Integrating genetic and transcriptomic data to identify genes underlying obesity risk loci.

Background: Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI.

Methods: We analyzed genotype and blood gene expression data from up to 5619 samples in the Framingham Heart Study (FHS). Using 3992 single-nucleotide polymorphisms (SNPs) at 97 BMI loci and 1408 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (P_BMI and P_SNP, respectively) and then a correlated meta-analysis between the full summary data sets (P_META). Transcripts were prioritized if we identified transcripts that met Bonferroni-corrected significance within each omic, showed stronger associations in the correlated meta-analysis than each omic, and had corresponding SNPs in the SNP-transcript-BMI association that were at least nominally associated with BMI in FHS data. We tested for generalization of identified association in a Hispanic ancestry sample of blood gene expression data and other samples in hypothalamus, nucleus accumbens, liver, and visceral adipose tissue (VAT) with significant threshold: P_META < 0.05 & P_META < P_SNP & P_META < P_BMI.

Results: Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. We generalized results for SNAPC3 and YPEL3 in Hispanic ancestry sample, for YPEL3 in the nucleus accumbens, ZNF646 and GSTM3 in VAT, and NT5C2, SNAPC3, TMEM245, YPEL3, and ZNF646 in liver.

Conclusion: The identified genes help link the genetic variation at obesity-risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.