Using hidden genealogical structure to study the architecture of human disease

ended

Large-scale genome-wide association studies (GWAS) have yielded thousands of genetic as-sociations to heritable traits, but for most common diseases, these signals collectively explain only a small fraction of phenotypic variation. The phenotypic impact of recent, rare genetic variants, in particular, is poorly understood, but currently available data sets and analytical tools cannot be used to effectively study this class of variation. To address this problem, we propose to develop new computational methodology that will enable studying the phenotypic role of recent, rare genetic variation. This will improve our understanding of the architecture of heritable complex traits, inform the design of future studies, and increase our ability to detect novel associations. This project will address three specific aims. The first aim is to devise new methods to accurately reconstruct the complex network of genealogical relationships of individuals using high/low-coverage sequencing or microarray data. The second is to leverage these genealogical structures to infer the presence of unobserved genetic variation, with the goal of analyzing variance components of narrow sense heritability attributable to rare variants and studying the evolutionary history of heritable traits. Finally, in the third aim, we will develop new approaches to detect association to both rare and common variants, increasing the statistical power of GWAS methodology.