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Mapping of quantitative trait loci using the skew-normal distribution
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Advisor(s)
Abstract(s)
In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture
model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data
transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise
interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting
method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special
case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main
peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution
are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross
experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed
via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of
QTL location as compared to standard IM and nonparametric IM.
Description
This deposit is in restrictedAccess (it can't be in open access to the public), and can only be accessed by two ways: either by requesting a legal copy from the author (the email contact present in this deposit) or by visiting the following link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2064950/
Keywords
Interval mapping (IM) Quantitative trait loci (QTL) Skew-normal distribution Expectation-maximization (EM)
Citation
Fernandes, E., Pacheco, A., Penha-Gonçalves, C. (2007). Mapping of quantitative trait loci using the skew-normal distribution. J Zhejiang Univ Sci B 8(11):792-801