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

Area of Science:

• Genetics and Genomics
• Metabolic Disease Research
• Systems Biology

Background:

• Genome-wide association studies (GWAS) have identified numerous genetic loci associated with body mass index (BMI).
• However, the biological mechanisms connecting these risk loci to BMI regulation remain largely unknown.
• Integrative omics analyses offer a powerful approach to elucidate these complex biological pathways.

Purpose of the Study:

• To identify genes and biological pathways that link genetic variations at BMI risk loci to BMI.
• To integrate genotype and gene expression data for a comprehensive understanding of BMI regulation.
• To translate findings from genetic associations to functional biological insights.

Main Methods:

• Analysis of genotype and blood gene expression data from the Framingham Heart Study (FHS) in up to 5,619 samples.
• Association analyses of single nucleotide polymorphisms (SNPs) with BMI (P_BMI) and with transcript levels (P_SNP).
• Correlated meta-analysis (P_META) of SNP and transcript data, followed by Bonferroni correction and validation in independent datasets and specific brain and liver tissues.

Main Results:

• Seven candidate genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, ZNF646) were identified in five BMI-associated regions.
• Results for SNAPC3 and YPEL3 were validated in independent blood gene expression data.
• Significant associations were observed for YPEL3 in the nucleus accumbens and for NT5C2, SNAPC3, TMEM245, YPEL3, and ZNF646 in the liver.

Conclusions:

• The identified genes provide crucial links between genetic variations at obesity risk loci and underlying biological mechanisms.
• These findings contribute to translating GWAS discoveries into functional understanding of BMI regulation.
• The study highlights the utility of integrative omics approaches for dissecting complex traits like obesity.