qgg

qgg: Quantitative Genetic and Genomic Analysis in R

qgg implements linear mixed models for quantitative genetic and genomic analyses, enabling estimation of genetic parameters, construction of genomic relationship matrices, genomic prediction, genetic risk profiling, and single- and multi-marker association analyses using large-scale genotype and phenotype data.

Key Features:

• Linear Mixed Models: Fits linear mixed models to estimate fixed and random effects in genetic data.
• Genomic Relationship Matrices: Constructs marker-based genomic relationship matrices from genotypic data.
• Estimation of Genetic Parameters: Estimates heritability and genetic correlation to characterize trait architecture.
• Genomic Prediction and Risk Profiling: Performs genomic prediction of phenotypes and genetic risk profiling based on genotype data.
• Association Analyses: Conducts single-marker and multi-marker association analyses to identify trait-associated genomic regions.

Scientific Applications:

• Quantitative Trait and Complex Disease Analysis: Analyzes large-scale genomic data to investigate quantitative traits and complex diseases.
• Genomic Feature-Based Analysis: Evaluates genes, chromosomes, and biological pathways as genomic feature sets.
• Causal Variant Enrichment Testing: Tests hypotheses of enrichment of causal variants within defined genomic regions.

Methodology:

Applies linear mixed model frameworks and marker-based genomic relationship matrices under the hypothesis that specific genomic features are enriched for causal variants influencing traits, enabling partitioning of genetic variation across defined feature classes.