利用 GWAS 目录中的数据推断人类特征中遗传缺失的性质。
Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog.
机构信息
Departamento de Bioquímica, Genética e Inmunología, Universidade de Vigo, 36310, Spain
Departamento de Bioquímica, Genética e Inmunología, Universidade de Vigo, 36310, Spain.
出版信息
Genetics. 2019 Jul;212(3):891-904. doi: 10.1534/genetics.119.302077. Epub 2019 May 13.
Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.
在过去的十年中,通过全基因组关联研究(GWAS)已经检测到了数千个与人类许多疾病和其他常见特征相关的基因。然而,迄今为止发现的候选因果变异通常只能解释通过家族数据估计的遗传率的一小部分。对于这种观察结果,最常见的解释是,缺失的遗传率对应于由于缺乏统计能力而未被发现的变异,无论是罕见的还是常见的,其效应都非常小。我们进行了一项荟萃分析,使用 NHGRI-EBI GWAS 目录中的数据,以探索一组 42 种复杂特征的观察到的基因座效应分布,并量化它们对狭义遗传率的贡献。利用手头的数据,我们能够预测 16 种疾病和特征的预期基因座效应分布、它们对遗传率的预期贡献,以及尚未发现的完全解释家族遗传率估计的基因座数量。我们的研究结果表明,对于 16 种特征中的 6 种,大量基因座的加性贡献无法解释家族(广义)遗传率,这表明 GWAS 和家族遗传率估计之间的差距可能永远无法对于这些特征来说是封闭的。相比之下,对于其他 10 种特征,如果是这种情况,数百或数千个尚未发现的基因座的加性贡献可能有潜力解释家族遗传率估计值。计算机模拟用于说明非加性遗传效应对 GWAS 和家族遗传率估计值之间差距的可能贡献。
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