Correlation Estimation Research Articles

ADCMDES: Design of an augmented cross-domain collaborative recommendation model using novel distance metric and ensemble stratification

Collaborative recommendation involves leveraging information from one entity to suggest attributes of other entities through pattern analysis. These models are invaluable for understanding data behaviour’s impact on related entities. Researchers propose various pattern recognition and correlation models, employing distance metrics like Jaccard, Cosine, etc., to estimate correlations between user queries and recommendation datasets. However, these models become less efficient as dataset size increases due to exponential correlation estimation delays. To enhance scalability while retaining recommendation quality, a new approach is introduced: ADCMDES. This novel augmented cross-domain collaborative model employs a hybrid distance metric and ensemble stratification for dataset pruning. It operates semi-supervisedly, requiring information about the entities being collaborated upon. This data is used to cluster similar collaborative entities, producing a condensed cluster with greater relevancy to user queries. Utilizing word2vec, records are transformed into features for an ensemble classification engine. The resultant model categorizes user input, directing it to the most relevant cluster. Entries within this cluster are ranked using a hybrid metric amalgamating 18 distance measures, enhancing correlation between input queries and recommendations. In testing, ADCMDES exhibited 15% better accuracy, 8% better precision, 9% better recall, and 3% lower RMSE compared to standard models across datasets. While some delay is introduced due to ensemble classification and augmented feature pooling, this doesn't considerably affect long-term recommendation performance and can be mitigated through parallel processing and redundancy reduction techniques.

Continuous-Time Multivariate Analysis

The starting point for much of multivariate analysis (MVA) is an n × p data matrix whose n rows represent observations and whose p columns represent variables. Some multivariate datasets, however, may be best conceptualized not as n discrete p-variate observations, but as p curves or functions defined on a common time interval. Here we introduce a framework for extending techniques of multivariate analysis to such settings. The proposed continuous-time multivariate analysis (CTMVA) framework rests on the assumption that the curves can be represented as linear combinations of basis functions such as B-splines, as in the Ramsay-Silverman representation of functional data; but whereas functional data analysis extends MVA to the case of observations that are curves rather than vectors—heuristically, n × p data with p infinite—we are instead concerned with what happens when n is infinite. We present continuous-time extensions of the classical MVA methods of covariance and correlation estimation, principal component analysis, Fisher’s linear discriminant analysis, and k-means clustering. We show that CTMVA can improve on the performance of classical MVA, in particular for correlation estimation and clustering, and can be applied in some settings where classical MVA cannot, including variables observed at disparate time points. CTMVA is illustrated with a novel perspective on a well-known Canadian weather dataset, and with applications to datasets involving international development, brain signals, and air quality. The proposed methods are implemented in the publicly available R package ctmva. Supplementary materials for this article are available online.

341 Impact of genomic selection for growth on feet and leg structure in Angus cattle

Abstract Historically fitness and production traits have shown an antagonistic relationship at the genetic level. Genetic selection mainly focused on growth traits (GT) might have influenced feet and leg structure traits (FT; recently added trait), causing changes over time. We aimed to examine genetic parameter changes in FT and GT over time in Angus cattle. Currently, genetic parameters are estimated using Variance Components Estimation (VCE), which has limitations in handling large datasets and is time demanding. To address these limitations, we explored the use of predictivity-based formulas to estimate heritability and genetic correlations in GT. The analysis was conducted using the blupf90+ family of programs. To estimate variance components (VCE), sampled data (2011-2022) from the American Angus population included 311K GT records [birth (BW), weaning weight (WW), and post-weaning gain (PWG)], ~70K FT records [foot claw set (CS) and foot angle (FA)], and 142K genotypes. FT are taken on the worst foot of the animal and can be scored multiple times throughout the life of the animal, but in this study, we did not include repeated records. The dataset was split into 5-yr intervals. Since FT ranged from 5 to 9, and 5 is ideal, a negative correlation is desired between FT and GT. The correlation FA-PWG, changed in an undesirable way over time, as it changed from -0.10 to 0.02 in the last interval. The correlation FA-WW is becoming more favorable over time, shifting from 0.05 to -0.15. The GT selection process shifted the correlations between CS and GT in the desired direction, changing from positive (~0.1) to almost zero in the more recent two intervals. To test the predictivity-based formulas, Genomic Estimated Breeding Values (GEBV) were estimated using GT data from 6.3M animals born from 2000 to 2022, and 900K genotyped animals. The data were divided into four slices, with a 2-yr validation period. Over time, heritabilities remained constant: ~0.3 for BW and WW, and ~0.2 for PWG. Correlations between GT also remained constant, approximately at 0.45 for BW-WW, 0.30 for BW-PWG, and 0.75 for WW-PWG. Although the formulas show promising results for heritabilities, interpreting the outcomes of genetic correlation estimation remains unclear, needing further tests. While certain correlations between FT and GT suggest a positive impact of genetic selection, some undesirable outcomes are realized. Probably due to the early FT genetic selection implementation (2019). This emphasizes the need for a multi-trait genomic selection approach to mitigate genetic antagonism. Adopting predictivity-based formulas reduces the time for genetic parameter estimation and allows the use of complete data instead of sampled data, as currently done in VCE.

Metrological non-classical correlations and quantum coherence in hybrid (1/2,1) system under decoherence channels

Abstract Our research focuses on the interplay between thermal noise and decoherence channels on quantum coherence and nonclassical correlations in a hybrid ( 1 / 2 , 1 ) Heisenberg model. This hybrid system integrates the Dzyaloshinsky–Moriya interaction (DMI) and operates under the influence of an external magnetic field. We use local quantum Fisher information (LQFI) for correlation estimation and relative entropy of coherence for coherence measurement in the considered system. Our investigation encompasses various parameters of the hybrid system, the strength of the DMI and the intensity of the external magnetic field. Our findings underscore that elevated temperature compromises both nonclassical correlations and coherence. On the other hand, the robustness of the DMI mitigates the impact of thermal noise on quantum Fisher information correlations and relative entropy of coherence in the hybrid system. Additionally, we inspect the impact of decohering channels-specifically, dephasing, phase flip, and bit- and trit-flip channels-on thermal coherence and quantum correlations. The introduction of various decoherence processes into the hybrid qubit-qutrit system leads to a competition with thermal fluctuations, thereby giving rise to out-of-equilibrium states. Our results indicate that as the decoherence strength parameter (p) increases, both LQFI and relative entropy of coherence exhibit similar behaviors in the dephasing and phase flip channels. These resources gradually diminish, eventually disappearing entirely at p = 1. In the context of the bit- and trit-flip channels, quantum coherence displays notable distinctions compared to what is observed under dephasing and phase flip channels, revealing that coherence can be preserved if the DMI is strong and the intensity of the external magnetic field is reduced. These findings are important since it is crucial to first understand the decoherence process, arising from the interaction with the environment, and then to find ways to hinder this decoherence in order to avoid the complete loss of the quantum resources necessary to quantum information processing.

A Study On The Effectiveness Of Workplace Health And Safety Practices Upon Employee Wellbeing At Sri Vigneshwara Cold Forge

This research paper explores effectiveness of employee workplace health and safety upon employee wellbeing which includes physical health, mental health, engagement, productivity and job satisfaction. Key variables including training programs, ergonomics measures, safety protocols and organizational support will be examined in relation to employee physical and mental wellbeing indicators such as injury rates, absenteeism, job satisfaction and stress levels. The total population is 200 and sample size is 132. The tools used for non-parametric U test, H test and Spearman Rank Correlation, Chi-square, Weighted average and Interval Estimation. Through a combination of qualitative and quantitative research methods, including surveys, interviews and data analysis, this research aims to suggest suitable health and safety measures for employee wellbeing for Sri Vigneshwara Cold Forge. Key words: Physical Health Impact, Safety Perception, Hazard Identification, Policy Compliance, Training Programs.

Ecological risk assessment of commonly used antibiotics in aquatic ecosystems along the coast of China

The consistent input of antibiotics into aquatic environments may pose risks to various creatures and ecosystems. However, risk assessment of pharmaceuticals and personal care products (PPCPs) in aquatic environments is frequently limited by the lack of toxicity data. To investigate the risk of commonly used antibiotics to various aquatic creatures, we focused on the distribution patterns and temporal dynamics of antibiotics in the coastal estuary area of China and performed a comprehensive ecological risk assessment for four antibiotics: erythromycin (ERY), tetracycline (TCN), norfloxacin (NOR) and sulfamethoxazole (SMX). An interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) combined model was applied to predict the toxicity data of untested aquatic species, and an accurate ecological risk assessment procedure was developed to evaluate the risk level of PPCPs. The results of risk quotient assessments and probabilistic risk assessments (PRAs) suggested that four objective antibiotics in the Chinese coastal estuary area were at a low risk level. These antibiotics posed a high risk in antibiotic-related global hot spots, with probabilistic risk values for ERY, NOR, SMX, and TCN of 81.33 %, 27.08 %, 21.13 %, and 15.44 %, respectively. We applied an extrapolation method to overcome the lack of toxicity data in ecological risk assessment, enhanced the ecological reality of water quality criteria derivation and reduced the uncertainty of risk assessment for antibiotics.

Method for Monitoring Wheat Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing

An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management.

Deriving ecological risk thresholds for soil molybdenum in China based on interspecies correlation estimation and quantitative ion character–activity relationship models

Soil molybdenum (Mo) levels can reach ecologically hazardous levels. China has not yet established the relevant thresholds, posing challenges for environmental management. Therefore, we present our data relevant to Mo toxicity for several important species. By normalizing soil properties, we obtained a correlation model of Mo toxicity to Hordeum vulgare, as well as 31 models for the toxicity of other elements including Cu and Ni to invertebrates and microbial processes. Using interspecies correlation estimation (ICE) extrapolation, the sensitivity coefficient (0.12–0.71) for five plants were found. For invertebrates and microbial processes lacking Mo data, we used regression analysis to establish Mo toxicity models based on the soil quantitative ion character–activity relationships (s–QICAR; R2 =0.70–0.95) and known toxicities of other metal elements to invertebrate and microbial processes. Furthermore, combining species sensitivity distribution calculations, the HC5 values for protecting 95% of soil species from Mo in three typical soil scenarios in China were calculated. After correction, the predicted no-effect concentrations were 6.8, 4.8, and 3.4 mg/kg, respectively. This study innovatively combined ICE and s − QICAR to derive soil Mo thresholds. Our results can provide a basis for decision-making in the assessment and management of soil Mo pollution.

Instantaneous Velocity Estimation Based on Envelope Cross Correlation and Joint Frequency-Phase Estimation for Wideband LFM Radar

Instantaneous Velocity Estimation Based on Envelope Cross Correlation and Joint Frequency-Phase Estimation for Wideband LFM Radar

Aquatic life criteria of hydrothermal liquefaction wastewater via ecotoxicity test and modeling

Wastewater resulting from hydrothermal liquefaction (HTL-AP) of biowaste is gaining attention as an emerging hazardous material. However, there is a lack of specific and systematic ecotoxicity studies on HTL-AP. This study addresses this gap by conducting acute toxicity tests on HTL-AP using typical aquatic species and integrating these results with predicted toxicity values from interspecies correlation estimation models to establish aquatic life criteria. HTL-AP exhibited significant toxicity with LC50 of 956.12–3645.4 mg/L, but demonstrated moderate toxicity compared to common freshwater pollutants like commercial microbicides, personal care products, and insect repellents. The resulting hazardous concentration for 5 % of species (HC5), the criterion maximum concentration, and the short-term water quality criteria for aquatic were 506.0, 253.0, and 168.7 mg/L, respectively. Notably, certain organisms like Misgurnus anguillicaudatus and Cipangopaludina chinensis showed high tolerance to HTL-AP, likely due to their metabolic capabilities on HTL-AP components. The significant decrease in HC5 values for some HTL-AP substances compared to pure compounds could indicate the synergistic inhibition effects among HTL-AP compositions. Furthermore, according to the established criteria, HTL-AP required significantly less diluted water (13 t) than carbendazim (1009 t) to achieve biosafety, indicating a safer release. This research establishes a preliminary water quality criterion for HTL-AP, offering a valuable reference for risk assessment and prediction in the utilization of HTL-AP within environmental contexts.

Linearized maximum rank correlation estimation when covariates are functional

This paper extends the linearized maximum rank correlation (LMRC) estimation proposed by Shen et al. (2023) to the setting where the covariate is a function. However, this extension is nontrivial due to the difficulty of inverting the covariance operator, which may raise the ill-posed inverse problem, for which we integrate the functional principal component analysis to the LMRC procedure. The proposed estimator is robust to outliers in response and computationally efficient. We establish the rate of convergence of the proposed estimator, which is minimax optimal under certain smoothness assumptions. Furthermore, we extend the proposed estimation procedure to handle discretely observed functional covariates, including both sparse and dense sampling designs, and establish the corresponding rate of convergence. Simulation studies demonstrate that the proposed estimators outperform the other existing methods for some examples. Finally, we apply our method to a real data to illustrate its usefulness.

Differential Privacy Preservation for Continuous Release of Real-Time Location Data.

Continuous real-time location data is very important in the big data era, but the privacy issues involved is also a considerable topic. It is not only necessary to protect the location privacy at each release moment, but also have to consider the impact of data correlation. Correlated Laplace Mechanism (CLM) is a sophisticated method to implement differential privacy on correlated time series. This paper aims to solve the key problems of applying CLM in continuous location release. Based on the finding that the location increment is approximately stationary in many scenarios, a location correlation estimation method based on the location increment is proposed to solve the problem of nonstationary location data correlation estimation; an adaptive adjustment model for the CLM filter based on parameter quantization idea (QCLM) as well as its effective implementation named QCLM-Lowpass utilizing the lowpass spectral characteristics of location data series is proposed to solve the problem of output deviations due to the undesired transient response of the CLM filter in time-varying environments. Extensive simulations and real data experiments validate the effectiveness of the proposed approach and show that the privacy scheme based on QCLM-Lowpass can offer a better balance between the ability to resist correlation-based attacks and data availability.

Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds.

Interspecies correlation estimation (ICE) models are log-linear relationships of acute sensitivity between two species that estimate the sensitivity of an untested species from the known sensitivity of a surrogate. As ICE model use increases globally, additional user guidance is required to ensure consistent use across chemicals and applications. The present study expands ICE uncertainty analyses and user guidance with a focus on low toxicity compounds whose acute values (i.e., reported as mg/L) can be greater than those used to develop a model. In these cases, surrogate values may be outside the ICE model domain and require additional extrapolations to predict acute toxicity. We use the extensive, standardized acute toxicity database underlying ICE models to broadly summarize inter-test variability of acute toxicity data as a measure by which model prediction accuracy can be evaluated. Using the data and models found on the USEPA Web-ICE (www3.epa.gov/webice), we created a set of "truncated" models from data corresponding to the lower 75th percentile of surrogate toxicity. We predicted toxicity for chemicals in the upper 25th percentile as both μg/L beyond the model domain and converted to mg/L (i.e., "scaled" value) and compared these predictions with those from cross-validation of whole ICE models and to the measured value. For ICE models with slopes in the range 0.66-1.33, prediction accuracy of scaled values did not differ from the accuracy of the models when data were entered as μg/L within or beyond the model domain. An uncertainty analysis of ICE confidence intervals was conducted and an interval range of two orders of magnitude was determined to minimize type I and II errors when accepting or rejecting ICE predictions. We updated the ICE user guidance based on these analyses to advance the state of the science for ICE model application and interpretation. Integr Environ Assess Manag 2024;20:1554-1565. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

The Effects of Working Capital Management on Firms’ Efficiency: Case of Turkish and Zimbabwean Commercial Banks.

This research studies management of working capital and its impact on commercial bank efficiency in Zimbabwe and Turkey. The study examined the financial statements of 10 banks from each country between 2009 and 2022. The research evaluated performance of banks using return on asset (ROA) as well as return on equity (ROE) as efficiency indicators. Working capital was assessed through the current ratio, current ratio, and total cash ratio, while bank size and leverage were considered as control variables. Correlation analysis, estimation equations, and regression models were used to examine the data, which included pooled cross-sectional and time series data, using SPSS (version 20.0). The data demonstrated a favourable and substantial association between ROE and the current ratio, demonstrating that adequate management of working capital has a beneficial impact on the banks’ profitability. A negative correlation, in contrast, between current ratio and ROE was noted, implying a balance of profitability and liquidity. Furthermore, no statistically significant association was seen between cash payment ability, total cash ratio, and ROA. However, a significant negative correlation was identified between the current ratio and ROA. Comparing the two countries, Turkish banks demonstrate superior liquidity management, potentially attributed to their adherence to the Basel III reform guidelines. The study recommends that Zimbabwean banks adopt similar measures to enhance their competitiveness in the international banking sector. In conclusion, the research emphasises the significance of good management of working capital in maximising bank efficiency. The findings provide valuable insights for policymakers and practitioners in the banking industry, urging them to implement liquidity management strategies for sustained success.

A local correlation estimation surrogate-assisted bi-objective evolutionary algorithm for heterogeneous objectives

Most existing evolutionary algorithms for solving bi-objective optimization problems are based on the assumption that both objectives can be evaluated once at the same time. However, in real-world optimization problems, there is a significant difference in function evaluation time between objectives, and these problems are defined as the problems with heterogeneous objectives. To utilize the latency of objectives, we propose a local correlation estimation based surrogate-assisted bi-objective evolutionary algorithm for problems with heterogeneous objectives. In the proposed algorithm, surrogate models are employed to approximate the objective functions. The proposed local correlation estimation (LCE) method is used to analyze the correlation between objectives within a local region, guiding the search direction for one objective while identifying promising solutions for the other objective. Finally, the ablation experiment for the LCE method validates the effectiveness of the proposed strategy, and the comparative results on various expensive bi-objective test problems demonstrate that the proposed algorithm is promising on heterogeneous bi-objective optimization problems.

A multitrait genetic study of hemostatic factors and hemorrhagic transformation after stroke treatment

BackgroundThrombolytic recombinant tissue plasminogen activator (r-tPA) treatment is the only pharmacologic intervention available in the ischemic stroke acute phase. This treatment is associated with an increased risk of intracerebral hemorrhages, known as hemorrhagic transformations (HTs), which worsen the patient’s prognosis. ObjectivesTo investigate the association between genetically determined natural hemostatic factors’ levels and increased risk of HT after r-tPA treatment. MethodsUsing data from genome-wide association studies on the risk of HT after r-tPA treatment and data on 7 hemostatic factors (factor [F]VII, FVIII, von Willebrand factor [VWF], FXI, fibrinogen, plasminogen activator inhibitor-1, and tissue plasminogen activator), we performed local and global genetic correlation estimation multitrait analyses and colocalization and 2-sample Mendelian randomization analyses between hemostatic factors and HT. ResultsLocal correlations identified a genomic region on chromosome 16 with shared covariance: fibrinogen-HT, P = 2.45 × 10−11. Multitrait analysis between fibrinogen-HT revealed 3 loci that simultaneously regulate circulating levels of fibrinogen and risk of HT: rs56026866 (PLXND1), P = 8.80 × 10−10; rs1421067 (CHD9), P = 1.81 × 10−14; and rs34780449, near ROBO1 gene, P = 1.64 × 10−8. Multitrait analysis between VWF-HT showed a novel common association regulating VWF and risk of HT after r-tPA at rs10942300 (ZNF366), P = 1.81 × 10−14. Mendelian randomization analysis did not find significant causal associations, although a nominal association was observed for FXI-HT (inverse-variance weighted estimate [SE], 0.07 [−0.29 to 0.00]; odds ratio, 0.87; 95% CI, 0.75-1.00; raw P = .05). ConclusionWe identified 4 shared loci between hemostatic factors and HT after r-tPA treatment, suggesting common regulatory mechanisms between fibrinogen and VWF levels and HT. Further research to determine a possible mediating effect of fibrinogen on HT risk is needed.

Device comparison study to measure nasal nitric oxide in relation to primary ciliary dyskinesia

Primary ciliary dyskinesia (PCD) is a genetic respiratory disease characterized by chronic cough, recurrent respiratory infections, and rhinosinusitis. The measurement of nasal nitric oxide (nNO) against resistance has been suggested as a sensitive screening method. However, current recommendations argue for the use of expensive, chemiluminescence devices to measure nNO. This study aimed to compare nNO measurement using three different devices in distinguishing PCD patients from healthy controls and cystic fibrosis (CF) patients and to evaluate their diagnostic precision. The study included 16 controls, 16 PCD patients, and 12 CF patients matched for age and sex. nNO measurements were performed using a chemiluminescence device (Eco Medics CLD 88sp), and two devices based on electrochemical sensors (Medisoft FeNO+ and NIOX Vero) following standardized guidelines. Correlation estimation, Bland–Altman, ROC curve, and one-way ANOVA were used to assess device differences and diagnostic performance. Significantly lower nNO output values were observed in PCD and CF patients compared to controls during exhalation against resistance. The correlation analysis showed high agreement among the three devices. ROC curve analysis demonstrated 100% sensitivity and specificity at different cut-off values for all devices in distinguishing PCD patients from controls (optimal cut-offs: EcoMedics 73, Medisoft 92 and NIOX 87 (nl min−1 )). Higher nNO output values were obtained with the Medisoft and NIOX devices as compared to the EcoMedics device, with a bias of−19 nl min−1 (95% CI: −73–35) and −21 nl min−1 (−73–31) accordingly. These findings indicate that all three tested devices can potentially serve as diagnostic tools for PCD if device specific cut-off values are used. This last-mentioned aspect warrants further studies and consideration in defining optimal cut-offs for individual device.

Pervasive biases in GWAS using family history of Alzheimer’s disease as proxy phenotypes

AbstractBackgroundUsing family health history as proxy phenotype has become a common approach in genome‐wide association studies (GWAS) of Alzheimer’s disease (AD). This approach leverages large samples in population biobanks where most mid‐aged participants do not have any AD diagnosis. However, methodological issues in GWAS‐by‐proxy (GWAX) and quality of its association results have not been carefully investigated.MethodWe performed GWAX on parental AD history in UK Biobank (N = 48,031 proxy cases and 315,286 controls). We then applied GWAS‐by‐subtraction to obtain the non‐AD component underlying AD GWAX. We computed genetic correlations of AD case‐control GWAS, AD GWAX, and the non‐AD component underlying GWAX with a suite of complex traits. We also explored the impact of various quality control procedures and additional covariates in AD GWAX analysis.ResultAD GWAS and GWAX showed drastically different genetic correlations with several other complex traits especially cognitive abilities (Figure 1). AD GWAX had a surprising genetic correlation with higher education (rg = 0.167, p = 1.7e‐11) as opposed to the negative AD‐education correlation observed for GWAS (rg = ‐0.133, p = 2.4e‐5). The positive genetic correlation between AD and cognition is explained by the non‐AD component in AD GWAX (rg = 0.260 with education, p = 5.2e‐11). The non‐AD component in GWAX also showed correlations with lower risks of health outcomes such as coronary artery disease (rg = ‐0.136, p = 2.8e‐3) and diabetes (rg = ‐0.206, p = 2.8e‐3), indicating substantial survival bias in samples reporting parental AD history. Adjusting for parental age in GWAX substantially reduced but did not fully remove the genetic correlation of AD and higher education (rg = 0.013, p = 0.67), hinting at additional unaccounted factors in the current GWAX practice. We also provide evidence that genetic correlation between family health history awareness and education (rg = 0.285, p = 1.4e‐10) leads to these biases in GWAX associations.ConclusionGWAX based on parental health history introduces substantial and systematic biases in AD genetic associations due to the non‐representativeness of the biobank health history survey. Naively combining GWAX with regular case‐control GWAS will lead to misleading results both at the single variant level and in genome‐wide analyses such as genetic correlation estimation and polygenic risk score applications.

Genetics of sleep medication purchases suggests causality from sleep problems to psychiatric traits.

Over 10% of the population in Europe and in the United States use sleep medication to manage sleep problems. Our objective was to elucidate genetic risk factors and clinical correlates that contribute to sleep medication purchase and estimate the comorbid impact of sleep problems. We performed epidemiological analysis for psychiatric diagnoses, and genetic association studies of sleep medication purchase in 797 714 individuals from FinnGen Release 7 (N = 311 892) and from the UK Biobank (N = 485 822). Post-association analyses included genetic correlation, co-localization, Mendelian randomization (MR), and polygenic risk estimation. In a GWAS we identified 27 genetic loci significantly associated with sleep medication, located in genes associated with sleep; AUTS2, CACNA1C, MEIS1, KIRREL3, PAX8, GABRA2, psychiatric traits; CACNA1C, HIST1H2BD, NUDT12. TOPAZ1 and TSNARE1. Co-localization and expression analysis emphasized effects on the KPNA2, GABRA2, and CACNA1C expression in the brain. Sleep medications use was epidemiologically related to psychiatric traits in FinnGen (OR [95% (CI)] = 3.86 [3.78 to 3.94], p < 2 × 10-16), and the association was accentuated by genetic correlation and MR; depression (rg = 0.55 (0.027), p = 2.86 × 10-89, p MR = 4.5 × 10-5), schizophrenia (rg = 0.25 (0.026), p = 2.52 × 10-21, p MR = 2 × 10-4), and anxiety (rg = 0.44 (0.047), p = 2.88 × 10-27, p MR = 8.6 × 10-12). These results demonstrate the genetics behind sleep problems and the association between sleep problems and psychiatric traits. Our results highlight the scientific basis for sleep management in treating the impact of psychiatric diseases.

Clustering-based inter-regional correlation estimation

A novel non-parametric estimator of the correlation between grouped measurements of a quantity is proposed in the presence of noise. The main motivation is functional brain network construction from fMRI data, where brain regions correspond to groups of spatial units, and correlation between region pairs defines the network. The challenge resides in the fact that both noise and intra-regional correlation lead to inconsistent inter-regional correlation estimation using classical approaches. While some existing methods handle either one of these issues, no non-parametric approaches tackle both simultaneously. To address this problem, a trade-off between two procedures is proposed: correlating regional averages, which is not robust to intra-regional correlation; and averaging pairwise inter-regional correlations, which is not robust to noise. To that end, the data is projected onto a space where Euclidean distance is used as a proxy for sample correlation. Hierarchical clustering is then leveraged to gather together highly correlated variables within each region prior to inter-regional correlation estimation. The convergence of the proposed estimator is analyzed, and the proposed approach is empirically shown to surpass several other popular methods in terms of quality. Illustrations on real-world datasets that further demonstrate its effectiveness are provided.