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R. W. Doerge

Abstract

The detection and location of quantitative trait loci (QTL) that control quantitative characters is a problem of great interest to the genetic mapping community. Interval mapping has proved to be a useful tool in locating QTL, but has recently been challenged by faster, more sophisticated regression methods (e.g. .. composite interval mapping). Regardless of the method used to locate QTL. the distribution of the test statistic (LOD score or likelihood ratio test) is unknown. Due to the quantitative trait values following a mixture distribution rather than a single distribution, the asymptotic distribution of the test statistic is not from a standard family, such as chi-square. The purpose of this work is to introduce interval mapping, discuss the distribution of the resulting test statistic, and then present empirical threshold values for the declaration of major QTL. as well as minor QTL. Empirical threshold values are obtained by permuting the actual experimental trait data, under a fixed and known genetic map. for the purpose of representing the distribution of the test statistic under the null hypothesis of no QTL effect. Not only is a permutation test statistically justified in this case, the test reflects the specifics of the experimental situation under investigation (i. e., sample size, marker density, skewing, etc.), and may be used in a conditional sense to derive thresholds for minor QTL once a major effect has been determined.

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Apr 26th, 10:15 AM

STATISTICAL THRESHOLD VALUES FOR LOCATING QUANTITATIVE TRAIT LOCI

The detection and location of quantitative trait loci (QTL) that control quantitative characters is a problem of great interest to the genetic mapping community. Interval mapping has proved to be a useful tool in locating QTL, but has recently been challenged by faster, more sophisticated regression methods (e.g. .. composite interval mapping). Regardless of the method used to locate QTL. the distribution of the test statistic (LOD score or likelihood ratio test) is unknown. Due to the quantitative trait values following a mixture distribution rather than a single distribution, the asymptotic distribution of the test statistic is not from a standard family, such as chi-square. The purpose of this work is to introduce interval mapping, discuss the distribution of the resulting test statistic, and then present empirical threshold values for the declaration of major QTL. as well as minor QTL. Empirical threshold values are obtained by permuting the actual experimental trait data, under a fixed and known genetic map. for the purpose of representing the distribution of the test statistic under the null hypothesis of no QTL effect. Not only is a permutation test statistically justified in this case, the test reflects the specifics of the experimental situation under investigation (i. e., sample size, marker density, skewing, etc.), and may be used in a conditional sense to derive thresholds for minor QTL once a major effect has been determined.