Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies
Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies
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DOI：
10.1371/journal.pgen.1006495
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发表时间：
2017-01-01
期刊：
影响因子：
4.5
通讯作者：
Koellinger, Philipp D.
中科院分区：
文献类型：
--
作者：
de Vlaming, Ronald;Okbay, Aysu;Koellinger, Philipp D.
大规模全基因组关联结果通常是从跨越不同地区和/或时间段的多项研究的GWAS汇总统计数据的固定效应荟萃分析中获得的。这种方法平均了所有研究中基因变异的估计影响。如果遗传效应在不同的研究中是不同的，GWAS的统计能力和多基因得分的预测准确性就会减弱，从而导致所谓的“遗漏遗传性”。在这里，我们描述了在线Meta-Gwas精度和功率(MetaGAP)计算器(可在www..狼吞虎咽。它基于一种新的多项研究框架对这种衰减进行了量化。通过模拟研究，我们表明，在广泛的遗传结构下，该计算器提供的统计能力和预测精度是准确的。我们将来自MetaGAP计算器的预测与GWAS文献中获得的实际结果进行了比较。具体地说，我们使用基因组相关矩阵限制最大似然法来估计SNP遗传度，并在三个大样本中交叉研究身高、BMI、教育年限和自我评估健康的遗传相关性。这些估计值用作MetaGAP计算器的输入参数。来自计算器的结果表明，交叉研究的异质性导致了在最近大规模的GWAS关于这些性状的努力中统计能力和预测准确性的减弱(例如，对于多年的教育，我们估计全基因组显著基因座的数量的相对损失为51-62%，多基因得分R-2的相对损失为36-38%)。因此，交叉研究的异质性导致了遗漏的遗传性。
Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/ or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called 'missing heritability'. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www. devlaming. eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51-62% in the number of genome-wide significant loci and a relative loss in polygenic score R-2 of 36-38%). Hence, cross-study heterogeneity contributes to the missing heritability.