Baypass

Baypass implements a Bayesian framework to identify genetic markers under selection and associations with population-specific covariates while accounting for the neutral covariance structure of population allele frequencies.

Key Features:

• Bayesian Framework: Employs a Bayesian framework to characterize adaptive genetic differentiation across populations and to estimate population covariance matrices.
• BayEnv-based Modeling Extensions: Incorporates extensions of the BayEnv model for improved estimation accuracy of the population covariance matrix and association analyses.
• XtX Calibration: Identifies significantly differentiated SNPs through a calibration procedure of the XtX statistics.
• Covariate Models: Supports alternative covariate models for association analyses with quantitative and categorical population-specific covariables.
• Auxiliary Variable Model: Implements an auxiliary variable model to address multiple testing and, when marker positions are available, to capture linkage disequilibrium information.
• Simulation-based Evaluation: Performance assessed by comprehensive simulation studies comparing power, robustness, and computational efficiency to BayEnv2, BayScEnv, BayScan, flk, and lfmm.

Scientific Applications:

• Cattle selection scans: Applied to genotyping data from 18 French cattle breeds to detect 13 significant selection signatures, including four associated with piebald coloration near KITLG, KIT, EDN3, and ALB and one linked to morphological differences around PLAG1.
• Ecological studies in nonmodel species: Used on Pool-Seq data from 12 Littorina saxatilis populations across ecotypes to investigate ecological adaptation.

Methodology:

Implements a Bayesian framework with BayEnv-derived modeling extensions, estimation of the population covariance matrix, calibration of XtX statistics for SNP differentiation, alternative covariate association models, an auxiliary variable model to handle multiple testing and capture linkage disequilibrium when marker positions are available, and evaluation via simulation studies.