Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data

Meuwissen, Theo H. E.; Goddard, Michael E.

doi:10.1051/gse:2004001

Cited by 55 publications

(69 citation statements)

References 19 publications

(31 reference statements)

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“…The applied models to estimate genomic breeding values described in this paper, are derived from a multiple QTL mapping model described by Meuwissen and Goddard [1]. The methods are implemented using variable (i.e.…”

Section: Introductionmentioning

confidence: 99%

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

2009

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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. Both models were applied with or without polygenic effect. A fifth model included only polygenic effects and no genomic information.The models needed to estimate 366,959 effects for the haplotype/IBD approach, but only 11,850 effects for the single SNP approach. The four genomic models identified 11 to 14 regions that had a posterior QTL probability >0.1. Accuracies of genomic selection breeding values for animals in generations 4–6 ranged from 0.84 to 0.87 (haplotype/IBD vs. SNP).It can be concluded that including a polygenic effect in the genomic model had no effect on the accuracy of the total EBVs or prediction of the QTL positions. The SNP model yielded slightly higher accuracies for the total EBVs, while both models were able to detect nearly all QTL that explained at least 0.5% of the total phenotypic variance.

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Section: Introductionmentioning

confidence: 99%

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

2009

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“…The genomic prediction model was based on the Bayesian multi-QTL model of Meuwissen and Goddard (2004), where the effects of dense SNP across the whole genome are fitted directly, without the use of haplotypes or identical-by-descent probabilities (Calus et al, 2008), using a fast algorithm with right-hand-side updating (Calus, 2010(Calus, , 2014. Each trait was analyzed separately.…”

Section: Computation Of Genomic Evaluationmentioning

confidence: 99%

Genomic evaluation of a relatively small dairy cattle population by combination with a larger population

Weller

Stoop

Eding

et al. 2015

Journal of Dairy Science

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The objectives were to investigate the accuracy of genomic evaluations obtained for a small dairy cattle population (Israeli Holsteins) via joint evaluation with a larger population (Dutch Holsteins), and to evaluate the use of pedigree data from foreign bulls computed by Interbull without daughter records in Israel. The training population included 4,010 Dutch bulls and 713 Israeli bulls. The validation population included 185 Israeli bulls with daughter records for milk production traits and slightly fewer bulls for the nonproduction traits. Milk, fat, and protein yields, somatic cell score, longevity, female fertility, direct and maternal calving ease, direct and maternal stillbirth, and the Israeli breeding index were analyzed. The genomic prediction model was based on the Bayesian multi-QTL model of Meuwissen and Goddard, where the effects of dense single nucleotide polymorphisms across the whole genome are fitted directly, without the use of haplotypes or identical-by-descent probabilities. Correlations of May 2014 estimated breeding values (EBV14) with genomic EBV (GEBV) were higher than the correlations of EBV14 with parent averages (PA) computed from the June 2009 evaluation for all traits. For the Israel selection index, the difference between EBV14 and GEBV correlation on the one hand and EBV14 and PA computed using Interbull data on the other hand was 15 percentage points. For protein, the difference between the corresponding correlations was 14 percentage points. Generally, correlations of EBV14 with PA based on Israeli EBV only were similar to correlations of EBV14 with PA including Interbull evaluations. Relative to EBV14, milk production traits were biased upwards for both GEBV and PA, but the bias was greater for PA. The Y-intercepts of regressions of EBV14 were significantly different from zero for regression on GEBV for all 3 milk production traits and the Israeli selection index. This was not the case for regression of EBV14 on PA. The regression line intersected with the line of unbiased estimation near the EBV of the bulls with highest values. Because only bulls with high evaluations are of interest for selection, GEBV for these bulls were less biased compared with that of bulls with lower evaluations. The difference in mean EBV14 between bulls born during 2007-2008 selected by GEBV and PA was 65 units. If half of all inseminations are by young bulls, then the annual genetic gain obtained by implementation of genomic evaluation will be 8 units per year (65/8). Because annual gain is currently 107 units, this is a gain of 7%.

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“…The theory of SSVS was further developed [13] and many other stochastic searching schemes have been proposed, e.g., the simplified method of Kuo and Mallick [14], the Gibbs variable selection [15], Geweke’s BVS with block-updates [16], and the reverse jump MCMC algorithm [17]. BVS algorithms were also extended to much wider settings, e.g., generalized linear models (GLMs) [18], [19]; multivariate regression models [20]; and even mixed-effects models [21], [22]; see O’Hara and Sillanpää [23] for a detailed review.…”

Section: Introductionmentioning

confidence: 99%

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

et al. 2013

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The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

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Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data

Cited by 55 publications

References 19 publications

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

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

Genomic evaluation of a relatively small dairy cattle population by combination with a larger population

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

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