CMplot is available on CRAN, so it can be installed with the following R code:
> install.packages("CMplot")
> library("CMplot")
# if you want to use the latest version on GitHub:
> source("https://raw.githubusercontent.com/YinLiLin/CMplot/master/R/CMplot.r")There are two datasets built-in CMplot package (i.e. pig 60K SNPs and cattle 50K SNPs) in which users can export and view the details by following R code:
- The first three columns in both datasets are names, chromosome, position of SNPs respectively.
- The rest of columns in pig data are the p-values of GWAS and in cattle data are SNP effects of genomic selection for given traits.
- The number of traits added after those first three columns related to SNP information is unlimited.
> data(pig60K) # calculated p-values by MLM
> data(cattle50K) # calculated SNP effects by rrblup
> head(pig60K)
SNP Chromosome Position trait1 trait2 trait3
1 ALGA0000009 1 52297 0.7738187 0.51194318 0.51194318
2 ALGA0000014 1 79763 0.7738187 0.51194318 0.51194318
3 ALGA0000021 1 209568 0.7583016 0.98405289 0.98405289
4 ALGA0000022 1 292758 0.7200305 0.48887140 0.48887140
5 ALGA0000046 1 747831 0.9736840 0.22096836 0.22096836
6 ALGA0000047 1 761957 0.9174565 0.05753712 0.05753712
> head(cattle50K)
SNP chr pos Somatic cell score Milk yield Fat percentage
1 SNP1 1 59082 0.000244361 0.000484255 0.001379210
2 SNP2 1 118164 0.000532272 0.000039800 0.000598951
3 SNP3 1 177246 0.001633058 0.000311645 0.000279427
4 SNP4 1 236328 0.001412865 0.000909370 0.001040161
5 SNP5 1 295410 0.000090700 0.002202973 0.000351394
6 SNP6 1 354493 0.000110681 0.000342628 0.000105792
Note: if plotting SNP_Density, only the first three columns are needed.
CMplot could handle not only Genome-wide association study results (p-values), but also SNP effects, Fst, tajima's D and so on.
CMplot has been integrated into our developed GWAS package rMVP, please cite the following paper:
Yin, L. et al. rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study, Genomics, Proteomics & Bioinformatics (2021), doi: 10.1016/j.gpb.2020.10.007.