Analysis of a Multi-Environment Trial for Black Raspberry (Rubus occidentalis L.) Quality Traits.

U.S. black raspberry (BR) production is currently limited by narrowly adapted, elite germplasm. An improved understanding of genetic control and the stability of pomological traits will inform the development of improved BR germplasm and cultivars. To this end, the analysis of a multiple-environment trial of a BR mapping population derived from a cross that combines wild ancestors introgressed with commercial cultivars on both sides of its pedigree has provided insights into genetic variation, genotype-by-environment interactions, quantitative trait loci (QTL), and QTL-by-environment interactions (QEI) of fruit quality traits among diverse field environments. The genetic components and stability of four fruit size traits and six fruit biochemistry traits were characterized in this mapping population following their evaluation over three years at four distinct locations representative of current U.S. BR production. This revealed relatively stable genetic control of the four fruit size traits across the tested production environments and less stable genetic control of the fruit biochemistry traits. Of the fifteen total QTL, eleven exhibited significant QEI. Closely overlapping QTL revealed the linkage of several fruit size traits: fruit mass, drupelet count, and seed fraction. These and related findings are expected to guide further genetic characterization of BR fruit quality, management of breeding germplasm, and development of improved BR cultivars for U.S. production.

Saturated linkage map construction in Rubus idaeus using genotyping by sequencing and genome-independent imputation.

BACKGROUND: Rapid development of highly saturated genetic maps aids molecular breeding, which can accelerate gain per breeding cycle in woody perennial plants such as Rubus idaeus (red raspberry). Recently, robust genotyping methods based on high-throughput sequencing were developed, which provide high marker density, but result in some genotype errors and a large number of missing genotype values. Imputation can reduce the number of missing values and can correct genotyping errors, but current methods of imputation require a reference genome and thus are not an option for most species. RESULTS: Genotyping by Sequencing (GBS) was used to produce highly saturated maps for a R. idaeus pseudo-testcross progeny. While low coverage and high variance in sequencing resulted in a large number of missing values for some individuals, a novel method of imputation based on maximum likelihood marker ordering from initial marker segregation overcame the challenge of missing values, and made map construction computationally tractable. The two resulting parental maps contained 4521 and 2391 molecular markers spanning 462.7 and 376.6 cM respectively over seven linkage groups. Detection of precise genomic regions with segregation distortion was possible because of map saturation. Microsatellites (SSRs) linked these results to published maps for cross-validation and map comparison. CONCLUSIONS: GBS together with genome-independent imputation provides a rapid method for genetic map construction in any pseudo-testcross progeny. Our method of imputation estimates the correct genotype call of missing values and corrects genotyping errors that lead to inflated map size and reduced precision in marker placement. Comparison of SSRs to published R. idaeus maps showed that the linkage maps constructed with GBS and our method of imputation were robust, and marker positioning reliable. The high marker density allowed identification of genomic regions with segregation distortion in R. idaeus, which may help to identify deleterious alleles that are the basis of inbreeding depression in the species.

Strategies for transcriptome analysis in nonmodel plants.

Even with recent reductions in sequencing costs, most plants lack the genomic resources required for successful short-read transcriptome analyses as performed routinely in model species. Several approaches for the analysis of short-read transcriptome data are reviewed for nonmodel species for which the genome of a close relative is used as the reference genome. Two approaches using a data set from Phytophthora-challenged Rubus idaeus (red raspberry) are compared. Over 70000000 86-nt Illumina reads derived from R. idaeus roots were aligned to the Fragaria vesca genome using publicly available informatics tools (Bowtie/TopHat and Cufflinks). Alignment identified 16956 putatively expressed genes. De novo assembly was performed with the same data set and a publicly available transcriptome assembler (Trinity). A BLAST search with a maximum e-value threshold of 1.0 × 10(-3) revealed that over 36000 transcripts had matches to plants and over 500 to Phytophthora. Gene expression estimates from alignment to F. vesca and de novo assembly were compared for raspberry (Pearson's correlation = 0.730). Together, alignment to the genome of a close relative and de novo assembly constitute a powerful method of transcriptome analysis in nonmodel organisms. Alignment to the genome of a close relative provides a framework for differential expression testing if alignments are made to the predefined gene-space of a close relative and de novo assembly provides a more robust method of identifying unique sequences and sequences from other organisms in a system. These methods are considered experimental in nonmodel systems, but can be used to generate resources and specific testable hypotheses.

Inheritance of Phytophthora root rot resistance in red raspberry determined by generation means and molecular linkage analysis.

Classical and molecular methodologies were used to determine the inheritance of Phytophthora root rot (PRR) resistance in red raspberry. The varieties 'Latham' and 'Titan,' resistant and susceptible, respectively, were used to create F(1), F(2), B(1), B(2), and S(1) populations for analysis. Generational means analysis was used to calculate the components of genetic variation and estimates of narrow and broad sense heritability for the plant disease index and the incidence of petiole lesions. The plant disease index showed additive genetic variation with additional significant interactions, but the incidence of petiole lesions was non-additive. A dominant, two-gene model was shown to be the best fit for the observed segregation ratios when classification for resistance was based on a combination of all criteria measured. Molecular linkage maps were generated from the segregating B(2) population. Linkage maps of both parents were constructed from amplified fragment length polymorphism (AFLP), Random amplified polymorphic DNA (RAPD), and uncharacterized resistant gene analog polymorphism (RGAP) markers with seven linkage groups each totaling 440 and 370 cM of genetic distance, respectively. An analysis of the distributional extremes of the B(2) population identified several RAPD markers clustered on two linkage groups associated with PRR resistance. QTL analysis identified two similar genomic regions on each map that explained significant percentages of phenotypic variation observed for the disease assessment criteria. Genetic mapping supports the dominant two-gene model developed from generational means analysis. The results reconcile conflicting reports on inheritance of PRR resistance, provide a basis for further investigation of durable resistance to Phytophthora caused diseases, and indicates that recurrent selection is the appropriate approach for the development of new resistant cultivars.