The genomic architecture of bud phenology and height growth remains poorly known in most forest trees. In non model species, QTL studies have shown limited application because most often QTL data ...could not be validated from one experiment to another. The aim of our study was to overcome this limitation by basing QTL detection on the construction of genetic maps highly-enriched in gene markers, and by assessing QTLs across pedigrees, years, and environments.
Four saturated individual linkage maps representing two unrelated mapping populations of 260 and 500 clonally replicated progeny were assembled from 471 to 570 markers, including from 283 to 451 gene SNPs obtained using a multiplexed genotyping assay. Thence, a composite linkage map was assembled with 836 gene markers.For individual linkage maps, a total of 33 distinct quantitative trait loci (QTLs) were observed for bud flush, 52 for bud set, and 52 for height growth. For the composite map, the corresponding numbers of QTL clusters were 11, 13, and 10. About 20% of QTLs were replicated between the two mapping populations and nearly 50% revealed spatial and/or temporal stability. Three to four occurrences of overlapping QTLs between characters were noted, indicating regions with potential pleiotropic effects. Moreover, some of the genes involved in the QTLs were also underlined by recent genome scans or expression profile studies.Overall, the proportion of phenotypic variance explained by each QTL ranged from 3.0 to 16.4% for bud flush, from 2.7 to 22.2% for bud set, and from 2.5 to 10.5% for height growth. Up to 70% of the total character variance could be accounted for by QTLs for bud flush or bud set, and up to 59% for height growth.
This study provides a basic understanding of the genomic architecture related to bud flush, bud set, and height growth in a conifer species, and a useful indicator to compare with Angiosperms. It will serve as a basic reference to functional and association genetic studies of adaptation and growth in Picea taxa. The putative QTNs identified will be tested for associations in natural populations, with potential applications in molecular breeding and gene conservation programs. QTLs mapping consistently across years and environments could also be the most important targets for breeding, because they represent genomic regions that may be least affected by G × E interactions.
Background Nasal polyps often are associated with asthma. The phenotype of these patients is unknown. Objective To identify the mucosal factors associated with asthma comorbidity, we analyzed the ...inflammatory patterns of nasal polyps. Methods Nasal polyps from 70 Belgian patients, 34% with asthma, were analyzed for type of inflammation, T-cell cytokines, and IgE antibodies to Staphylococcus aureus enterotoxins. The same investigations were repeated in 93 Chinese patients with polyps, a group with a low asthma comorbidity rate (8%). Results In Belgian patients with polyps, 54% of samples showed eosinophilic inflammation. A classification tree evaluation identified IL-5 as the main positive determinant. Enterotoxin IgE in tissue (37%) was associated with significantly increased total IgE and eosinophil cationic protein concentrations. Expression of enterotoxin IgE, total IgE at greater than 1,442 kU/L, and eosinophil cationic protein at greater than 17,109 μg/L in samples with a total IgE concentration of greater than 246 kU/L significantly predicted asthma (odds ratio, 5.8-13). Only 7.5% of the samples from Chinese patients with polyps showed eosinophilic inflammation. IL-5 was confirmed as a positive determinant of eosinophilic inflammation, and enterotoxin IgE in tissue (17% of patients) was associated with significantly increased total IgE and eosinophil cationic protein concentrations. The expression of IL-5 or total IgE at greater than 790 kU/L in samples with an IL-5 concentration of greater than 194 pg/mL significantly predicted comorbid asthma (odds ratio, 17.2-96). Conclusion Mucosal inflammation in nasal polyps orchestrated by TH 2 cytokines and amplified by S aureus enterotoxins is characterized by an increased eosinophilic inflammation and formation of IgE antibodies. This phenotype is associated with comorbid asthma in white and Asian patients with nasal polyps.
Abstract
Motivation
In the modern genomics era, genome sequence assemblies are routine practice. However, depending on the methodology, resulting drafts may contain considerable base errors. Although ...utilities exist for genome base polishing, they work best with high read coverage and do not scale well. We developed ntEdit, a Bloom filter-based genome sequence editing utility that scales to large mammalian and conifer genomes.
Results
We first tested ntEdit and the state-of-the-art assembly improvement tools GATK, Pilon and Racon on controlled Escherichia coli and Caenorhabditis elegans sequence data. Generally, ntEdit performs well at low sequence depths (<20×), fixing the majority (>97%) of base substitutions and indels, and its performance is largely constant with increased coverage. In all experiments conducted using a single CPU, the ntEdit pipeline executed in <14 s and <3 m, on average, on E.coli and C.elegans, respectively. We performed similar benchmarks on a sub-20× coverage human genome sequence dataset, inspecting accuracy and resource usage in editing chromosomes 1 and 21, and whole genome. ntEdit scaled linearly, executing in 30–40 m on those sequences. We show how ntEdit ran in <2 h 20 m to improve upon long and linked read human genome assemblies of NA12878, using high-coverage (54×) Illumina sequence data from the same individual, fixing frame shifts in coding sequences. We also generated 17-fold coverage spruce sequence data from haploid sequence sources (seed megagametophyte), and used it to edit our pseudo haploid assemblies of the 20 Gb interior and white spruce genomes in <4 and <5 h, respectively, making roughly 50M edits at a (substitution+indel) rate of 0.0024.
Availability and implementation
https://github.com/bcgsc/ntedit
Supplementary information
Supplementary data are available at Bioinformatics online.
Plantation‐grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to ...face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single‐ and multi‐trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate‐to‐high heritability and low genotype‐by‐environment interactions. Weevil resistance was genetically positively correlated with tree height, height‐to‐diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi‐trait models performed similarly as single‐trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi‐trait GS models. A GS index that corresponded to the breeders’ priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high‐quality, weevil‐resistant Norway spruce reforestation stock with high accuracy achieved from single‐trait or multi‐trait GS.
Background
Google Trends (GTs) is a web‐based surveillance tool that explores the searching trends of specific queries via Google. This tool proposes to reflect the real‐life epidemiology of allergic ...rhinitis and asthma. However, the validation of GTs against pollen concentrations is missing at the country level.
Objectives
In the present study, we used GTs (a) to compare the terms related to allergy in France, (b) to assess seasonal variations across the country for 5 years and (c) to compare GTs and pollen concentrations for 2016.
Methods
Google Trends queries were initially searched to investigate the terms reflecting pollen and allergic diseases. 13‐ and 5‐year GTs were used in France. Then, 5‐year GTs were assessed in all metropolitan French regions to assess the seasonality of GTs. Finally, GTs were compared with pollen concentrations (Réseau National de Surveillance en Aerobiology) for 2016 in seven regions (GTs) and corresponding cities (pollen concentrations).
Results
The combination of searches for “allergy” as a disease, “pollen” as a disease cause and “ragweed” as a plant was needed to fully assess the pollen season in France. “Asthma” did not show any seasonality. Using the 5‐year GTs, an annual and clear seasonality of queries was found in all regions depending on the predicted pollen exposure for spring and a summer peak but not for winter peaks. The agreement between GT queries and pollen concentrations is usually poor except for spring trees and grasses. Moreover, cypress pollens are insufficiently reported by GTs.
Conclusions
Google Trends cannot predict the pollen season in France.
Google Trends (GTs) is a web‐based tool that explores searching trends. This tool was used to search the terms related to allergy in France, to assess 5‐year seasonal variations across the country and to compare GTs with the pollen concentrations for 2016. The combination of terms “allergy,” “pollen” and “ragweed” was needed to fully assess the pollen season in France. GTs cannot predict the pollen season in France.
Genomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate tree ...breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long breeding cycles. In the present study, we tested GS in an advanced-breeding population of the boreal black spruce (Picea mariana Mill. BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy.
The study relied on 734 25-year-old trees belonging to 34 full-sib families derived from 27 parents and that were established on two contrasting sites. Genomic profiles were obtained from 4993 Single Nucleotide Polymorphisms (SNPs) representative of as many gene loci distributed among the 12 linkage groups common to spruce. GS models were obtained for four growth and wood traits. Validation using independent sets of trees showed that GS model accuracy was high, related to trait heritability and equivalent to that of conventional pedigree-based models. In forward selection, gains per unit of time were three times higher with the GS approach than with conventional selection. In addition, models were also accurate across sites, indicating little genotype-by-environment interaction in the area investigated. Using information from half-sibs instead of full-sibs led to a significant reduction in model accuracy, indicating that the inclusion of relatedness in the model contributed to its higher accuracies. About 500 to 1000 markers were sufficient to obtain GS model accuracy almost equivalent to that obtained with all markers, whether they were well spread across the genome or from a single linkage group, further confirming the implication of relatedness and potential long-range linkage disequilibrium (LD) in the high accuracy estimates obtained. Only slightly higher model accuracy was obtained when using marker subsets that were identified to carry large effects, indicating a minor role for short-range LD in this population.
This study supports the integration of GS models in advanced-generation tree breeding programs, given that high genomic prediction accuracy was obtained with a relatively small number of markers due to high relatedness and family structure in the population. In boreal spruce breeding programs and similar ones with long breeding cycles, much larger gain per unit of time can be obtained from genomic selection at an early age than by the conventional approach. GS thus appears highly profitable, especially in the context of forward selection in species which are amenable to mass vegetative propagation of selected stock, such as spruces.
Allergen-specific immunotherapy (SIT) is an etiology-based treatment for respiratory and Hymenoptera -allergic diseases. Although introduced a century ago, SIT was not widely accepted for many years ...until its efficacy in the treatment of both allergic rhinoconjunctivitis and allergic asthma was demonstrated in appropriate double-blind, placebo-controlled trials and its mechanism of action was better understood. The indications for allergen-specific immunotherapy have been specified in consensus reports. Allergen-specific immunotherapy is primarily targeted to benefit patients with Hymenoptera allergy or severe upper and mild to moderate lower allergic respiratory diseases that are poorly controlled by pharmacologic treatments or who are unable or unwilling to use the latter. Several recent developments have helped to reinforce the position of SIT in the overall therapeutic management of respiratory allergies: (1) improvement in the quality of allergen extracts as a result of standardization, (2) better understanding of SIT's mechanism of action, (3) the introduction of sublingual tablets and their rigorous registration as pharmaceutical therapies by regulatory agencies, and (4) rationalization of prescribing patterns. There is a requirement for additional well designed, well executed, randomized trials in adults and children with allergic rhinitis and asthma, with a special focus on optimal patient selection, dosage, and treatment duration. In this review, the authors put into perspective current international expert recommendations on the use of SIT (in relation to levels of clinical evidence) and analyze what is needed for the future.
Allergy diagnosis based on purified allergen molecules provides detailed information regarding the individual sensitization profile of allergic patients, allows monitoring of the development of ...allergic disease and of the effect of therapies on the immune response to individual allergen molecules. Allergen microarrays contain a large variety of allergen molecules and thus allow the simultaneous detection of allergic patients’ antibody reactivity profiles towards each of the allergen molecules with only minute amounts of serum. In this article we summarize recent progress in the field of allergen microarray technology and introduce the MeDALL allergen-chip which has been developed for the specific and sensitive monitoring of IgE and IgG reactivity profiles towards more than 170 allergen molecules in sera collected in European birth cohorts. MeDALL is a European research program in which allergen microarray technology is used for the monitoring of the development of allergic disease in childhood, to draw a geographic map of the recognition of clinically relevant allergens in different populations and to establish reactivity profiles which are associated with and predict certain disease manifestations. We describe technical advances of the MeDALL allergen-chip regarding specificity, sensitivity and its ability to deliver test results which are close to in vivo reactivity. In addition, the usefulness and numerous advantages of allergen microarrays for allergy research, refined allergy diagnosis, monitoring of disease, of the effects of therapies, for improving the prescription of specific immunotherapy and for prevention are discussed.
Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species ...and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested.
Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71-0.79) and moderately high for growth (r = 0.52-0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.
Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.
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