Trinculo

Trinculo performs multinomial logistic regression to analyze multi-category phenotypes in genome-wide association studies (GWAS), enabling investigation of how common genetic risk variants influence multiple related diseases or disease subtypes.

Key Features:

• Multinomial Logistic Regression: Implements multinomial logistic regression to model associations between genetic variants and multi-category phenotypes.
• Multi-category Phenotype Analysis: Enables simultaneous analysis of several phenotype categories to investigate shared genetic effects across related conditions and subtypes.
• GWAS-scale Performance: Provides computational performance suitable for large-scale genome-wide association study datasets.
• Implementation: Implemented in C to support computational efficiency.

Scientific Applications:

• Dissecting Genetic Architecture: Dissects the genetic architecture of diseases that manifest through multiple phenotypic expressions or subtypes.
• Identifying Shared Risk Variants: Identifies shared common genetic risk variants across multiple phenotype categories.
• Complex Trait Genetics: Supports analyses in complex trait genetics where overlapping genetic factors influence multiple phenotypes.

Methodology:

Trinculo employs multinomial logistic regression to model relationships between genetic variants and multi-category phenotypic outcomes, allowing simultaneous analysis of several phenotype categories; it is implemented in C to provide computational performance for large-scale GWAS datasets.