pcadapt

pcadapt performs PCA-based genome scans on genotype matrices to detect outlier loci associated with local adaptation (R package pcadapt2, version 4).

Key Features:

• Enhanced computational efficiency: Implements a novel genotype storage format and an optimized algorithm for computing principal components, reducing PCA computation time by approximately 20–60× versus earlier versions.
• Optimized principal component computation: Improves the algorithm for computing principal components on large genotype matrices to enable analysis of large datasets.
• Integration and accuracy preservation: Incorporates computational enhancements into the existing pcadapt framework while maintaining result accuracy and reliability.

Scientific Applications:

• Local adaptation scans: Identifies outlier loci that may be under selection to investigate the genetic basis of adaptation to specific environmental conditions.
• Evolutionary and ecological genetics: Supports genome-wide analyses in evolutionary biology and ecological genetics across diverse species and populations.
• Large-scale genome scans and GWAS: Enables large-scale genome scans and genome-wide association studies focused on adaptation by handling large genotype datasets.

Methodology:

Principal Component Analysis (PCA) is applied to genotype matrices using a novel genotype storage format and an optimized algorithm for computing principal components.