The premature germination of seeds before harvest, known as preharvest sprouting (PHS), is a serious problem in all wheat growing regions of the world. In order to determine genetic control of PHS resistance in white wheat from the relatively uncharacterized North American germplasm, a doubled haploid population consisting of 209 lines from a cross between the PHS resistant variety Cayuga and the PHS susceptible variety Caledonia was used for QTL mapping. A total of 16 environments were used to detect 15 different PHS QTL including a major QTL, QPhs.cnl-2B.1, that was significant in all environments tested and explained from 5 to 31% of the trait variation in a given environment. Three other QTL QPhs.cnl-2D.1, QPhs.cnl-3D.1, and QPhs.cnl-6D.1 were detected in six, four, and ten environments, respectively. The potentially related traits of heading date (HD), plant height (HT), seed dormancy (DOR), and rate of germination (ROG) were also recorded in a limited number of environments. HD was found to be significantly negatively correlated with PHS score in most environments, likely due to a major HD QTL, QHd.cnl-2B.1, found to be tightly linked to the PHS QTL QPhs.cnl-2B.1. Using greenhouse grown material no overlap was found between seed dormancy and the four most consistent PHS QTL, suggesting that greenhouse environments are not representative of field environments. This study provides valuable information for marker-assisted breeding for PHS resistance, future haplotyping studies, and research into seed dormancy.

Reference populations are valuable resources in genetics studies for determining marker order, marker selection, trait mapping, construction of large-insert libraries, cross-referencing marker platforms, and genome sequencing. Reference populations can be propagated indefinitely, they are polymorphic and have normal segregation. Described are two new reference populations who share the same parents of the original wheat reference population Synthetic W7984 (Altar84/ Aegilops tauschii (219) CIGM86.940) x Opata M85, an F(1)-derived doubled haploid population (SynOpDH) of 215 inbred lines and a recombinant inbred population (SynOpRIL) of 2039 F(6) lines derived by single-plant self-pollinations. A linkage map was constructed for the SynOpDH population using 1446 markers. In addition, a core set of 42 SSR markers was genotyped on SynOpRIL. A new approach to identifying a core set of markers used a step-wise selection protocol based on polymorphism, uniform chromosome distribution, and reliability to create nested sets starting with one marker per chromosome, followed by two, four, and six. It is suggested that researchers use these markers as anchors for all future mapping projects to facilitate cross-referencing markers and chromosome locations. To enhance this public resource, researchers are strongly urged to validate line identities and deposit their data in GrainGenes so that others can benefit from the accumulated information.

processing and end-use characteristics, which depend on protein hydration and development through mixing. Hybridizations between hard and soft wheat types could be a sourceHard wheat is generally used for making bread-type of novel variation for wheat quality improvement. This study was conducted to identify genomic regions related to differences in milling and products, and soft wheat is generally preferred for baking quality between a soft and a hard cultivar of hexaploid wheat pastry-type products. Hard grain requires more energy (Triticum aestivum L.). A population of 101 double-haploid lines was to be reduced to flour than soft grain, and its starch generated from a cross between Grandin, a hard spring wheat variety, granules are damaged more during milling. Damaged and AC Reed, a soft spring wheat variety. The genetic map included starch granules absorb more water, thereby altering sev-320 markers in 43 linkage groups and spanned 3555 cM. Quadrumateral baking properties (Mok and Dick, 1991). milled flour yield, softness equivalent, flour protein content and alkaline Hybridizations between hard and soft wheat types water retention capacity were evaluated for three locations and one year, could expand the genetic base of wheat breeding and and Allis-Chalmers milling, mixograph, and cookie baking tests were create new possibilities for combinations of desirable completed without replication. The effect of qualitative variation for alleles from both germplasm subgroups. However, this kernel texture, caused by the segregation of the Hardness gene, was controlled by regression on texture class. The residual variance was used type of cross is not common practice in wheat breeding for composite interval mapping, and QTLs on 1A, 1B, 1A/D, 2A, 2B, because the two classes have distinct quality goals. 2D, 3A/B, 4B, 5B and 6B were detected. The effect of some QTLs was Carver (1996) compared interclass hybrids, backcrosses opposite to the direction expected on the basis of parental phenotypes. and progeny from a hard ϫ hard cross, and concluded The hard wheat parent contributed alleles favorable for soft wheat variethat the interclass crosses resulted in progenies with ties at QTLs on 1AS,L, 1BL-2, and 6B, whereas the soft parent contribhigher grain yield but lower flour yield and larger variuted alleles for higher protein content at QTLs on 2BL-1, 4B-1, and 6B ability for quality traits, and that recovering the quality and higher flour yield on 2BL-2 and 4B-2. These results indicated that profile of the hard type through intensive selection would hard ϫ soft wheat crosses have considerable potential for improving be feasible. Identification of quantitative trait loci (QTL) milling and baking quality of either class.related to quality differences between classes could help in planning complementary crosses and backcrosses, and in designing selection schemes to recover the quality char-

Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Abstract Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.