This project is concerned with developing new statistical methodology for population genetic data. Attention will be focused on dependencies among sets of alleles: the applications of Hardy-Weinberg testing in high-dimensional genetic data and the characterization of population structure with rare variants and related populations. Theory will be developed at least in part in response to the needs of current whole-genome SNP surveys and whole-genome sequence variant surveys for humans. This work will be extended to analyses of population structure and ancestry proportions to the intensely selected and medically important HLA region to provide an in-depth analysis of population structure and ancestry proportions. The work is proposed by investigators in the Department of Biostatistics at the University of Washington. They will be joined by Jerome Goudet at the University of Lausanne, Jan Graffelman at the Universitat Politecnica de Catalunya and Diogo Meyer at the University of Sao Paulo.

Public Health Relevance

As population genetic datasets grow, there is both the need and the opportunity to quantify the dependencies among alleles within and between individuals, or within and between populations. Individual-level dependencies address inbreeding and relatedness and can lead to estimates of heritability of complex human traits, while population level dependencies can be used to infer the presence of natural selection in the history of the populations. Work is proposed to strengthen ways of estimating allelic dependencies, with attention being paid to the variation imposed by the evolutionary process as well as the variation from sampling individuals from current populations, with particular attention to HLA genes in the major histocompatility region.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM075091-10
Application #
9563269
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Krasnewich, Donna M
Project Start
2006-05-01
Project End
2021-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
10
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Washington
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Graffelman, Jan; Weir, Bruce S (2018) Multi-allelic exact tests for Hardy-Weinberg equilibrium that account for gender. Mol Ecol Resour 18:461-473
Goudet, Jérôme; Kay, Tomas; Weir, Bruce S (2018) How to estimate kinship. Mol Ecol 27:4121-4135
Qi, Ting; Wu, Yang; Zeng, Jian et al. (2018) Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun 9:2282
Graffelman, Jan; Weir, Bruce S (2018) On the testing of Hardy-Weinberg proportions and equality of allele frequencies in males and females at biallelic genetic markers. Genet Epidemiol 42:34-48
Yengo, Loic; Zhu, Zhihong; Wray, Naomi R et al. (2018) Reply to Kardos et al.: Estimation of inbreeding depression from SNP data. Proc Natl Acad Sci U S A 115:E2494-E2495
Xue, Angli; Wu, Yang; Zhu, Zhihong et al. (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 9:2941
Zheng, Xiuwen; Gogarten, Stephanie M; Lawrence, Michael et al. (2017) SeqArray-a storage-efficient high-performance data format for WGS variant calls. Bioinformatics 33:2251-2257
Galván-Femenía, Iván; Graffelman, Jan; Barceló-I-Vidal, Carles (2017) Graphics for relatedness research. Mol Ecol Resour 17:1271-1282
Puig, X; Ginebra, J; Graffelman, J (2017) A Bayesian test for Hardy-Weinberg equilibrium of biallelic X-chromosomal markers. Heredity (Edinb) 119:226-236
Graffelman, Jan; Jain, Deepti; Weir, Bruce (2017) A genome-wide study of Hardy-Weinberg equilibrium with next generation sequence data. Hum Genet 136:727-741

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