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首页> 美国卫生研究院文献>other >Methodological Considerations in Estimation of Phenotype Heritability Using Genome-Wide SNP Data Illustrated by an Analysis of the Heritability of Height in a Large Sample of African Ancestry Adults
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Methodological Considerations in Estimation of Phenotype Heritability Using Genome-Wide SNP Data Illustrated by an Analysis of the Heritability of Height in a Large Sample of African Ancestry Adults

机译：使用全基因组SNP数据估算表型遗传力的方法学考虑通过对大量非洲祖先成年人的身高遗传力进行分析说明

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Height has an extremely polygenic pattern of inheritance. Genome-wide association studies (GWAS) have revealed hundreds of common variants that are associated with human height at genome-wide levels of significance. However, only a small fraction of phenotypic variation can be explained by the aggregate of these common variants. In a large study of African-American men and women (n = 14,419), we genotyped and analyzed 966,578 autosomal SNPs across the entire genome using a linear mixed model variance components approach implemented in the program GCTA (Yang et al Nat Genet 2010), and estimated an additive heritability of 44.7% (se: 3.7%) for this phenotype in a sample of evidently unrelated individuals. While this estimated value is similar to that given by Yang et al in their analyses, we remain concerned about two related issues: (1) whether in the complete absence of hidden relatedness, variance components methods have adequate power to estimate heritability when a very large number of SNPs are used in the analysis; and (2) whether estimation of heritability may be biased, in real studies, by low levels of residual hidden relatedness. We addressed the first question in a semi-analytic fashion by directly simulating the distribution of the score statistic for a test of zero heritability with and without low levels of relatedness. The second question was addressed by a very careful comparison of the behavior of estimated heritability for both observed (self-reported) height and simulated phenotypes compared to imputation R2 as a function of the number of SNPs used in the analysis. These simulations help to address the important question about whether today's GWAS SNPs will remain useful for imputing causal variants that are discovered using very large sample sizes in future studies of height, or whether the causal variants themselves will need to be genotyped de novo in order to build a prediction model that ultimately captures a large fraction of the variability of height, and by implication other complex phenotypes. Our overall conclusions are that when study sizes are quite large (5,000 or so) the additive heritability estimate for height is not apparently biased upwards using the linear mixed model; however there is evidence in our simulation that a very large number of causal variants (many thousands) each with very small effect on phenotypic variance will need to be discovered to fill the gap between the heritability explained by known versus unknown causal variants. We conclude that today's GWAS data will remain useful in the future for causal variant prediction, but that finding the causal variants that need to be predicted may be extremely laborious.
机译：身高具有极其多基因的遗传模式。全基因组关联研究（GWAS）已揭示了数百种常见变异体，这些变异体在全基因组意义上与人类身高相关。但是，这些共同变体的总和只能解释一小部分的表型变异。在一项针对非裔美国人（n = 14,419）的大型研究中，我们使用了GCTA计划中实施的线性混合模型方差成分方法对整个基因组中的966,578个常染色体SNP进行了基因分型和分析（Yang等，Nat Genet，2010年），并在明显无关的个体样本中估计此表型的加和遗传力为44.7％（se：3.7％）。尽管此估计值与Yang等人的分析结果相似，但我们仍然关注两个相关问题：（1）在完全没有隐藏的相关性的情况下，当非常大时方差分量法是否具有足够的能力来估计遗传力分析中使用了多个SNP； （2）在实际研究中，是否可以通过低水平的残留隐性关联度对遗传力的估计产生偏差。我们通过半模拟分数统计量的分布，以半遗传分析的方式解决了第一个问题，该方法用于在有或没有低相关性的情况下进行零遗传力测试。第二个问题是通过非常仔细地比较了观察到的（自我报告的）身高和模拟表型的遗传力估计值与估算的R 2 的行为之间的相关性，该行为是用于SNPs数量的函数分析。这些模拟有助于解决一个重要问题，即今天的GWAS SNP是否仍可用于估算在未来的高度研究中使用非常大的样本量发现的因果变异，或者是否需要从头对因果变异进行基因分型才能建立一个预测模型，该模型最终可以捕获高度变化的很大一部分，并暗示其他复杂的表型。我们的总体结论是，当研究规模很大（5,000左右）时，使用线性混合模型对身高的累加遗传力估计值不会明显偏向上。然而，在我们的模拟中有证据表明，将需要发现大量因果变异（成千上万种），每个对表型方差的影响很小，以填补已知因果变异与未知因果变异所解释的遗传力之间的空白。我们得出的结论是，今天的GWAS数据将在将来用于因果变量预测，但发现需要预测的因果变量可能非常费力。

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