Effect of BLUP prediction on genomic selection: practical considerations to achieve greater accuracy in genomic selection

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Effect of BLUP prediction on genomic selection: practical considerations to achieve greater accuracy in genomic selection

Background Prediction of breeding values (BV) using only genotypic information is the final goal of Genomic Selection (GS) [1]. Commonly, BV prediction from traditional BLUP analysis is the input for constructing GS prediction models, and GS predicted BVs are correlated with traditional BLUP BVs to estimate the accuracy of GS models. The use of GS in plant breeding depends on the accuracy of th...

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GENOMIC SELECTION DAIRRy-BLUP: A High-Performance Computing Approach to Genomic Prediction

In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression–best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR...

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The Impact of Different Genetic Architectures on Accuracy of Genomic Selection Using Three Bayesian Methods

Genome-wide evaluation uses the associations of a large number of single nucleotide polymorphism (SNP) markers across the whole genome and then combines the statistical methods with genomic data to predict the genetic values. Genomic predictions relieson linkage disequilibrium (LD) between genetic markers and quantitative trait loci (QTL) in a population. Methods that use all markers simultaneo...

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On the Accuracy of Genomic Selection

Genomic selection is focused on prediction of breeding values of selection candidates by means of high density of markers. It relies on the assumption that all quantitative trait loci (QTLs) tend to be in strong linkage disequilibrium (LD) with at least one marker. In this context, we present theoretical results regarding the accuracy of genomic selection, i.e., the correlation between predicte...

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The aim of this study was to determine the effect of marker density, level of heritability, number of QTLs, and size of training set on the genomic accuracy over three generations. Thereby, a trait was simulated with heritability of 0.10, 0.25 or 0.40. For each animal, a genome with 20 chromosomes, 1 Morgan each, was simulated. Different marker densities (2000, 4000 and 6000 markers) and 400 an...

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ژورنال

عنوان ژورنال: BMC Proceedings

سال: 2011

ISSN: 1753-6561

DOI: 10.1186/1753-6561-5-s7-p49