Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.
![plot asreml-r type plot asreml-r type](https://media.springernature.com/original/springer-static/image/chp%3A10.1007%2F978-3-319-55177-7_3/MediaObjects/325391_1_En_3_Figc_HTML.gif)
#PLOT ASREML R TYPE SOFTWARE#
The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. # the order of 'highlight.text' must be consistent with 'highlight' # : value or vecter, control the size of added text # : value or vecter, control the font of added text # : value or vecter or list for multiple traits, -1, 0, 1 limited, control the position of text around the highlighted SNPs: -1(left), 0(center), 1(right) # models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. # Note: # 'highlight', 'highlight.text', '', '' could be vector or list, if it is a vector, # all traits will use the same highlighted SNPs index and text, if it is a list, the length of the list should equal to the number of traits. =c( "red ", "blue ", "green "), threshold = 0.05 /nrow( pig60K), threshold.lty = 2,Īmplify = FALSE, file = "jpg ", memo = " ", dpi = 300, file.output = TRUE, verbose = TRUE, width = 14, height = 6) l =c( "red ", "blue ", "green "), highlight.cex = 1, highlight.pch =c( 15 : 17), highlight.text = genes, > CMplot( pig60K, plot.type = "m ", LOG10 = TRUE, col =c( "gre圓0 ", "grey60 "), highlight = SNPs,
![plot asreml-r type plot asreml-r type](https://d3i71xaburhd42.cloudfront.net/3e431f395681cc17d8b2366d571fd4a4d2c0d8f6/112-Figure8.1-1.png)
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. Total 50~ parameters are available in CMplot, typing ?CMplot can get the detail function of all parameters.ĬMplot has been integrated into our developed GWAS package rMVP, please cite the following paper:
![plot asreml-r type plot asreml-r type](https://i1.rgstatic.net/publication/275406715_Spatial_Analysis_of_Small_Plot_Field_Trials_Using_ASReml/links/553b67d40cf245bdd7647977/largepreview.png)
Now CMplot could handle not only Genome-wide association study results, but also SNP effects, Fst, tajima's D and so on. Note: if plotting SNP_Density, only the first three columns are needed.
![plot asreml-r type plot asreml-r type](https://biosci-production-mma2-dashboard.s3.eu-west-2.amazonaws.com/public/Blogs/MMforRepeatedMeasures/7.png)
data( pig60K) #calculated p-values by MLM > data( cattle50K) #calculated SNP effects by rrblup > head( pig60K)