BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies

Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we propose a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM is replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection is used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical bayes. Finally, shared memory and parallel computing schemes were used to reduce the computation time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, FarmCPU, and the control method of BLUPmrMLM in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. According to the reanalysis of two large rice datasets, compared with the above methods, BLUPmrMLM significantly reduced the computation time and identified more previously reported genes. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).