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Mapping genome-wide QTL of ratio traits with Bayesian shrinkage analysis for its component traits

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Abstract

The ratio trait is defined as a ratio of two regular quantitative traits with normal distribution, which is distinguished from regular quantitative traits in the genetic analysis because it does not follow the normal distribution. On the basis of maximum likelihood method that uses a special linear combination of the two component traits, we develop a Bayesian mapping strategy for ratio traits, which firstly analyzes the two component traits by Bayesian shrinkage method, and then generates a new posterior sample of genetic effects for a ratio trait from ones of population means and genetic effects for the two component traits, finally, infers QTL for the ratio trait via post MCMC analysis for the new posterior sample. A simulation study demonstrates that the new method has higher detecting power of the QTL than maximum likelihood method. An application is illustrated to map genome-wide QTL for relative growth rate of height on soybean.

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Acknowledgments

The research was supported by the Chinese National Natural Science Foundation Grant 30972077 to RY.

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Authors and Affiliations

  1. College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, 163319, Daqing, People’s Republic of China

    Runqing Yang

  2. School of Agriculture and Biology, Shanghai Jiaotong University, 201101, Shanghai, China

    Runqing Yang

  3. Department of Biology, Northwest University, National Engineering Research Center for Miniaturized Detection System, 710069, Xi’an, People’s Republic of China

    Tianbo Jin

  4. Agricultural College, Northeast Agricultural University, 150030, Harbin, People’s Republic of China

    Wenbin Li

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  1. Runqing Yang
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  2. Tianbo Jin
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  3. Wenbin Li
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Correspondence to Runqing Yang.

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Yang, R., Jin, T. & Li, W. Mapping genome-wide QTL of ratio traits with Bayesian shrinkage analysis for its component traits. Genetica 138, 853–860 (2010). https://doi.org/10.1007/s10709-010-9468-6

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  • DOI: https://doi.org/10.1007/s10709-010-9468-6

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