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.
|Reshef, Yakir A; Finucane, Hilary K; Kelley, David R et al. (2018) Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nat Genet 50:1483-1493|
|Hormozdiari, Farhad; Gazal, Steven; van de Geijn, Bryce et al. (2018) Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat Genet 50:1041-1047|
|Ganna, Andrea; Satterstrom, F Kyle; Zekavat, Seyedeh M et al. (2018) Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum. Am J Hum Genet 102:1204-1211|
|Kaplanis, Joanna; Gordon, Assaf; Shor, Tal et al. (2018) Quantitative analysis of population-scale family trees with millions of relatives. Science 360:171-175|
|Gazal, Steven; Loh, Po-Ru; Finucane, Hilary K et al. (2018) Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations. Nat Genet 50:1600-1607|
|Turley, Patrick; Walters, Raymond K; Maghzian, Omeed et al. (2018) Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 50:229-237|
|Palamara, Pier Francesco; Terhorst, Jonathan; Song, Yun S et al. (2018) High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nat Genet 50:1311-1317|
|Loh, Po-Ru; Genovese, Giulio; Handsaker, Robert E et al. (2018) Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations. Nature 559:350-355|
|Pasaniuc, Bogdan; Price, Alkes L (2017) Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 18:117-127|
|Gazal, Steven; Finucane, Hilary K; Furlotte, Nicholas A et al. (2017) Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat Genet 49:1421-1427|
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