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.
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.
|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