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  • Chikowore, Tinashe, et al. (författare)
  • GWAS transethnic meta-analysis of BMI in similar to 700k individuals reveals novel gene-smoking interaction in African populations
  • 2020
  • Ingår i: Genetic Epidemiology. - : John Wiley & Sons. - 0741-0395 .- 1098-2272. ; 44:5, s. 475-476
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • Sixty two percent of the 1.12 billion obese people globally reside in low‐middle income countries, 77% of which are in Africa. There is paucity of data on gene‐lifestyle interactions associated with the increasing prevalence of obesity among Africans. We hypothesised that gene‐environment interacting (GEI) variants exhibit heterogenous effects on obesity in transethnic meta‐analysis of marginal SNP associations as a result of modification by an unknown exposure that varies across populations.Body mass index (BMI) genome‐wide association study (GWAS) summary statistics for 678,671 individuals representative of the major global ancestries were aggregated at 21,338,816 SNPs via fixed‐effects meta‐analysis. Lead SNPs attaining genome‐wide significance (P  < 5 × 10−8) were tested for heterogeneity in effects between GWAS. Lead SNPs with significant evidence of heterogeneity after Bonferroni correction were then selected for interaction analysis with selected lifestyle factors in an independent AWI‐Gen study of 10,500 African participants. Significant interaction findings were then replicated in 3,177 individuals of African ancestry in the UK Biobank.Of 881 lead SNPs, five had significant heterogenous effects on BMI (P  < 5.7 × 10−5). Rs471094, at the CDKAL1 locus had significant interaction with smoking status, which reduced the effect of the BMI raising allele in current smokers (Betaint = −0.949 kg/m2; P int = .002) compared with non‐smokers in AWI‐Gen. This finding was validated in the UK Biobank (Betaint = −1.471 kg/m2, P int = .020; meta‐analysis Betaint = −1.050 kg/m2, P int = .0002). Our results highlight the first gene‐lifestyle interaction on BMI in Africans and demonstrate the utility of transethnic meta‐analysis of GWAS for identifying GEI effects.
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