Comparison of genome-wide and phenotypic selection indices in maize

Môro, Gustavo Vitti; Santos, M. F.; Júnior, Cláudio Lopes de Souza

doi:10.1007/s10681-019-2401-x

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…Analogously, under optimal conditions, the gain increased from 0.34 (PS) to 0.55 (GS) per cycle, which translates to 0.084 (PS) and 0.140 (GS) per year. Also for maize, Môro et al [ 11 ] reported a similar selection gain when using GS or PS. For soybean [ Glycine max (L.) Merr.…”

Section: Introductionmentioning

confidence: 81%

A review of deep learning applications for genomic selection

et al. 2021

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Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

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Section: Introductionmentioning

confidence: 81%

A review of deep learning applications for genomic selection

et al. 2021

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“…For wheat yield, Lozada et al [49] reported 32% lower actual response to selection for wheat yield from GS relative to PS, whereas Michel et al [50] found greater prediction accuracy for independent environments of GS relative to PS. For maize yield, Beyene et al [51] reported the greater efficiency of GS over pedigree-based conventional PS when comparing actual yield gains from GS with ordinary gains reported for PS; Beyene et al [52] found similar actual yield gains for GS and PS in a second study; and Môro et al [53] observed 12% greater response to selection for GS relative to PS. Additionally, Sallam and Smith [54] reported similar actual yield gains of GS and PS for barley.…”

Section: Discussionmentioning

confidence: 99%

Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought

Annicchiarico¹,

N.²,

Laouar

et al. 2020

IJMS

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Terminal drought is the main stress limiting pea (Pisum sativum L.) grain yield in Mediterranean environments. This study aimed to investigate genotype × environment (GE) interaction patterns, define a genomic selection (GS) model for yield under severe drought based on single nucleotide polymorphism (SNP) markers from genotyping-by-sequencing, and compare GS with phenotypic selection (PS) and marker-assisted selection (MAS). Some 288 lines belonging to three connected RIL populations were evaluated in a managed-stress (MS) environment of Northern Italy, Marchouch (Morocco), and Alger (Algeria). Intra-environment, cross-environment, and cross-population predictive ability were assessed by Ridge Regression best linear unbiased prediction (rrBLUP) and Bayesian Lasso models. GE interaction was particularly large across moderate-stress and severe-stress environments. In proof-of-concept experiments performed in a MS environment, GS models constructed from MS environment and Marchouch data applied to independent material separated top-performing lines from mid- and bottom-performing ones, and produced actual yield gains similar to PS. The latter result would imply somewhat greater GS efficiency when considering same selection costs, in partial agreement with predicted efficiency results. GS, which exploited drought escape and intrinsic drought tolerance, exhibited 18% greater selection efficiency than MAS (albeit with non-significant difference between selections) and moderate to high cross-population predictive ability. GS can be cost-efficient to raise yields under severe drought.

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“…However, the traits of protein content, oil content, oleic acid, and linoleic acid chosen through the GS selection process (by BayesB and GBLUP) outperformed (P < .05) those chosen by PS (Smallwood et al, 2019). In the case of maize, Môro et al (2019) reported that no significant differences were observed between the two, with PS being based on selection indices, rather than on each trait separately, in 256 plants of an F 2 population (S 1 lines) genotyped with 177 microsatellite molecular markers. In soybeans, Smallwood et al (2019) found that for fatty acid traits, the genomic prediction method (with GBLUP and BayesB) outperformed PS, whereas for traits such as protein and oil, no significant differences were found between them.…”

Section: Introductionmentioning

confidence: 96%

Application of a Poisson deep neural network model for the prediction of count data in genome‐based prediction

et al. 2021

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Genomic selection (GS) is revolutionizing conventional ways of developing new plants and animals. However, because it is a predictive methodology, GS strongly depends on statistical and machine learning to perform these predictions. For continuous outcomes, more models are available for GS. Unfortunately, for count data outcomes, there are few efficient statistical machine learning models for large datasets or for datasets with fewer observations than independent variables. For this reason, in this paper, we applied the univariate version of the Poisson deep neural network (PDNN) proposed earlier for genomic predictions of count data. The model was implemented with (a) the negative log-likelihood of Poisson distribution as the loss function, (b) the rectified linear activation unit as the activation function in hidden layers, and (c) the exponential activation function in the output layer. The advantage of the PDNN model is that it captures complex patterns in the data by implementing many nonlinear transformations in the hidden layers. Moreover, since it was implemented in Tensorflow as the back-end, and in Keras as the front-end, the model can

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Comparison of genome-wide and phenotypic selection indices in maize

Cited by 7 publications

References 37 publications

A review of deep learning applications for genomic selection

A review of deep learning applications for genomic selection

Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought

Application of a Poisson deep neural network model for the prediction of count data in genome‐based prediction

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