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An investigation of the power for separating closely linked QTL in experimental populations

  • CHEN-HUNG KAO (a1) and MIAO-HUI ZENG (a1)

Summary

Hu & Xu (2008) developed a statistical method for computing the statistical power for detecting a quantitative trait locus (QTL) located in a marker interval. Their method is based on the regression interval mapping method and allows experimenters to effectively investigate the power for detecting a QTL in a population. This paper continues to work on the power analysis of separating multiple-linked QTLs. We propose simple formulae to calculate the power of separating closely linked QTLs located in marker intervals. The proposed formulae are simple functions of information numbers, variance inflation factors and genetic parameters of a statistical model in a population. Both regression and maximum likelihood interval mappings suitable for detecting QTL in the marker intervals are considered. In addition, the issue of separating linked QTLs in the progeny populations from an F2 subject to further self and/or random mating is also touched upon. One of the primary keys to our approach is to derive the genotypic distributions of three and four loci for evaluating the correlation structures between pairwise unobservable QTLs in the model across populations. The proposed formulae allow us to predict the power of separation when several factors, such as sample sizes, sizes and directions of QTL effects, distances between QTLs, interval sizes and relative QTL positions in the intervals, are considered together at a time in different experimental populations. Numerical justifications and Monte Carlo simulations were provided for confirmation and illustration.

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Copyright

Corresponding author

*Corresponding author: Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, Republic of China. Tel: (02) 2783-5611 ext 418. Fax: (02) 2783-1523. e-mail: chkao@stat.sinica.edu.tw

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An investigation of the power for separating closely linked QTL in experimental populations

  • CHEN-HUNG KAO (a1) and MIAO-HUI ZENG (a1)

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