Biology subjects

Sorrells, M. E.

Publications and source records attributed to Sorrells, M. E..

10 recordsLinked to original sources

bioRxiv2026

Genome wide association analysis of resistance to scald in an adapted multiparent winter malting barley population

Scald, caused by the fungus Rhynchosporium graminicola Heinsen 1897, is a major foliar disease in winter malting barley (Hordeum vulgare L). Resistance to scald in winter malting barley is controlled by major and minor resistance genes. We used a population of 377 lines derived from biparental crosses among five winter malting barley parents to analyze resistance to scald and associated agronomic traits. Increased winter survival and later heading dates were negatively correlated with increased resistance, whereas increased height was positively correlated with resistance. A genome-wide association study (GWAS) for resistance to scald was analyzed with multiple models, using 14,789 SNP and 374 lines. The similarities and differences between the models were identified in SNP trait associations and phenotypic effect sizes. SNP associations identified a large region on chromosome 3H across models. FarmCPU identified additional associations on chromosomes 2H, 3H, 4H and 7H. Linkage disequilibrium on chromosome 3H and GWAS for resistance to scald using the Rrs1-linked marker, HVS3, as a covariate confirmed Rrs1 was segregating in this population. GWAS for winter survival, heading date and plant height identified associations across the genome, with chromosome 2H showing SNP-trait colocalizations between resistance to scald, winter survival, heading date and plant height. Breeding for durable resistance to scald in winter malting barley can include pyramiding major resistance loci, such as Rrs1, as well as QTL for disease resistance and agronomic traits. PLAIN LANGUAGE SUMMARYO_ST_ABSGenetic architecture of resistance to scald in winter malting barleyC_ST_ABSScald is an important foliar pathogen in winter malting barley, affecting both grain yield and quality. While resistance to scald is controlled by major and minor resistance genes, agronomic traits are also known to limit the spread of scald in barley. We determined the genetic architecture using a large multiparent population of winter malting barley. The FarmCPU genome-wide association model proved optimal for defining the resistance genes, with the major resistance gene, Rrs1, conferring 29% of the variation in this population. Fewer days to heading and taller plants contributed to plant avoidance of scald. Reduced canopy coverage in plants with low winter survival led to less scald severity. A region of the genome contributing a minor resistance effect was co-localized with a region for plant height, heading date and winter survival. Core IdeasO_LIResistance to scald in a large multiparent population was derived from a major resistance gene (Rrs1) and several smaller effect QTLs C_LIO_LIRrs1 resistance was derived from Lightning and is located within a large linkage block on Chromosome 3H C_LIO_LIFewer days to heading and taller plants were correlated with less disease in the multiparent winter malting barley population in NY state C_LIO_LIA QTL for resistance to scald co-localized on chromosome 2H with winter survival, heading date, and plant height C_LIO_LIFarmCPU was an optimal model for association analysis for resistance to scald in the multiparent unbalanced diallel population. C_LI

plant biology↗

bioRxiv2021

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel models that encompassed metabolome-wide mGWAS results, or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel, consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We tested if similar metabolites had consistent model ranks and found that, while different metrics of similarity had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.

genomics↗

bioRxiv2021

Selection for seed size has indirectly shaped specialized metabolite abundance in oat (Avena sativa L.)

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties and their agronomic role in mitigating disease severity, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic and phenotypic analyses for three types of seed specialized metabolites - avenanthramides, avenacins, and avenacosides - we found reduced genetic variation in modern germplasm compared to diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes, but avenacoside abundance decreases with seed size and plant breeding intensity. Overall, we show that increased seed size associated with plant breeding has uneven effects on the seed metabolome, and broadly contributes to understanding how selection shapes plant specialized metabolism.

plant biology↗

bioRxiv2021

Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 93% imputation accuracy with 0.01x sequence coverage, which was only slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.9%). Compared to Beagle, on average, PHG imputation was ~4% (p-value = 0.00027) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. The reduced accuracy of imputation with GBS data (90.4%) is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in our reference panel. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

genomics↗

bioRxiv2021

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

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.

genetics↗

bioRxiv2020

Translating insights from the seed metabolome into improved prediction for healthful compounds in oat

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Despite these characteristics, little is known regarding the genetic controllers of variation for these compounds in oat seed. We sought to characterize natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leverage this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for eight of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional ‘omics’ data to a set of biologically-meaningful variables and translate inferences on these data into improved breeding decisions.View Full Text

genomics↗

bioRxiv2020

Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

Breeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

genetics↗

bioRxiv2018

Use of Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype x environment (G x E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregon, Mexico over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.

genomics↗

bioRxiv2018

A low resolution epistasis mapping approach to identify chromosome arm interactions in allohexaploid wheat

1Epistasis is an important contributor to genetic variance, even in inbred populations where it is present as additive by additive interactions. Testing for epistasis presents a multiple testing problem as the search space for modest numbers of markers is large. Additionally, single markers do not necessarily track functional units of interacting chromatin as well as haplotype based methods do. To harness the power of multiple markers while drastically minimizing the number of tests conducted, we present a low resolution test for epistatic interactions across whole chromosome arms. Two additive genetic covariance matrices are constructed from markers on two different chromosome arms. The Hadamard product of these additive covariance matrices is then used to produce the additive by additive epistasis covariance matrix between the two chromosome arms. The covariance matrices are subsequently used to estimate an epistatic interaction variance parameter in a mixed model framework, while correcting for background additive and epistatic effects. We find significant epistatic interactions for 2% of interactions tested for four agronomic traits in a population of winter wheat. Interactions across homeologous chromosome arms were identified, but were less abundant than other interaction chromosome arm pairs. Of these, homeologous chromosome arm pair 4BL and 4DL showed a strong relationship between the product of their additive effects and the interaction effect that may be indicative of functional redundancy. Several chromosome arms were involved in many interactions across the genome, suggesting that they may contain important large effect regulatory factors. The differential patterns of epistasis across different traits suggests that detection of epistatic interactions is robust when correcting for background additive and epistatic effects in the population. The low resolution epistasis mapping method presented here identifies important epistatic interactions with a limited number of statistical tests at the cost of relatively lower precision.

genetics↗

bioRxiv2018

Prediction of subgenome additive and interaction effects in allohexaploid wheat

1Whole genome duplications have played an important role in the evolution of angiosperms. These events often occur through hybridization between closely related species, resulting in an allopolyploid with multiple subgenomes. With the availability of affordable genotyping and a reference genome to locate markers, breeders of allopolyploids now have the opportunity to manipulate subgenomes independently. This also presents a unique opportunity to investigate epistatic interactions between homeologous orthologs across subgenomes. We present a statistical framework for partitioning genetic variance to the subgenomes of an allopolyploid, predicting breeding values for each subgenome, and determining the importance of inter-genomic epistasis. We demonstrate using an allohexaploid wheat breeding population evaluated in Ithaca, NY and an important wheat dataset previously shown to demonstrate non-additive genetic variance. Subgenome covariance matrices were constructed and used to calculate subgenome interaction covariance matrices across subgenomes for variance component estimation and genomic prediction. We propose a method to extract population structure from all subgenomes at once before covariances are calculated to reduce collinearity between subgenome estimates. Variance parameter estimation was shown to be reliable for additive subgenome effects, but was less reliable for subgenome interaction components. Predictive ability was equivalent to current genomic prediction methods. Including only inter-genomic interactions resulted in the same increase in accuracy as modeling all pairwise marker interactions. Thus, we provide a new tool for breeders of allopolyploid crops to characterize the genetic architecture of existing populations, determine breeding goals, and develop new strategies for selection of additive effects and fixation of inter-genomic epistasis.

genetics↗