英国布里斯托尔大学Neil M. Davies和Laurence J. Howe共同合作，近期取得重要工作进展。他们研究发现家族内全基因组关联分析减少了直接遗传效应估计值的偏差。这一研究成果2022年5月9日在线发表于《自然—遗传学》上。

研究人员结合了19个队列的178,086个兄弟姐妹的数据，产生了25个表型的群体（家族间）和兄弟姐妹内（家族内）的GWAS估计值。在身高、教育程度、初产年龄、子女数量、认知能力、抑郁症状和吸烟方面，家族内GWAS估计值小于人口估计值。在下游SNP遗传率、遗传相关性和孟德尔随机化分析中观察到一些差异。例如，兄弟姐妹间受教育程度和身体质量指数之间的遗传相关性减弱到零。相反，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。他们还发现了关于身高的多基因适应的家族内证据。在此，我们说明了基于家族的GWAS数据对受到人口统计学和间接遗传效应影响的表型的重要性。

据介绍，无关系个体的全基因组关联研究（GWAS）的估计值可以捕捉到遗传变异（直接效应）、人口学（人口分层、同种交配）和亲属（间接遗传效应）的影响。基于家庭GWAS设计可以控制人口学和间接遗传效应，但一直缺乏大规模的家庭数据集。

附：英文原文

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

Author: Howe, Laurence J., Nivard, Michel G., Morris, Tim T., Hansen, Ailin F., Rasheed, Humaira, Cho, Yoonsu, Chittoor, Geetha, Ahlskog, Rafael, Lind, Penelope A., Palviainen, Teemu, van der Zee, Matthijs D., Cheesman, Rosa, Mangino, Massimo, Wang, Yunzhang, Li, Shuai, Klaric, Lucija, Ratliff, Scott M., Bielak, Lawrence F., Nygaard, Marianne, Giannelis, Alexandros, Willoughby, Emily A., Reynolds, Chandra A., Balbona, Jared V., Andreassen, Ole A., Ask, Helga, Baras, Aris, Bauer, Christopher R., Boomsma, Dorret I., Campbell, Archie, Campbell, Harry, Chen, Zhengming, Christofidou, Paraskevi, Corfield, Elizabeth, Dahm, Christina C., Dokuru, Deepika R., Evans, Luke M., de Geus, Eco J. C., Giddaluru, Sudheer, Gordon, Scott D., Harden, K. Paige, Hill, W. David, Hughes, Amanda, Kerr, Shona M., Kim, Yongkang, Kweon, Hyeokmoon, Latvala, Antti, Lawlor, Deborah A., Li, Liming, Lin, Kuang, Magnus, Per, Magnusson, Patrik K. E., Mallard, Travis T., Martikainen, Pekka, Mills, Melinda C., Njlstad, Pl Rasmus, Overton, John D., Pedersen, Nancy L., Porteous, David J., Reid, Jeffrey, Silventoinen, Karri, Southey, Melissa C.

Issue&Volume: 2022-05-09

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. Within-sibship genome-wide association analyses using data from 178,076 siblings illustrate differences between population-based and within-sibship GWAS estimates for phenotypes influenced by demographic and indirect genetic effects.

DOI: 10.1038/s41588-022-01062-7

Source: https://www.nature.com/articles/s41588-022-01062-7