PS_SNP

PS_SNP selects single nucleotide polymorphisms (SNPs) for multi-stage (two-stage) whole-genome association studies to reduce genotyping burden while preserving power to detect disease associations.

Key Features:

• Efficient Multi-Stage Design: Implements a two-stage framework where a subset of samples is densely genotyped in stage one to inform a reduced marker set for later stages.
• Correlation and Interaction Analysis: Incorporates analysis of correlations (linkage disequilibrium) among SNPs and SNP–SNP interactions with the phenotype when selecting markers.
• Reduced SNP Set with High Discriminatory Power: Identifies a smaller set of SNPs from stage-one data that maximizes discriminative power for subsequent genotyping and testing.
• Combined Analysis for Improved Power: Performs combined analysis across stages and provides theoretical derivations of the significance level for the combined statistic.
• Extensive Simulation Validation: Validates performance through extensive simulations to assess reduction in SNP number and gain in detection power.
• Application to Real-World Data: Has been applied to a genome-wide association study dataset on sporadic amyotrophic lateral sclerosis (ALS) to identify candidate SNPs.

Scientific Applications:

• Complex disease GWAS: Facilitates detection of genetic associations in complex diseases by prioritizing SNPs for follow-up genotyping in multi-stage designs.
• Cost-efficient study design: Enables resource-efficient whole-genome association studies by reducing the number of SNPs genotyped in later stages.
• ALS genetic analysis: Supports discovery of candidate SNPs in genome-wide association study datasets such as sporadic amyotrophic lateral sclerosis (ALS).

Methodology:

Uses stage-one dense genotyping to analyze linkage disequilibrium and SNP interactions, selects a reduced SNP set for stage two, performs combined-stage analysis with theoretical significance-level derivation, and validates via extensive simulations and application to an ALS GWAS dataset.