MARV

MARV analyzes rare genetic variants across multiple phenotypes to detect loci associated with complex traits and assess pleiotropic effects.

Key Features:

• Collapsing Rare Variants: Aggregates rare variants within genomic regions and models the proportion of minor alleles across variants.
• Multi-phenotype Analysis: Evaluates all possible phenotype combinations simultaneously to increase power and enable detection of pleiotropic effects.
• Model Selection with Bayesian Information Criterion (BIC): Computes BIC for each analysis to support selection of statistical models.
• Computational Efficiency: Running time scales primarily with the size of genetic data rather than the number of phenotypes, keeping memory requirements manageable.

Scientific Applications:

• Complex trait locus discovery: Identifies loci associated with complex traits by aggregating rare variants across multiple phenotypes.
• Pleiotropy detection: Detects genetic loci that influence multiple phenotypes through combined analyses.
• Example application to cohort data: Application to the Northern Finland Birth Cohort 1966 revealed multi-phenotype effects at loci including APOA5 and ZNF259 with stronger combined association than single-phenotype analyses.

Methodology:

MARV collapses rare variants within genomic regions, models the proportion of minor alleles as a linear combination of multiple phenotypes while evaluating all phenotype combinations, and computes Bayesian Information Criterion (BIC) for model selection, with running time primarily determined by genetic data size.