biMM

biMM estimates genetic variances and covariances using a computationally efficient bivariate linear mixed model to decompose trait variances into genetic and environmental components for high-dimensional phenotype data.

Key Features:

• Bivariate linear mixed model implementation: Computationally efficient implementation of a bivariate linear mixed model for analysis of multiple traits measured across partially overlapping individuals.
• High-dimensional phenotype analysis: Tailored for settings with high-dimensional phenotype data, including studies with hundreds of traits.
• Variance and covariance decomposition: Partitions trait variances and covariances into genetic and environmental components.
• Computational efficiency: Optimized to enable analysis of large-scale studies without prohibitive resource demands.
• Implemented in R: Software implementation provided in R.

Scientific Applications:

• Genetic architecture of complex traits: Estimating genetic variances and covariances to dissect the genetic architecture of complex traits.
• Genetic correlation and pleiotropy studies: Identifying genetic correlations between traits to investigate pleiotropy and shared genetic pathways.
• GWAS and large-scale phenotype analyses: Application in genome-wide association studies and other large-scale analyses involving multiple phenotypes and overlapping cohorts.
• Disease mechanism and trait evolution inference: Supporting analyses that can inform disease mechanisms and trait evolution.

Methodology:

biMM applies a computationally efficient bivariate linear mixed model framework to partition trait variances and covariances into genetic and environmental components while accommodating partially overlapping individuals and simultaneous analysis of multiple traits.