Abstract
BackgroundGenome-wide dense markers have been used to detect genes and estimate relative genetic values. Among many methods, Bayesian techniques have been widely used and shown to be powerful in genome-wide breeding value estimation and association studies. However, computation is known to be intensive under the Bayesian framework, and specifying a prior distribution for each parameter is always required for Bayesian computation. We propose the use of hierarchical likelihood to solve such problems.ResultsUsing double hierarchical generalized linear models, we analyzed the simulated dataset provided by the QTLMAS 2010 workshop. Marker-specific variances estimated by double hierarchical generalized linear models identified the QTL with large effects for both the quantitative and binary traits. The QTL positions were detected with very high accuracy. For young individuals without phenotypic records, the true and estimated breeding values had Pearson correlation of 0.60 for the quantitative trait and 0.72 for the binary trait, where the quantitative trait had a more complicated genetic architecture involving imprinting and epistatic QTL.ConclusionsHierarchical likelihood enables estimation of marker-specific variances under the likelihoodist framework. Double hierarchical generalized linear models are powerful in localizing major QTL and computationally fast.
Highlights
Genome-wide dense markers have been used to detect genes and estimate relative genetic values
We propose the use of smoothed double hierarchical generalized linear model (DHGLM) since it reduces the noise in marker-specific variance estimates and highlights the signals of quantitative trait loci (QTL). r, regarded as a spatial correlation parameter, was chosen to be 0.9 in this paper, which nicely shrank the single nucleotide polymorphism (SNP) with zero effect
Estimation of SNP effects The effect of each SNP was estimated by a smoothed DHGLM with spatial correlation parameter r = 0.9 for both traits (Figure 1)
Summary
Genome-wide dense markers have been used to detect genes and estimate relative genetic values. Bayesian techniques have been widely used and shown to be powerful in genome-wide breeding value estimation and association studies. A kinship matrix can be calculated using the pedigree data, which can be used in a generalized linear mixed model (GLMM) to estimate breeding values. By including genetic marker information, genomic estimated breeding values (GEBV) can be obtained taking into account the information from these markers, and quantitative trait loci (QTL) can be mapped by associating genotypes at a certain locus to the phenotype observations. Dense marker genotypes along genome can be affordably obtained due to new and efficient methods likelihood function named hierarchical likelihood (hlikelihood) [8]. GEBV are calculated from the estimated marker effects, and QTL are mapped by the estimated marker-specific variances
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