SeqBreed

SeqBreed simulates populations and performs genomic prediction in Python to enable evaluation and optimization of genomic selection and genome-wide association study designs.

Key Features:

• Simulation of complex phenotypes: Simulates any number of complex phenotypes influenced by multiple causal loci.
• Population simulation under genomic selection: Simulates populations under genomic selection scenarios.
• Genomic prediction methods: Implements BLUP, Genomic Best Linear Unbiased Prediction (GBLUP), and single-step GBLUP (SSTEP).
• Support for diverse genomic architectures: Accommodates sex chromosomes, mitochondrial DNA, and autopolyploidy.
• Visualization outputs: Generates Principal Component Analysis (PCA) and genome-wide association study (GWAS) plots.
• Implementation and extensibility: Implemented in Python as a generic, flexible framework that can be modified for specific research needs.

Scientific Applications:

• Genomic prediction experiment design: Used to design and optimize genomic prediction and selection strategies.
• Genome-wide association studies (GWAS): Used to evaluate GWAS designs and interpret trait–marker associations.
• Cross-species and ploidy investigations: Applicable across species and ploidy levels, including analyses involving autopolyploids.
• Empirical dataset examples: Applied to Drosophila Genome Reference Panel (DGRP) sequence data and tetraploid potato genotypes.

Methodology:

Simulates populations and complex phenotypes influenced by multiple causal loci, implements BLUP, GBLUP and single-step GBLUP (SSTEP) genomic prediction methods, and produces PCA and GWAS plots.