AGHmatrix: R Package to Construct Relationship Matrices for Autotetraploid and Diploid Species: A Blueberry Example

Abstract

Progress in the rate of improvement in autopolyploid species has been limited compared with diploids, mainly because software and methods to apply advanced prediction and selection methodologies in autopolyploids are lacking. The objectives of this research were to (i) develop an R package for autopolyploids to construct the relationship matrix derived from pedigree information that accounts for autopolyploidy and double reduction and (ii) use the package to estimate the level and effect of double reduction in an autotetraploid blueberry breeding population with extensive pedigree information. The package is unique, as it can create A-matrices for different levels of ploidy and double reduction, which can then be used by breeders to fit mixed models in the context of predicting breeding values (BVs). Using the data from this blueberry population, we found for all the traits that tetrasomic inheritance creates a better fit than disomic inheritance. In one of the five traits studied, the level of double reduction was different from zero, decreasing the estimated heritability, but it did not affect the prediction of BVs. We also discovered that different depths of pedigree would have significant implications on the estimation of double reduction using this approach. This freely available R package is available for autopolyploid breeders to estimate the level of double reduction present in their populations and the impact in the estimation of genetic parameters as well as to use advanced methods of prediction and selection.

Genetic Covariance of Environments in the Potato National Chip Processing Trial

“…A was calculated with R package AGHmatrix version 0.0.3003 (Amadeu et al, 2016) under the assumption of random chromosome segregation (i.e., no double reduction). A was calculated with R package AGHmatrix version 0.0.3003 (Amadeu et al, 2016) under the assumption of random chromosome segregation (i.e., no double reduction).…”

Insights Into the Genetic Basis of Blueberry Fruit-Related Traits Using Diploid and Polyploid Models in a GWAS Context

“…To assess the genetic structure of blueberry population, the Principal Components Analysis (PCA) was performed using the marker-based relationship matrix as input. Diploid and tetraploid genomic relationship matrices were computed with the AGHmatrix R-package (Amadeu et al, 2016). The Discriminant Analysis of Principal Components (DAPC) was conducted to cluster genetically similar individuals using the Bayesian Information Criterion (BIC) to select the best supported model, as implemented in the R package adegenet v. 1.3-1 (Jombart and Ahmed, 2011).…”

“…For tetraploid, the K-matrix was constructed assuming tetrasomic inheritance (Slater et al, 2013), while for the diploid model it was built considering the algorithm proposed by VanRaden (2008). Both matrices were computed using the AGHmatrix R-package (Amadeu et al, 2016). To correct for population structure, PCA analysis was computed internally using the GWASpoly package and the four principal components were further used in GWAS analyses.…”

“…However, it is noteworthy that this conclusion is limited to our breeding population. In more complex pedigrees, for example, the usage of relationship matrix for autotetraploids might impact the final results (Kerr et al, 2012;Amadeu et al, 2016). LD is another population parameter that significantly affects GWAS results.…”

Association Mapping Considering Allele Dosage: An Example of Forage Traits in an Interspecific Segmental Allotetraploid Urochloa spp. Panel

“…In turn, Inostroza et al (2018) only detected significant SNP–trait association in the allotetraploid clover ( Trifolium repens L.) when GWAS analyses were performed with tetraploid genetic models, showing the importance of considering the correct allele dosage. Additionally, improvements in genomic studies in polyploids were provided by the use of new methods to estimate the relationship matrix in tetraploids as described by Endelman et al (2018) and Amadeu et al (2016), followed by the development of new software to estimate the genotype call in tetraploids such as ClusterCall (Schmitz Carley et al, 2017) and updog (Gerard et al, 2018).…”