mrMLM v4.0+ Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for Genome-Wide Association Study

Introduction

Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen et al. Brief Bioinform 2018, 19(4): 700-712. Wang et al. Sci Rep 6: 19444. Tamba et al. PLoS Computat Biol 2017, 13(1): e1005357. Zhang et al. Heredity 2017, 118(6): 517-524. Ren et al. Heredity 2018, 120(3): 208-218. Zhang et al. Frontiers in Plant Science 10: 100. Zhang et al. (eds) 2019. The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits. Lausanne: Frontiers Media. (ISBN 978-2-88945-834-9). Tamba et al. A fast mrMLM algorithm for multi-locus genome-wide association studies. bioRxiv, 2018. doi: 10.1101/341784.

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Credits

  1. Yuan-Ming Zhang soyzhang@mail.hzau.edu.cn
    Investigator

    college of plant science and technology, Huazhong Agricultural University, China

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Summary
AccessionBT007077
Tool TypeToolkit
CategorymGWAS
PlatformsLinux/Unix, MAC OS X, Windows
TechnologiesR
User InterfaceTerminal Command Line
Latest ReleasemrMLM v4.0 (October 22, 2019)
Download Count5591
Country/RegionChina
Submitted ByYuan-Ming Zhang
Fundings

The work was supported by National Natural Science Foundation of China (31571268, 31871242, U1602261, 31701071 and 21873034), Huazhong Agricultural University Scientific & Technological Self-innovation Foundation (2014RC020), and State Key Laboratory of Cotton Biology Open Fund (CB2019B01)