丹麦奥尔胡斯大学Doug Speed等研究人员发现，汇总统计可用于评估和改进遗传力模型。2020年3月23日，国际知名学术期刊《自然—遗传学》发表了这一研究成果。

研究人员表示，目前，全基因组中遗传力如何变化的最佳模型存在很多争论。GCTA的作者推荐了GCTA-LDMS-I模型，LD分数回归的作者推荐了基线LD模型，而他们也推荐了LDAK模型。

研究人员提供了一个统计学框架用于评估遗传力模型，这一框架使用了全基因组关联研究的汇总统计。基于复杂人类特征的31项研究（平均样本量为136,000），研究人员发现基线LD模型比其他现有的遗传性模型更为可靠，但还可以通过合并LDAK模型的功能来进行改进。这一框架还提供了一种用于评估汇总统计中选择相关参数α的方法。研究人员发现负向全基因组特征选择的强力证据（P <1×10^-6），包括身高、收缩压和大学教育，并且选择的影响在功能类别内（如编码的SNP和启动子区域）更强。

附：英文原文

Title: Evaluating and improving heritability models using summary statistics

Author: Doug Speed, John Holmes, David J. Balding

Issue&Volume: 2020-03-23

Abstract: 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<1×106) 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.

DOI: 10.1038/s41588-020-0600-y

Source: https://www.nature.com/articles/s41588-020-0600-y