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Reverse gene–environment interaction approach to identify variants influencing body-mass index in humans

Nature Metabolism volume 1, pages 630–642 (2019)Cite this article

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Abstract

Identifying gene–environment (G×E) interactions contributing to human cardiometabolic disorders is challenging. Here we apply a reverse G×E candidate search by deriving candidate variants from promoter–enhancer interactions that respond to dietary fatty acid challenge through altered chromatin accessibility in primary human adipocytes. We then test all variants residing in lipid-responsive open chromatin sites in adipocyte promoter–enhancer contacts for interaction effects between genotype and dietary saturated fat intake on body-mass index (BMI) in the UK Biobank. We discover 14 new G×E variants in 12 lipid-responsive promoters, including in well-known lipid-related genes (LIPE, CARM1 and PLIN2) and newly associated genes, such as LDB3, for which we provide further functional and integrative genomic evidence. We further identify 24 G×E variants in enhancers, for a total of 38 new G×E variants for BMI in the UK Biobank, demonstrating that molecular genomics data produced in physiologically relevant contexts can be applied to discover new functional G×E mechanisms in humans.

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Fig. 1: ATAC-seq analysis comparing primary human preadipocytes and adipocytes indicates successful adipocyte differentiation and widespread changes in chromatin accessibility.
Fig. 2: Lipid-responsive regions fall within adipocyte accessible regions of the genome, as well as within context-dependent regions that are not present in untreated adipocytes.
Fig. 3: The 154 genes with lipid-responsive promoters within chromosomal interactions exhibit cross-species conservation and constraints on loss-of-function mutations, in line with their potential importance for energy homeostasis and survival.
Fig. 4: A lipid-responsive open chromatin region in human primary adipocytes at the 11q12.2 FADS1–FADS2–FADS3 locus harbours GWAS SNPs for serum lipid traits.
Fig. 5: Fine-mapping of the gene–diet interaction for BMI in the LDB3 promoter region.
Fig. 6: Analytical approach.

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Data availability

The ATAC-seq data for primary human preadipocytes and adipocytes (untreated and lipid-challenged cells) and the pCHi-C data for primary human adipocytes under lipid-challenge conditions have been deposited in the Gene Expression Omnibus under accession GSE129574 and are available upon request from the corresponding author.

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Acknowledgements

This research has been conducted using the UK Biobank Resource under application number 33934. We thank the individuals who participated in the METSIM and UK Biobank studies. We also thank the UNGC sequencing core at UCLA for performing the DNA and RNA sequencing. This study was funded by National Institutes of Health (NIH) grants HL-095056, HL-28481 and U01DK105561. K.M.G. was supported by NIH-NHLBI grant 1F31HL142180, M.A. was supported by an HHMI Gilliam grant, D.Z.P. was supported by NIH-NCI grant T32LM012424 and NIH-NIDDK grant F31DK118865, and A.K. was supported by NIH grant F31HL127921. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the article.

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Authors and Affiliations

  1. Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Kristina M. Garske, David Z. Pan, Zong Miao, Yash V. Bhagat, Caroline Comenho, Jihane N. Benhammou, Marcus Alvarez, Joseph R. Pisegna, Janet S. Sinsheimer & Päivi Pajukanta

  2. Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA

    David Z. Pan, Zong Miao & Päivi Pajukanta

  3. Department of Computer Science, UCLA, Los Angeles, CA, USA

    Christopher R. Robles

  4. Vache and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, USA

    Jihane N. Benhammou & Joseph R. Pisegna

  5. Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Arthur Ko

  6. Institute for Human Genetics, Department of Epidemiology and Biostatistics and Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA

    Chun Jimmie Ye

  7. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    Karen L. Mohlke

  8. Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Janet S. Sinsheimer

  9. Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland

    Markku Laakso & Päivi Pajukanta

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  1. Kristina M. Garske
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Contributions

K.M.G. and P.P. designed the study. K.M.G., D.Z.P., Z.M., J.R.P., C.J.Y., J.S.S. and P.P. performed methods development and statistical analysis. K.M.G., D.Z.P., Z.M., M.A. and C.R.R. performed computational analysis of the data. K.M.G., Y.V.B., M.A., C.C. and J.N.B. performed the experiments. M.L., K.M. and P.P. produced the METSIM RNA-seq data. A.K. performed quality control of the METSIM RNA-seq data. K.M.G. and P.P. wrote the manuscript and all authors read, reviewed and/or edited the manuscript.

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Correspondence to Päivi Pajukanta.

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Supplementary information

Supplementary Information

Supplementary Figures 1–6 and Supplementary Tables 1, 3, 5, 6, 8, 10–14, 16 and 21

Reporting Summary

Supplementary Table 2

Differentially accessible ATAC-seq peaks between human preadipocytes and adipocytes. Peaks were considered differentially accessible at a cutoff of FDR < 0.05. FDR was calculated (adjusting for n = 154,647 ATAC-seq peaks) from the P values of the QL F test for differential accessibility between preadipocytes and adipocytes by using ATAC-seq libraries from three replicates per cell type. Related to Fig. 1.

Supplementary Table 4

Differentially accessible ATAC-seq peaks in lipid-challenged human adipocytes. The table lists the significant differential ATAC-seq peaks in human primary adipocytes that were treated with saturated (palmitic) or monounsaturated (oleic) fatty acids or vehicle (BSA) control. Peaks were considered differentially accessible at a cutoff of FDR < 0.05. FDR was calculated (adjusting for n = 122,252 ATAC-seq peaks) from the P values of the QL F test in one-way ANOVA. For the post hoc test to determine which comparison was significant after one-way ANOVA (OA vs. BSA, PA vs. BSA or OA vs. PA), we determined the least significant difference. Related to Fig. 2.

Supplementary Table 7

154 genes with lipid-responsive promoters in chromosomal interactions in adipocytes. The table lists the Ensembl ID and gene symbol for genes with promoters in interactions in adipocyte promoter-capture Hi-C that also had lipid-responsive ATAC-seq peaks. Related to Fig. 3.

Supplementary Table 9

323 gene promoters physically interact with lipid-responsive enhancers in adipocytes. The table lists the Ensembl ID and gene symbol for genes with promoters that interact with enhancers that contained lipid-responsive ATAC-seq peaks. Related to Supplementary Fig. 5.

Supplementary Table 15

75 lipid-responsive peaks in gene promoters contain SNPs with MAF > 0.05 in the UK Biobank. The table lists the lipid-responsive ATAC-seq peaks within gene promoters involved in adipocyte chromosomal interactions that contain SNPs with MAF > 0.05 in the UK Biobank (n = 75/91 peaks). The SNPs in these regions were tested for gene–environment interactions in the UK Biobank. Related to Fig. 3 and Table 2.

Supplementary Table 17

142 lipid-responsive peaks in enhancers contain SNPs with MAF > 0.05 in the UK Biobank. The table lists the lipid-responsive ATAC-seq peaks within enhancers involved in adipocyte chromosomal interactions that contain SNPs with MAF > 0.05 in the UK Biobank (n = 142/169 peaks). The SNPs in these regions were tested for gene–environment interactions in the UK Biobank. Related to Supplementary Fig. 5 and Supplementary Table 18.

Supplementary Table 18

Significant G×E interactions with BMI from a multivariable linear model for 410 enhancer SNPs. †The cis-eQTLs were identified in adipose tissue from the METSIM cohort; §When more than one non-independent SNP (LD r2 > 0.2) has a significant G×E P value for the lipid-responsive region, both SNPs are listed together in order of more to less significant. ‡Genes in separate promoter-containing baits are marked when a lipid-responsive enhancer with a G×E SNP is interacting with more than one bait in adipocyte pCHi-C. The reported P values are from the multivariable linear model (see equation (2) in the Methods), where g is the number of minor alleles of the genotype and e is saturated fat intake. Here p-g indicates the P value for the genotype effect and p-g*e indicates the P value for the G×E effect; beta values follow the same notation. For the multivariable linear model, there were a total of 410 SNPs and 18,318 individuals with no missing data available for study.

Supplementary Table 19

DeepSEA analysis of the 20 G×E SNPs in interacting lipid-responsive gene promoters. The table lists the predicted functional impact of promoter G×E SNPs on chromatin features such as transcription factor binding and histone marks. Related to Table 2.

Supplementary Table 20

DeepSEA analysis of the 26 G×E SNPs in interacting lipid-responsive enhancers. The table lists the predicted functional impact of enhancer G×E SNPs on chromatin features such as transcription factor binding and histone marks. Related to Supplementary Table 18.

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Garske, K.M., Pan, D.Z., Miao, Z. et al. Reverse gene–environment interaction approach to identify variants influencing body-mass index in humans. Nat Metab 1, 630–642 (2019). https://doi.org/10.1038/s42255-019-0071-6

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