Integrating genetic and transcriptomic data to identify genes underlying obesity risk loci
Authors' 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.
Study context
- Population
- Framingham Heart Study samples with genotype and blood gene expression data; 3992 single-nucleotide polymorphisms (SNPs) at 97 body mass index (BMI) loci and 1408 transcripts within 1 Mbup to
- Participants
- 5,619 samples
- Outcome
- Genes prioritized from SNP-transcript-BMI associations
Methodological detailscorrelated meta-analysis, Bonferroni-corrected significance, generalization in independent samples
- Correlated meta-analysis
- Bonferroni-corrected significance
- Generalization in independent samples
Pooled result
As reported in the abstract
Significant SNP-transcript-BMI associations
308
Genes identified (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, ZNF646) in five association regions
7
Further pooled results
Generalization in other samples
- Hispanic ancestry blood sample: SNAPC3 and YPEL3 generalized
- Nucleus accumbens: YPEL3 generalized
- Visceral adipose tissue (VAT): ZNF646 and GSTM3 generalized
- Liver: NT5C2, SNAPC3, TMEM245, YPEL3 and ZNF646 generalized
Original publication
Integrating genetic and transcriptomic data to identify genes underlying obesity risk loci. Int J Obes (Lond). Published 26 September 2025. doi:10.
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