The overall objective is ttie development of methods for the enhanced detection and resolution of genes contributing to complex quantitative genetic traits observed in individuals not known to be related. The approach will be through using dense SNP marker data for the detection and estimation of segments of gene identity by descent (ibd) shared among sets of individuals. Locus-specific inferred ibd among individuals will be analyzed in conjunction with their phenotypic similarities and differences, in order to detect and resolve causal loci. We will develop and assess hidden Markov models (HMM) and methods for detection of ibd genome segments between pairs of members of populations from dense SNP data or sequence variants. We will assess the effects on performance of our methods of linkage disequilibrium, data error and copynumber variants, and the efficacy of prior haplotype imputation, data cleaning, and screening for regions of allelic similarity. We will extend our models and methods to the inference of ibd among larger sets of chromosomes using both HMM and coalescent models, and develop Markov chain Monte Carlo methods for sampling of ibd genome segments, conditional on dense SNP marker or sequence variant data in candidate gene regions. We will develop and assess methods for analyzing trait data on individuals conditional on the patterns of ibd genome segments inferred among them, by assessing location-specific levels and regional chromosomal extent of ibd segments among sampled chromosomes in relation to quantitative trait values. We will assess our methods and compare with alternative approaches, by first testing methods in simulated population structures, where latent ibd is known, but in which founder haplotypes are provided by real-data population samples. Then, in real data sets available to us, where latent ibd is unknown, we will compare results of our methods with those of other approaches developed both within the P01 group and elsewhere. We will develop software implementing our methods, and document, distribute and support this software.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Program Projects (P01)
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Special Emphasis Panel (ZRG1-GGG-M)
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University of Washington
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Weir, Bruce S; Goudet, Jérôme (2017) A Unified Characterization of Population Structure and Relatedness. Genetics 206:2085-2103
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
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
Pappas, D J; Lizee, A; Paunic, V et al. (2017) Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest. Pharmacogenomics J :
Visscher, Peter M; Wray, Naomi R; Zhang, Qian et al. (2017) 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet 101:5-22
Wang, Bowen; Sverdlov, Serge; Thompson, Elizabeth (2017) Efficient Estimation of Realized Kinship from Single Nucleotide Polymorphism Genotypes. Genetics 205:1063-1078
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
Zeng, Biao; Lloyd-Jones, Luke R; Holloway, Alexander et al. (2017) Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression. G3 (Bethesda) 7:2533-2544
Hodonsky, Chani J; Jain, Deepti; Schick, Ursula M et al. (2017) Genome-wide association study of red blood cell traits in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos. PLoS Genet 13:e1006760
Zhan, Xiang; Zhao, Ni; Plantinga, Anna et al. (2017) Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits. Genetics 206:1779-1790

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