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

ABSTRACT:

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 w

SUBMITTER: Xu H 

PROVIDER: S-EPMC12583137 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature