摘要
Previous studies have 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 for multi-locus GWAS are available.Therefore,we developed an R software named mr MLM v4.0.2.This software integrates mr MLM,FASTmr MLM,FASTmr EMMA,p LARm EB,p KWm EB,and ISIS EM-BLASSO methods developed by our lab.There are four components in mr MLM v4.0.2,including dataset input,parameter setting,software running,and result output.The fread function in data.table is used to quickly read datasets,especially big datasets,and the do Parallel package is used to conduct parallel computation using multiple CPUs.In addition,the graphical user interface software mr MLM.GUI v4.0.2,built upon Shiny,is also available.To confirm the correctness of the aforementioned programs,all the methods in mr MLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets.The results confirm the superior performance of mr MLM v4.0.2 to other methods currently available.False positive rates are effectively controlled,albeit with a less stringent significance threshold.mr MLM v4.0.2 is publicly available at Bio Code(https://bigd.big.ac.cn/biocode/tools/BT007077)or R(https://cran.r-project.org/web/packages/mr MLM.GUI/index.html)as an open-source software.
基金
supported by the National Natural Science Foundation of China(Grant Nos.31871242,U1602261,31701071,21873034,and 31571268)
the Huazhong Agricultural University Scientific&Technological Self-innovation Foundation,China(Grant No.2014RC020)
the State Key Laboratory of Cotton Biology Open Fund,China(Grant No.CB2019B01)
作者简介
Corresponding author:Yuan-Ming Zhang,E-mail:soyzhang@mail.hzau.edu.cn,ORCID:0000-0003-2317-2190;Equal contribution:Cox Lwaka Tamba,ORCID:0000-0002-7239-9271;Equal contribution:Ya-Wen Zhang,ORCID:0000-0003-0193-094X;Yang-Jun Wen,ORCID:0000-0002-0282-5183;Pei Li,ORCID:0000-0002-2681-8842;Wen-Long Ren,ORCID:0000-0001-5634-8012;Yuan-Li Ni,ORCID:0000-0003-3645-3979;Jun Gao,ORCID:0000-0002-6692-1828
