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

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 (P_BMI and P_SNP, respectively) and then a correlated meta-analysis between the full summary data sets (P_META). We identified transcripts that met Bonferroni-corrected signifi

SUBMITTER: Xu H 

PROVIDER: S-EPMC11188121 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature