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  4. Power Prediction of Separating Closely Linked QTL under Three Models of Genomic Structures in Recombinant Inbred Populations
 
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Power Prediction of Separating Closely Linked QTL under Three Models of Genomic Structures in Recombinant Inbred Populations

Date Issued
2010
Date
2010
Author(s)
Wang, Hsiu-Hui
URI
http://ntur.lib.ntu.edu.tw//handle/246246/253672
Abstract
The statistical model for QTL mapping is generally a normal mix- ture model and is usually proposed for F2 population. Recombinant inbred (RI) populations are also popular as they can provide greater power in mapping closely linked QTL by providing different genome structures to benefit QTL detection. In F2 population, the genome structure has the Markovian property. Therefore, the mixing propor- tions in the mixture model can be easily obtained by using pairwise genotypic distributions of two loci. In RI populations, their genome structures do not have such property due to multi-meiosis cycles. The derivation of the mixing proportions is more complicated as it in- volves the use of the genotypic distribution of three loci. We use three different methods, including Kao and Zeng’s method (Kao and Zeng, 2009), Lynch andWalsh’s method (Lynch andWalsh, 1998), and Jiang and Zeng’s method (Jiang and Zeng, 1997), to obtain the genotypic distributions of three loci for computing the mixing proportions and investigating the correlation structures between two putative QTL in the RI populations. Then, the powers of separating two linked QTL detection predicted by these methods are compared under the frame- work of regression interval mapping model (Haley and Knott, 1992). As compared to the power predicted by KZ method, it is found that JZ method, which has assumption of Markovian property, overesti- mated the power, and LW method, which replaces the recombination rate with the proportion of the recombinants in RI population, under- predicted the power. Numerical analyses are provided for illustration.
Subjects
QTL
interval mapping
normal mixture model
RI populations
Markovian property
regression interval mapping
Type
thesis
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