QTL mapping in outbred half-sib families using Bayesian model selection

Fang, Ming; Liu, J.; Sun, Dongxiao; Zhang, Y; Zhang, Q; Zhang, S

doi:10.1038/hdy.2011.15

Cited by 5 publications

(7 citation statements)

References 62 publications

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“…We also experimented with other scalings of the posterior covariance matrix, but the theoretical formula turned out to work well enough in our applications. This scaling factor (2.38) 2 /d was also employed in the MCMC sampler of Fang et al (2011), who introduced a new method for QTL mapping. In their sampling scheme, they utilized REML estimates in the construction of the proposal covariance matrix.…”

Section: Discussionmentioning

confidence: 99%

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

et al. 2012

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Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.

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Section: Discussionmentioning

confidence: 99%

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

et al. 2012

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“…Other recent Bayesian approaches have been proposed that take unique QTL allelic effects for each of the mapping parents (Hayashi and Iwata 2009 ) with their simulated datasets containing a common reference parent in a star design. Fang et al ( 2011 ) assumed the QTL alleles of all mapping individuals as samples from a normal distribution with a covariance matrix proportional to the IBD matrix that was calculated from the marker information on the mapping offspring and their parents. The modeling of the QTL as a random effect in a mixed model can be solved more efficiently using restricted maximum likelihood approaches.…”

Section: Discussionmentioning

confidence: 99%

“…In conventional QTL linkage mapping it is common to assume independence among the parental alleles of the mapping population(s) (Blanc et al 2006 ; Crepieux et al 2005 ; Fang et al 2011 ; Hayashi and Iwata 2009 ). Here, this assumption is replaced by allowing for putative dependencies between the parents based on ancestral pedigree and marker information.…”

Section: Methodsmentioning

confidence: 99%

“…This approach is also expected to yield more consistent QTL mapping results. However, a common assumption in this approach is that the parents of the connected populations are unrelated and thus can be treated as independent (Blanc et al 2006 ; Crepieux et al 2005 ; Fang et al 2011 ; Hayashi and Iwata 2009 ). While this assumption is convenient from the standpoint of the statistical analysis, it does not reflect the reality of most breeding programs and leads to loss of power in QTL estimation when the parents are in fact related.…”

Section: Introductionmentioning

confidence: 99%

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QTL linkage analysis of connected populations using ancestral marker and pedigree information

et al. 2012

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The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand.

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“…Bayesian model selection with a variance component model has been developed to map multiple QTLs in LA (Fang et al, 2009(Fang et al, , 2011, in which a huge number of random QTLs effects are intergrated out and the number of model parameters is reduced dramatically, so that it has excellent mixing character. In this research, we applied the Bayesian model selection to fine map multiple QTLs combined LD and LA information simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model selection for multiple QTLs mapping combining linkage disequilibrium and linkage

Jiang¹,

Ma²,

Yang³

et al. 2014

Genet. Res.

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Linkage disequilibrium (LD) mapping is able to localize quantitative trait loci (QTL) within a rather small region (e.g. 2 cM), which is much narrower than linkage analysis (LA, usually 20 cM). The multilocus LD method utilizes haplotype information around putative mutation and takes historical recombination events into account, and thus provides a powerful method for further fine mapping. However, sometimes there are more than one QTLs in the region being studied. In this study, the Bayesian model selection implemented via the Markov chain Monte Carlo (MCMC) method is developed for fine mapping of multiple QTLs using haplotype information in a small region. The method combines LD as well as linkage information. A series of simulation experiments were conducted to investigate the behavior of the method. The results showed that this new multiple QTLs method was more efficient in separating closely linked QTLs than single-marker association studies.

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QTL mapping in outbred half-sib families using Bayesian model selection

Cited by 5 publications

References 62 publications

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

QTL linkage analysis of connected populations using ancestral marker and pedigree information

Bayesian model selection for multiple QTLs mapping combining linkage disequilibrium and linkage

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