CCmed

CCmed detects cis-mediated trans-associations across conditions and studies to identify replicable trans-acting eQTL effects mediated by cis-gene expression.

Key Features:

• Cross-Condition Mediation Analysis: Detects cis-mediated trans-associations that exhibit replicable effects across multiple conditions and studies.
• Integration of Multi-Tissue Data: Integrates cis-association statistics (between SNP and cis-gene) and gene–gene conditional correlation statistics from multiple tissues or studies.
• Bimodal Effect-Sharing Models: Implements two variations, CCmedmost and CCmedspec, motivated by bimodal effect-sharing patterns to detect cross-tissue and tissue-specific trans-associations, respectively.
• Application to GTEx Brain Tissues: Applied to Genotype-Tissue Expression (GTEx) data across 13 brain tissues to identify trios with cis-mediated trans-associations relevant to complex traits such as schizophrenia.

Scientific Applications:

• Gene Regulation Mapping: Maps genetic variants that influence distal gene expression through cis-gene mediation to elucidate regulatory networks.
• Complex Trait Genetics: Identifies mediated trans-associations relevant to complex traits and disease susceptibility, exemplified by analyses related to schizophrenia.
• Cross-Tissue and Tissue-Specific Discovery: Distinguishes associations that are shared across tissues from those that are tissue-specific to inform context-dependent regulatory mechanisms.

Methodology:

Integrates cis-association statistics (SNP–cis-gene) and gene–gene conditional correlation statistics from multiple tissues or studies to identify cis-mediated trans-associations, with CCmedmost and CCmedspec implemented for cross-tissue and tissue-specific detection.