scholarly journals Inter- and Intra-Generation Genomic Predictions for Douglas-fir Growth in Unobserved Environments

2019 ◽  
Author(s):  
Blaise Ratcliffe ◽  
Francis Thistlethwaite ◽  
Omnia Gamal El-Dien ◽  
Eduardo P. Cappa ◽  
Ilga Porth ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Environmental Heterogeneity ◽  
Prediction Models ◽  
Reaction Norm ◽  
Douglas Fir ◽  
Single Step ◽  
Exome Capture ◽  
Experimental Population ◽  
Training Population ◽  
The Impact

ABSTRACTConifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previous studies have shown much reduced prediction accuracies (PA) of breeding values in unobserved environments, which may impede its adoption. The impact of explicit environmental heterogeneity modeling including genotype-by-environment (G×E) interaction effects using environmental covariates (EC) in a reaction-norm genomic prediction model was tested using single-step GBLUP (ssGBLUP). A three-generation coastal Douglas-fir experimental population with 14 genetic trials (n = 13,615) permitted estimation of intra- and inter-generation PA in unobserved environments using 66,969 SNPs derived from exome capture. Intra- and inter-generation PAs ranged from 0.447-0.640 and 0.317-0.538, respectively. The inclusion of ECs in the prediction models explained up to 23% of the phenotypic variation for the fully specified model and resulted in the best model fit. Modeling G×E effects in the training population increased PA up to 6% and 13% over the base model for inter- and intra-generations, respectively. GS-PA can be substantially improved using ECs to explain environmental heterogeneity and G×E effects. The ssGBLUP methodology allows historical genetic trials containing non-genotyped samples to contribute in genomic prediction, and, thus, effectively boosting training population size which is a critical step. Further pheno- and enviro-typing developments may improve GS-PA.

scholarly journals GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2050
Author(s):  
Beatriz Castro Dias Cuyabano ◽  
Gabriel Rovere ◽  
Dajeong Lim ◽  
Tae Hun Kim ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Phenotypic Expression ◽  
Genetic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Korean Cattle ◽  
Evaluation Programs ◽  
The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

scholarly journals Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Frances R. Thistlethwaite ◽  
Blaise Ratcliffe ◽  
Jaroslav Klápště ◽  
Ilga Porth ◽  
Charles Chen ◽  
...  
Keyword(s):  
Wood Density ◽  
Genomic Prediction ◽  
Douglas Fir ◽  
Exome Capture ◽  
Space And Time ◽  
Genotyping Platform

scholarly journals Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1591
Author(s):  
Sebastian Michel ◽  
Franziska Löschenberger ◽  
Ellen Sparry ◽  
Christian Ametz ◽  
Hermann Bürstmayr
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Wheat Breeding ◽  
Single Step ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Record Keeping ◽  
Line Breeding ◽  
Cost Efficient

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain.

scholarly journals Genomic prediction accuracies and abilities for growth and wood quality traits of Scots pine, using genotyping-by-sequencing (GBS) data

2019 ◽  
Author(s):  
Ainhoa Calleja-Rodriguez ◽  
Jin Pan ◽  
Tomas Funda ◽  
Zhi-Qiang Chen ◽  
John Baison ◽  
...  
Keyword(s):  
Scots Pine ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Wood Quality ◽  
Genotyping By Sequencing ◽  
Tree Breeding ◽  
Snp Markers ◽  
Progeny Testing ◽  
Quality Traits ◽  
The Impact

ABSTRACTHigher genetic gains can be achieved through genomic selection (GS) by shortening time of progeny testing in tree breeding programs. Genotyping-by-sequencing (GBS), combined with two imputation methods, allowed us to perform the current genomic prediction study in Scots pine (Pinus sylvestrisL.). 694 individuals representing 183 full-sib families were genotyped and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic prediction models. In addition, the impact on the predictive ability (PA) and prediction accuracy to estimate genomic breeding values was evaluated by assigning different ratios of training and validation sets, as well as different subsets of SNP markers. Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed higher PAs and prediction accuracies than Bayesian LASSO (BL). A subset of approximately 4000 markers was sufficient to provide the same PAs and accuracies as the full set of 8719 markers. Furthermore, PAs were similar for both pedigree- and genomic-based estimations, whereas accuracies and heritabilities were slightly higher for pedigree-based estimations. However, prediction accuracies of genomic models were sufficient to achieve a higher selection efficiency per year, varying between 50-87% compared to the traditional pedigree-based selection.

Single‐Step Reaction Norm Models for Genomic Prediction in Multienvironment Recurrent Selection Trials

Crop Science ◽  
2018 ◽  
Vol 58 (2) ◽  
pp. 592-607 ◽  
Author(s):  
Odilon P. Morais Júnior ◽  
João Batista Duarte ◽  
Flávio Breseghello ◽  
Alexandre S. G. Coelho ◽  
Orlando P. Morais ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Recurrent Selection ◽  
Reaction Norm ◽  
Single Step ◽  
Single Step Reaction

scholarly journals Optimizing whole-genomic prediction for autotetraploid blueberry breeding

Heredity ◽  
2020 ◽  
Vol 125 (6) ◽  
pp. 437-448 ◽  
Author(s):  
Ivone de Bem Oliveira ◽  
Rodrigo Rampazo Amadeu ◽  
Luis Felipe Ventorim Ferrão ◽  
Patricio R. Muñoz
Keyword(s):  
Genomic Prediction ◽  
Total Yield ◽  
Substantial Reduction ◽  
Breeding Population ◽  
Fruit Weight ◽  
Sequencing Depth ◽  
Breeding Cycle ◽  
Marker Density ◽  
Training Population ◽  
The Impact

Abstract Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions toward resource allocation (e.g., genotyping and phenotyping) in order to maximize prediction accuracy. These findings have the potential to allow for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry. We anticipate that the benefits and pipeline described in our study can be applied to optimize genomic prediction for other diploid and polyploid species.

scholarly journals Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome

Heredity ◽  
2019 ◽  
Vol 124 (2) ◽  
pp. 274-287 ◽  
Author(s):  
Emre Karaman ◽  
Mogens S. Lund ◽  
Guosheng Su
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Single Step ◽  
Bayesian Regression ◽  
Practical Implementation ◽  
Whole Genome ◽  
Single Nucleotide ◽  
Variance Estimates ◽  
Region Size ◽  
Variance Structure

Abstract Widely used genomic prediction models may not properly account for heterogeneous (co)variance structure across the genome. Models such as BayesA and BayesB assume locus-specific variance, which are highly influenced by the prior for (co)variance of single nucleotide polymorphism (SNP) effect, regardless of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects. In this study, we propose a multi-trait Bayesian whole genome regression method (BayesN0), which is based on grouping a number of predefined SNPs to account for heterogeneous (co)variance structure across the genome. This model was also implemented in single-step Bayesian regression (ssBayesN0). For practical implementation, we considered multi-trait single-step SNPBLUP models, using (co)variance estimates from BayesN0 or ssBayesN0. Genotype data were simulated using haplotypes on first five chromosomes of 2200 Danish Holstein cattle, and phenotypes were simulated for two traits with heritabilities 0.1 or 0.4, assuming 200 quantitative trait loci (QTL). We compared prediction accuracy from different prediction models and different region sizes (one SNP, 100 SNPs, one chromosome or whole genome). In general, highest accuracies were obtained when 100 adjacent SNPs were grouped together. The ssBayesN0 improved accuracies over BayesN0, and using (co)variance estimates from ssBayesN0 generally yielded higher accuracies than using (co)variance estimates from BayesN0, for the 100 SNPs region size. Our results suggest that it could be a good strategy to estimate (co)variance components from ssBayesN0, and then to use those estimates in genomic prediction using multi-trait single-step SNPBLUP, in routine genomic evaluations.

scholarly journals An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments

2021 ◽  
Vol 12 ◽  
Author(s):  
Sarah Widener ◽  
George Graef ◽  
Alexander E. Lipka ◽  
Diego Jarquin
Keyword(s):  
Grain Yield ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Extreme Environments ◽  
Extreme Environment ◽  
Future Research ◽  
Nested Association Mapping ◽  
Estimated Breeding Values ◽  
The Impact ◽  
Gp Model

The effects of climate change create formidable challenges for breeders striving to produce sufficient food quantities in rapidly changing environments. It is therefore critical to investigate the ability of multi-environment genomic prediction (GP) models to predict genomic estimated breeding values (GEBVs) in extreme environments. Exploration of the impact of training set composition on the accuracy of such GEBVs is also essential. Accordingly, we examined the influence of the number of training environments and the use of environmental covariates (ECs) in GS models on four subsets of n = 500 lines of the soybean nested association mapping (SoyNAM) panel grown in nine environments in the US-North Central Region. The ensuing analyses provided insights into the influence of both of these factors for predicting grain yield in the most and the least extreme of these environments. We found that only a subset of the available environments was needed to obtain the highest observed prediction accuracies. The inclusion of ECs in the GP model did not substantially increase prediction accuracies relative to competing models, and instead more often resulted in negative prediction accuracies. Combined with the overall low prediction accuracies for grain yield in the most extreme environment, our findings highlight weaknesses in current GP approaches for prediction in extreme environments, and point to specific areas on which to focus future research efforts.

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