GCTA

GCTA estimates the proportion of phenotypic variance explained by single nucleotide polymorphisms (SNPs) across the genome to quantify genetic contributions to complex traits.

Key Features:

• Variance Estimation (GREML): Uses Genomic-Relatedness-Based Restricted Maximum Likelihood (GREML) to estimate the proportion of phenotypic variance explained by all SNPs collectively.
• Genetic Relationship Estimation: Constructs genetic relatedness matrices among individuals from SNP data for downstream variance component analyses.
• Mixed Linear Model Analysis: Applies mixed linear models to partition trait variation into genetic and environmental components.
• Linkage Disequilibrium Structure Estimation: Estimates linkage disequilibrium (LD) structure among SNPs to inform genetic-architecture analyses.
• GWAS Simulation: Performs simulation studies of GWAS data to test hypotheses and validate analytical approaches.
• Data Management for GWAS: Handles large-scale GWAS datasets for the above analyses.

Scientific Applications:

• Genome-wide SNP heritability estimation: Quantifies the cumulative contribution of common SNPs to complex-trait heritability using GWAS data.
• Partitioning of variance components: Separates genetic and environmental contributions to phenotypic variance via mixed models and GREML.
• X chromosome analyses and dosage compensation testing: Estimates variance explained by SNPs on the X chromosome and supports testing hypotheses related to dosage compensation.
• Method validation and power analysis: Uses GWAS simulations to test hypotheses and assess performance of heritability estimation approaches.

Methodology:

Computational methods explicitly include GREML (Genomic-Relatedness-Based Restricted Maximum Likelihood), construction of genetic relatedness matrices from SNP data, mixed linear models for variance component analysis, estimation of LD structure among SNPs, and GWAS simulation.