Lucía Gutiérrez

The Genetics of Winterhardiness in Barley: Perspectives from Genome-Wide Association Mapping

Winterhardiness is a complex trait that involves low temperature tolerance (LTT), vernalization sensitivity, and photoperiod sensitivity. Quantitative trait loci (QTL) for these traits were first identified using biparental mapping populations; candidate genes for all loci have since been identified and characterized. In this research we used a set of 148 accessions consisting of advanced breeding lines from the Oregon barley (Hordeum vulgare L. subsp vulgare) breeding program and selected cultivars that were extensively phenotyped and genotyped with single nucleotide polymorphisms. Using these data for genome‐wide association mapping we detected the same QTL and genes that have been systematically characterized using biparental populations over nearly two decades of intensive research. In this sample of germplasm, maximum LTT can be achieved with facultative growth habit, which can be predicted using a three‐locus haplotype involving FR‐H1, FR‐H2, and VRN‐H2. The FR‐H1 and FR‐H2 LTT QTL explained 25% of the phenotypic variation, offering the prospect that additional gains from selection can be achieved once favorable alleles are fixed at these loci.

Association Mapping of Malting Quality Quantitative Trait Loci in Winter Barley: Positive Signals from Small Germplasm Arrays

Malting quality comprises one of the most economically relevant set of traits in barley (Hordeum vulgare L.). It is a complex phenotype, expensive and difficult to measure, that would benefit from a marker‐assisted selection strategy. Malting quality is a target of the U.S. Barley Coordinated Agricultural Project (CAP) and development of winter habit malting barley varieties is a key objective of the U.S. barley research community. The objective of this work was to detect quantitative trait loci (QTL) for malting quality traits in a winter breeding program that is a component of the U.S. Barley CAP. We studied the association between five malting quality traits and 3072 single nucleotide polymorphisms (SNPs) from the barley oligonucleotide pool assay (BOPA) 1 and 2, assayed in advanced inbred lines from the Oregon State University (OSU) breeding program from three germplasm arrays (CAP I, CAP II, and CAP III). After comparing 16 models we selected a structured association model with posterior probabilities inferred from software STRUCTURE (QK) approach to use on all germplasm arrays. Most of the marker‐trait associations are germplasm‐ and environment‐specific and close to previously mapped genes and QTL relevant for malt and beer quality. We found alleles fixed by random genetic drift, novel unmasked alleles, and genetic‐background interaction. In a relatively small population size study we provide strong evidence for detecting true QTL.

Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America.

Key messageMulti-environment multi-QTL mixed models were used in a GWAS context to identify QTL for disease resistance. The use of mega-environments aided the interpretation of environment-specific and general QTL.AbstractDiseases represent a major constraint for barley (Hordeum vulgare L.) production in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f.sp. hordei) and leaf rust (caused by Puccinia hordei) are three of the most important diseases that affect the crop in the region. Since fungicide application is not an economically or environmentally sound solution, the development of durably resistant varieties is a priority for breeding programs. Therefore, new resistance sources are needed. The objective of this work was to detect genomic regions associated with field level plant resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Disease severities measured in multi-environment trials across the Americas and 1,096 SNPs in a population of 360 genotypes were used to identify genomic regions associated with disease resistance. Optimized experimental design and spatial modeling were used in each trial to estimate genotypic means. Genome-Wide Association Mapping (GWAS) in each environment was used to detect Quantitative Trait Loci (QTL). All significant environment-specific QTL were subsequently included in a multi-environment-multi-QTL (MEMQ) model. Geographical origin and inflorescence type were the main determinants of population structure. Spot blotch severity was low to intermediate while leaf and stripe rust severity was high in all environments. Mega-environments were defined by locations for spot blotch and leaf rust. Significant marker-trait associations for spot blotch (9 QTL), leaf (6 QTL) and stripe rust (7 QTL) and both global and environment-specific QTL were detected that will be useful for future breeding efforts.

Strategies for selecting crosses using genomic prediction in two wheat breeding programs

Cross prediction strategies for grain yield and baking quality traits were compared. Crosses for all parent combinations were obtained via genomic prediction models. Mid‐parent selection was similar to accounting for variance when selecting yield. The variance had a larger impact in cross predictions for quality traits.

Genome-Wide Association Mapping Identifies Disease-Resistance QTLs in Barley Germplasm from Latin America

Diseases are the main problem for barley in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f. sp. hordei), and leaf rust (caused by Puccinia hordei) are three of the most important diseases that attack the crop in the region. Chemical control of those diseases is both economically and environmentally inappropriate, making the development of durable resistant varieties a priority for breeding programs. However, the availability of new resistance sources is a limiting factor. The objective of this work was to detect genomic regions associated to durable resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Associations between disease severities measured in several environments across the Americas and 1,536 SNPs (belonging to the barley OPA1) in a population of 360 genotypes were used to identify genomic regions associated with disease. Several models for association mapping with mixed models were compared. These models considered either the structure of the population (Q) through PCA analysis, the identity by descent through coancestry information (K), or both. Results show significant marker-trait associations for spot blotch and leaf and stripe rust. Associations are environment specific.

Ascertainment bias from imputation methods evaluation in wheat.

BackgroundWhole-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.ResultsIn 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.ConclusionsPoorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.

Genome-Wide Association Mapping of Malting Quality Traits in Relevant Barley Germplasm in Uruguay

Knowledge about the genetic components of major malting quality traits is needed for efficient barley breeding, and although some of these traits have been mapped, little information about germplasm-specific QTL is known for South American germplasm. The aim of this study was to determine by genome-wide association mapping the key genetic basis of malting quality traits in a population of 76 different genotypes consisting of historical varieties, commercial cultivars, and advanced lines representative of barley breeding in Uruguay. Samples obtained in five contrasting environments were micromalted in order to obtain a phenotypic database. The population was genotyped with 1,033 polymorphic SNPs using the Illumina BOPA1. Marker-trait associations were detected through linkage disequilibrium mapping using a mixed linear model (MLM) Q + K containing a structure matrix (PCA) and a kinship matrix (K). Preliminary results showed QTL effects detected for all traits, with some genomic regions showing a high concentration of significant associations. Most QTL were environment specific. We are presently studying the relationship between malting quality traits and linkage disequilibrium blocks found in the population. The results provide some of the first data regarding genetic basis of malting quality relevant traits in the germplasm used in the region.

Genome-Wide Association Study Using Historical Breeding Populations Discovers Genomic Regions Involved in High-Quality Rice

Genome‐wide association study (GWAS) for rice quality was performed in two breeding populations. Twenty‐two putative quantitative trait loci (QTL) were associated to rice quality. A genomic region on chromosome 6 was associated with all quality traits in the tropical japonica population. Markers for favorable haplotypes are ready for immediate use for selection.

Resistance to Multiple Temperate and Tropical Stem and Sheath Diseases of Rice

Reaction to sheath blight, stem rot, and aggregated sheath spot were tested in 641 tropical japonica and indica rice lines. Disease resistance was mapped independently from flowering time and plant height. Quantitative trait loci of major effect for resistance to the three diseases were found. A multiple disease resistance quantitative trait locus was found on chromosome 9 across tropical japonica and indica populations.

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality

Key MessageMulti-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.AbstractMulti-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.