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Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits

Abstract

Dietary intake is a major contributor to the global obesity epidemic and represents a complex behavioural phenotype that is partially affected by innate biological differences. Here, we present a multivariate genome-wide association analysis of overall variation in dietary intake to account for the correlation between dietary carbohydrate, fat and protein in 282,271 participants of European ancestry from the UK Biobank (n = 191,157) and Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (n = 91,114), and identify 26 distinct genome-wide significant loci. Dietary intake signals map exclusively to specific brain regions and are enriched for genes expressed in specialized subtypes of GABAergic, dopaminergic and glutamatergic neurons. We identified two main clusters of genetic variants for overall variation in dietary intake that were differently associated with obesity and coronary artery disease. These results enhance the biological understanding of interindividual differences in dietary intake by highlighting neural mechanisms, supporting functional follow-up experiments and possibly providing new avenues for the prevention and treatment of prevalent complex metabolic diseases.

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Fig. 1: SNP-based association with overall variation in dietary intake in the multivariate genome-wide analysis of 282,271 individuals from CHARGE and the UKBB.
Fig. 2: Implicated tissue and cell expression profiles for dietary intake in the multivariate genome-wide analysis of 282,271 individuals from CHARGE and the UKBB.
Fig. 3: Association of cluster-specific PRS and cardiometabolic phenotypes.

Data availability

The summary GWAS statistics will be publicly available at the UKBB website (http://biobank.ctsu.ox.ac.uk/), database of Genotypes and Phenotypes (accession no. phs000930) and Type 2 Diabetes Knowledge Portal (http://www.kp4cd.org/dataset_downloads/t2d). The single-cell expression datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= under accession nos. GSE93374, GSE104276 and GSE763816,46,65. The ldsc command line tool can be found at https://github.com/bulik/ldsc20.

Code availability

The code used to reproduce the analyses for this manuscript will be made available on publication at http://sites.bu.edu/fhspl/publications/ and https://github.com/perslab/Merino_2020. The CELLEX precomputed expression specificity files are available at https://github.com/perslab/CELLECT/wiki/Precomputed-CELLEX-datasets.

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Acknowledgements

This study was designed and carried out by the CHARGE Consortium Nutrition Working Group. Part of this work was conducted using the UKBB resource under application no. 27892. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 703787. J.M. is supported by the National Institutes of Health (NIH) grant no. P30 DK040561. H.S.D. and R.S. are supported by NIH grant nos. R01 DK105072 and DK107859. R.S. is supported by NIH grant no. R01 DK102696 and the MGH Research Scholar Fund. J.M.L. is supported by NIH grant nos. F32 DK102323 and T32 HL007901. C.S., J.C.F. and J.D. are supported by NIH grant no. U01 DK078616. J.C.F. is supported by NIH grant no. K24 DK110550. J.C. is supported by the American Diabetes Association Pathway to Stop Diabetes award no. 1-18-INI-14. T.H.P. and P.V.T. acknowledge the Novo Nordisk Foundation (no. NNF16OC0021496). T.H.P. acknowledges the Lundbeck Foundation (no. R190­2014­3904). This research was supported in part by the Intramural Research Program of the National Institute on Aging. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

Authors

Contributions

J.M., H.S.D., C.S., A.Y.C., D.I.C., J.C.F. and R.S. conceived and designed the study. J.D., J.C.F. and R.S. oversaw the study. H.S.D., J.M., C.S., J.M.L. and Y.S. served as analysts. Phenotype definitions were developed by J.M., H.S.D., C.S., J.D. and R.S. C.S. performed the quality control and meta-analyses. Heritability and genetic correlation were performed by H.S.D. The bioinformatic analyses were performed and interpreted by J.M., H.S.D., C.S., J.M.L., Y.S., H.W., J.K., C.T., T.T., D.I.C., J.C.F. and R.S. The single-cell expression analyses were conducted and interpreted by J.M., P.V.T., T.H.P., J.C., L.T. and J.C.F. The figures were created by J.M., H.S.D., C.S., M.S.U., Y.S., P.V.T., T.H.P., J.D. and D.I.C. M.K.R. provided helpful advice and feedback on study design and manuscript writing. J.M., H.S.D., C.S., J.D., J.C.F. and R.S., made major contributions to manuscript writing and editing. All authors contributed to and critically reviewed the manuscript and approved its final version.

Corresponding authors

Correspondence to Josée Dupuis, Jose C. Florez or Richa Saxena.

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Competing interests

A.Y.C. is currently employed by Merck Research Laboratories. M.K.R. reports receiving research funding from Novo Nordisk, consultancy fees from Novo Nordisk and Roche Diabetes Care and modest owning of shares in GlaxoSmithKline. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 UK Biobank sample selection.

A total of 502,536 participants were available in UK Biobank at the beginning of this study. Thirty participants withdrew consent during the implementation of the analysis plan. We excluded 892 participants with invalid diet data based on previously defined quality control filtering criteria for diet data. A total of 16,034 participants did not pass quality control criteria based on UK Biobank quality control definitions (high heterozygosity & high missing rate, sex aneuploidy, submitted sex different from inferred sex). We excluded 27,965 participants based on a non-European self-reported ancestry, and 5,955 European ancestry outliers based on + /-6SD from the mean in the subset of 192,025 participants based on the first 4 PCs. Among 451,660 participants remaining for the discovery of genetic variants for dietary intake, 260,503 had missing diet data (n = 258,393) or invalid values (n = 2,110). Due to the skewed distribution of macronutrient intake, we winsorized at mean + /− 5 SD for each phenotype.

Extended Data Fig. 2 Schematic of the study design in the multi-trait genome-wide association meta-analysis for dietary intake in 282,271 individuals.

The genome-wide association meta-analysis of dietary intake comprised data from 191,157 participants from the UK Biobank and 91,114 participants from the CHARGE Consortium. Single-trait macronutrient GWAS from the UK Biobank and CHARGE Consortium were meta-analyzed using METAL and then combined into a multi-trait GWAS using the multi-trait CPASSOC method. Downstream in silico analyses were conducted to identify biological features of identified loci including functional annotation, tissue and pathway enrichment, and single-cell RNA expression analyses. The Bayesian nonnegative matrix factorization clustering algorithm was used to classify dietary intake genetic loci into subgroups based on potential functional and clinical similarities. Cluster-based polygenic risk scores were built to investigate patterns of metabolic risk.

Extended Data Fig. 3 Quantile-quantile plot of the SNP-based associations with single-trait and multi-trait genome-wide association meta-analyses of 282,271 individuals.

Quantile–quantile plot of the SNP-based associations with multivariate (a), carbohydrate (b), fat (c), and protein intake (d). SNP P values were computed in METAL by weighting effect size estimates using the inverse of the corresponding standard errors.

Extended Data Fig. 4 Associations between environmental factors and macronutrient intake.

Shown are effect estimates (betas) and 95% confidence intervals for the association between environmental factors and macronutrient intake among UK Biobank participants. Environmental factors were all added to the initial model used for the primary main UK Biobank analyses. A null model was first run using SAIGE to evaluate the association of each environmental factor with each macronutrient intake. Only significant factors were retained in the model. Genetic association analyses were then conducted, similarly to the primary main UK Biobank analyses, using SAIGE.

Extended Data Fig. 5 Schematic overview of the Bayesian nonnegative matrix factorization clustering algorithm.

The input for the Bayesian nonnegative matrix factorization clustering algorithm (bNMF) was the set of 31 genetic variants reaching nominal significance association with proportion fat intake. Next summary association statistics for 22 dietary intake traits from the UK Biobank were aggregated for each dietary intake variant. Our analyses involved variants aligned by their alleles associated with increased fat intake. We generated standardized effect sizes for variant trait associations from GWAS by dividing the estimated regression coefficient beta by the standard error, using the UK Biobank summary statistic results (variant-trait association matrix (31 by 22)). The defining features of each cluster were determined by the most highly associated traits, which is a natural output of the bNMF approach. bNMF algorithm was performed in R for 1,000 iterations with different initial conditions, and the maximum posterior solution at the most probable number of clusters was selected for downstream analysis.

Extended Data Fig. 6 Trait and loci association to clusters.

Clustering of variant-trait associations was performed for 31 genetic variants reaching nominal significance association with proportion fat intake and 22 nutritional traits derived from GWAS using the Bayesian nonnegative matrix factorization clustering algorithm, with identification of two clusters present on 80% of iterations. Loci and traits defining each cluster were based on a cut-off of weighting of 0.94 (Methods). a) trait association to cluster, b) loci association to cluster: 1 NEGR1, RARB, RP11.161I6.2, RP11.22P4.1, TMEM108. Cluster 2: ADH1A, DNMT3A, FGF21, FUT1, FUT2, GCKR, KLB, PLEKHM1, PPP1R3B, RP11.696N14.1, TSPAN5.

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Merino, J., Dashti, H.S., Sarnowski, C. et al. Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits. Nat Hum Behav 6, 155–163 (2022). https://doi.org/10.1038/s41562-021-01182-w

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