PCHAT

PCHAT performs dimensionality reduction by deriving a heritability‑maximizing composite phenotype to improve detection of associations between single nucleotide polymorphisms (SNPs) and multiple correlated traits.

Key Features:

• Dimension reduction through heritability maximization: Estimates a vector of loadings for each SNP to reduce multiple correlated phenotypes to a single composite trait that maximizes heritability across phenotypes.
• Enhanced detection power: Concentrates association testing on the heritability‑maximizing derived trait to increase power for detecting SNP‑trait associations compared with testing individual phenotypes or principal components of phenotypes (PCP).
• Iterated sample splitting for cross‑validation: Employs iterated sample splitting into training and testing subsets to estimate loadings in unrelated subjects and to control type I error via cross‑validation.

Scientific Applications:

• Genome‑wide association studies (GWAS): Applied in GWAS with multiple correlated phenotypes to improve SNP discovery for complex traits.
• Multitrait genetic analysis: Used to investigate shared genetic architecture and coordinated control by genotyped polymorphisms to uncover subtle genetic influences on multifactorial diseases and traits.

Methodology:

Estimation of per‑SNP loading vectors to maximize heritability, reduction of multiple phenotypes to a single composite trait, and iterated sample splitting into training and testing subsets with cross‑validation to control type I error.