Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Hanfei Xu; Shreyash Gupta; Ian R. Dinsmore; Abbey Kollu; Anne Marie Cawley; Mohammad Yaser Anwar; Hung‐Hsin Chen; Lauren E. Petty; Sudha S. Seshadri; Mariaelisa Graff; Jennifer Elizabeth Below; Jennifer A. Brody; Geetha Chittoor; Susan P. Fisher-Hoch; Nancy L. Heard‐Costa; Daniel A Levy; Honghuang Lin; Ruth J. F. Loos; Joseph B. McCormick; Jerome I. Rotter; Tooraj Mirshahi; Christopher D. Still; Anita L. DeStefano; L. Adrienne Cupples; Karen L. Mohlke; Kari E. North; Anne E. Justice; Ching‐Ti Liu

doi:10.1038/s41366-025-01898-z

Genetic & Genomic MedicineInternational Journal of ObesitySep 26, 2025

Peer-reviewed journal article

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Hanfei Xu iD, Shreyash Gupta iD, Ian R. Dinsmore iD, Abbey Kollu, Anne Marie Cawley, Mohammad Yaser Anwar iD, Hung‐Hsin Chen iD, Lauren E. Petty iD, Sudha S. Seshadri iD, Mariaelisa Graff iD, Jennifer Elizabeth Below iD, Jennifer A. Brody iD, Geetha Chittoor iD, Susan P. Fisher-Hoch, Nancy L. Heard‐Costa iD, Daniel A Levy iD, Honghuang Lin iD, Ruth J. F. Loos iD, Joseph B. McCormick iD, Jerome I. Rotter iD, Tooraj Mirshahi iD, Christopher D. Still iD, Anita L. DeStefano iD, L. Adrienne Cupples iD, Karen L. Mohlke iD, , Ching‐Ti Liu iD

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Abstract

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. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA<PSNP & PMETA<PBMI) results for YPEL3 in nucleus accumbens and NT5C2, SNAPC3, TMEM245, YPEL3, and ZNF646 in liver. The identified genes help link the genetic variation at obesity risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.