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.

No abstract

The first soybean [Glycine max (L.) Merr.] breeding program in China was established in the northeast in 1913. A trend analysis of widely grown cultivars across Chinese soybean breeding history may provide a better perspective on the genetic progress in soybean. The objective of the current study was to assess the genetic change of 15 phenological, yield, and agronomic traits on widely grown cultivars in northeast China. Sixty-four soybean cultivars representing a span of 84 yr of release were included. The field experiments were conducted at three sites in each region during 2009, 2010, and 2011, and the annual genetic changes were obtained by regression analysis. The results showed that the yield gain in widely grown cultivars of different regions ranged from 6 to 16 kg ha −1 yr −1 due to improvements in different yield components in the last nine decades. In addition, modern cultivars demonstrated more upright plant architecture, fewer branches, shorter height, higher lodging resistance, and earlier flowering than obsolete cultivars. However, changes were insignificant in the height of the bottom pod and the node number. The changing rates of yield and phenological traits across these decades were constant, while that of agronomic traits were discontinuous. Days to flowering, branch number, and lodging score were more responsive to environments in new cultivars than in old cultivars. In conclusion, these findings indicate a substantial improvement in the yield, agronomic, and phenological traits resulted from long-term genetic breeding. This study also provides insight into developing new strategies for soybean genetic improvement in China and worldwide. Corresponding authors (hantianfu@ caas.cn; wucunxiang@caas.cn). Abbreviations: 100-SW, 100-seed weight; BLUE, best linear unbiased estimator; BLUP, best linear unbiased predictor; BN, branch number; C, cultivar; CV, coefficient of variability; DTF, days to first flower; DTM, days to maturity; E, environment; HBP, height of the bottom pod; JL, Jilin-Liaoning region; LS, lodging score; MG, maturity group; MSH, midsouth Heilongjiang region; NH, north Heilongjiang region; NN, node number; PH, plant height; PPP, number of pods per plant; R/V, ratio of the reproductive period to the vegetative period; RP, reproductive period; SPP, seeds per plant; SPPOD, seeds per pod; YPP, yield per plant.

Key message We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding. Abstract Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.

Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2,214 representative soybean accessions, we developed the ZDX1 high-throughput functional soybean array, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to agronomically important traits. We genotyped 817 soybean accessions using ZDX1, including parental lines, non-parental lines, and progeny from a practical breeding pipeline. It was clarified that non-parental lines had highest genetic diversity, and 235 SNPs were identified to be fixed in the progeny. The unknown soybean cyst nematode-resistant and early maturity accessions were identified by using allele combinations. Notably, we found that breeding index was a good indicator for progeny selection, in which the superior progeny were derived from the crossing more distantly related parents with at least one parent having a higher breeding index. Based on this rule, two varieties were directionally developed. Meanwhile, redundant parents were screened out and potential combinations were formulated. GBLUP analysis displayed that the markers in genic regions had priority to be higher accuracy on predicting four agronomic traits compared with either whole genome or intergenic markers. Then we used progeny to expand the training population to increase the prediction accuracy of breeding selection by 32.1%. Collectively, our work provided a versatile array for high accuracy selecting and predicting both parents and progeny that can greatly accelerate soybean breeding.