4.6 Article

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions

Journal

PLOS GENETICS
Volume 13, Issue 6, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pgen.1006812

Keywords

-

Funding

  1. European Commission [CoG-2015_681742_NASCENT]
  2. Swedish Research Council
  3. Swedish Heart Lung Foundation
  4. Novo Nordisk Foundation
  5. Swedish Research Council [4.1-2016-00416]
  6. Novo Nordisk Foundation Postdoctoral Fellowship within Endocrinology/Metabolism at International Elite Research Environments [NNF160C0020698]
  7. Bundesministerium fur Bildung and Forschung [BMBF-01ER1206, BMBF-01ER1507]
  8. Wellcome Trust [WT098017]
  9. National Heart, Lung and Blood Institute [N01-HC-25195, HHSN2682015000011]
  10. Affymetrix, Inc [N02-HL-6-4278]
  11. Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center
  12. National Institute of Diabetes and Digestive and Kidney Diseases [R01-DK089256]
  13. Danish Council for Independent Research [DFF-1333-00124]
  14. Sapere Aude program [DFF-1331-00730B]
  15. EU [692145, 676550, 654248, 692065]
  16. Estonian Research Council [IUT20-60]
  17. PerMed 1, NIASC, EIT Health and European Union through the European Regional Development Fund [2014-2020.4.01.15-0012]
  18. MRC [MC_UU_12015/2, MC_UU_12015/1, MC_UU_12015/7, MC_UU_12015/5] Funding Source: UKRI
  19. Medical Research Council [MC_UU_12015/1, MC_UU_12015/2, MC_UU_12015/5, MC_UU_12015/7] Funding Source: researchfish
  20. National Institute for Health Research [NF-SI-0512-10135] Funding Source: researchfish
  21. Novo Nordisk Fonden [NNF17OC0026828, NNF16OC0021020] Funding Source: researchfish

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Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (GxE) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (P-v), GxE interaction effects (with smoking and physical activity), and marginal genetic effects (P-m). Correlations between P-v and P-m were stronger for SNPs with established marginal effects (Spearman's rho = 0.401 for triglycerides, and rho = 0.236 for BMI) compared to all SNPs. When P-v and P-m were compared for all pruned SNPs, only BMI was statistically significant (Spearman's rho = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the P-v distribution (P-binomial < 0.05). SNPs from the top 1% of the P-m distribution for BMI had more significant P-v values (Pmann-Whitney = 1.46x10(-5)), and the odds ratio of SNPs with nominally significant (< 0.05) P-m and P-v was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant GxE interaction P-values (Pint < 0.05) were enriched with nominally significant P-v values (P-binomial = 8.63x10(-9) and 8.52x10(-7) for SNP x smoking and SNP x physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for GxE, and variance-based prioritization can be used to identify them.

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