SeqBreed：一个用于评估复杂场景中基因组预测的 Python 工具。
SeqBreed: a python tool to evaluate genomic prediction in complex scenarios.
机构信息
Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193, Bellaterra, Barcelona, Spain.
ICREA, Passeig de Lluís Companys 23, 08010, Barcelona, Spain.
出版信息
Genet Sel Evol. 2020 Feb 10;52(1):7. doi: 10.1186/s12711-020-0530-2.
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.
背景
基因组预测（GP）是一种利用 DNA 多态性信息来预测复杂性状的育种值的方法。尽管 GP 可以显著提高预测准确性，但它可能昂贵且难以实施。为了帮助设计最优的育种计划和实验，包括全基因组关联研究和基因组选择实验，我们开发了 SeqBreed，这是一种用 python3 编写的通用且灵活的正向模拟器。
结果
SeqBreed 可以容纳性染色体和线粒体染色体以及同源多倍体。它可以模拟任何数量的由任意数量的因果基因座决定的复杂表型。SeqBreed 实现了几种 GP 方法，包括基因组最佳线性无偏预测（GBLUP）、一步 GBLUP、基于系谱的 BLUP 和大规模选择。我们使用果蝇基因组参考面板（DGRP）序列数据和四倍体马铃薯基因型数据来说明其功能。
结论
SeqBreed 是一种灵活易用的工具，可以用于优化 GP 或全基因组关联研究。它包含了一些最流行的 GP 方法，并包括几个可视化工具。代码是开放的，可以自由修改。软件、文档和示例可在 https://github.com/miguelperezenciso/SeqBreed 上获得。
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