Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.)

Abstract

Genomic selection is a promising molecular breeding strategy enhancing genetic gain per unit time. The objectives of our study were to (1) explore the prediction accuracy of genomic selection for plant height and yield per plant in soybean [Glycine max (L.) Merr.], (2) discuss the relationship between prediction accuracy and numbers of markers, and (3) evaluate the effect of marker preselection based on different methods on the prediction accuracy. Our study is based on a population of 235 soybean varieties which were evaluated for plant height and yield per plant at multiple locations and genotyped by 5361 single nucleotide polymorphism markers. We applied ridge regression best linear unbiased prediction coupled with fivefold cross-validations and evaluated three strategies of marker preselection. For plant height, marker density and marker preselection procedure impacted prediction accuracy only marginally. In contrast, for grain yield, prediction accuracy based on markers selected with a haplotype block analyses-based approach increased by approximately 4 % compared with random or equidistant marker sampling. Thus, applying marker preselection based on haplotype blocks is an interesting option for a cost-efficient implementation of genomic selection for grain yield in soybean breeding.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-016-0504-9) contains supplementary material, which is available to authorized users.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…For instance, Jarquín et al (2014) obtained a high prediction accuracy of 0.64 by deploying GS for improving yield and agronomic traits using genotypingby-sequencing in a breeding program. In addition, GS for yield ridge regression best linear unbiased prediction (BLUP) coupled with fivefold cross-validations and marker preselection based on haplotype blocks is an interesting option for a cost-efficient implementation of genomic selection for grain yield in soybean breeding (Ma et al, 2016). In the case of pea, using GS, Tayeh et al (2015c) reported mean cross-environment prediction accuracies of 0.83 for thousand-seed weight, 0.68 for number of seeds per plant, and 0.65 for date of flowering.…”

Improving accuracy of genomic prediction in maize single-crosses through different kernels and reducing the marker dataset

“…Several factors influence the accuracy of genomic prediction, such as genetic architecture, non-additive effects, models, the presence of G×E interaction, and marker density Spindel et al 2015;Cuevas et al 2016;Ma et al 2016). Many statistical methods have been proposed to estimate the GEBVs in the training population.…”

“…Several studies had shown that low-density arrays can improve the accuracy of genomic prediction (Moser et al 2010;Spindel et al 2015;Hoffstetter et al 2016;Ma et al 2016). It was demonstrated that high genotyping density does not always increase accuracy and markers subset sometimes outperform the entire dataset (Zhang et al 2010;Ma et al 2016). Standard sets of selected SNPs are a tendency for low-cost of genotyping in plant breeding.…”

“…Therefore, marker density has been investigated in some studies, which has shown divergent results regarding the use of low or higher density SNPs datasets (Habier et al 2009;Weigel et al 2009;Moser et al 2010;Crossa et al 2013;Perez-Rodriguez et al 2013;Tayeh et al 2015). Furthermore, higher genotyping density does not always increase accuracy and markers subset sometimes outperform the entire dataset (Zhang et al 2010;Ma et al 2016). …”

“…They did not observe a reduction in prediction accuracies in any of the traits evaluated. Moreover, Ma et al (2016) showed that the marker preselection based on haplotype block analyses is an interesting option to reduce the implementation cost of genomic selection.…”