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

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 multipleinterval 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.

Quantitative trait locus mapping analysis of multiple traits when using genotype data with potential errors

“…Extensive simulation studies have validated that the new method is advantageous for parameter estimation in the QTL mapping of multiple traits and it can effectively overcome the impact of genotype errors/loss and estimate all parameters simultaneously, compared with existing methods. The proposed method can also be used to deal with the problems of other cases, for example, other experimental populations, or different error rates on different loci, in which we only need to change the conditional probabilities provided in Table 1, or adjust the presentation of the joint error rate φ j (Tong et al, 2015), respectively. The statistical Model (1) considered in this study can also be further generalized; for example, genotype information of markers can be added and considered simultaneously, which will lead to a composite multi-interval mapping model.…”

“…Performing this operation until all markers are completely analyzed will save much running time of the algorithm. Alternatively, we suggest using the idea of a two-step mapping method (Tong et al, 2015), i.e., first detect and retain the markers with larger effects of all the markers. Marker intervals can then be constructed using the selected markers so that the proposed method in this paper can be used with less computational burden.…”

“…However, due to the constraints in genotype scoring software and biochemical anomalies, most of the data that researchers have used may contain certain potential genotype errors. The genotype errors cannot be neglected arbitrarily in statistical analysis and may have a significant impact on the study of genetic linkage analysis (Ronin et al, 2015;Tong et al, 2015;Yan et al, 2016).…”

Marker genotyping error effects on genomic predictions under different genetic architectures

“…A wide range of parameters can be predicted for the genome, such as mutation rates, the number of chromosomes, markers and QTLs, marker allele frequencies, rate of the missing marker, and rate of marker genotyping error (Sargolzei and Schenkel 2013). There are many studies on QTL mapping, most of which assume that the observed genotypes are correct (Tong et al 2015). The error of genotyping would be considered as the rate of mistyping in all called genotypes and may happen because of the unsuitable calling of allele, nonspecificity of experimental evaluation, or random assessment variability (Hao et al 2004).…”