Registration of the S2MET Barley Mapping Population for Multi‐Environment Genomewide Selection

Neyhart, Jeffrey L.; Sweeney, Daniel W.; Sorrells, Mark E.; Kapp, Christian; Kephart, K. D.; Sherman, Jamie; Stockinger, Eric J.; Fisk, Scott; Hayes, Patrick; Daba, Sintayehu; Mohammadi, Mohsen; Hughes, Nia; Lukens, Lewis; Barrios, Pablo González; Gutiérrez, Lucı́a; Smith, Kevin P.

doi:10.3198/jpr2018.06.0037crmp

Cited by 12 publications

(24 citation statements)

References 42 publications

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“…Collaborative TPs that span years and mega‐environments such as that described by Neyhart et al. (2019) may provide datasets that enable accurate parental selection for new breeding programs based on expected progeny genetic variance. Initial empirical results from Neyhart and Smith (2019) suggest this approach may work for high heritability traits, but not for those of low heritability.…”

Section: Discussionmentioning

confidence: 99%

A connected half‐sib family training population for genomic prediction in barley

et al. 2020

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Genomic prediction accuracy is affected by population size, trait heritability, relatedness of training and validation populations, marker density, and genetic architecture. Nested association mapping (NAM) populations have advantages in many of these features compared with biparental families and may be an effective strategy for increasing prediction accuracy. The classic NAM design was modified to create a two‐row spring malting barley (Hordeum vulgare L.) population of 1341 F3:F4 lines in seven families that was phenotyped for heading date, plant height, leaf rust, spot blotch, pre‐harvest sprouting, and grain protein. Quantitative trait loci (QTL) were detected for plant height, leaf rust, pre‐harvest sprouting, and spot blotch with genome‐wide association analyses. Prediction accuracies were assessed in validation populations consisting of a single family or multiple families. Across‐family prediction accuracy (.607–.811) generally surpassed within‐family prediction accuracy, particularly for traits with high across‐family variance. Reductions in marker density (70–80%) and training population size (25–50%) did not cause significant loss of prediction accuracy. Addition of fixed marker effects from genome‐wide association had minimal impact on prediction accuracy in the full training population but improved accuracy in reduced training populations. Within‐family prediction for traits highly influenced by family structure was improved by adding half‐sibs to the training population. Connected half‐sib training populations could be useful for new and established breeding programs looking to implement genomic selection due to benefits of family structure on prediction accuracy, genotyping, genetic diversity, and genetic mapping.

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Section: Discussionmentioning

confidence: 99%

A connected half‐sib family training population for genomic prediction in barley

et al. 2020

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“…The assembly and genotyping of this population is described in Neyhart et al (2019), and we describe relevant details below. The assembly and genotyping of this population is described in Neyhart et al (2019), and we describe relevant details below.…”

Section: Training Populationmentioning

confidence: 99%

“…These lines were derived from crosses made between individuals in the TP (described in Neyhart et al, 2019) and were genotyped for the same 6361 GBS markers. These lines were derived from crosses made between individuals in the TP (described in Neyhart et al, 2019) and were genotyped for the same 6361 GBS markers.…”

Section: Training Populationmentioning

confidence: 99%

“…A training population (TP) of 175 two-row barley lines was used for genomewide prediction. The assembly and genotyping of this population is described in Neyhart et al (2019), and we describe relevant details below. The TP was phenotyped in Crookston andSt.…”

Section: Training Populationmentioning

confidence: 99%

“…Simulated biparental populations were used to inform the selection of parent combinations among a pool of 813 candidate lines. These lines were derived from crosses made between individuals in the TP (described in Neyhart et al, 2019) and were genotyped for the same 6361 GBS markers. We used the PopVar package in R to predict the mean, variance, and superior progeny mean (denoted m , ĝ V , and SP m , respectively) for each of C(813, 2) = 330,078 possible nonreciprocal parent combinations.…”

Section: Training Populationmentioning

confidence: 99%

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Validating Genomewide Predictions of Genetic Variance in a Contemporary Breeding Program

Neyhart

Smith

2019

Crop Science

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Predicting the genetic variance among progeny from a cross—prior to making said cross—would be a valuable metric for plant breeders to discriminate among possible parent combinations. The use of genomewide markers and simulated populations is one proposed method for making such predictions. Our objective was to assess the predictive ability of this method for three relevant quantitative traits within a breeding program regularly using genomewide selection. Using a training population of two‐row barley (Hordeum vulgare L.) lines, we predicted the mean (μ), genetic variance (VG), and superior progeny mean (μSP, mean of the best 10% of lines) of 330,078 possible parent combinations for Fusarium head blight (FHB) severity, heading date, and plant height. Twenty‐seven of these combinations were chosen to develop biparental populations, which were subsequently phenotyped for the same traits. We found that the predictive abilities (rMP) for μ and μSP were moderate to high (rMP = 0.46–0.69), whereas those for VG were lower (rMP = 0.01–0.48). Unsurprisingly, predictive ability was likely a function of trait heritability, as rMP estimates for heading date (the most heritable trait) were highest, and rMP estimates for FHB severity (the least heritable trait) were lowest. We observed strong negative bias when predicting VG (on average −83 to −96%), but the relative consistency of this bias across validation families indicates that it may have little impact when selecting crosses. We concluded that accurate predictions of VG and μSP are feasible, but as with any implementation of genomewide selection, reliable phenotypic data are critical.

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Using environmental similarities to design training sets for genomewide selection

2020

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In plant breeding, the goal of genomewide selection is to predict the merit of unobserved individuals, particularly those in the next breeding generation. Predictions of these individuals in unobserved or future environments would be of additional use to a breeder. For many of the complex traits targeted in breeding, this may require management of genotype × environment interactions by, for example, using data from homogeneous groups of environments. Our objectives were to assess the accuracy of genomewide predictions in unobserved environments both within and between breeding generations; we aimed to compare training sets that included data from all possible environments with those that included data from (a) decreasingly similar environments or (b) discrete clusters of similar environments. A 183‐line spring barley (Hordeum vulgare L.) training population and 50‐line offspring test population were phenotyped in 29 location–year environments for grain yield, heading date, and plant height. Environmental similarities were measured using phenotypic data, geographic distance, or environmental covariables. When using training data from more, but decreasingly similar environments, prediction accuracy increased, but marginal gains declined; in some cases, accuracy declined with additional data. Clusters of environments informed by phenotypes (i.e., phenotypic correlations or multiplicative models) typically improved prediction accuracy within a generation, but not between generations (offspring population). Our study suggests that, as an alternative to using data from all available environments, informative subsets may be advantageous for genomewide predictions within a single breeding generation, but not between generations.

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Registration of the S2MET Barley Mapping Population for Multi‐Environment Genomewide Selection

Cited by 12 publications

References 42 publications

A connected half‐sib family training population for genomic prediction in barley

A connected half‐sib family training population for genomic prediction in barley

Validating Genomewide Predictions of Genetic Variance in a Contemporary Breeding Program

Using environmental similarities to design training sets for genomewide selection

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