An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.

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The statistical analysis of quality traits in plant improvement programs with application to the mapping of milling yield in wheat

Australian Journal of Agricultural Research ◽

10.1071/ar01058 ◽

2001 ◽

Vol 52 (12) ◽

pp. 1207 ◽

Cited By ~ 31

Author(s):

A. B. Smith ◽

B. R. Cullis ◽

R. Appels ◽

A. W. Campbell ◽

G. B. Cornish ◽

...

Keyword(s):

Molecular Genetic ◽

Triticum Aestivum L ◽

Quality Trait ◽

Quality Traits ◽

Response To Selection ◽

Improvement Programs ◽

Selection For ◽

Milling Yield ◽

Marker Information ◽

Phase Process

It is well known that the response to selection for grain yield is improved with the use of appropriate experimental designs and statistical analyses. The issues are more complex for quality traits since the data are obtained from a 2-phase process in which samples are collected from the field then processed in the laboratory. This paper presents a method of analysis for quality trait data that allows for variation arising from both the field and laboratory phases. Initially, an analysis suitable for standard varietal selection is presented. This is extended to include molecular genetic marker information for the purpose of detecting quantitative trait loci. The technique is illustrated using two doubled haploid wheat (Triticum aestivum L.) populations in which the trait of interest is milling yield.

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Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana)

BMC Genomics ◽

10.1186/s12864-017-3715-5 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 43

Author(s):

Patrick R.N. Lenz ◽

Jean Beaulieu ◽

Shawn D. Mansfield ◽

Sébastien Clément ◽

Mireille Desponts ◽

...

Keyword(s):

Genomic Selection ◽

Picea Mariana ◽

Black Spruce ◽

Wood Quality ◽

Breeding Population ◽

Quality Traits ◽

Factors Affecting ◽

Selection For

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

Genes ◽

10.3390/genes12020210 ◽

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.

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

Crop Science ◽

10.2135/cropsci2016.06.0496 ◽

2017 ◽

Vol 57 (3) ◽

pp. 1325-1337 ◽

Cited By ~ 22

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

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Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits

Evolutionary Applications ◽

10.1111/eva.13076 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2704-2722 ◽

Cited By ~ 1

Author(s):

Jean Beaulieu ◽

Simon Nadeau ◽

Chen Ding ◽

Jose M. Celedon ◽

Aïda Azaiez ◽

...

Keyword(s):

Genomic Selection ◽

Wood Quality ◽

Spruce Budworm ◽

White Spruce ◽

Quality Traits ◽

Selection For

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Genomic Selection for Fruit Quality Traits in Apple (Malus×domestica Borkh.)

PLoS ONE ◽

10.1371/journal.pone.0036674 ◽

2012 ◽

Vol 7 (5) ◽

pp. e36674 ◽

Cited By ~ 121

Author(s):

Satish Kumar ◽

David Chagné ◽

Marco C. A. M. Bink ◽

Richard K. Volz ◽

Claire Whitworth ◽

...

Keyword(s):

Genomic Selection ◽

Fruit Quality ◽

Malus Domestica ◽

Quality Traits ◽

Malus Domestica Borkh ◽

Fruit Quality Traits ◽

Selection For

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Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.)

Plants ◽

10.3390/plants9060719 ◽

2020 ◽

Vol 9 (6) ◽

pp. 719

Author(s):

Mulusew Fikere ◽

Denise M. Barbulescu ◽

M. Michelle Malmberg ◽

Pankaj Maharjan ◽

Phillip A. Salisbury ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Seed Quality ◽

Agronomic Traits ◽

Genetic Correlations ◽

Quality Traits ◽

Blackleg Disease ◽

Genetic Progress ◽

Seed Quality Traits

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.

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Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits

PLoS ONE ◽

10.1371/journal.pone.0169234 ◽

2017 ◽

Vol 12 (1) ◽

pp. e0169234 ◽

Cited By ~ 44

Author(s):

Elisa Biazzi ◽

Nelson Nazzicari ◽

Luciano Pecetti ◽

E. Charles Brummer ◽

Alberto Palmonari ◽

...

Keyword(s):

Association Mapping ◽

Medicago Sativa ◽

Genomic Selection ◽

Forage Quality ◽

Genome Wide Association ◽

Quality Traits ◽

Genome Wide ◽

Selection For

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Bayesian genomic models boost prediction accuracy for survival to Streptococcus agalactiae infection in Nile tilapia (Oreochromus nilioticus)

Genetics Selection Evolution ◽

10.1186/s12711-021-00629-y ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Rajesh Joshi ◽

Anders Skaarud ◽

Alejandro Tola Alvarez ◽

Thomas Moen ◽

Jørgen Ødegård

Keyword(s):

Genomic Selection ◽

Streptococcus Agalactiae ◽

Nile Tilapia ◽

Prediction Accuracy ◽

Predictive Accuracy ◽

Challenge Test ◽

Bacterial Disease ◽

Nucleotide Polymorphisms ◽

Genomic Selection Model ◽

Selection For

Abstract Background Streptococcosis is a major bacterial disease in Nile tilapia that is caused by Streptococcus agalactiae infection, and development of resistant strains of Nile tilapia represents a sustainable approach towards combating this disease. In this study, we performed a controlled disease trial on 120 full-sib families to (i) quantify and characterize the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia, and (ii) identify the best genomic model and the optimal density of single nucleotide polymorphisms (SNPs) for this trait. Methods In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were used in the challenge test. Mortalities were recorded every 3 h for 35 days. After quality control, genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. Genetic parameters were obtained using various genomic selection models (genomic best linear unbiased prediction (GBLUP), BayesB, BayesC, BayesR and BayesS) and a traditional pedigree-based model (PBLUP). The pedigree-based analysis used a deep 17-generation pedigree. Prediction accuracy and bias were evaluated using five replicates of tenfold cross-validation. The genomic models were further analyzed using 10 subsets of SNPs at different densities to explore the effect of pruning and SNP density on predictive accuracy. Results Moderate estimates of heritabilities ranging from 0.15 ± 0.03 to 0.26 ± 0.05 were obtained with the different models. Compared to a pedigree-based model, GBLUP (using all the SNPs) increased prediction accuracy by 15.4%. Furthermore, use of the most appropriate Bayesian genomic selection model and SNP density increased the prediction accuracy up to 71%. The 40 to 50 SNPs with non-zero effects were consistent for all BayesB, BayesC and BayesS models with respect to marker id and/or marker locations. Conclusions These results demonstrate the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia. Compared to the PBLUP and GBLUP models, Bayesian genomic models were found to boost the prediction accuracy significantly.

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