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

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

Family-based study designs have been applied to resolve confounding by population stratification, dynastic effects and assortative mating in genetic association analyses. Here, Brumpton et al. describe theory and simulations for overcoming such biases in Mendelian randomization through within-family...

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Autores principales: 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, The Within-family Consortium, The 23andMe Research Team, 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, Neil M. Davies
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Science
Q
Acceso en línea:https://doaj.org/article/85accda5604c471488ab1aecb46cd352
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Sumario:Family-based study designs have been applied to resolve confounding by population stratification, dynastic effects and assortative mating in genetic association analyses. Here, Brumpton et al. describe theory and simulations for overcoming such biases in Mendelian randomization through within-family studies.

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