Search

Garden roses are an economically important horticultural crop worldwide, and two major fungal pathogens, black spot (Diplocarpon rosae F.A. Wolf) and cercospora leaf spot of rose (Rosisphaerella rosicola Pass.), affect both the health and ornamental value of the plant. Most studies on black spot disease resistance have focused on diploid germplasm, and little work has been performed on cercospora leaf spot resistance. With the use of newly developed software tools for autopolyploid genetics, two interconnected tetraploid garden rose F1 populations (phenotyped over the course of 3 years) were used for quantitative trait locus (QTL) analysis of black spot and cercospora leaf spot resistance as well as plant defoliation. QTLs for black spot resistance were mapped to linkage groups (LGs) 1–6. QTLs for cercospora resistance and susceptibility were found in LGs 1, 4, and 5 and for defoliation in LGs 1, 3, and 5. The major locus on LG 5 for black spot resistance coincides with the previously discovered Rdr4 locus inherited from Rosa L. ‘Radbrite’ (Brite Eyes™), the common parent used in these mapping populations. This work is the first report of any QTL for cercospora resistance/susceptibility in tetraploid rose germplasm and the first report of defoliation QTL in roses. A major QTL for cercospora susceptibility coincides with the black spot resistance QTL on LG 5 (Rdr4). A major cercospora resistance QTL was found on LG 1. These populations provide a genetic resource that will further the knowledge base of rose genetics as more traits are studied. Studying more traits from these populations will allow for the stacking of various QTLs for desirable traits.

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline.

Genome-wide association mapping studies (GWAS) based on Big Data are a potential approach to improve marker-assisted selection in plant breeding. The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing. Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects, but is hindered by data heterogeneity and lack of interoperability. In this study, we used genomic and phenotypic data sets, focusing on Central European winter wheat populations evaluated for heading date. We explored strategies for integrating these data and subsequently the resulting potential for GWAS. Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols, resulting in high quality integrated phenotypic data. In this context, genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series, which was low in our case. Contrary to expectations, fewer associations between markers and traits were found in the larger combined data than in the individual experimental series. However, the predictive power based on the marker-trait associations of the integrated data set was higher across data sets. Therefore, the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS. The results encourage further efforts to standardize and share data in the plant breeding community.

Abstract Background The growing availability of genomic resources in radiata pine paves the way for significant advances in fundamental and applied genomic research. We constructed robust high-density linkage maps based on exome-capture genotyping in two F1 populations, and used these populations to perform quantitative trait locus (QTL) scans, genomic prediction and quantitative analyses of genetic architecture for key traits targeted by tree improvement programmes. Results Our mapping approach used probabilistic error correction of the marker data, followed by an iterative approach based on stringent parameters. This approach proved highly effective in producing high-density maps with robust marker orders and realistic map lengths (1285–4674 markers per map, with sizes ranging from c. 1643–2292 cM, and mean marker intervals of 0.7–2.1 cM). Colinearity was high between parental linkage maps, although there was evidence for a large chromosomal rearrangement (affecting ~ 90 cM) in one of the parental maps. In total, 28 QTL were detected for growth (stem diameter) and wood properties (wood density and fibre properties measured by Silviscan) in the QTL discovery population, with 1–3 QTL of small to moderate effect size detected per trait in each parental map. Four of these QTL were validated in a second, unrelated F1 population. Results from genomic prediction and analyses of genetic architecture were consistent with those from QTL scans, with wood properties generally having moderate to high genomic heritabilities and predictive abilities, as well as somewhat less complex genetic architectures, compared to growth traits. Conclusions Despite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. The maps produced in this study will be a valuable resource for several applications, including the selection of marker panels for genomic prediction and anchoring a recently completed de novo whole genome assembly. We also provide the first map-based evidence for a large genomic rearrangement in radiata pine. Finally, results from our QTL scans, genomic prediction, and genetic architecture analyses are informative about the genomic basis of variation in important phenotypic traits.