Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max)

Abstract

Key messageTwenty-two loci for soybean SW and candidate genes conditioning seed development were identified; and prediction accuracies of GS and MAS were estimated through cross-validation and validation with unrelated populations.AbstractSoybean (Glycine max) is a major crop for plant protein and oil production, and seed weight (SW) is important for yield and quality in food/vegetable uses of soybean. However, our knowledge of genes controlling SW remains limited. To better understand the molecular mechanism underlying the trait and explore marker-based breeding approaches, we conducted a genome-wide association study in a population of 309 soybean germplasm accessions using 31,045 single nucleotide polymorphisms (SNPs), and estimated the prediction accuracy of genomic selection (GS) and marker-assisted selection (MAS) for SW. Twenty-two loci of minor effect associated with SW were identified, including hotspots on Gm04 and Gm19. The mixed model containing these loci explained 83.4 % of phenotypic variation. Candidate genes with Arabidopsis orthologs conditioning SW were also proposed. The prediction accuracies of GS and MAS by cross-validation were 0.75–0.87 and 0.62–0.75, respectively, depending on the number of SNPs used and the size of training population. GS also outperformed MAS when the validation was performed using unrelated panels across a wide range of maturities, with an average prediction accuracy of 0.74 versus 0.53. This study convincingly demonstrated that soybean SW is controlled by numerous minor-effect loci. It greatly enhances our understanding of the genetic basis of SW in soybean and facilitates the identification of genes controlling the trait. It also suggests that GS holds promise for accelerating soybean breeding progress. The results are helpful for genetic improvement and genomic prediction of yield in soybean.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-015-2614-x) contains supplementary material, which is available to authorized users.

Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy

“…This type of panel is expected to exhibit a greater genetic diversity than panels constituted of lines from breeding programs. Zhang et al (2016) confirmed that seed weight was under the influence of many minor-effect loci in their panel, and they obtained an equivalent prediction accuracy using 2000 or 31,045 SNPs.…”

“…Jarquín et al (2014) reported robustness of prediction accuracy for yield when using different sets of markers in a panel of experimental lines from a breeding program. Zhang et al (2016) reported results from association analyses and assessment of genomic prediction accuracy for seed weight, a component trait of yield, in a panel of accessions from the USDA germplasm collection. This type of panel is expected to exhibit a greater genetic diversity than panels constituted of lines from breeding programs.…”

“…Jarquín et al (2014) reported the prediction accuracies for yield and other agronomic traits obtained using genotyping-by-sequencing data. Zhang et al (2016) reported results from association analysis and assessed genomic prediction accuracy for seed weight, which is a component trait of grain yield. In both studies, the authors concluded that genomic selection holds good potential to expedite genetic gain for yield in soybean breeding programs.…”

“…For soybean, studies have reported genomic selection accuracy for production traits, development traits, or resistance to pathogens ( Jarquín et al, 2014;Bao et al, 2015;Zhang et al, 2016). Jarquín et al (2014) reported the prediction accuracies for yield and other agronomic traits obtained using genotyping-by-sequencing data.…”

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F₂ populations by using genomic selection models

“…The objective of genomic selection is to identify possible markers in linkage disequilibrium with the gene regions of interest. Since this pioneering study, several authors have used this technique to predict the genetic value in several plant species, such as corn (Beyene et al, 2015), soybeans (Zhang et al, 2016), wheat (Bassi et al, 2016), forest species (Cros et al, 2015), sugarcane (Gouy et al, 2013), and rice (Spindel et al, 2015).…”