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Bayesian shrinkage mapping of quantitative trait loci in variance component models.

Fang M - BMC Genet. (2010)

Bottom Line: The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL.The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.The newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Science College, Heilongjiang August First Land Reclamation University, Daqing, China. fangming618@126.com

ABSTRACT

Background: In this article, I propose a model-selection-free method to map multiple quantitative trait loci (QTL) in variance component model, which is useful in outbred populations. The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL. It is analogous to Xu's Bayesian shrinkage estimation method, but his method is based on allelic substitution model, while the new method is based on the variance component models.

Results: Extensive simulation experiments were conducted to investigate the performance of the proposed method. The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.

Conclusions: The newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.

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Traces of polygenic variance and residual variance obtained from the proposed method.
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Figure 2: Traces of polygenic variance and residual variance obtained from the proposed method.

Mentions: To demonstrate the special character of the proposed method, I firstly analyzed the data from the simulated zero-QTL model. The profiles of QTL intensity and weighted QTL variance are plotted in Figure 1a and Figure 1b, respectively. The profile of weighted QTL variance gives very noisy signals for QTL detection, but the values of weighted QTL variance are very tiny and the profile is much flat, which reflects that the proposed method can effectively shrink the values of the variance of zero-effect QTL infinitely close to zero. Figure 2 gives the MCMC traces of the polygenic variance and the residual variance, and indicates that the estimates of them are all close to their true values, 1; moreover, the Markov chains of them converge fast and mix well.


Bayesian shrinkage mapping of quantitative trait loci in variance component models.

Fang M - BMC Genet. (2010)

Traces of polygenic variance and residual variance obtained from the proposed method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2874758&req=5

Figure 2: Traces of polygenic variance and residual variance obtained from the proposed method.
Mentions: To demonstrate the special character of the proposed method, I firstly analyzed the data from the simulated zero-QTL model. The profiles of QTL intensity and weighted QTL variance are plotted in Figure 1a and Figure 1b, respectively. The profile of weighted QTL variance gives very noisy signals for QTL detection, but the values of weighted QTL variance are very tiny and the profile is much flat, which reflects that the proposed method can effectively shrink the values of the variance of zero-effect QTL infinitely close to zero. Figure 2 gives the MCMC traces of the polygenic variance and the residual variance, and indicates that the estimates of them are all close to their true values, 1; moreover, the Markov chains of them converge fast and mix well.

Bottom Line: The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL.The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.The newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Science College, Heilongjiang August First Land Reclamation University, Daqing, China. fangming618@126.com

ABSTRACT

Background: In this article, I propose a model-selection-free method to map multiple quantitative trait loci (QTL) in variance component model, which is useful in outbred populations. The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL. It is analogous to Xu's Bayesian shrinkage estimation method, but his method is based on allelic substitution model, while the new method is based on the variance component models.

Results: Extensive simulation experiments were conducted to investigate the performance of the proposed method. The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.

Conclusions: The newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.

Show MeSH
Related in: MedlinePlus