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rrBLUP specifications


Unique identifier OMICS_25064
Name rrBLUP
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 4.5
Stability Stable
Source code URL https://cran.r-project.org/src/contrib/rrBLUP_4.6.tar.gz
Maintained Yes




No version available


  • person_outline Jeffrey Endelman
  • person_outline Jeffrey Endelman

Additional information


Publication for rrBLUP

rrBLUP citations


Genomic selection of agronomic traits in hybrid rice using an NCII population

PMCID: 5945574
PMID: 29748895
DOI: 10.1186/s12284-018-0223-4

[…] 0 markers were used for prediction of all traits. Research in an elite rice breeding population genotyped with 73,147 markers revealed that prediction accuracy reached a plateau at 7142 SNPs with the rrBLUP method (Spindel et al. ). Therefore, a low-density marker panel is desired to obtain a favorable cost-benefit ratio for GS. With respect to the size of training population, it has strong effect […]


Genomic heritability estimates in sweet cherry reveal non additive genetic variance is relevant for industry prioritized traits

BMC Genet
PMCID: 5894190
PMID: 29636022
DOI: 10.1186/s12863-018-0609-8

[…] ndividuals included in the study and eY is a 3 × 3 matrix of year error terms using a general correlation structure implemented in ASReml. The genomic additive relationship matrix was computed with R/rrBLUP [] using the VanRaden method []:Ga=HHT2∑jpj(1−pj)where pj is frequency of the positive allele for a single marker column, and H was computed as equal to centered marker data, {H}ij = {M}ij − 2( […]


Genomic prediction applied to high biomass sorghum for bioenergy production

PMCID: 5893689
PMID: 29670457
DOI: 10.1007/s11032-018-0802-5

[…] .85 for NDF to 0.66 for days to flowering.All tested genomic selection models yielded similar predictive abilities for each of the nine traits. Even though differences between models were modest, the RRBLUP model showed the best predictions overall, while the Bayes Lasso model showed the lowest predictive abilities. For example, for the trait plant height, the best and worst models provided values […]


Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat

Front Plant Sci
PMCID: 5871932
PMID: 29619035
DOI: 10.3389/fpls.2018.00276

[…] s of their expected selection index in GS1 (Figure ). For each trait included in the selection index, a prediction model was built with genomic best linear unbiased prediction (G-BLUP) (in R package “rrBLUP”; Endelman, ). The expected selection index values were calculated from the expected values of the seven traits with equation 1 (Figure ). Because all traits except days to first flowering were […]


Genomic Selection Outperforms Marker Assisted Selection for Grain Yield and Physiological Traits in a Maize Doubled Haploid Population Across Water Treatments

Front Plant Sci
PMCID: 5869257
PMID: 29616072
DOI: 10.3389/fpls.2018.00366

[…] Genomic prediction was implemented in rrBLUP package (Endelman, ) in DH population. SNPs in the genetic map were used for genomic prediction. Details of the implementation of rrBLUP were described earlier (Zhao et al., ). A five-fold cros […]


Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments

PMCID: 5940163
PMID: 29559536
DOI: 10.1534/g3.118.200140

[…] Missing data (4.8% of data points) were replaced by the mean value of the non-missing data for the loci, using the “mean” option implemented in the ridge regression best linear unbiased predictions (rrBLUP) package in R (). A set of 13,330 polymorphic SNPs were used for model training and genomic predictions (see Supplemental material File S1). These SNPs were distributed across all the 26 chromo […]


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rrBLUP institution(s)
Department of Crop and Soil Sciences, Washington State University, Mount Vernon, WA, USA

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