作者
Ben Brumpton, Eleanor Sanderson, Fernando Pires Hartwig, Sean Harrison, Gunnhild Åberge Vie, Yoonsu Cho, Laura D Howe, Amanda Hughes, Dorret I Boomsma, Alexandra Havdahl, John Hopper, Michael Neale, Michel G Nivard, Nancy L Pedersen, Chandra A Reynolds, Elliot M Tucker-Drob, Andrew Grotzinger, Laurence Howe, Tim Morris, Shuai Li, MR within-family Consortium, Wei-Min Chen, Johan Håkon Bjørngaard, Kristian Hveem, Cristen Willer, David M Evans, Jaakko Kaprio, Bjørn Olav Åsvol, George Davey Smith, Bjørn Olav Åsvold, Gibran Hemani, Neil M Davies
发表日期
2019/4/9
期刊
BioRxiv
页码范围
602516
出版商
Cold Spring Harbor Laboratory
简介
Mendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trøndelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.
引用总数
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