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

Journal of genetics

Chaofeng Yuan¹  Weijun Ma¹  Liang Tong¹  Ying Zhou1¹  Haidong Liu¹ 

[1] School of Mathematical Sciences, Heilongjiang University, Harbin 150080, People’s Republic of China$$

关键词: backcross model;    EM algorithm;    genotyping errors;    maximum likelihood estimation;    QTL mapping.;   

DOI  :  

学科分类：生物科学（综合）

来源: Indian Academy of Sciences

【 摘 要 】

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.

【 授权许可】

Unknown