高通量基因分型到基因组估计育种值(GEBVs)的实用工作流程。

Practical Workflow from High-Throughput Genotyping to Genomic Estimated Breeding Values (GEBVs).

机构信息

CREA Research Centre for Vegetable and Ornamental Crops, Pontecagnano Faiano, Italy.

Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy.

出版信息

Methods Mol Biol. 2021;2264:119-135. doi: 10.1007/978-1-0716-1201-9_9.

Abstract

The global climate is changing, resulting in significant economic losses worldwide. It is thus necessary to speed up the plant selection process, especially for complex traits such as biotic and abiotic stresses. Nowadays, genomic selection (GS) is paving new ways to boost plant breeding, facilitating the rapid selection of superior genotypes based on the genomic estimated breeding value (GEBV). GEBVs consider all markers positioned throughout the genome, including those with minor effects. Indeed, although the effect of each marker may be very small, a large number of genome-wide markers retrieved by high-throughput genotyping (HTG) systems (mainly genotyping-by-sequencing, GBS) have the potential to explain all the genetic variance for a particular trait under selection. Although several workflows for GBS and GS data have been described, it is still hard for researchers without a bioinformatics background to carry out these analyses. This chapter has outlined some of the recently available bioinformatics resources that enable researchers to establish GBS applications for GS analysis in laboratories. Moreover, we provide useful scripts that could be used for this purpose and a description of key factors that need to be considered in these approaches.

摘要

全球气候正在发生变化,给全世界造成了巨大的经济损失。因此,有必要加快植物的选择过程,特别是对于生物和非生物胁迫等复杂性状。如今,基因组选择(GS)正在为促进植物育种开辟新途径,根据基因组估计育种值(GEBV)快速选择优良基因型。GEBV 考虑了基因组中所有位置的标记,包括那些具有较小效应的标记。实际上,尽管每个标记的效应可能非常小,但高通量基因分型(HTG)系统(主要是测序分型,GBS)检索到的大量全基因组标记具有潜力来解释在选择下特定性状的所有遗传方差。尽管已经描述了用于 GBS 和 GS 数据的几个工作流程,但对于没有生物信息学背景的研究人员来说,仍然很难进行这些分析。本章概述了一些最近可用的生物信息学资源,这些资源使研究人员能够在实验室中建立 GBS 应用程序进行 GS 分析。此外,我们还提供了可用于此目的的有用脚本,并描述了这些方法中需要考虑的关键因素。

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