Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals
Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals
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从大量无关个体样本中估计人类复杂性状的非加性遗传方差
DOI：
10.1101/2020.11.09.375501
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
P. Visscher
中科院分区：
文献类型：
--
作者：
Valentin Hivert;J. Sidorenko;F. Rohart;M. Goddard;Jian Yang;N. Wray;L. Yengo;P. Visscher
复杂性状的非加性遗传方差传统上是从亲缘关系数据中估计的。众所周知，在非实验室物种（包括人类）中进行无偏倚的估计是非常困难的，因为可能会与亲属之间的环境协方差相混淆。原则上，可归因于常见DNA变异的非加性方差可以从具有全基因组SNP数据的无关个体的随机样本中估计。在这里，我们联合估计的比例解释的加性，显性和加性加性遗传方差在一个单一的分析模型的方差。我们首先通过模拟表明，我们的模型导致无偏估计，并提供新的理论来预测标准误差估计使用最小二乘或最大似然。然后，我们将该模型应用于70个复杂性状，使用来自英国生物库的254，679个无关个体和1.1M基因分型和插补SNP。我们发现了强有力的证据，加性方差（平均性状。相比之下，跨性状的平均估计值为0.001，这意味着由常见SNP标记的因果变异的显性方差可以忽略不计。性状间的平均上位性方差为0.058，由于抽样方差较大，与零无显著差异。我们的研究结果提供了新的证据表明，复杂性状的遗传方差主要是加性的，需要数百万无关个体的样本量来估计上位方差具有足够的精度。
Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive , dominance and additive-by-additive genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits . In contrast, the average estimate of across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance across the traits was 0.058, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive, and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.
影响因子：
9.8
作者：
Zhu, Zhihong;Bakshi, Andrew;Yang, Jian
通讯作者：
Yang, Jian
影响因子：
30.8
作者：
Tin, Adrienne;Marten, Jonathan;Koettgen, Anna
通讯作者：
Koettgen, Anna