Abstract

Genetic association analysis of rare variants is an area of growing importance in genetics. Rare variants may explain a large portion of the tieritability that has not been explained by common variants, and will lead to new insights into the aetiology of common diseases. Increasing amounts of sequence data are being generated in order to investigate rare variants; however, statistical methods for analysis of these data are in their infancy. We will develop statistical methodology to leverage genome-wide association data for rare variant analysis through imputation, haplotypic testing and selection of samples for sequencing, and we will compare methods for rare variant association analysis. We will also develop statistical methodology to detect population structure and correct for it in association analysis, with a particular focus on rare variant analysis.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Program Projects (P01)
Project #
4P01GM099568-05
Application #
9137726
Study Section
Special Emphasis Panel (ZRG1)
Project Start
Project End
Budget Start
2016-05-01
Budget End
2017-04-30
Support Year
5
Fiscal Year
2016
Total Cost
Indirect Cost

  Institution

Name
University of Washington
Department
Type
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195

  Publications

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
Marigorta, Urko M; Rodríguez, Juan Antonio; Gibson, Greg et al. (2018) Replicability and Prediction: Lessons and Challenges from GWAS. Trends Genet 34:504-517
Pappas, D J; Lizee, A; Paunic, V et al. (2018) Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest. Pharmacogenomics J 18:367-376
Mo, Angela; Marigorta, Urko M; Arafat, Dalia et al. (2018) Disease-specific regulation of gene expression in a comparative analysis of juvenile idiopathic arthritis and inflammatory bowel disease. Genome Med 10:48
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
Yengo, Loic; Visscher, Peter M (2018) Assortative mating on complex traits revisited: Double first cousins and the X-chromosome. Theor Popul Biol 124:51-60
Browning, Sharon R; Browning, Brian L; Daviglus, Martha L et al. (2018) Ancestry-specific recent effective population size in the Americas. PLoS Genet 14:e1007385
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
Chen, Guo-Bo; Lee, Sang Hong; Montgomery, Grant W et al. (2017) Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method. BMC Med Genet 18:94
Brown, Lisa A; Sofer, Tamar; Stilp, Adrienne M et al. (2017) Admixture Mapping Identifies an Amerindian Ancestry Locus Associated with Albuminuria in Hispanics in the United States. J Am Soc Nephrol 28:2211-2220

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