Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
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TLDR
It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.Abstract:
Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.read more
Citations
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Journal ArticleDOI
Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.)
Marcio Pais de Arruda,Alexander E. Lipka,Patrick J. Brown,A. M. Krill,Carrie Thurber,Gina Brown-Guedira,Yanhong Dong,Bradley J. Foresman,Frederic L. Kolb +8 more
TL;DR: This study compares genomic selection and marker-assisted selection in their ability to predict six traits associated with resistance to a destructive wheat disease, Fusarium head blight, and indicates that GS is a more appropriate strategy for FHB resistance.
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Genotypic Context and Epistasis in Individuals and Populations
TL;DR: It is argued that predicting genotype from phenotype for individuals based on population studies is difficult and, especially in human genetics, likely to result in underestimating the effects of genotypic context.
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Genome-wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm.
Manje Gowda,Biswajit Das,Dan Makumbi,Raman Babu,Kassa Semagn,George Mahuku,Michael Olsen,Jumbo M. Bright,Yoseph Beyene,Boddupalli M. Prasanna +9 more
TL;DR: Genome-wide association analysis in tropical and subtropical maize germplasm revealed that resistance to MLND is controlled by multiple loci with small to medium effects and the SNPs identified by GWAS can be used as potential candidates in MLND resistance breeding program.
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Genomic selection and prediction in plant breeding
José Crossa,Paulino Pérez,Gustavo de los Campos,George Mahuku,Susanne Dreisigacker,Cosmos Magorokosho +5 more
TL;DR: Results showed that models including marker information yielded important gains in predictive ability relative to that of a pedigree-based model, this with a modest number of markers, indicating that genotype × environment interaction was an important component of genetic variability.
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Genome-wide prediction of discrete traits using bayesian regressions and machine learning
Oscar González-Recio,Selma Forni +1 more
TL;DR: The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed, and among the different alternatives proposed to analyze discrete traits, machine-learning showed some advantages over Bayesian regressions.
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