Leveraging family data to design Mendelian randomization that is provably robust to population stratification
- 1Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA;
- 2Department of Statistics, University of California Los Angeles, Los Angeles, California 90095, USA;
- 3Department of Computational Medicine, University of California Los Angeles, Los Angeles, California 90095, USA;
- 4Department of Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA
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↵5 These authors contributed equally to this work.
Abstract
Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.277664.123.
- Received January 5, 2023.
- Accepted April 16, 2023.
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