Coarse-to-fine Discovery for Genetic Association

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Mendelian traits are governed by single genes, and methods to identifythese genes have been remarkably successful. In stark contrast,complex traits result from multiple genetic variants that areindividually neither necessary nor sufficient, often interacting witheach other and the environment. Indeed, collectively, geneticvariants identified to date for complex traits typically explain lessthan 10% of the phenotype variance. No unified computational andstatistical framework has been advanced for organizing the discoveryprocess. To date, a single strategy has dominated: staticvariant-by-variant analysis. In contrast, the investigators propose anew coarse-to-fine statistical framework motivated by the biomedicalhypothesis that mutations contributing to a specific disease clusterin specific pathways, and in genes within these pathways. Simulationsdemonstrate that multi-scale, hierarchical coarse-to-fine sequentialtests have greater power than conventional methods under thishypothesis. The researchers convert these heuristics into mathematicsand provide a comprehensive analysis, both empirical and theoretical,of the trade-offs resulting from the introduction of carefully chosenbiases about the distribution of active variants within genes andpathways. The new methods are applied to data from real genome-wideassociation studies (GWAS) with large cohorts to validate theirutility.Knowing the genetic variants that contribute to cardiovasculardisease, diabetes, autism, and other prevalent disorders would havegreat value in identifying drug targets, predicting people at risk,and suggesting personalized therapies. These diseases are not causedby mutations in single genes, however, but by multiple mutations thatcombine to disrupt multi-gene biological pathways. The investigatorstherefore develop a new statistical framework that begins the searchfor disease-risk genes at the pathway level, then sequentially narrowsthe search to genes within pathways and alleles within genes.Successful applications to ongoing human genetic studies involvingtens to hundreds of thousands of people identify genes contributing tocardiovascular disease. More generally, the coarse-to-finestatistical framework has great value in the current era of "bigdata", with increasingly large data volumes calling for innovativestatistical methods.