Evidence map›Paper›PMID 41674649›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Biologically informed instrument selection for dietary Mendelian randomization using chemosensory receptor variants.

Liang-Dar Hwang, Cailu Lin, David M Evans, Nicholas G Martin, Danielle R Reed, Paule V Joseph

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Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Liang-Dar HwangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.ORCID 0000-0002-5535-2199
Cailu LinMonell Chemical Senses Center, Philadelphia, PA, USA.
David M EvansInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
Nicholas G MartinQIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Danielle R ReedMonell Chemical Senses Center, Philadelphia, PA, USA.
Paule V JosephSection of Sensory Science and Metabolism (SenSMet), National Institute on Alcohol Abuse and Alcoholism & National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Background: Mendelian randomization (MR) is increasingly used for causal inference in nutritional epidemiology; however, dietary MR studies often rely on instruments statistically selected from genome-wide association studies of self-reported intake, which are vulnerable to pleiotropy and reverse causation and may violate core MR assumptions. We aimed to develop and evaluate a biologically informed framework for selecting valid genetic instruments for dietary exposures, based on genes encoding taste and olfactory receptors that mediate chemosensory inputs and shape food preferences and dietary behaviour. Methods: We prioritised 1,214 nonsynonymous variants in 30 taste and 295 olfactory receptor genes with minor allele frequency ≥1%. Associations with 140 food-liking traits were tested in UK Biobank participants aged 37 to 73 years. Candidate variants were evaluated using a multi-stage filtering pipeline designed to improve instrument validity. This included replication in an independent younger cohort (Avon Longitudinal Study of Parents and Children, age 25), concordance between food liking and intake, exclusion of associations with socioeconomic status, assessment of food specificity accounting for linkage disequilibrium and co-consumption patterns, and directionality testing to reduce reverse causation. Retained variants were applied as instruments in MR analyses to assess cardiometabolic outcomes. Results: We identified 268 nonsynonymous variants within 101 olfactory and 16 taste receptor genes associated with 96 food-liking traits. The filtering process yielded 28 candidate instruments for 24 foods. Among these, the instrument for onion liking uniquely satisfied all criteria for classification as high confidence. To demonstrate clinical relevance, genetically proxied onion liking was associated with lower blood pressure and a reduced risk of type 2 diabetes in MR analyses, with no evidence of effects on body mass index, glycaemic traits, or serum lipid levels. Conclusions: Guiding genetic instrument selection using chemosensory receptor genes provides a biologically informed strategy for dietary Mendelian randomization that reduces susceptibility to pleiotropy and reverse causation. This framework enables more robust causal evaluation of diet-disease relationships and strengthens inference in nutritional epidemiology and public health research.

Indexed as

ALSPACblood pressuredietary exposurefood likingMendelian randomizationolfactory receptortaste receptortype 2 diabetesUK Biobank

Identifiers

PMID41674649
PMCPMC12889861

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.