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ABSTRACT In medical research, individual‐level patient data provide invaluable information, but the patients' right to confidentiality remains of utmost priority. This poses a huge challenge when estimating statistical models such as a linear mixed model, which is an extension of linear regression models that can account for potential heterogeneity whenever data come from different data providers. Federated learning tackles this hurdle by estimating parameters without retrieving individual‐level data. Instead, iterative communication of parameter estimate updates between the data providers and analysts is required. In this article, we propose an alternative framework to federated learning for fitting linear mixed models. Specifically, our approach only requires the mean, covariance, and sample size of multiple covariates from different data providers once. Using the principle of statistical sufficiency within the likelihood framework as theoretical support, this proposed strategy achieves estimates identical to those derived from actual individual‐level data. We demonstrate this approach through real data on 15 068 patient records from 70 clinics at the Children's Hospital of Pennsylvania. Assuming that each clinic only shares summary statistics once, we model the COVID‐19 polymerase chain reaction test cycle threshold as a function of patient information. Simplicity, communication efficiency, generalisability, and wider scope of implementation in any statistical software distinguish our approach from existing strategies in the literature. [ABSTRACT FROM AUTHOR]

Research into vaccine hesitancy is a critical component of the public health enterprise, as rates of communicable diseases preventable by routine childhood immunization have been increasing in recent years. It is therefore important to estimate proportions of "never‐vaccinators" in various subgroups of the population in order to successfully target interventions to improve childhood vaccination rates. However, due to privacy issues, it may be difficult to obtain individual patient data (IPD) needed to perform the appropriate time‐to‐event analyses: state‐level immunization information services may only be willing to share aggregated data with researchers. We propose statistical methodology for the analysis of aggregated survival data that can accommodate a cured fraction based on a polynomial approximation of the mixture cure model log‐likelihood function relying only on summary statistics. We study the performance of the method through simulation studies and apply it to a real‐world data set from a study examining reminder/recall approaches to improve human papillomavirus (HPV) vaccination uptake. The proposed methods may be generalized for use when there is interest in fitting complex likelihood‐based models but IPD is unavailable due to data privacy or other concerns. [ABSTRACT FROM AUTHOR]

How do we maintain a rich and stable perceptual experience across the entire visual scene, even when we are focusing on a subset of visual inputs? The current study explored this question by investigating whether the visual system processes summary statistics of multiple features regardless of task relevance, and how they interact with subsequent perception. To test the processing of multifeature summary statistics under different attentional requirements, we presented multiple Gabor patches with heterogeneous orientations/colors and asked participants to attend to a single feature dimension (Experiments 1 and 3) or a single item (Experiment 2) for the memory task. During the memory maintenance period (before memory response), we asked the participants to perform a discrimination task (Experiments 1 and 2) or a boundary localization task (Experiment 3) to test how the memory of the ensemble representation alters the subsequent perceptual experience. We found evidence for obligatory processing of scene summary statistics presented for the memory task, which interacted with the subsequent perceptual sensitivity. Specifically, not only summary statistics relevant but also those of task-irrelevant feature (Experiments 1 and 3) and outside the focus of attention (Experiment 2) were encoded and bidirectionally interacted with subsequent perception. These results suggest obligatory processing of summary statistics of a scene, which may allow rich and stable visual experience by integrating temporally adjacent visual inputs. [ABSTRACT FROM AUTHOR]

Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.

Abstract Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis. [ABSTRACT FROM AUTHOR]

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency. [ABSTRACT FROM AUTHOR]

Gene-based association analysis is a powerful tool for identifying genes that explain trait variability. An essential step of this analysis is a conditional analysis. It aims to eliminate the influence of SNPs outside the gene, which are in linkage disequilibrium with intragenic SNPs. The popular conditional analysis method, GCTA-COJO, accounts for the influence of several top independently associated SNPs outside the gene, correcting the z statistics for intragenic SNPs. We suggest a new TauCOR method for conditional gene-based analysis using summary statistics. This method accounts the influence of the full regional polygenic background, correcting the genotype correlations between intragenic SNPs. As a result, the distribution of z statistics for intragenic SNPs becomes conditionally independent of distribution for extragenic SNPs. TauCOR is compatible with any gene-based association test. TauCOR was tested on summary statistics simulated under different scenarios and on real summary statistics for a 'gold standard' gene list from the Open Targets Genetics project. TauCOR proved to be effective in all modelling scenarios and on real data. The TauCOR's strategy showed comparable sensitivity and higher specificity and accuracy than GCTA-COJO on both simulated and real data. The method can be successfully used to improve the effectiveness of gene-based association analyses. [ABSTRACT FROM AUTHOR]

When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants are known for a gene but we do not know the correlations between genotypes of variants, one of which is imputed, and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK Biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT, and PCA methods on reconstructed and real matrices using modelled summary statistics and calculated summary statistics on real phenotypes showed high quality of reconstruction and robustness of the method to different gene structures. [ABSTRACT FROM AUTHOR]

Ensemble coding allows observers to form an average to represent a set of elements. However, it is unclear whether observers can extract an average from a cross-category set. Previous investigations on this issue using low-level stimuli yielded contradictory results. The current study addressed this issue by presenting high-level stimuli (i.e., a crowd of facial expressions) simultaneously (Experiment 1) or sequentially (Experiment 2), and asked participants to complete a member judgment task. The results showed that participants could extract average information from a group of cross-category facial expressions with a short perceptual distance. These findings demonstrate cross-category ensemble coding of high-level stimuli, contributing to the understanding of ensemble coding and providing inspiration for future research. [ABSTRACT FROM AUTHOR]

Graphs in research articles can increase the comprehension of statistical data but may mislead readers if poorly designed. We propose a new plot type, the sea stack plot, which combines vertical histograms and summary statistics to represent large univariate datasets accurately, usefully, and efficiently. We compare five commonly used plot types (dot and whisker plots, boxplots, density plots, univariate scatter plots, and dot plots) to assess their relative strengths and weaknesses when representing distributions of data commonly observed in biological studies. We find the assessed plot types are either difficult to read at large sample sizes or have the potential to misrepresent certain distributions of data, showing the need for an improved method of data visualisation. We present an analysis of the plot types used in four ecology and conservation journals covering multiple areas of these research fields, finding widespread use of uninformative bar charts and dot and whisker plots (60% of all panels showing univariate data from multiple groups for the purpose of comparison). Some articles presented more informative figures by combining plot types (16% of panels), generally boxplots and a second layer such as a flat density plot, to better display the data. This shows an appetite for more effective plot types within conservation and ecology, which may further increase if accurate and user‐friendly plot types were made available. Finally, we describe sea stack plots and explain how they overcome the weaknesses associated with other alternatives to uninformative plots when used for large and/or unevenly distributed data. We provide a tool to create sea stack plots with our R package 'seastackplot', available through GitHub. [ABSTRACT FROM AUTHOR]

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene–Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene–environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB. [ABSTRACT FROM AUTHOR]

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab's Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets. [ABSTRACT FROM AUTHOR]

BackgroundThe relationship between socioeconomic status and frailty has been extensively investigated in the literature, but it remains unclear whether a causal relationship exists. Our goal is to evaluate the causal relationship between six socioeconomic traits and the frailty index using summary-level data for single nucleotide polymorphisms from large genome-wide association studies with individuals of European ancestry.MethodsA two-sample MR was performed. We applied the inverse variance weighted (IVW) method for the primary estimate, with sensitivity analyses conducted using alternative MR methods to evaluate the robustness of the findings. A subsequent multivariable MR was undertaken to adjust for the effects of body mass index (BMI). Finally, the MR Steiger directionality test was performed to confirm the causal direction.ResultsThe IVW MR analysis revealed significant associations between various socioeconomic factors and the frailty index. Specifically, genetically predicated age completed full time education (β = −0.477, 95% confidence interval [CI]: −0.634 to −0.319) and average total household income before tax (β = −0.321, 95% CI: −0.410 to −0.232) were negatively associated with the frailty index. On the other hand, genetically predicted job involves heavy manual or physical work (β = 0.298, 95% CI: 0.113 to 0.484), job involves mainly walking or standing (β = 0.179, 95% CI: 0.013 to 0.345), Townsend deprivation index at recruitment (β = 0.535, 95% CI: 0.285 to 0.785), and social isolation/loneliness (β = 1.344, 95% CI: 0.834 to 1.853) were positively associated with the frailty index. Sensitivity analysis using other MR methods and multivariable MR analysis adjusting for BMI yielded stable results. The MR Steiger directionality test confirmed the causal direction.ConclusionOur findings highlight the importance of socioeconomic factors in affecting frailty risk. Future research should focus on unraveling the pathways through which these socioeconomic factors exert their effects on frailty, with the ultimate goal of developing targeted strategies to mitigate the risk of frailty.

Abstract Background The human brain can rapidly represent sets of similar stimuli by their ensemble summary statistics, like the average orientation or size. Classic models assume that ensemble statistics are computed by integrating all elements with equal weight. Challenging this view, here, we show that ensemble statistics are estimated by combining parafoveal and foveal statistics in proportion to their reliability. In a series of experiments, observers reproduced the average orientation of an ensemble of stimuli under varying levels of visual uncertainty. Results Ensemble statistics were affected by multiple spatial biases, in particular, a strong and persistent bias towards the center of the visual field. This bias, evident in the majority of subjects and in all experiments, scaled with uncertainty: the higher the uncertainty in the ensemble statistics, the larger the bias towards the element shown at the fovea. Conclusion Our findings indicate that ensemble perception cannot be explained by simple uniform pooling. The visual system weights information anisotropically from both the parafovea and the fovea, taking the intrinsic spatial anisotropies of vision into account to compensate for visual uncertainty.

Current genome-wide association studies of frontotemporal dementia (FTD) are underpowered due to limited samples. Further, common genetic etiologies between FTD and amyotrophic lateral sclerosis (ALS) remain unknown. Using the largest summary statistics of FTD (3526 cases and 9402 controls) and ALS (27,205 cases and 110,881 controls), we found a significant genetic correlation between them ( r ˆ g = 0.637, P = 0.032) and identified 190 FTD-related variants within 5 loci (3p22.1, 5q35.1, 9p21.2, 19p13.11, and 20q13.13). Among these, ALS and FTD had causal variants in 9p21.2 and 19p13.11. Moreover, MOBP (3p22.1), C9orf72 (9p21.2), MOB3B (9p21.2), UNC13A (19p13.11), SLC9A8 (20q13.13), SNAI1 (20q13.13), and SPATA2 (20q13.13) were discovered by both SNP- and gene-level analyses, which together discovered 15 FTD-associated genes, with 10 not detected before (IFNK , RNF114 , SLC9A8 , SPATA2 , SNAI1 , SCFD1 , POLDIP2 , TMEM97, G2E3 , and PIGW). Functional analyses showed these genes were enriched in heart left ventricle, kidney cortex, and some brain regions. Overall, this study provides insights into genetic determinants of FTD and shared genetic etiology underlying FTD and ALS. [ABSTRACT FROM AUTHOR]

The objects we encounter in everyday settings are composed of multicolored surfaces. To utilize the color information of these surfaces, it is necessary to efficiently summarize the color distribution of object surfaces. Studies examining such summarization (averaging) processes have shown that the three attributes of color -- hue, saturation, and brightness -- are averaged in different manners. Hue can be averaged accurately and approximated by the arithmetic mean on the uniform chromaticity scale diagram. Averaging of saturation exhibits a bias, with the average tending to shift towards a more saturated direction than the colorimetric mean. Furthermore, this bias becomes more pronounced when there is greater variability (noise) in the color distribution. While there is also a bias observed in the averaging of brightness, the underlying processing strategy seems to be adaptively adjusted depending on the task at hand. These summarizing strategies are believed to be acquired to enhance the reliability and certainty of sensory information in noisy natural environments. Identifying the factors that determine these processing strategies remains a challenge for future research. [ABSTRACT FROM AUTHOR]

Recent research has revealed that the human visual system is capable of comprehending various pieces of information "at a glance." Simultaneously, humans continuously receive information from the natural and social environments, which undergo constant changes. Thus, humans can identify patterns and regularities not only in a single moment but also in long-term changes. How the visual system estimates the spatiotemporal ensemble, i.e., the summary statistics of information that varies over time and space? Here, we introduce a series of our research findings that demonstrate how the visual system estimate spatiotemporal-statistical properties of the information from the visual events which probabilistically changes in the spatiotemporal domain, focusing on orientation as a representative visual feature. [ABSTRACT FROM AUTHOR]

LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.

Abstract Graphs in research articles can increase the comprehension of statistical data but may mislead readers if poorly designed. We propose a new plot type, the sea stack plot, which combines vertical histograms and summary statistics to represent large univariate datasets accurately, usefully, and efficiently. We compare five commonly used plot types (dot and whisker plots, boxplots, density plots, univariate scatter plots, and dot plots) to assess their relative strengths and weaknesses when representing distributions of data commonly observed in biological studies. We find the assessed plot types are either difficult to read at large sample sizes or have the potential to misrepresent certain distributions of data, showing the need for an improved method of data visualisation. We present an analysis of the plot types used in four ecology and conservation journals covering multiple areas of these research fields, finding widespread use of uninformative bar charts and dot and whisker plots (60% of all panels showing univariate data from multiple groups for the purpose of comparison). Some articles presented more informative figures by combining plot types (16% of panels), generally boxplots and a second layer such as a flat density plot, to better display the data. This shows an appetite for more effective plot types within conservation and ecology, which may further increase if accurate and user‐friendly plot types were made available. Finally, we describe sea stack plots and explain how they overcome the weaknesses associated with other alternatives to uninformative plots when used for large and/or unevenly distributed data. We provide a tool to create sea stack plots with our R package ‘seastackplot’, available through GitHub.

Finger millet [Eleusine coracana (L.) Gaertn.], a staple cereal amongst the rural population of large parts of Africa and South Asia. It is well-known for its nutritional status, especially abundant quantities of iron and calcium. Despite these advantages, finger millet is far from extensive conventional crop improvement programs. To popularize finger millet production and increase its productivity, primarily, the characterization of its germplasm is crucial. In the present study, 587 finger millet genotypes collected from different sources were analyzed for various estimates of genetic variability parameters, genetic divergence through K-means clustering followed by principal component analysis (PCA). The analysis of variance revealed substantial amount of genetic variability for the seven quantitative traits among 587 finger millet germplasm accessions. The genotypes were classified into two clusters following K-means clustering algorithm. The cluster I had higher number of genotypes (343) than cluster II (244). Thus, the grouping of accessions shall be of practical value to finger millet researchers to select and tap the genetic potential of elite accessions from different clusters as donor parentsin crossing programs. Based on PCA, three principal components (PC) with eigen values greater than one contributed 72.55% of the total variability. The PC1 accounted highest proportion of total variance (40.82 %), followed by PC2, PC3, PC4, PC5, PC6 and PC7that revealed 16.74 %, 14.98 %, 11.06 %, 9.19 %, 7.12% and 0.06 % variation, respectively.

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

BackgroundApproximate Bayesian Computation (ABC) has become a key tool for calibrating the parameters of discrete stochastic biochemical models. For higher dimensional models and data, its performance is strongly dependent on having a representative set of summary statistics. While regression-based methods have been demonstrated to allow for the automatic construction of effective summary statistics, their reliance on first simulating a large training set creates a significant overhead when applying these methods to discrete stochastic models for which simulation is relatively expensive. In this τ work, we present a method to reduce this computational burden by leveraging approximate simulators of these systems, such as ordinary differential equations and τ-Leaping approximations.ResultsWe have developed an algorithm to accelerate the construction of regression-based summary statistics for Approximate Bayesian Computation by selectively using the faster approximate algorithms for simulations. By posing the problem as one of ratio estimation, we use state-of-the-art methods in machine learning to show that, in many cases, our algorithm can significantly reduce the number of simulations from the full resolution model at a minimal cost to accuracy and little additional tuning from the user. We demonstrate the usefulness and robustness of our method with four different experiments.ConclusionsWe provide a novel algorithm for accelerating the construction of summary statistics for stochastic biochemical systems. Compared to the standard practice of exclusively training from exact simulator samples, our method is able to dramatically reduce the number of required calls to the stochastic simulator at a minimal loss in accuracy. This can immediately be implemented to increase the overall speed of the ABC workflow for estimating parameters in complex systems.

Background: Although extensive discussions on the influence of maternal educational attainment on offspring birthweight, the conclusion remains controversial, and it is challenging to comprehensively assess the causal association between them. Methods: To estimate effect of maternal educational attainment on the birthweight of first child, we first conducted an individual-level analysis with UK Biobank participants of white ancestry (n = 208,162). We then implemented Mendelian randomization (MR) methods including inverse variance weighted (IVW) MR and multivariable MR to assess the causal relation between maternal education and maternal-specific birthweight. Finally, using the UK Biobank parent-offspring trio data (n = 618), we performed a polygenic score based MR to simultaneously adjust for confounding effects of fetal-specific birthweight and paternal educational attainment. We also conducted simulations for power evaluation and sensitivity analyses for horizontal pleiotropy of instruments. Results: We observed that birthweight of first child was positively influenced by maternal education, with 7 years of maternal education as the reference, adjusted effect = 44.8 (95%CIs 38.0–51.6, P = 6.15 × 10−38), 54.9 (95%CIs 47.6–62.2, P = 4.21 × 10−128), and 89.4 (95%CIs 82.1–96.7, P = 4.28 × 10−34) for 10, 15 and 20 years of maternal educational attainment, respectively. A causal relation between maternal education and offspring birthweight was revealed by IVW MR (estimated effect = 0.074 for one standard deviation increase in maternal education years, 95%CIs 0.054–0.093, P = 2.56 × 10−13) and by complementary MR methods. This connection was not substantially affected by paternal education or horizontal pleiotropy. Further, we found a positive but insignificant causal association (adjusted effect = 24.0, 95%CIs −150.1–198.1, P = 0.787) between maternal education and offspring birthweight after simultaneously controlling for fetal genome and paternal education; this null causality was largely due to limited power of small sample sizes of parent-offspring trios. Conclusion: This study offers supportive evidence for a causal association between maternal education and offspring birthweight, highlighting the significance of enhancing maternal education to prevent low birthweight.

There is a growing number of frail patients whose health conditions require constant monitoring by physicians. Unfortunately, the budget restrictions of hospitals and the concomitant decision that patients should stay home require that this monitoring is to be carried out remotely. Today, Internet of Things (IoT) wearables are the most promising technology solution for sensing patients' physiological values 24/7. These measurements need to be stored permanently and then processed in order to provide support to physicians in charge of making real-time clinical decisions consistent with the patient's health status. This paper elaborates on the combination of the IoT and DBMS technologies. In detail, a PostgreSQL relational database stores patient-generated health data (PGHD), while a set of SQL views generates standard summary statistical indicators. [ABSTRACT FROM AUTHOR]

The heritability explained by local ancestry markers in an admixed population (h γ 2) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of h γ 2 can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA h γ 2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe h ˆ γ 2 in the 20 phenotypes range from 0.0025 to 0.033 (mean h ˆ γ 2 = 0.012 ± 9.2 × 10−4), which translates to h ˆ 2 ranging from 0.062 to 0.85 (mean h ˆ 2 = 0.30 ± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 ± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies. This study reports a method to estimate heritability explained by local ancestry using admixture mapping summary statistics and evaluates potential biases in admixture mapping. The method provides a strategy for summary statistic-based heritability estimation in admixed populations and controlling false positives due to ancestral population stratification in admixture mapping studies. [ABSTRACT FROM AUTHOR]

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, although down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci; the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses were found robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who generate and share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers' use of the summary statistics. [ABSTRACT FROM AUTHOR]

Background Epidemiological studies demonstrated that adverse in utero environment was associated with increased risk of offspring high blood pressure, by using birthweight as the proxy of maternal intrauterine exposure; however, the nature of such association remains less understood. Methods With maternal/fetal-specific summary statistics of birthweight (n  =   297 356 for own birthweight and n  =   210 248 for offspring birthweight) and summary statistics of blood pressure [i.e. systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP)] (n  =   757 601), we evaluated the genetic correlation between fetal-specific birthweight and blood pressure using cross-trait linkage disequilibrium score regression, and next detected pleiotropic genes for them with a pleiotropy mapping method called mixture-adjusted intersect-union pleiotropy test. Furthermore, we conducted a genetic risk score (GRS)-based Mendelian randomization analysis in parent-offspring pairs (n  =   6031) of the UK Biobank cohort, to assess the causal relation between maternal-specific GRS and blood pressure conditioning on fetal genotypes. Results We found fetal-specific birthweight had a negative genetic correlation with DBP (⁠ ρ ^ g  = −0.174, P  =   1.68 × 10–10), SBP (⁠ ρ ^ g  = −0.198, P  =   8.09 × 10–12), and PP (⁠ ρ ^ g  = −0.152, P  =   6.04 × 10–8), and detected 143, 137 and 135 pleiotropic genes shared between fetal-specific birthweight and PP, SBP and DBP, respectively. These genes often exhibited opposite genetic effects, and were more likely to be differentially expressed in pancreas, liver, heart, brain, whole blood and muscle skeletal tissues. A causal negative association of maternal-specific birthweight was identified with SBP (P  =   2.20 × 10–2) and PP (P  =   7.67 × 10–3) but not DBP (P  =   0.396) in mother-offspring pairs, after accounting for the influence of fetal-specific GRS; and the two significant relations were robust against the horizontal pleiotropy of instruments and the confounding influence of gestational duration and preterm birth. However, these causal associations could not be detected in father-offspring pairs. Conclusions This study revealed common genetic components underlying birthweight and blood pressure, and provided important insight into aetiology and early prevention of high blood pressure. [ABSTRACT FROM AUTHOR]

Abstract Background Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently. Results By leveraging summary statistics of 37 traits from East Asian (N max=254,373) or European (N max=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρ g ) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with $${\widehat{\rho }}_{g}$$ ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs. Conclusions Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.

Abstract Background Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear. Results By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection. Conclusions Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups.

Ensemble coding allows observers to form an average to represent a set of elements. However, it is unclear whether observers can extract an average from a cross-category set. Previous investigations on this issue using low-level stimuli yielded contradictory results. The current study addressed this issue by presenting high-level stimuli (i.e., a crowd of facial expressions) simultaneously (Experiment 1) or sequentially (Experiment 2), and asked participants to complete a member judgment task. The results showed that participants could extract average information from a group of cross-category facial expressions with a short perceptual distance. These findings demonstrate cross-category ensemble coding of high-level stimuli, contributing to the understanding of ensemble coding and providing inspiration for future research.

Objective: Low back pain is a prevalent and debilitating condition worldwide, with significant implications for individuals' quality of life and productivity. The aim of this study was to assess the relationship between mood instability and the risk of developing chronic low back pain, using a rigorously designed mendelian randomization methodology. Method: The study incorporated both univariate and multivariate mendelian randomization to analysis the causal relationship between mood instability and the risk of developing chronic low back pain. The data on mood instability from the Integrative Epidemiology Unit (IEU) opened Genome-Wide Association Studies (GWAS) project (IEU-opened GWAS project). Data on low back pain were collected from two sources: One source is the IEU open GWAS project (discovery data). Another source is a GWAS meta-analysis (replication data). Inverse variance weighted method, weighted median method, MR-Egger regression, and mendelian randomization pleiotropy residual sum and outlier method were used for mendelian randomization analysis. Result: The univariable mendelian randomization analysis shows a statistically significant correlation between mood instability and the risk of low back pain. Several methods were performed, including inverse variance weighting (discovery data: odds ratio= 3.544, 95% confidence interval= 1.785-7.039, p = 0.000; replication data: odds ratio= 3.167, 95% confidence interval= 2.476-4.052, p= 0.000), MREgger (discovery data: odds ratio= 7.178, 95% confidence interval= 0.057-909.525, p = 0.429; replication data: odds ratio= 2.262, 95% confidence interval= 0.580-8.825, p = 0.246), weighted median (discovery data: odds ratio= 2.730, 95% confidence interval= 1.112-6.702, p = 0.028; replication data: odds ratio= 3.243, 95% confidence interval= 2.378-4.422, p = 0.000), MR-PRESSO (discovery data: odds ratio= 3.544, 95% confidence interval= 1.785-7.039, p = 0.001; replication data: odds ratio= 3.167, 95% confidence interval= 2.476-4.052, p= 0.000) methods. The results were consistent across these methods. The results obtained from discovery data are consistent with those obtained from discovery data. In the multivariable mendelian randomization, after adjusting for various covariates such as body mass index, current tobacco smoking, alcohol intake frequency, Total body bone mineral density, and vigorous physical activity, there is a consistent correlation between mood instability and chronic low back pain. Conclusion: This study provides robust evidence supporting a causal relationship between mood instability and the development of low back pain. Our findings suggest that addressing mood instability may play a crucial role in prevention and management strategies for individuals experiencing low back pain. [ABSTRACT FROM AUTHOR]

One of the main necessities for population geneticists is the availability of sensitive statistical tools that enable to accept or reject the standard Wright–Fisher model of neutral evolution. A number of statistical tests have been developed to detect specific deviations from the null frequency spectrum in different directions (e.g., Tajima's D, Fu and Li's F and D tests, Fay and Wu's H). A general framework exists to generate all neutrality tests that are linear functions of the frequency spectrum. In this framework, it is possible to develop a family of optimal tests with almost maximum power against a specific alternative evolutionary scenario. In this paper we provide a thorough discussion of the structure and properties of linear and nonlinear neutrality tests. First, we present the general framework for linear tests and emphasise the importance of the property of scalability with the sample size (that is, the interpretation of the tests should not depend on the sample size), which, if missing, can lead to errors in interpreting the data. After summarising the motivation and structure of linear optimal tests, we present a more general framework for the optimisation of linear tests, leading to a new family of tunable neutrality tests. In a further generalisation, we extend the framework to nonlinear neutrality tests and we derive nonlinear optimal tests for polynomials of any degree in the frequency spectrum. [ABSTRACT FROM AUTHOR]

The object of research is histograms of the dynamics of dangerous parameters of the gas environment, the values of which are measured in real time at the intervals of absence and ignition of materials. The method of determining histograms during a typical selection of measurements is described. This method allows one to determine histograms for samples of an arbitrary position and the size of the data interval of measurements of the dynamics of dangerous parameters of the gas environment. On the basis of histograms on the intervals of the absence and occurrence of fires of test materials, indicators of their summary statistics can be determined. Laboratory experiments were conducted to study the features of the histograms of carbon monoxide concentration, smoke density, and temperature of the gas medium for intervals of reliable absence and appearance of ignition of materials in the form of alcohol and textiles. The results of the analysis of the histograms clearly show that the dynamics of the studied dangerous parameters at the indicated intervals differ from the Gaussian. At the same time, the histograms differ in shape, which depends on the type of ignition material and the corresponding dangerous parameter. Based on the features of the histograms of the dynamics of dangerous parameters on the intervals of the absence and appearance of fires of test materials, the simplest indicators of summary statistics in the form of the range, number, and position of the modes are determined. It was established that when alcohol ignites, the variation range of carbon monoxide concentration, smoke density, and gas temperature increases from 0.545, 0.068, and 0.161 to 7.121, 0.523, and 8.71, respectively. At the same time, the range of variation of these parameters during textile ignition increases from 0.182, 0.205, and 0.323 to 0.394, 0.386, and 2.903, respectively. The obtained results in aggregate or one by one can be used in practice for early detection of fires in order to prevent the occurrence of fires in premises. [ABSTRACT FROM AUTHOR]

Abstract Background Generating polygenic risk scores for diseases and complex traits requires high quality GWAS summary statistic files. Often, these files can be difficult to acquire either as a result of unshared or incomplete data. To date, bioinformatics tools which focus on restoring missing columns containing identification and association data are limited, which has the potential to increase the number of usable GWAS summary statistics files. Results SumStatsRehab was able to restore rsID, effect/other alleles, chromosome, base pair position, effect allele frequencies, beta, standard error, and p-values to a better extent than any other currently available tool, with minimal loss. Conclusions SumStatsRehab offers a unique tool utilizing both functional programming and pipeline-like architecture, allowing users to generate accurate data restorations for incomplete summary statistics files. This in turn, increases the number of usable GWAS summary statistics files, which may be invaluable for less researched health traits.

Background: Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear. Results: By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection. Conclusions: Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently. Results: By leveraging summary statistics of 37 traits from East Asian (Nmax=254,373) or European (Nmax=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρg) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs. Conclusions: Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping. [ABSTRACT FROM AUTHOR]

Bayes factors are an increasingly popular tool for indexing evidence from experiments. For two competing population models, the Bayes factor reflects the relative likelihood of observing some data under one model compared to the other. Computing Bayes factors can be difficult, requiring one to integrate the product of the likelihood and a prior distribution on the population parameter(s) for both competing models. Previous work has obviated this difficulty for independent-groups designs. In this paper, we develop a new analytic formula for computing Bayes factors directly from minimal summary statistics in repeated-measures designs. This work is an improvement on previous methods for computing Bayes factors from summary statistics (e.g., the BIC method), which produce Bayes factors that violate the Sellke upper bound of evidence for smaller sample sizes. The new approach taken in this paper requires knowing only the F -statistic and degrees of freedom, both of which are commonly reported in most empirical work. In addition to providing computational examples, we report a simulation study that benchmarks the new formula against other methods for computing Bayes factors in repeated-measures designs. Our new method provides an easy way for researchers to compute Bayes factors directly from a minimal set of summary statistics, allowing users to index the evidential value of their own data, as well as data reported in published studies. [ABSTRACT FROM AUTHOR]

Our visual system possesses a remarkable ability to extract summary statistical information from groups of similar objects, known as ensemble perception. It remains elusive whether the processing of ensemble statistics exerts influences on our perceptual decision-making and what roles consciousness and attention play in this process. In a series of experiments, we demonstrated that the processing of ensemble statistics can exert significant modulation effects on our perceptual decision-making, which is independent of consciousness but relies on attentional resources. More intriguingly, the conscious and unconscious ensemble representations respectively induce repulsive and attractive modulation effects, with the unconscious effect susceptible to the temporal separation and the distinction between the inducers and the targets. These results not only suggest that the conscious and unconscious ensemble representations engage different visual processing mechanisms but also highlight the distinct roles of consciousness and attention in ensemble perception. [ABSTRACT FROM AUTHOR]

Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Extensive evidence from genome-wide association studies (GWAS) has shown that jointly analyzing multiple phenotypes can improve the power of the association test compared to the traditional single variant versus single trait approach. Here we propose an adaptive test based on principal components (ATPC) that is powerful and efficient for discovering the association between a single variant and multiple traits. Our method only needs GWAS summary statistics that are often available. We first estimate the trait correlation matrix by LD score regression. Then, based on the correlation matrix, we construct a series of test statistics that contain different numbers of principal components. The ultimate test statistic combines the P values of these principal component-based statistics by using the aggregated Cauchy association test. The analytical P-value of the test statistic can be computed quickly without the permutation process, which is the notable feature of our proposed method. The extensive simulation studies demonstrate that ATPC can control the type I error rates and have powerful and robust performance compared to several existing tests in a wide range of simulation settings. The analysis of the lipids GWAS summary data from the Global Lipids Genetics Consortium shows that ATPC identifies 230 new SNPs that are missed by the original single trait association analysis. By searching the GWAS Catalog, some SNPs and mapped genes identified by ATPC are reported to be associated with lipid traits. Through further analysis for GWAS results, we also find some Gene Ontology terms and biological pathways related to lipids. [ABSTRACT FROM AUTHOR]

Analysis of genome-wide association studies (GWAS) is characterized by a large number of univariate regressions where a quantitative trait is regressed on hundreds of thousands to millions of single-nucleotide polymorphism (SNP) allele counts, one at a time. This article proposes an estimator of the SNP heritability of the trait, defined here as the fraction of the variance of the trait explained by the SNPs in the study. The proposed GWAS heritability (GWASH) estimator is easy to compute, highly interpretable, and is consistent as the number of SNPs and the sample size increase. More importantly, it can be computed from summary statistics typically reported in GWAS, not requiring access to the original data. The estimator takes full account of the linkage disequilibrium (LD) or correlation between the SNPs in the study through moments of the LD matrix, estimable from auxiliary datasets. Unlike other proposed estimators in the literature, we establish the theoretical properties of the GWASH estimator and obtain analytical estimates of the precision, allowing for power and sample size calculations for SNP heritability estimates, and forming a firm foundation for future methodological development.

Abstract Background Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking. Methods By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement. Results Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones. Conclusion Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

Abstract Background As levels of anthropogenic noise in the marine environment rise, it is crucial to quantify potential associated effects on marine mammals. Yet measuring responses is challenging because most species spend the majority of their time submerged. Consequently, much of their sub-surface behavior is difficult or impossible to observe and it can be difficult to determine if—during or following an exposure to sound—an observed dive differs from previously recorded dives. We propose a method for initial assessment of potential behavioral responses observed during controlled exposure experiments (CEEs), in which animals are intentionally exposed to anthropogenic sound sources. To identify possible behavioral responses in dive data collected from satellite-linked time–depth recorders, and to inform the selection and parameters for subsequent individual and population-level response analyses, we propose to use kernel density estimates of conditional distributions for quantitative comparison of pre- and post-exposure behavior. Results We apply the proposed method to nine Cuvier’s beaked whales (Ziphius cavirostris) exposed to a lower-amplitude simulation of Mid-Frequency Active Sonar within the context of a CEE. The exploratory procedure provides evidence that exposure to sound causes animals to change their diving behavior. Nearly all animals tended to dive deep immediately following exposure, potentially indicating avoidance behavior. Following the initial deep dive after exposure, the procedure provides evidence that animals either avoided deep dives entirely or initiated deep dives at unusual times relative to their pre-exposure, baseline behavior patterns. The procedure also provides some evidence that animals exposed as a group may tend to respond as a group. Conclusions The exploratory approach we propose can identify potential behavioral responses across a range of diving parameters observed during CEEs. The method is particularly useful for analyzing data collected from animals for which neither the baseline, unexposed patterns in dive behavior nor the potential types or duration of behavioral responses is well characterized in the literature. The method is able to be applied in settings where little a priori knowledge is known because the statistical analyses employ kernel density estimates of conditional distributions, which are flexible non-parametric techniques. The kernel density estimates allow researchers to initially assess potential behavioral responses without making strong, model-based assumptions about the data.