Coarse-to-fine Discovery for Genetic Association

孟德尔性状是由单基因控制的，鉴定这些基因的方法已经非常成功。 与此形成鲜明对比的是，复杂的性状是由多个遗传变异引起的，这些遗传变异在个体上既不是必要的，也不是充分的，它们经常与彼此和环境相互作用。 事实上，总的来说，迄今为止发现的复杂性状的遗传变异通常解释不到10%的表型变异。 没有统一的计算和统计框架已经先进的组织发现过程。 到目前为止，一个单一的策略占主导地位：静态变量分析。 相反，研究人员提出了一种新的由粗到细的统计框架，其动机是生物医学假设，即导致特定疾病的突变聚集在特定的通路中，以及这些通路中的基因中。 仿真结果表明，多尺度，分层的粗到细的顺序测试有更大的权力比传统的方法在此假设。 研究人员将这些理论转化为实证，并提供了一个全面的分析，既有经验的，也有理论的，对引入精心选择的关于基因和途径内活性变体分布的偏见所产生的权衡。 这些新方法被应用于真实的全基因组关联研究（GWAS）的大样本数据，以验证其效用。了解导致心血管疾病、糖尿病、自闭症和其他流行疾病的遗传变异，将对确定药物靶点、预测高危人群和提出个性化治疗方案具有重要价值。 然而，这些疾病不是由单个基因突变引起的，而是由多个突变结合起来破坏多基因生物途径引起的。 因此，研究人员开发了一个新的统计框架，从通路水平开始寻找疾病风险基因，然后依次将搜索范围缩小到通路内的基因和基因内的等位基因。成功应用于正在进行的涉及数万至数十万人的人类遗传研究，可以识别出导致心血管疾病的基因。 更一般地说，从粗到细的统计框架在当前的“大数据”时代具有巨大价值，越来越大的数据量需要创新的统计方法。

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.