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

Liang Tong; Ying Zhou; Yixing Guo; Hui Ding; Donghai Ji

doi:10.7717/peerj.12187

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

PeerJ. 2021 Sep 24:9:e12187. doi: 10.7717/peerj.12187. eCollection 2021.

Authors

Liang Tong^{1

2}, Ying Zhou³, Yixing Guo⁴, Hui Ding², Donghai Ji¹

Affiliations

¹ School of Science, Harbin University of Science and Technology, Harbin, P. R. China.
² School of Information Engineering, Suihua University, Suihua, P. R. China.
³ School of Mathematical Sciences, Heilongjiang University and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Harbin, P. R. China.
⁴ Dalian University of Science and Technology, Dalian, P. R. China.

Abstract

Background: Quantitative trait locus (QTL) analysis aims to locate and estimate the effects of the genes influencing quantitative traits and infer the relationship between gene variants and changes in phenotypic characteristics using statistical methods. Some methods have been developed to map QTLs of multiple traits in the case of no genotype error in a given dataset. However, practical genetic data that people use may contain some potential errors because of the limitations of biotechnology. Common genetic data correction methods can only reduce errors, but cannot calculate the degree of error. In this paper, we propose a QTL mapping strategy for multiple traits in the presence of genotype errors.

Methods: The additive effect, dominant effect, recombination rate, error rate, and other parameters of QTLs can be simultaneously obtained using this new method in the framework of multiple-interval mapping.

Results: Our simulation results show that the accuracy of parameter estimation can be improved by considering the errors of marker genotypes during the analysis of genetic data. Real data analysis also shows that the new method proposed in this paper can map the QTLs of multiple traits more accurately.

Keywords: EM algorithm; Error rate; Multiple traits; Multiple-interval mapping; QTL; Recombination rate.

Grants and funding

This research was supported by the National Natural Science Foundation of China (Grant No. 12071114), the Natural Science Foundation of Heilongjiang Province of China (LH2019A020 and LH2019D018), the Fundamental Research Funds on Basic Research Project of Heilongjiang Provincial Colleges and Universities (KYYWF10236190103), and the Innovation Project for University Students of Heilongjiang Province (201910236004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.