Thanks to technological advances in high-density genome scans, genetic association studies routinely have data for hundreds of thousand or millions of genetic markers across the entire genome. Despite these advancements, the mapping of many complex traits has proven to be difficult, illustrating the need for new and more powerful methods for the identification of loci that influence complex traits. Statistical methods for the analysis of genetic data have primarily been developed for markers on the autosomal chromosomes and significantly less attention has been given to the analysis of the X-chromosome, despite the potential for identifying X-linked genes that influence complex traits. This project is concerned with development and application of statistical methodology for the analysis of X-chromosome data. We will develop statistical methodology for association testing of X-linked variants in samples with related individuals as well as methodology for relatedness inference on the X. We will also develop statistical methodology for estimating and adjusting for population structure on the X-chromosome in samples from populations with admixed ancestry, such as African Americans and Hispanics.

Public Health Relevance

Very few genetic associations for human diseases and traits have beed identified on the X-chromosome. Many genetic analyses exclude variants on the X due to insufficient methodology in the scientific literature for analyzing X-chromosome data. The aim of this project is to develop new statistical methodology for the the analysis of data on the X-chromosome.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Program Projects (P01)
Project #
3P01GM099568-02S1
Application #
8609114
Study Section
Special Emphasis Panel (ZRG1-GGG-T (40))
Program Officer
Eckstrand, Irene A
Project Start
2012-06-05
Project End
2017-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
2
Fiscal Year
2014
Total Cost
$49,050
Indirect Cost
$15,291
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
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
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
Marigorta, Urko M; Denson, Lee A; Hyams, Jeffrey S et al. (2017) Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn's disease. Nat Genet 49:1517-1521

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