10,585 research outputs found
Biobanking across the phenome - at the center of chronic disease research
No full textRecognized public health relevant risk factors such as obesity, physical inactivity, smoking or air pollution are common to many non-communicable diseases (NCDs). NCDs cluster and co-morbidities increase in parallel to age. Pleiotropic genes and genetic variants have been identified by genome-wide association studies (GWAS) linking NCD entities hitherto thought to be distant in etiology. These different lines of evidence suggest that NCD disease mechanisms are in part shared. Identification of common exogenous and endogenous risk patterns may promote efficient prevention, an urgent need in the light of the global NCD epidemic. The prerequisite to investigate causal risk patterns including biologic, genetic and environmental factors across different NCDs are well characterized cohorts with associated biobanks. Prospectively collected data and biospecimen from subjects of various age, sociodemographic, and cultural groups, both healthy and affected by one or more NCD, are essential for exploring biologic mechanisms and susceptibilities interlinking different environmental and lifestyle exposures, co-morbidities, as well as cellular senescence and aging. A paradigm shift in the research activities can currently be observed, moving from focused investigations on the effect of a single risk factor on an isolated health outcome to a more comprehensive assessment of risk patterns and a broader phenome approach. Though important methodological and analytical challenges need to be resolved, the ongoing international efforts to establish large-scale population-based biobank cohorts are a critical basis for moving NCD disease etiology forward. Future epidemiologic and public health research should aim at sustaining a comprehensive systems view on health and disease. The political and public discussions about the utilitarian aspect of investing in and contributing to cohort and biobank research are essential and are indirectly linked to the achievement of public health programs effectively addressing the global NCD epidemic
Proportions of differential missing SNPs by storage environment among DBS archived at the Michigan Neonatal Biobank between 1992 and 2008.
No full textProportions of differential missing SNPs by storage environment among DBS archived at the Michigan Neonatal Biobank between 1992 and 2008.</p
MANOVA modelling of a chiropractic longitudinal study using multiple imputation
No full textThe purpose of this report is to present the detailed statistical analysis of a randomised, placebo-controlled trial comparing two different treatment modalities to an intervention of no known benefit for people with acute or subacute thoracic spine pain.
The therapy arms consist of Spinal Manipulative Therapy (SMT) and Graston Technique (GT) and the placebo is a non-functional ultrasound. A placebo group was utilised because at present there are no proven treatments for non-specific thoracic pain. This trial is registered with the Australia and New Zealand Clinical Trials Registry. Ethics approval has been granted by Murdoch University Human Research and Ethics Committee, number 2007/274.
The aim of this three arm trial was to test the efficacy of SMT and GT as independent modalities compared to detuned ultrasound for the outcomes of pain and disability. The latter were measured using the Visual Analogue Scale (VAS) and a modified Oswestry Back Pain Disability Index. The study was conducted at the Murdoch University Chiropractic student clinic in Perth, Australia, and the protocol published in Crothers et al (2008).
In this report, Section 2 provides an initial exploratory analysis of the data, Section 3 outlines the statistical models used in the final analysis, Section 4 defines these models in mathematical terms, Section 5 discusses the management of missing values via multiple imputation and Section 6 presents the results of the statistical modelling and hypothesis tests. The clinical study will be published in full elsewhere
The missing field
No full textPublished work by a Douglas College Student Alumni. Jennifer Zilm's poetry collection, "The Missing Field", concerns themes of translation, preservation and the engagement with the transitory documents of everyday life, whether a snapshot of a Vancouver bus, postcards from the Middle East, lecture notes on Euripides, a van Gogh museum catalogue or marginalia in water damaged collection of Rilke poems. Part of the "Essential poets (255)" series. --Provided by publisher.Poems© Author
Detailed information of 180 complex traits used in this research from the UK Biobank.
No full textN_full: the total sample size of this trait in the UK Biobank. N_matched: the sample size of this trait after the alignment with the genotype and brain imaging data. Coding: the UK Biobank coding identifier. Missing: the values encoded as missing for the trait. Mapping: the json-object describing the mapping used. (XLSX)</p
Characteristics of UK Biobank participants.
No full text<p>Number and performance of participants completing each auditory and cognitive measure. Percentages for each N other than gender are corrected for missing data. Mean ± sd: descriptive statistics for age, SRT, cognitive tests. % +ve: percent positive responses to each question. Cognitive test performance shows number of questions correctly answered (FI), % correct responses (PM), and number of incorrectly chosen pairs (VM).</p><p>Characteristics of UK Biobank participants.</p
Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.
No full textBiobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or fill-in missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets
Additional file 1 of Risk of hip fracture in meat-eaters, pescatarians, and vegetarians: a prospective cohort study of 413,914 UK Biobank participants
No full textAdditional file 1: Supplementary Figures: Fig S1: Flow chart of UK Biobank participants for this study. Fig S2: Directed Acyclic Graph showing the relationship between diet group, hip fracture incidence, and related factors. Fig S3: Risk of hip fracture in occasional meat-eaters, pescatarians, and vegetarians compared to regular meat-eaters in the UK Biobank with multiple imputation via chained equations for missing covariate data. Fig S4: Log(-log) survival plot for regular meat-eaters, occasional meat-eaters, pescatarians, and vegetarians in the UK Biobank. Fig S5: A) Time until hip fracture and B) Age at hip fracture in regular meat-eaters, occasional meat-eaters, pescatarians, and vegetarians in the UK Biobank. Fig S6: Risk of hip fracture in occasional meat-eaters, pescatarians, and vegetarians compared to regular meat-eaters in the UK Biobank with multiple imputation via chained equations for missing covariate data. Supplementary Tables: Table S1: Strengthening the Reporting of Observational studies in Nutritional Epidemiology (STROBE-Nut) checklist. Table S2: Diet group categorisation and definitions. Table S3: Summary of mediation analyses using the inverse odds ratio weighting method in the UK Biobank. Table S4: Diet group classifications at recruitment and at the latest point of available follow-up in UK Biobank participants. Table S5: Dietary characteristics of UK Biobank participants by diet group at recruitment. Table S6: Characteristics of UK Biobank participants by diet group at recruitment, stratified by sex. Table S7: Characteristics of UK Biobank participants at recruitment that were included or excluded from analyses. Table S8: Adjusted and relative means (95% confidence intervals) of potential mediators at recruitment across diet groups in the UK Biobank. Table S9: Risk of hip fracture by diet group in the UK Biobank with varying restrictions. Supplementary Methods: Diet group classification. Other dietary measurements. Derivation of potential mediators. Derivation of covariates. Calculating absolute risk differences. Mediation analyses. Supplementary results: Diet group at recruitment and follow-up. Dietary characteristics at recruitment. Descriptive characteristics at recruitment with varying restrictions
Data from: Metabolomics - Emory Cardiovascular Biobank
No full textUntargeted high-resolution plasma metabolomic profiling among patients with coronary artery disease. Patients recruited from the Emory Cardiovascular Biobank into independent discovery and validation cohorts.This DATSETNAMEreadme.txt file was generated on 2020-08-20 by ANURAG MEHTA and CHANG LIU
GENERAL INFORMATION
1. Title of Dataset: Emory Cardiovascular Biobank Metabolomics
2. Author Information
A. Principal Investigator Contact Information
Name: Arshed A. Quyyumi, MD
Institution: Emory University School of Medicine
Address: 1462 Clifton Road NE, Suite 507, Atlanta, Georgia 30329
Email: [email protected]
B. Associate or Co-investigator Contact Information
Name: Anurag Mehta, MD
Institution: Emory University School of Medicine
Address: 1462 Clifton Road NE, Suite 513, Atlanta, Georgia 30329
Email: [email protected]
C. Alternate Contact Information
Name: Chang Liu
Institution: Emory University School of Medicine
Address: 1462 Clifton Road NE, Atlanta, Georgia 30329
Email: [email protected]
3. Date of data collection: 2004 to 2016
4. Information about funding sources that supported the collection of the data: National Heart, Lung, and Blood Institute grant 1P20HL113451
SHARING/ACCESS INFORMATION
1. Links to publications that cite or use the data: https://doi.org/10.1371/journal.pone.0237579
2. Links to other publicly accessible locations of the data: https://doi.org/10.5061/dryad.866t1g1mt
3. Links/relationships to ancillary data sets: none
4. Was data derived from another source? no
5. Recommended citation for this dataset: Mehta A, Liu C, Quyyumi AA. Emory Cardiovascular Biobank Metabolomics. 2020
DATA & FILE OVERVIEW
1. File List: Analysis_data_first_cohort.csv; Analysis_data_second_cohort.csv
2. Relationship between files: data of two separate cohorts
3. Additional related data collected that was not included in the current data package: none
4. Are there multiple versions of the dataset? no
METHODOLOGICAL INFORMATION: please see description in materials and methods section of the manuscript
DATA-SPECIFIC INFORMATION FOR: Analysis_data_first_cohort.csv
1. Number of variables: 6796
2. Number of cases/rows: 454
3. Variable List:
GENEID: subject IDs
timetodeath3yr: time to death event or censoring at three years
death3yr: death event at three years
age: age in years
male: male gender, 1=male, 0=female
BlackRace: black race, 1=black, 0=non-black
batch: batch of metabolomics profiling
Strokehx: history of stroke, 1=yes, 0=no
CABGhx: prior CABG, 1=yes, 0=no
PVDhx: peripheral artery disease, 1=yes, 0=no
eGFR_max120_lt60: estimated glomerular filtration rate less than 60 ml/min/1.73m2, 1=yes, 0=no
curr_smoking: current smoking, 1=yes, 0=no
HFhx: heart failure history, 1=yes, 0=no
HTN: hypertension, 1=yes, 0=no
DM: diabetes, 1=yes, 0=no
mzXX_tXX: intensities of metabolic features
4. Missing data codes: NA
DATA-SPECIFIC INFORMATION FOR: Analysis_data_second_cohort.csv
1. Number of variables: 8729
2. Number of cases/rows: 322
3. Variable List:
GENEID: subject IDs
timetodeath3yr: time to death event or censoring at three years
death3yr: death event at three years, 1=yes, 0=no
age: age in years
male: male gender, 1=male, 0=female
BlackRace: black race, 1=black, 0=non-black
batch: batch of metabolomics profiling
Strokehx: history of stroke, 1=yes, 0=no
CABGhx: prior CABG, 1=yes, 0=no
PVDhx: peripheral artery disease, 1=yes, 0=no
eGFR_max120_lt60: estimated glomerular filtration rate less than 60 ml/min/1.73m2, 1=yes, 0=no
curr_smoking: current smoking, 1=yes, 0=no
HFhx: heart failure history, 1=yes, 0=no
HTN: hypertension, 1=yes, 0=no
DM: diabetes, 1=yes, 0=no
mzXX_tXX: intensities of metabolic features
4. Missing data codes: NA
Funding provided by: National Heart, Lung, and Blood InstituteCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000050Award Number: 1P20HL113451Funding provided by: American Heart AssociationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000968Award Number: 19POST34400057Dataset collected as part of Emory Cardiovascular Biobank - a prospective regsitry of patients with coronary artery diseas
State Estimation of Linear Systems With Sparse Inputs and Markov-Modulated Missing Outputs
No full textIn this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the Markov chain models intermittent outages due to communication channels and occlusions corresponding to moving objects. We rely on the sparse Bayesian learning framework to derive an estimation algorithm that uses Kalman smoothing to handle temporal correlation and the Viterbi algorithm to handle missing data. Further, we demonstrate the utility of our algorithm by applying it to the frequency division duplexed multiple input multiple output downlink channel estimation problem.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
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