Literature DB >> 33864366

Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c.

Kenneth E Westerman1,2,3, Jenkai Miao4, Daniel I Chasman5,6,7,8, Jose C Florez2,3,9, Han Chen10,11, Alisa K Manning1,2,3, Joanne B Cole2,4,9.   

Abstract

Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (P < 5 × 10-8): two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534 and milk type [dairy vs. other] with 4:131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycaemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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Year:  2021        PMID: 33864366      PMCID: PMC8411984          DOI: 10.1093/hmg/ddab109

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   5.121


  4 in total

1.  Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.

Authors:  Kenneth E Westerman; Timothy D Majarian; Franco Giulianini; Dong-Keun Jang; Jenkai Miao; Jose C Florez; Han Chen; Daniel I Chasman; Miriam S Udler; Alisa K Manning; Joanne B Cole
Journal:  Nat Commun       Date:  2022-07-09       Impact factor: 17.694

Review 2.  Do gene-environment interactions have implications for the precision prevention of type 2 diabetes?

Authors:  Thorkild I A Sørensen; Sophia Metz; Tuomas O Kilpeläinen
Journal:  Diabetologia       Date:  2022-01-07       Impact factor: 10.460

3.  Interactions between Polygenic Risk Scores, Dietary Pattern, and Menarche Age with the Obesity Risk in a Large Hospital-Based Cohort.

Authors:  Sunmin Park; Hye Jeong Yang; Min Jung Kim; Haeng Jeon Hur; Soon-Hee Kim; Myung-Sunny Kim
Journal:  Nutrients       Date:  2021-10-25       Impact factor: 5.717

4.  Gene-Environment Interaction on Type 2 Diabetes Risk among Chinese Adults Born in Early 1960s.

Authors:  Chao Song; Weiyan Gong; Caicui Ding; Rui Wang; Hongyun Fang; Ailing Liu
Journal:  Genes (Basel)       Date:  2022-04-05       Impact factor: 4.141

  4 in total

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