The AGILE Mission Barbiellini, G.; Argan, A.; Boffelli, F. ...
Astronomy & astrophysics,
08/2009, Letnik:
502, Številka:
3
Journal Article
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Context. AGILE is an Italian Space Agency mission dedicated to observing the gamma-ray Universe. The AGILE's very innovative instrumentation for the first time combines a gamma-ray imager (sensitive ...in the energy range 30 MeV–50 GeV), a hard X-ray imager (sensitive in the range 18–60 keV), a calorimeter (sensitive in the range 350 keV–100 MeV), and an anticoincidence system. AGILE was successfully launched on 2007 April 23 from the Indian base of Sriharikota and was inserted in an equatorial orbit with very low particle background. Aims. AGILE provides crucial data for the study of active galactic nuclei, gamma-ray bursts, pulsars, unidentified gamma-ray sources, galactic compact objects, supernova remnants, TeV sources, and fundamental physics by microsecond timing. Methods. An optimal sky angular positioning (reaching 0.1 degrees in gamma-rays and 1–2 arcmin in hard X-rays) and very large fields of view (2.5 sr and 1 sr, respectively) are obtained by the use of Silicon detectors integrated in a very compact instrument. Results. AGILE surveyed the gamma-ray sky and detected many Galactic and extragalactic sources during the first months of observations. Particular emphasis is given to multifrequency observation programs of extragalactic and galactic objects. Conclusions. AGILE is a successful high-energy gamma-ray mission that reached its nominal scientific performance. The AGILE Cycle-1 pointing program started on 2007 December 1, and is open to the international community through a Guest Observer Program.
Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a ...means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data.
We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability.
Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.
Emphasizing increased profit through increased dairy cow production has revealed a negative relationship of production with fitness and health traits. Decreased cow health can affect herd ...profitability through increased rates of involuntary culling and decreased or lost milk sales. The development of genomic selection methodologies, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving profitability. The principal objective of this study was to use health data collected from on-farm computer systems in the United States to estimate variance components and heritability for health traits commonly experienced by dairy cows. A single-step analysis was conducted to estimate genomic variance components and heritabilities for health events, including cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, and retained placenta. A blended H matrix was constructed for a threshold model with fixed effects of parity and year-season and random effects of herd-year and sire. The single-step genomic analysis produced heritability estimates that ranged from 0.02 (standard deviation = 0.005) for lameness to 0.36 (standard deviation = 0.08) for retained placenta. Significant genetic correlations were found between lameness and cystic ovaries, displaced abomasum and ketosis, displaced abomasum and metritis, and retained placenta and metritis. Sire reliabilities increased, on average, approximately 30% with the incorporation of genomic data. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data are feasible in the United States, and that the inclusion of genomic data substantially improves reliabilities for these traits.
Genotype by environment interaction (G × E) in dairy cattle productive traits has been shown to exist, but current genetic evaluation methods do not take this component into account. As several ...environmental descriptors (e.g., climate, farming system) are known to vary within the United States, not accounting for the G × E could lead to reranking of bulls and loss in genetic gain. Using test-day records on milk yield, somatic cell score, fat, and protein percentage from all over the United States, we computed within herd-year-season daughter yield deviations for 1,087 Holstein bulls and regressed them on genetic and environmental information to estimate variance components and to assess prediction accuracy. Genomic information was obtained from a 50k SNP marker panel. Environmental effect inputs included herd (160 levels), geographical region (7 levels), geographical location (2 variables), climate information (7 variables), and management conditions of the herds (16 total variables divided in 4 subgroups). For each set of environmental descriptors, environmental, genomic, and G × E components were sequentially fitted. Variance components estimates confirmed the presence of G × E on milk yield, with its effect being larger than main genetic effect and the environmental effect for some models. Conversely, G × E was moderate for somatic cell score and small for milk composition. Genotype by environment interaction, when included, partially eroded the genomic effect (as compared with the models where G × E was not included), suggesting that the genomic variance could at least in part be attributed to G × E not appropriately accounted for. Model predictive ability was assessed using 3 cross-validation schemes (new bulls, incomplete progeny test, and new environmental conditions), and performance was compared with a reference model including only the main genomic effect. In each scenario, at least 1 of the models including G × E was able to perform better than the reference model, although it was not possible to find the overall best-performing model that included the same set of environmental descriptors. In general, the methodology used is promising in accounting for G × E in genomic predictions, but challenges exist in identifying a unique set of covariates capable of describing the entire variety of environments.
This study leveraged a growing dataset of producer-recorded phenotypes for mastitis, reproductive diseases (metritis and retained placenta), and metabolic diseases (ketosis, milk fever, and displaced ...abomasum) to investigate the potential presence of inbreeding depression for these disease traits. Phenotypic, pedigree, and genomic information were obtained for 354,043 and 68,292 US Holstein and Jersey cows, respectively. Total inbreeding coefficients were calculated using both pedigree and genomic information; the latter included inbreeding estimates obtained using a genomic relationship matrix and runs of homozygosity. We also generated inbreeding coefficients based on the generational inbreeding for recent and old pedigree inbreeding, for different run-of-homozygosity length classes, and for recent and old homozygous-by-descent segment-based inbreeding. Estimates on the liability scale revealed significant evidence of inbreeding depression for reproductive-disease traits, with an increase in total pedigree and genomic inbreeding showing a notable effect for recent inbreeding. However, we found inconsistent evidence for inbreeding depression for mastitis or any metabolic diseases. Notably, in Holsteins, the probability of developing displaced abomasum decreased with inbreeding, particularly for older inbreeding. Estimates of disease probability for cows with low, average, and high inbreeding levels did not significantly differ across any inbreeding coefficient and trait combination, indicating that although inbreeding may affect disease incidence, it likely plays a smaller role compared with management and environmental factors.
Clinical mastitis (CM) is one of the health disorders with large impacts on dairy farming profitability and animal welfare. The objective of this study was to perform a genome-wide association study ...(GWAS) for CM in first-lactation Holstein. Producer-recorded mastitis event information for 103,585 first-lactation cows were used, together with genotype information on 1,361 bulls from the Illumina BovineSNP50 BeadChip. Single-step genomic-BLUP methodology was used to incorporate genomic data into a threshold-liability model. Association analysis confirmed that CM follows a highly polygenic mode of inheritance. However, 10-adjacent-SNP windows showed that regions on chromosomes 2, 14 and 20 have impacts on genetic variation for CM. Some of the genes located on chromosome 14 (LY6K, LY6D, LYNX1, LYPD2, SLURP1, PSCA) are part of the lymphocyte-antigen-6 complex (LY6) known for its neutrophil regulation function linked to the major histocompatibility complex. Other genes on chromosome 2 were also involved in regulating immune response (IFIH1, LY75, and DPP4), or are themselves regulated in the presence of specific pathogens (ITGB6, NR4A2). Other genes annotated on chromosome 20 are involved in mammary gland metabolism (GHR, OXCT1), antibody production and phagocytosis of bacterial cells (C6, C7, C9, C1QTNF3), tumor suppression (DAB2), involution of mammary epithelium (OSMR) and cytokine regulation (PRLR). DAVID enrichment analysis revealed 5 KEGG pathways. The JAK-STAT signaling pathway (cell proliferation and apoptosis) and the 'Cytokine-cytokine receptor interaction' (cytokine and interleukines response to infectious agents) are co-regulated and linked to the 'ABC transporters' pathway also found here. Gene network analysis performed using GeneMania revealed a co-expression network where 665 interactions existed among 145 of the genes reported above. Clinical mastitis is a complex trait and the different genes regulating immune response are known to be pathogen-specific. Despite the lack of information in this study, candidate QTL for CM were identified in the US Holstein population.
The principal objective of this study was to analyze the plausibility of health data recorded through on-farm recording systems throughout the United States. Substantial progress has been made in the ...genetic improvement of production traits while health and fitness traits of dairy cattle have declined. Health traits are generally expensive and difficult to measure, but health event data collected from on-farm computer management systems may provide an effective and low-cost source of health information. To validate editing methods, incidence rates of on-farm recorded health event data were compared with incidence rates reported in the literature. Putative relationships among common health events were examined using logistic regression for each of 3 timeframes: 0 to 60, 61 to 90, and 91 to 150 d in milk. Health events occurring on average before the health event of interest were included in each model as predictors when significant. Calculated incidence rates ranged from 1.37% for respiratory problems to 12.32% for mastitis. Most health events reported had incidence rates lower than the average incidence rate found in the literature. This may partially represent underreporting by dairy farmers who record disease events only when a treatment or other intervention is required. Path diagrams developed using odds ratios calculated from logistic regression models for each of 13 common health events allowed putative relationships to be examined. The greatest odds ratios were estimated to be the influence of ketosis on displaced abomasum (15.5) and the influence of retained placenta on metritis (8.37), and were consistent with earlier reports. The results of this analysis provide evidence for the plausibility of on-farm recorded health information.
Genetic improvement of dairy cattle health through the use of producer-recorded data has been determined to be feasible. Low estimated heritabilities indicate that genetic progress will be slow. ...Variation observed in lowly heritable traits can largely be attributed to nongenetic factors, such as the environment. More rapid improvement of dairy cattle health may be attainable if herd health programs incorporate environmental and managerial aspects. More than 1,100 herd characteristics are regularly recorded on farm test-days. We combined these data with producer-recorded health event data, and parametric and nonparametric models were used to benchmark herd and cow health status. Health events were grouped into 3 categories for analyses: mastitis, reproductive, and metabolic. Both herd incidence and individual incidence were used as dependent variables. Models implemented included stepwise logistic regression, support vector machines, and random forests. At both the herd and individual levels, random forest models attained the highest accuracy for predicting health status in all health event categories when evaluated with 10-fold cross-validation. Accuracy (SD) ranged from 0.61 (0.04) to 0.63 (0.04) when using random forest models at the herd level. Accuracy of prediction (SD) at the individual cow level ranged from 0.87 (0.06) to 0.93 (0.001) with random forest models. Highly significant variables and key words from logistic regression and random forest models were also investigated. All models identified several of the same key factors for each health event category, including movement out of the herd, size of the herd, and weather-related variables. We concluded that benchmarking health status using routinely collected herd data is feasible. Nonparametric models were better suited to handle this complex data with numerous variables. These data mining techniques were able to perform prediction of health status and could add evidence to personal experience in herd management.
Identifying individual genetic variation in drug metabolism pathways is of importance not only in livestock, but also in humans in order to provide the ultimate goal of giving the right drug at the ...right dose at the right time. Our objective was to identify individual genes and gene networks involved in metabolizing fenbendazole (FBZ) and flunixin meglumine (FLU) in swine liver. The population consisted of female and castrated male pigs that were sired by boars represented by 4 breeds. Progeny were randomly placed into groups: no drug (UNT), FLU or FBZ administered. Liver transcriptome profiles from 60 animals with extreme (i.e. fast or slow drug metabolism) pharmacokinetic (PK) profiles were generated from RNA sequencing. Multiple cytochrome P450 (CYP1A1, CYP2A19 and CYP2C36) genes displayed different transcript levels across treated versus UNT. Weighted gene co-expression network analysis identified 5 and 3 modules of genes correlated with PK parameters and a portion of these were enriched for biological processes relevant to drug metabolism for FBZ and FLU, respectively. Genes within identified modules were shown to have a higher transcript level relationship (i.e. connectivity) in treated versus UNT animals. Investigation into the identified genes would allow for greater insight into FBZ and FLU metabolism.
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