Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection

2022 ◽  
Author(s):  
Pauline Robert ◽  
Jérôme Auzanneau ◽  
Ellen Goudemand ◽  
François-Xavier Oury ◽  
Bernard Rolland ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Wheat Breeding ◽  
Factors Influencing

scholarly journals Breeding Program Optimization for Genomic Selection in Winter Wheat

2020 ◽  
Author(s):  
Megan Calvert ◽  
Byron Evers ◽  
Xu Wang ◽  
Allan Fritz ◽  
Jesse Poland
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Economic Benefit ◽  
Prediction Accuracy ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Program Optimization ◽  
Crop Species ◽  
Wheat Breeding Program ◽  
Basic Set

AbstractDeveloping methodologies in the fields of phenomics and genomic prediction have the potential to increase the production of crop species by accelerating germplasm improvement. The integration of these technologies into germplasm improvement and breeding programs requires evidence that there will be a direct economic benefit to the program. We determined a basic set of parameters, such as prediction accuracy greater than 0.3, the ability to genotype over 7 lines for the cost of one phenotypic evaluation, and heritability levels below 0.4, at which the use of genomic selection would be of economic benefit in terms of genetic gain and operational costs to the Kansas State University (KSU) winter wheat breeding program. The breeding program was then examined to determine whether the parameters benefitting genomic selection were observed or achievable in a practical sense. Our results show that the KSU winter wheat breeding program is at a decision point with regards to their primary means of selection. A few operational changes to increase prediction accuracy would place the program in the parameter space where genomic selection is expected to outpace the current phenotypic selection methodology at a parity of the operation cost and would be of greatest benefit to the program.

scholarly journals Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker × Environment Interaction Genomic Selection Model

2015 ◽  
Vol 5 (4) ◽  
pp. 569-582 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Jose Crossa ◽  
David Bonnett ◽  
Susanne Dreisigacker ◽  
Jesse Poland ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Wheat Breeding ◽  
Selection Model ◽  
Environment Interaction ◽  
Genomic Selection Model

scholarly journals Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 210
Author(s):  
Sang V. Vu ◽  
Cedric Gondro ◽  
Ngoc T. H. Nguyen ◽  
Arthur R. Gilmour ◽  
Rick Tearle ◽  
...  
Keyword(s):  
Moisture Content ◽  
Genomic Selection ◽  
Shell Length ◽  
Prediction Accuracy ◽  
Genomic Information ◽  
Morphometric Traits ◽  
Shell Weight ◽  
Shell Width ◽  
Crassostrea Angulata ◽  
Selection For

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

scholarly journals Increased genomic prediction accuracy in wheat breeding using a large Australian panel

2017 ◽  
Vol 130 (12) ◽  
pp. 2543-2555 ◽  
Author(s):  
Adam Norman ◽  
Julian Taylor ◽  
Emi Tanaka ◽  
Paul Telfer ◽  
James Edwards ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Wheat Breeding

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3372
Author(s):  
Cesar A. Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Medicago Sativa ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Breeding Value ◽  
Phenotypic Traits ◽  
Genome Wide ◽  
Medicago Sativa L

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy

Crop Science ◽  
2017 ◽  
Vol 57 (3) ◽  
pp. 1325-1337 ◽  
Author(s):  
Alexandra Duhnen ◽  
Amandine Gras ◽  
Simon Teyssèdre ◽  
Michel Romestant ◽  
Bruno Claustres ◽  
...  
Keyword(s):  
Protein Content ◽  
Genomic Selection ◽  
Prediction Accuracy ◽  
Seed Protein ◽  
Breeding Program ◽  
Seed Protein Content ◽  
Selection For ◽  
Program Data

Tools for transforming wheat breeding: genomic selection, rapid generation advance and database-based decision support.

2021 ◽  
pp. 213-224
Author(s):  
Umesh R. Rosyara ◽  
Kate Dreher ◽  
Bhoja R. Basnet ◽  
Susanne Dreisigacker
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Genomic Selection ◽  
Rapid Evolution ◽  
Wheat Breeding ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Breeding Methodology ◽  
Rapid Generation Advance ◽  
Rapid Generation

Abstract This chapter discusses the increased implications in the current breeding methodology of wheat, such as rapid evolution of new sequencing and genotyping technologies, automation of phenotyping, sequencing and genotyping methods and increased use of prediction and machine learning methods. Some of the strategies that will further transform wheat breeding in the next few years are also presented.

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