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  • Published: 29 October 2020

The genome editing revolution: review

  • Ahmad M. Khalil   ORCID: orcid.org/0000-0002-1081-7300 1  

Journal of Genetic Engineering and Biotechnology volume  18 , Article number:  68 ( 2020 ) Cite this article

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Development of efficient strategies has always been one of the great perspectives for biotechnologists. During the last decade, genome editing of different organisms has been a fast advancing field and therefore has received a lot of attention from various researchers comprehensively reviewing latest achievements and offering opinions on future directions. This review presents a brief history, basic principles, advantages and disadvantages, as well as various aspects of each genome editing technology including the modes, applications, and challenges that face delivery of gene editing components.

Genetic modification techniques cover a wide range of studies, including the generation of transgenic animals, functional analysis of genes, model development for diseases, or drug development. The delivery of certain proteins such as monoclonal antibodies, enzymes, and growth hormones has been suffering from several obstacles because of their large size. These difficulties encouraged scientists to explore alternative approaches, leading to the progress in gene editing. The distinguished efforts and enormous experimentation have now been able to introduce methodologies that can change the genetic constitution of the living cell. The genome editing strategies have evolved during the last three decades, and nowadays, four types of “programmable” nucleases are available in this field: meganucleases, zinc finger nucleases, transcription activator-like effector nucleases, and the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) (CRISPR/Cas-9) system. Each group has its own characteristics necessary for researchers to select the most suitable method for gene editing tool for a range of applications. Genome engineering/editing technology will revolutionize the creation of precisely manipulated genomes of cells or organisms in order to modify a specific characteristic. Of the potential applications are those in human health and agriculture. Introducing constructs into target cells or organisms is the key step in genome engineering.


Despite the success already achieved, the genome editing techniques are still suffering certain difficulties. Challenges must be overcome before the full potential of genome editing can be realized.

In classical genetics, the gene-modifying activities were carried out selecting genetic sites related to the breeder’s goal. Subsequently, scientists used radiation and chemical mutagens to increase the probability of genetic mutations in experimental organisms. Although these methods were useful, they were time-consuming and expensive. Contrary to this, reverse genetics goes in the opposite direction of the so-called forward genetic screens of classical genetics. Reverse genetics is a method in molecular genetics that is used to help understanding the function of a gene by analyzing the phenotypic effects of specific engineered gene sequences. Robb et al. [ 68 ] defined and compared the three terms: “genome engineering”, “genome editing”, and “gene editing”. Genome engineering is the field in which the sequence of genomic DNA is designed and modified. Genome editing and gene editing are techniques for genome engineering that incorporate site-specific modifications into genomic DNA using DNA repair mechanisms. Gene editing differs from genome editing by dealing with only one gene.

This review briefly presents the evolution of genome editing technology over the past three decades using PubMed searches with each keyword of genome-editing techniques regarding the brief history, basic principles, advantages and disadvantages, as well as various aspects of each genome editing technology including the modes, future perspective, applications, and challenges.

Genome-wide editing is not a new field, and in fact, research in this field has been active since the 1970s. The real history of this technology started with pioneers in genome engineering [ 36 , 59 ]. The first important step in gene editing was achieved when researchers demonstrated that when a segment of DNA including homologous arms at both ends is introduced into the cell, it can be integrated into the host genome through homologous recombination (HR) and can dictate wanted changes in the cell [ 10 ]. Employing HR alone in genetic modification posed many problems and limitations including inefficient integration of external DNA and random incorporation in undesired genomic location. Consequently, the number of cells with modified genome was low and uneasy to locate among millions of cells. Evidently, it was necessary to develop a procedure by which scientists can promote output. Out of these limitations, a breakthrough came when it was figured out that, in eukaryotic cells, more efficient and accurate gene targeting mechanisms could be attained by the induction of a double stranded break (DSB) at a specified genomic target [ 70 ].

Furthermore, scientists found that if an artificial DNA restriction enzyme is inserted into the cell, it cuts the DNA at specific recognition sites of double-stranded DNA (dsDNA) sequences. Thus, both the HR and non-homologous end joining (NHEJ) repair can be enhanced [ 14 ]. Various gene editing techniques have focused on the development and the use of different endonuclease-based mechanisms to create these breaks with high precision procedures [ 53 , 78 ] (Fig. 1 ). The mode of action of what is known as site-directed nucleases is based on the site-specific cleavage of the DNA by means of nuclease and the triggering of the cell’s DNA repair mechanisms: HR and NHEJ.

figure 1

Genome editing outcomes. Genome editing nucleases induce double-strand breaks (DSBs). The breaks are repaired through two ways: by non-homologous end joining (NHEJ) in the absence of a donor template or via homologous recombination (HR) in the presence of a donor template. The NHEJ creates few base insertions or deletion, resulting in an indel, or in frameshift that causes gene disruption. In the HR pathway, a donor DNA (a plasmid or single-stranded oligonucleotide) can be integrated to the target site to modify the gene, introducing the nucleotides and leading to insertion of cDNA or frameshifts induction. (Adapted from [ 78 ])

One of the limitations in this procedure is that it has to be activated only in proliferating cells, adding that the level of activity depends on cell type and target gene locus [ 72 ]. Tailoring of repair templates for correction or insertion steps will be affected by these differences. Several investigations have determined ideal homology-directed repair (HDR) donor configurations for specific applications in specific models systems [ 67 ]. The differences in the activities of the DNA repair mechanisms will also influence the efficiency of causing indel mutations through NHEJ or the classical microhomology-mediated end joining (c-MMEJ) pathway, and even the survival of the targeted cells. The production of such repair in the cell is a sign of a characteristic that errors may occur during splicing the ends and cause the insertion or deletion of a short chain. Simply speaking, gene editing tools involve programmed insertion, deletion, or replacement of a specific segment of in the genome of a living cell. Potential targets of gene editing include repair of mutated gene, replacement of missing gene, interference with gene expression, or overexpression of a normal gene.

The human genome developments paved the way to more extensive use of the reverse genetic analysis technique. Nowadays, two methods of gene editing exist: one is called “targeted gene replacement” to produce a local change in an existing gene sequence, usually without causing mutations. The other one involves more extensive changes in the natural genome of species in a subtler way.

In the field of targeted nucleases and their potential application to model and non-model organisms, there are four major mechanisms of site-specific genome editing that have paved the way for new medical and agricultural breakthroughs. In particular, meganucleases (MegNs), zinc finger nucleases (ZFNs), transcription activator-like effector nuclease (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) (CRISPR/Cas-9) (Fig. 2 ).

figure 2

Schematic diagram of the four endonucleases used in gene editing technologies. a Meganuclease (MegN) that generally cleaves its DNA substrate as a homodimer. b Zinc finger nuclease (ZFN) recognizes its target sites which is composed of two zinc finger monomers that flank a short spacer sequence recognized by the FokI cleavage domain. c Transcription activator-like effector nuclease (TALEN) consists of two monomers; TALEN recognizes target sites which flank a fok1 nuclease domain to cut the DNA. d CRISPR/Cas9 system is made of a Cas9 protein with two nuclease domains: human umbilical vein endothelium cells (HuvC) split nuclease and the HNH, an endonuclease domain named for the characteristic histidine and asparagine residue, as well as a single guide RNA (sgRNA). (Adapted from [ 1 , 51 ]; Gaj et al., 2016 [ 53 ];)

Meganucleases (MegNs)

Meganucleases (MegNs) are naturally occurring endodeoxyribonucleases found within all forms of microbial life as well as in eukaryotic mitochondria and chloroplasts. The genes that encode MegNs are often embedded within self-splicing elements. The combination of molecular functions is mutually advantageous: the endonuclease activity allows surrounding introns and inteins to act as invasive DNA elements, while the splicing activity allows the endonuclease gene to invade a coding sequence without disrupting its product. The high specificity of these enzymes is based on their ability to cleave dsDNA at specific recognition sites comprising 14–40 bp (Fig. 2 a). Unlike restriction enzymes, which provide defenses to bacteria against invading DNA, MegNs facilitate lateral mobility of genetic elements within an organism. This process is referred to as “homing” and gives the name homing endonucleases to these enzymes. The high DNA specificity of MegNs makes them a powerful protein scaffold to engineer enzymes for genome manipulation. A deep understanding of their molecular recognition of DNA is an important prerequisite to generate engineered enzymes able to cleave DNA in specific desired genome sites. Crystallographic analyses of representatives from all known MegNs families have illustrated both their mechanisms of action and their evolutionary relationships to a wide range of host proteins. The functional capabilities of these enzymes in DNA recognition vary widely across the families of MegNs. In each case, these capabilities, however, make a balance between what is called orthogonal requirements of (i) recognizing a target of adequate length to avoid overt toxicity in the host, while (ii) accommodating at least a small amount of sequence drift within that target. Indirect readout in protein-DNA recognition is the mechanism by which the protein achieves partial sequence specificity by detecting structural features on the DNA.

Several homing endonucleases have been used as templates to engineer tools that cleave DNA sequences other than their original wild-type targets.

Meganucleases can be divided into five families based on sequence and structure motifs: LAGLIDADG, GIY-YIG, HNH, His-Cys box, and PD-(D/E) XK [ 74 ]. I-CreI is a homodimeric member of MegNs family, which recognizes and cleaves a 22-bp pseudo-palindromic target (5′-CAAAACGTCGTGAGACAGTTTG-3′). The important role of indirect readout in the central region of the target DNA of these enzymes I-CreI suggested that indirect readout may play a key role in the redesign of protein-DNA interactions. The sequences of the I-CreI central substrate region, four bp (± 1 and ± 2) called 2NN, along with the adjacent box called 5NNN, are key for substrate cleavage [ 64 ]. Changes in 2NN significantly affect substrate binding and cleavage because this region affects the active site rearrangement, the proper protein-DNA complex binding, and the catalytic ion positioning to lead the cleavage.

An exhaustive review of each MegN can be found in Stoddard [ 75 ] as well as in Petersen and Niemann [ 63 ]. Several MegNs have been used as templates to engineer tools that cleave DNA sequences other than their original wild-type targets. This technology have advantages of high specificity of MegNs to target DNA because of their very long recognition sites, ease in delivery due to relatively small size, and giving rise to more recombinant DNA (i.e., more recombinogenic for HDR) due to production of a 3′ overhang after DNA cleavage. This lowers the potential cytotoxicity [ 53 , 78 ].

Meganucleases have several promising applications; they are more specific than other genetic editing tools for the development of therapies for a wide range of inherited diseases resulting from nonsense codons or frameshift mutations. However, an obvious drawback to the use of natural MegNs lies in the need to first introduce a known cleavage site into the region of interest. Additionally, it is not easy to separate the two domains of MegNs: the DNA-binding and the DNA-cleavage domains, which present a challenge in its engineering. Another drawback of MegNs is that the design of sequence-specific enzymes for all possible sequences is time-consuming and expensive. Therefore, each new genome engineering target requires an initial protein engineering step to produce a custom MegN. Thus, in spite of the so many available MegNs, the probability of finding an enzyme that targets a desired locus is very small and the production of customized MegNs remains really complex and highly inefficient. Therefore, routine applications of MegNs in genome editing is limited and proved technically challenging to work with [ 24 ].

Zinc finger nucleases (ZFNs)

The origin of genome editing technology began with the introduction of zinc finger nucleases (ZFNs). Zinc finger nucleases are artificially engineered restriction enzymes for custom site-specific genome editing. Zinc fingers themselves are transcription factors, where each finger recognizes 3–4 bases. Zinc finger nucleases are hybrid heterodimeric proteins, where each subunit contains several zinc finger domains and a Fok1 endonuclease domain to induce DSB formation. The first is zinc finger, which is one of the DNA binding motifs found in the DNA binding domain of many eukaryotic transcription factors responsible for DNA identification. The second domain is a nuclease (often from the bacterial restriction enzyme FokI) [ 6 ]. When the DNA-binding and the DNA-cleaving domains are fused together, a highly specific pair of “genomic scissors” is created (Fig. 2b ). In principle, any gene in any organism can be targeted with a properly designed pair of ZFNs. Zinc finger recognition depends only on a match to DNA sequence, and mechanisms of DNA repair, both HR and NHEJ, are shared by essentially all species. Several studies have reported that ZFNs with a higher number of zinc fingers (4, 5, and 6 finger pairs) have increased the specificity and efficiency and improved targeting such as using modular assembly of pre-characterized ZFs utilizing standard recombinant DNA technology.

Since they were first reported [ 41 ], ZFN was appealing and showed considerable promise and they were used in several living organisms or cultured cells [ 11 ]. The discovery of ZFNs overcame some of the problems associated with MegNs applications. They facilitated targeted editing of the gene by inducing DSBs in DNA at specific sites. One major advantage of ZFNs is that they are easy to design, using combinatorial assembly of preexisting zinc fingers with known recognition patterns. This approach, however, suffered from drawbacks for routine applications. One of the major disadvantages of the ZFN is what is called “context-dependent specificity” (how well they cleave target sequence). Therefore, these specificities can depend on the context in the adjacent zinc fingers and DNA. In other terms, their specificity does not only depend on the target sequence itself, but also on adjacent sequences in the genome. This issue may cause genome fragmentation and instability when many non-specific cleavages occur. It only targets a single site at a time and as stated above. Although the low number of loci does not usually make a problem for knocking-out editing, it poses limitation for knocking in manipulation [ 32 ]. In addition, ZFNs cause overt toxicity to cells because of the off-target cleavages. The off-target effect is the probability of inaccurate cut of target DNA due to single nucleotide substitutions or inappropriate interaction between domains.

Transcription activator-like effector nucleases (TALENs)

The limitations mentioned in the previous section paved the way for the development of a new series of nucleases: transcription activator-like effector nucleases (TALENs), which were cheaper, safer, more efficient, and capable of targeting a specified region in the genome [ 13 ].

In principle, the TALENs are similar to ZFNs and MegNs in that the proteins must be re-engineered for each targeted DNA sequence. The ZFNs and TALENs are both modular and have natural DNA-binding specificities. The TALEN is similar to ZFN in that it is an artificial chimeric protein that result from fusing a non-specific FokI restriction endonuclease domain to a DNA-binding domain recognizing an arbitrary base sequence (Fig. 2c ). This DNA-binding domain consists of highly conserved repeats derived from transcription activator-like effectors (TALE). When genome editing is planned, a pair of TALEN is used like ZFNs. The TALE protein made of three domains: an amino-terminal domain having a transport signal, a DNA-binding domain which is made of repeating sequences of 34 amino acids arranged in tandem, and a carboxyl-terminal domain having a nuclear localization signal and a transcription activation domain. Of the 34 amino acids, there is a variable region of two amino acid residues located at positions 12 and 13 called repeat variable di-residues (RVD). This region has the ability to confer specificity to one of the any four nucleotide bps [ 15 ].

Unlike ZFNs, TALENs had advantages in that one module recognizes just one nucleotide in its DNA-binding domain, as compared with 3 bps recognized by the first single zinc finger domains [ 39 ]. So, interference of the recognition sequence does not occur even when several modules are joined. In theory, because cleavage of the target sequence is more specific than ZFN, it became possible to target any DNA sequence of any organism genome. This difference facilitates creation of TALEN systems which recognize more target sequences. Another benefit of the TALEN system over ZFN’s for genome editing is that the system is more efficient in producing DSBs in both somatic cells and pluripotent stem cells [ 35 ]. In addition, TALENs exhibit less toxicity in human cell lines due to off-target breaks that result in unwanted changes and toxicity in the genome. Another advantage of TALENs is a higher percentage of success in genome editing through cytoplasmic injection of TALEN mRNA in livestock embryos than observed with ZFN induction [ 39 ]. In addition, TALENs have been more successfully used in plant genome engineering [ 88 ]. It is hoped that TALENs will be applied in the generation of genetically modified laboratory animals, which may be utilized as a model for human disease research [ 24 , 39 ].

The TALEN-like directed development of DNA binding proteins was employed to improve TALEN specificity by phage-assisted continuous evolution (PACE). The improved version was used to create genetically modified organisms [ 34 ]. Nucleases which contain designable DNA-binding sequences can modify the genomes and have the promise for therapeutic applications. DNA-binding PACE is a general strategy for the laboratory evolution of DNA-binding activity and specificity. This system can be used to generate TALEN with highly improved DNA cutting specificity, establishing DB-PACE as a diverse approach for improving the accuracy of genome editing tools. Thus, similar to ZFN, TALEN is used for DSBs as well as for knocking in/knocking out. In comparison with the ZFN, two important advantages for this editing technique have been reported: first, the simple design, and second, the low number of off-target breaks [ 35 ].

In spite of the improvement and simplification of the TALEN method, it is complicated for whom not familiar with molecular biological experiments. Moreover, it is confronted with some limitations, such as their large size (impeding delivery) in comparison to ZFN [ 24 , 39 ]. The superiority of TALEN relative to ZFN could be attributed to the fact that in the TALEN each domain recognizes only one nucleotide, instead of recognizing DNA triplets in the case of ZEF. The design of TALEN is commonly more obvious than ZNF. This results in less intricate interactions between the TALEN-derived DNA-binding domains and their target nucleotides than those among ZNF and their target trinucleotides [ 35 , 39 ].

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)

The CRISPR/Cas system is the most recent platform in the field of genome editing. The system was developed in 2013 and is known as the third generation genomic editing tools. The clustered regularly interspaced short palindromic repeats, which are sometimes named “short regularly spaced repeats” were discovered in the 1980s. Computational analysis of these elements showed they were found in more than 40% of sequenced bacteria and 90% of archaea [ 37 , 56 ]. The acronym CRISPR was suggested, and a group of genes adjacent to the CRISPR locus, which was termed “CRISPR-associated system”, or Cas was established [ 37 ]. Cas proteins coded by these genes carry functional domains similar to endonucleases, helicases, polymerases, and nucleotide-binding proteins. In addition, the role of CRISPRs as bacterial and archaeal adaptive immunity system against invading bacteriophages and other and in DNA repair was realized [ 17 , 77 ].

Unlike the two previous technologies (ZFN and TALEN), in which the recognition of the DNA site was based on the sequence recognition by artificial proteins requiring interaction between protein and DNA, the DNA recognition of the CRISPR/Cas system is based on RNA-DNA interactions. This offers several advantages over ZFNs and TALENs. These include easy design for any genomic targets, easy prediction regarding off-target sites, and the probability of modifying several genomic sites simultaneously (multiplexing). CRISPR-Cas systems are diverse and have been classified thus far into two classes, six types, and over 20 subtypes based on locus arrangement and signature cas genes [ 33 , 44 , 51 ]. Types I, III, and IV, with multiprotein crRNA-effector complexes, are class 1 systems; types II, V, and VI, with a single protein-crRNA effector complex, are class 2. All CRISPR-Cas systems require Cas proteins and crRNAs for function, and CRISPR- cas expression is a prerequisite to acquire new spacers, process pre-crRNA, and assemble ribonucleoprotein crRNA interference complexes for target degradation. Herein, we will focus on the CRISPR-Cas9 technology, the reader should keep in mind other available variants of the system such as CRISPR-Cas6 [ 5 ], CRISPR-Cas12a, -Cas12b [ 42 ], as well as the most recently discovered c2c2 (Cas13a) and c2c6 (Cas13b [ 19 , 69 ]. The CRISPR/Cas9 system is made of Cas9 nuclease and single-guide RNA (sgRNA). The sgRNA is an engineered single RNA molecule containing crispr RNA and tracr RNA parts. The sgRNA recognizes the target sequence by standard Watson-Crick base pairing. It has to be followed by a DNA motif called a protospacer adjacent motif (PAM). The commonly used wild-type Streptococcus pyogenes Cas (SpCas9) protein has a specific PAM sequence, 5’-NGG-3’, where “N” can be any nucleotide base followed by two guanine (“G”) nucleobases. This sequence is located directly downstream of the target sequence in the genomic DNA, on the non-target strand. Targeting is constrained to every 14 bp (12 bp from the seed sequence and 2 bp from PAM) [ 15 ]. SpCas9 variants may increase the specificity of genome modifications at DNA targets adjacent to NGG PAM sequences when used in place of wild-type SpCas9.

DNA cleavage is performed by Cas9 nuclease and can result in DSB in the the case of a wild-type enzyme, or in a SSB when using mutant Cas9 variants called nickases (Fig. 2d ). It should be emphasized that the utilization of this approach in editing eukaryotes’ genome only needs the manipulation of a short sequence of RNA, and there is no need for complicated manipulations in the protein domain. This enables a faster and more cost-effective design of the DNA recognition moiety compared with ZFN and TALEN technologies. Applications of CRISPR-Cas9 systems are variable like those for ZFNs, TALENs, and MegNs. But, because of the relative simplicity of this system, its great efficiency and high tendency for multiple functions and library construction, it can be applied to different species and cell types [ 35 ].

As shown in Fig. 3 , in all CRISPR/Cas systems, immunity occurs in three distinct stages [ 77 , 81 ]: (1) adaptation or new spacer acquisition, (2) CRISPR transcription and processing (crRNA generation), and (3) interference or silencing. The advantages of the CRISPR/Cas system superseded those of both of the TALEN and ZFN tools, the ZFN in particular. This is due to its target design simplicity since the target specificity depends on ribonucleotide complex formation and non-protein/DNA recognition. In addition, the CRISPR/Cas approach is more efficient because changes can be introduced directly by injecting RNAs that encode the Cas protein and gRNA into developing embryos. Moreover, multigene mutations can be induced simultaneously by injecting them with multiple gRNAs. This is an example that explains the rapid spread of CRISPR/Cas 9 application in various fields. Still, the system has certain drawbacks. Although the CRISPR/Cas9 is much less complicated than TALEN, in terms of execution and construction, the off-target effect in CRISPR/Cas9 is higher than TALEN. Since the DSB results only after accurate binding of a pair of TALEN to the target sequence, the off-target effect problem is considered to be low. These two are different in restriction of target sequence. CRISPR/Cas9 is much more efficient than TALEN in multiple simultaneous modification. Table 1 compares the three main systems of site-directed synthetic nuclease employed in genome editing: ZFN, TALEN, and CRISPR/Cas9.

figure 3

Schematic representation of CRISPR loci and targeting of DNA sequence, which include Cas genes, a leader sequence, and several spacer sequences derived from engineered or foreign DNA that are separated by short direct repeat sequences. The three major steps of CRISPR-Cas immune systems. In the adaptation phase, Cas proteins excise specific fragments from foreign DNA and integrate it into the repeat sequence neighboring the leader at the CRISPR locus. Then, CRISPR arrays are transcribed and processed into multiple crRNAs, each carrying a single spacer sequence and part of the adjoining repeat sequence. Finally, at the interference phase, the crRNAs are assembled into different classes of protein targeting complexes (cascades) that anneal to, and cleave, spacer matching sequences on either invading element or their transcripts and thus destroy them. (Adapted from [ 3 , 53 , 78 ])

The off-target effect is an essential subject for future studies if CRISPR/Cas9 is to achieve its promises as a powerful method for genome editing. Non-specific and unintended genetic modifications (off-target effect) can result from the use of CRISPR/Cas9 system which is one of the drawbacks of this tool. Therefore, this point should be considered for use in researches. One strategy to reduce the off-target activity is to replace the Streptococcus pyogenes Cas9 enzyme (SpyCas9) for a mutant Cas9 nickase (nSpyCas9; ncas9), which cleaves a single strand through the inactivation of a nuclease domain Ruvc or HNH [ 9 ]. Our understanding of off-target effects remains fragmentary. A deeper understanding of this phenomenon is needed. Several approaches that could be followed to characterize the binding domains and consequently Cas9 targeting specificity have been reviewed and summarized [ 83 ].

It has previously been stated that CRISPR/Cas9 system needs both gRNA and PAM to detect its target sequence of interest by integration of a gRNA component that binds to complementary double-stranded DNA sequences. Cell culture studies have shown that off-target effects may be due to the incorrect detection of genomic sequences by sgRNA. This, in turn, affects cleavage when the mismatch is in the vicinity of the PAM (up to 8 bases), but if the PAM is too far apart, these effects will be small [ 4 ], even a slight mismatch between sgRNA and target sequences can lead to a failure. Dependence of this method on specific PAM sequences to act functionally limits the number of target loci, and it can reduce off-target breaks [ 86 ]. For this goal, another type of specific PAM-containing nucleases has been prepared to compensate for this limitation. Genetic engineering and enzyme changing have also been able to overcome the limitation [ 42 ]. For a sgRNA, many similar sequences depending on the genome size of the species may exist [ 86 ]. Interestingly, the initial targeting scrutiny of the CRISPR/Cas9-sgRNA complex showed that not every nucleotide base in the gRNA is necessary to be complementary to the target DNA sequence to effect Cas9 nuclease activity. Regarding that where the similar sequences are found in the genome, their breaks could lead to malignancies or even death [ 86 ]. Various methods have been proposed to prevent off-target breaks, among which the double nicking method, the FokI-dCas9 fusion protein method, and the truncated sgRNA method [ 76 ] (Fig. 4 ).

figure 4

a Summary of the Cas9 nickases methods in efficient genome editing. Two gRNAs target opposite strands of DNA. These double nicks create a DSB that is repaired using non-homologous end joining (NHEJ) or edits via homology-directed repair (HDR) (adapted from www.addgene.org/crispr/nick ). b FokI-dCas 9 fusion protein method. Two FokI-dCas9 fusion proteins are used to adjacent target sites by two different sgRNAs to facilitate FokI dimerization and DNA cleavage. These fusions would have enhanced specificity compared to the standard monomeric Cas9 nucleases and the paired nickase system because they should require two sgRNAs for activity. c Truncated sgRNA method. Cas9 interacting with either a full-length sgRNA (20 nucleotide sequence complementary to target site) or truncated gRNA (less than 15 nucleotide sequence complementary to target site). (Retrieved from blog.addgene.org )

To overcome these problems, researchers explored another generation of base editing technologies, which combine CRISPR and cytidine deaminase (Fig. 5 ). This is a diverse method called CRISPR-SKIP (Fig. 6 ) which uses cytidine deaminase single-base editors to program exon skipping by mutating target DNA bases within splice acceptor sites [ 25 ]. Given its simplicity and precision, CRISPR-SKIP will be widely applicable in gene therapy. Base editing utilizes Cas9 D10A nickases fused to engineered base deaminase enzymes to make single base changes in the DNA sequence without the need of DNA DSB. Also, base editing does not require an external repair template. The Cas9 nickase part of the base editor protein plays a dual function. The first is to target the deaminase activity to the wanted region and the second is to localize the enzyme to certain regions of double-stranded RNA. The deaminase domains in base editors (BEs) occur in two versions: either adenosine deaminase or cytosine deaminase, which catalyze only base transitions (C to T and A to G) and cannot produce base transversions [ 26 , 68 ]. In these base editing tools, the targeted activity of adenosine deaminase can result in an A:T to G:C sequence alteration in a very similar way [ 26 , 68 ].This approach avoided the requirement of breaking DNA to induce an oligonucleotide. In addition, compared to knocking system, it exerted a higher output with lower off-targets [ 40 , 43 ]. Adenosine is deaminated to inosine (I) that is subsequently utilized to repair the nicked strand with a cytosine, and the I:C base pair is resolved to G:C [ 26 ]. More recently, new genome editing technologies have been developed: glycosylase base editors (GBEs), which consist of a Cas9 nickase, a cytidine deaminase, and a uracil-DNA glycosylase (Ung), are capable of transversion mutations by changing C to A in bacterial cells and from C to G in mammalian cells [ 45 , 89 ]. The new BEs can also be designed to minimize unwanted (“off-target”) mutations that could potentially cause undesirable side effects. The novel BE platform may help researchers understand and correct genetic diseases by selective editing of single DNA “alphabets” across nucleobase classes. However, the technique with this new class of transversion BEs is still at an early stage and requires additional optimization, so it would be premature to say this is ready for the clinic applications.

figure 5

Base editing uses engineered Cas9 variants to induce base changes in a target sequence. Cas9 nickase is fused to a base deaminase domain. The deaminase domain works on a targeted region within the R-loop after target binding and R-loop formation. Simultaneously, the target strand is nicked. DNA repair is started in response to the nick using the strand which contains the deaminated base as a repair template. Repair leads to a transition mutations: C:G to T:A and A:T to G:C for cytosine and adenosine base editors, respectively [ 68 ]

figure 6

Essential steps in CRISPR-SKIP targeting approach: a Nearly every intron ends with a guanosine (asterisked G). It is hypothesized that mutations that disrupt this highly conserved G within the splice acceptor of any given exon in genomic DNA would lead to exon skipping by preventing incorporation of the exon into mature transcripts base. b In the presence of an appropriate PAM sequence, this G can be effectively mutated by converting the complementary cytidine to thymidine using CRISPR-Cas9 C>T single-base editors. (From [ 25 ])

Gene delivery

From biotechnology’s point of view, the main obstacle that is facing molecular technology is to select the right method that is simple but effective to transfer the gene to the host cell. The components of gene editing have to be transferred to the cell/nucleus of interest using in vivo, ex vivo, or in vitro route. In this regard, several concerns must be considered including physical barriers (cell membranes, nuclear membranes) as well as digestion by proteases or nucleases of the host. Another important issue is the possible rejection by the immune system of the host if the components are delivered in vivo. In general, the gene delivery routes can be categorized in three classes of physical delivery, viral vectors, and non-viral agents. Although the direct delivery of construct plasmids may sound easy and more efficient and specific than the physical and the chemical methods, it proves to be an inappropriate choice because the successful gene delivery system requires the foreign genetic molecule to remain stable within the host cells [ 52 ]. The other possible procedure is to use viruses. However, because plant cells have thick walls, the gene transfer systems for plants involve transient and stable transformation using protoplast-plasmid in vitro [ 54 ]: agrobacterium-mediated transformation, gene gun and viral vectors (transient expression by protoplast transformation), and agro-infiltration [ 1 ]. Viruses may present a suitable vehicle to transfer genome engineering components to all plant parts because they do not require transformation and/or tissue culture for delivering and mutated seeds could easily recovered. For many years, scientists employed different species of Agrobacterium to systematically infect a large number of plant species and generate transgenic plants. These bacterial species have small genome size and this facilitates cloning and agroinfections, and the virus genome does not integrate into plant genomes [ 1 ].

Of the challenges and approaches of delivering CRISPR, it was pointed out [ 18 , 51 ] that although the present genome engineering is in favor of CRISPR tools, TALENs may still be of a primary choice in certain experimental species. For example, TALENs have been utilized in targeted genomic editing in Xenopus tropicalis by knocking-out Klf4 [ 49 , 50 ] or thyroid hormone receptor α [ 23 ]. In addition, TALENs have been utilized to modify genome of human stem cells [ 47 ]. Also TALEN approach has been applied to create amniotic mesenchymal stem cells overexpressing anti-fibrotic interleukin-10 [ 12 ]. Lately, a geminivirus genome has been prepared to deliver various nucleases platforms (including ZFN, TALENs, and the CRISPR/Cas system) and repair template for HR of DSBs [ 62 ].

To deliver the carrying DNA sequence to target cells, non-viral techniques such as electroporation, lipofection, and microinjection can also be used [ 18 ]. In addition, these techniques also reduce off-target cleavages problems. Gene transfer via microinjection is considered the gold standard procedure since its efficiency is approximately 100% [ 85 ]. The advantage of this approach is its high efficacy and less constrains on the size of the delivery. A disadvantage is that it can be employed only in in vitro or ex vivo cargo. Recently, small RNAs, including small interfering RNA (siRNA) and microRNA (miRNA), have been widely adopted in research to replace laboratory animals and cell lines. Development of innovative nanoparticle-based transfer systems that deliver CRISPR/Cas9 constructs and maximize their effectiveness has been tested in the last few years [ 29 , 58 ].

Applications of gene technology

The ability of the abovementioned gene delivery systems to target and manipulate the genome of living organisms has been attractive to many researchers worldwide. Despite all limitations, the interest in this technology has developed its capabilities and enhanced its scope of applications. Genome/gene engineering technology is relatively applicable and has potential to effectively and rapidly revolutionize genome surgery and will soon transform agriculture, nutrition, and medicine. Some of the most important applications are briefly described below.

Plant-based genome editing

The appearance of genome editing has been appealing especially to agricultural experts. One of the major goals for utilizing genome editing tools in plants is to generate improved crop varieties with higher yields and clear-cut addition of valuable traits such as high nutritional value, extended shelf life, stress tolerance, disease and pest resistance, or removal of undesirable traits [ 1 ]. However, several obstacles related to the precision of the genetic manipulations and the incompatibility of the host species have hampered the development of crop improvements [ 2 ]. The use of site-specific nucleases is one of the important promising techniques of gene editing that helped overcome certain limitations by specifically targeting a suitable site in a gene/genome. The employment of the gene editing technologies, including those discussed in this review, seems to be endless ever since their emergence, and several improvements in original tools have further brought accuracy and precision in these methods [ 78 ].

Animal-based genome editing

Recent genome editing techniques has been extensively applied in many organisms, such as bacteria, yeast, and mouse [ 53 , 73 ]. Genetic manipulation tools cover a wide range of fields, including the generation of transgenic animals using embryonic stem cells (ESC), functional analysis of genes, model development for diseases, or drug development. Genome editing techniques have been used in many various organisms. Among the livestock and aquatic species, ZFN is only used for zebrafish, but two other technologies, TALEN and CRISPR, have been used at the cell level in chicken, sheep, pig, and cattle. Engineered endonucleases or RNA-guided endonucleases (RGENs) mediated gene targeting has been applied directly in a great number of animal organisms including nematodes and zebrafish [ 20 , 57 ], as well as pigs [ 71 , 85 ]. Since the first permission to use CRISPR/Cas9 in human embryos and in vivo genome editing via homology-independent targeted integration (HITI), an increasing number of studies have identified striking differences between mouse and human pre-implantation development and pluripotency [ 66 ], highlighting the need for focused studies in human embryos. Therefore, more specific criteria and widely accepted standards for clinical research have to be met before human germline editing would be deemed permissible [ 31 ]. In this regard, results of some research on the human genome editing have been questioned. The “He Jiankui experiments at the beginning of 2019”, which claimed to have created the world’s first genetically edited babies, is simply the most recent example. He Jiankui said he edited the babies’ genes at conception by selecting CRISPR/cas9 to edit the chemokine receptor type 5 (CCR5) gene in cd4+ cells in hopes of making children resistant to the AIDS virus, as their father was HIV-positive. Researchers said He’s actions exposed the twins to unknown health risks, possibly including a higher susceptibility to viral illnesses. For more information on the scientific reactions around the world, the reader may find helpful several excellent sources of information [ 38 , 49 , 79 , 84 ].

  • Gene therapy

The original principles of gene therapy arose during the 1960s and early 1970s when restriction enzymes were utilized to manipulate DNA [ 22 ]. Since then, researchers have done great efforts to treat genetic diseases but treatment for multiple mutations is difficult. Different clinical therapy applications have been attempted to overcome these problems. Much of the interest in CRISPR and other gene editing methods revolves around their potential to cure human diseases. It is hoped that eradication of human diseases is not too far to achieve via the CRISPR system because it was employed in other fields of biological sciences such as genetic improvement and gene therapy. It is important to mention that the therapeutic efficiency of gene editing depends on several factors, such as editing efficacy, which varies widely depending on the cell type, senescence status, and cell cycle status of the target [ 69 ]. Other factors that also influence therapeutic effectiveness include cell aptitude, which refers to the feasibility of accomplishing a therapeutic modification threshold, and the efficient transfer of programmable nuclease system to the target tissue, which is only considered to be effective if the engineered nuclease system reaches safely and efficiently to the nucleus of the target cell. Finally, the precision of the editing procedure is another important aspect, which refers to only editing the target DNA without affecting any other genes [ 80 ].

The genome editing tools have enabled scientists to utilize genetically programmed animals to understand the cause of various diseases and to understand molecular mechanisms that can be explored for better therapeutic strategies (Fig. 7 ). Genome editing gives the basis of the treatment of many kinds of diseases. In preliminary experiments, the knocking-in procedure was used to reach this goal. There are examples of gene editing techniques applied in different genetic diseases in cell lines, disease models, and human [ 48 , 53 , 82 ]. These encouraging results suggest the therapeutic capability of these gene editing strategies to treat human genetic diseases including Duchenne muscular dystrophy [ 8 , 28 , 55 ], cystic fibrosis [ 21 ], sickle cell anemia [ 62 ], and Down syndrome [ 7 ]. In addition, this technology has been employed in curing Fanconi anemia by correcting point mutation in patient-derived fibroblasts [ 60 ], as well as in hemophilia for the restoration of factor VIII deficiency in mice [ 61 , 87 ]. The CRISPR tools have also demonstrated promising results in diagnosis and curing fatal diseases such as AIDS and cancer [ 16 , 30 , 84 ].

figure 7

Outline of the ex vivo and in vivo genome editing procedures for clinical therapy. Top: In the ex vivo editing therapy, cells are removed from a patient to be treated, corrected by gene editing and then re-engrafted back to the patient. To achieve therapeutic success, the target cells must be capable of surviving in vitro and autologous transplantation of the corrected cells. Below: In the in vivo editing therapy, designed nucleases are administered using viral or non-viral techniques and directly injected locally to the affected tissue, such as the eye, brain, or muscle. (Adapted from [ 48 ])

Other applications

The applications mentioned above were more about knock out or modification of genes Gapinske et al. [ 25 ]. However due to inactivate nuclease activity nature of the dCas9, CRISPR can be used in other applications as well. By selecting the target sequence, gene expression can be controlled by inhibiting the transcription rate of RNA polymerase II (polII) or inhibiting the transcription factor binding [ 65 ]. Additionally, combining gene expression inhibitors such as Krüppel-associated box with the inactivated Cas9 has led to generate a special kind of gene inhibitors, which are called CRISPR interference (CRISPRi), and downregulate gene expression [ 46 ]. It is also possible to control gene expression by fusing transcription-activating molecule, the transcription-repressing molecule, or the genome-modifying molecule to dCas9 [ 27 ].

Genome editing is a fast-growing field. Editing nucleases have revolutionized genomic engineering, allowing easy editing of the mammalian genome. Much progress has been accomplished in the improvement of gene editing technologies since their discovery. Of the four major nucleases used to cut and edit the genome, each has its own advantages and disadvantages, and the choice of which gene editing method depends on the specific situation. The current genome editing techniques are still buckling up with problems, and it is difficult to perform genome editing in cells with low transfection efficiency or in some cultured cells such as primary cultured cells. Genotoxicity is an inherent problem of enzymes that act on nucleic acids, though one can expect that highly specific endonucleases would reduce or abolish this issue. Exceptional efforts are needed in future to complement and offer something novel approaches in addition to the already existing ones. It is anticipated that research in gene editing is going to continue and tremendously advance. With the development of next-generation sequencing technology, new extremely important clinical applications, such as manufacturing engineered medical products, eradication of human genetic diseases, treatment of AIDS and cancers, as well as improvement of crop and food, will be introduced. Combination of genomic modifications induced by targeted nucleases to their own self-degradation, self-inactivating vectors may help overcoming confronting limitations discussed above to improve the specificity of genome editing, especially because the frequency of off-target modifications. Our understanding of off-target effects remains poor. This is a vital area for continued study if CRISPR/Cas9 is to realize its promise. Regarding gene cargo delivery systems, this remains the greatest obstacle for CRISPR/Cas9 use, and an all-purpose delivery method has yet to emerge. The union between genome engineering and regenerative medicine is still in its infancy; realizing the full potential of these technologies in reprograming the fate of stem/progenitor cells requires that their functional landscape be fully explored in these genetic backgrounds. Humankind can only wait to see what the potential of these technologies will be. One major question is whether or not the body’s immune response will accept or reject the foreign genetic elements within the cells. Another important concern is that along with the revolutionary advances of this biotechnology and related sciences, bioethical concerns and legal problems related to this issue are still increasing in view of the possibility of human genetic manipulation and the unsafety of procedures involved [ 49 , 50 , 66 ]. The enforcement of technical and ethical guidelines, and legislations should be considered and need serious attention as soon as possible.

Availability of data and materials

Not applicable


CRISPR-associated protein 9

Clustered regularly interspaced short palindromic repeats

Double-stranded break

Embryonic stem cells

Homology-directed repair

Homology-independent targeted integration

Homologous recombination

Human umbilical vein endothelium cells

Intron-encoded endonuclease

  • Meganucleases

Microhomology-mediated end joining

Non-homologous end joining

Phage-assisted continuous evolution

Protospacer adjacent motifs

RNA-guided endonucleases

Repeat variable di-residues

Single guide RNA

Streptococcus pyogenes Cas9

Single-strand break

Transcription activator-like effector nuclease

Zinc finger nucleases

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  • v.26(1); 2018 Jan

One small edit for humans, one giant edit for humankind? Points and questions to consider for a responsible way forward for gene editing in humans

Heidi c. howard.

1 Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden

Carla G. van El

2 Department of Clinical Genetics, Section Community Genetics and EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands

Francesca Forzano

3 Department of Clinical Genetics, Great Ormond Street Hospital, London, UK

Dragica Radojkovic

4 Laboratory for Molecular Genetics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia

Emmanuelle Rial-Sebbag

5 UMR 1027, Inserm, Faculté de médecine Université Toulouse 3, Paul Sabatier, Toulouse France

Guido de Wert

7 Department of Health, Ethics and Society, Research Schools CAPHRI and GROW, Maastricht University, Maastricht, The Netherlands

Pascal Borry

6 Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, Leuven Institute for Genomics and Society, KU Leuven, Kapucijnenvoer 35 Box 7001, 3000 Leuven, Belgium

Martina C. Cornel

Gene editing, which allows for specific location(s) in the genome to be targeted and altered by deleting, adding or substituting nucleotides, is currently the subject of important academic and policy discussions. With the advent of efficient tools, such as CRISPR-Cas9, the plausibility of using gene editing safely in humans for either somatic or germ line gene editing is being considered seriously. Beyond safety issues, somatic gene editing in humans does raise ethical, legal and social issues (ELSI), however, it is suggested to be less challenging to existing ethical and legal frameworks; indeed somatic gene editing is already applied in (pre-) clinical trials. In contrast, the notion of altering the germ line or embryo such that alterations could be heritable in humans raises a large number of ELSI; it is currently debated whether it should even be allowed in the context of basic research. Even greater ELSI debates address the potential use of germ line or embryo gene editing for clinical purposes, which, at the moment is not being conducted and is prohibited in several jurisdictions. In the context of these ongoing debates surrounding gene editing, we present herein guidance to further discussion and investigation by highlighting three crucial areas that merit the most attention, time and resources at this stage in the responsible development and use of gene editing technologies: (1) conducting careful scientific research and disseminating results to build a solid evidence base; (2) conducting ethical, legal and social issues research; and (3) conducting meaningful stakeholder engagement, education and dialogue.


Gene editing, which allows for specific location(s) in the genome to be targeted and changed by deleting, adding or substituting nucleotides, is currently the subject of much academic, industry and policy discussions. While not new per se, gene editing has become a particularly salient topic primarily due to a relatively novel tool called CRISPR-Cas9. This specific tool distinguishes itself from its counterparts, (e.g., zinc-finger nucleases and TAL effector nucleases (TALENs)) due to a mixture of increased efficiency (number of sites altered), specificity (at the exact location targeted), ease of use and accessibility for researchers (e.g., commercially available kits), as well as a relatively affordable price [ 1 ]. These attributes make CRISPR-Cas9 an extremely useful and powerful tool that can (and has) been used in research in order to alter the genes in cells from a large range of different organisms, including plants, non-human animals and microorganisms, as well as in human cells [ 2 ]. Ultimately, CRISPR-Cas9 is becoming increasingly available to a larger number of scientists, who have used it, or intend to use it for a myriad of reasons in many different research domains. When such powerful and potentially disruptive technologies or tools (begin to) show a tendency to become widely used, it is common for debate and discussion to erupt. Germane to this debate is the fact that with the advent of CRISPR-Cas9 and other similar tools (e.g., CRISPR Cpf1), the possibility of using the technique of gene editing in a potentially safe and effective manner in humans—whether for somatic or germ line/heritable 1 gene editing—has become feasible in the near to medium future.

With some clinical trials underway, somatic genetic editing for therapeutic purposes is certainly much closer to being offered in the clinic. For example, several clinical trials on HIV are ongoing [ 3 , 4 ]; in 2015 an infant with leukaemia was treated with modified immunes cells (using TALENs) from a healthy donor [ 5 ]. Moreover, in the autumn of 2016, a Chinese group became 'the first to inject a person with cells that contain genes edited using the CRISPR-Cas9 technique' within the context of a clinical trial for aggressive lung cancer [ 6 ]. With such tools, gene editing is being touted as a feasible approach to treat or even cure certain single-gene diseases such as beta-thalassaemia and sickle-cell disease through somatic gene editing [ 3 ].

Beyond somatic cell gene editing, there is also discussion that through the manipulation of germ line cells or embryos, gene editing could be used to trans-generationally 'correct' or avoid single-gene disorders entirely. Notably, (ethical) concerns about heritable gene editing in humans were heightened when in April 2015, a group at Sun Yat-sen University in Guangzhou, China, led by Dr. Junjiu Huang reported they had successfully used gene editing in human embryos [ 7 ]. They used CRISPR-Cas9 to modify the beta-globin gene in non-viable (triplonuclear) spare embryos from in vitro fertility treatments. The authors concluded that while the experiments were successful overall, it is difficult to predict all the intended and unintended outcomes of gene editing in embryos (e.g., mosaicism, off-target events) and that 'clinical applications of the CRISPR-Cas9 system may be premature at this stage' [ 7 ]. Partly in anticipation/response to these experiments and to the increasing use of CRISPR-Cas9 in many different areas, a number of articles were published [ 2 , 8 – 14 ] and meetings were organized [ 9 , 10 , 15 – 17 ] in order to further discuss the scientific, ethical, legal, policy and social issues of gene editing, particularly regarding heritable human gene editing and the responsible way forward.

Internationally, some first position papers on human gene editing were published in 2015 and 2016. Interestingly, these different recommendations and statements do not entirely concur with one another. The United Nations Educational, Scientific and Cultural Organisation (UNESCO) called for a temporary ban on any use of germ line gene editing [ 18 ]. The Society for Developmental Biology 'supports a voluntary moratorium by members of the scientific community on all manipulation of pre- implantation human embryos by genome editing ' [ 19 ]. The Washington Summit (2015) organizers (National Academy of Sciences, the U.S. National Academy of Medicine, the Chinese Academy of Sciences and the U.K.’s Royal Society) recommended against any use of it in the clinic at present [ 17 ] and specified that with increasing scientific knowledge and advances, this stance 'should be revisited on regular basis' [ 17 ]. Indeed, this was done, to some extent, in a follow-up report by the US National Academy of Sciences and National Academy of Medicine, in which the tone of the recommendations appear much more open towards allowing germ line modifications in the clinic [ 20 , 21 ]. Meanwhile, the 'Hinxton group' also stated that gene editing 'is not sufficiently developed to consider human genome editing for clinical reproductive purposes at this time' [ 22 ] and they proposed a set of general recommendations to move the science of gene editing ahead in an established and accepted regulatory framework. Despite these differences, at least two arguments are consistent throughout these guidance documents: (1) the recognition of the need for further research regarding the risks and benefits; and (2) the recognition of the need for on-going discussion and/or education involving a wide range of stakeholders (including lay publics) regarding the potential clinical use and ethical and societal issues and impacts of heritable gene editing. It should be noted, however, that in the 2017 National Academies of Science, and of Medicine Report, the role of public engagement (PE) and dialogue was presented within the context of having to discuss the use of gene editing for enhancement vs. therapy (rather than somatic vs. heritable gene editing, which was the case in the 2015 summit report) [ 20 , 21 ].

Although many stakeholders, including scientists, clinicians and patients are enthusiastic about the present and potential future applications of these more efficient tools in both the research and clinical contexts, there are also important concerns about moving forward with gene editing technologies for clinical use in humans, and to some extent, for use in the laboratory as well. As we have learned from other ethically sensitive areas in the field of genetics and genomics, such as newborn screening, reproductive genetics or return of results, normative positions held by different stakeholders may be dissimilar and even completely incompatible. This might be influenced by various factors, such as commercial pressure, a technological imperative, ideological or political views, or personal values. Furthermore, it is clear that associated values often differ between different stakeholder groups, different cultures and countries (e.g., where some may be more/less liberal), making widespread or global agreement on such criteria very difficult, if not impossible to reach [ 23 , 24 ].

From this perspective, it was important to study the opportunities and challenges created by the use of gene editing (with CRISPR-Cas9 and other similar tools) within the Public and Professional Policy Committee (PPPC) 2 of the European Society of Human Genetics (ESHG; https://www.eshg.org/pppc.0.html ). Our committee advances that ESHG members and related stakeholders should be aware of, and if possible, take part in the current debates surrounding gene editing. Although not all genetics researchers will necessarily use gene editing in their research, and while gene editing as a potential treatment strategy, may appear, initially, somewhat separate from the diagnostics-focused present day Genetics Clinic, we believe that these stakeholders have an important role to play in the discussions around the development of these tools. For one, their expertise in the science of genetics and in dealing with patients with genetic diseases makes them a rare set of stakeholders who are particularly well placed to not only understand the molecular aspects and critically assess the scientific discourse, but also understand current clinic/hospital/health system resources, as well as human/patient needs. Furthermore, in more practical terms, one could consider that clinical genetics laboratories could be involved in the genome sequencing needed to verify for off-target events in somatic gene editing; and that clinical geneticists and/or genetic counsellors could be involved in some way in the offer of such treatment, especially in any counselling related to the genetic condition for which treatment is sought.

The PPPC is an interdisciplinary group of clinicians and researchers with backgrounds in different fields of expertise including Genetics, Health Law, Bioethics, Philosophy, Sociology, Health Policy, Psychology, as well as Health Economics. As a first step, a sub-committee was assigned the task to specifically study the subject of gene editing (including attending international meetings on the subject) and report back to the remaining members. Subsequently, all PPPC members contributed to a collective discussion during the January 2016 PPPC meeting in Zaandam, The Netherlands (15–16 January 2016). At this meeting, a decision was reached to develop an article outlining the main areas that need to be addressed in order to proceed responsibly with human gene editing, including a review of the critical issues for a multidisciplinary audience and the formulation of crucial questions that require answers as we move forward. A first draft of the article was developed by the sub-committee. This draft was further discussed during the 2016 ESHG annual meeting in Barcelona (21–24 May 2016). A second draft was developed and sent out for comments by all PPPC members and a final draft of the article was concluded based on these comments. Although the work herein acts as guidance for further discussion, reflection and research, the ESHG will be publishing separate recommendations on germ line gene editing (accepted during the 2017 annual meeting in Copenhagen, Denmark).

In the context of the ongoing discussion and debate surrounding gene editing, we present herein three crucial areas that merit the most attention at this stage in the responsible development and use of these gene editing technologies, particularly for uses that directly or indirectly affect humans:

  • Conducting careful scientific research to build an evidence base.
  • Conducting ethical, legal and social issues (ELSI) research.
  • Conducting meaningful stakeholder engagement, education, and dialogue (SEED).

Although the main focus of this discussion article is on the use of gene editing in humans (or in human cells) in research and in the clinic for both somatic and heritable gene editing, we also briefly mention the use of gene editing in non-humans as this will also affect humans indirectly.

Conduct ongoing responsible scientific research to build a solid evidence base

The benefits, as well as risks and negative impacts encountered when conducting gene editing in any research context should be adequately monitored and information about these should be made readily available. Particular attention should be paid to the dissemination of the information by reporting and/or publishing both the 'successful' and 'unsuccessful' experiments including the benefits and risks involved in experiments using gene editing in both human and non-human cells and organisms (Table  1 ).

Example of questions that should be addressed regarding building a scientific evidence base for gene editing

An evidence base regarding actual (and potential) health risks and benefits relevant to the use of gene editing in the human context still needs to be built. Therefore, a discussion needs to be held regarding what type of monitoring, reporting and potential proactive search for any physically based risks and benefits should be conducted by researchers using gene editing. Hereby, various questions emerge: are the current expectations and practices of sharing the results of academic and commercial research adequate for the current and future field of gene editing? Should there be a specific system established for the (systematic) monitoring of some types of basic and (pre-) clinical research? If so, which stakeholders/agencies should or could be responsible for this? How could or should an informative long-term medical surveillance of human patients be organized? Following treatment, would patients be obliged to commit to lifelong follow-up? And, if relevant, how could long-term consequences be monitored for future generations? For example, if heritable gene editing was allowed, from logistical and ELSI perspectives, there would be many challenges in attempting to ensure that the initial patients (in whom gene editing was conducted), as well as their offspring would report for some form of follow-up medical check-ups to assess the full impact of gene editing on future generations while still respecting these individuals’ autonomy.

Although the availability of results and potential monitoring are especially important in a biomedical context for all experiments and assays conducted in human cells, and especially in any ex vivo or in vivo trials with humans, relevant and useful information (to the human context and/or affecting humans) can also be gleaned from the results of experiments with non-human animals and even plants. Furthermore, as clearly explained by Caplan et al. [ 2 ], gene editing in insects, plants and non-human animals are currently taking place and may have very concrete and important impacts on human health long before any gene editing experiments are used in any regular way in the health-care setting. As such, while keeping a focus on human use, there should also be monitoring of the results in non-human and non-model organism experiments and potential applications [ 2 ]. Effects might include change of the ecosystem, of microbial environment, (including the microbiome, of parasites and zoonosis, which can involve new combinations with some disappearing, and/or new unexpected ones appearing), change to vegetation, which has a reflection on our vegetal food and on animals’ food and natural niche [ 25 ]. All this will have an impact on the environment, and consequently on organisms (including humans) who are exposed to this altered environment, hence the monitoring of risks and benefits is very important. Especially with gene editing of organisms for human consumption (in essence, genetically modified organisms), it will be important to note that the absence of obvious harms does not mean that there are no harms. Proper studies must be conducted and information regarding these should be made readily available.

Ongoing reflection, research and dialogue on the ELSI of gene editing as it pertains to humans

Research on the ELSI and impacts of human gene editing should be conducted in tandem with the basic scientific research, as well as with any implementations of gene editing in the clinic. Appropriate resources and priority should be granted to support and promote ELSI research; it should be performed unabated, in a meaningful way and by individuals from a diverse range of disciplines (Table  2 ).

Example of questions needed to be addressed for the ethical, legal, and social issues research (ELSI) of gene editing

Ongoing research, reflection and dialogue should address all ELSI 3 salient to gene editing. With respect to gene editing in humans, both somatic and germ line/heritable embryonic gene editing contexts should be addressed. As stated above, we should also study the ELSI of gene editing in non-human and non experimental/model organisms, including issues surrounding the potential (legal and logistical related to implementation) confusions surrounding the use of the terms genetically modified organisms vs. the term gene-edited organisms.

Somatic gene editing

Although somatic gene editing is not free from ethical, legal and social implications—it is, in many respects, similar to more traditional 'gene therapy' approaches in humans—it has been suggested that in many cases, the use of somatic gene editing does not challenge existing ethical, legal and social frameworks as much as heritable gene editing. However, as with any new experimental therapeutic, the unknowns still outweigh what is known and issues of risk assessment and safety, risk/benefit calculation, patient monitoring (potentially for long periods), reimbursement, equity in access to new therapies and the potential for the unjustified draining of resources from more pressing (albeit less novel) therapies, particular protection for vulnerable populations (e.g., fetuses, children (lacking competencies)), and informed consent remain important to study further [ 26 ].

Furthermore, as with any new (disruptive) technology or application, there often remains a gap to be filled between the setting of abstract principles or guidelines and how to apply these in practice. Indeed, important questions and uncertainties surrounding somatic gene editing both in research and in the clinic remain, including, but not limited to: do the established (national and international) legal and regulatory frameworks (e.g., Regulation (EC) no. 1394/2007 on advanced therapy medicinal products) need further shaping/revisions to appropriately address somatic gene editing (including not just issues with the products per se but also for issues related to potential health tourism)? And if so, how would this best be accomplished? Do present clinical trial principles and protocols suffice? How exactly will trials in somatic gene editing be conducted and evaluated? Do we need particular protection or status for patients in such trials? What procedures will be instilled for patients receiving such treatments (e.g., consent, genetic counselling, follow-up monitoring)? Furthermore, to what extent will commercial companies be able to, or be allowed to offer, potentially upon consumer request, treatments based on techniques where so much uncertainty regarding harms remains? Importantly, which health-care professionals will be involved in the provision of somatic gene therapy and the care of patients who undergo such treatments? Who will decide on roles and responsibilities in this novel context? And, based on what criteria will the eligible diseases/populations to be treated be chosen? Indeed, these questions can also all be applied to the context of heritable gene editing, which is discussed below.

Germ line/heritable gene editing

With respect to germ line or heritable gene editing in humans, the ELSI are more challenging than for somatic gene editing, yet they are not all new per se either. Some of these previously discussed concerns include, but are not limited to: issues addressing sanctity of human life, and respect for human dignity, the moral status of the human embryo, individual autonomy, respect and protection for vulnerable persons, respect for cultural and biological diversity and pluralism, disability rights, protection of future generations, equitable access to new technologies and health care, the potential reduction of human genetic variation, stakeholder roles and responsibilities in decision making, as well as how to conduct 'globally responsible' science [ 16 , 2 , 11 , 18 ]. Discussions and debates over some of these topics have been held numerous times in the last three decades, especially within the context of in vitro fertilization, transgenic animals, cloning, pre-implantation genetic diagnosis (PGD), research with stem cells and induced pluripotent stem cells, as well as related to the large scope of discussion around 'enhancement' [ 13 ]. Although it is important to identify and reflect on more general ELSI linked with heritable gene editing and these different contexts, it is also vital to reflect on the ELSI that may be (more) specific to this novel approach. For example, would the fact that for the first time a human (scientist or clinician) would be directly editing the nuclear DNA of another human in a heritable way cause some form of segregation of types of humans? Creators and the created? [ 27 ] Clearly, we need time for additional reflection and discussion on such topics. Distinguishing the ELSI between different yet related contexts will allow for a deeper understanding of the issues and the rationale behind their (un)acceptability by different stakeholders.

A major contextual difference in the current discussions regarding germ line/heritable gene editing is that we have never been so close to having the technology to perform it in humans in a potentially safe and effective manner. Hence, as we move closer to this technical possibility and as we work out the scientific issues of efficiency and safety, the discussions orient themselves increasingly towards the ELSI regarding whether or not we want to even use heritable gene editing in a laboratory or clinical setting, and if so, how we want it to be used, by whom and based on which criteria? This includes, but is not limited to the following questions: should gene editing of human germ line cells, gametes and embryos be allowed in basic research—for the further understanding of human biology (e.g., human development) and without the intention of being used for creating modified human life? Some jurisdictions, such as the UK, have already answered this question, and are allowing this technique in the research setting in human cells in vitro (they will not be placed in a human body, the research will only involve studying the human embryos outside of the body) whereby researchers need to apply for permission to conduct such research. Some believe that allowing this will inevitably lead to the technology being used in the clinic (the so-called 'slippery slope' argument). This, then, brings us to the question at the centre of the debate: should gene editing of germ line cells, gametes or embryos or any other cell that results in a heritable alteration be allowed in humans in a clinical setting? Germane to this issue is another vital question: what, if any, principles or reasoning would justify the use of hereditary gene editing in humans in a clinical context given the current ban on such techniques in many jurisdictions? The new EU clinical trial Regulation (536/2014 Art 90 al.2.) does not allow germ line modification in humans. Should there be leeway for reconsidering this ban in the future in view of the possible benefits of therapeutic germ line gene editing? Should we first understand the risks and benefits of somatic gene editing before even seriously considering heritable gene editing? If we consider that it could be used in some situations, should we only consider using germ line gene editing in the clinic if there are absolutely no other alternatives? Should already established and potentially safer 4 reproductive alternatives, like PGD, be the approaches of choice before even considering germ line gene editing? If we do entertain its use, what, if any criteria, will be safe enough according to different stakeholders (scientists, ethicists, clinicians, policy makers, patients, general public) for it to be legitimate to consider using gene editing for reproductive use? Who will set this safety threshold and based on what risk/benefit calculations? Furthermore, if ever allowed, should heritable human gene editing be permitted only for specific medical purposes with a particular high chance of developing a disease (e.g., only when parents have a-near-100% risk of having a child affected with a serious disorder), and if so, would it matter if the risk is not 100%, but (much) lower? In addition, how can we, or should we define/demarcate medical reasons from enhancement? And, as was posed above for the use in somatic cells, for what medical conditions will gene editing be considered appropriate for use? What will the criteria be and who will decide?

Taking a step back and looking at the issues from a more general perspective, such ELSI research and reflection will need to address, among others, questions that fall under the following themes:

  • the balance of risks and benefits for individual patients and also for the larger community and ecosystem as a whole;
  • the ethical, governance and legislative frameworks;
  • the motivations and interests 'pushing' gene editing to be used;
  • the roles and responsibilities of different stakeholders in ensuring the ethically acceptable use of gene editing, including making sure that every stakeholder voice is heard;
  • the commercial presence, influence, and impact on (the use of) gene editing;
  • the rationale behind the allocation of resources for health care and research and if and which kind of shift might be expected with the new technologies on the rise.

Additional overarching issues relating to ELSI include the need to take a historical perspective and consider previous attempts to deal with genetic technologies and what or how we can learn from these; the need to consider how group actors could or should accept a shared global responsibility when it comes to the governance of gene editing; the potential eugenic tendencies related to new technologies used to eliminate disease phenotypes; the responsibility of current society for future generations; the way different stakeholders may perceive and desire to eliminate (genetic) risk and/or uncertainty by using new technologies such as gene editing; and the potential role(s) different stakeholders, including 'experts', may inadvertently play in propagating a false sense of control over human health.

Although the human context is where much of the attention currently resides, and is indeed, the focus of this article, as mentioned above, we also stress that many concerns and ELSI also stem from the use of gene editing in non-human organisms (plants, insects and microorganisms), the study of which, could inform the human context. More importantly, given that the use of gene editing in these organisms is currently taking place in laboratories and, if released, some of these gene-edited organisms could have a large impact on the environment and society [ 2 ], the ELSI of gene editing in non-human organisms should also be seriously addressed. In this respect, the current debates over definitions and whether plants and non-human animals in which gene editing is performed are considered (legally) genetically modified organisms (GMOs) are particularly important to consider; indeed, this legal stance may be a misleading way to describe the scientific differences in practice. Moreover, the manipulation of definitions may also be used to circumvent the negative press and opinions surrounding GMOs in Europe. Last, but not least, the use of gene editing for the creation of biologic weapons is a possibility that must be discussed and adequately managed [ 2 ].

In order to ensure that the appropriate ELSI research is conducted to answer these myriad questions, ELSI researchers must ensure adequate understanding of scientific facts and possibilities of gene editing, ensure appropriate use of robust methods [ 29 ] to answer specific ELSI questions, as well as learn from previous research on related themes such as (traditional) gene therapy, reproductive technologies, and GMOs. Furthermore, funding will have to be prioritized for ELSI research. National and European funding agencies should ensure that ELSI funding is given in certain proportion to how much gene editing research is being conducted in the laboratory and (pre) clinical domain. In practice, this will mean ensuring that there are adequate review panels for stand-alone ELSI grants, which do not usually fall within any one traditional academic field (e.g., philosophy, law or social sciences). The requirement of including ELSI work packages within science grants may also be useful if such work packages are conducted by ELSI experts (and this is verified by the funding agencies), that they are given enough budget to conduct research and not only offer services, and that the ELSI work package is not co-opted by the science agenda. Spending money on ELSI research has already allowed for the information to be used in more applied ways. Among others, ELSI research has contributed to helping individual researchers understand what kind of research they are (not) allowed to do in certain countries or regions; helped to design appropriate consent forms for research and clinic; and has helped inform policy decisions.

As ELSI are identified, studied and discussed, it will be of utmost importance to communicate these with as many publics as relevant and possible in a clear and comprehensive way so that the largest number of different stakeholders can understand and engage in a discussion about these issues. With respect to engaging non-academic and non-expert audiences in meaningful dialogue, the challenges are greater. Yet, as this is a vital element of conducting science and preparing clinical applications in a responsible manner and stretches beyond the academic focus of ELSI we propose to distinguish a third domain dedicated to such stakeholder engagement, education and dialogue (SEED) described below.

Stakeholder engagement, education and dialogue (SEED)

To deliver socially responsible research (and health care), an ongoing robust and meaningful multidisciplinary dialogue among a diverse group of stakeholders, including lay publics, should be initiated and maintained to discuss scientific and ethically relevant issues related to gene editing. Publics must not only be asked to engage in the discussion, but they should also be given proper information and education regarding the known facts, as well as the uncertainties regarding the use of gene editing in research and in the clinic. In this way, the two focal areas described above will feed into these SEED goals. Stakeholders should also be given the tools to be able to reflect on the ethically relevant issues in order to help informed decision making. Appropriate resources and prioritization should be granted to support and promote SEED (Table  3 ).

Examples of questions to be answered regarding stakeholder, engagement, education and dialogue (SEED) for gene editing

As mentioned in the introduction, the statements addressing gene editing published  by different groups and organizations have highlighted the need for an ongoing discussion about human gene editing among all stakeholders, including experts, and the general public(s) [ 8 , 9 , 17 ], In calling for an 'ongoing international forum to discuss the potential clinical uses of gene editing', the organizing committee of the International Summit on Human Gene Editing stated that

'The forum should be inclusive among nations and engage a wide range of perspectives and expertise – including from biomedical scientists, social scientists, ethicists, health care providers, patients and their families, people with disabilities, policymakers, regulators, research funders, faith leaders, public interest advocates, industry representatives, and members of the general public' [ 17 ].

Hence, this implies that not only should different expertise be represented in this ongoing discussion, but lay publics should also be included. For this to be a meaningful and impactful endeavour, all stakeholders involved should be appropriately informed and educated about the basic science and possibilities of gene editing. Academic/professional silos, differences in language, definitions, approaches and general lack of experience with multi- and inter-disciplinary work are all barriers to involving different expert stakeholders in meaningful exchange and dialogue. Some first constructive steps have included the posting online of meeting and conference presentations on gene editing (e.g., the 3 days of the Washington Summit ( http://www.nationalacademies.org/gene-editing/Gene-Edit-Summit/index.htm .), Eurordis webinars and meetings aimed at informing patients, http://www.eurordis.org/tv ). Beyond this, one important barrier to having a truly meaningful and inclusive multidisciplinary discussion about new technologies is the (potential) lack of knowledge and/or understanding of different publics [ 30 ]. Indeed, it is not reasonable for experts to expect that all concerned stakeholders are properly informed about the science and/or the social and ethical issues, which are important requisites for having meaningful and productive conversations about responsible gene editing. Furthermore, a pitfall we must avoid is using PE with the aim of persuading or gaining acceptance of technologies instead of 'true participation' [ 31 ] and as a means to allow for supporting informed opinions.

Another critical issue is the role and influence of different stakeholders, including the media, in educating and informing the public. What are the roles and responsibilities of different stakeholders in setting up and maintaining responsible engagement and dialogue? What will, and what should be the role of scientists in popular media communications and other SEED activities? Where will the funding for these activities come from? Financial and temporal resources will have to be reserved for such SEED regarding gene editing. Resources will also be needed to conduct further research on the best way to engage different publics and to study whether engagement strategies are successful.

Moreover, before engaging different publics and asking for their feedback, whichever stakeholders take on this task must seriously reflect on the precise reasons for which lay publics are being engaged. What is the goal? And, what method of engagement will best meet these goals? There is also a need for honest evaluation of engagement efforts to report on their impacts and outcomes. Indeed, the purposes of PE in science can vary widely, including, among others, informing, consulting and/or collaborating; [ 32 ] clearly each of these implies different levels of participation by publics, and by extension, different levels of influence on a topic. Importantly, there are a long list of questions that also need to be answered for PE (Table  3 ), including but not limited to how different voices will be weighed and if or how they will be used in any policy or decision making.

The value of PE in the form of public dialogue in a democratic society, (and we would specify its contribution to responsible science) is very well summarized by Mohr and Raman (2012) in a perspective piece on the UK Stem Cell Dialogue: [ 31 ]

'The value of public dialogue in a democratic society is twofold. From a normative perspective, the process of PE is in itself a good thing in that the public should be consulted on decisions in which they have a stake. From a substantive standpoint, PE generates manifold perspectives, visions, and values that are relevant to the science and technologies in question, and could potentially lead to more socially robust outcomes (which may differ from the outcomes envisaged by sponsors or scientists)' [ 31 ].

Particularly for the purposes of gene editing, we consider SEED a way to try to ensure that decisions on a subject that is filled with uncertainties, and could have important implications for society for generations to come, is not left in the hands of a few. We want to underline the need for: lay publics to be informed to support transparency; lay publics to be educated to support autonomy and informed opinion/decision making; different voices and concerns to be heard and considered through ongoing dialogue to help ensure that no one stakeholder group pursue their interests unchecked. Although it is beyond the scope of this article to go into any detail, it is important to take the time to learn from past and ongoing engagement efforts in science in general [ 32 ], as well as in biomedicine, including areas like stem cell research [ 31 ] and genetics [ 30 , 33 ]. For example, we can learn about: how PE can generate value and impact for a society, as well as how to conceive of and evaluate a PE programme [ 32 ]; the nuances around 'representative samples' and if they really are representative [ 31 ]; how letting citizens be the 'architects' rather than just participants of engagement (activities) could help to ward against the generation of 'predetermined outcomes' [ 31 ]; the utility of deliberative PE to 'offer useful information to policy makers [ 30 ]. Given all the different reasons for PE, and given the higher standards expected for PE in recent years [ 34 ] it is to be expected that each PE activity will have to be adjusted for the specific context. There are, also, useful tools for PE from a European funded project called 'PE2020, Public Engagement Innovations for Horizon 2020' [ 35 ], which has as an aim to 'to identify, analyse and refine innovative public engagement (PE) tools and instruments for dynamic governance in the field of Science in Society (SiS)' [ 35 ].

As already mentioned above for ELSI research, funding agencies will have to prioritize resources for these SEED activities, and the strategies we outlined for ELSI, could also apply for SEED.

In the midst of a plethora of debate over gene editing, different stakeholder views, preferences, agendas and messages, it is crucial to focus our limited resources, including human resources, time and finances on the most important areas that will enable and support the responsible use of gene editing. We have identified the following three areas that merit an equitable distribution of attention and resources in the immediate and medium-term future:

  • Conducting ELSI research.

Indeed, one way to ensure that each of these three important areas receive adequate financial support to conduct the necessary work would be for international and national funding agencies to announce specific funding calls on gene editing. They could also encourage or require that scientific projects focused on gene editing include ELSI and SEED along with the scientific work packages. Furthermore, understandably, priorities need to be made with respect to resource allocation in the biomedical sciences, especially in such uncertain financial contexts, however, as expressed at the World Science Forum in Budapest in November 2011, we must ward against scarce funding being funnelled to single disciplines since it is common knowledge that much of the most valuable work is now multidisciplinary [ 36 ]. Moreover, at such a time funding entities must not 'expel' the social sciences 'from the temple' but rather, the hard sciences should 'invite them in to help public engagement' [ 36 ].


We thank all members of the Public and Professional Policy Committee of the ESHG for their valuable feedback and generosity in discussions. Members of PPPC in 2015–2017 were Caroline Benjamin, Pascal Borry, Angus Clarke, Martina Cornel, Carla van El, Florence Fellmann, Francesca Forzano, Heidi Carmen Howard, Hulya Kayserili, Bela Melegh, Alvaro Mendes, Markus Perola, Dragica Radijkovic, Maria Soller, Emmanuelle Rial-Sebbag and Guido de Wert. We also thank the anonymous reviewers for their constructive comments, which have helped to improve the article. Part of this work has been supported by the Swedish Foundation for Humanities and Social Science under grant M13-0260:1, and the CHIP ME COST Action IS1303.

Compliance with ethical standards

Conflict of interest.

The authors declare that they have no competing interests.

1 In this category, we include the editing of germ line cells, or embryonic cells, or even somatic cells that are edited and promoted to then become germ line cells in such a way that the alterations would be heritable.

2 This group studies salient ethical, legal, social, policy and economic aspects relating to genetics and genomics.

3 Herein, the terms 'ethical', 'legal' and 'social' are used in a broad sense, where, for example, issues such as economic evaluations, public health prioritization and other related areas would also be included. Indeed the first goal of 'SEED' (see below) is also, to some extent, part of ELSI research, however, given the paucity of meaningful PE in the past, combined with strong consensus regarding the current need and importance of such activities, we have chosen to highlight it separately. We also wish to stress the difference between academic ELSI research and the work of ethics review committees. Although both deal with ethical and legal issues, the former has as a main goal to advance research and does not act as a policing body, nor does it have an agenda per se. Furthemore, ELSI research does not only identify issues to be addressed but also works with scientists and policy makers to address the issues responsibly.

4 It is important to note that despite attempts at addressing these issues, even for technologies such as PGD [ 28 ].

  • Published: 31 October 2020

A review on genetic algorithm: past, present, and future

  • Sourabh Katoch 1 ,
  • Sumit Singh Chauhan 1 &
  • Vijay Kumar   ORCID: orcid.org/0000-0002-3460-6989 1  

Multimedia Tools and Applications volume  80 ,  pages 8091–8126 ( 2021 ) Cite this article

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In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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1 introduction.

In the recent years, metaheuristic algorithms are used to solve real-life complex problems arising from different fields such as economics, engineering, politics, management, and engineering [ 113 ]. Intensification and diversification are the key elements of metaheuristic algorithm. The proper balance between these elements are required to solve the real-life problem in an effective manner. Most of metaheuristic algorithms are inspired from biological evolution process, swarm behavior, and physics’ law [ 17 ]. These algorithms are broadly classified into two categories namely single solution and population based metaheuristic algorithm (Fig.  1 ). Single-solution based metaheuristic algorithms utilize single candidate solution and improve this solution by using local search. However, the solution obtained from single-solution based metaheuristics may stuck in local optima [ 112 ]. The well-known single-solution based metaheuristics are simulated annealing, tabu search (TS), microcanonical annealing (MA), and guided local search (GLS). Population-based metaheuristics utilizes multiple candidate solutions during the search process. These metaheuristics maintain the diversity in population and avoid the solutions are being stuck in local optima. Some of well-known population-based metaheuristic algorithms are genetic algorithm (GA) [ 135 ], particle swarm optimization (PSO) [ 101 ], ant colony optimization (ACO) [ 47 ], spotted hyena optimizer (SHO) [ 41 ], emperor penguin optimizer (EPO) [ 42 ], and seagull optimization (SOA) [ 43 ].

figure 1

Classification of metaheuristic Algorithms

Among the metaheuristic algorithms, Genetic algorithm (GA) is a well-known algorithm, which is inspired from biological evolution process [ 136 ]. GA mimics the Darwinian theory of survival of fittest in nature. GA was proposed by J.H. Holland in 1992. The basic elements of GA are chromosome representation, fitness selection, and biological-inspired operators. Holland also introduced a novel element namely, Inversion that is generally used in implementations of GA [ 77 ]. Typically, the chromosomes take the binary string format. In chromosomes, each locus (specific position on chromosome) has two possible alleles (variant forms of genes) - 0 and 1. Chromosomes are considered as points in the solution space. These are processed using genetic operators by iteratively replacing its population. The fitness function is used to assign a value for all the chromosomes in the population [ 136 ]. The biological-inspired operators are selection, mutation, and crossover. In selection, the chromosomes are selected on the basis of its fitness value for further processing. In crossover operator, a random locus is chosen and it changes the subsequences between chromosomes to create off-springs. In mutation, some bits of the chromosomes will be randomly flipped on the basis of probability [ 77 , 135 , 136 ]. The further development of GA based on operators, representation, and fitness has diminished. Therefore, these elements of GA are focused in this paper.

The main contribution of this paper are as follows:

The general framework of GA and hybrid GA are elaborated with mathematical formulation.

The various types of genetic operators are discussed with their pros and cons.

The variants of GA with their pros and cons are discussed.

The applicability of GA in multimedia fields is discussed.

The main aim of this paper is two folds. First, it presents the variants of GA and their applicability in various fields. Second, it broadens the area of possible users in various fields. The various types of crossover, mutation, selection, and encoding techniques are discussed. The single-objective, multi-objective, parallel, and hybrid GAs are deliberated with their advantages and disadvantages. The multimedia applications of GAs are elaborated.

The remainder of this paper is organized as follows: Section 2 presents the methodology used to carry out the research. The classical genetic algorithm and genetic operators are discussed in Section 3 . The variants of genetic algorithm with pros and cons are presented in Section 4 . Section 5 describes the applications of genetic algorithm. Section 6 presents the challenges and future research directions. The concluding remarks are drawn in Section 7 .

2 Research methodology

PRISMA’s guidelines were used to conduct the review of GA [ 138 ]. A detailed search has been done on Google scholar and PubMed for identification of research papers related to GA. The important research works found during the manual search were also added in this paper. During search, some keywords such as “Genetic Algorithm” or “Application of GA” or “operators of GA” or “representation of GA” or “variants of GA” were used. The selection and rejection of explored research papers are based on the principles, which is mentioned in Table 1 .

Total 27,64,792 research papers were explored on Google Scholar, PubMed and manual search. The research work related to genetic algorithm for multimedia applications were also included. During the screening of research papers, all the duplicate papers and papers published before 2007 were discarded. 4340 research papers were selected based on 2007 and duplicate entries. Thereafter, 4050 research papers were eliminated based on titles. 220 research papers were eliminated after reading of abstract. 70 research papers were left after third round of screening. 40 more research papers were discarded after full paper reading and facts found in the papers. After the fourth round of screening, final 30 research papers are selected for review.

Based on the relevance and quality of research, 30 papers were selected for evaluation. The relevance of research is decided through some criteria, which is mentioned in Table 1 . The selected research papers comprise of genetic algorithm for multimedia applications, advancement of their genetic operators, and hybridization of genetic algorithm with other well-established metaheuristic algorithms. The pros and cons of genetic operators are shown in preceding section.

3 Background

In this section, the basic structure of GA and its genetic operators are discussed with pros and cons.

3.1 Classical GA

Genetic algorithm (GA) is an optimization algorithm that is inspired from the natural selection. It is a population based search algorithm, which utilizes the concept of survival of fittest [ 135 ]. The new populations are produced by iterative use of genetic operators on individuals present in the population. The chromosome representation, selection, crossover, mutation, and fitness function computation are the key elements of GA. The procedure of GA is as follows. A population ( Y ) of n chromosomes are initialized randomly. The fitness of each chromosome in Y is computed. Two chromosomes say C1 and C2 are selected from the population Y according to the fitness value. The single-point crossover operator with crossover probability (C p ) is applied on C1 and C2 to produce an offspring say O . Thereafter, uniform mutation operator is applied on produced offspring ( O ) with mutation probability (M p ) to generate O′ . The new offspring O′ is placed in new population. The selection, crossover, and mutation operations will be repeated on current population until the new population is complete. The mathematical analysis of GA is as follows [ 126 ]:

GA dynamically change the search process through the probabilities of crossover and mutation and reached to optimal solution. GA can modify the encoded genes. GA can evaluate multiple individuals and produce multiple optimal solutions. Hence, GA has better global search capability. The offspring produced from crossover of parent chromosomes is probable to abolish the admirable genetic schemas parent chromosomes and crossover formula is defined as [ 126 ]:

where g is the number of generations, and G is the total number of evolutionary generation set by population. It is observed from Eq.( 1 ) that R is dynamically changed and increase with increase in number of evolutionary generation. In initial stage of GA, the similarity between individuals is very low. The value of R should be low to ensure that the new population will not destroy the excellent genetic schema of individuals. At the end of evolution, the similarity between individuals is very high as well as the value of R should be high.

According to Schema theorem, the original schema has to be replaced with modified schema. To maintain the diversity in population, the new schema keep the initial population during the early stage of evolution. At the end of evolution, the appropriate schema will be produced to prevent any distortion of excellent genetic schema [ 65 , 75 ]. Algorithm 1 shows the pseudocode of classical genetic algorithm.

Algorithm 1: Classical Genetic Algorithm (GA)

figure a

3.2 Genetic operators

GAs used a variety of operators during the search process. These operators are encoding schemes, crossover, mutation, and selection. Figure 2 depicts the operators used in GAs.

figure 2

Operators used in GA

3.2.1 Encoding schemes

For most of the computational problems, the encoding scheme (i.e., to convert in particular form) plays an important role. The given information has to be encoded in a particular bit string [ 121 , 183 ]. The encoding schemes are differentiated according to the problem domain. The well-known encoding schemes are binary, octal, hexadecimal, permutation, value-based, and tree.

Binary encoding is the commonly used encoding scheme. Each gene or chromosome is represented as a string of 1 or 0 [ 187 ]. In binary encoding, each bit represents the characteristics of the solution. It provides faster implementation of crossover and mutation operators. However, it requires extra effort to convert into binary form and accuracy of algorithm depends upon the binary conversion. The bit stream is changed according the problem. Binary encoding scheme is not appropriate for some engineering design problems due to epistasis and natural representation.

In octal encoding scheme, the gene or chromosome is represented in the form of octal numbers (0–7). In hexadecimal encoding scheme, the gene or chromosome is represented in the form of hexadecimal numbers (0–9, A-F) [ 111 , 125 , 187 ]. The permutation encoding scheme is generally used in ordering problems. In this encoding scheme, the gene or chromosome is represented by the string of numbers that represents the position in a sequence. In value encoding scheme, the gene or chromosome is represented using string of some values. These values can be real, integer number, or character [ 57 ]. This encoding scheme can be helpful in solving the problems in which more complicated values are used. As binary encoding may fail in such problems. It is mainly used in neural networks for finding the optimal weights.

In tree encoding, the gene or chromosome is represented by a tree of functions or commands. These functions and commands can be related to any programming language. This is very much similar to the representation of repression in tree format [ 88 ]. This type of encoding is generally used in evolving programs or expressions. Table 2 shows the comparison of different encoding schemes of GA.

3.2.2 Selection techniques

Selection is an important step in genetic algorithms that determines whether the particular string will participate in the reproduction process or not. The selection step is sometimes also known as the reproduction operator [ 57 , 88 ]. The convergence rate of GA depends upon the selection pressure. The well-known selection techniques are roulette wheel, rank, tournament, boltzmann, and stochastic universal sampling.

Roulette wheel selection maps all the possible strings onto a wheel with a portion of the wheel allocated to them according to their fitness value. This wheel is then rotated randomly to select specific solutions that will participate in formation of the next generation [ 88 ]. However, it suffers from many problems such as errors introduced by its stochastic nature. De Jong and Brindle modified the roulette wheel selection method to remove errors by introducing the concept of determinism in selection procedure. Rank selection is the modified form of Roulette wheel selection. It utilizes the ranks instead of fitness value. Ranks are given to them according to their fitness value so that each individual gets a chance of getting selected according to their ranks. Rank selection method reduces the chances of prematurely converging the solution to a local minima [ 88 ].

Tournament selection technique was first proposed by Brindle in 1983. The individuals are selected according to their fitness values from a stochastic roulette wheel in pairs. After selection, the individuals with higher fitness value are added to the pool of next generation [ 88 ]. In this method of selection, each individual is compared with all n-1 other individuals if it reaches the final population of solutions [ 88 ]. Stochastic universal sampling (SUS) is an extension to the existing roulette wheel selection method. It uses a random starting point in the list of individuals from a generation and selects the new individual at evenly spaced intervals [ 3 ]. It gives equal chance to all the individuals in getting selected for participating in crossover for the next generation. Although in case of Travelling Salesman Problem, SUS performs well but as the problem size increases, the traditional Roulette wheel selection performs relatively well [ 180 ].

Boltzmann selection is based on entropy and sampling methods, which are used in Monte Carlo Simulation. It helps in solving the problem of premature convergence [ 118 ]. The probability is very high for selecting the best string, while it executes in very less time. However, there is a possibility of information loss. It can be managed through elitism [ 175 ]. Elitism selection was proposed by K. D. Jong (1975) for improving the performance of Roulette wheel selection. It ensures the elitist individual in a generation is always propagated to the next generation. If the individual having the highest fitness value is not present in the next generation after normal selection procedure, then the elitist one is also included in the next generation automatically [ 88 ]. The comparison of above-mentioned selection techniques are depicted in Table 3 .

3.2.3 Crossover operators

Crossover operators are used to generate the offspring by combining the genetic information of two or more parents. The well-known crossover operators are single-point, two-point, k-point, uniform, partially matched, order, precedence preserving crossover, shuffle, reduced surrogate and cycle.

In a single point crossover, a random crossover point is selected. The genetic information of two parents which is beyond that point will be swapped with each other [ 190 ]. Figure 3 shows the genetic information after swapping. It replaced the tail array bits of both the parents to get the new offspring.

figure 3

Swapping genetic information after a crossover point

In a two point and k-point crossover, two or more random crossover points are selected and the genetic information of parents will be swapped as per the segments that have been created [ 190 ]. Figure 4 shows the swapping of genetic information between crossover points. The middle segment of the parents is replaced to generate the new offspring.

figure 4

Swapping genetic information between crossover points

In a uniform crossover, parent cannot be decomposed into segments. The parent can be treated as each gene separately. We randomly decide whether we need to swap the gene with the same location of another chromosome [ 190 ]. Figure 5 depicts the swapping of individuals under uniform crossover operation.

figure 5

Swapping individual genes

Partially matched crossover (PMX) is the most frequently used crossover operator. It is an operator that performs better than most of the other crossover operators. The partially matched (mapped) crossover was proposed by D. Goldberg and R. Lingle [ 66 ]. Two parents are choose for mating. One parent donates some part of genetic material and the corresponding part of other parent participates in the child. Once this process is completed, the left out alleles are copied from the second parent [ 83 ]. Figure 6 depicts the example of PMX.

figure 6

Partially matched crossover (PMX) [ 117 ]

Order crossover (OX) was proposed by Davis in 1985. OX copies one (or more) parts of parent to the offspring from the selected cut-points and fills the remaining space with values other than the ones included in the copied section. The variants of OX are proposed by different researchers for different type of problems. OX is useful for ordering problems [ 166 ]. However, it is found that OX is less efficient in case of Travelling Salesman Problem [ 140 ]. Precedence preserving crossover (PPX) preserves the ordering of individual solutions as present in the parent of offspring before the application of crossover. The offspring is initialized to a string of random 1’s and 0’s that decides whether the individuals from both parents are to be selected or not. In [ 169 ], authors proposed a modified version of PPX for multi-objective scheduling problems.

Shuffle crossover was proposed by Eshelman et al. [ 20 ] to reduce the bias introduced by other crossover techniques. It shuffles the values of an individual solution before the crossover and unshuffles them after crossover operation is performed so that the crossover point does not introduce any bias in crossover. However, the utilization of this crossover is very limited in the recent years. Reduced surrogate crossover (RCX) reduces the unnecessary crossovers if the parents have the same gene sequence for solution representations [ 20 , 139 ]. RCX is based on the assumption that GA produces better individuals if the parents are sufficiently diverse in their genetic composition. However, RCX cannot produce better individuals for those parents that have same composition. Cycle crossover was proposed by Oliver [ 140 ]. It attempts to generate an offspring using parents where each element occupies the position by referring to the position of their parents [ 140 ]. In the first cycle, it takes some elements from the first parent. In the second cycle, it takes the remaining elements from the second parent as shown in Fig.  7 .

figure 7

Cycle Crossover (CX) [ 140 ]

Table 4 shows the comparison of crossover techniques. It is observed from Table 4 that single and k-point crossover techniques are easy to implement. Uniform crossover is suitable for large subsets. Order and cycle crossovers provide better exploration than the other crossover techniques. Partially matched crossover provides better exploration. The performance of partially matched crossover is better than the other crossover techniques. Reduced surrogate and cycle crossovers suffer from premature convergence.

3.2.4 Mutation operators

Mutation is an operator that maintains the genetic diversity from one population to the next population. The well-known mutation operators are displacement, simple inversion, and scramble mutation. Displacement mutation (DM) operator displaces a substring of a given individual solution within itself. The place is randomly chosen from the given substring for displacement such that the resulting solution is valid as well as a random displacement mutation. There are variants of DM are exchange mutation and insertion mutation. In Exchange mutation and insertion mutation operators, a part of an individual solution is either exchanged with another part or inserted in another location, respectively [ 88 ].

The simple inversion mutation operator (SIM) reverses the substring between any two specified locations in an individual solution. SIM is an inversion operator that reverses the randomly selected string and places it at a random location [ 88 ]. The scramble mutation (SM) operator places the elements in a specified range of the individual solution in a random order and checks whether the fitness value of the recently generated solution is improved or not [ 88 ]. Table 5 shows the comparison of different mutation techniques.

Table 6 shows the best combination of encoding scheme, mutation, and crossover techniques. It is observed from Table 6 that uniform and single-point crossovers can be used with most of encoding and mutation operators. Partially matched crossover is used with inversion mutation and permutation encoding scheme provides the optimal solution.

4 Variants of GA

Various variants of GA’s have been proposed by researchers. The variants of GA are broadly classified into five main categories namely, real and binary coded, multiobjective, parallel, chaotic, and hybrid GAs. The pros and cons of these algorithms with their application has been discussed in the preceding subsections.

4.1 Real and binary coded GAs

Based on the representation of chromosomes, GAs are categorized in two classes, namely binary and real coded GAs.

4.1.1 Binary coded GAs

The binary representation was used to encode GA and known as binary GA. The genetic operators were also modified to carry out the search process. Payne and Glen [ 153 ] developed a binary GA to identify the similarity among molecules. They used binary representation for position of molecule and their conformations. However, this method has high computational complexity. Longyan et al. [ 203 ] investigated three different method for wind farm design using binary GA (BGA). Their method produced better fitness value and farm efficiency. Shukla et al. [ 185 ] utilized BGA for feature subset selection. They used mutual information maximization concept for selecting the significant features. BGAs suffer from Hamming cliffs, uneven schema, and difficulty in achieving precision [ 116 , 199 ].

4.1.2 Real-coded GAs

Real-coded GAs (RGAs) have been widely used in various real-life applications. The representation of chromosomes is closely associated with real-life problems. The main advantages of RGAs are robust, efficient, and accurate. However, RGAs suffer from premature convergence. Researchers are working on RGAs to improve their performance. Most of RGAs are developed by modifying the crossover, mutation and selection operators.

Crossover operators

The searching capability of crossover operators are not satisfactory for continuous search space. The developments in crossover operators have been done to enhance their performance in real environment. Wright [ 210 ] presented a heuristics crossover that was applied on parents to produce off-spring. Michalewicz [ 135 ] proposed arithmetical crossover operators for RGAs. Deb and Agrawal [ 34 ] developed a real-coded crossover operator, which is based on characteristics of single-point crossover in BGA. The developed crossover operator named as simulated binary crossover (SBX). SBX is able to overcome the Hamming cliff, precision, and fixed mapping problem. The performance of SBX is not satisfactory in two-variable blocked function. Eshelman et al. [ 53 ] utilized the schemata concept to design the blend crossover for RGAs. The unimodal normal distribution crossover operator (UNDX) was developed by Ono et al. [ 144 ]. They used ellipsoidal probability distribution to generate the offspring. Kita et al. [ 106 ] presented a multi-parent UNDX (MP-UNDX), which is the extension of [ 144 ]. However, the performance of RGA with MP-UNDX is much similar to UNDX. Deep and Thakur [ 39 ] presented a Laplace crossover for RGAs, which is based on Laplacian distribution. Chuang et al. [ 27 ] developed a direction based crossover to further explore the all possible search directions. However, the search directions are limited. The heuristic normal distribution crossover operator was developed by Wang et al. [ 207 ]. It generates the cross-generated offspring for better search operation. However, the better individuals are not considered in this approach. Subbaraj et al. [ 192 ] proposed Taguchi self-adaptive RCGA. They used Taguchi method and simulated binary crossover to exploit the capable offspring.

Mutation operators

Mutation operators generate diversity in the population. The two main challenges have to tackle during the application of mutation. First, the probability of mutation operator that was applied on population. Second, the outlier produced in chromosome after mutation process. Michalewicz [ 135 ] presented uniform and non-uniform mutation operators for RGAs. Michalewicz and Schoenauer [ 136 ] developed a special case of uniform mutation. They developed boundary mutation. Deep and Thakur [ 38 ] presented a novel mutation operator based on power law and named as power mutation. Das and Pratihar [ 30 ] presented direction-based exponential mutation operator. They used direction information of variables. Tang and Tseng [ 196 ] presented a novel mutation operator for enhancing the performance of RCGA. Their approach was fast and reliable. However, it stuck in local optima for some applications. Deb et al. [ 35 ] developed polynomial mutation that was used in RCGA. It provides better exploration. However, the convergence speed is slow and stuck in local optima. Lucasius et al. [ 129 ] proposed a real-coded genetic algorithm (RCGA). It is simple and easy to implement. However, it suffers from local optima problem. Wang et al. [ 205 ] developed multi-offspring GA and investigated their performance over single point crossover. Wang et al. [ 206 ] stated the theoretical basis of multi-offspring GA. The performance of this method is better than non-multi-offspring GA. Pattanaik et al. [ 152 ] presented an improvement in the RCGA. Their method has better convergence speed and quality of solution. Wang et al. [ 208 ] proposed multi-offspring RCGA with direction based crossover for solving constrained problems.

Table 7 shows the mathematical formulation of genetic operators in RGAs.

4.2 Multiobjective GAs

Multiobjective GA (MOGA) is the modified version of simple GA. MOGA differ from GA in terms of fitness function assignment. The remaining steps are similar to GA. The main motive of multiobjective GA is to generate the optimal Pareto Front in the objective space in such a way that no further enhancement in any fitness function without disturbing the other fitness functions [ 123 ]. Convergence, diversity, and coverage are main goal of multiobjective GAs. The multiobjective GAs are broadly categorized into two categories namely, Pareto-based, and decomposition-based multiobjective GAs [ 52 ]. These techniques are discussed in the preceding subsections.

4.2.1 Pareto-based multi-objective GA

The concept of Pareto dominance was introduced in multiobjective GAs. Fonseca and Fleming [ 56 ] developed first multiobjective GA (MOGA). The niche and decision maker concepts were proposed to tackle the multimodal problems. However, MOGA suffers from parameter tuning problem and degree of selection pressure. Horn et al. [ 80 ] proposed a niched Pareto genetic algorithm (NPGA) that utilized the concept of tournament selection and Pareto dominance. Srinivas and Deb [ 191 ] developed a non-dominated sorting genetic algorithm (NSGA). However, it suffers from lack of elitism, need of sharing parameter, and high computation complexity. To alleviate these problems, Deb et al. [ 36 ] developed a fast elitist non-dominated sorting genetic algorithm (NSGA-II). The performance of NSGA-II may be deteriorated for many objective problems. NSGA-II was unable to maintain the diversity in Pareto-front. To alleviate this problem, Luo et al. [ 130 ] introduced a dynamic crowding distance in NSGA-II. Coello and Pulido [ 28 ] developed a multiobjective micro GA. They used an archive for storing the non-dominated solutions. The performance of Pareto-based approaches may be deteriorated in many objective problems [ 52 ].

4.2.2 Decomposition-based multiobjective GA

Decomposition-based MOGAs decompose the given problem into multiple subproblems. These subproblems are solved simultaneously and exchange the solutions among neighboring subproblems [ 52 ]. Ishibuchi and Murata [ 84 ] developed a multiobjective genetic local search (MOGLS). In MOGLS, the random weights were used to select the parents and local search for their offspring. They used generation replacement and roulette wheel selection method. Jaszkiewicz [ 86 ] modified the MOGLS by utilizing different selection mechanisms for parents. Murata and Gen [ 141 ] proposed a cellular genetic algorithm for multiobjective optimization (C-MOGA) that was an extension of MOGA. They added cellular structure in MOGA. In C-MOGA, the selection operator was performed on the neighboring of each cell. C-MOGA was further extended by introducing an immigration procedure and known as CI-MOGA. Alves and Almeida [ 11 ] developed a multiobjective Tchebycheffs-based genetic algorithm (MOTGA) that ensures convergence and diversity. Tchebycheff scalar function was used to generate non-dominated solution set. Patel et al. [ 151 ] proposed a decomposition based MOGA (D-MOGA). They integrated opposition based learning in D-MOGA for weight vector generation. D-MOGA is able to maintain the balance between diversity of solutions and exploration of search space.

4.3 Parallel GAs

The motivation behind the parallel GAs is to improve the computational time and quality of solutions through distributed individuals. Parallel GAs are categorized into three broad categories such as master-slave parallel GAs, fine grained parallel GAs, and multi-population coarse grained parallel Gas [ 70 ]. In master-slave parallel GA, the computation of fitness functions is distributed over the several processors. In fine grained GA, parallel computers are used to solve the real-life problems. The genetic operators are bounded to their neighborhood. However, the interaction is allowed among the individuals. In coarse grained GA, the exchange of individuals among sub-populations is performed. The control parameters are also transferred during migration. The main challenges in parallel GAs are to maximize memory bandwidth and arrange threads for utilizing the power of GPUs [ 23 ]. Table 8 shows the comparative analysis of parallel GAs in terms of hardware and software. The well-known parallel GAs are studied in the preceding subsections.

4.3.1 Master slave parallel GA

The large number of processors are utilized in master-slave parallel GA (MS-PGA) as compared to other approaches. The computation of fitness functions may be increased by increasing the number of processors. Hong et al. [ 79 ] used MS-PGA for solving data mining problems. Fuzzy rules are used with parallel GA. The evaluation of fitness function was performed on slave machines. However, it suffers from high computational time. Sahingzo [ 174 ] implemented MS-PGA for UAV path finding problem. The genetic operators were executed on processors. They used multicore CPU with four cores. Selection and fitness evaluation was done on slave machines. MS-PGA was applied on traffic assignment problem in [ 127 ]. They used thirty processors to solve this problem at National University of Singapore. Yang et al. [ 213 ] developed a web-based parallel GA. They implemented the master slave version of NSGA-II in distributed environment. However, the system is complex in nature.

4.3.2 Fine grained parallel GA

In last few decades, researchers are working on migration policies of fine grained parallel GA (FG-PGA). Porta et al. [ 161 ] utilized clock-time for migration frequency, which is independent of generations. They used non-uniform structure and static configuration. The best solution was selected for migration and worst solution was replaced with migrant solution. Kurdi [ 115 ] used adaptive migration frequency. The migration procedure starts until there is no change in the obtained solutions after ten successive generations. The non-uniform and dynamic structure was used. In [ 209 ], local best solutions were synchronized and formed a global best solutions. The global best solutions were transferred to all processors for father execution. The migration frequency depends upon the number of generation. They used uniform structure with fixed configuration. Zhang et al. [ 220 ] used parallel GA to solve the set cover problem of wireless networks. They used divide-and-conquer strategy to decompose the population into sub-populations. Thereafter, the genetic operators were applied on local solutions and Kuhn-Munkres was used to merge the local solutions.

4.3.3 Coarse grained parallel GA

Pinel et al. [ 158 ] proposed a GraphCell. The population was initialized with random values and one solution was initialized with Min-min heuristic technique. 448 processors were used to implement the proposed approach. However, coarse grained parallel GAs are less used due to complex in nature. The hybrid parallel GAs are widely used in various applications. Shayeghi et al. [ 182 ] proposed a pool-based Birmingham cluster GA. Master node was responsible for managing global population. Slave node selected the solutions from global population and executed it. 240 processors are used for computation. Roberge et al. [ 170 ] used hybrid approach to optimize switching angle of inverters. They used four different strategies for fitness function computation. Nowadays, GPU, cloud, and grid are most popular hardware for parallel GAs [ 198 ].

4.4 Chaotic GAs

The main drawback of GAs is premature convergence. The chaotic systems are incorporated into GAs to alleviate this problem. The diversity of chaos genetic algorithm removes premature convergence. Crossover and mutation operators can be replaced with chaotic maps. Tiong et al. [ 197 ] integrated the chaotic maps into GA for further improvement in accuracy. They used six different chaotic maps. The performance of Logistic, Henon and Ikeda chaotic GA performed better than the classical GA. However, these techniques suffer from high computational complexity. Ebrahimzadeh and Jampour [ 48 ] used Lorenz chaotic for genetic operators of GA to eliminate the local optima problem. However, the proposed approach was unable to find relationship between entropy and chaotic map. Javidi and Hosseinpourfard [ 87 ] utilized two chaotic maps namely logistic map and tent map for generating chaotic values instead of random selection of initial population. The proposed chaotic GA performs better than the GA. However, this method suffers from high computational complexity. Fuertes et al. [ 60 ] integrated the entropy into chaotic GA. The control parameters are modified through chaotic maps. They investigated the relationship between entropy and performance optimization.

Chaotic systems have also used in multiobjective and hybrid GAs. Abo-Elnaga and Nasr [ 5 ] integrated chaotic system into modified GA for solving Bi-level programming problems. Chaotic helps the proposed algorithm to alleviate local optima and enhance the convergence. Tahir et al. [ 193 ] presented a binary chaotic GA for feature selection in healthcare. The chaotic maps were used to initialize the population and modified reproduction operators were applied on population. Xu et al. [ 115 ] proposed a chaotic hybrid immune GA for spectrum allocation. The proposed approach utilizes the advantages of both chaotic and immune operator. However, this method suffers from parameter initialization problem.

4.5 Hybrid GAs

Genetic Algorithms can be easily hybridized with other optimization methods for improving their performance such as image denoising methods, chemical reaction optimization, and many more. The main advantages of hybridized GA with other methods are better solution quality, better efficiency, guarantee of feasible solutions, and optimized control parameters [ 51 ]. It is observed from literature that the sampling capability of GAs is greatly affected from population size. To resolve this problem, local search algorithms such as memetic algorithm, Baldwinian, Lamarckian, and local search have been integrated with GAs. This integration provides proper balance between intensification and diversification. Another problem in GA is parameter setting. Finding appropriate control parameters is a tedious task. The other metaheuristic techniques can be used with GA to resolve this problem. Hybrid GAs have been used to solve the issues mentioned in the preceding subsections [ 29 , 137 , 186 ].

4.5.1 Enhance search capability

GAs have been integrated with local search algorithms to reduce the genetic drift. The explicit refinement operator was introduced in local search for producing better solutions. El-Mihoub et al. [ 54 ] established the effect of probability of local search on the population size of GA. Espinoza et al. [ 50 ] investigated the effect of local search for reducing the population size of GA. Different search algorithms have been integrated with GAs for solving real-life applications.

4.5.2 Generate feasible solutions

In complex and high-dimensional problems, the genetic operators of GA generate infeasible solutions. PMX crossover generates the infeasible solutions for order-based problems. The distance preserving crossover operator was developed to generate feasible solutions for travelling salesman problem [ 58 ]. The gene pooling operator instead of crossover was used to generate feasible solution for data clustering [ 19 ]. Konak and Smith [ 108 ] integrated a cut-saturation algorithm with GA for designing the communication networks. They used uniform crossover to produce feasible solutions.

4.5.3 Replacement of genetic operators

There is a possibility to replace the genetic operators which are mentioned in Section 3.2 with other search techniques. Leng [ 122 ] developed a guided GA that utilizes the penalties from guided local search. These penalties were used in fitness function to improve the performance of GA. Headar and Fukushima [ 74 ] used simplex crossover instead of standard crossover. The standard mutation operator was replaced with simulated annealing in [ 195 ]. The basic concepts of quantum computing are used to improve the performance of GAs. The heuristic crossover and hill-climbing operators can be integrated into GA for solving three-matching problem.

4.5.4 Optimize control parameters

The control parameters of GA play a crucial role in maintaining the balance between intensification and diversification. Fuzzy logic has an ability to estimate the appropriate control parameters of GA [ 167 ]. Beside this, GA can be used to optimize the control parameters of other techniques. GAs have been used to optimize the learning rate, weights, and topology of neutral networks [ 21 ]. GAs can be used to estimate the optimal value of fuzzy membership in controller. It was also used to optimize the control parameters of ACO, PSO, and other metaheuristic techniques [ 156 ]. The comparative analysis of well-known GAs are mentioned in Table 9 .

5 Applications

Genetic Algorithms have been applied in various NP-hard problems with high accuracy rates. There are a few application areas in which GAs have been successfully applied.

5.1 Operation management

GA is an efficient metaheuristic for solving operation management (OM) problems such as facility layout problem (FLP), supply network design, scheduling, forecasting, and inventory control.

5.1.1 Facility layout

Datta et al. [ 32 ] utilized GA for solving single row facility layout problem (SRFLP). For SRFLP, the modified crossover and mutation operators of GA produce valid solutions. They applied GA to large sized problems that consists of 60–80 instances. However, it suffers from parameter dependency problem. Sadrzadeh [ 173 ] proposed GA for multi-line FLP have multi products. The facilities were clustered using mutation and heuristic operators. The total cost obtained from the proposed GA was decreased by 7.2% as compared to the other algorithms. Wu et al. [ 211 ] implemented hierarchical GA to find out the layout of cellular manufacturing system. However, the performance of GA is greatly affected from the genetic operators. Aiello et al. [ 7 ] proposed MOGA for FLP. They used MOGA on the layout of twenty different departments. Palomo-Romero et al. [ 148 ] proposed an island model GA to solve the FLP. The proposed technique maintains the population diversity and generates better solutions than the existing techniques. However, this technique suffers from improper migration strategy that can be utilized for improving the population. GA and its variants has been successfully applied on FLP [ 103 , 119 , 133 , 201 ].

5.1.2 Scheduling

GA shows the superior performance for solving the scheduling problems such as job-shop scheduling (JSS), integrated process planning and scheduling (IPPS), etc. [ 119 ]. To improve the performance in the above-mentioned areas of scheduling, researchers developed various genetic representation [ 12 , 159 , 215 ], genetic operators, and hybridized GA with other methods [ 2 , 67 , 147 , 219 ].

5.1.3 Inventory control

Besides the scheduling, inventory control plays an important role in OM. Backordering and lost sales are two main approaches for inventory control [ 119 ]. Hiassat et al. [ 76 ] utilized the location-inventory model to find out the number and location of warehouses. Various design constraints have been added in the objective functions of GA and its variants for solving inventory control problem [].

5.1.4 Forecasting and network design

Forecasting is an important component for OM. Researchers are working on forecasting of financial trading, logistics demand, and tourist arrivals. GA has been hybridized with support vector regression, fuzzy set, and neural network (NN) to improve their forecasting capability [ 22 , 78 , 89 , 178 , 214 ]. Supply network design greatly affect the operations planning and scheduling. Most of the research articles are focused on capacity constraints of facilities [ 45 , 184 ]. Multi-product multi-period problems increases the complexity of supply networks. To resolve the above-mentioned problem, GA has been hybridized with other techniques [ 6 , 45 , 55 , 188 , 189 ]. Multi-objective GAs are also used to optimize the cost, profit, carbon emissions, etc. [ 184 , 189 ].

5.2 Multimedia

GAs have been applied in various fields of multimedia. Some of well-known multimedia fields are encryption, image processing, video processing, medical imaging, and gaming.

5.2.1 Information security

Due to development in multimedia applications, images, videos and audios are transferred from one place to another over Internet. It has been found in literature that the images are more error prone during the transmission. Therefore, image protection techniques such as encryption, watermarking and cryptography are required. The classical image encryption techniques require the input parameters for encryption. The wrong selection of input parameters will generate inadequate encryption results. GA and its variants have been used to select the appropriate control parameters. Kaur and Kumar [ 96 ] developed a multi-objective genetic algorithm to optimize the control parameters of chaotic map. The secret key was generated using beta chaotic map. The generated key was use to encrypt the image. Parallel GAs were also used to encrypt the image [ 97 ].

5.2.2 Image processing

The main image processing tasks are preprocessing, segmentation, object detection, denoising, and recognition. Image segmentation is an important step to solve the image processing problems. Decomposing/partitioning an image requires high computational time. To resolve this problem, GA is used due to their better search capability [ 26 , 102 ]. Enhancement is a technique to improve the quality and contrast of an image. The better image quality is required to analyze the given image. GAs have been used to enhance natural contrast and magnify image [ 40 , 64 , 99 ]. Some researchers are working on hybridization of rough set with adaptive genetic algorithm to merge the noise and color attributes. GAs have been used to remove the noise from the given image. GA can be hybridized with fuzzy logic to denoise the noisy image. GA based restoration technique can be used to remove haze, fog and smog from the given image [ 8 , 110 , 146 , 200 ]. Object detection and recognition is a challenging issue in real-world problem. Gaussian mixture model provides better performance during detection and recognition process. The control parameters are optimized through GA [ 93 ].

5.2.3 Video processing

Video segmentation has been widely used in pattern recognition, and computer vision. There are some critical issues that are associated with video segmentation. These are distinguishing object from the background and determine accurate boundaries. GA can be used to resolve these issues [ 9 , 105 ]. GAs have been implemented for gesture recognition successfully by Chao el al. [ 81 ] used GA for gesture recognition. They applied GAs and found an accuracy of 95% in robot vision. Kaluri and Reddy [ 91 ] proposed an adaptive genetic algorithm based method along with fuzzy classifiers for sign gesture recognition. They reported an improved recognition rate of 85% as compared to the existing method that provides 79% accuracy. Beside the gesture recognition, face recognition play an important role in criminal identification, unmanned vehicles, surveillance, and robots. GA is able to tackle the occlusion, orientations, expressions, pose, and lighting condition [ 69 , 95 , 109 ].

5.2.4 Medical imaging

Genetic algorithms have been applied in medical imaging such as edge detection in MRI and pulmonary nodules detection in CT scan images [ 100 , 179 ]. In [ 120 ], authors used a template matching technique with GA for detecting nodules in CT images. Kavitha and Chellamuthu [ 179 ] used GA based region growing method for detecting the brain tumor. GAs have been applied on medical prediction problems captured from pathological subjects. Sari and Tuna [ 176 ] used GA used to solve issues arises in biomechanics. It is used to predict pathologies during examination. Ghosh and Bhattachrya [ 62 ] implemented sequential GA with cellular automata for modelling the coronavirus disease 19 (COVID-19) data. GAs can be applied in parallel mode to find rules in biological datasets [ 31 ]. The authors proposed a parallel GA that runs by dividing the process into small sub-generations and evaluating the fitness of each individual solution in parallel. Genetic algorithms are used in medicine and other related fields. Koh et al. [ 61 ] proposed a genetic algorithm based method for evaluation of adverse effects of a given drug.

5.2.5 Precision agriculture

GAs have been applied on various problems that are related to precision agriculture. The main issues are crop yield, weed detection, and improvement in farming equipment. Pachepsky and Acock [ 145 ] implemented GA to analyze the water capacity in soil using remote sensing images. The crop yield can be predicted through the capacity of water present in soil. The weed identification was done through GA in [ 142 ]. They used aerial image for classification of plants. In [ 124 ], color image segmentation was used to discriminate the weed and plant. Peerlink et al. [ 154 ] determined the appropriate rate of fertilizer for various portions of agriculture field. They GA for determining the nitrogen in wheat field. The energy requirements in water irrigation systems can be optimized by viewing it as a multi-objective optimization problem. The amount of irrigation required and thus power requirements change continuously in a SMART farm. Therefore, GA can be applied in irrigation systems to reduce the power requirements [ 33 ].

5.2.6 Gaming

GAs have been successfully used in games such as gomoku. In [ 202 ], the authors shown that the GA based approach finds the solution having the highest fitness than the normal tree based methods. However, in real-time strategy based games, GA based solutions become less practical to implement [ 82 ]. GAs have been implemented for path planning problems considering the environment constraints as well as avoiding the obstacles to reach the given destination. Burchardt and Salomon [ 18 ] described an implementation for path planning for soccer games. GA can encode the path planning problems via the coordinate points of a two-dimensional playing field, hence resulting in a variable length solution. The fitness function in path planning considers length of path as well as the collision avoiding terms for soccer players.

5.3 Wireless networking

Due to adaptive, scalable, and easy implementation of GA, it has been used to solve the various issues of wireless networking. The main issues of wireless networking are routing, quality of service, load balancing, localization, bandwidth allocation and channel assignment [ 128 , 134 ]. GA has been hybridized with other metaheuristics for solving the routing problems. Hybrid GA not only producing the efficient routes among pair of nodes, but also used for load balancing [ 24 , 212 ].

5.3.1 Load balancing

Nowadays, multimedia applications require Quality-of-Service (QoS) demand for delay and bandwidth. Various researchers are working on GAs for QoS based solutions.GA produces optimal solutions for complex networks [ 49 ]. Roy et al. [ 172 ] proposed a multi-objective GA for multicast QoS routing problem. GA was used with ACO and other search algorithms for finding optimal routes with desired QoS metrics. Load balancing is another issue in wireless networks. Scully and Brown [ 177 ] used MicroGAs and MacroGAs to distribute the load among various components of networks. He et al. [ 73 ] implemented GA to determine the balance load in wireless sensor networks. Cheng et al. [ 25 ] utilized distributed GA with multi-population scheme for load balancing. They used load balancing metric as a fitness function in GA.

5.3.2 Localization

The process of determining the location of wireless nodes is called as localization. It plays an important role in disaster management and military services. Yun et al. [ 216 ] used GA with fuzzy logic to find out the weights, which are assigned according to the signal strength. Zhang et al. [ 218 ] hybridized GA with simulated annealing (SA) to determine the position of wireless nodes. SA is used as local search to eliminate the premature convergence.

5.3.3 Bandwidth and channel allocation

The appropriate bandwidth allocation is a complex task. GAs and its variants have been developed to solve the bandwidth allocation problem [ 92 , 94 , 107 ]. GAs were used to investigate the allocation of bandwidth with QoS constraints. The fitness function of GAs may consists of resource utilization, bandwidth distribution, and computation time [ 168 ]. The channel allocation is an important issue in wireless networks. The main objective of channel allocation is to simultaneously optimize the number of channels and reuse of allocated frequency. Friend et al. [ 59 ] used distributed island GA to resolve the channel allocation problem in cognitive radio networks. Zhenhua et al. [ 221 ] implemented a modified immune GA for channel assignment. They used different encoding scheme and immune operators. Pinagapany and Kulkarni [ 157 ] developed a parallel GA to solve both static and dynamic channel allocation problem. They used decimal encoding scheme. Table 10 summarizes the applications of GA and its variants.

6 Challenges and future possibilities

In this section, the main challenges faced during the implementation of GAs are discussed followed by the possible research directions.

6.1 Challenges

Despite the several advantages, there are some challenges that need to be resolved for future advancements and further evolution of genetic algorithms. Some major challenges are given below:

6.1.1 Selection of initial population

Initial population is always considered as an important factor for the performance of genetic algorithms. The size of population also affects the quality of solution [ 160 ]. The researchers argue that if a large population is considered, then the algorithm takes more computation time. However, the small population may lead to poor solution [ 155 ]. Therefore, finding the appropriate population size is always a challenging issue. Harik and Lobo [ 71 ] investigated the population using self-adaption method. They used two approaches such as (1) use of self-adaption prior to execution of algorithm, in which the size of population remains the same and (2) in which the self-adaption used during the algorithm execution where the population size is affected by fitness function.

6.1.2 Premature convergence

Premature convergence is a common issue for GA. It can lead to the loss of alleles that makes it difficult to identify a gene [ 15 ]. Premature convergence states that the result will be suboptimal if the optimization problem coincides too early. To avoid this issue, some researchers suggested that the diversity should be used. The selection pressure should be used to increase the diversity. Selection pressure is a degree which favors the better individuals in the initial population of GA’s. If selection pressure (SP1) is greater than some selection pressure (SP2), then population using SP1 should be larger than the population using SP2. The higher selection pressure can decrease the population diversity that may lead to premature convergence [ 71 ].

Convergence property has to be handled properly so that the algorithm finds global optimal solution instead of local optimal solution (see Fig. 8 ). If the optimal solution lies in the vicinity of an infeasible solution, then the global nature of GA can be combined with local nature of other algorithms such as Tabu search and local search. The global nature of genetic algorithms and local nature of Tabu search provide the proper balance between intensification and diversification.

figure 8

Local and global optima [ 149 ]

6.1.3 Selection of efficient fitness functions

Fitness function is the driving force, which plays an important role in selecting the fittest individual in every iteration of an algorithm. If the number of iterations are small, then a costly fitness function can be adjusted. The number of iterations increases may increase the computational cost. The selection of fitness function depends upon the computational cost as well as their suitability. In [ 46 ], the authors used Davies-Bouldin index for classification of documents.

6.1.4 Degree of mutation and crossover

Crossover and mutation operators are the integral part of GAs. If the mutation is not considered during evolution, then there will be no new information available for evolution. If crossover is not considered during evolution, then the algorithm can result in local optima. The degree of these operators greatly affect the performance of GAs [ 72 ]. The proper balance between these operators are required to ensure the global optima. The probabilistic nature cannot determine the exact degree for an effective and optimal solution.

6.1.5 Selection of encoding schemes

GAs require a particular encoding scheme for a specific problem. There is no general methodology for deciding whether the particular encoding scheme is suitable for any type of real-life problem. If there are two different problems, then two different encoding schemes are required. Ronald [ 171 ] suggested that the encoding schemes should be designed to overwhelm the redundant forms. The genetic operators should be implemented in a manner that they are not biased towards the redundant forms.

6.2 Future research directions

GAs have been applied in different fields by modifying the basic structure of GA. The optimality of a solution obtained from GA can be made better by overcoming the current challenges. Some future possibilities for GA are as follows:

There should be some way to choose the appropriate degree of crossover and mutation operators. For example Self-Organizing GA adapt the crossover and mutation operators according to the given problem. It can save computation time that make it faster.

Future work can also be considered for reducing premature convergence problem. Some researchers are working in this direction. However, it is suggested that new methods of crossover and mutation techniques are required to tackle the premature convergence problem.

Genetic algorithms mimic the natural evolution process. There can be a possible scope for simulating the natural evolution process such as the responses of human immune system and the mutations in viruses.

In real-life problems, the mapping from genotype to phenotype is complex. In this situation, the problem has no obvious building blocks or building blocks are not adjacent groups of genes. Hence, there is a possibility to develop novel encoding schemes to different problems that does not exhibit same degree of difficulty.

7 Conclusions

This paper presents the structured and explained view of genetic algorithms. GA and its variants have been discussed with application. Application specific genetic operators are discussed. Some genetic operators are designed for representation. However, they are not applicable to research domains. The role of genetic operators such as crossover, mutation, and selection in alleviating the premature convergence is studied extensively. The applicability of GA and its variants in various research domain has been discussed. Multimedia and wireless network applications were the main attention of this paper. The challenges and issues mentioned in this paper will help the practitioners to carry out their research. There are many advantages of using GAs in other research domains and metaheuristic algorithms.

The intention of this paper is not only provide the source of recent research in GAs, but also provide the information about each component of GA. It will encourage the researchers to understand the fundamentals of GA and use the knowledge in their research problems.

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Katoch, S., Chauhan, S.S. & Kumar, V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80 , 8091–8126 (2021). https://doi.org/10.1007/s11042-020-10139-6

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A Research on Genetic Engineering in Different Fields

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2020, Middle East Journal of Applied Science & Technology

Genetic engineering is the best technology that is promoting the world and this technology is applied to many plants, animals and microorganisms. It has wider applications in the field of Biology, Medicine, Industry, Research, Agriculture and many other fields of science. In this research paper I update the Roles of Genetic Engineering in Agriculture, Animals, Human enhancement and Evolution, Bacteriophage Against Infectious Diseases, Medicines, Phage in Infectious Diseases, Biofuels Production and Improve Plant Performance Under Drought.

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genetic engineering essay

Genetic Engineering Essay Guide With 70 Hot Topics

Genetic engineering has been a subject of heated debate. You will find many essays on genetic engineering, asking you to debate for or against, discuss its ethical implications, or emerging congenital disease.

With all these at hand, you may be tempted to opt-out immediately. However, this top-notch guide seeks to make genetics essay writing as fun and as straightforward as possible. Ride along to see the magic!

What Is An Essay on Genetic Engineering?

Now, genetic engineering in itself is the use of biotechnology to manipulate an organism’s genes directly. Therefore, essays on genetics will require students to explore the set of technologies used to change cells’ genetic makeup. These include the transfer of genes within and across species boundaries to produce novel or improved organisms.

We have various areas of genetic engineering, such as:

  • Human genetic engineering definition: Deals with genetic engineering techniques applied to humans
  • Genetic engineering in plants: Concentrates on genetically modified plant species

Genetic engineering is mostly applied in medicine and thus its technicality. I know this is a field that most students approach with reverence and uttermost humility. Nonetheless, it doesn’t have to be that way. The next few lines might change your opinion on genetic engineering forever!

Why is genetic engineering necessary?

Importance of Genetic Engineering

It is essential in the following ways:

  • Ensures that seed companies can protect modified seed varieties as intellectual property.
  • Leads to production o organisms with better traits
  • Helps maintain the ecosystem

You can see why this field is unavoidable regardless of the negative talk behind it.

Genetically Engineering Plants and Animals – Essay Sample

Young in practice, a little over forty years old, genetic engineering has provided the scientific community with an abundance of knowledge once thought absurd. Genetic engineering means deliberately changing the genome of an organism to acquire some desired traits during its cultivation. On the whole, genetic engineering has a multitude of advantages and disadvantages when it comes to using it on animals and plants; the most prominent advantages include disease resistance, increased crop yields, and a decrease in need for pesticides and antibiotics, whereas disadvantages include the potential for emergence of stronger pathogens, as well as various unexpected consequences. This current paper discusses the pros and cons of using genetic engineering on plants, animals, and provides a synthesis, arguing that, despite its disadvantages, it still serves as a pivotal advantage not only within the scientific community, but also society.

The Advantages of Using Genetic Engineering

The impact of genetic engineering on society can be seen at various aspects, affecting various aspects of social and physical organic life, especially in terms of human beings. The practice consists of the specific selection and removal of genes from organic organisms and inserting them into another. The practice, though still young in practice and not yet deemed completely socially acceptable, makes the possibility of curing diseases once thought incurable a reality, thereby inherently improving the life of both humans and non-human animals. It has many positive effects on society, an example being in Uganda bananas, a main source of caloric intake, are susceptible to the emergence of new diseases that affects their production because of the disease’s potency. Ugandan scientists have successfully used a genetic modification, inserting a pepper gene into bananas, which prevents the fruit from getting the disease (Bohanec, 2015). Furthermore, through genetic engineering, tissue, skin cells, and other forms of organic matter can be grown and used in replacing damaged, worn, or malfunctioning organs and tissues thereby prolonging human life and benefiting their quality of life. The practice helps better advance both the scientific and medical field, both of which are essential in discovering how to better life on Earth.

Genetic engineering, as previously mentioned, can be used to grow and replace damaged tissue or organs, aiding in the betterment and prolonging of human life; it can cure diseases once though incurable, an example being AIDS and cancer. Millions of people around the world suffer from AIDS and cancer, both posing a severe risk to the overall health of the person. More than 900,000 lives were taken by AIDS in 2017 (UNAIDS, 2018). Similarly, over 600,000 were taken by cancer in the following year (NIH, 2018). Genetic engineering makes the possibility of eradicating these diseases a reality. In theory, genetic engineering can help those who suffer from these diseases live longer, healthier, fuller lives by eradicating the disease in its entirety. Though it would not be an easy feat, nor a cheap one, it could still help further advance and better human life and prolong the human life span. People would no longer live in fear of dying from these prolific diseases. Furthermore, genetic engineering, despite the naysayers and opposers of the practice, is another step in organic evolution. From plants to animals, the practice has the chance to achieve strides within scientific history that can greatly benefit the planet in its entirety. From eliminating hunger, to eradicating once prolific diseases, genetic engineering can provide a better, longer, and higher quality of life and tackle bounds once thought impossible the scientific community.

Genetically engineered plants and animals may provide a wide array of benefits that might be pivotal for humanity in the modern world. These benefits include the possibility of developing such plant cultivars that would be resistant to a wide variety of pathogens and diseases caused by microorganisms such as viruses (Ginn, Alexander, Edelstein, Abedi, & Wixon, 2013). If such plant cultivars are created, it might become unnecessary to use chemicals in order to battle these plant diseases. This is clearly a major benefit, since it means better preserving the natural environment and avoiding the use of chemicals that may contaminate soils and waters, as well as kill wildlife.

The Disadvantages of Using Genetic Engineering

The use of genetic engineering to alter plants and animals used in agriculture and husbandry may also have a variety of adverse consequences. For instance, it should be noted that high rates of resistance to disease might have a serious flip side. More specifically, the pathogenic microorganisms (such as bacteria and viruses) can usually mutate quickly in order to adapt to the new conditions. This means that if new cultivars or breeds of plants or animals with high resistance to diseases are created, the pathogens may adapt to these changes in their “hosts” and turn stronger, thus becoming capable of infecting the new cultivars or breeds (Ayres, n.d.). This might again necessitate the use of chemicals or antibiotics; only now stronger drugs or pesticides would be needed. In addition, the old cultivars or breeds may also become infected by the new microorganism strains, and these strains will probably cause more severe diseases in the “original” plants and animals and will be more difficult to cure or prevent.

Another negative possibility is accidentally creating some invasive species that may harm the local ecosystems. For instance, if new plants are made in such a manner that the local species of animals cannot eat them, and then humans lose control over their growth, the new plants may pose a danger to the original plants growing in the given ecosystem, therefore disrupting the ecosystem. For example, in 1984 a patch of seaweed labelled as Caulerpa taxifolia was bred with another robust strain of seaweed identified by scientists as Caulerpa taxifolia (Vahl) C. Agandh . The initial objective was to breed an aquarium plant, however, after a sample escaped in 1984 into the Mediterranean Sea, being found off the coast of both the United States and Australia in 2000, it was found that the strain’s taste was subpar to marine wild life. It was eventually poisoned by the California state government to avoid further damage to marine life and the marine ecosystem and was consequently outlawed by hundreds of countries. The World Conservation Union named it one of the 100 World’s Worst Invasive Alien Species, despite it being manmade (Cellania, 2008).

Finally, there is always the risk of “going too far” when practicing genetic engineering (Bruce & Bruce, 2013). Indeed, it should be noted that the humanity has used various methods of cultivation for millennia in order to breed for specific traits. For example, in 1956, Warwick Kerr, a Brazilian geneticist, imported an aggressive breed of African honeybee to breed with a European species to aid in the decreasing bee population epidemic. Provoked by even the smallest of instigation, after over 26 swarms of the aggressive bee escaped from the apiary in Sao Paulo, they wreaked havoc in North and South America, found in the United States in the early 90s. Nevertheless, genetic engineering is a fast and radical method to change organisms, and very little, if any, data is available to predict the potential adverse impacts of its utilization. It may be difficult to tell when (if at any point) one must stop the process of genetic engineering to avoid unexpected adverse influences of its utilization.

Genetic engineering, despite its disadvantages, can help progress humanity in ways that once seemed impossible. With the environmental and physical epidemics surrounding the planet, the practice can serve as a benefit to resolving the hunger crisis, the preservation of endangered plant and animal species, bringing certain species back from extinction, and so much more. It should be stressed that the utilization of biotechnology and genetic engineering may bring a wide array of significant benefits, which may be of great use to the humanity nowadays. The creation of breeds and cultivars which are immune to disease, resistant to harsh environmental conditions, are cheap to grow, and provide better nutritional value for people might be extremely helpful in reducing the amount of chemicals, pesticides, and antibiotics needed to grow these animals or plants, and, consequently, to help preserve the environment. However, it should also be remembered that genetic engineering might have a wide array of adverse impacts, such as the emergence of new, stronger pathogens, the creation of invasive species, and a multitude of negative consequences that no one knew to expect.

Genetic Engineering Essay Structure

A top-rated genetic engineering essay comes in the manner outlined below:

  • Genetic engineering essay introduction: Provide context for your paper by giving a well-researched background on the subject of discussion. Include the thesis statement which will provide the direction of your writing.
  • Body: Discuss the main points in detail with relevant examples and evidence from authentic and reliable sources. You can use diagrams or illustrations to support your argument if need be.
  • Genetic engineering conclusion: Finalize your paper with a summative statement and a restatement of the thesis statement while showing the genetic engineering process’s implication. Does it add any value to society?

Armed with this great treasure of knowledge, you are good to begin writing your paper. However, we have quality genetic engineering essay topics from expert writers to start you off:

Interesting Genetic Engineering Persuasive Essay Topics

  • How human curiosity has led to new advancements and technologies in genetics
  • History of genetically modified food
  • Discuss the process of genetic engineering in crops
  • Evaluate the acceptance of genetically modified crops worldwide
  • Analyze the leading countries implementing genetic engineering
  • Does genetic engineering produce a desired characteristic?
  • What are the legal implications of genetic engineering
  • The role of scientists in making the world a better place
  • Why coronavirus is a game-changer in the field of genetic engineering
  • The effectiveness of genetic engineering as a course in college

Great Topics on the Disadvantages of Genetic Engineering in Humans

  • Why changing the sequence of nucleotides of the DNA affects human code structure
  • Impact of genetic engineering human lifespans
  • Genetic engineering and population control
  • Ethical questions to consider in human genetic engineering
  • Unintended side effects on humans
  • Increasing the risk of allergies
  • The foundation of new weapon technologies
  • Disadvantages of trait selection before birth
  • The greater risk of stillbirth
  • Why ladies are at risk with genetic engineering

Why is Genetic Engineering Good Essay Topics

  • Genetic engineering and disease prevention
  • The creation of a healthy and better society
  • Production of drought-resistant crops
  • Crop pollen spreads further than expected
  • Survival of human species
  • Birth of healthy children with desirable traits
  • Solving food insecurity problems globally
  • Elimination of fertility issues for couples
  • Medical advancements as a result of genetic engineering
  • Reducing the prevalence of schizophrenia and depression

Good Genetic Engineering Topics

  • The development of genetic engineering in the modern world
  • Application of ethics in genetic engineering
  • Societal class versus genetic engineering
  • Impact of genetic engineering on natural selection and adaptation
  • Detection of toxins from GMO foods
  • Social effects of genetic engineering
  • Why people are becoming increasingly resistant to antibiotics
  • How gene editing affects the human germline
  • Medical treatment opportunities in genetic engineering
  • The relationship between molecular cloning and genetic engineering

Impressive Genetic Engineering Research Paper Topics

  • Impact of genetic engineering on food supply
  • The taste of GMO food versus ordinary food
  • GMOs and their need for environmental resources
  • Why genetic engineering may face out the use of pesticides
  • Reduced cost of living and longer shelf life.
  • Growth rates of plants and animals
  • Application of genetic engineering on soil bacteria
  • New allergens in the food supply
  • Production of new toxins
  • Enhancement of the environment for toxic fungi

Latest Genetic Engineering Ideas

  • The discovery of vaccines through genetic engineering
  • Biological warfare on the rise
  • Change in herbicide use patterns
  • Mutation effects in plants and animals
  • Impact of gene therapies
  • Does genetic engineering always lead to the desired phenotype?
  • Genetic engineering in mass insulin production
  • Role of genetic engineering in human growth hormones
  • Treating infertility
  • Development of monoclonal antibodies

Pro and Cons of Genetic Engineering in Humans Topic Ideas

  • Possibility of increased economic inequality
  • Increased human suffering
  • The emergence of large-scale eugenic programmes
  • Rise of totalitarian control over human lives
  • The concentration of toxic metals in genetic engineering
  • Creation of animal models of human diseases
  • Using somatic gene therapy on Parkinson’s disease
  • Production of allergens in the food supply
  • Redesigning the world through genetic engineering
  • Bioterrorism: A study of the issue of emerging infectious diseases

I believe that by now explain genetic engineering in a sentence and write an essay on it effortlessly. If this still seems complicated for you, we have professional essay writers at your disposal.

You are sure of trustful, custom essay writing help at the convenience of your bedroom study table. It is cheap and high-quality.

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