In the past five years, genetic association studies have evaluated the contribution of common SNP variation to complex traits at an unprecedented level of detail. These genome wide studies relied not only on advances in genotyping technologies but also on improved study designs and advances in statistical and computational methods - ranging from the development of cost-effective two stage designs, to new strategies to control for population structure, to methods and software for genotype imputation and for cross study meta-analyses. In the next five years, great advances are again expected in genotyping and sequencing technologies. Effectively using these technologies to further our understanding of complex traits will require continued advances in methods for the design and analysis of genetic studies. In this application, we build on our record of developing practical useful analytical methods, computational tools, and study designs for human genetic studies. We set out to develop computational and statistical methods that will enable studies of complex traits in humans to effectively exploit these new technologies. Specifically, we will develop new methods and computational tools for genotype imputation and for the interpretation of short read sequence data, evaluate sequence and genotyping based design strategies for complex trait studies, and develop statistical methods that facilitate the prioritization of likely functional variants in genetic association studies. !
In the next few years, continued advances in laboratory methods will allow geneticists to examine sequence variation in great detail and in progressively larger numbers of individuals. Here, we propose to develop statistical tools, computational methods and study designs that will allow geneticists to more fully exploit these new laboratory methods to study complex traits in humans. We expect methods developed here will lead directly to improved understanding of the molecular basis of many human traits and diseases - an important step in the path towards new treatments and therapies. !
| Chiang, Charleston W K; Marcus, Joseph H; Sidore, Carlo et al. (2018) Genomic history of the Sardinian population. Nat Genet 50:1426-1434 |
| Yang, Jingjing; Chen, Sai; Abecasis, Gonçalo et al. (2018) Improved score statistics for meta-analysis in single-variant and gene-level association studies. Genet Epidemiol 42:333-343 |
| Gamazon, Eric R; Segrè, Ayellet V; van de Bunt, Martijn et al. (2018) Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat Genet 50:956-967 |
| Steri, Maristella; Orrù, Valeria; Idda, M Laura et al. (2017) Overexpression of the Cytokine BAFF and Autoimmunity Risk. N Engl J Med 376:1615-1626 |
| van den Berg, Marten E; Warren, Helen R; Cabrera, Claudia P et al. (2017) Discovery of novel heart rate-associated loci using the Exome Chip. Hum Mol Genet 26:2346-2363 |
| McCarthy, Shane; Das, Sayantan; Kretzschmar, Warren et al. (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279-83 |
| Loh, Po-Ru; Danecek, Petr; Palamara, Pier Francesco et al. (2016) Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet 48:1443-1448 |
| Wen, Xiaoquan; Lee, Yeji; Luca, Francesca et al. (2016) Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of Posteriors. Am J Hum Genet 98:1114-1129 |
| Das, Sayantan; Forer, Lukas; Schönherr, Sebastian et al. (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284-1287 |
| Flickinger, Matthew; Jun, Goo; Abecasis, Gonçalo R et al. (2015) Correcting for Sample Contamination in Genotype Calling of DNA Sequence Data. Am J Hum Genet 97:284-90 |
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