489 research outputs found

    Genetic analysis suggests high misassignment rates in clinical Alzheimer's cases and controls

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    Genetic case-control association studies are often based on clinically ascertained cases and population or convenience controls. It is known that some of the controls will contain cases, as they are usually not screened for the disease of interest. However, even clinically assessed cases and controls can be misassigned. For Alzheimer's disease (AD), it is important to know the accuracy of the clinical assignment. The predictive accuracy of AD risk by polygenic risk score analysis has been reported in both clinical and pathologically confirmed cohorts. The genetic risk prediction can provide additional insights to inform classification of subjects to case and control sets at a preclinical stage. In this study, we take a mathematical approach and aim to assess the importance of a genetic component for the assignment of subjects to AD-positive and -negative groups, and provide an estimate of misassignment rates (MARs) in AD case/control cohorts accounting for genetic prediction modeling results. The derived formulae provide a tool to estimate MARs in any sample. This approach can also provide an estimate of the maximal and minimal MARs and therefore could be useful for statistical power estimation at the study design stage. We illustrate this approach in 2 independent clinical cohorts and estimate misdiagnosis rate up to 36% in controls unscreened for the APOE genotype, and up to 29% when E3 homozygous subjects are used as controls in clinical studies

    Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity

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    Influenza viruses cause a significant amount of morbidity and mortality. Understanding host immune control efficacy and how different factors influence lung injury and disease severity are critical. We established and validated dynamical connections between viral loads, infected cells, CD

    Estimating and Reporting on the Quality of Inpatient Stroke Care by Veterans Health Administration Medical Centers

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    Background—Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error caused by small numbers of eligible patients per facility. We applied multilevel modeling and empirical Bayes (EB) estimation in addressing this issue in performance reporting of stroke care quality in the Medical Centers. Methods and Results—We studied a retrospective cohort of 3812 veterans admitted to 106 Medical Centers with ischemic stroke during fiscal year 2007. The median number of study patients per facility was 34 (range, 12–105). Inpatient stroke care quality was measured with 13 evidence-based QIs. Eligible patients could either pass or fail each indicator. Multilevel modeling of a patient's pass/fail on individual QIs was used to produce facility-level EB-estimated QI pass rates and confidence intervals. The EB estimation reduced interfacility variation in QI rates. Small facilities and those with exceptionally high or low rates were most affected. We recommended 8 of the 13 QIs for performance reporting: dysphagia screening, National Institutes of Health Stroke Scale documentation, early ambulation, fall risk assessment, pressure ulcer risk assessment, Functional Independence Measure documentation, lipid management, and deep vein thrombosis prophylaxis. These QIs displayed sufficient variation across facilities, had room for improvement, and identified sites with performance that was significantly above or below the population average. The remaining 5 QIs were not recommended because of too few eligible patients or high pass rates with little variation. Conclusions—Considerations of statistical uncertainty should inform the choice of QIs and their application to performance reporting

    24-Hour Rhythms of DNA Methylation and Their Relation with Rhythms of RNA Expression in the Human Dorsolateral Prefrontal Cortex

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    Circadian rhythms modulate the biology of many human tissues, including brain tissues, and are driven by a near 24-hour transcriptional feedback loop. These rhythms are paralleled by 24-hour rhythms of large portions of the transcriptome. The role of dynamic DNA methylation in influencing these rhythms is uncertain. While recent work in Neurospora suggests that dynamic site-specific circadian rhythms of DNA methylation may play a role in modulating the fungal molecular clock, such rhythms and their relationship to RNA expression have not, to our knowledge, been elucidated in mammalian tissues, including human brain tissues. We hypothesized that 24-hour rhythms of DNA methylation exist in the human brain, and play a role in driving 24-hour rhythms of RNA expression. We analyzed DNA methylation levels in post-mortem human dorsolateral prefrontal cortex samples from 738 subjects. We assessed for 24-hour rhythmicity of 420,132 DNA methylation sites throughout the genome by considering methylation levels as a function of clock time of death and parameterizing these data using cosine functions. We determined global statistical significance by permutation. We then related rhythms of DNA methylation with rhythms of RNA expression determined by RNA sequencing. We found evidence of significant 24-hour rhythmicity of DNA methylation. Regions near transcription start sites were enriched for high-amplitude rhythmic DNA methylation sites, which were in turn time locked to 24-hour rhythms of RNA expression of nearby genes, with the nadir of methylation preceding peak transcript expression by 1–3 hours. Weak ante-mortem rest-activity rhythms were associated with lower amplitude DNA methylation rhythms as were older age and the presence of Alzheimer's disease. These findings support the hypothesis that 24-hour rhythms of DNA methylation, particularly near transcription start sites, may play a role in driving 24-hour rhythms of gene expression in the human dorsolateral prefrontal cortex, and may be affected by age and Alzheimer's disease

    Epigenetic and Genetic Influences on DNA Methylation Variation in Maize Populations

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    DNA methylation is a chromatin modification that is frequently associated with epigenetic regulation in plants and mammals. However, genetic changes such as transposon insertions can also lead to changes in DNA methylation. Genome-wide profiles of DNA methylation for 20 maize (Zea mays) inbred lines were used to discover differentially methylated regions (DMRs). The methylation level for each of these DMRs was also assayed in 31 additional maize or teosinte genotypes, resulting in the discovery of 1966 common DMRs and 1754 rare DMRs. Analysis of recombinant inbred lines provides evidence that the majority of DMRs are heritable. A local association scan found that nearly half of the DMRs with common variation are significantly associated with single nucleotide polymorphisms found within or near the DMR. Many of the DMRs that are significantly associated with local genetic variation are found near transposable elements that may contribute to the variation in DNA methylation. Analysis of gene expression in the same samples used for DNA methylation profiling identified over 300 genes with expression patterns that are significantly associated with DNA methylation variation. Collectively, our results suggest that DNA methylation variation is influenced by genetic and epigenetic changes that are often stably inherited and can influence the expression of nearby genes
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