sutomato / aghmatrix

An R package to compute relationship matrices for diploid and autotetraploid species

AGHmatrix

AGHmatrix is an R package to compute A (pedigree), G (genomic-base), and H (A corrected by G) matrices for diploid and autopolyploid species. It suports any even ploidy.

The following matrices are implemented:

Pedigree-based relationship matrix (A matrix)

	Additive	Non-Additive
Diploid	Henderson (1976)	Cockerham (1954)
Polyploid	Kerr (2012), Slater (2014)

Molecular-based relationship matrix (G matrix)

	Additive	Non-Additive
Diploid	Yang (2010), VanRaden (2012)	Su (2012), Vitezica (2013)
Polyploid	Slater (2016), VanRaden (2012)	Slater (2016), Endelman (2018)

Combined pedigree and molecular-based relationship matrix (H matrix)

	Any Effect
Any Ploidy	Munoz (2014), Martini (2018)

An original manuscript about AGHmatrix development and application in autotetraploids is described on Amadeu, R. R., C. Cellon, J. W. Olmstead, A. A. Garcia, M. F. Resende, and P. R. Muñoz, 2016 AGHmatrix: R package to construct relationship matrices for autotetraploid and diploid species: a blueberry example. The Plant Genome 9. doi:10.3835/plantgenome2016.01.0009..

How to install

From CRAN

Within R:

install.packages("AGHmatrix")

From github (development version)

Within R, you need to install and load the package devtools:

install.packages("devtools")
library(devtools)

This will allow you to automatically build and install packages from github. If you use Windows, first install Rtools. On a Mac, you will need Xcode (available on the App Store). On Linux, you are good to go.

Then, to install AGHmatrix from github:

install_github("prmunoz/AGHmatrix")

Tutorials

You can read the AGHmatrix tutorial going to the vignettes of the installed package, or clicking below. Please, start with the overview, that will guide you through other chapters.

AGHmatrix Tutorial

More about us

Bluberry Breeding & Genomics Lab, University of Florida - USA

Statistical-Genetics Lab, University of Sao Paulo - Brazil

References

Amadeu, RR, et al., 2016 AGHmatrix: R package to construct relationship matrices for autotetraploid and diploid species: a blueberry example. The Plant Genome 9(4). https://doi.org/10.3835/plantgenome2016.01.0009

Ashraf, BH, et a., 2016 Estimating genomic heritabilities at the level of family-pool samples of perennial ryegrass using genotyping-by-sequencing. Theoretical and Applied Genetics 129: 45-52. https://doi.org/0.1007/s00122-015-2607-9

Endelman, JB, et al., 2018. Genetic variance partitioning and genome-wide prediction with allele dosage information in autotetraploid potato. Genetics, 209(1) pp. 77-87. https://doi.org/10.1534/genetics.118.300685

Hamilton, MG, et al., 2017 Computation of the inverse additive relationship matrix for autopolyploid and multiple-ploidy populations. Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-017-3041-y

Henderson, C, 1976 A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics pp. 69–83. https://doi.org/10.2307/2529339

Kerr, RJ, et al., 2012 Use of the numerator relation ship matrix in genetic analysis of autopolyploid species. Theoretical and Applied Genetics 124: 1271–1282. https://doi.org/10.1007/s00122-012-1785-y

Martini, JW, et al., 2018, The effect of the H$^{1}$ scaling factors $\tau$ and $\omega$ on the structure of H in the single-step procedure. Genetics Selection Evolution, 50(1), 16. https://doi.org/10.1186/s12711-018-0386-x

Mrode, R. A., 2014 Linear models for the prediction of animal breeding values. Cabi. 3rd ed.

Munoz, PR, et al., 2014 Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics 198: 1759–1768. https://doi.org/10.1534/genetics.114.171322

R Core Team, 2016 R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Resende, MF, et al., 2012 Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda l.). Genetics 190: 1503–1510. https://doi.org/10.1534/genetics.111.137026

Slater, AT, et al., 2014 Improving the analysis of low heritability complex traits for enhanced genetic gain in potato. Theoretical and applied genetics 127: 809–820. https://doi.org/10.1007/s00122-013-2258-7

Slater AT, et al., 2016 Improving genetic gain with genomic selection in autotetraploid potato. The Plant Genome 9. https://doi.org/10.3835/plantgenome2016.02.0021

Su, G, et al., 2012 Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PloS one 7:e45293. https://doi.org/10.1371/journal.pone.0045293

VanRaden, P, 2008 Efficient methods to compute genomic predictions. Journal of dairy science 91: 4414–4423. https://doi.org/10.3168/jds.2007-0980

Vitezica, ZG, et al., 2013 On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics 195: 1223–1230. https://doi.org/10.1534/genetics.113.155176

Yang, J, et al., 2010 Common snps explain a large proportion of the heritability for human height. Nature genetics 42: 565–569. https://doi.org/10.1038/ng.608