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Quantifying the Relative Contribution of the Heterozygous Class to QTL Detection Power

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  • McClosky Benjamin

    (Nature Source Genetics)

  • Ma Xiwen

    (Nature Source Genetics)

  • Tanksley Steven D.

    (Nature Source Genetics)

Abstract

Basic statistical theory implies that genotypic class cardinalities play a strong role in determining power to detect QTL, but the classes do not contribute equal information to the model. For example, while it is generally accepted that homozygotes contribute more to the detection of additive effects, heterozygotes are necessary to detect dominance effects. The literature on QTL detection often mentions the importance of genotypic class sizes in passing (Belknap (1998); Belknap et al. (1996); Jin et al. (2004); Kliebenstein (2007); Kao (2006); Martinez et al. (2002)), but no rigorous study of their relative values appears to exist. The purpose of this paper is to quantify the relative contribution of the heterozygous class. Researchers can use these results in evaluating the tradeoff between gain in statistical power and the cost of developing populations with specified genotypic class sizes. In addition, we arrive at the surprising conclusion that a misspecified additive model often outperforms a full model that incorporates dominance. This result is significant because standard software packages normally use the full model by default.

Suggested Citation

  • McClosky Benjamin & Ma Xiwen & Tanksley Steven D., 2011. "Quantifying the Relative Contribution of the Heterozygous Class to QTL Detection Power," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-19, May.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:27
    DOI: 10.2202/1544-6115.1622
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    References listed on IDEAS

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    1. Karl W. Broman & Terence P. Speed, 2002. "A model selection approach for the identification of quantitative trait loci in experimental crosses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 641-656, October.
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    1. B. McClosky & S. D. Tanksley, 2013. "Optimizing Experimental Design in Genetics," Journal of Optimization Theory and Applications, Springer, vol. 157(2), pages 520-532, May.

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