The efficiency of phenotype-based assessments of plant variety protection and registration could be improved by the integration of DNA-based testing. We review the current and proposed models in the era of next generation breeding.

Identification and registration of new rice varieties are very important to be free from environmental effects and using molecular markers that are more reliable. The objectives of this study were, first, the identification and distinction of 40 rice varieties consisting of local varieties of Iran, improved varieties, and IRRI varieties using PIC, and discriminating power, second, cluster analysis based on Dice similarity coefficient and UPGMA algorithm, and, third, determining the ability of microsatellite markers to separate varieties utilizing the best combination of markers. For this research, 12 microsatellite markers were used. In total, 83 polymorphic alleles (6.91 alleles per locus) were found. In addition, the variation of PIC was calculated from 0.52 to 0.9. The results of cluster analysis showed the complete discrimination of varieties from each other except for IR58025A and IR58025B. Moreover, cluster analysis could detect the most of the improved varieties from local varieties. Based on the best combination of markers analysis, five pair primers together have shown the same results of all markers for detection among all varieties. Considering the results of this research, we can propose that microsatellite markers can be used as a complementary tool for morphological characteristics in DUS tests.

Many traits play essential roles in determining crop yield. Wide variation for morphological traits exists in Hordeum vulgare L., but the genetic basis of this morphological variation is largely unknown. To understand genetic basis controlling morphological traits affecting yield, a barley doubled haploid population (146 individuals) derived from Clipper × Sahara 3771 was used to map chromosome regions underlying days to awn appearance, plant height, fertile spike number, flag leaf length, spike length, harvest index, seed number per plant, thousands kernel weight, and grain yield. Twenty-seven QTLs for nine traits were mapped to the barley genome that described 3–69% of phenotypic variations; and some genomic regions harbor a given QTL for more than one trait. Out of 27 QTLs identified, 19 QTLs were novel. Chromosomal regions on 1H, 2H, 4H, and 6H associated with seed grain yield, and chromosome regions on 2H and 6H had major effects on grain yield (GY). One major QTL for seed number per plant was flanked by marker VRS1-KSUF15 on chromosome 2H. This QTL was also associated with GY. Some loci controlling thousands kernel weight (TKW), fertile spike number (FSN), and GY were the same. The major grain yield QTL detected on linkage PSR167 co-localized with TAM10. Two major QTLs controlling TKW and FSN were also mapped at this locus. Eight QTLs on chromosomes 1H, 2H, 3H, 4H, 5H, 6H, and 7H consistently affected spike characteristics. One major QTL (ANIONT1A-TACMD) on 4H affected both spike length (SL) and spike number explained 9 and 5% of the variation of SL and FSN, respectively. In conclusion, this study could cast some light on the genetic basis of the studied pivotal traits. Moreover, fine mapping of the identified major effect markers may facilitate the application of molecular markers in barley breeding programs.

Recent innovations in breeding technologies have reduced the timeframe to develop improved plant varieties compared to conventional breeding processes. Technologies like speed breeding, or rapid generation advancement, may also accelerate the process of statutory variety registration. Within this procedure, improved varieties are required to satisfy distinctness, uniformity and stability (DUS) criteria to establish the unique identity of a given submission during the variety registration process. The DUS standard also provides a solid basis for seed certification, Plant Breeders' Rights, as well as variety maintenance throughout commercial lifespan of varieties. Currently, the overall timeline of variety registration may vary from two to four years, depending on crop type and country. In this article, we propose the concept of 'Speed DUS Testing': a rapid phenotype-based method, which could be integrated with approaches that take advantage of DNA markers. We compare methods and discuss how DUS testing could be modernized to fast-track variety registration.

Elucidating marker-trait associations would have fruitful implications in distinctness, uniformity, and stability (DUS) tests of new varieties required for both variety registration and granting plant breeders' rights. As the number of new varieties with narrow genetic bases increases, the necessity for deployment of molecular markers to complement morphological DUS traits gets particular attention. We used simple sequence repeats (SSRs) and sequence related amplification polymorphisms (SRAPs) markers in association mapping of morphological traits in a collection of 143 barley landraces and advanced breeding lines. This panel represented a diverse and uniform sample in terms of both quantitative and categorical traits whilst it was structurally partitioned by number of ear rows (six-and two-rowed) and seasonal growth habit (winter and spring types) characteristics. SSRs were more powerful compared with SRAPs in separating six-and two-rowed genotypes based on both model-based Bayesian and neighbor joining clustering methods. A number of associated SSR and SRAP markers were found for 15 out of 36 DUS traits after considering Bonferroni correction through linear models (GLM and MLM) and chi-square-based tests (SA and AAT). This is also the first report of association of awn roughness and grain color with molecular markers in barley. Moreover, SSR marker BMAC0113 appeared associated with time of ear emergence (TEE), confirming previous findings. These markers could be beneficial to complement and speed up DUS testing of new varieties, as well as for improving management of barley reference collections.Additional keywords: LD mapping; Hordeum vulgare L.; DUS traits; SSRs & SRAPs markers. Abbreviations used: AAT (allelic association test); AFLP (amplified fragment length polymorphism); AR (awn roughness); DUS (distinctness, uniformity, stability); ED (ear density); GLM (general linear model); GSLN (spiculation of inner lateral nerves of lemma); KCAL (color of grain aleurone layer); MLM (mixed linear model); NER (number of ear rows); SA (stratified analysis); SGH (seasonal growth habit); SRAP (sequence-related amplified polymorphism); SSR (simple sequence repeats); TEE (time of ear emergence).