Nolte Ilja M, van der Most Peter J, Alizadeh Behrooz Z, de Bakker Paul Iw, Boezen H Marike, Bruinenberg Marcel, Franke Lude, van der Harst Pim, Navis Gerjan, Postma Dirkje S, Rots Marianne G, Stolk Ronald P, Swertz Morris A, Wolffenbuttel Bruce Hr, Wijmenga Cisca, Snieder Harold
Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
Eur J Hum Genet. 2017 Jun;25(7):877-885. doi: 10.1038/ejhg.2017.50. Epub 2017 Apr 12.
Despite the recent explosive rise in number of genetic markers for complex disease traits identified in genome-wide association studies, there is still a large gap between the known heritability of these traits and the part explained by these markers. To gauge whether this 'heritability gap' is closing, we first identified genome-wide significant SNPs from the literature and performed replication analyses for 32 highly relevant traits from five broad disease areas in 13 436 subjects of the Lifelines Cohort. Next, we calculated the variance explained by multi-SNP genetic risk scores (GRSs) for each trait, and compared it to their broad- and narrow-sense heritabilities captured by all common SNPs. The majority of all previously-associated SNPs (median=75%) were significantly associated with their respective traits. All GRSs were significant, with unweighted GRSs generally explaining less phenotypic variance than weighted GRSs, for which the explained variance was highest for height (15.5%) and varied between 0.02 and 6.7% for the other traits. Broad-sense common-SNP heritability estimates were significant for all traits, with the additive effect of common SNPs explaining 48.9% of the variance for height and between 5.6 and 39.2% for the other traits. Dominance effects were uniformly small (0-1.5%) and not significant. On average, the variance explained by the weighted GRSs accounted for only 10.7% of the common-SNP heritability of the 32 traits. These results indicate that GRSs may not yet be ready for accurate personalized prediction of complex disease traits limiting widespread adoption in clinical practice.
尽管全基因组关联研究中识别出的复杂疾病性状的遗传标记数量最近呈爆发式增长,但这些性状的已知遗传力与这些标记所解释的部分之间仍存在很大差距。为了评估这种“遗传力差距”是否正在缩小,我们首先从文献中识别出全基因组显著的单核苷酸多态性(SNP),并在生命线队列研究的13436名受试者中,针对五个广泛疾病领域的32个高度相关性状进行了重复分析。接下来,我们计算了每个性状的多SNP遗传风险评分(GRS)所解释的方差,并将其与所有常见SNP所捕获的广义和狭义遗传力进行比较。所有先前关联的SNP中的大多数(中位数 = 75%)与其各自的性状显著相关。所有GRS均具有显著性,未加权的GRS通常比加权GRS解释的表型方差少,加权GRS对身高解释的方差最高(15.5%),对其他性状解释的方差在0.02%至6.7%之间。所有性状的广义常见SNP遗传力估计均具有显著性,常见SNP的加性效应解释了身高方差的48.9%,对其他性状解释的方差在5.6%至39.2%之间。显性效应一致较小(0 - 1.5%)且不显著。平均而言,加权GRS所解释的方差仅占32个性状常见SNP遗传力的10.7%。这些结果表明,GRS可能尚未准备好用于复杂疾病性状的准确个性化预测,这限制了其在临床实践中的广泛应用。