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SeqBreed: a python tool to evaluate genomic prediction in complex scenarios.

Miguel Pérez-Enciso1,2, Lino C Ramírez-Ayala3, Laura M Zingaretti3,4

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Summary

SeqBreed is a flexible Python tool for genomic prediction (GP) and genome-wide association studies. It simulates complex traits and incorporates popular GP methods, aiding in optimizing breeding programs.

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Area of Science:

  • Quantitative genetics
  • Bioinformatics
  • Computational biology

Background:

  • Genomic prediction (GP) uses DNA polymorphism to estimate breeding values for complex traits.
  • GP enhances predictive accuracy but can be costly and difficult to implement.
  • Designing optimal breeding programs and experiments requires efficient simulation tools.

Purpose of the Study:

  • To develop SeqBreed, a generic and flexible forward simulator for genomic prediction and genome-wide association studies.
  • To provide a tool that aids in designing optimum breeding programs and experiments.
  • To offer a user-friendly platform for simulating complex genetic scenarios.

Main Methods:

  • SeqBreed is a forward simulator programmed in Python 3.
  • It accommodates sex and mitochondrion chromosomes, and autopolyploidy.
  • The simulator can model complex phenotypes determined by multiple causal loci.

Main Results:

  • SeqBreed implements various genomic prediction methods, including GBLUP, single-step GBLUP, pedigree-based BLUP, and mass selection.
  • The simulator was validated using Drosophila genome reference panel (DGRP) and tetraploid potato genotype data.
  • Functionality was demonstrated with diverse genetic data, including autopolyploid and sex-linked traits.

Conclusions:

  • SeqBreed is a flexible and user-friendly tool for optimizing genomic prediction and genome-wide association studies.
  • It integrates popular GP methods and visualization tools.
  • The open-source code, documentation, and examples are available for modification and use.