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Statistical Genetics

Statistical geneticists at SPH develop statistical methods for understanding the genetic basis of human diseases and traits.  These methods involve large-scale data sets from candidate-gene, genome-wide and resequencing studies, using both unrelated and related individuals.  SPH statistical geneticists collaborate with other investigators at SPH and around the world on studies of cancer, heart disease, diabetes, respiratory disease, psychiatric disease, and health-related behaviors (e.g. smoking, diet).  They have close ties to the Program in Quantitative Genomics and Computational Biology and Bioinformatics group at SPH.  Training encompasses basic statistics; Mendelian and population genetics; design and analysis of genetic association studies; gene expression and epigenetic markers; and gene-environment interaction.

Students holding a degree in mathematics, computer science, statistics or a related field and an interest in genetics are invited to apply to our Doctoral or Master’s degree programs.  Faculty in the PGSG advise students in both the Epidemiology and Biostatistics departments. Prospective students can apply to either department. While it is possible to apply to both departments, it is typically not recommended. It is Graduate School policy that an individual may submit no more than three applications during the course of his or her academic career. Prospective students are encouraged to discuss which program will best fit their needs with potential advisors. More details about the application process can be found here .

Postdoctoral training positions are also available, with support coming from individual Principal Investigators or appropriate training grants.  Prospective students or postdoctoral fellows with an interest in statistical genetics at SPH should contact Alkes Price .

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Mathematics, genetics and evolution

  • Published: 06 February 2013
  • Volume 1 , pages 9–31, ( 2013 )

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  • Warren J. Ewens 1  

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The importance of mathematics and statistics in genetics is well known. Perhaps less well known is the importance of these subjects in evolution. The main problem that Darwin saw in his theory of evolution by natural selection was solved by some simple mathematics. It is also not a coincidence that the re-writing of the Darwinian theory in Mendelian terms was carried largely by mathematical methods. In this article I discuss these historical matters and then consider more recent work showing how mathematical and statistical methods have been central to current genetical and evolutionary research.

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Ewens, W.J. Mathematics, genetics and evolution. Quant Biol 1 , 9–31 (2013). https://doi.org/10.1007/s40484-013-0003-5

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Received : 08 October 2012

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Accepted : 06 November 2012

Published : 06 February 2013

Issue Date : March 2013

DOI : https://doi.org/10.1007/s40484-013-0003-5

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Qiao, Dandi. "Statistical Approaches for Next-Generation Sequencing Data." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10689.

Bruen, Trevor Cormac Vincent. "Discrete and statistical approaches to genetics." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102964.

Baillie, John Kenneth. "Statistical genetics in infectious disease susceptibility." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/17620.

Oldmeadow, Christopher. "Latent variable models in statistical genetics." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/31995/1/Christopher_Oldmeadow_Thesis.pdf.

Mitchell, Brittany L. "Statistical genetic analyses of neuropsychological traits." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227852/14/Brittany%20Mitchell%20Thesis.pdf.

Hudson, Julie. "Maternal Gene-Environment Effects: An Evaluation of Statistical Approaches to Detect Effects and an Investigation of the Effect of Violations of Model Assumptions." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39637.

Casale, Francesco Paolo. "Multivariate linear mixed models for statistical genetics." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/267465.

Csilléry, Katalin. "Statistical inference in population genetics using microsatellites." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3865.

Sperrin, Matthew. "Statistical methodology motivated by problems in genetics." Thesis, Lancaster University, 2010. http://eprints.lancs.ac.uk/49088/.

Lange, Christoph. "Generalized estimating equation methods in statistical genetics." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269921.

ZHANG, GE. "STATISTICAL METHODS IN GENETIC ASSOCIATION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744.

Wright, David Jonathan. "Investigating statistical homogeneity of a human chromosome." Thesis, Queen Mary, University of London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338927.

Ngong, Chiano Mathias. "Statistical problems in human genetic linkage analysis." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339750.

Liesch, Rahel. "Statistical Genetics for the Budset in Norway Spruce." Thesis, Uppsala University, Department of Mathematics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121386.

Jung, Min Kyung. "Statistical methods for biological applications." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278454.

Choy, Yan-tsun. "Statistical evaluation of mixed DNA stains." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664287.

Yu, Xiaoqing. "Statistical Methods and Analyses for Next-generation Sequencing Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1403708200.

Yung, Godwin Yuen Han. "Statistical methods for analyzing genetic sequencing association studies." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493313.

Zang, Yong, and 臧勇. "Robust tests under genetic model uncertainty in case-control association studies." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46419123.

Choy, Yan-tsun, and 蔡恩浚. "Statistical evaluation of mixed DNA stains." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664287.

Shringarpure, Suyash. "Statistical Methods for studying Genetic Variation in Populations." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/117.

Cordell, Heather Jane. "Statistical methods in the genetic analysis of type 1 diabetes." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296834.

Mathieson, Iain. "Genes in space : selection, association and variation in spatially structured populations." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:85f051b6-2121-49cf-9468-3ca7ba77cc4a.

Ahiska, Bartu. "Reference-free identification of genetic variation in metagenomic sequence data using a probabilistic model." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.561121.

Bos, David H. "Statistical genetics and molecular evolution of major histocompatibility complex genes." Thesis, University of Canterbury. Biological Sciences, 2005. http://hdl.handle.net/10092/6773.

Lundell, Jill F. "Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7594.

Vaez, Torshizi Rasoul. "Quantitative genetic analyses of production and reproduction traits in Australian merino sheep." Thesis, The University of Sydney, 1996. https://hdl.handle.net/2123/27593.

Lee, Yiu-fai, and 李耀暉. "Analysis for segmental sharing and linkage disequilibrium: a genomewide association study on myopia." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43912217.

Lu, Li. "Some actuarial and statistical investigations into topics on genetics and insurance." Thesis, Heriot-Watt University, 2006. http://hdl.handle.net/10399/154.

Shen, Xia. "Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170091.

Golding, Pauline Lindsay. "Development of a statistical method for the identification of gene-environment interactions." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6520.

Zorrilla, Luc. "Beyond high mutation highrecombination limit in statisticalgenetics." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296875.

Ciampa, Julia Grant. "Multilocus approaches to the detection of disease susceptibility regions : methods and applications." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:8f82a624-7d80-438c-af3e-68ce983ff45f.

Guturu, Harendra. "Deciphering human gene regulation using computational and statistical methods." Thesis, Stanford University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3581147.

It is estimated that at least 10-20% of the mammalian genome is dedicated towards regulating the 1-2% of the genome that codes for proteins. This non-coding, regulatory layer is a necessity for the development of complex organisms, but is poorly understood compared to the genetic code used to translate coding DNA into proteins. In this dissertation, I will discuss methods developed to better understand the gene regulatory layer. I begin, in Chapter 1, with a broad overview of gene regulation, motivation for studying it, the state of the art with a historically context and where to look forward.

In Chapter 2, I discuss a computational method developed to detect transcription factor (TF) complexes. The method compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid transcription factor (TF) complexes. Structural data were integrated to explore overlapping motif arrangements while ensuring physical plausibility of the TF complex. Using this approach, I predicted 422 physically realistic TF complex motifs at 18% false discovery rate (FDR). I found that the set of complexes is enriched in known TF complexes. Additionally, novel complexes were supported by chromatin immunoprecipitation sequencing (ChIP-seq) datasets. Analysis of the structural modeling revealed three cooperativity mechanisms and a tendency of TF pairs to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. The TF complexes and associated binding site predictions are made available as a web resource at http://complex.stanford.edu.

Next, in Chapter 3, I discuss how gene enrichment analysis can be applied to genome-wide conserved binding sites to successfully infer regulatory functions for a given TF complex. A genomic screen predicted 732,568 combinatorial binding sites for 422 TF complex motifs. From these predictions, I inferred 2,440 functional roles, which are consistent with known functional roles of TF complexes. In these functional associations, I found interesting themes such as promiscuous partnering of TFs (such as ETS) in the same functional context (T cells). Additionally, functional enrichment identified two novel TF complex motifs associated with spinal cord patterning genes and mammary gland development genes, respectively. Based on these predictions, I discovered novel spinal cord patterning enhancers (5/9, 56% validation rate) and enhancers active in MCF7 cells (11/19, 53% validation rate). This set replete with thousands of additional predictions will serve as a powerful guide for future studies of regulatory patterns and their functional roles.

Then, in Chapter 4, I outline a method developed to predict disease susceptibility due to gene mis-regulation. The method interrogates ensembles of conserved binding sites of regulatory factors disrupted by an individual's variants and then looks for their most significant congregation next to a group of functionally related genes. Strikingly, when the method is applied to five different full human genomes, the top enriched function for each is reflective of their very different medical histories. These results suggest that erosion of gene regulation results in function specific mutation loads that manifest as disease predispositions in a familial lineage. Additionally, this aggregate analysis method addresses the problem that although many human diseases have a genetic component involving many loci, the majority of studies are statistically underpowered to isolate the many contributing loci.

Finally, I conclude in Chapter 5 with a summary of my findings throughout my research and future directions of research based on my findings.

Hu, Xianghong. "Statistical methods for Mendelian randomization using GWAS summary data." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/639.

Li, Yong-Jun. "The application of statistical physics in bioinformatics /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202003%20LI.

Allchin, Lorraine Doreen May. "Statistical methods for mapping complex traits." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:65f392ba-1b64-4b00-8871-7cee98809ce1.

McCaskie, Pamela Ann. "Multiple-imputation approaches to haplotypic analysis of population-based data with applications to cardiovascular disease." University of Western Australia. School of Population Health, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0160.

Silver, Matthew. "Statistical methods in neuroimaging genetics : pathways sparse regression and cluster size inference." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11124.

Kecskemetry, Peter D. "Computationally intensive methods for hidden Markov models with applications to statistical genetics." Thesis, University of Oxford, 2014. https://ora.ox.ac.uk/objects/uuid:8dd5d68d-27e9-4412-868c-0477e438a2c5.

Dilthey, Alexander Tilo. "Statistical HLA type imputation from large and heterogeneous datasets." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:1bca18bf-b9d5-4777-b58e-a0dca4c9dbea.

Sharif, Maarya. "Statistical issues in modelling the ancestry from Y-chromosome and surname data." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3407/.

Fernandez, Daniel. "Cell States and Cell Fate: Statistical and Computational Models in (Epi)Genomics." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:14226043.

Crisci, Jessica L. "On Identifying Signatures of Positive Selection in Human Populations: A Dissertation." eScholarship@UMMS, 2013. https://escholarship.umassmed.edu/gsbs_diss/664.

Crisci, Jessica L. "On Identifying Signatures of Positive Selection in Human Populations: A Dissertation." eScholarship@UMMS, 2006. http://escholarship.umassmed.edu/gsbs_diss/664.

Katsumata, Yuriko. "STATISTICAL ANALYSES TO DETECT AND REFINE GENETIC ASSOCIATIONS WITH NEURODEGENERATIVE DISEASES." UKnowledge, 2017. https://uknowledge.uky.edu/epb_etds/17.

Ramasamy, Adaikalavan. "Increasing statistical power and generalizability in genomics microarray research." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:81ccede7-a268-4c7a-9bf8-a2b68634846d.

Silva, Heyder Diniz. "Aspectos biométricos da detecção de QTL'S ("Quantitative Trait Loci") em espécies cultivadas." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-18102002-162652/.

AMALAPURAPU, SUCHITRA S. "A STATISTICAL ANALYSIS OF AMINO ACID CHANGES IN THE HUMAN GENOME." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1051720394.

Su, Zhan. "Statistical methods for the analysis of genetic association studies." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:98614f8b-63fe-4fa1-9a24-422216ad14cf.

VIDEO

  1. Lecture

  2. genetics terminology

  3. Genetics introduction

  4. Bio121 Lecture 25 Mendelian Genetics

  5. Thank you from Dr Mathias Seviiri

  6. Phylogenetic comparative approaches to uncover the genomic basis of species’ phenotypic differences

COMMENTS

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