P-value Threshold Research Articles

Effect of high variation in transcript expression on identifying differentially expressed genes in RNA-seq analysis.

Great efforts have been made on the algorithms that deal with RNA-seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p-value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p-value and fold change thresholds on DE analyses, with RNA-seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp-values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA-seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification.

The fragility of randomized placebo-controlled trials for irritable bowel syndrome.

The fragility index (FI) represents the number of participants whose status in a trial would have to change from a non-event (not experiencing the primary endpoint) to an event (experiencing the primary endpoint) in order to turn a statistically significant result into a non-significant result. We sought to evaluate the fragility indices of irritable bowel syndrome [IBS-mixed (IBS-M), IBS-constipation (IBS-C), & IBS-diarrhea (IBS-D)] trials. Irritable bowel syndrome trials published in high-impact journals were identified from Medline. Trials had to be in adults, randomized, parallel-armed, with at least one statistically significant binary outcome, and an achieved primary endpoint of therapeutic efficacy. FI and correlation coefficients were calculated, and regression modeling used to identify predictors of a high FI. Twelve trials were analyzed with a median FI of 6 (range: 0-38). Median sample size in all trials was 366 (range: 44-856). Trial publication year (p=0.71), journal impact factor (p=0.52), duration of study (p=0.12), and number need to treat [NNT] (p=0.29) were not predictive of a high FI. While a lower p-value correlated with a higher FI (p=0.039), no correlation was noted between FI and impact factor (R=-0.20, p=0.52), trial publication year (R=0.12, p=0.71), duration of trial (R=-0.46, p=0.13), NNT (R=-0.34, p=0.29), and sample size (R=0.23, p=0.5). The highest FI was in a Ramosetron trial (FI=30) for IBS-D. A median of six participants is needed to nullify results in the included IBS trials suggesting how easily statistical significance based on a threshold p-value may be overturned.

1 Correlation Between New York City Hot Spotting Policy and Mobility to Reduce COVID-19 Spread

In October 2020, New York State initiated a micro-cluster strategy (“hot spotting”) that divides into three categories based on COVID-19 cases and hospital capacity, each with successively more restrictions: Yellow, Orange, and Red Zones. Our objectives were to evaluate the influence of hot spotting on mobility and subsequent mortality, and then to identify underlying social determinants of health associated with the neighborhoods most affected by hot spotting. We combine several data sources in our analysis. Time-dependent data were obtained from SafeGraph for cellphone mobility at the Census Block Group, New York State Governor’s Office for hot spotting, school and indoor dining, and NYC Department of Health and Mental Hygiene (DOHMH) for COVID-19 cases and mortality. Using the DOHMH’s “Modified Zip Code Tabulation Areas” (MODZCTA), we matched these to community-level data obtained from 2018 American Community Survey 5-year estimates for population density. Our main outcomes are Average Median Percentage Time Home (AMPTH) and Device-Weighted Average Median Percentage Time Home (DWAMPTH) from SafeGraph Social Distancing Metrics summarized to MODZCTA boundaries. Home is defined as the common nighttime location of each mobile device over a 6-week period to a Geohash-7 granularity (∼153m x ∼153m). We implemented the Wilcoxon rank-sum test with a <0.05 p-value threshold for each day since hot spotting policy to compare MODZCTA with any of the Zone's designation to those without designation. Our main outcomes are Average Median Percentage Time Home (AMPTH) and Device-Weighted Average Median Percentage Time Home (DWAMPTH) from SafeGraph Social Distancing Metrics summarized to MODZCTA boundaries. NYC residents from October 5, 2020, to December 31, 2020 (87 days total) using the 177 MODZCTA within NYC as geographic unit of analysis. For the AMPTH measurement, MODZCTAs with hot spotting Zone's designation had 84 days (95% of the days) with statistically significantly lower mobility than non-intervention MODZCTAs, and for the DWAMPTH measurement, 83 days (97% of the days) had statistically significantly lower mobility. 58 of the days had p-value<0.001 for AMPTH and 49 had p-value<0.001 for DWAMPTH, and only a minority of days had p-value>0.1 (2 days for AMPTH and 3 for DWAMPTH). Looking at individual boroughs, Brooklyn had 42 statistically significant days for AMPTH and 49 for DWAMPTH, while Queens had 12 statistically significant days for AMPTH and 7 for DWAMPTH. New York State’s micro-cluster focus Zones is associated with decreased mobility in high COVID-19 prevalence areas. Our study suggests that shutdowns targeted at small geographic areas may reduce mobility and thus can potentially help control COVID-19 spread.

Identifying individuals with high risk of Alzheimer\u2019s disease using polygenic risk scores

Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

Validity of observational evidence on putative risk and protective factors: appraisal of 3744 meta-analyses on 57 topics

BackgroundThe validity of observational studies and their meta-analyses is contested. Here, we aimed to appraise thousands of meta-analyses of observational studies using a pre-specified set of quantitative criteria that assess the significance, amount, consistency, and bias of the evidence. We also aimed to compare results from meta-analyses of observational studies against meta-analyses of randomized controlled trials (RCTs) and Mendelian randomization (MR) studies.MethodsWe retrieved from PubMed (last update, November 19, 2020) umbrella reviews including meta-analyses of observational studies assessing putative risk or protective factors, regardless of the nature of the exposure and health outcome. We extracted information on 7 quantitative criteria that reflect the level of statistical support, the amount of data, the consistency across different studies, and hints pointing to potential bias. These criteria were level of statistical significance (pre-categorized according to 10−6, 0.001, and 0.05 p-value thresholds), sample size, statistical significance for the largest study, 95% prediction intervals, between-study heterogeneity, and the results of tests for small study effects and for excess significance.Results3744 associations (in 57 umbrella reviews) assessed by a median number of 7 (interquartile range 4 to 11) observational studies were eligible. Most associations were statistically significant at P < 0.05 (61.1%, 2289/3744). Only 2.6% of associations had P < 10−6, ≥1000 cases (or ≥20,000 participants for continuous factors), P < 0.05 in the largest study, 95% prediction interval excluding the null, and no large between-study heterogeneity, small study effects, or excess significance. Across the 57 topics, large heterogeneity was observed in the proportion of associations fulfilling various quantitative criteria. The quantitative criteria were mostly independent from one another. Across 62 associations assessed in both RCTs and in observational studies, 37.1% had effect estimates in opposite directions and 43.5% had effect estimates differing beyond chance in the two designs. Across 94 comparisons assessed in both MR and observational studies, such discrepancies occurred in 30.8% and 54.7%, respectively.ConclusionsAcknowledging that no gold-standard exists to judge whether an observational association is genuine, statistically significant results are common in observational studies, but they are rarely convincing or corroborated by randomized evidence.

An exploratory study of body composition as a predictor of dose-limiting toxicity in metastatic pancreatic cancer treated with gemcitabine plus nab-paclitaxel

Body composition is increasingly being studied as a method of predicting chemotherapy toxicity. Our study aimed to evaluate associations of body composition with treatment toxicity in a group of pancreatic cancer patients treated with gemcitabine plus nab-paclitaxel. A retrospective review was performed for all patients who received first-line gemcitabine plus nab-paclitaxel for metastatic pancreatic cancer at a northern Alberta cancer institute (Canada) from 2014 to 2017. Total lean body mass (LBM) was derived from measurements of muscle surface area at L3 on baseline computed tomography (CT) scans. Optimal stratification, or minimal p-value analysis, was used to assess for a threshold of nab-paclitaxel dose per LBM (mg/kg) associated with a higher risk of dose-limiting toxicity (DLT). 152 patients were included in the study, of whom 62 (40.8%) experienced DLT. nab-Paclitaxel dose/LBM ranged from 0.98 to 8.76mg/kg. A threshold for nab-paclitaxel dose/LBM that optimally predicted risk of DLT was identified at 5.83mg/kg. Above this cut-off, 18/31 (58.1%) patients experienced DLT, compared to 44/121 (36.4%) patients below (p=0.028). Patients above this cut-off had a higher incidence of peripheral neuropathy compared to those below, though this was not statistically significant based on an adjusted p-value threshold (48.4 vs. 29.8% respectively, p=0.050). Body mass index, body surface area, and absolute initial doses of nab-paclitaxel or gemcitabine did not significantly impact likelihood of DLT. nab-Paclitaxel dose normalized to LBM, based on CT-derived measures of skeletal muscle, has potential to predict risk of chemotherapy toxicity. Chemotherapy dosing based on body composition, rather than conventional anthropometric measures, may be effective in reducing treatment toxicity.

Abstract LB090: Associations of somatically mutated genes and pathways with colorectal cancer specific survival in 4,500 colorectal cancer patients

Abstract Colorectal cancer (CRC) is a heterogenous disease that develops through somatic mutations in driver genes, leading to activation of diverse neoplastic pathways. To systematically examine if somatically mutated genes and pathways impact survival, we sequenced tumor and normal DNA samples for 4,512 CRC cases using a targeted panel. We performed Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for associations of disease-specific (DS)-survival with somatically mutated genes and pathways, adjusting for age and sex, and stratifying baseline hazards by study population. We assessed statistical significance using Bonferroni p-value thresholds to account for multiple testing of 214 genes (2.34x10-4) and 6 key CRC pathways (8.3x10-3). We limited analyses to non-silent mutations. We observed that DS-survival was significantly more favorable among individuals with hypermutated (HM) tumors, primarily consisting of microsatellite unstable and POLE-mutated tumors, as compared to those with non-hypermutated (NHM) tumors (HR=0.4, 95% CI: 0.3-0.5, p=1.2x10-17). BRAF V600E mutations were associated with poorer survival (HR=2.0, 95% CI: 1.6-2.5, p=1.6x10-10). This association was more pronounced among NHM tumors (HR=2.3, 95% CI: 1.8-2.9, p=4.3x10-12). We identified suggestive associations (p-values &amp;lt; 5.0x10-3 in overall or stratified analyses) between DS-survival and mutations in B2M, TP53, and SMAD4. Mutations in B2M may provide more favorable prognosis for survival (HR=0.5, 95% CI: 0.3-0.8, p=4.4x10-3), with similar effect sizes in HM and NHM tumors. Poorer survival may be associated with mutations in TP53 (HR=1.2, 95% CI:1.2-1.4, p=9.3x10-4) and SMAD4 (HR=1.3, 95% CI:1.1-1.6, p=3.2x10-3) in NHM tumors. We further observed statistically significant associations between survival and three mutated pathways: TP53/ATM (HR=1.2, 95% CI:1.1-1.4, p=9.0x10-4), receptor tyrosine kinases (RTK) and RAS (HR=1.3, 95% CI:1.1-1.5, p=4.5x10-5), and TGF-beta (HR=1.3, 95% CI:1.1-1.5, p=2.9x10-4). Findings for TP53/ATM and RTK/RAS were primarily due to mutations in one gene within each pathway (TP53 and BRAF, respectively). However, the TGF-beta pathway finding, which was more pronounced in NHM tumors, included two genes with p-values less than 0.05 (SMAD4 p=3.2x10-3 and TGFBR2 p=0.01). Despite our large sample size, relatively few somatically mutated genes were significantly associated with DS-survival. It may be that pathway-level testing affords more power for analyses and that larger sample sizes are needed. It is of interest that non-silent mutations in B2M, whose product is a component of the class I major histocompatibility complex, was associated with improved DS-survival, though our finding was not statistically significant after multiple testing correction so further exploration and replication is needed. Citation Format: Tabitha A. Harrison, Syed H. Zaidi, Conghui Qu, Amanda I. Phipps, Robert S. Steinfelder, Quang M. Trinh, Sonja I. Berndt, Daniel D. Buchanan, Peter T. Campbell, Andrew T. Chan, Kim F. Doheny, David A. Drew, Jane C. Figueiredo, Steven J. Gallinger, Marios Giannakis, Andrea Gsur, Marc J. Gunter, Michael Hoffmeister, Wen-Yi Huang, Paul J. Limburg, Victor Moreno, Polly A. Newcomb, Shuji Ogino, Ross L. Prentice, Tameka Shelford, Wei Sun, Stephen N. Thibodeau, Li Hsu, Ulrike Peters. Associations of somatically mutated genes and pathways with colorectal cancer specific survival in 4,500 colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB090.

Recent Preoperative Concussion and Postoperative Complications: A Retrospective Matched-cohort Study.

Physiological alterations during the perianesthetic period may contribute to secondary neurocognitive injury after a concussion. Patients exposed to concussion and who received an anesthetic within 90 days were matched to unexposed patients without concussion. Intraoperative and postoperative events were compared. Subgroup analyses assessed relationships among patients with a concussion in the prior 30, 31 to 60, and 61 to 90 days and their respective unexposed matches. To facilitate identification of potential targets for further investigation, statistical comparisons are reported before, as well as after, correction for multiple comparisons. Sixty concussion patients were matched to 176 unexposed patients. Before correction, 28.3% postconcussion versus 14.8% unexposed patients reported postanesthesia care unit pain score≥7 (P=0.02); 16.7% concussion versus 6.5% unexposed patients reported headache within 90 days of anesthesia (P=0.02) and 23.5% of patients who received surgery and anesthesia within 30 days of concussion experienced headache within 90 days of anesthesia compared with 7.1% in the unexposed group (P=0.01). Patients who experienced concussion and had anesthesia between 31 and 60 days after injury had a postanesthesia care unit Richmond Agitation and Sedation Scale score of -1.61±1.29 versus a score of -0.2±0.45 in unexposed patients (P=0.002). After adjusting the P-value threshold for multiple comparisons, the P-value for significance was instead 0.0016 for the overall cohort. Our study revealed no significant associations with application of adjusted significance thresholds. There were no differences in intraoperative and postoperative outcomes in patients with recent concussion compared with unexposed patients. Before correction for multiple comparisons, several potential targets for further investigation are identified. Well-powered studies are warranted.

Prognostic Factors and Survival Outcomes in Phase I Patients with Hematologic Malignancies

Introduction: Patient selection is a challenge in designing effective phase I clinical trials in hematologic malignancies. Potential candidates typically have relapsed or refractory disease, and have had poor response to multiple prior lines of therapy. Such patients are at risk for early death, which reduces both the scientific yield and the therapeutic value of clinical trials: patients whose disease has already entered a terminal phase, and who subsequently die within a few months, provide little meaningful data to advance the understanding of an experimental treatment, and likely derive little clinical benefit from that treatment.Currently, eligibility criteria for most trials rely on ad hoc laboratory thresholds, and on subjective assessments of performance status and life expectancy. It is desirable to have an objective, statistically validated means of predicting survival, to optimize patient selection for phase I clinical trials. A prognostic scoring system for this purpose has been developed and validated for patients with solid tumors (Garrido-Laguna et al. Cancer . 2012;118(5):1422-1428); this model incorporates variables on tumor type and number of metastatic sites, which are not directly applicable outside of solid tumors. Similar work has recently been reported in hematologic malignancies (Benajiba et al. Anti-cancer drugs . 2017;28(5):540-545), using only three variables: performance status, serum albumin, and disease class. We developed a novel prognostic scoring system to estimate progression free (PFS) and overall (OS) survival for patients enrolled in phase I clinical trials for treatment of hematologic malignancy.Methods: We retrospectively reviewed the electronic medical records of 106 patients enrolled in phase I clinical trials for treatment of hematologic malignancies at the UT Health San Antonio Cancer Center between 2000 and 2014. Descriptive, univariate, and multivariate analyses were performed to determine the parameters that carry prognostic significance. Cox proportional hazards regression models of Progression Free and Overall Survival (PFS, OS) were used for multivariate analyses. A backward elimination process with a relaxed P-value (0.2) threshold was used to select variables for adjustment.Results: Our analysis identified Hispanic ethnicity (P=0.03), low albumin level (<3.5 g/dL), low hemoglobin (<10 g/dL, P=0.02), hyperbilirubinemia (>1.0 mg/dL, P=0.03), abnormal white blood cell count (<4 k/µL or >10 k/µL, P<0.01), elevated lactate dehydrogenase (LDH) (>240 IU/L, P<0.01), and diagnosis of acute leukemia (P<0.01) as independent predictors of poor overall survival. The patient cohort was stratified by risk score into good (0-1), intermediate (2-4), and poor (5-7) risk categories, which were compared with respect to PFS and OS using Kaplan-Meier curves and log rank testing (see figures 1 and 2).Conclusion: A simple score incorporating diagnosis, ethnicity, albumin, hemoglobin, bilirubin, white blood count, and LDH can identify patients with advanced hematologic malignancy who are likely to have short OS and PFS. Efforts are underway to validate this score against a separate cohort of phase I patients. Once validated, it could be incorporated in phase I trial design, potentially making these trials easier, faster, and more cost-effective to conduct, while allocating experimental treatments to those patients most likely to derive benefit. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Impact of the p-Value Threshold on Interpretation of Trial Outcomes in Obstetrics and Gynecology.

Randomized controlled trials (RCTs) are considered the highest level of evidence to inform clinical practice. However, the reproducibility crisis has raised concerns about the scientific rigor of published RCT findings. Some advocate for a lower p-value threshold. We aimed to review published OB/Gyn topical RCTs in three representative OB/Gyn journals and three high impact non-OB/Gyn journals to determine if their interpretations would change with adoption of a p-value threshold for significance of 0.005. Secondarily, we evaluated if there were differences in methodologic characteristics between those that did and did not lose significance. A manual search was performed to identify all OB/Gyn RCTs published in the selected journals between July 2017 and June 2019. Data were collected on primary outcome(s), methodology, and p-values. We determined the proportion of primary outcomes that would remain statistically significant with adoption of a p-value significance threshold of 0.005 versus be reinterpreted as "suggestive" (defined as p-value between 0.005 and 0.05). Chi-square or Fisher's exact test were used to compare study characteristics. Overall, 202 RCTs met inclusion criteria; 52% in obstetrics and 48% in gynecology. Of 90 studies considered significant with p <0.05 at the time of publication, 54.4% (n = 49) would maintain significant (p < 0.005), while 45.6% (n = 41) would become suggestive using the lower threshold. Most RCTs utilized a single (90.1%) versus composite (8.9%) primary outcome type, used an intent-to-treat analysis (73.3%), and studied a drug intervention (46.5%). Methodologically, 23.7% did not prespecify analysis type, 28.2% did not meet the pre-determined sample size, and 9.4% did not report an a priori sample size calculation. Studies maintaining significance were more likely to be international and report a funding source. Adopting a p-value significance threshold of 0.005 would require reinterpretation of almost half of RCT results in the OB/Gyn literature. Highly variable methodological quality was identified. · New p-value threshold results in reinterpretation of nearly half of RCT results in OB/Gyn literature.. · Highly variable methodological quality was identified.. · Reduced use of binary interpretations of significance is necessary..

DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies.

Deep learning has revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence has emerged as an area of research that goes beyond pure prediction improvement by extracting knowledge from deep learning methodologies through the interpretation of their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers’ decisions by applying layer-wise relevance propagation as one example from the pool of explanation techniques. The resulting importance scores are eventually used to determine a subset of the most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw P-value thresholding and other baseline methods. Two novel disease associations (rs10889923 for hypertension, rs4769283 for type 1 diabetes) were identified.

PAX8/PAX8-AS1 DNA methylation levels are associated with objective sleep duration in persons with unexplained hypersomnolence using a deep phenotyping approach.

Patients with unexplained hypersomnolence have significant impairment related to daytime sleepiness and excessive sleep duration, the biological bases of which are poorly understood. This investigation sought to examine relationships between objectively measured hypersomnolence phenotypes and epigenetic modification of candidate hypersomnolence genes to advance this line of inquiry. Twenty-eight unmedicated clinical patients with unexplained hypersomnolence were evaluated using overnight ad libitum polysomnography, multiple sleep latency testing, infrared pupillometry, and the psychomotor vigilance task. DNA methylation levels on CpG sites annotated to 11 a priori hypersomnolence candidate genes were assessed for statistical association with hypersomnolence measures using independent regression models with adjusted local index of significance (aLIS) P-value threshold of 0.05. Nine CpG sites exhibited significant associations between DNA methylation levels and total sleep time measured using ad libitum polysomnography (aLIS p-value < .05). All nine differentially methylated CpG sites were annotated to the paired box 8 (PAX8) gene and its related antisense gene (PAX8-AS1). Among these nine differentially methylated positions was a cluster of five CpG sites located in the body of the PAX8 gene and promoter of PAX8-AS1. This study demonstrates that PAX8/PAX8-AS1 DNA methylation levels are associated with total sleep time in persons with unexplained hypersomnolence. Given prior investigations that have implicated single nucleotide polymorphisms in PAX8/PAX8-AS1 with habitual sleep duration, further research that clarifies the role of DNA methylation levels on these genes in the phenotypic expression of total sleep time is warranted.

Sphingosine Kinase 1 Regulates the Pulmonary Vascular Immune Response.

The aberrant proliferation of pulmonary artery smooth muscle (PASMCs) cells is a defining characteristic of pulmonary arterial hypertension (PAH) and leads to increased vascular resistance, elevated pulmonary pressure, and right heart failure. The sphingosine kinase 1 (SPHK1)/sphingosine-1 phosphate/sphingosine-1 phosphate receptor 2 pathway promotes vascular remodeling and induces PAH. The aim of this study was to identify genes and cellular processes that are modulated by over-expression of SPHK1 in human PASMCs (hPASMCs). RNA was purified and submitted for RNA sequencing to identify differentially expressed genes. Using a corrected p-value threshold of <0.05, there were 294 genes significantly up-regulated while 179 were significantly down-regulated. Predicted effects of these differentially expressed genes were evaluated using the freeware tool Enrichr to assess general gene set over-representation (enrichment) and ingenuity pathway analysis (IPA™) for upstream regulator predictions. We found a strong change in genes that regulated the cellular immune response. IL6, STAT1, and PARP9 were elevated in response to SPHK1 over-expression in hPASMCs. The gene set enrichment mapped to a few immune-modulatory signaling networks, including IFNG. Furthermore, PARP9 and STAT1 protein were elevated in primary hPASMCs isolated from PAH patients. In conclusion, these data suggest a role of Sphk1 regulates pulmonary vascular immune response in PAH.

The Impact of COVID-19 Lockdowns on Particulate Matter Emissions in Lombardy and Italian Citizens' Consumption Habits

Italy has been one of the first nations in the world to be heavily affected by COVID-19. A wide range of containment measures has been adopted from February to December 2020 to mitigate the pandemic. In this regard, the present research sets out to evaluate two aspects: (i) the impact of lockdowns on the concentrations of particulate matter (PM) 10 and 2.5 in the Lombardy region, and (ii) how anti-COVID-19 restrictions influenced Italian citizens' consumption habits. To do this, the average daily concentrations of PM10 and PM2.5 during 2020 in all the provinces of Lombardy were compared with those of the previous years through Welch's t-test. The same procedure was adopted to estimate the change in Google relative search volumes of home delivery services and smart working on a national scale. Two mean values were considered statistically confident when t &amp;lt; 1.5, suspiciously non-confident when 1.5 ≤ t &amp;lt; 1.9, and non-confident when t ≥ 1.9. Seasonalities and trends were assessed both graphically and with Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski-Phillips-Schmidt-Shin tests. Finally, Pearson and Spearman correlations between changes in citizens' behavior and specific key events related to COVID-19 have been dealt with. The P-value threshold was indicatively set at 0.05. Microsoft Excel 2020 and Google Sheets were used as data analysis software. This paper showed: (i) the limited or insufficient effectiveness of lockdowns in reducing PM10 and PM2.5 concentrations in Lombardy, and (ii) a significant change in the consumption habits of Italian citizens, thus leading to both positive and negative results in terms of sustainability. Therefore, it is high time that both Italian and international environmental protection authorities thoroughly investigated the role of non-mobility-related sources of particulate emissions to impose effective rules on home delivery services. Moreover, further research is required for the understanding of anthropogenic, environmental, and atmospheric phenomena that influence the concentrations of PM10 and PM2.5.

White matter tracts in Bipolar Disorder patients: A comparative study based on diffusion kurtosis and tensor imaging

BackgroundDiffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), is a powerful tool for studying human brain.The purpose is to investigate differences between DKI and DTI by comparing parameters in same analysis methods with bipolar disorder (BD) patients. MethodsIn this study, we attained in 47 BD patients and 49 age-, sex-, and education-matched healthy controls, complimented DTI and DKI scanning and got Fractional Anisotropy (FA), Mean Diffusion (MD) and Mean Kurtosis (MK). Voxel-wise statistical analysis was performed by the tract-based spatial statistics (TBSS) analysis and atlas-based regional data analysis. ResultsTBSS analysis showed more widespread regions and higher fidelity in DKI parameters than DTI parameters with the same p-value threshold, and DKI parameters showed significant alterations after Family-Wise Error correction. The DKI-FA value in the corpus callosum, bilateral cingulum (cingulate gyrus), bilateral superior corona radiata, left anterior corona radiata and left posterior corona radiata of BD patients was negatively correlated with the duration of illness. In the atlas-based regional data analysis, the effect size of DTI-FA, DTI-MD, DKI-FA and DKI-MD were quantified using Cohen's d value. DKI-FA and DKI-MD demonstrated more between-group different regions and the higher (p < 0.001) absolute Cohen's d value than DTI-FA. LimitationsThis study did not consider the difference between sub-types of BD. ConclusionsCompared to DTI parameters, DKI parameters were more sensitive and stable to probe the local microstructure, and particularly powerful to exploit cerebral alterations in BD patients.

Risk factors for metabolic bone disease among preterm infants less than 32\u2009weeks gestation with Bronchopulmonary dysplasia

BackgroundBronchopulmonary dysplasia (BPD) infants present an increased incidence of metabolic bone disease (MBD), but it is unknown which factors contribute to this. The aim of this study was to determine the risk factors for developing MBD in BPD infants.MethodsA retrospective review of the medical records of BPD infants admitted to the Neonatal intensive care unit at Zhangzhou Hospital between Jun 2016 and May 2020 was performed. BPD infants with MBD were identified, two contemporaneous without MBD matched by gestational age and gender were randomly selected as controls for each case of MBD. The association between putative risk factors and MBD was estimated with ORs and 95% CIs. A P-value threshold ≤0.2 was used in univariate analysis for inclusion into a multivariate (adjusted) model with a P-value of < 0.05 as statistically significant.ResultsA total of 156 BPD infants were enrolled with 52 cases of MBD and 104 controls. Fetal growth restriction (OR 6.00, 95% CI, 1.81–19.84), extremely low birth weight (OR 3.10, 95% CI, 1.07–8.94), feeding volume < 80 mL/kg/d at the end of the 4th week after birth (OR 14.98, 95% CI, 4.04–55.58), cholestasis (OR 4.44, 95% CI, 1.59–12.40), late onset sepsis (OR 3.95, 95% CI, 1.12–13.98) and prolonged (> 2 weeks) diuretics application (OR 5.45, 95% CI, 1.25–23.84) were found to be statistically significant risk factors for MBD in BPD infants.ConclusionIn BPD infants of homogeneous gestational age, fetal growth restriction, extremely low birth weight, feeding volume < 80 mL/kg/d at the end of the 4th week after birth, cholestasis and late onset sepsis are significant risk factors for MBD. These findings provide potential predictive factors for MBD in BPD infants and warrant prospective validation.

Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption

Coffee and tea are extensively consumed beverages worldwide which have received considerable attention regarding health. Intake of these beverages is consistently linked to, among others, reduced risk of diabetes and liver diseases; however, the mechanisms of action remain elusive. Epigenetics is suggested as a mechanism mediating the effects of dietary and lifestyle factors on disease onset. Here we report the results from epigenome-wide association studies (EWAS) on coffee and tea consumption in 15,789 participants of European and African-American ancestries from 15 cohorts. EWAS meta-analysis of coffee consumption reveals 11 CpGs surpassing the epigenome-wide significance threshold (P-value <1.1×10−7), which annotated to the AHRR, F2RL3, FLJ43663, HDAC4, GFI1 and PHGDH genes. Among them, cg14476101 is significantly associated with expression of the PHGDH and risk of fatty liver disease. Knockdown of PHGDH expression in liver cells shows a correlation with expression levels of genes associated with circulating lipids, suggesting a role of PHGDH in hepatic-lipid metabolism. EWAS meta-analysis on tea consumption reveals no significant association, only two CpGs annotated to CACNA1A and PRDM16 genes show suggestive association (P-value <5.0×10−6). These findings indicate that coffee-associated changes in DNA methylation levels may explain the mechanism of action of coffee consumption in conferring risk of diseases.

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

BackgroundBovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.ResultsThe host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively.ConclusionsThe results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

Evaluation of polygenic prediction methodology within a reference-standardized framework.

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.

Changes in DNA Methylation after 24 Hours of High‐Altitude Exposure

Acclimatization to high altitude involves a series of physiological changes required to improve oxygen delivery to tissue. These plastic changes are driven by shifts in gene expression patterns which may be influenced by epigenetic mechanisms. Recent studies have explored the impact of high-altitude exposure on DNA methylation in hypoxia-inducible factor (HIF) pathway genes. We expand on this work with a high-throughput investigation of methylation levels at over 850,000 CpG sites throughout the genome before and after high-altitude exposure. We hypothesized that acute high-altitude exposure would result in global hypomethylation, including reduced methylation at key HIF pathway genes including EPAS1 (HIF-2a) and EPO.DNA methylation was measured in 12 healthy participants (9 men and 3 women) between the ages of 19 and 32. DNA was isolated from whole blood collected during fasting at sea level (340m), and after 24 hours at high altitude (3800m). Illumina MethylationEPIC was used to profile methylation levels at individual CpG sites across the genome. Our preliminary analysis identified 24 differentially methylated sites (p-value threshold of 0.0001) (11 increased, 14 decreased). Of these sites, 17 were located in known gene regions. KEGG pathway analysis reveals these genes are associated with the TCA cycle (p = 0.02) and pyruvate metabolism (p = 0.03). After preprocessing, our dataset included 69 CpG sites associated with EPAS1 and 11 associated with EPO. While two EPAS1 sites show small changes in methylation status, they did not meet the significance threshold. These preliminary data suggest that DNA methylation may play a role in metabolic adaptations to oxygen limitation at high altitude.