The importance of using public data to validate reported associations.
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ABSTRACT:
SUBMITTER: Chenevix-Trench G
PROVIDER: S-EPMC6025853 | biostudies-literature | 2018 Jun
REPOSITORIES: biostudies-literature
Publications
The importance of using public data to validate reported associations.
Chenevix-Trench Georgia G Beesley Jonathan J Pharoah Paul D P PDP Berchuck Andrew A
PLoS genetics 20180629 6
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Project description:BackgroundPrevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates, such as seasonality and transmission distance. Passive disease surveillance (i.e., case reports by public) is a common method of monitoring emergence of wildlife diseases, but can be challenging to interpret due to spatial biases and limitations in data quantity and quality.Methodology/principal findingsWe obtained passive rabies surveillance data from dead striped skunks (Mephitis mephitis) in an epizootic in northern Colorado, USA. We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. We estimated the distance travelled per transmission event, direction of invasion, rate of spatial spread, and effects of infection density and season. We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic. Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread. Estimated mean transmission distances were 2.3 km (95% Highest Posterior Density (HPD95): 0.02, 11.9; phylogeographic) and 3.9 km (95% credible intervals (CI95): 1.4, 11.3; occupancy). Estimated rates of spatial spread in km/year were: 29.8 (HPD95: 20.8, 39.8; phylogeographic, branch velocity, homogenous model), 22.6 (HPD95: 15.3, 29.7; phylogeographic, diffusion rate, homogenous model) and 21.1 (CI95: 16.7, 25.5; occupancy). Initial colonization probability was twice as high in spring relative to fall.Conclusions/significanceSkunk-to-skunk transmission was primarily local (< 4 km) suggesting that if interventions were needed, they could be applied at the wave front. Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions. Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases.
| S-EPMC5552346 | biostudies-literature
Project description:Self-reported hypertension has not been validated in specific Hispanic subgroups (Puerto Ricans, Dominicans) and in Asian Americans. The objectives were to assess validity of self-reported hypertension in Hispanic and Asian American adults, and to recalibrate self-reported hypertension with measured values. Data were from the New York City Community Health Survey 2005-2008 and the Heart Follow-Up Study (HFUS) 2010 (included measured hypertension). Sensitivity and specificity were calculated in the HFUS data; recalibration was conducted using a previously described method by Mentz et al. Sensitivity was similar in Puerto Ricans and Dominicans versus whites. The differences in hypertension prevalence after recalibration were largest in Hispanics. No substantial differences occurred among Asian Americans. Factors such as low health literacy or insurance status are potential explanations for bias in self-reported hypertension among Hispanic subgroups. Surveillance systems may consider recalibration, potentially in areas with a high percentage of Hispanics or uninsured.
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Project description:Background: Salivary epigenetic biomarkers may detect esophageal cancer. Methods: A total of 256 saliva samples from esophageal adenocarcinoma patients and matched volunteers were analyzed with Illumina EPIC methylation arrays. Three datasets were created, using 64% for discovery, 16% for testing and 20% for validation. Modules of gene-based methylation probes were created using weighted gene coexpression network analysis. Module significance to disease and gene importance to module were determined and a random forest classifier generated using best-scoring gene-related epigenetic probes. A cost-sensitive wrapper algorithm maximized cancer diagnosis. Results: Using age, sex and seven probes, esophageal adenocarcinoma was detected with area under the curve of 0.72 in discovery, 0.73 in testing and 0.75 in validation datasets. Cancer sensitivity was 88% with specificity of 31%. Conclusion: We have demonstrated a potentially clinically viable classifier of esophageal cancer based on saliva methylation.
| S-EPMC10825730 | biostudies-literature
Project description:ObjectiveTransgender individuals are vulnerable to negative health risks and outcomes, but research remains limited because data sources, such as electronic medical records (EMRs), lack standardized collection of gender identity information. Most EMR do not include the gold standard of self-identified gender identity, but International Classification of Diseases (ICDs) includes diagnostic codes indicating transgender-related clinical services. However, it is unclear if these codes can indicate transgender status. The objective of this study was to determine the extent to which patients' clinician notes in EMR contained transgender-related terms that could corroborate ICD-coded transgender identity.MethodsData are from the US Department of Veterans Affairs Corporate Data Warehouse. Transgender patients were defined by the presence of ICD9 and ICD10 codes associated with transgender-related clinical services, and a 3:1 comparison group of nontransgender patients was drawn. Patients' clinician text notes were extracted and searched for transgender-related words and phrases.ResultsAmong 7560 patients defined as transgender based on ICD codes, the search algorithm identified 6753 (89.3%) with transgender-related terms. Among 22 072 patients defined as nontransgender without ICD codes, 246 (1.1%) had transgender-related terms; after review, 11 patients were identified as transgender, suggesting a 0.05% false negative rate.ConclusionsUsing ICD-defined transgender status can facilitate health services research when self-identified gender identity data are not available in EMR.
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Project description:Purpose : The goal of this study was to use RNA Seq to validate transcriptional data of two clinical isolates focussing on a subset of 74 transcript that were selected specifically for Nanostring analysis. Methods : mRNA profiles were generated for the clinical isolates FRD1 and CI224_M, in duplicate, by deep sequencing. Strains were grown for 8 hours in LB medium at 37C prior to RNA harvest. Ribosomal RNA was removed using the Ribi-Zero rRNA Removal Kit (Epicentre). mRNA reads were trimmed and mapped to the PAO1 NC_002516 reference genome from NCBI using the ClC Genomics Workbench platform and defaut parameters.
2016-08-09 | GSE83773 | GEO
Project description:While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for obtaining large amounts of medical information from a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web-based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in-depth questions to refine self-reported diagnoses. Our data suggest that online collection of self-reported data from a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations.
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Project description:ObjectiveIn response to a need for better sepsis diagnostics, several new gene expression classifiers have been recently published, including the 11-gene "Sepsis MetaScore," the "FAIM3-to-PLAC8" ratio, and the Septicyte Lab. We performed a systematic search for publicly available gene expression data in sepsis and tested each gene expression classifier in all included datasets. We also created a public repository of sepsis gene expression data to encourage their future reuse.Data sourcesWe searched National Institutes of Health Gene Expression Omnibus and EBI ArrayExpress for human gene expression microarray datasets. We also included the Glue Grant trauma gene expression cohorts.Study selectionWe selected clinical, time-matched, whole blood studies of sepsis and acute infections as compared to healthy and/or noninfectious inflammation patients. We identified 39 datasets composed of 3,241 samples from 2,604 patients.Data extractionAll data were renormalized from raw data, when available, using consistent methods.Data synthesisMean validation areas under the receiver operating characteristic curve for discriminating septic patients from patients with noninfectious inflammation for the Sepsis MetaScore, the FAIM3-to-PLAC8 ratio, and the Septicyte Lab were 0.82 (range, 0.73-0.89), 0.78 (range, 0.49-0.96), and 0.73 (range, 0.44-0.90), respectively. Paired-sample t tests of validation datasets showed no significant differences in area under the receiver operating characteristic curves. Mean validation area under the receiver operating characteristic curves for discriminating infected patients from healthy controls for the Sepsis MetaScore, FAIM3-to-PLAC8 ratio, and Septicyte Lab were 0.97 (range, 0.85-1.0), 0.94 (range, 0.65-1.0), and 0.71 (range, 0.24-1.0), respectively. There were few significant differences in any diagnostics due to pathogen type.ConclusionsThe three diagnostics do not show significant differences in overall ability to distinguish noninfectious systemic inflammatory response syndrome from sepsis, though the performance in some datasets was low (area under the receiver operating characteristic curve, < 0.7) for the FAIM3-to-PLAC8 ratio and Septicyte Lab. The Septicyte Lab also demonstrated significantly worse performance in discriminating infections as compared to healthy controls. Overall, public gene expression data are a useful tool for benchmarking gene expression diagnostics.
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Project description:Genomic data contribute invaluable information to the epidemiological investigation of pathogens of public health importance. However, whole-genome sequencing (WGS) of bacteria typically relies on culture, which represents a major hurdle for generating such data for a wide range of species for which culture is challenging. In this study, we assessed the use of culture-free target-enrichment sequencing as a method for generating genomic data for two bacterial species: (1) Bacillus anthracis, which causes anthrax in both people and animals and whose culture requires high-level containment facilities; and (2) Mycoplasma amphoriforme, a fastidious emerging human respiratory pathogen. We obtained high-quality genomic data for both species directly from clinical samples, with sufficient coverage (>15×) for confident variant calling over at least 80% of the baited genomes for over two thirds of the samples tested. Higher qPCR cycle threshold (Ct) values (indicative of lower pathogen concentrations in the samples), pooling libraries prior to capture, and lower captured library concentration were all statistically associated with lower capture efficiency. The Ct value had the highest predictive value, explaining 52 % of the variation in capture efficiency. Samples with Ct values ≤30 were over six times more likely to achieve the threshold coverage than those with a Ct > 30. We conclude that target-enrichment sequencing provides a valuable alternative to standard WGS following bacterial culture and creates opportunities for an improved understanding of the epidemiology and evolution of many clinically important pathogens for which culture is challenging.
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Project description:BackgroundDigital health (patient portals, remote monitoring devices, video visits) is a routine part of health care, though the digital divide may affect access.ObjectivesTo test and validate an electronic health record (EHR) screening tool to identify patients at risk of the digital divide.Materials and methodsWe conducted a retrospective EHR data extraction and cross-sectional survey of participants within 1 health care system. We identified 4 potential digital divide markers from the EHR: (1) mobile phone number, (2) email address, (3) active patient portal, and (4) >2 patient portal logins in the last year. We mailed surveys to patients at higher risk (missing all 4 markers), intermediate risk (missing 1-3 markers), or lower risk (missing no markers). Combining EHR and survey data, we summarized the markers into risk scores and evaluated its association with patients' report of lack of Internet access. Then, we assessed the association of EHR markers and eHealth Literacy Scale survey outcomes.ResultsA total of 249 patients (39.4%) completed the survey (53%>65 years, 51% female, 50% minority race, 55% rural/small town residents, 46% private insurance, 45% Medicare). Individually, the 4 EHR markers had high sensitivity (range 81%-95%) and specificity (range 65%-79%) compared with survey responses. The EHR marker-based score (high risk, intermediate risk, low risk) predicted absence of Internet access (receiver operator characteristics c-statistic=0.77). Mean digital health literacy scores significantly decreased as her marker digital divide risk increased (P <.001).DiscussionEach of the four EHR markers (Cell phone, email address, patient portal active, and patient portal actively used) compared with self-report yielded high levels of sensitivity, specificity, and overall accuracy.ConclusionUsing these markers, health care systems could target interventions and implementation strategies to support equitable patient access to digital health.
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Project description:Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field. In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.
| S-EPMC4535466 | biostudies-literature