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

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.

SUBMITTER: Brumpton B

PROVIDER: S-EPMC7360778 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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

Brumpton Ben B Sanderson Eleanor E Heilbron Karl K Hartwig Fernando Pires FP Harrison Sean S Vie Gunnhild Åberge GÅ Cho Yoonsu Y Howe Laura D LD Hughes Amanda A Boomsma Dorret I DI Havdahl Alexandra A Hopper John J Neale Michael M Nivard Michel G MG Pedersen Nancy L NL Reynolds Chandra A CA Tucker-Drob Elliot M EM Grotzinger Andrew A Howe Laurence L Morris Tim T Li Shuai S Auton Adam A Windmeijer Frank F Chen Wei-Min WM Bjørngaard Johan Håkon JH Hveem Kristian K Willer Cristen C Evans David M DM Kaprio Jaakko J Davey Smith George G Åsvold Bjørn Olav BO Hemani Gibran G Davies Neil M NM

Nature communications 20200714 1

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 sib ...[more]

PMID: 32665587

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Project description:Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias. Author summary There is growing evidence that genome-wide association studies capture associations relating to environmental factors, such as indirect effects from parental genotypes. Within-family models such as sibling comparisons can be used to disentangles these different sources of association but are limited by the paucity of sibling data in large biobanks. Within-spouse pair models are a potentially tractable model because spouses share environmental factors in adulthood and may also share early-life environmental factors. Here, we evaluated the application of within-spouse models in genetic association studies, specifically considering assortative mating, a phenomenon whereby individuals may select a phenotypically similar partner. We found that within-spouse pair models can detect genuine confounding in genetic association estimates but are potentially susceptible to collider bias induced by comparing assorted pairs. Within-spouse pair estimates could be useful when combining evidence from different study designs.

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Dataset Information

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

Publications

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

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