SeqBreed: a python tool to evaluate genomic prediction in complex scenarios
2020
Pérez-Enciso, Miguel | Ramírez-Ayala, Lino C. | Zingaretti, Laura M.
BACKGROUND: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3. RESULTS: SeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes that are determined by any number of causal loci. SeqBreed implements several GP methods, including genomic best linear unbiased prediction (GBLUP), single-step GBLUP, pedigree-based BLUP, and mass selection. We illustrate its functionality with Drosophila genome reference panel (DGRP) sequence data and with tetraploid potato genotype data. CONCLUSIONS: SeqBreed is a flexible and easy to use tool that can be used to optimize GP or genome-wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation, and examples are available at https://github.com/miguelperezenciso/SeqBreed.
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