A covering method for detecting genetic associations between rare variants and common phenotypes.
A covering method for detecting genetic associations between rare variants and common phenotypes.
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DOI：
10.1371/journal.pcbi.1000954
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发表时间：
2010-10-14
影响因子：
4.3
通讯作者：
Bafna V
中科院分区：
文献类型：
--
作者：
Bhatia G;Bansal V;Harismendy O;Schork NJ;Topol EJ;Frazer K;Bafna V
基因组全关联（GWA）研究，即检测共同遗传标记与疾病表型之间的关联，已经显示出不同程度的成功。虽然许多因素可能会混淆GWA研究，但我们关注的是多种罕见变异（RVs）可能共同影响疾病病因学的可能性。在这里，我们描述了一种用于RV分析的算法RareCover。该算法结合了不同的rv集合，具有低效果和适度的外显率。此外，它不要求稀有变体在位置上相邻。对一系列假设外显率和群体归因风险（PAR）值的广泛模拟表明，我们的方法比其他已发表的方法（包括崩溃和加权崩溃策略）更强大。为了展示该方法，我们将RareCover应用于289名处于身体质量指数分布极端（NCT00263042）的个体的队列数据重测序。对个体样本的两个基因FAAH和MGLL进行了重新测序，这两个基因已知与内源性大麻素代谢有关（148名肥胖患者和150名对照组的187Kbp）。RareCover分析在每个基因中准确地确定了一个显著相关区域，每个区域在上游调控区域约5kbp。数据表明，RVs有助于破坏这两个基因的表达，导致相应大麻素的代谢降低。总的来说，我们的结果表明，包括rv在测量遗传关联的力量。我们专注于检测多种罕见变异（rv）的问题，这些变异共同影响疾病表型。考虑到这个问题，我们认为因果罕见变异的检测必然不同于用于常见变异的典型单标记分析，并提出了一种新的算法RareCover来完成这种分析。RareCover结合了不同的rv集合，每个都具有非常低的效果和适度的外显率。对一系列外显率和群体归因风险（PAR）值的广泛模拟表明，我们的方法比其他已发表的方法（包括崩溃和加权和策略）更强大。为了展示该方法，我们将RareCover应用于289名处于身体质量指数分布极端（NCT00263042）的个体的数据，并围绕FAAH和MGLL基因进行测序。RareCover分析在每个基因中准确地确定了一个显著相关区域，每个区域在上游调控区域约5Kbp。数据表明，RVs有助于破坏这两种基因的表达，从而降低与肥胖相关的内源性大麻素的代谢。总之，我们的研究结果表明，包括RV在内的测量遗传关联的能力，并表明基于全基因组、DNA测序的关联研究调查RV效应是可行的。
Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations. We focus on the problem of detecting multiple rare variants (RVs) that act together to influence disease phenotypes. In considering this problem, we argue that the detection of causal rare variants must necessarily be different from typical single-marker analysis used for common variants and propose a novel algorithm, RareCover, to accomplish this analysis. RareCover combines a disparate collection of RVs, each with very low effect and modest penetrance. Extensive simulations over a range of values for penetrance and population attributable risk (PAR) illustrate the power of our approach over other published methods, including the collapsing and weighted-sum strategies. To showcase the method, we applied RareCover to data from 289 individuals at the extremes of Body Mass Index distribution (NCT00263042), sequenced around the FAAH and MGLL genes. RareCover analysis identified exactly one significantly associated region in each gene, each about 5Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes leading to lowered metabolism of the corresponding endocannabinoids previously linked with obesity. Overall, our results point to the power of including RVs in measuring genetic associations, and suggest that whole genome, DNA sequencing-based association studies investigating RV effects are feasible.
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影响因子：
48
作者：
Craig, David W.;Pearson, John V.;Szelinger, Szabolcs;Sekar, Aswin;Redman, Margot;Corneveaux, Jason J.;Pawlowski, Traci L.;Laub, Trisha;Nunn, Gary;Stephan, Dietrich A.;Homer, Nils;Huentelman, Matthew J.
通讯作者：
Huentelman, Matthew J.
影响因子：
6.9
作者：
Lieb, Wolfgang;Manning, Alisa K.;Florez, Jose C.;Dupuis, Josee;Cupples, L. Adrienne;McAteer, Jarred B.;Vasan, Ramachandran S.;Hoffmann, Udo;O'Donnell, Christopher J.;Meigs, James B.;Fox, Caroline S.
通讯作者：
Fox, Caroline S.
DOI：
10.1073/pnas.0812824106
发表时间：
2009-03-10
影响因子：
11.1
作者：
Kryukov, Gregory V.;Shpunt, Alexander;Sunyaev, Shamil R.
通讯作者：
Sunyaev, Shamil R.
影响因子：
7.7
作者：
Engeli, S;Böhnke, J;Jordan, J
通讯作者：
Jordan, J
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.