Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance

González-Camacho, Juan Manuel; Ornella, Leonardo; Pérez‐Rodríguez, Paulino; Gianola, Daniel; Dreisigacker, Susanne; Crossa, José

doi:10.3835/plantgenome2017.11.0104

Cited by 124 publications

(123 citation statements)

References 79 publications

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“…In this study, we compared three linear models characterized by two different assumptions with respect to the distribution of variance for marker effects. In GBLUP and KGD-GBLUP all marker effects are shrunk equally, assuming the predicted trait is controlled by many markers with small effect (Goddard et al 2011), whereas BayesCp assumes that the trait is a mixture of distributions with large and small effect markers (Habier et al 2011). Even with different prior assumptions, Figure 1 illustrates the similarity in predictive ability among the three methods for all nutritive traits, with only minor differences (Figure 1).…”

Section: Discussionmentioning

confidence: 99%

“…Three whole-genome regression methods, with two different prior assumptions regarding the distribution of marker effects, were used for generating GEBVs. The first method was a univariate linear mixed model, called GBLUP (Goddard et al 2011) in which markers effects were assumed to have equal variance. The linear model can be expressed follows:…”

Section: Genomic Prediction Modelingmentioning

confidence: 99%

“…While the heritability of a trait and the extent of LD in a training population cannot be easily optimized, the density of markers and the size and composition of the training population are two factors that can be controlled. Several methods have been developed for genomic prediction and can be broadly classified as whole-genome regression methods (discussed by De Los Campos et al (2013)) or machine learning methods (outlined by González-Camacho et al (2018)). Based on simulation and empirical results, Daetwyler et al (2013) concluded that genomic best linear unbiased predictor (GBLUP) and Bayesian variable selection methods (BayesB and BayesCp) were the benchmark for genomic prediction, as these methods are appropriate for a range of genetic architectures, from traits which are controlled by many genes with small effects (infinitesimal model) to traits with large SNP effects (variable selection model).…”

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confidence: 99%

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Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

Arojju

Cao²,

Jahufer³

et al. 2020

G3 Genes|Genomes|Genetics

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Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P , 0.05) family variance was detected for all nutritive traits and genomic heritability (h 2 g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCp genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

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Section: Discussionmentioning

confidence: 99%

Section: Genomic Prediction Modelingmentioning

confidence: 99%

mentioning

confidence: 99%

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Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

Arojju

Cao²,

Jahufer³

et al. 2020

G3 Genes|Genomes|Genetics

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“…ML had also been used in GS, and it might has the best performance in the interpretation of large-scale genomic data [6]. González et al suggested that ML was a valuable alternative to wellknown parametric methods for genomic selection [9]. Montesinos-López et al found that the predictions of the multi-trait deep learning model were very competitive with the Bayesian multitrait and multi-environment model [10].…”

Section: Introductionmentioning

confidence: 99%

A stacking ensemble learning framework for genomic prediction

Liang

Chang

et al. 2020

Preprint

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Background: Machine learning (ML) is perhaps the most useful for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) was unsatisfactory in existing research. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF) integrated three machine learning methods to predict genomic estimated breeding values (GEBVs). Results: We evaluated the prediction ability of SELF by three real datasets and compared the prediction accuracy of SELF, base learners, GBLUP and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF had an average 7.70% improvement compared with GBLUP in three datasets. Except for the milk fat percentage (MFP) traits of the German Holstein dairy cattle dataset, SELF more robust than BayesB in the remaining traits.Conclusions: In this study, we utilized a stacking ensemble learning framework (SELF) to genomic prediction and it performed much better than GBLUP and BayesB in three real datasets with different genetic architecture. Therefore, we believed SEFL had the potential to be promoted to estimate GEBVs in other animals and plants.

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“…Due to sugarcane's genomic complexity, simplified predictive models involving linear regression cannot capture the unknown nonlinear characteristics present in these datasets 31 , as described for other polyploid species [36][37][38] . To address this issue, machine learning (ML) methodologies represent a promising approach with high accuracy 31,[39][40][41] . Although GS was developed to address the problem of categorizing individuals using different populations, its application in biparental populations is suitable and might be highly efficient due to the significant amount of linkage disequilibrium between loci 42 , which would facilitate the initial cycles of breeding programs.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Aono

Costa

Rody

et al. 2020

Preprint

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Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane. 3/154/15 10/15

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Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance

Cited by 124 publications

References 79 publications

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

A stacking ensemble learning framework for genomic prediction

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

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