Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations

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Abstract

Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health.

Abstract

The choice of food intake is at least partially influenced by genetics, even though the effect sizes appear rather modest. Here, Cole et al. perform GWAS for food intake (85 individual food items and 85 derived dietary patterns) and test potential causal relationships with cardiometabolic traits using Mendelian randomization.

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A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Stephen S Lim, Theo Vos, Abraham D. Flaxman … (2012)

The Lancet, 380(9859), 2224-2260

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Orienting the causal relationship between imprecisely measured traits using GWAS summary data

Gibran Hemani, Kate Tilling, George Davey Smith (2018)

Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.

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Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence

James Boyle, Theodore Thompson, Edward Gregg … (2010)

Background People with diabetes can suffer from diverse complications that seriously erode quality of life. Diabetes, costing the United States more than $174 billion per year in 2007, is expected to take an increasingly large financial toll in subsequent years. Accurate projections of diabetes burden are essential to policymakers planning for future health care needs and costs. Methods Using data on prediabetes and diabetes prevalence in the United States, forecasted incidence, and current US Census projections of mortality and migration, the authors constructed a series of dynamic models employing systems of difference equations to project the future burden of diabetes among US adults. A three-state model partitions the US population into no diabetes, undiagnosed diabetes, and diagnosed diabetes. A four-state model divides the state of "no diabetes" into high-risk (prediabetes) and low-risk (normal glucose) states. A five-state model incorporates an intervention designed to prevent or delay diabetes in adults at high risk. Results The authors project that annual diagnosed diabetes incidence (new cases) will increase from about 8 cases per 1,000 in 2008 to about 15 in 2050. Assuming low incidence and relatively high diabetes mortality, total diabetes prevalence (diagnosed and undiagnosed cases) is projected to increase from 14% in 2010 to 21% of the US adult population by 2050. However, if recent increases in diabetes incidence continue and diabetes mortality is relatively low, prevalence will increase to 33% by 2050. A middle-ground scenario projects a prevalence of 25% to 28% by 2050. Intervention can reduce, but not eliminate, increases in diabetes prevalence. Conclusions These projected increases are largely attributable to the aging of the US population, increasing numbers of members of higher-risk minority groups in the population, and people with diabetes living longer. Effective strategies will need to be undertaken to moderate the impact of these factors on national diabetes burden. Our analysis suggests that widespread implementation of reasonably effective preventive interventions focused on high-risk subgroups of the population can considerably reduce, but not eliminate, future increases in diabetes prevalence.

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Author and article information

Contributors

Joel N. Hirschhorn: Joel.Hirschhorn@childrens.harvard.edu

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 19 March 2020

Publication date PMC-release: 19 March 2020

Publication date Collection: 2020

Volume: 11

Electronic Location Identifier: 1467

Affiliations

[1 ]GRID grid.66859.34, Programs in Metabolism and Medical and Population Genetics, , The Broad Institute of MIT and Harvard, ; Cambridge, MA USA

[2 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Diabetes Unit and Center for Genomic Medicine, , Massachusetts General Hospital, ; Boston, MA USA

[3 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Division of Endocrinology and Center for Basic and Translational Obesity Research, , Boston Children’s Hospital, ; Boston, MA USA

[4 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Medicine, , Harvard Medical School, ; Boston, MA USA

[5 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Genetics, , Harvard Medical School, ; Boston, MA USA

Author information

Joanne B. Cole http://orcid.org/0000-0001-9520-2788

Article

Publisher ID: 15193

DOI: 10.1038/s41467-020-15193-0

PMC ID: 7081342

PubMed ID: 32193382

SO-VID: 96661506-8f8b-4df8-aa90-e8a278f8e058

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 5 June 2019

Date accepted : 11 February 2020

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ScienceOpen disciplines: Uncategorized

Keywords: behavioural genetics,genome-wide association studies,nutrition

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ScienceOpen disciplines: Uncategorized

Keywords: behavioural genetics, genome-wide association studies, nutrition

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Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations

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Most cited references 20

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Orienting the causal relationship between imprecisely measured traits using GWAS summary data

Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence

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Cited by 44

Most referenced authors 4,672