Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)

Miller, Mark J.; Song, Qijian; Fallen, Benjamin; Li, Zenglu

doi:10.3389/fpls.2023.1171135

Cited by 10 publications

(4 citation statements)

References 58 publications

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“…Selection of QTLs with small effects that are not consistent across environments could prove difficult. Genomic selection has become a useful tool to select quantitative traits in breeding programs (Miller et al 2023a and 2023b ). These QTLs from PI 471938 for slow canopy wilting could be incorporated into the genomic selection models to help predict the performance of lines derived from PI 471938 for drought tolerance without having to phenotype them under drought stress in the early generations.…”

Section: Discussionmentioning

confidence: 99%

Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean

Menke,

Steketee,

Song

et al. 2024

Theor Appl Genet

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In soybean [Glycine max (L.) Merr.], drought stress is the leading cause of yield loss from abiotic stress in rain-fed US growing areas. Only 10% of the US soybean production is irrigated; therefore, plants must possess physiological mechanisms to tolerate drought stress. Slow canopy wilting is a physiological trait that is observed in a few exotic plant introductions (PIs) and may lead to yield improvement under drought stress. Canopy wilting of 130 recombinant inbred lines (RILs) derived from Hutcheson × PI 471938 grown under drought stress was visually evaluated and genotyped with the SoySNP6K BeadChip. Over four years, field evaluations of canopy wilting were conducted under rainfed conditions at three locations across the US (Georgia, Kansas, and North Carolina). Due to the variation in weather among locations and years, the phenotypic data were collected from seven environments. Substantial variation in canopy wilting was observed among the genotypes in the RIL population across environments. Three QTLs were identified for canopy wilting from the RIL population using composite interval mapping on chromosomes (Chrs) 2, 8, and 9 based on combined environmental analyses. These QTLs inherited the favorable alleles from PI 471938 and accounted for 11, 10, and 14% of phenotypic variation, respectively. A list of 106 candidate genes were narrowed down for these three QTLs based on the published information. The QTLs identified through this research can be used as targets for further investigation to understand the mechanisms of slow canopy wilting. These QTLs could be deployed to improve drought tolerance through a targeted selection of the genomic regions from PI 471938.

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Section: Discussionmentioning

confidence: 99%

Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean

Menke,

Steketee,

Song

et al. 2024

Theor Appl Genet

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“…Some studies reported an increase (Z. Hu et al, 2011;Jiang et al, 2018;Martini et al, 2017;Miller et al, 2023;Vojgani et al, 2021), while others reported a decrease (Jiang & Reif, 2015;Lorenzana & Bernardo, 2009) in PA upon modeling epistasis, indicating trait specific response. The difference in PA between rrBLUP and EGBLUP for RW_STI was 0.05, suggesting that these models would likely provide similar levels of realized genetic gain when applied.…”

Section: Discussionmentioning

confidence: 99%

Salinity stress tolerance prediction for biomass‐related traits in maize (Zea mays L.) using genome‐wide markers

Singh,

Krause,

Sandhu

et al. 2023

The Plant Genome

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Maize (Zea mays L.) is the third most important cereal crop after rice (Oryza sativa) and wheat (Triticum aestivum). Salinity stress significantly affects vegetative biomass and grain yield and, therefore, reduces the food and silage productivity of maize. Selecting salt‐tolerant genotypes is a cumbersome and time‐consuming process that requires meticulous phenotyping. To predict salt tolerance in maize, we estimated breeding values for four biomass‐related traits, including shoot length, shoot weight, root length, and root weight under salt‐stressed and controlled conditions. A five‐fold cross‐validation method was used to select the best model among genomic best linear unbiased prediction (GBLUP), ridge‐regression BLUP (rrBLUP), extended GBLUP, Bayesian Lasso, Bayesian ridge regression, BayesA, BayesB, and BayesC. Examination of the effect of different marker densities on prediction accuracy revealed that a set of low‐density single nucleotide polymorphisms obtained through filtering based on a combination of analysis of variance and linkage disequilibrium provided the best prediction accuracy for all the traits. The average prediction accuracy in cross‐validations ranged from 0.46 to 0.77 across the four derived traits. The GBLUP, rrBLUP, and all Bayesian models except BayesB demonstrated comparable levels of prediction accuracy that were superior to the other modeling approaches. These findings provide a roadmap for the deployment and optimization of genomic selection in breeding for salt tolerance in maize.

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“…However, it was unsuccessful for SY, whose correlation between F2 and F2:5 was low (r = -0.23). Several studies have indicated genomic selection use for traits with lower heritability to increase selection gains in soybean (Duhnen et Miller et al 2023). In this study, the phenotyping of yield components of the F2:3 and F2:5 generations and the genotyping of the F2 generation from the crossing of the lines BRQ16-5409 and BR13-9499 were performed in soybean plants under ASR pressure.…”

Section: Early-generation Selectionmentioning

confidence: 99%

Genomic prediction of yield components in soybean under Asian rust pressure for early-generation selection

de Carvalho,

Cruz,

Arias

et al. 2024

Preprint

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Asian soybean rust (ASR) has the potential do severely reduce soybean productivity in numerous regions of the world. Developing productive cultivars under disease pressure in genetic breeding programs is limited by low precision in the early stages and the large number of genotypes evaluated in more advanced stages. Information about their subsequent generations can increase genetic gain through selection and reduce the number of segregating lines carried forward in programs. The objective of this work was to predict the average soybean yield components in the F2:3 and F2:5 families and the 50% best lines of each F2:5 family (F2:5(50%)). Genotyping of the F2 generation from the cross of two elite breeding lines and phenotyping of advanced generations (F2:3 and F2:5) under ASR pressure were used to obtain and validate genomic prediction models. Phenotypes included seed yield per plant, 50-seed weight, days to maturity, and plant height. The genomic prediction models used were G-BLUP, principal component regression, Bagging, and Bayes C[[EQUATION]]. Bagging and Bayes C[[EQUATION]] most often showed the highest predictive ability. Phenotyping of F2:3 as opposed to F2 only increased the predictive above of 50-seed weight, but not for the other traits. Phenotyping of F2:5 and F2:5(50%) allowed us to obtain predictive abilities greater than 0.50 for all traits. Using these models for early-generation selection in a target population will allow better adaptation of the logistical structure of breeding programs to develop productive soybean genotypes under ASR pressure and increase selection gain.

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Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)

Cited by 10 publications

References 58 publications

Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean

Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean

Salinity stress tolerance prediction for biomass‐related traits in maize (Zea mays L.) using genome‐wide markers

Genomic prediction of yield components in soybean under Asian rust pressure for early-generation selection

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