Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data

Area of Science:

• Genetics and Genomics
• Molecular Biology
• Systems Biology

Background:

• Body mass index (BMI) is a complex trait influenced by genetic and environmental factors.
• Identifying genes associated with BMI is crucial for understanding its regulation and developing targeted interventions.

Approach:

• Integrated multi-omics data including genome-wide association study (GWAS), transcriptome-wide association study (TWAS), and epigenome-wide association study (EWAS).
• Employed a machine learning method, netWAS, utilizing adipose tissue-specific interaction networks to prioritize BMI-associated genes.
• Investigated potential brain-adipose interactions by incorporating brain-specific networks into the netWAS analysis.

Key Points:

• Identified 195 genes significantly associated with BMI through meta-analysis.
• Prioritized 21 adipose tissue-specific genes using netWAS, with six novel genes (FUS, STX4, CCNT2, FUBP1, NDUFS3, RAPSN) not previously linked to BMI.
• Discovered 11 genes associated with BMI in both adipose and brain tissues, suggesting potential brain-adipose crosstalk.

Conclusions:

• This integrated omics approach successfully identified novel BMI-associated genes.
• The findings provide new molecular targets and insights into the genetic architecture of BMI regulation.
• Future research can explore the identified genes' roles in the molecular mechanisms underlying BMI.