Genomic selection in soybean: accuracy and time gain in relation to phenotypic selection

Matei, Gilvani; Woyann, Leomar Guilherme; Milioli, Anderson Simionato; Oliveira, Ivone de Bem; Zdziarski, Andrei Daniel; Zanella, Rodrigo; Coelho, Alexandre Siqueira Guedes; Finatto, Taciane

doi:10.1007/s11032-018-0872-4

Cited by 39 publications

(33 citation statements)

References 46 publications

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“…Also, Matei et al. (2018) obtained a maximum of 0.70 for the same trait when working with recombinant inbred lines and varieties. Differently, our results (Figures 2 and 3) were inferior to the literature examples described above.…”

Section: Discussionmentioning

confidence: 96%

Genomic prediction enables early but low‐intensity selection in soybean segregating progenies

Mendonça

Galli

Malone³

et al. 2020

Crop Science

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In soybean [Glycine max (L.) Merr.], new commercial lines are commonly obtained from biparental crosses, and the selection is performed as homozygosity increases. However, it is difficult to select for quantitative traits in the early steps of breeding, due to the high heterozygosity level and a vast number of new progenies, which sometimes lead breeders to randomly select for these traits in this phase. Therefore, we aimed to assess the impact of genomic selection in early generations of a soybean breeding program. Working on germplasm derived from two different maturity regions in Brazil, genotyped in F2 and phenotyped in F2:4 for grain yield, plant height, maturity rating, and days to maturity, we compared the composition of different training populations, models with and without the genotype × environment (G × E) interaction effect, and two types of relationship measurements (genetic similarity and Euclidian distance). Results showed superior performance of the Euclidian distance kernel over the standard VanRaden kernel in major scenarios tested. In general, G × E models did not obtain superior performance compared with mean principal models, and the training population composed only of the nearest progenies had the highest prediction ability. The best models achieved prediction abilities between 0.40 and 0.56, thereby enabling application of a low‐intensity selection in F2. As a result, half of the progenies could be discarded without missing a great part of the good ones. Our results show that through genomic prediction, it is possible to select for quantitative traits in the early steps of breeding, which might increase the efficiency of the program in the advanced phases.

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Section: Discussionmentioning

confidence: 96%

Genomic prediction enables early but low‐intensity selection in soybean segregating progenies

Mendonça

Galli

Malone³

et al. 2020

Crop Science

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“…Despite the difference in heritability between the traits, Matei et al. (2018) obtained successful predictions for GY and MG; they achieved a value greater than 0.6 for predictive ability (PA) in the context of population structure. In some scenarios, MG was above 0.8, showing the high potential of GS for this trait.…”

Section: Introductionmentioning

confidence: 99%

The accuracy of different strategies for building training sets for genomic predictions in segregating soybean populations

Mendonça

Fritsche‐Neto

2020

Crop Science

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The design of the training set is a key factor in the success of the genomic selection approach. The nature of line inclusion in soybean [Sorghum bicolor (L.) Moench.] breeding programs is highly dynamic, so generating a training set that endures across the years and regions is challenging. Therefore, we aimed to define the best strategies for building training sets to apply genomic selection in segregating soybean populations for traits with different genetic architectures. We used two datasets for grain yield (GY) and maturity group (MG) from two different soybean breeding regions in Brazil. Five training set schemes were tested. In addition, we included a training set formed by an optimization algorithm based on the predicted error variance. The predictions achieved good values for both traits, reaching 0.5 in some scenarios. The best scenario changed according to the trait. Although the best performance was achieved with the use of full‐sibs in the MG dataset, for GY, full‐sibs and a set of advanced lines were equivalent. For both traits, no improvement in predictive ability resulted from training set optimization. Furthermore, the use of advanced lines from the same breeding program is recommended as a training set for GY, so the training set is continually renewed and closely related to the breeding populations, and no additional phenotyping is needed. On the other hand, to improve prediction accuracies for MG, it is necessary to use training sets with less genetic variability but with more segregation resolution.

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“…The establishment of GS for complex traits can permit the achievement of a rapid genetic gain per unit of time [18]. Matei et al [19] showed that selection cycle for yield and seed weight can be signi cantly shortened using GS. The e ciency of GS heavily relies on its accuracy.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola¹,

Qin²,

Shi³

et al. 2020

Preprint

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BackgroundSoybean [Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy.Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). Population structure was inferred using STRUCTURE 2.3.4, GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that: a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci (E2, E4, and Dt1) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900, Glyma.19g200800, Glyma.09g196700, and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP (if no annotated genes found close the top one) for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent.Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits in soybean through marker-assisted selection (MAS) and GS in breeding programs. After validation, the candidate genes can be transferred to new cultivars using the linked SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.

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Genomic selection in soybean: accuracy and time gain in relation to phenotypic selection

Cited by 39 publications

References 46 publications

Genomic prediction enables early but low‐intensity selection in soybean segregating progenies

Genomic prediction enables early but low‐intensity selection in soybean segregating progenies

The accuracy of different strategies for building training sets for genomic predictions in segregating soybean populations

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

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