PROJECT SUMIVIARY (See instructions): 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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Program Projects (P01)
Project #
1P01GM099568-01
Application #
8262856
Study Section
Special Emphasis Panel (ZRG1-GGG-M (40))
Project Start
Project End
Budget Start
2012-06-05
Budget End
2013-04-30
Support Year
1
Fiscal Year
2012
Total Cost
$236,924
Indirect Cost
$53,662
Name
University of Washington
Department
Type
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Chen, Guo-Bo; Lee, Sang Hong; Brion, Marie-Jo A et al. (2014) Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data. Hum Mol Genet 23:4710-20
(2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46:1173-86
Marigorta, Urko M; Gibson, Greg (2014) A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects. Front Genet 5:225
Hemani, Gibran; Shakhbazov, Konstantin; Westra, Harm-Jan et al. (2014) Detection and replication of epistasis influencing transcription in humans. Nature 508:249-53
Robinson, Matthew R; Wray, Naomi R; Visscher, Peter M (2014) Explaining additional genetic variation in complex traits. Trends Genet 30:124-32
Zheng, Chaozhi; Kuhner, Mary K; Thompson, Elizabeth A (2014) Joint inference of identity by descent along multiple chromosomes from population samples. J Comput Biol 21:185-200
Yang, Jian; Zaitlen, Noah A; Goddard, Michael E et al. (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat Genet 46:100-6
Gratten, Jacob; Wray, Naomi R; Keller, Matthew C et al. (2014) Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat Neurosci 17:782-90
Zheng, Chaozhi; Kuhner, Mary K; Thompson, Elizabeth A (2014) Bayesian inference of local trees along chromosomes by the sequential Markov coalescent. J Mol Evol 78:279-92
Preeprem, Thanawadee; Gibson, Greg (2014) SDS, a structural disruption score for assessment of missense variant deleteriousness. Front Genet 5:82

Showing the most recent 10 out of 27 publications