Genomic Prediction of Autotetraploids; Influence of Relationship Matrices, Allele Dosage, and Continuous Genotyping Calls in Phenotype Prediction

ABSTRACTEstimation of allele dosage in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Here we propose and compare the use of next generation sequencing read depth as continuous parameterization for autotetraploid genomic prediction of breeding values, using blueberry (Vaccinium corybosumspp.) as a model. Additionally, we investigated the influence of different sources of information to build relationship matrices in phenotype prediction; no relationship, pedigree, and genomic information, considering either diploid or tetraploid parameterizations. A real breeding population composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Analyses were based on extensive pedigree (since 1908) and high-density marker data (86K markers). Our results show that marker-based matrices can yield significantly better prediction than pedigree for most of the traits, based on model fitting and expected genetic gain. Continuous genotypic based models performed as well as the current best models and presented a significantly better goodness-of-fit for all traits analyzed. This approach also reduces the computational time required for marker calling and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. Accuracies are encouraging for application of genomic selection (GS) for blueberry breeding. Conservatively, GS could reduce the time for cultivar release by three years. GS could increase the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection.

Download Full-text

On the accuracy of genomic prediction models considering multi-trait and allele dosage in Urochloa spp. interspecific tetraploid hybrids

Molecular Breeding ◽

10.1007/s11032-019-1002-7 ◽

2019 ◽

Vol 39 (7) ◽

Cited By ~ 4

Author(s):

Filipe Inácio Matias ◽

Filipe Couto Alves ◽

Karem Guimarães Xavier Meireles ◽

Sanzio Carvalho Lima Barrios ◽

Cacilda Borges do Valle ◽

...

Keyword(s):

Genomic Prediction ◽

Prediction Models ◽

Allele Dosage

Download Full-text

Genomic prediction with allele dosage information in highly polyploid species

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03994-w ◽

2021 ◽

Author(s):

Lorena G. Batista ◽

Victor H. Mello ◽

Anete P. Souza ◽

Gabriel R. A. Margarido

Keyword(s):

Genomic Prediction ◽

Polyploid Species ◽

Allele Dosage

Download Full-text

Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana

The Plant Genome ◽

10.3835/plantgenome2017.10.0090 ◽

2018 ◽

Vol 11 (2) ◽

pp. 170090 ◽

Cited By ~ 19

Author(s):

Moses Nyine ◽

Brigitte Uwimana ◽

Nicolas Blavet ◽

Eva Hřibová ◽

Helena Vanrespaille ◽

...

Keyword(s):

Genomic Prediction ◽

Predictive Ability ◽

Genotype By Environment Interaction ◽

Environment Interaction ◽

Genotype By Environment ◽

Dosage Effects ◽

Allele Dosage

Download Full-text

Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean

Evolutionary Bioinformatics ◽

10.1177/1176934319831307 ◽

2019 ◽

Vol 15 ◽

pp. 117693431983130 ◽

Cited By ~ 4

Author(s):

Diego Jarquín ◽

Reka Howard ◽

George Graef ◽

Aaron Lorenz

Keyword(s):

Quality Control ◽

Molecular Marker ◽

Response Surface ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Missing Values ◽

Imputation Method ◽

Phenotypic Data ◽

Marker Data ◽

Allele Dosage

An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and missing marker values and the imputation of missing marker values. In this article, we evaluated how the prediction accuracy is influenced by the combination of 12 MAF values, 27 different percentages of missing marker values, and 2 imputation techniques (IT; naïve and Random Forest (RF)). We constructed a response surface of prediction accuracy values for the two ITs as a function of MAF and percentage of missing marker values using soybean data from the University of Nebraska–Lincoln Soybean Breeding Program. We found that both the genetic architecture of the trait and the IT affect the prediction accuracy implying that we have to be careful how we perform QC on the marker data. For the corresponding combinations MAF-percentage of missing values we observed that implementing the RF imputation increased the number of markers by 2 to 5 times than the simple naïve imputation method that is based on the mean allele dosage of the non-missing values at each loci. We conclude that there is not a unique strategy (combination of the QCs and imputation method) that outperforms the results of the others for all traits.

Download Full-text

Genomic prediction ability for carcass composition indicator traits in Nellore cattle

Livestock Science ◽

10.1016/j.livsci.2021.104421 ◽

2021 ◽

Vol 245 ◽

pp. 104421

Author(s):

Rosiane P. Silva ◽

Rafael Espigolan ◽

Mariana P. Berton ◽

Raysildo B. Lôbo ◽

Cláudio U. Magnabosco ◽

...

Keyword(s):

Genomic Prediction ◽

Carcass Composition ◽

Prediction Ability ◽

Nellore Cattle ◽

Indicator Traits

Download Full-text

The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

Scientific Reports ◽

10.1038/s41598-021-83567-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fatemeh Amini ◽

Felipe Restrepo Franco ◽

Guiping Hu ◽

Lizhi Wang

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Genomic Prediction ◽

In Planta ◽

Data Set ◽

Look Ahead ◽

Trace Back ◽

New Variant ◽

The Look ◽

Partially Observable

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

Download Full-text

Genomic prediction using training population design in interspecific soybean populations

Molecular Breeding ◽

10.1007/s11032-021-01203-6 ◽

2021 ◽

Vol 41 (2) ◽

Author(s):

Eduardo Beche ◽

Jason D. Gillman ◽

Qijian Song ◽

Randall Nelson ◽

Tim Beissinger ◽

...

Keyword(s):

Genomic Prediction ◽

Training Population ◽

Population Design

Download Full-text

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

Genetics Selection Evolution ◽

10.1186/s12711-021-00607-4 ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Theo Meuwissen ◽

Irene van den Berg ◽

Mike Goddard

Keyword(s):

Variable Selection ◽

Genome Sequence ◽

Genomic Prediction ◽

Milk Fat ◽

Genotype Imputation ◽

Whole Genome Sequence ◽

Genomic Relationship Matrix ◽

Polygenic Effect ◽

Relationship Matrix ◽

Whole Genome

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

Download Full-text

Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽

10.1093/genetics/iyab030 ◽

2021 ◽

Author(s):

Marco Lopez-Cruz ◽

Gustavo de los Campos

Keyword(s):

Sample Size ◽

Dna Sequences ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Regularization Parameter ◽

Selection Index ◽

Prediction Method ◽

Training Data ◽

Breeding Value ◽

Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

Download Full-text