Limitations of GCTA as a solution to the missing heritability problem
Limitations of GCTA as a solution to the missing heritability problem
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DOI：
10.1073/pnas.1520109113
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发表时间：
2016-01-05
影响因子：
11.1
通讯作者：
Tuljapurkar, Shripad
中科院分区：
文献类型：
--
作者：
Kumar, Siddharth Krishna;Feldman, Marcus W.;Tuljapurkar, Shripad
全基因组关联研究(GWAS)试图了解复杂表型(S)(例如身高)和多达数百万个单核苷酸多态(SNP)之间的关系。对GWAS的早期分析通常被认为“遗漏”了根据亲属之间的相关性估计的大部分加性遗传方差。一种较新的方法，全基因组复杂性状分析(GCTA)，使用基因相似个体之间相关的随机SNP效应模型，获得了更高的遗传力估计。GCTA现在已经被应用于从精神分裂症到学业成就的许多表型。然而，最近的研究质疑GCTA对遗传性的估计。在这里，我们表明，应用于当前SNP数据的GCTA不能产生可靠或稳定的遗传力估计。我们首先证明了GCTA敏感地依赖于高维遗传关联度矩阵(GRM)的所有奇异值。当GCTA中的假设完全满足时，我们发现GCTA产生的遗传力估计将是有偏的，标准误差很可能是不准确的。当总体是分层的时，我们发现GRM通常具有高度倾斜的奇异值，并且我们证明了许多小的奇异值不能可靠地估计。因此，GWAS数据必然会被GCTA过度拟合，因此产生了对遗传力的高估计。我们还表明，GCTA的遗传力估计对所选样本和表型中的测量误差很敏感。我们使用Framingham数据集来说明我们的结果。我们的分析表明，使用GCTA获得的结果以及对结果的定性解释应该非常谨慎地解释。
Genome-wide association studies (GWASs) seek to understand the relationship between complex phenotype(s) (e.g., height) and up to millions of single-nucleotide polymorphisms (SNPs). Early analyses of GWASs are commonly believed to have "missed" much of the additive genetic variance estimated from correlations between relatives. A more recent method, genome-wide complex trait analysis (GCTA), obtains much higher estimates of heritability using a model of random SNP effects correlated between genotypically similar individuals. GCTA has now been applied to many phenotypes from schizophrenia to scholastic achievement. However, recent studies question GCTA's estimates of heritability. Here, we show that GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability. We show first that GCTA depends sensitively on all singular values of a high-dimensional genetic relatedness matrix (GRM). When the assumptions in GCTA are satisfied exactly, we show that the heritability estimates produced by GCTA will be biased and the standard errors will likely be inaccurate. When the population is stratified, we find that GRMs typically have highly skewed singular values, and we prove that the many small singular values cannot be estimated reliably. Hence, GWAS data are necessarily overfit by GCTA which, as a result, produces high estimates of heritability. We also show that GCTA's heritability estimates are sensitive to the chosen sample and to measurement errors in the phenotype. We illustrate our results using the Framingham dataset. Our analysis suggests that results obtained using GCTA, and the results' qualitative interpretations, should be interpreted with great caution.