Our research focuses on the development of statistical methods for uncovering the genetic basis of human disease, and on the population genetics underlying these methods. Areas of interest include:
Functional components of heritability
Functional annotation data represents a great opportunity for the field of human genetics, as there is increasing evidence that genetic associations are concentrated in annotation classes spanning a small fraction of the genome. In contrast to approaches that rely on genome-wide significant associations to identify enriched regulatory and cell-type specific annotation classes, our research takes a heritability approach, leveraging genome-wide polygenic signals.
Functional heritability from summary statistics paper (Finucane et al. 2015). Also see BOLT-REML paper (Loh et al. 2015), Genetic correlations from summary statistics paper (Bulik-Sullivan et al. 2015) and Functional heritability paper (Gusev et al. 2014).
Disease mapping in structured populations
Structured populations pose several technical challenges for disease mapping.
Population stratification motivates the use of mixed linear models (MLM) and/or principal components analysis (PCA), but the question of how to maximize power using these methods is complex. In addition, it is currently unclear how to best combine data from multiple ethnicities.
BOLT-LMM paper (Loh et al. 2015). Also see Liability Threshold MLM paper (Hayeck et al. 2015), MLM Perspective paper (Yang et al. 2014), African-American selection paper (Bhatia et al. 2014), Fst paper (Bhatia et al. 2013), PCA SNP weights paper (Chen et al. 2013), MIXSCORE paper (Pasaniuc et al. 2011), Africa selection paper (Bhatia et al. 2011) and Admixture review paper (Seldin et al. 2011).
Common vs. rare variant architectures of complex traits
The gap between the estimates of the heritability explained by SNPs and total narrow-sense heritability has several possible explanations, including a rare variant contribution to complex trait architectures. We aim to understand the relative contribution of common and rare variants, informing strategies for disease mapping and polygenic prediction using whole-genome sequencing data.
Admixture-based heritability paper (Zaitlen et al. 2014). Also see 2VC prediction paper (Tucker et al. 2015), LDpred paper (Vilhjalmsson et al. 2015), Local heritability paper (Gusev et al. 2013), deCODE heritability paper (Zaitlen et al. 2013) (Research Highlight), Low-coverage sequencing paper (Pasaniuc et al. 2012), Excess-of-rare-variants paper (Price et al. 2010).