Analyses of common and rare genetic variation have produced key biological insights for many complex diseases. However, for most diseases, including psychiatric disease, the bulk of heritability remains unexplained. The genetics community is increasingly focusing on rare variants, motivated by improvements in technology that are enabling the generation of large whole-genome and whole exome sequencing (WGS and WES) data sets. A growing number of high-profile studies on rare and common variant analysis have been published, including studies published by the PIs of this renewal application and funded by R01MH101244. Nonetheless, there are many unanswered questions about the genetic architecture of complex diseases. Here, we propose a research program that will investigate complex disease architectures and develop methods to optimally leverage rare and common variant contributions to produce new biological discoveries. We will assess contributions to disease heritability across the allele frequency spectrum; identify gene sets and functional annotations that are enriched for disease heritability; and leverage these findings to increase statistical power in studies of rare and common variants while controlling for confounding. Our collaboration has multiple strengths: our statistical and computational expertise; our extensive publication record in the previous funding cycle; our track record of producing practical software that is widely used by the community; and our data- driven approach, which ensures that the methods we develop will be broadly applied to psychiatric and other disease data sets. We will guide our research using hundreds of thousands of samples from large psychiatric GWAS, WES and WGS disease data sets.
This renewal application proposes to characterize genetic architecture of psychiatric and other complex diseases. We will quantify heritability due to rare and common alleles, and identify functional groups of alleles and gene sets that are mostly responsible for the heritable component of disease risk. Building on the results we will develop new methods to identify disease risk loci in sequencing studies.
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