Genomic prediction using training population design in interspecific soybean populations

Beche, Eduardo; Gillman, Jason D.; Song, Qijian; Nelson, Randall L.; Beissinger, Tim; Decker, Jared E.; Shannon, Grover; Scaboo, Andrew

doi:10.1007/s11032-021-01203-6

Cited by 11 publications

(14 citation statements)

References 55 publications

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“…The first demonstration of GS in pre-breeding was in exotic maize populations successfully selecting for yield ( Combs and Bernardo, 2013 ). Genomic selection in CWR pre-breeding was also validated recently in soybean ( Beche et al, 2021 ). Very good prediction accuracies for several quantitative traits were reported using a NAM breeding population with three wild Plant Inventory parents ( Beche et al, 2021 ).…”

Section: Pre-breeding Techniques For Transferring Useful Traits To Th...mentioning

confidence: 99%

“…Genomic selection in CWR pre-breeding was also validated recently in soybean ( Beche et al, 2021 ). Very good prediction accuracies for several quantitative traits were reported using a NAM breeding population with three wild Plant Inventory parents ( Beche et al, 2021 ). An excellent example for pre-breeding for marginal lands using focused identification of germplasm strategy (FIGS) is found in studies with barley, wheat and faba bean landraces ( Endresen et al, 2011 ; Khazaei et al, 2013 ).…”

Section: Pre-breeding Techniques For Transferring Useful Traits To Th...mentioning

confidence: 99%

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How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments?

et al. 2022

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Alongside the use of fertilizer and chemical control of weeds, pests, and diseases modern breeding has been very successful in generating cultivars that have increased agricultural production several fold in favorable environments. These typically homogeneous cultivars (either homozygous inbreds or hybrids derived from inbred parents) are bred under optimal field conditions and perform well when there is sufficient water and nutrients. However, such optimal conditions are rare globally; indeed, a large proportion of arable land could be considered marginal for agricultural production. Marginal agricultural land typically has poor fertility and/or shallow soil depth, is subject to soil erosion, and often occurs in semi-arid or saline environments. Moreover, these marginal environments are expected to expand with ongoing climate change and progressive degradation of soil and water resources globally. Crop wild relatives (CWRs), most often used in breeding as sources of biotic resistance, often also possess traits adapting them to marginal environments. Wild progenitors have been selected over the course of their evolutionary history to maintain their fitness under a diverse range of stresses. Conversely, modern breeding for broad adaptation has reduced genetic diversity and increased genetic vulnerability to biotic and abiotic challenges. There is potential to exploit genetic heterogeneity, as opposed to genetic uniformity, in breeding for the utilization of marginal lands. This review discusses the adaptive traits that could improve the performance of cultivars in marginal environments and breeding strategies to deploy them.

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Section: Pre-breeding Techniques For Transferring Useful Traits To Th...mentioning

confidence: 99%

Section: Pre-breeding Techniques For Transferring Useful Traits To Th...mentioning

confidence: 99%

How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments?

et al. 2022

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“…From a cross between cultivated and wild progenitors of soybean ( G. max X G. sojae ), Beche et al (2021) reported that the yield-related alleles were associated with the cultivated elite line, but the protein content alleles were from the wild progenitor. The difference in the distribution of trait-contributing alleles in such crosses has a greater impact on their predictive accuracy.…”

Section: Gs: Implications In Crop Improvementmentioning

confidence: 99%

“…Contiguous breeding programs are very common where new cross combinations are added each year. In such cases, using nested association mapping (NAM) population is better in terms of prediction accuracy (for yield 0.68 and oil and for protein content 0.76) than biparental population, showing the potential of NAM where connectedness is there among the population on the basis of the common parent ( Beche et al, 2021 ). Similarly, Stewart-Brown et al (2019) have reported that, for better predictions in soybean, it is important to have good relatedness among training and breeding populations.…”

Section: Gs: Implications In Crop Improvementmentioning

confidence: 99%

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

et al. 2022

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Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

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“…Because reGBS has high rates of missing data and under calls heterozygous alleles, a 6K Illumina Infinium chip (BARCSoySNP6K) is still actively used by the soybean community (Song et al, 2020, Beche et al, 2021, Li et al, 2021). This chip allows researchers to obtain low missing data with high accuracy of calling heterozygotes and has an easy workflow for processing samples and data analysis.…”

Section: Introductionmentioning

confidence: 99%

Development of Molecular Inversion Probes for Soybean Progeny Genomic Selection Genotyping

Wang

Happ

McConaughy

et al. 2022

Preprint

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Increasing rate of genetic gain for key agronomic traits through genomic selection requires the development of new molecular methods to run genome-wide single nucleotide polymorphisms (SNPs). The main limitation of current methods is the cost is too high to screen breeding populations. Molecular inversion probes (MIPs) is a targeted genotyping-by-sequencing method that could be used for soybeans that is both cost effective, high-throughput, and provides high data quality to screen breeder’s germplasm for genomic selection. A 1K MIP SNP set was developed for soybean with uniformly distributed markers across the genome. The SNPs were selected to maximize the number of informative markers in germplasm being tested in soybean breeding programs located in the North Central and Mid-South regions of the United States. The 1K SNP MIP set was tested on diverse germplasm and a recombinant inbred line population. Targeted sequencing with MIPs obtained an 85% enrichment for the targeted SNPs. MIP’s genotyping accuracy was 93% overall while homozoygous call accuracy was 98% with less than 10% missing data. The accuracy of MIPs combined with its low per sample cost makes it a powerful tool to enable genomic selection within soybean breeding programs.

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Genomic prediction using training population design in interspecific soybean populations

Cited by 11 publications

References 55 publications

How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments?

How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments?

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

Development of Molecular Inversion Probes for Soybean Progeny Genomic Selection Genotyping

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