Genomic prediction in maize breeding populations with genotyping-by-sequencing.
Genomic prediction in maize breeding populations with genotyping-by-sequencing.
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DOI：
10.1534/g3.113.008227
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发表时间：
2013-11-06
期刊：
影响因子：
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通讯作者：
Babu R
中科院分区：
文献类型：
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作者：
Crossa J;Beyene Y;Kassa S;Pérez P;Hickey JM;Chen C;de los Campos G;Burgueño J;Windhausen VS;Buckler E;Jannink JL;Lopez Cruz MA;Babu R
测序基因分型（GBS）技术已被证明有能力提供大量的标记基因型，与标准的单核苷酸多态性（SNP）阵列相比，其确定偏倚可能更小。因此，GBS已成为一种有吸引力的基因组选择的替代技术。然而，GBS数据的使用带来了重要的挑战，目前正在对包括玉米、小麦和木薯在内的几种作物进行GBS基因组预测的准确性进行调查。本研究的主要目的是评估各种方法，将GBS信息，并比较它们与系谱模型预测的遗传值，从两个玉米群体的不同性状在不同的环境（实验1和2）进行评估。鉴于GBS数据中有很大比例的未命名基因型，我们评估了使用不同长度（短或长）的非插补、插补和GBS推断单倍型的方法。GBS和系谱数据被纳入统计模型，使用基因组最佳线性无偏预测（GBLUP）或再生核希尔伯特空间（RKHS）回归，预测精度进行了量化，使用交叉验证方法。结果表明：相对于系谱模型或仅标记模型，系谱和GBS数据相结合在预测准确性上有一致的提高，在实验1中使用估算或非估算的GBS数据比推断的单倍型，或在实验2中使用非估算的GBS和基于信息的估算的短和长单倍型，与其他方法相比，预测能力都有提高;实验2中使用GBS数据获得的预测准确度水平与先前作者使用SNP阵列分析该数据集所报道的水平相当;在试验1中，GBLUP和RKHS模型对三个性状的预测相关性最好，而对于实验2，RKHS对干旱胁迫环境的预测略好于GBLUP，两种模型在水源充足的环境中提供了相似的预测。
Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.