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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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Posterior distribution of the number of QTL from the RJMCMC method.
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Figure 7: Posterior distribution of the number of QTL from the RJMCMC method.

Mentions: The QTL intensity profiles of both methods are plotted in Figure 6a. It shows that the profile of the new method is higher than that of the RJMCMC method. But it is not sufficient to prove that the new method is more powerful than the RJMCMC method, because the QTL intensity is not used to detect QTL in shrinkage method. The profile of the weighted QTL variance is given in Figure 6b, and five clear bumps are found around their true simulated positions, which shows that the five simulated QTL are all detected by the new method. However, in the RJMCMC method, the estimated average number of QTL equaled to 3.37. The profile of the posterior QTL intensity is depicted in Figure 7, showing that the trait is mostly affected by three or four QTL with probability 0.565 or 0.359, and the estimated number of QTL is clearly smaller than the true number of QTL. The results suggest that my new method is competitive with the RJMCMC method, and may even out-perform it. The computing time of the proposed method and the RJMCMC method were nearly equal and they took ~ 24 hr on a Pentium IV PC with a 2.60-GHz processor and 1.00 GB RAM.


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

Fang M - BMC Genet. (2010)

Posterior distribution of the number of QTL from the RJMCMC method.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Posterior distribution of the number of QTL from the RJMCMC method.
Mentions: The QTL intensity profiles of both methods are plotted in Figure 6a. It shows that the profile of the new method is higher than that of the RJMCMC method. But it is not sufficient to prove that the new method is more powerful than the RJMCMC method, because the QTL intensity is not used to detect QTL in shrinkage method. The profile of the weighted QTL variance is given in Figure 6b, and five clear bumps are found around their true simulated positions, which shows that the five simulated QTL are all detected by the new method. However, in the RJMCMC method, the estimated average number of QTL equaled to 3.37. The profile of the posterior QTL intensity is depicted in Figure 7, showing that the trait is mostly affected by three or four QTL with probability 0.565 or 0.359, and the estimated number of QTL is clearly smaller than the true number of QTL. The results suggest that my new method is competitive with the RJMCMC method, and may even out-perform it. The computing time of the proposed method and the RJMCMC method were nearly equal and they took ~ 24 hr on a Pentium IV PC with a 2.60-GHz processor and 1.00 GB RAM.

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