PCAtag

PCAtag applies principal component analysis (PCA) to select tag single nucleotide polymorphisms (tSNPs) that optimally capture intra-genic genetic variation for candidate gene association studies.

Key Features:

• Identification of Linkage Disequilibrium Groups: Identifies groups of SNPs in linkage disequilibrium (LD-groups) without requiring the SNPs to be contiguous.
• Principal Component Analysis Application: Applies principal component analysis (PCA) to evaluate multivariate SNP correlations and infer LD-groups.
• Optimal SNP Set Establishment: Determines a minimal set of group-tagging SNPs (gtSNPs) to maximize coverage of intra-genic genetic variation.
• Comparison with Existing Methods: Performs comparably to haplotype-tagging SNP (htSNP) methods while indicating the optimal number of SNPs required for analysis.
• Robustness and Validation: Validation using multiple replicates of simulated data demonstrates robustness of the PCA-based SNP selection approach.

Scientific Applications:

• Candidate Gene Association Studies: Optimizes selection of tSNPs for studies associating genetic variants and haplotypes with disease.
• Genetic Diversity Analysis: Captures intra-genic diversity to support analyses of complex traits and diseases.

Methodology:

Assumes identification of all SNPs above a predefined minor allele frequency threshold; applies PCA to evaluate multivariate SNP correlations, infer non-contiguous LD-groups, and select group-tagging SNPs (gtSNPs); evaluates performance using multiple replicates of simulated data.