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Achieving durable resistance to leaf rust (LR), caused by Puccinia triticina, in wheat has been one of the main objectives of breeding programs. Durability of LR resistance is considered to be associated with adult plant resistance (APR) genes that are quantitatively inherited and whose expression is largely influenced by environment. Our objective was to study the effects and interactions of APR genes Lr34, Lr68 and Sr2 on LR response in Uruguay using two BC1F6 populations (‘LE2304*2/Parula’ and ‘ORL99192*2/Parula’). The experimental material was screened for LR in three artificially inoculated environments (La Estanzuela 2012 and 2013 and Young 2012). Linked molecular markers were used to detect the presence of the genes conferring resistance to LR. Gene Lr34 was fixed in the ‘LE2304*2/Parula’ population. Lr68+Sr2 resulted in a reduction of 79 % of the LR area under the disease progress curve (AUDPC), while the presence of Lr68 alone resulted in a reduction of 51 %. In the ‘ORL99192*2/Parula’ population, the combined effect of Lr34+Lr68+Sr2 led to the highest reduction of LR AUDPC (73 %). Individually, Lr34 and Lr68 resulted in a reduction of the LR AUDPC of 14 and 50 %, respectively. Sr2 alone did not reduce LR AUDPC but enhanced the effect of either Lr34 or Lr68. Unlike previous reports Lr68 conferred a higher level of LR resistance than Lr34 in Uruguay.

In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

Background Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. Results In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. Conclusions Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3120-5) contains supplementary material, which is available to authorized users.

Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding community from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and comparable to genomic selection. Despite the fact that the two methodological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome), they both consider the targeted traits (e.g. grain yield, growth, phenology, plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e. physiological) putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.

Manhattan plots of the GWAS analysis for real phenotype wheat data with 35 % missing rate and a Bonferroni threshold corrected by the effective number of independent markers. For each trait measured and each marker score matrix evaluated, a manhattan plot of the GWAS analysis is presented. The phenotype traits are: DH, Days to Heading; PH, Plant Height; SPM, Spikes Per Square Meter; TKW, Thousands Kernel Weight. The marker score matrices were: NImp (not imputed), Mean (mean imputed), MVN-EM (Multivariate Normal Expectation Maximization method) and RF (Random Forest method). QTL detected by the NImp matrix are in turquoise, QTL detected exclusively by the MVN-EM matrix are in coral, QTL detected exclusively by the Mean matrix are in green, and QTL detected exclusively by the RF matrix are in orchid. (PDF 745 KB)

Power (PO) and false positives rate (FPR) with 25 QTL and 50 % missing rate, for major and minor QTL to evaluate the GWAS performance based on simulated matrix with a α = 0.01 threshold. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM and Ysim-Mean ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 36 KB)

Power (PO) and false positives rate (FPR) with 25 QTL, for major and minor QTL for ascertainment bias in imputation performance comparison in barley, with a α = 0.01 threshold. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM and Ysim-Mean ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 34 KB)

QQ plots of the p-values from the GWAS analysis from real phenotype wheat data with 25 % missing rate and a Bonferroni threshold corrected by the effective number of independent markers. For each trait measured and each marker score matrix evaluated, a qq-plot of the p-values resulted form the GWAS analysis is presented. The marker score matrices were: NImp (not imputed) in turquoise, Mean (mean imputed) in green, MVN-EM (Multivariate Normal Expectation Maximization method) in coral and RF (Random Forest method) in orchid. The phenotype traits are: DH, days to heading; PH, Plant Height; SPM, Spikes Per Square Meter; TKW, Thousands Kernel Weight. (PDF 359 KB)

Power (PO) and false positives rate (FPR) with 25 QTL and 50 % missing rate, for major and minor QTL to evaluate the GWAS performance based on simulated matrix with a Bonferroni threshold. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM and Ysim-Mean ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 35 KB)

Power (PO) and false positives rate (FPR) with 25 QTL and 35 % missing rate, for major and minor QTL to evaluate the GWAS performance based on simulated matrix with a Bonferroni threshold corrected by the effective number of independent markers. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM, Ysim-Mean and Ysim-RF ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM, G Mean and G RF ). (PDF 156 KB)

Power (PO) and false positives rate (FPR) with 25 QTL and 25 % missing rate, for major and minor QTL to evaluate the GWAS performance based on simulated matrix with a Bonferroni threshold corrected by the effective number of independent markers. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM, Ysim-Mean and Ysim-RF ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM, G Mean and G RF ). (PDF 156 KB)

Power (PO) and false positives rate (FPR) for major and minor QTL with 25 QTL, for the golden standard from barley, with α = 0.01 threshold. Each parameter was calculated for the combinations of: number of QTL (q), heritabilties (h 2 ), a marker score matrix to simulate the QTL (i.e. Ysim-NoNA ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 28 KB)

Power (PO) and false positives rate (FPR) with 25 QTL, for major and minor QTL for ascertainment bias in imputation performance comparison in barley, with a Bonferroni threshold. Each parameter was calculated for the combinations of: heritabilties (h 2 ), marker score matrices to simulate the QTL (i.e. Ysim-NImp , Ysim-MVN-EM and Ysim-Mean ), and marker score matrices to perform the GWAS analysis (i.e. G NImp, G MVN-EM and G Mean ). (PDF 34 KB)

We investigated the allelic nature and map locations of Hordeum vulgare (barley) homologs to three classes of Arabidopsis low temperature (LT) regulatory genes—CBFs, ICE1, and ZAT12—to determine if there were any candidates for winterhardiness-related quantitative trait loci (QTL). We phenotyped the Dicktoo × Morex (D×M) mapping population under controlled freezing conditions and in addition to the previously reported 5H-L Fr-H1 QTL, observed three additional LT tolerance QTLs on 1H-L, 4H-S, and 4H-L. We identified and assigned either linkage map or chromosome locations to 1 ICE1 homolog, 2 ZAT12 homologs, and 17 of 20 CBF homologs. Twelve of the CBF genes were located on 5H-L and the 11 with assigned linkage map positions formed 2 tandem clusters on 5H-L. A subset of these CBF genes was confirmed to be physically linked, validating the map position clustering. The tandem CBF clusters are not candidates for the D×M LT tolerance Fr-H1 QTL, as they are ~30 cM distal to the QTL peak. No LT tolerance QTL was detected in conjunction with the CBF gene clusters in Dicktoo × Morex. However, comparative mapping using common markers and BIN positions established the CBF clusters are coincident with reported Triticeae LT tolerance and COR gene accumulation QTLs and suggest one or more of the CBF genes may be candidates for Fr-H2 in some germplasm combinations. These results suggest members of the CBF gene family may function as components of winterhardiness in the Triticeae and underscore both the importance of extending results from model systems to economically important crop species and in viewing QTL mapping results in the context of multiple germplasm combinations.

Association mapping has been proposed to identify polymorphisms involved in phenotypic variations and may prove useful in identifying interesting alleles for breeding purposes. Using this approach, a total of 382 cultivars and advanced lines of spring wheat obtained from three breeding programs (Chile, Uruguay and CIMMYT) were evaluated for plant height (PH), kernels per spike (KS), 1,000 kernel weight (TKW), grain yield and carbon isotope discrimination (Δ13C) and tested for genotyping-by-sequencing-derived SNP markers across the hexaploid wheat genome. A Bayesian clustering approach via Markov chain Monte Carlo was performed to examine the genetic differentiation (F ST) among different genetic groups. The results indicated the existence of two distinct and strongly differentiated genetic groups. Cluster I contained 215 genotypes (56.3 %), over 60 % (137/215) of which were collected from CIMMYT. Cluster II showed the highest F ST value, according to 95 % credible interval. Linkage disequilibrium (LD) among SNPs was calculated for the A, B and D genomes and at the whole-genome level. LD decayed over a longer genetic distance for the D genome than for the A and B genomes. In the A and B genomes, LD declined to 50 % of its initial value at about 2 cM. In the D genome, LD was much more extensive, declining to 50 % of its initial value only at 22 cM. In the whole genome, LD declined to 50 % of its initial value at an average of 4 cM. Important genomic regions associated with complex traits in spring wheat were identified. Selection on these regions may increase the efficiency of the current breeding programs. Although most of the associations were environment specific, some stable associations were detected for Δ13C, KS, PH and TKW. Chromosomes 1A, 3A, 4A and 5A were the most important chromosomes, as they comprised quantitative trait loci (QTL) for Δ13C, a trait that can be used as an indirect tool for increased water-use efficiency in wheat. Environment-specific genomic regions were detected, indicating the presence of QTL-by-environment interaction. To produce suitable genotypes under contrasting water availability conditions, QTL × E interactions (and genotype-by-environment interaction) should be considered in the current spring wheat breeding program.

Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding community from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and comparable to genomic selection. Despite the fact that the two methodological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome), they both consider the targeted traits (e.g. grain yield, growth, phenology, plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e. physiological) putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.

The vernalization gene VRN-1 has been identified as a MADS-box transcription factor orthologous to the meristem identity gene APETALA1 (AP1). A single copy of this gene was found in diploid wheat, but 2 copies were reported in barley. In this study, we present a detailed characterization of these 2 copies to understand their respective roles in the vernalization response. We identified 2 groups of barley bacterial artificial chromosomes (BACs), each containing 1 AP1 copy designated hereafter as HvAP1a and HvAP1b. A physical map of the VRN-H1 region showed that the HvAP1a BACs were part of the VRN-H1 region but that the HvAP1b BACs were not. Numerous structural changes were observed between the barley and wheat VRN-1 physical maps. In a population segregating for VRN-H1, the HvAP1a gene cosegregated with growth habit, suggesting that HvAP1a is the barley vernalization gene VRN-H1. The other copy, HvAP1b, was mapped on the centromeric region of chromosome 1H, the chromosome where vernalization gene VRN-H3 was previously mapped. We developed a mapping population segregating for VRN-H3 and showed that 2 molecular makers flanking HvAP1b locus were not linked to growth habit. The HvAP1b copy has a complete deletion of the first 2 exons, suggesting that it is a truncated pseudogene and not a candidate for VRN-H3. In summary, this study contributed a detailed physical map of the barley VRN-H1 region, showed several structural differences with the orthologous wheat region, and clarified the identity of the barley VRN-H1 gene.Key words: barley, vernalization, Vrn-1, physical map.

Vernalization, the requirement of a period of low temperature to induce transition from the vegetative to reproductive state, is an evolutionarily and economically important trait in the Triticeae. The genetic basis of vernalization in cultivated barley (Hordeum vulgare subsp. vulgare) can be defined using the two-locus VRN-H1/VRN-H2 model. We analyzed the allelic characteristics of HvBM5A, the candidate gene for VRN-H1, from ten cultivated barley accessions and one wild progenitor accession (subsp. spontaneum), representing the three barley growth habits - winter, facultative, and spring. We present multiple lines of evidence, including sequence, linkage map location, and expression, that support HvBM5A being VRN-H1. While the predicted polypeptides from different growth habits are identical, spring accessions contain a deletion in the first intron of HvBM5A that may be important for regulation. While spring HvBM5A alleles are typified by the intron-localized deletion, in some cases, the promoter may also determine the allele type. The presence/absence of the tightly linked ZCCT-H gene family members on chromosome 4H perfectly correlates with growth habit and we conclude that one of the three ZCCT-H genes is VRN-H2. The VRN-H2 locus is present in winter genotypes and deleted from the facultative and spring genotypes analyzed in this study, suggesting the facultative growth habit (cold tolerant, vernalization unresponsive) is a result of deletion of the VRN-H2 locus and presence of a winter HvBM5A allele. All reported barley vernalization QTLs can be explained by the two-locus VRN-H1/VRN-H2 model based on the presence/absence of VRN-H2 and a winter vs. spring HvBM5A allele., This work was supported by the United States Barley Genome Project (PMH, THHC), National Science Foundation Plant Genome Research (Grant DBI0110124, PHM, THHC), United States Department of Agriculture CSREES NRI (Grants 2003-00929 and 2004-01783, JD), Ministero dell’Università e della Ricerca Scientifica (Grant RBAU01MHMR_004, EF, NP), and the Spanish Ministry of Education and Research (CICYT, AGL2001-2289, AC).

Mol Gen Genomics (2005) 273: 54–65 DOI 10.1007/s00438-004-1095-4 O R I GI N A L P A P E R Daolin Fu AE Pe´ter Szu } cs AE Liuling Yan Marcelo Helguera AE Jeffrey S. Skinner Jarislav von Zitzewitz AE Patrick M. Hayes Jorge Dubcovsky Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat Received: 15 September 2004 / Accepted: 22 November 2004 / Published online: 3 February 2005 Springer-Verlag 2005 Abstract The broad adaptability of wheat and barley is in part attributable to their flexible growth habit, in that spring forms have recurrently evolved from the ancestral winter growth habit. In diploid wheat and barley growth habit is determined by allelic variation at the VRN-1 and/or VRN-2 loci, whereas in the polyploid wheat species it is determined primarily by allelic variation at VRN-1. Dominant Vrn-A1 alleles for spring growth habit are frequently associated with mutations in the promoter region in diploid wheat and in the A genome of common wheat. However, several dominant Vrn-A1, Vrn-B1, Vrn-D1 (common wheat) and Vrn-H1 (barley) alleles show no polymorphisms in the promoter region relative to their respective recessive alleles. In this study, we sequenced the complete VRN-1 gene from these accessions and found that all of them have large dele- tions within the first intron, which overlap in a 4-kb region. Furthermore, a 2.8-kb segment within the 4-kb region showed high sequence conservation among the different recessive alleles. PCR markers for these dele- tions showed that similar deletions were present in all the accessions with known Vrn-B1 and Vrn-D1 alleles, and in 51 hexaploid spring wheat accessions previously shown to have no polymorphisms in the VRN-A1 pro- moter region. Twenty-four tetraploid wheat accessions Communicated by W.R. McCombie D. Fu AE L. Yan AE J. Dubcovsky (&) Department of Plant Sciences, University of California, One Shields Av, Davis, CA 95616, USA P. Szu } cs Agricultural Research Institute of the Hungarian Academy of Sciences, 2462 Martonva´sa´r, Hungary M. Helguera EEA INTA Marcos Jua´rez, CC 21, 2580 Marcos Jua´rez, Argentina J. S. Skinner AE J. von Zitzewitz AE P. M. Hayes Department of Crop and Soil Science and Horticulture, Oregon State University, Corvallis, OR 97331, USA had a similar deletion in VRN-A1 intron 1. We hypothesize that the 2.8-kb conserved region includes regulatory elements important for the vernalization requirement. Epistatic interactions between VRN-H2 and the VRN-H1 allele with the intron 1 deletion suggest that the deleted region may include a recognition site for the flowering repression mediated by the product of the VRN-H2 gene of barley. Keywords Wheat AE Barley AE Vernalization AE VRN-1 AE Allelic variation Introduction Many cereal crops, such as wheat, barley and oat, are divided into spring and winter types based on their growth habit. Winter varieties require an extended per- iod of exposure to cold in order to flower, a process known as vernalization. Vernalization is defined as ‘‘the acquisition or acceleration of the ability to flower by a chilling treatment’’ (Chouard 1960). The physiology of vernalization has been studied extensively in cereals, but only a few genes from this molecular pathway are cur- rently known (Yan et al. 2003, 2004b). In wheat and barley, the determination of the ver- nalization requirement involves an epistatic interaction between the genetic loci VRN-1 and VRN-2 (Takahashi and Yasuda 1971; Tranquilli and Dubcovsky 2000). Positional cloning of these two genes from diploid wheat (Triticum monococcum L., 2n=14, genome A m A m ) re- vealed that VRN-1 encodes a MADS-box transcription factor that is orthologous to the Arabidopsis meristem identity gene APETALA1 (Yan et al. 2003), while the VRN-2 gene codes for a zinc finger-CCT domain tran- scription factor with no clear orthologues in Arabidopsis or rice (Yan et al. 2004b). The VRN-1 gene is dominant for the spring growth habit and it is up-regulated by vernalization in winter lines (Danyluk et al. 2003; Tre- vaskis et al. 2003; Yan et al. 2003), whereas the VRN-2