False Discovery Rate Procedure Research Articles

Changes in temporal lobe activation during a sound stimulation task in patients with sensorineural tinnitus: a multi-channel near-infrared spectroscopy study

BackgroundThe subjective sign of a serious pandemic in human work and life is mathematical neural tinnitus. fNIRS (functional near-infrared spectroscopy) is a new non-invasive brain imaging technology for studying the neurological activity of the human cerebral cortex. It is based on neural coupling effects. This research uses the fNIRS approach to detect differences in the neurological activity of the cerebral skin in the sound stimulation mission in order to better discriminate between the sensational neurological tinnitus.MethodsIn the fNIRS brain imaging method, 14 sensorineural tinnitus sufferers and 14 healthy controls listened to varied noise and quiet for fNIRS data collection. Linear fitting was employed in MATLAB to eliminate slow drifts during preprocessing and event-related design analysis. The false discovery rate (FDR) procedure was applied in IBM SPSS Statistics 26.0 to control the false positive rate in multiple comparison analyses.ResultsWhen the ill group and the healthy control group were stimulated by pink noise, there was a significant difference in blood oxygen concentration (P < 0.05), and the healthy control group exhibited a high activation, according to the fNIRS measurement data. The blood oxygen concentration level in the patient group was dramatically enhanced after one month of acupuncture therapy under the identical stimulation task settings, and it was favorably connected with the levels of THI and TEQ scales.ConclusionsUsing sensorineural tinnitus illness as an example, fNIRS technology has the potential to disclose future pathological study on subjective diseases throughout time. Other clinical disorders involving the temporal lobe and adjacent brain areas may also be examined, in addition to tinnitus-related brain alterations.

COVID-19 hotspot detection in a university setting.

The onset of the COVID-19 pandemic commenced an era of widespread disruptions in the academic world, including shut downs, periodic shifts to online learning, and disengagement from students. In an effort to transition back to in-person learning, many universities and schools tried to implement policy that balanced student learning with community health. While academic administrators have little control over some aspects of COVID-19 spread, they often choose to use temporary shutdowns of in-person teaching based on perceived hotspots of COVID-19. Specifically, if administrators have substantial evidence of within-group transmission for a class or other academic unit (a "hotspot"), the activities of that class or division of the university might be temporarily moved online. In this article, we describe an approach used to make these types of decisions. Using demographic information and weekly COVID-19 testing outcomes for university students, we use an XGBoost model that produces an estimated probability of testing positive for each student. We discuss variables engineered from the demographic information that increased model fit. As part of our approach, we simulate semesters under the null hypothesis of no in-class transmission, and compare the distribution of simulated outcomes to the observed group positivity rates to find an initial p-value for each group (e.g., section, housing area, or major). Using a simulation-based modification of a standard false discovery rate procedure, we identify possible hot spots-classes or groups whose COVID-19 rates exceed the levels expected for the demographic mix of students in each group of interest. We use simulation experiments and an anonymized example from Fall 2020 to illustrate the performance of our approach. While our example is based on hotspot detection in a university setting, the approach can be used for monitoring the spread of infectious disease within any interconnected organization or population.

Human Milk Composition Is Associated with Maternal Body Mass Index in a Cross-Sectional, Untargeted Metabolomics Analysis of Human Milk from Guatemalan Mothers

BackgroundMaternal overweight and obesity has been associated with poor lactation performance including delayed lactogenesis and reduced duration. However, the effect on human milk composition is less well understood. ObjectivesWe evaluated the relationship of maternal BMI on the human milk metabolome among Guatemalan mothers. MethodsWe used data from 75 Guatemalan mothers who participated in the Household Air Pollution Intervention Network trial. Maternal BMI was measured between 9 and <20 weeks of gestation. Milk samples were collected at a single time point using aseptic collection from one breast at 6 mo postpartum and analyzed using high-resolution mass spectrometry. A cross-sectional untargeted high-resolution metabolomics analysis was performed by coupling hydrophilic interaction liquid chromatography (HILIC) and reverse phase C18 chromatography with mass spectrometry. Metabolic features associated with maternal BMI were determined by a metabolome-wide association study (MWAS), adjusting for baseline maternal age, education, and dietary diversity, and perturbations in metabolic pathways were identified by pathway enrichment analysis. ResultsThe mean age of participants at baseline was 23.62 ± 3.81 y, and mean BMI was 24.27 ± 4.22 kg/m2. Of the total metabolic features detected by HILIC column (19,199 features) and by C18 column (11,594 features), BMI was associated with 1026 HILIC and 500 C18 features. Enriched pathways represented amino acid metabolism, galactose metabolism, and xenobiotic metabolic metabolism. However, no significant features were identified after adjusting for multiple comparisons using the Benjamini–Hochberg false discovery rate procedure (FDRBH < 0.2). ConclusionsFindings from this untargeted MWAS indicate that maternal BMI is associated with metabolic perturbations of galactose metabolism, xenobiotic metabolism, and xenobiotic metabolism by cytochrome p450 and biosynthesis of amino acid pathways. Significant metabolic pathway alterations detected in human milk were associated with energy metabolism-related pathways including carbohydrate and amino acid metabolism.This trial was registered at clinicaltrials.gov as NCT02944682.

Exposure to benzotriazoles and benzothiazoles in Czech male population and its associations with biomarkers of liver function, serum lipids and oxidative stress.

Benzotriazoles and benzothiazoles (BTs) are high-production volume chemicals as well as widely distributed emerging pollutants with potential health risk. However, information about human exposure to BTs and associated health outcomes is limited. We aimed to characterise exposure to BTs among Czech men, including possible occupational exposure among firefighters, its predictors, and its associations with liver function, serum lipids and oxidative stress. 165participants (including 110 firefighters) provided urine and blood samples that were used to quantify the urinary levels of 8 BTs (high-performance liquid chromatography-tandem mass spectrometry), and 4 liver enzymes, cholesterol, low-density lipoprotein, and 8-hydroxy-2'-deoxyguanosine. Linear regression was used to assess associations with population characteristics and biomarkers of liver function, serum lipids and oxidative stress. Regression models were adjusted for potential confounding variables and false discovery rate procedure was applied to account for multiplicity. The BTs ranged from undetected up to 46.8ng/mL. 2-hydroxy-benzothiazole was the most predominant compound (detection frequency 83%; median 1.95ng/mL). 1-methyl-benzotriazole (1M-BTR) was measured in human samples for the first time, with a detection frequency 77% and median 1.75ng/mL. Professional firefighters had lower urinary 1M-BTR compared to non-firefighters. Urinary 1M-BTR was associated with levels of γ-glutamyl transferase (β = -17.54%; 95% CI: -26.127,-7.962). This is the first study to investigate BT exposure in Central Europe, including potentially exposed firefighters. The findings showed a high prevalence of BTs in the study population, the relevance of 1M-BTR as a new biomarker of exposure, and an urgent need for further research into associated adverse health outcomes.

Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data

BackgroundControlling the False Discovery Rate (FDR) in Multiple Comparison Procedures (MCPs) has widespread applications in many scientific fields. Previous studies show that the correlation structure between test statistics increases the variance and bias of FDR. The objective of this study is to modify the effect of correlation in MCPs based on the information theory. We proposed three modified procedures (M1, M2, and M3) under strong, moderate, and mild assumptions based on the conditional Fisher Information of the consecutive sorted test statistics for controlling the false discovery rate under arbitrary correlation structure. The performance of the proposed procedures was compared with the Benjamini–Hochberg (BH) and Benjamini–Yekutieli (BY) procedures in simulation study and real high-dimensional data of colorectal cancer gene expressions. In the simulation study, we generated 1000 differential multivariate Gaussian features with different levels of the correlation structure and screened the significance features by the FDR controlling procedures, with strong control on the Family Wise Error Rates.ResultsWhen there was no correlation between 1000 simulated features, the performance of the BH procedure was similar to the three proposed procedures. In low to medium correlation structures the BY procedure is too conservative. The BH procedure is too liberal, and the mean number of screened features was constant at the different levels of the correlation between features. The mean number of screened features by proposed procedures was between BY and BH procedures and reduced when the correlations increased. Where the features are highly correlated the number of screened features by proposed procedures reached the Bonferroni (BF) procedure, as expected. In real data analysis the BY, BH, M1, M2, and M3 procedures were done to screen gene expressions of colorectal cancer. To fit a predictive model based on the screened features the Efficient Bayesian Logistic Regression (EBLR) model was used. The fitted EBLR models based on the screened features by M1 and M2 procedures have minimum entropies and are more efficient than BY and BH procedures.ConclusionThe modified proposed procedures based on information theory, are much more flexible than BH and BY procedures for the amount of correlation between test statistics. The modified procedures avoided screening the non-informative features and so the number of screened features reduced with the increase in the level of correlation.

Single‐cell Transcriptomes Reveal Cell‐type Specific Roles of VEGF Family Genes in the Pathogenesis of Alzheimer’s Disease

AbstractBackgroundThe role of the vascular endothelial growth factors (VEGFs) in the pathogenesis of Alzheimer’s disease (AD) has been recently described, including notable changes along the VEGFB/FLT1 signaling pathway. Yet, cell‐specific alterations are not well characterized. To disentangle VEGFs’ cell‐specific effect on AD‐related traits, we performed association analyses of mRNA expression from 10 VEGF genes with cognitive performance and AD pathology in eight brain cell types using a large single nucleus RNA sequencing dataset from human brain.MethodThis project utilized recently published single‐cell transcriptomes derived from dorsolateral prefrontal cortex tissues of 424 donors collected at Rush Alzheimer’s Disease Center. Processed single cell RNA sequencing data were acquired from AD Knowledge Portal (syn2580853). Mean age at death was 89 years, 68% were females, and 37% were AD cases. Negative binomial lognormal mixed models implemented in the NEBULA R package were used to detect differential expression between AD cases and cognitively normal controls, and to analyze the associations with immunohistochemical amyloid and tangles, and finally to assess cross‐sectional and longitudinal global cognitive scores. Correction for multiple comparisons was completed using the false discovery rate procedure (Pfdr&lt;0.05).ResultPrefrontal cortical FLT1 expression was upregulated in AD participants compared to cognitively normal controls in both endothelial (endo) and microglial (micro) cells. Higher micro‐FLT1, endo‐FLT4, and oligodendrocyte (oligo) VEGFB expression was associated with greater β‐amyloid burden in AD samples, whereas higher VEGFB expression in inhibitory neurons was associated with lower β‐amyloid burden. Higher expression of micro‐FLT1 and oligo‐VEGFB associated with worse cognitive trajectories. Higher micro‐ and endo‐ expression of FLT1 associated with lower cognition scores at the last clinic visit before death. Lastly, higher astrocyte‐expressed NRP1 was associated with lower tangle burden.ConclusionConsistent with our previously reported bulk tissue results, prefrontal cortical expression of FLT1 and FLT4 were associated with global cognition, longitudinal cognitive trajectories, and AD neuropathology, and these associations appear to be limited to endothelial and microglial cells. In contrast, VEGFB expression seems to have opposite effects on amyloid burden depending on the cell types, suggesting its more dynamic role in AD physiopathology.

Predicted gene expression identifies novel tissue‐specific gene predictors of memory performance in older adults

AbstractBackgroundMemory performance can serve as a strong endophenotype for Alzheimer’s disease (AD) that changes early and continues to decline with disease progression. Yet, the genetic architecture of memory is not well characterized in older adults. Here, we build on existing memory GWAS studies by performing predicted gene expression analysis (PrediXcan) among older (60+) individuals from four cohorts of aging and investigate specific gene‐tissue drivers of genetically regulated gene expression associated with memory performance.MethodTissue‐specific (49 tissues, 5455 genes) PrediXcan models were built following the method described in Gamazon et al. (Nature Genetics 2015) leveraging model weights derived in GTEx (v8 release, build 38). Baseline and longitudinal memory scores were harmonized leveraging cognitive item‐level data on 19,707 non‐Hispanic White participants from four cohort studies of aging and AD (mean age 75.6±7.7, 55% female) using confirmatory factor analyses models. PrediXcan analyses were run adjusting for age at baseline, sex, and 5 population PCs and then meta‐analyzed using a fixed effects model. Correction for multiple comparisons accounting for all gene‐tissue combinations (267,267) was completed with the false discovery rate procedure (fdr‐p&lt;0.05). Sensitivity analyses excluded all non‐AD dementia and other comorbid conditions (N = 16,373; 57% females).ResultAs expected, several signals emerged from chromosome 19, including 48 gene‐tissue combinations near the APOE locus. Outside of APOE, we identified 20 gene‐tissue combinations from 17 known AD loci and three novel loci: higher predicted PUS7 expression in the caudate (β = ‐0.018, p = 0.034) and higher RP11‐18C3.1 expression in colon (β = ‐0.0165, fdr‐p = 0.034) related to faster cognitive decline, while higher predicted LRRC25 in the nucleus accumbens related to slower cognitive decline (β = 0.009, fdr‐p = 0.015). These signals remain comparable in sensitivity analysis.ConclusionWe identified multiple candidates for future mechanistic analysis. LRRC25 is a particularly interesting candidate that is differentially expressed in the AD brain, regulates autophagy in myeloid cells, and is in a co‐expression network with other known AD genes in the immune pathway like MS4A4A (https://agora.adknowledgeportal.org/). Future work will test for replication of these effects and deconvolve genetically‐regulated versus measured gene expression effects in the AD brain.

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

We proposed a method to extract causal relations of spatial clusters from multi-dimensional event sequence data, also known as a spatio-temporal point process. The proposed Granger cluster sequence mining algorithm identifies the pairs of spatial data clusters that have causality over time with each other. It extended the cluster sequence mining algorithm, which utilized a statistical inference technique to identify the occurrence relation, with a causality inference based on the Granger causality. In addition, the proposed method utilizes a false discovery rate procedure to control the significance of the causality. Based on experiments on both synthetic and semi-real data, we confirmed that the algorithm is able to extract the synthetic causal relations from multiple different sets of data, even when disturbed with high level of spatial noise. False discovery rate procedure also helps to increase the accuracy even more under such case and also make the algorithm less-sensitive to the hyperparameters.

Geometry-derived statistical significance: A probabilistic framework for detecting true positive findings in MRI data.

The false discovery rate (FDR) procedure does not incorporate the geometry of the random field and requires high statistical power at each voxel, a requirement not satisfied by the limited number of participants in imaging studies. Topological FDR, threshold free cluster enhancement (TFCE), and probabilistic TFCE improve statistical power by incorporating local geometry. However, topological FDR requires specifying a cluster defining threshold and TFCE requires specifying transformation weights. Geometry-derived statistical significance (GDSS) procedure overcomes these limitations by combining voxelwise p-values for the test statistic with the probabilities computed from the local geometry for the random field, thereby providing substantially greater statistical power than the procedures currently used to control for multiple comparisons. We use synthetic data and real-world data to compare its performance against the performance of these other, previously developed procedures. GDSS provided substantially greater statistical power relative to the comparator procedures, which was less variable to the number of participants. GDSS was more conservative than TFCE: that is, it rejected null hypotheses at voxels with much higher effect sizes than TFCE. Our experiments also showed that the Cohen's D effect size decreases as the number of participants increases. Therefore, sample size calculations from small studies may underestimate the participants required in larger studies. Our findings also suggest effect size maps should be presented along with p-value maps for correct interpretation of findings. GDSS compared with the other procedures provides considerably greater statistical power for detecting true positives while limiting false positives, especially in small sized (<40 participants) imaging cohorts.

Associations between placental DNA methylation and emotional and behavioral outcomes in preschoolers: insights from the EDEN Mother-Child Cohort study

IntroductionBehavioral (externalizing) and emotional (internalizing) problems were showed to be associated with the prenatal environment. Changes in placental DNA methylation was identified as a relevant potential mechanism of such association.ObjectivesWe aimed to explore the associations between placental DNA methylation and child behavior in order to explore pathways that could link prenatal exposures to child behavior.MethodsData including 441 children of 3 years of age from the EDEN mother-child cohort. Child behavior assessed using the Strengths and Difficulties Questionnaire (SDQ). Both hypotheses-driven and exploratory analyses (including epigenome-wide association studies (EWAS) and differentially methylated regions (DMR) analyses) were conducted. The analyses were adjusted for confounding and technical factors and estimated placental cell composition. All the p-values were corrected using a false discovery rate (FDR) procedure for multiple tests.ResultsIn the hypothesis-driven analysis, cg26703534 (AHRR), was significantly associated with emotional problems (pFDR = 0.03). In the exploratory analyses, cg09126090 (pFDR = 0.04) and cg10305789 (PPP1R16B; pFDR &lt; 0.01) were significantly associated with peer-relationship problems and 33 DMRs were significantly associated with at least one of the SDQ subscales. Placental DNA methylation showed more associations with internalizing than externalizing symptoms, especially among girls. DMRs tented to include highly methylated CpGs.ConclusionsThis study investigated for the first time the associations between placental DNA methylation and internalizing and externalizing symptoms in preschoolers. Further analyses, such as consortium meta-analyses would be necessary to confirm and extend our results.Disclosure of InterestNone Declared

Effects of pesticide exposure on oxidative stress and DNA methylation urinary biomarkers in Czech adults and children from the CELSPAC-SPECIMEn cohort

Current-use pesticide (CUP) exposure occurs mainly through diet and environmental application in both agricultural and urban settings. While pesticide exposure has been associated with many adverse health outcomes, the intermediary molecular mechanisms are still not completely elucidated. Among others, their roles in epigenetics (DNA methylation) and DNA damage due to oxidative stress are presumed. Scientific evidence on urinary biomarkers of such body response in general population is limited, especially in children.A total of 440 urine samples (n = 110 parent-child pairs) were collected during the winter and summer seasons in order to describe levels of overall DNA methylation (5-mC, 5-mdC, 5-hmdC, 7-mG, 3-mA) and oxidative stress (8-OHdG) biomarkers and investigate their possible associations with metabolites of pyrethroids (3-PBA, t/c-DCCA), chlorpyrifos (TCPY), and tebuconazole (TEB-OH). Linear mixed-effects models accounting for intraindividual and intrahousehold correlations were utilized. We applied false discovery rate procedure to account for multiplicity and adjusted for potential confounding variables.Higher urinary levels of most biological response biomarkers were measured in winter samples. In adjusted repeated measures models, interquartile range (IQR) increases in pyrethroid metabolites were associated with higher oxidative stress. t/c-DCCA and TCPY were associated with higher urinary levels of cytosine methylation biomarkers (5-mC and/or 5-mdC). The most robust association was observed for tebuconazole metabolite with 3-mA (−15.1% change per IQR increase, 95% CI = −23.6, −5.69) suggesting a role of this pesticide in reduced demethylation processes through possible DNA glycosylase inhibition.Our results indicate an urgent need to extend the range of analyzed environmental chemicals such as azole pesticides (e.g. prothioconazole) in human biomonitoring studies. This is the first study to report urinary DNA methylation biomarkers in children and associations between CUP metabolites and a comprehensive set of biomarkers including methylated and oxidized DNA alterations. Observed associations warrant further large-scale research of these biomarkers and environmental pollutants including CUPs.

A phase I change‐point method for high‐dimensional process with sparse mean shifts

AbstractAlthough Phase I analysis of multivariate processes has been extensively discussed, the discussion on techniques for Phase I monitoring of high‐dimensional processes is still limited. In high‐dimensional applications, it is common to observe that a large number of components but only a limited number of them change at the same time. The shifted components are often sparse and unknown a priori in practice. Motivated by this, this article studies Phase I monitoring of high‐dimensional process mean vectors under an unknown sparsity level of shifts. The basic idea of the proposed monitoring scheme is to first employ the false discovery rate procedure to estimate the sparsity level of mean shifts, and then to monitor the mean changes based on the maximum of the directional likelihood ratio statistics over all the possible shift directions. The comparison results based on extensive simulations favor the proposed monitoring scheme. A real example is presented to illustrate the implementation of the new monitoring scheme.

ZAP:Z-Value Adaptive Procedures for False Discovery Rate Control with Side Information

AbstractAdaptive multiple testing with covariates is an important research direction that has gained major attention in recent years. It has been widely recognised that leveraging side information provided by auxiliary covariates can improve the power of false discovery rate (FDR) procedures. Currently, most such procedures are devised with p-values as their main statistics. However, for two-sided hypotheses, the usual data processing step that transforms the primary statistics, known as p-values, into p-values not only leads to a loss of information carried by the main statistics, but can also undermine the ability of the covariates to assist with the FDR inference. We develop a p-value based covariate-adaptive (ZAP) methodology that operates on the intact structural information encoded jointly by the p-values and covariates. It seeks to emulate the oracle p-value procedure via a working model, and its rejection regions significantly depart from those of the p-value adaptive testing approaches. The key strength of ZAP is that the FDR control is guaranteed with minimal assumptions, even when the working model is misspecified. We demonstrate the state-of-the-art performance of ZAP using both simulated and real data, which shows that the efficiency gain can be substantial in comparison with p-value-based methods. Our methodology is implemented in the R package zap.

Cardiovascular disease-wide association study to investigate shared genetics with peak oxygen uptake

Abstract Introduction Cardiorespiratory fitness, measured as peak oxygen uptake (VO2peak), is a strong predictor of cardiovascular disease (CVD) morbidity and mortality and is estimated to have a large genetic component (∼60%). However, the genetic determinants are yet to be established. Our research group recently identified novel genetic variants associated with directly measured VO2peak. Interestingly, most were specific to females. Looking into genetic pleiotropy between VO2peak and CVD can help elucidate the biological mechanisms explaining the link between this fitness related trait and disease, and potentially identify new therapeutic targets. Purpose The association study aims to investigate if genetic variants found to be associated with VO2peak in women is associated with any cardiovascular disease phenotypes. Methods 34,188 female participants with genotype data from the Trøndelag Health Study (HUNT) were included in this study. ICD-coded hospital data related to cardiovascular disease was collected on each participant and the ICD codes were mapped to Phecodes, resulting in 100 disease-specific phenotypes. 26 single nucleotide polymorphisms (SNPs) previously found to be associated with directly measured VO2peak (p&amp;lt;5e-6) were included in the analyses, all common variants (minor allele frequency ≥1%). Each of the 26 genetic variants were tested for association with the 100 phenotypes using a logistic mixed model as implemented in SAIGE. The analysis was adjusted for birthyear and ten principal components of ancestry. The Benjamini-Hochberg false discovery rate (FDR) procedure controlling the FDR at 0.05 was used to correct for the number of tested SNPs and phenotypes. Results After testing each of the 26 SNPs for association with 100 cardiovascular disease phenotypes, 133 SNP-phenotype associations were nominally significant (p&amp;lt;0.05). The association between rs17066736 and myocarditis had the lowest p-value (1.7e-4). Among the other tested SNP-phenotype pairs were phenotypes related to ischemic heart disease, cardiac conduction disorders, heart failure, cerebrovascular disease, and diseases of arteries and veins. However, when adjusting for multiple testing, none reached overall statistical significance. Conclusions The findings showed no statistically significant associations between genetic variants associated with VO2peak and cardiovascular disease phenotypes in women. The role of inborn VO2peak in prediction of CVD (in women) needs further assessment. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): NTNU biotechnology

Bayesian local false discovery rate for sparse count data with application to the discovery of hotspots in protein domains

In cancer research at the molecular level, it is critical to understand which somatic mutations play an important role in the initiation or progression of cancer. Recently, studying cancer somatic variants at the protein domain level is an important area for uncovering functionally related somatic mutations. The main issue is to find the protein domain hotspots which have significantly high frequency of mutations. Multiple testing procedures are commonly used to identify hotspots; however, when data is not large enough, existing methods produce unreliable results with failure in controlling a given type I error rate. We propose multiple testing procedures, based on Bayesian local false discovery rate, for sparse count data and apply it in the identification of clusters of somatic mutations across entire gene families using protein domain models. In multiple testing for count data, it is not clear what kind of the null distribution should be admitted. In our proposed algorithms, we implement the zero assumption in the context of Bayesian methods to identify the null distribution for count data rather than using any theoretical null distribution. Furthermore, we also address different types of modeling of alternative distributions. The proposed fully Bayesian models are efficient when the number of count data is small (50≤N<200) while the local false discovery rate procedures, based on the empirical Bayes, is desirable for a large number of data (N>800). We provide numerical studies to show that the proposed fully Bayesian methods can control a given level of false discovery rate for small number of positions while existing approaches based on nonparametric empirical Bayes fail in controlling a false discovery rate. In addition, we present real data examples of protein domain data to select hotspots in protein domain data.

Time series graphical lasso and sparse VAR estimation

A two-stage sparse vector autoregression method is proposed. It relies on the more recent and powerful technique of time series graphical lasso to estimate sparse inverse spectral density matrices in the first stage, and its second stage refines non-zero entries of the AR coefficient matrices using a false discovery rate (FDR) procedure. Compared to a recent approach, the method has the advantage of avoiding the inversion of the spectral density matrix, but has to deal with optimization over Hermitian matrices with complex-valued entries. Such modifications significantly improve the computational time with a little loss in forecasting performance. The algorithmic and computational properties of the method have been studied and the performance of the two methods is compared using simulated and a real macro-economic dataset. The simulation results show that the proposed modification is preferred over the existing method when the goal is to learn the structure of the AR coefficient matrices while the latter outperforms the former when forecasting is the ultimate task.

Adverse events following third dose of mRNA COVID-19 vaccination among nursing home residents who received the primary series.

BackgroundWe sought to compare rates of adverse events among nursing home residents who received an mRNA COVID‐19 vaccine booster dose with those who had not yet received their booster.MethodsWe assessed a prospective cohort of 11,200 nursing home residents who received a primary COVID‐19 mRNA vaccine series at least 6 months prior to September 22, 2021 and received a third “booster dose” between September 22, 2021 and February 2, 2022. Residents lived in 239 nursing homes operated by Genesis HealthCare, spanning 21 U.S. states. We screened electronic health records for 20 serious vaccine‐related adverse events that are monitored following receipt of COVID‐19 vaccination by the CDC's Vaccine Safety Datalink. We matched boosted and yet‐to‐be boosted residents during the same time period, comparing rates of events occurring 14 days after booster administration with those occurring 14 days prior to booster administration. To supplement previously reported background rates of adverse events, we report background rates of medical conditions among nursing home residents during 2020, before COVID‐19 vaccines were administered in nursing homes. Events occurring in 2021–2022 were confirmed by physician chart review. We report unadjusted rates of adverse events and used a false discovery rate procedure to adjust for multiplicity of events tested.ResultsNo adverse events were reported during the 14 days post‐booster. A few adverse events occurred prior to booster (ischemic stroke: 49.4 per 100,000 residents, 95% CI: 21.2, 115.7; venous thromboembolism: 9.9 per 100,000 residents, 95% CI: 1.7, 56.0), though differences in event rates pre‐ versus post‐booster were not statistically significant (p < 0.05) after adjusting for multiple comparisons. No significant differences were detected between post‐booster vaccination rates and prior year 14‐day background rates of medical conditions.ConclusionsNo safety signals were detected following a COVID‐19 mRNA vaccine booster dose in this large multi‐state sample of nursing home residents.

The Effect of Hydro-Morphology and Habitat Alterations on the Functional Diversity and Composition of Macrophyte Communities in the Large River

Macrophyte communities have major role in the functioning of freshwater ecosystems. However, there is gap in knowledge about how natural and anthropogenic hydro-morphological disturbances affect their functional diversity and trait structure, particularly in the temperate large rivers. In this study we investigated the effect of hydro-morphology on functional diversity and trait structure of macrophyte communities in the middle section of the Danube course. We collected macrophyte and environmental data from 947 sampling units in the main river channel and connected side waterbodies. We extracted data on 18 traits with 65 trait states and calculated seven functional diversity metrics and cumulative weighted means of trait states (CWMs). We applied redundancy analysis (RDA) to investigate the response of functional diversity metrics to the environmental variables, and Variation Partitioning to determine whether natural, or anthropogenic subset of hydro-morphological factors is more important predictor of functional diversity. To relate CWMs and environmental variables, we performed RLQ and fourth-corner analysis, followed by false discovery rate procedure. Hydro-morphological variables explained 36.7% of the variability in the functional diversity metrics. Combined effect of two subsets of environmental variables explained largest part of the variability in functional diversity metrics. Six associations between traits and environmental variables were found. We found that functional diversity metrics indicate prevailing ecological processes, from environmental to biotic filtering, along the natural—anthropogenic hydro-morphological gradient. We concluded that functional diversity metrics are potentially useful tools in the identification of the causes of ecological degradation, and could be applied in river bioassessments and management.

Leveraging Gene-Level Prediction as Informative Covariate in Hypothesis Weighting Improves Power for Rare Variant Association Studies.

Gene-based rare variant association studies (RVASs) have low power due to the infrequency of rare variants and the large multiple testing burden. To correct for multiple testing, traditional false discovery rate (FDR) procedures which depend solely on P-values are often used. Recently, Independent Hypothesis Weighting (IHW) was developed to improve the detection power while maintaining FDR control by leveraging prior information for each hypothesis. Here, we present a framework to increase power of gene-based RVASs by incorporating prior information using IHW. We first build supervised machine learning models to assign each gene a prediction score that measures its disease risk, using the input of multiple biological features, fed with high-confidence risk genes and local background genes selected near GWAS significant loci as the training set. Then we use the prediction scores as covariates to prioritize RVAS results via IHW. We demonstrate the effectiveness of this framework through applications to RVASs in schizophrenia and autism spectrum disorder. We found sizeable improvements in the number of significant associations compared to traditional FDR approaches, and independent evidence supporting the relevance of the genes identified by our framework but not traditional FDR, demonstrating the potential of our framework to improve power of gene-based RVASs.

High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases.

Increasing availability of medico-administrative databases has prompted the development of automated pharmacovigilance signal detection methodologies. Self-controlled approaches have recently been proposed. They account for time-independent confounding factors that may not be recorded. So far, large numbers of drugs have been screened either univariately or with LASSO penalized regressions. We propose and assess a new method that combines the case-crossover self-controlled design with propensity scores (propensity score-adjusted case-crossover) built from high-dimensional data-driven variable selection, to account for co-medications or possibly other measured confounders. Comparison with the univariate and LASSO case-crossover was performed from simulations and a real-data study. Multiple regressions (LASSO, propensity score-adjusted case-crossover) accounted for co-medications and no other covariates. For the univariate and propensity score-adjusted case-crossover methods, the detection threshold was based on a false discovery rate procedure, while for LASSO, it relied on the Akaike Information Criterion. For the real-data study, two drug safety experts evaluated the signals generated from the analysis of 4099 patients with acute myocardial infarction from the French national health database. On simulations, our approach ranked the signals similarly to the LASSO and better than the univariate method while controlling the false discovery rate at the prespecified level, contrary to the univariate method. The LASSO provided the best sensitivity at the cost of larger false discovery rate estimates. On the application, our approach showed similar performances to the LASSO and better performances than the univariate method. It highlighted 43 signals out of 609 drug candidates: 22 (51%) were considered as potentially pharmacologically relevant, including seven (16%) regarded as highly relevant. Our findings show the interest of a propensity score combined with a case-crossover for pharmacovigilance. They also confirm that indication bias remains a challenge when mining medico-administrative databases.