Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips

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Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips

BACKGROUND The simulated dataset of the 13th QTL-MAS workshop was analysed to i) detect QTL and ii) predict breeding values for animals without phenotypic information. Several parameterisations considering all SNP simultaneously were applied using Gibbs sampling. RESULTS Fourteen QTL were detected at the different time points. Correlations between estimated breeding values were high between m...

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Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian Methods

BACKGROUND Bayesian methods allow prediction of genomic breeding values (GEBVs) using high-density single nucleotide polymorphisms (SNPs) covering the whole genome with effective shrinkage of SNP effects using appropriate priors. In this study we applied a modification of the well-known BayesA and BayesB methods to estimate the proportion of SNPs with zero effects (π) and a common variance for ...

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Estimating genomic breeding values from the QTL-MAS Workshop Data using a single SNP and haplotype/IBD approach

Genomic breeding values were estimated using a Gibbs sampler that avoided the use of the Metropolis-Hastings step as implemented in the BayesB model of Meuwissen et al., Genetics 2001, 157:1819-1829.Two models that estimated genomic estimated breeding values (EBVs) were applied: one used constructed haplotypes (based on alleles of 20 markers) and IBD matrices, another used single SNP regression...

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BACKGROUND Genomic breeding value estimation is the key step in genomic selection. Among many approaches, BLUP methods and Bayesian methods are most commonly used for estimating genomic breeding values. Here, we applied two BLUP methods, TABLUP and GBLUP, and three Bayesian methods, BayesA, BayesB and BayesCπ, to the common dataset provided by the 15th QTL-MAS Workshop to evaluate and compare t...

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ژورنال

عنوان ژورنال: BMC Proceedings

سال: 2010

ISSN: 1753-6561

DOI: 10.1186/1753-6561-4-s1-s9