• Ying Zhou

      Articles written in Journal of Genetics

    • A new strategy for estimating two-locus recombination fractions under some natural inequality restrictions

      Ying Zhou Weijun Ma Xiaona Sheng Huakun Wang

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      Linkage analysis is now being widely used to map markers on each chromosome in the human genome, to map genetic diseases, and to identify genetic forms of common diseases. Two-locus linkage analysis and multi-locus analysis have been investigated comprehensively, and many computer programs have been developed to perform linkage analysis. Yet there exists a shortcoming in traditional methods, i.e., the parameter space of two-locus recombination fractions has not been emphasized sufficiently in the usual analyses. In this paper, we propose a new strategy for estimating the two-locus recombination fractions based on data of backcross family in the framework of some natural and necessary parameter restrictions. The new strategy is based on a restricted projection algorithm, which can provide fast reasonable estimates of recombination fraction, and can therefore serve as a superior alternative algorithm. Results obtained from both real and simulated data indicate that the new algorithm performs well in the estimation of recombination fractions and outperforms current methods.

    • An estimating function approach to linkage heterogeneity

      He Gao Ying Zhou Weijun Ma Haidong Liu Linan Zhao

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      Testing linkage heterogeneity between two loci is an important issue in genetics. Currently, there are four methods (K-test, A-test, B-test and D-test) for testing linkage heterogeneity in linkage analysis, which are based on the likelihood-ratio test. Among them, the commonly used methods are the K-test and A-test. In this paper, we present a novel test method which is different from the above four tests, called G-test. The new test statistic is based on estimating function, possessing a theoretic asymptotic distribution, and therefore demonstrates its own advantages. The proposed test is applied to analyse a real pedigree dataset. Our simulation results also indicate that the G-test performs well in terms of power of testing linkage heterogeneity and outperforms the current methods to some degree.

    • Simultaneous estimation of QTL effects and positions when using genotype data with errors

      Liang Tong Weijun Ma Haidong Liu Chaofeng Yuan Ying Zhou

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      Accurate genetic data are important prerequisite of performing genetic linkage test or association test. Currently, most analytical methods assume that the observed genotypes are correct. However, due to the constraint at the technical level, most of the genetic data that people used so far contain errors. In this paper, we considered the problem of QTL mapping based on biological data with genotyping errors. By analysing all possible genotypes of each individual in framework of multiple-interval mapping, we proposed an algorithm of inferring all model parameters through the expectation-maximization (EM) algorithm and discussed the hypothesis testing of the existence of QTL. We carried out extensive simulation studies to assess the proposed method. Simulation results showed that the new method outperforms the method that does not take the genotyping errors into account, and therefore it can decrease the impact of genotyping errors on QTL mapping. The proposed method was also applied to analyse a real barley dataset.

    • Simultaneous estimation of QTL parameters for mapping multiple traits


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      The analysis of quantitative trait loci (QTLs) aims at mapping and estimating the positions and effects of the genes that may affect the quantitative trait, and evaluating the relationship between the gene variation and the phenotype. In existing studies, most methods mainly focus on the association/linkage between multiple gene loci and one trait, in which some useful joint information of multiple traits may be ignored. In this paper, we proposed a method of simultaneously estimating all QTL parameters in the framework of multiple-trait multiple-interval mapping. Simulation results show that in accuracy aspect, the proposed methodoutperforms an existing method for mapping multiple traits. A real example is also provided to validate the performance of the new method.

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