Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark.
Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.
Nat Genet. 2020 Apr;52(4):458-462. doi: 10.1038/s41588-020-0600-y. Epub 2020 Mar 23.
There is currently much debate regarding the best model for how heritability varies across the genome. The authors of GCTA recommend the GCTA-LDMS-I model, the authors of LD Score Regression recommend the Baseline LD model, and we have recommended the LDAK model. Here we provide a statistical framework for assessing heritability models using summary statistics from genome-wide association studies. Based on 31 studies of complex human traits (average sample size 136,000), we show that the Baseline LD model is more realistic than other existing heritability models, but that it can be improved by incorporating features from the LDAK model. Our framework also provides a method for estimating the selection-related parameter α from summary statistics. We find strong evidence (P < 1 × 10) of negative genome-wide selection for traits, including height, systolic blood pressure and college education, and that the impact of selection is stronger inside functional categories, such as coding SNPs and promoter regions.
目前，关于遗传率如何在基因组中变化的最佳模型存在很多争论。GCTA 的作者推荐 GCTA-LDMS-I 模型，LD Score Regression 的作者推荐 Baseline LD 模型，而我们推荐 LDAK 模型。在这里，我们提供了一个使用全基因组关联研究的汇总统计数据评估遗传率模型的统计框架。基于 31 项复杂人类特征的研究（平均样本量为 136,000），我们表明 Baseline LD 模型比其他现有的遗传率模型更现实，但通过纳入 LDAK 模型的特征可以对其进行改进。我们的框架还提供了一种从汇总统计数据估计与选择相关的参数 α 的方法。我们发现了强有力的证据（P<1×10）表明，包括身高、收缩压和大学教育在内的特征存在全基因组范围内的负向选择，并且选择的影响在功能类别（如编码 SNP 和启动子区域）内更强。
