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同一家系全基因组关联分析可减少直接遗传效应估计的偏差。

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

机构信息

Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

出版信息

Nat Genet. 2022 May;54(5):581-592. doi: 10.1038/s41588-022-01062-7. Epub 2022 May 9.

DOI:10.1038/s41588-022-01062-7
PMID:35534559
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9110300/
Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

摘要

全基因组关联研究（GWAS）对无关联个体的估计捕获了遗传变异（直接效应）、人口统计学（群体分层、交配选择）和亲属（间接遗传效应）的影响。基于家庭的 GWAS 设计可以控制人口统计学和间接遗传效应，但缺乏大规模的家庭数据集。我们结合了来自 19 个队列的 178086 对兄弟姐妹的数据，为 25 种表型生成了群体（家系间）和家系内（家系内）GWAS 估计值。在家系内的 GWAS 估计值比身高、教育程度、首次生育年龄、子女数量、认知能力、抑郁症状和吸烟等表型的群体估计值小。在下游 SNP 遗传力、遗传相关性和孟德尔随机化分析中观察到一些差异。例如，教育程度和体重指数之间的家系内遗传相关性趋于零。相比之下，对大多数分子表型（例如，低密度脂蛋白胆固醇）的分析通常是一致的。我们还在家系内发现了更高身高的多基因适应的证据。在这里，我们说明了受人口统计学和间接遗传效应影响的表型的基于家庭的 GWAS 数据的重要性。

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