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.

Polyploidization is an ancient and recurrent process in plant evolution, impacting the diversification of natural populations and plant breeding strategies. Polyploidization occurs in many important crops; however, its effects on inheritance of many agronomic traits are still poorly understood compared with diploid species. Higher levels of allelic dosage or more complex interactions between alleles could affect the phenotype expression. Hence, the present study aimed to dissect the genetic basis of fruit-related traits in autotetraploid blueberries and identify candidate genes affecting phenotypic variation. We performed a genome-wide association study (GWAS) assuming diploid and tetraploid inheritance, encompassing distinct models of gene action (additive, general, different orders of allelic interaction, and the corresponding diploidized models). A total of 1,575 southern highbush blueberry individuals from a breeding population of 117 full-sib families were genotyped using sequence capture and next-generation sequencing, and evaluated for eight fruit-related traits. For the diploid allele calling, 77,496 SNPs were detected; while 80,591 SNPs were obtained in tetraploid, with a high degree of overlap (95%) between them. A linear mixed model that accounted for population and family structure was used for the GWAS analyses. By modeling tetraploid genotypes, we detected 15 SNPs significantly associated with five fruit-related traits. Alternatively, seven significant SNPs were detected for only two traits using diploid genotypes, with two SNPs overlapping with the tetraploid scenario. Our results showed that the importance of tetraploid models varied by trait and that the use of diploid models has hindered the detection of SNP-trait associations and, consequently, the genetic architecture of some commercially important traits in autotetraploid species. Furthermore, 14 SNPs co-localized with candidate genes, five of which lead to non-synonymous amino acid changes. The potential functional significance of these SNPs is discussed.

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