Jarquín Diego, Kocak Kyle, Posadas Luis, Hyma Katie, Jedlicka Joseph, Graef George, Lorenz Aaron
Department of Agronomy and Horticulture, University of Nebraska, 363 Keim Hall, Lincoln, NE 68583, USA.
BMC Genomics. 2014 Aug 29;15(1):740. doi: 10.1186/1471-2164-15-740.
Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations.
Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller.
Using GBS for genomic prediction in soybean holds good potential to expedite genetic gain. Our results suggest that standard additive G-BLUP models can be used on unfiltered, imputed GBS data without loss in accuracy.
基因分型技术的进步，如测序基因分型（GBS），使基因组预测更具吸引力，可减少育种周期时间以及与表型分析相关的成本。基因组预测和选择已在多种作物中得到研究，但大豆方面尚无相关报道。本研究的目的是：（i）评估在典型大豆育种计划中使用GBS进行基因组选择的前景；（ii）评估GBS标记选择和填补对基因组预测准确性的影响。为实现这些目标，从内布拉斯加大学大豆育种计划中抽取了一组大豆品系，使用GBS进行基因分型，并在多个内布拉斯加地点对产量和其他农艺性状进行评估。
测序基因分型在301个优良大豆育种品系上鉴定出16,502个单核苷酸多态性（SNP），其小等位基因频率（MAF）> 0.05且缺失值百分比≤ 5%。当纳入缺失值高达80%的SNP时，共鉴定出52,349个SNP。使用交叉验证评估的籽粒产量预测准确性估计为0.64，表明在大豆中使用基因组选择进行籽粒产量预测具有良好潜力。基于缺失数据百分比过滤SNP对预测准确性几乎没有影响，尤其是在使用随机森林填补法填补缺失值时。当对所有SNP使用随机森林填补法时观察到最高准确性，但差异不显著。标准加性G-BLUP模型稳健；对加性×加性上位性进行建模并未提高预测准确性。训练群体大小对准确性的影响在约100时开始趋于平稳，但在本分析中使用最大可能大小时，准确性稳步上升。当训练群体较小时，仅纳入MAF > 0.30的SNP可提供更高的准确性。
在大豆中使用GBS进行基因组预测具有加快遗传增益的良好潜力。我们的结果表明，标准加性G-BLUP模型可用于未过滤的、填补后的GBS数据，而不会损失准确性。
