Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses

Ben Brumpton (), Eleanor Sanderson, Karl Heilbron, 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, Adam Auton, Frank Windmeijer, Wei-Min Chen, Johan Håkon Bjørngaard, Kristian Hveem, Cristen Willer, David M. Evans, Jaakko Kaprio, George Davey Smith, Bjørn Olav Åsvold, Gibran Hemani and Neil M. Davies ()
Additional contact information
Ben Brumpton: Norwegian University of Science and Technology
Eleanor Sanderson: University of Bristol
Karl Heilbron: 23andMe, Inc.
Fernando Pires Hartwig: University of Bristol
Sean Harrison: University of Bristol
Gunnhild Åberge Vie: Norwegian University of Science and Technology
Yoonsu Cho: University of Bristol
Laura D. Howe: University of Bristol
Amanda Hughes: University of Bristol
Dorret I. Boomsma: Vrije Universiteit Amsterdam
Alexandra Havdahl: University of Bristol
John Hopper: The University of Melbourne
Michael Neale: Virginia Commonwealth University
Michel G. Nivard: Vrije Universiteit Amsterdam
Nancy L. Pedersen: Karolinska Institutet
Chandra A. Reynolds: University of California Riverside
Elliot M. Tucker-Drob: University of Texas at Austin
Andrew Grotzinger: University of Texas at Austin
Laurence Howe: University of Bristol
Tim Morris: University of Bristol
Shuai Li: The University of Melbourne
Adam Auton: 23andMe, Inc.
Wei-Min Chen: University of Virginia
Johan Håkon Bjørngaard: Norwegian University of Science and Technology
Kristian Hveem: Norwegian University of Science and Technology
Cristen Willer: University of Michigan
David M. Evans: University of Bristol
Jaakko Kaprio: University of Helsinki
George Davey Smith: University of Bristol
Bjørn Olav Åsvold: Norwegian University of Science and Technology
Gibran Hemani: University of Bristol
Neil M. Davies: Norwegian University of Science and Technology

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies.

Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17117-4

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DOI: 10.1038/s41467-020-17117-4

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