Using genetic variation to disentangle the complex relationship between food intake and health outcomes

Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide ev...

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Published in:PLoS genetics Vol. 18; no. 6; p. e1010162
Main Authors: Pirastu, Nicola, McDonnell, Ciara, Grzeszkowiak, Eryk J., Mounier, Ninon, Imamura, Fumiaki, Merino, Jordi, Day, Felix R., Zheng, Jie, Taba, Nele, Concas, Maria Pina, Repetto, Linda, Kentistou, Katherine A., Robino, Antonietta, Esko, Tõnu, Joshi, Peter K., Fischer, Krista, Ong, Ken K., Gaunt, Tom R., Kutalik, Zoltán, Perry, John R. B., Wilson, James F.
Format: Journal Article
Language:English
Published: United States Public Library of Science 01.06.2022
Public Library of Science (PLoS)
Subjects:
Alcoholic beverages
Biobanks
Biology and Life Sciences
Blood pressure
Cardiovascular disease
Causality
Cholesterol
Coffee
Diet
Dietary intake
Eating
Educational attainment
Food
Food intake
Genetic analysis
Genetic diversity
Genetic markers
Genetic Variation
Genomes
Humans
Interactive computer systems
Low density lipoprotein
Medicine and Health Sciences
Nutrition research
Outcome Assessment, Health Care
Risk Factors
Single-nucleotide polymorphism
Software
United Kingdom > UK
ISSN:1553-7404, 1553-7390, 1553-7404
Online Access:Get full text
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Summary:Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes.
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I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr Joshi is a paid consultant to Global Gene Corp and Humanity Inc.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1010162