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A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics

机译:一种基于汇总关联统计估计区域遗传关联对复杂性状贡献的方法

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Despite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method, LD Adjusted Regional Genetic Variance (LARGV), to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to a multiple linear regression model when no interaction or haplotype effects are present. It has several applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by two or more regions. Using height and BMI data from the Health Retirement Study (N?=?7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.
机译:尽管付出了巨大的努力,但已知的遗传关联仅能解释预测的遗传力的一小部分。区域协会结合了来自多个连续遗传变异的信息,可以改善已建立的协会基因座所解释的差异。但是,由于对连锁不平衡(LD)的敏感性,使用汇总关联统计信息不容易进行区域关联的估计。现在,我们提出了一种新的方法,即LD调整的区域遗传方差(LARGV),以在考虑LD的同时使用汇总统计信息来估计由区域协会解释的表型差异。当不存在交互作用或单倍型效应时,我们的方法渐近等效于多元线性回归模型。它具有多种应用,例如根据解释的方差对遗传区域进行排序或比较两个或多个区域所解释的方差。使用健康退休研究中的身高和BMI数据(N?=?7,776),我们显示出大多数遗传变异都位于基因组的一小部分,并且先前确定的连锁峰具有高于预期的区域变异。

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