Abstract
Previous studies reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages are available. Therefore, an R software mrMLM, which includes our six multi-locus methods, was developed. mrMLM includes three components: dataset input, parameter setting and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0, built upon Shiny, is also available. To confirm the correctness of the above programs, the same simulation datasets as used in previous studies, along with three real datasets, were re-analyzed by all the methods in mrMLM v4.0 and three widely-used methods. The results confirmed the advantages of our multi-locus methods over the current methods. The conclusion is also consistent with those in a Research Topic in Frontiers in Plant Science. Although a less stringent significance threshold is adopted, the false positive rates are effectively controlled. mrMLM is publicly available at https://cran.r-project.org/web/packages/mrMLM/index.html or https://bigd.big.ac.cn/biocode/tools/BT007077 as an open-source software.