scholarly journals Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

Genetics ◽  
2012 ◽  
Vol 192 (4) ◽  
pp. 1513-1522 ◽  
Author(s):  
Yi Jia ◽  
Jean-Luc Jannink
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Selection Methods ◽  
Multiple Trait ◽  
Value Prediction ◽  
Genetic Value

A comparison of genomic selection methods for breeding value prediction

2015 ◽  
Vol 60 (10) ◽  
pp. 925-935 ◽  
Author(s):  
Xin Wang ◽  
Zefeng Yang ◽  
Chenwu Xu
Keyword(s):  
Genomic Selection ◽  
Breeding Value ◽  
Selection Methods ◽  
Value Prediction

scholarly journals Genomic selection for lentil breeding: empirical evidence

2019 ◽  
Author(s):  
Teketel A. Haile ◽  
Taryn Heidecker ◽  
Derek Wright ◽  
Sandesh Neupane ◽  
Larissa Ramsay ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Statistical Models ◽  
Prediction Accuracy ◽  
Recombinant Inbred Lines ◽  
Genotype By Environment Interaction ◽  
Exome Capture ◽  
Environment Interaction ◽  
Multiple Trait ◽  
Similar Accuracy ◽  
Population Prediction

AbstractGenomic selection (GS) is a type of marker-based selection which was initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models and approaches have been developed to implement GS; however, none of these methods have been tested for use in lentil breeding. This study was conducted to evaluate different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait models, two multiple-trait models, and models that account for population structure and genotype-by-environment interaction (GEI) using a lentil diversity panel and two recombinant inbred lines (RIL) populations that were genotyped using a custom exome capture assay. Within-population, across-population and across-environment predictions were made for five phenology traits. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, traits, and statistical models. Single-trait models showed similar accuracy for each trait in the absence of large effect QTL but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and multiple-trait (MT) models increased prediction accuracy for a low heritability trait by up to 66% and 14% but accuracy did not improve for traits of high heritability. Moderate to high accuracies were obtained for within-population and across-environment predictions but across-population prediction accuracy was very low. This suggests that GS can be implemented in lentil to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.

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 Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

2020 ◽  
Author(s):  
Matthew McGowan ◽  
Zhiwu Zhang ◽  
Jiabo Wang ◽  
Haixiao Dong ◽  
Xiaolei Liu ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Markers ◽  
Random Effects ◽  
Genome Wide Association Study ◽  
Single Step ◽  
Hybrid Breeding ◽  
Multiple Trait ◽  
Underlying Distribution ◽  
Genome Wide ◽  
Before And After

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

scholarly journals Evaluation of the efficiency of artificial neural networks for genetic value prediction

2016 ◽  
Vol 15 (1) ◽  
Author(s):  
G.N. Silva ◽  
R.S. Tomaz ◽  
I.C. Sant’Anna ◽  
V.Q. Carneiro ◽  
C.D. Cruz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Value Prediction ◽  
Artificial Neural ◽  
Genetic Value

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
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