Package: mrMLM 5.0.1
mrMLM: Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for GWAS
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 significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, <doi:10.1093/bib/bbw145>), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, <doi:10.1016/j.molp.2022.02.012>).
Authors:
mrMLM_5.0.1.tar.gz
mrMLM_5.0.1.zip(r-4.5)mrMLM_5.0.1.zip(r-4.4)mrMLM_5.0.1.zip(r-4.3)
mrMLM_5.0.1.tgz(r-4.4-x86_64)mrMLM_5.0.1.tgz(r-4.4-arm64)mrMLM_5.0.1.tgz(r-4.3-x86_64)mrMLM_5.0.1.tgz(r-4.3-arm64)
mrMLM_5.0.1.tar.gz(r-4.5-noble)mrMLM_5.0.1.tar.gz(r-4.4-noble)
mrMLM_5.0.1.tgz(r-4.4-emscripten)mrMLM_5.0.1.tgz(r-4.3-emscripten)
mrMLM.pdf |mrMLM.html✨
mrMLM/json (API)
# Install 'mrMLM' in R: |
install.packages('mrMLM', repos = c('https://yuanmingzhang.r-universe.dev', 'https://cloud.r-project.org')) |
- Gen - Genotype data
- Genotype - Genotype of real data
- Phe - Phenotype dataset
- Phenotype - Phenotype of real data
- ResultFinal - Final result used to draw manhattan plot.
- ResultIntermediate - Intermediate result used to draw manhattan plot.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:a4a5083333. Checks:OK: 4 NOTE: 5. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 07 2024 |
R-4.5-win-x86_64 | NOTE | Nov 07 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 07 2024 |
R-4.4-win-x86_64 | NOTE | Nov 07 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 07 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 07 2024 |
R-4.3-win-x86_64 | OK | Nov 07 2024 |
R-4.3-mac-x86_64 | OK | Nov 07 2024 |
R-4.3-mac-aarch64 | OK | Nov 07 2024 |
Exports:DoDataFASTmrEMMAFASTmrMLMinputDataISISmrMLMmrMLMFunMultiManhattanmultiplication_speedpKWmEBpLARmEBReadData
Dependencies:BEDMatrixcodetoolscoincrochetdata.tabledoParallelforeachiteratorslarslatticelibcoinlpSolveMASSMatrixmatrixStatsmodeltoolsmultcompmvtnormncvregRcppRcppEigensamplingsandwichsblsurvivalTH.datazoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
process raw data | DoData |
To perform GWAS with FASTmrEMMA method | FASTmrEMMA |
To perform GWAS with FASTmrMLM method | FASTmrMLM |
Genotype data | chrom Gen pos rs# |
Genotype of real data | 33-16 A4226 Genotype Nov-38 |
Input data which have been transformed | inputData |
To perform GWAS with ISIS EM-BLASSO method | ISIS |
Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for GWAS | mrMLM |
To perform GWAS with mrMLM method | mrMLMFun |
Drawing multi-locus Manhattan plot | MultiManhattan |
Matrix multiplication acceleration algorithm. | multiplication_speed |
Phenotype dataset | <phenotype> Phe rep-1 rep-2 rep-3 |
Phenotype of real data | Phenotype trait1 trait2 trait3 |
To perform GWAS with pKWmEB method | pKWmEB |
To perform GWAS with pLARmEB method | pLARmEB |
read raw data | ReadData |
Final result used to draw manhattan plot. | Chromosome LOD.score QTN.effect ResultFinal |
Intermediate result used to draw manhattan plot. | ResultIntermediate RS. Trait.ID Trait.name |