A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model

Montesinos‐López, Osval A.; Montesinos‐López, Abelardo; Crossa, J.; Kismiantini,; Ramírez-Alcaraz, Juan Manuel; Singh, Ravi P.; Mondal, Suchismita; Juliana, Philomin

doi:10.1038/s41437-018-0109-7

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…Although the mixed-model methodology by the frequentist approach has several desirable characteristics [49], the adoption of Bayesian statistical inference for genetic evaluation in the breeding of crop species has shown to be advantageous. Bayesian models have been used since 1986 [63] and further exploited in recent years [23–25,64,65] due to the great computational advancements and new methodological applications and elucidations.…”

Section: Discussionmentioning

confidence: 99%

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Volpato¹,

Alves²,

Teodoro³

et al. 2019

PLoS ONE

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At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F 2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; ) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of . Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.

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Section: Discussionmentioning

confidence: 99%

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Volpato¹,

Alves²,

Teodoro³

et al. 2019

PLoS ONE

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“…Two types of MT models were implemented. Firstly, by performing single value decomposition (SVD) of the matrix of all phenotypes, as proposed by Montesinos-López et al (2019a), whereby each of the decomposed and uncorrelated vectors from all the traits were predicted as traits themselves using the same genomic models as for ST predictions. The predictions of vectors were then back-transformed to the original trait scales to derive the MT predictions per-trait.…”

Section: Methodsmentioning

confidence: 99%

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture in long-term genetic gains in wheat

Fradgley

Gardner

Bentley

et al. 2022

Preprint

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Crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, a focus on selection for yield has negatively impacted other important traits. To better understand this selection process, and how it might be optimised, we analysed extensive genotypic and phenotypic data from a diverse, 16-founder wheat multi-parent advanced generation inter-cross (MAGIC) population. Compared to single-trait models, multi-trait ensemble models increased prediction accuracy for almost 90% of traits, improving grain yield prediction accuracy by 3-52%. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10-36%. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimised long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits which involved optimisation of antagonistic trait relationships. We found that multi-trait selection indices can be used to optimise undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes and provide insights into mechanisms for continued genetic gain in a limited gene pool and optimisation of multiple traits for crop improvement.

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“…Accuracy of GS for low heritability traits (e.g., grain yield) can also be significantly increased by multivariate models when a correlated highly heritability trait is available [88,92]. MT models can improve indirect selection due to increased genetic correlation estimates [23,73,93]. Montesinos-López et al [77] showed the higher the genetic correlation between traits, the higher the prediction accuracy and benefit of MT over single trait models.…”

Section: Multi-trait Modelsmentioning

confidence: 99%

Optimizing Plant Breeding Programs For Genomic Selection

Merrick¹,

Herr²,

Sandhu³

et al. 2022

Preprint

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Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding .

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A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model

Cited by 11 publications

References 30 publications

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture in long-term genetic gains in wheat

Optimizing Plant Breeding Programs For Genomic Selection

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