Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections

Rolling, William; Dorrance, Anne E.; McHale, Leah K.

doi:10.1007/s00122-020-03679-w

Cited by 12 publications

(8 citation statements)

References 82 publications

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“…The accuracy of GS models varies with the genetic architecture of a trait due to their assumptions and treatment of marker effects. In our study, there was a lack of significant differences between the common parametric Bayesian models, rrBLUP and gBLUP, which support results presented previously in predicting complex traits and are statistically equivalent (Daetwyler et al., 2010; Endelman, 2011; Heffner, Jannink, & Sorrells, 2011; Heslot et al., 2012; Lorenz et al., 2012; Rolling et al., 2020). In early simulation studies for GS, BayesB had higher accuracy than gBLUP, but in more empirical studies, the results are very similar (Daetwyler et al., 2010; Habier et al., 2007; Meuwissen et al., 2001).…”

Section: Discussionsupporting

confidence: 90%

Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

Merrick

Carter

2021

The Plant Genome

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Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genetic basis. One such trait, seedling emergence of wheat (Triticum aestivum L.) from deep planting, presents a unique opportunity to explore the best method to use and implement genetic selection (GS) models to predict a complex trait. Seventeen GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel phenotyped from 2015 to 2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA, with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross‐validations. However, the consistent moderate accuracy of the parametric models indicates little advantage of using nonparametric models within individual years, but the nonparametric models show a slight increase in accuracy when combing years for complex traits. There was an increase in accuracy using cross‐validations from 0.40 to 0.41 using diversity panels lines to breeding lines. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using nonparametric machine learning models within their own breeding programs with increased accuracy as they combine training populations over the years.

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Section: Discussionsupporting

confidence: 90%

Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

Merrick

Carter

2021

The Plant Genome

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“…The accuracy of GS models varies with the genetic architecture of a trait due to their assumptions and treatment of marker effects. In our study there was a lack of significant differences between the common parametric Bayesian models, rrBLUP and gBLUP which support results presented previously in predicting complex traits (Daetwyler et al 2010;Heffner et al 2011b;Heslot et al 2012;Lorenz et al 2012;Rolling et al 2020). In early simulation studies for GS, BayesB had higher accuracy than gBLUP, but in more empirical studies the results are very similar (Meuwissen et al 2001;Habier et al 2007;Daetwyler et al 2010).…”

Section: Genomic Selection Modelssupporting

confidence: 89%

Comparison of Genomic Selection Models for Exploring Predictive Ability of Complex Traits in Breeding Programs

Merrick

Carter

2021

Preprint

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Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genetic basis. One such trait, seedling emergence of wheat (Triticum aestivum L.) from deep planting, presents a unique opportunity to explore the best method to use and implement GS models to predict a complex trait. 17 GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel (DP) phenotyped from 2015-2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross-validations. However, the consistent moderate accuracy of cBLUP and other parametric models indicates no need to implement computationally demanding non-parametric models for complex traits. There was an increase in accuracy using cross-validations from 0.40 to 0.41 and independent validations from 0.10 to 0.17 using diversity panels lines to breeding lines. The environmental effects of complex traits can be overcome by combining years of the same populations. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using parametric models within their own breeding programs with increased accuracy as they combine training populations over the years.

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“…Thus, it has a great potential for improving the genetic gain associated with yield and other complex traits within a limited time frame in different crop plants ( Crossa et al, 2017 ; Voss-Fels et al, 2019 ; Lebedev et al, 2020 ). Moreover, GP has been used in soybean for improving different traits such as cyst nematode infestation ( Ravelombola et al, 2020 ), disease resistance ( Rolling et al, 2020 ), agronomic traits ( Beche et al, 2021 ), and seed yield ( Mendonça et al, 2020 ). The results of these studies have demonstrated the potential of GP for improving complex traits in soybean.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.)

et al. 2022

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Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.

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Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections

Cited by 12 publications

References 82 publications

Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

Comparison of Genomic Selection Models for Exploring Predictive Ability of Complex Traits in Breeding Programs

Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.)

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