C-GWAS

C-GWAS integrates summary statistics from multiple potentially correlated traits to detect single nucleotide polymorphisms (SNPs) with multi-trait effects and increase statistical power in genome-wide association studies.

Key Features:

• Integration of GWAS summary statistics: Combines summary statistics across multiple potentially correlated traits to enable joint analysis.
• Detection of SNPs with multi-trait effects: Identifies single nucleotide polymorphisms (SNPs) exhibiting pleiotropic effects across traits.
• Increased statistical power versus MinGWAS and MTAG: Provides higher power compared with minimal p-value approaches (MinGWAS) and the MTAG method.
• Performance validated by simulations: Extensive computer simulations demonstrated superior performance in identifying significant genetic loci with multi-trait effects.
• Application to facial GWAS meta-analysis: Applied to a meta-analysis of 78 facial GWAS studies involving 10,115 European participants, identifying 56 study-wide suggestively significant loci including 17 novel loci.
• Replication across expanded dataset: Replication in an expanded dataset including 13,622 European and Asian individuals confirmed 46 (82%) of the initially identified loci at nominal significance and showed consistent allele effects for 9 (53%) of the novel loci.
• Functional analysis support: Functional analyses reinforced the reliability of identified loci and provided insights into the genetic architecture of human facial appearance.
• Implementation: Provided as a computationally efficient open-source R package.

Scientific Applications:

• Multi-trait GWAS meta-analysis: Combine summary statistics across correlated traits to detect pleiotropic loci affecting multiple phenotypes.
• Genetic analysis of facial morphology: Identify loci affecting human facial appearance and discover novel genetic associations for facial traits.
• Cross-population replication and validation: Validate identified loci across European and Asian cohorts using discovery and replication datasets.
• Functional interpretation of loci: Support downstream functional analyses to interpret the biological relevance of associated loci.

Methodology:

Combines GWAS summary statistics across multiple potentially correlated traits to detect SNPs with multi-trait effects; assesses performance via extensive computer simulations; applied to a meta-analysis of 78 facial GWAS (10,115 Europeans) for discovery and an expanded dataset of 13,622 Europeans and Asians for replication; functional analyses were performed to interpret identified loci.