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

Journal of Genetics volume 94, pages 27–34 (2015)Cite this article

Abstract

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.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (nos. 11201129 and 11371083), the Natural Science Foundation of Heilongjiang Province of China (A201207), the Scientific Research Foundation of Department of Education of Heilongjiang Province of China (no. 1253G044) and the Science and Technology Innovation Team in Higher Education Institutions of Heilongjiang Province (no. 2014TD005). This work was partly supported by the Science and Technology Programme of Suihua of China (no. KJZD20130112) and the Youth Fund Project in 2013 of Suihua University of China (KQ 1302004).

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Affiliations

  1. School of Mathematical Sciences, Heilongjiang University, Harbin, 150080, People’s Republic of China

    LIANG TONG, WEIJUN MA, HAIDONG LIU, CHAOFENG YUAN & YING ZHOU

  2. School of Information Engineering, Suihua University, Suihua, 152061, People’s Republic of China

    LIANG TONG

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  1. LIANG TONG
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  2. WEIJUN MA
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  3. HAIDONG LIU
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  4. CHAOFENG YUAN
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  5. YING ZHOU
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Corresponding author

Correspondence to YING ZHOU.

Additional information

[Tong L., Ma W., Liu H., Yuan C. and Zhou Y. 2015 Simultaneous estimation of QTL effects and positions when using genotype data with errors. J. Genet. 94, xx–xx]

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TONG, L., MA, W., LIU, H. et al. Simultaneous estimation of QTL effects and positions when using genotype data with errors. J Genet 94, 27–34 (2015). https://doi.org/10.1007/s12041-015-0487-z

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  • DOI: https://doi.org/10.1007/s12041-015-0487-z

Keywords

  • backcross model
  • EM algorithm
  • genotyping errors
  • maximum likelihood estimation
  • QTL mapping.