Improvement of key agronomical traits in soybean through genomic prediction of superior crosses

Jean, Martine; Cober, Elroy R.; O’Donoughue, Louise S.; Rajcan, Istvan; Belzile, François

doi:10.1002/csc2.20583

Cited by 11 publications

(20 citation statements)

References 27 publications

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“…Here, using 3D-GBS, we showed that it is possible to produce a lower number of restriction fragments, well and uniformly distributed across the genome, to reduce the number of reads needed to provide sufficient read coverage to call genotypes efficiently. Here, we found that 100K reads is sufficient to conduct GS with 3D-GBS, and that is significantly lower compared to previous studies where GBS has been used (e.g., Qin et al [ 48 ] with ~ 3.3M reads/sample, Jarquín et al [ 27 ] with ~ 2.6M reads/sample and Jean et al [ 28 ] with ~ 1.2M reads/sample). Similarly, we estimated the optimal number of reads per sample for an efficient genotyping of bi- and multi-parent populations.…”

Section: Resultscontrasting

confidence: 60%

“…To estimate the gain of 3D-GBS over the standard GBS approach, we selected two studies conducted internally, using ApeKI-based GBS protocol and with the lowest number of reads per sample for GS [ 28 ] and QTL mapping [ 12 ]. In these study cases, based on the optimal number of reads/sample estimated previously, with the same population, experimental design and goal, the application of 3D-GBS for GS and QTL mapping would have led to similar results with a significant reduction in per-sample sequencing cost: ~ 92% (~ 1.2M vs 100K reads/sample) and ~ 86% (~ 1.4M vs 200K reads/sample), respectively.…”

Section: Resultsmentioning

confidence: 99%

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3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes

et al. 2023

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Despite the increased efficiency of sequencing technologies and the development of reduced-representation sequencing (RRS) approaches allowing high-throughput sequencing (HTS) of multiplexed samples, the per-sample genotyping cost remains the most limiting factor in the context of large-scale studies. For example, in the context of genomic selection (GS), breeders need genome-wide markers to predict the breeding value of large cohorts of progenies, requiring the genotyping of thousands candidates. Here, we introduce 3D-GBS, an optimized GBS procedure, to provide an ultra-high-throughput and ultra-low-cost genotyping solution for species with small to medium-sized genome and illustrate its use in soybean. Using a combination of three restriction enzymes (PstI/NsiI/MspI), the portion of the genome that is captured was reduced fourfold (compared to a “standard” ApeKI-based protocol) while reducing the number of markers by only 40%. By better focusing the sequencing effort on limited set of restriction fragments, fourfold more samples can be genotyped at the same minimal depth of coverage. This GBS protocol also resulted in a lower proportion of missing data and provided a more uniform distribution of SNPs across the genome. Moreover, we investigated the optimal number of reads per sample needed to obtain an adequate number of markers for GS and QTL mapping (500–1000 markers per biparental cross). This optimization allows sequencing costs to be decreased by ~ 92% and ~ 86% for GS and QTL mapping studies, respectively, compared to previously published work. Overall, 3D-GBS represents a unique and affordable solution for applications requiring extremely high-throughput genotyping where cost remains the most limiting factor.

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Section: Resultscontrasting

confidence: 60%

Section: Resultsmentioning

confidence: 99%

3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes

et al. 2023

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“…To explore this question, Jean et al (2021) used genotypic and phenotypic information on a set of 350 lines to predict the mean performance of over 60,000 potential crosses for yield and maturity, two key traits of prime concern to soybean breeders. To assess the accuracy of these predictions, a subset of 101 crosses that had been performed and subjected to selection in the course of past breeding work was examined.…”

Section: Main Achievements Of the Soyagen Projectmentioning

confidence: 99%

“…The gray rectangles showcase crosses with above-average yield for a given maturity. Reproduced with permission from Jean et al (2021) .…”

Section: Main Achievements Of the Soyagen Projectmentioning

confidence: 99%

The SoyaGen Project: Putting Genomics to Work for Soybean Breeders

et al. 2022

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The SoyaGen project was a collaborative endeavor involving Canadian soybean researchers and breeders from academia and the private sector as well as international collaborators. Its aims were to develop genomics-derived solutions to real-world challenges faced by breeders. Based on the needs expressed by the stakeholders, the research efforts were focused on maximizing realized yield through optimization of maturity and improved disease resistance. The main deliverables related to molecular breeding in soybean will be reviewed here. These include: (1) SNP datasets capturing the genetic diversity within cultivated soybean (both within a worldwide collection of > 1,000 soybean accessions and a subset of 102 short-season accessions (MG0 and earlier) directly relevant to this group); (2) SNP markers for selecting favorable alleles at key maturity genes as well as loci associated with increased resistance to key pathogens and pests (Phytophthora sojae, Heterodera glycines, Sclerotinia sclerotiorum); (3) diagnostic tools to facilitate the identification and mapping of specific pathotypes of P. sojae; and (4) a genomic prediction approach to identify the most promising combinations of parents. As a result of this fruitful collaboration, breeders have gained new tools and approaches to implement molecular, genomics-informed breeding strategies. We believe these tools and approaches are broadly applicable to soybean breeding efforts around the world.

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“…It aims to predict the phenotype of an individual based only on its genotype (Crossa et al 2017). Recently, the use of GS models to predict superior crosses has been explored in soybean with very promising results (Jean et al 2021). This approach relies on the production of a simulated set of progenies, with the number and location of recombination events (REs) on each chromosome being predicted based on the genetic distances between markers on a genetic map (Mohammadi et al 2015).…”

Section: Introductionmentioning

confidence: 99%

The construction of a high-density consensus genetic map for soybean based on SNP markers derived from genotyping-by-sequencing

Fallah

Jean

St-Amour

et al. 2022

Genome

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Genetic linkage maps are used to localize markers on the genome based on the recombination frequency. Most often, these maps are based on the segregation observed within a single biparental population of limited size (n<300) where relatively few recombination events are sampled and in which some genomic regions are monomorphic because both parents carry the same alleles. Together, these two limitations affect both the resolution and extent of genome coverage of such maps. Consensus genetic maps overcome the limitations of individual genetic maps by merging the information from multiple segregating populations derived from a greater diversity of parental combinations, thus increasing the number of recombination events and reducing the number of monomorphic regions. The aim of this study was to construct a high-density consensus genetic map for SNP markers obtained through a genotyping-by-sequencing (GBS) approach. Individual genetic maps were generated from six F<sub>4:5</sub> mapping populations (n=278-365), totaling 1857 individuals. The six linkage maps were then merged to produce a consensus map comprising a total of 16,311 mapped SNPs that jointly cover 99.5% of the soybean genome with only two gaps larger than 10 cM. Compared to previous soybean consensus maps, it offers a more extensive and uniform coverage.

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Improvement of key agronomical traits in soybean through genomic prediction of superior crosses

Cited by 11 publications

References 27 publications

3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes

3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes

The SoyaGen Project: Putting Genomics to Work for Soybean Breeders

The construction of a high-density consensus genetic map for soybean based on SNP markers derived from genotyping-by-sequencing

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