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The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait. [ABSTRACT FROM AUTHOR]

Genomic selection uses marker-based predictions to improve and accelerate the breeding process. Numerous studies have investigated the accuracy of genomic predictions; however, few studies have directly compared genomic and phenotypic selection. We compared genomic and phenotypic selection in five sets of selection candidates from a barley breeding program. In each set, about 96 breeding lines were genotyped with 1536 single nucleotide polymorphism (SNP) markers and phenotyped for yield, Fusarium head blight (FHB) severity, and deoxynivalenol (DON) concentration. A set of 168 lines and the same set of SNP markers were used to train a prediction model and predict the performance of the selection candidates using ridge regression best linear unbiased prediction. The best-performing 10% of the breeding lines in each selection candidate set were selected using both methods and revaluated in several trials. We found similar significant response to selection using genomic and phenotypic selection for FHB severity and DON concentration, and no significant response for yield using either method. For all traits, genomic selection significantly increased genetic similarity compared with the selection candidates. In addition, genomic selection, compared with phenotypic selection, resulted in an increase in the frequency of favorable alleles. Three indirect selection methods for DON concentration, (predicted FHB severity, empirical FHB severity, and predicted DON concentration) performed similarly to direct phenotypic selection for DON, but differed considerably in cost. We conclude that the use of genomic selection for yield and FHB breeding in barley should result in gains from response to selection that are similar to the gains obtained using phenotypic selection, but with a shorter breeding cycle time and lower cost. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) and its associated mycotoxin, deoxynivalenol (DON), are the major biotic factors limiting cereal production in many parts of the world. A recent association mapping (AM) study of US six-row spring barley identified several modest effect quantitative trait loci (QTL) for DON and FHB. To date, few studies have attempted to verify the results of association analyses, particularly for complex traits such as DON and FHB resistance in barley. While AM methods use measures to control for the effects of population structure and multiple testing, false positive associations may still occur. A previous AM study used elite breeding germplasm to identify QTL for FHB and DON. To verify the results of that study, we evaluated the effects of the nine DON QTL using near-isogenic lines (NILs). We created families of contrasting homozygous haplotypes from lines in the original AM populations that were heterozygous for the DON QTL. Seventeen NIL families were evaluated for FHB and DON in three field experiments. Significant differences between contrasting NIL haplotypes were detected for three QTL across environments and/or genetic backgrounds, thereby confirming QTL from the original AM study. Several explanations for those QTL that were not confirmed are discussed, including the effect of genetic background and incomplete sampling of relevant haplotypes. [ABSTRACT FROM AUTHOR]

Assignment or exclusion of an individual to specific populations or cultivars based on molecular genetic markers provides an attractive approach for varietal identification at the individual level in cross-pollinated plant species. The objectives of this study were (i) to explore the molecular diversity and relationships between Australasian perennial ryegrass (Lolium perenne L.) populations; (ii) to investigate accuracy of assignment of individuals to different types of populations including ecotypic, nonrestricted- and restricted-based cultivars; and (iii) to determine the effect of variable number of SSR loci and different statistical analysis methods on assignment accuracy. Eight forage perennial ryegrass populations comprising 48 individual plants per population were genotyped with 29 simple sequence repeat (SSR) marker loci. The number of alleles per locus ranged from 3.72 to 6.76. The mean observed heterozygosity varied from 0.419 to 0.538. Various genetic distance estimates and clustering methods obtained results consistent with breeding history. Genetic variation among (8.7%) and within populations (91.3%) was significant. Accuracy of individual assignment differed by population type, and it was higher (>90%) for restricted-based cultivars than for ecotypic and nonrestricted-based populations. These results indicate that SSR marker profiles can be effectively used to assign individuals for outbreeding populations such as perennial ryegrass. The approach used in this study may be useful for ryegrass germplasm management issues such as cultivar identification at the individual level. [ABSTRACT FROM AUTHOR]

The symbiotic association between perennial ryegrass (Lolium perenne L.) and the fungal endophyte Neotyphodium lolii is associated with host-specific adaptations, particularly in response to abiotic and biotic stresses. Knowledge of the origin of the symbiosis and the contribution of endophyte genotype to host phenotypic variation is currently limited. Simple sequence repeat (SSR) markers were used to assess endophyte genetic diversity in a globally distributed collection of perennial ryegrass accessions. Consistent in planta detection was achieved with 18 of 22 SSR markers (primer pairs). Endophytes representing as many as four different taxa were detected in 42 accessions from 20 different countries, N. lolii being predominant. A total of 33 unique N. lolii genotypes were discriminated, of which 29 clustered into three major groups with limited within-group variation. The three major N. lolii groups were associated with distinct perennial ryegrass chloroplast haplotypes. The alkaloid profiles of accessions were apparently associated with the presence of specific N. lolii genotypes. Genotypic analysis provides a powerful method for genetic dissection of the grass-endophyte interaction and prediction of phenotypic variation based on genotypic variation. [ABSTRACT FROM AUTHOR]

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Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical. [ABSTRACT FROM AUTHOR]

Septoria speckled leaf blotch (SSLB), caused by Septoria passerinhi, has emerged as one of the most important foliar diseases of barley in the Upper Midwest region of the United States. To map and tag genes for SSLB resistance, we developed two populations derived from the resistant accessions CIho 4780 and CIho 10644 and the susceptible malting cv. Foster. Segregation analysis of F2 plants or F2:3 families from the Foster/CIho 4780 and Foster/CIho 10644 populations revealed that a single dominant gene conferred resistance at the seedling stage. Bulked segregant analysis identified an amplified fragment length polymorphism marker, E-ACT/M-CAA-170, that co-segregated with the SSLB resistance gene Rsp2 in the Foster/CIho 4780 F2 population. Southern hybridization analysis with DNA from the wheat/barley addition lines localized E-ACT/M-CAA-170 on the short arm of the barley chromosome 5(1H). Restriction fragment length polymorphism analysis with DNA clones previously mapped to the short arm of chromosome 5(1H) placed Rsp2 at a position flanked by the markers Act8 and ksuD14. A sequence-characterized amplified region (SCAR) marker (E-ACT/M-CAA-170a) was developed that co-segregated with not only Rsp2 in the Foster/CIho 4780 population but also resistance gene Rsp3 in the Foster/CIho 10644 population. This result indicates that Rsp3 is closely linked to Rsp2 on the short arm of chromosome 5(1H). The utility of SCAR marker E-ACT/M-CAA-170a for selecting Rsp2 in two different breeding populations was validated. [ABSTRACT FROM AUTHOR]