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. !
|Pistis, Giorgio; Porcu, Eleonora; Vrieze, Scott I et al. (2015) Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs. Eur J Hum Genet 23:975-83|
|Vrieze, Scott I; Malone, Stephen M; Pankratz, Nathan et al. (2014) Genetic associations of nonsynonymous exonic variants with psychophysiological endophenotypes. Psychophysiology 51:1300-8|
|Vrieze, Scott I; Malone, Stephen M; Vaidyanathan, Uma et al. (2014) In search of rare variants: preliminary results from whole genome sequencing of 1,325 individuals with psychophysiological endophenotypes. Psychophysiology 51:1309-20|
|Iacono, William G; Malone, Stephen M; Vaidyanathan, Uma et al. (2014) Genome-wide scans of genetic variants for psychophysiological endophenotypes: a methodological overview. Psychophysiology 51:1207-24|
|Holmen, Oddgeir L; Zhang, He; Fan, Yanbo et al. (2014) Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nat Genet 46:345-51|
|Wen, Xiaoquan (2014) Bayesian model selection in complex linear systems, as illustrated in genetic association studies. Biometrics 70:73-83|
|Feng, Shuang; Liu, Dajiang; Zhan, Xiaowei et al. (2014) RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30:2828-9|
|Lee, Seunggeung; Abecasis, Gonçalo R; Boehnke, Michael et al. (2014) Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 95:5-23|
|Liu, Dajiang J; Peloso, Gina M; Zhan, Xiaowei et al. (2014) Meta-analysis of gene-level tests for rare variant association. Nat Genet 46:200-4|
|Iacono, William G; Vaidyanathan, Uma; Vrieze, Scott I et al. (2014) Knowns and unknowns for psychophysiological endophenotypes: integration and response to commentaries. Psychophysiology 51:1339-47|
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