Bayesian shrinkage mapping of quantitative trait loci in variance component models

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.

Figure 1

Profiles of QTL intensity (a) and weighted QTL variance (b) obtained from the proposed method under simulated zero-QTL model.

Figure 2

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

Figure 3

Typical profiles of QTL intensity (a) and weighted QTL variance (b) from the proposed method.

Figure 4

The change of the acceptance rate against the degree of freedom by the proposed method.

Figure 5

Profiles of F statistic obtained from QTL Express.

Figure 6

Profiles of QTL intensity (a) and weighted QTL variance (b) in the genome-wide mapping. The true locations of the simulated QTL are indicated by upward arrows on the horizontal axis.

Figure 7

Posterior distribution of the number of QTL from the RJMCMC method.

Figure 8

Profiles of weighted QTL variance for q = 11 (a) and q = 14 (b) in the genome-wide mapping. The true locations of the simulated QTL are indicated by upward arrows on the horizontal axis.