GAUSS: a summary-statistics-based R package for accurate estimation of linkage disequilibrium for variants, Gaussian imputation, and TWAS analysis of cosmopolitan cohorts.

MOTIVATION: As the availability of larger and more ethnically diverse reference panels grows, there is an increase in demand for ancestry-informed imputation of genome-wide association studies (GWAS), and other downstream analyses, e.g. fine-mapping. Performing such analyses at the genotype level is computationally challenging and necessitates, at best, a laborious process to access individual-level genotype and phenotype data. Summary-statistics-based tools, not requiring individual-level data, provide an efficient alternative that streamlines computational requirements and promotes open science by simplifying the re-analysis and downstream analysis of existing GWAS summary data. However, existing tools perform only disparate parts of needed analysis, have only command-line interfaces, and are difficult to extend/link by applied researchers. RESULTS: To address these challenges, we present Genome Analysis Using Summary Statistics (GAUSS)-a comprehensive and user-friendly R package designed to facilitate the re-analysis/downstream analysis of GWAS summary statistics. GAUSS offers an integrated toolkit for a range of functionalities, including (i) estimating ancestry proportion of study cohorts, (ii) calculating ancestry-informed linkage disequilibrium, (iii) imputing summary statistics of unobserved variants, (iv) conducting transcriptome-wide association studies, and (v) correcting for "Winner's Curse" biases. Notably, GAUSS utilizes an expansive, multi-ethnic reference panel consisting of 32 953 genomes from 29 ethnic groups. This panel enhances the range and accuracy of imputable variants, including the ability to impute summary statistics of rarer variants. As a result, GAUSS elevates the quality and applicability of existing GWAS analyses without requiring access to subject-level genotypic and phenotypic information. AVAILABILITY AND IMPLEMENTATION: The GAUSS R package, complete with its source code, is readily accessible to the public via our GitHub repository at https://github.com/statsleelab/gauss. To further assist users, we provided illustrative use-case scenarios that are conveniently found at https://statsleelab.github.io/gauss/, along with a comprehensive user guide detailed in Supplementary Text S1.

KOMPUTE: imputing summary statistics of missing phenotypes in high-throughput model organism data.

Motivation: The International Mouse Phenotyping Consortium (IMPC) is striving to build a comprehensive functional catalog of mammalian protein-coding genes by systematically producing and phenotyping gene-knockout mice for almost every protein-coding gene in the mouse genome and by testing associations between gene loss-of-function and phenotype. To date, the IMPC has identified over 90 000 gene-phenotype associations, but many phenotypes have not yet been measured for each gene, resulting in largely incomplete data; ∼75.6% of association summary statistics are still missing in the latest IMPC summary statistics dataset (IMPC release version 16). Results: To overcome these challenges, we propose KOMPUTE, a novel method for imputing missing summary statistics in the IMPC dataset. Using conditional distribution properties of multivariate normal, KOMPUTE estimates the association Z-scores of unmeasured phenotypes for a particular gene as a conditional expectation given the Z-scores of measured phenotypes. Our evaluation of the method using simulated and real-world datasets demonstrates its superiority over the singular value decomposition matrix completion method in various scenarios. Availability and implementation: An R package for KOMPUTE is publicly available at https://github.com/statsleelab/kompute, along with usage examples and results for different phenotype domains at https://statsleelab.github.io/komputeExamples.

Transformation of valence signaling in a striatopallidal circuit.

The ways in which sensory stimuli acquire motivational valence through association with other stimuli is one of the simplest forms of learning. Though we have identified many brain nuclei that play various roles in reward processing, a significant gap remains in understanding how valence encoding transforms through the layers of sensory processing. To address this gap, we carried out a comparative investigation of the olfactory tubercle (OT), and the ventral pallidum (VP) - 2 connected nuclei of the basal ganglia which have both been implicated in reward processing. First, using anterograde and retrograde tracing, we show that both D1 and D2 neurons of the OT project primarily to the VP and minimally elsewhere. Using 2-photon calcium imaging, we then investigated how the identity of the odor and reward contingency of the odor are differently encoded by neurons in either structure during a classical conditioning paradigm. We find that VP neurons robustly encode reward contingency, but not identity, in low-dimensional space. In contrast, OT neurons primarily encode odor identity in high-dimensional space. Though D1 OT neurons showed larger response vectors to rewarded odors than other odors, we propose this is better interpreted as identity encoding with enhanced contrast rather than as valence encoding. Finally, using a novel conditioning paradigm that decouples reward contingency and licking vigor, we show that both features are encoded by non-overlapping VP neurons. These results provide a novel framework for the striatopallidal circuit in which a high-dimensional encoding of stimulus identity is collapsed onto a low-dimensional encoding of motivational valence.

Hepatocyte-specific deletion of Bis causes senescence in the liver without deteriorating hepatic function.

Bcl-2-interacting cell death suppressor (BIS), also called as BAG3, regulates numerous physiological processes, such as apoptosis, protein quality control, and senescence. Whole-body Bis-knockout (KO) mice exhibit early lethality following cardiac and skeletal muscle dysfunction. The first attempt to generate organ-specific knockout mice resulted in constitutive or inducible heart-specific Bis-knockout mice, which exhibited cardiac dilation and underwent premature death. Here, we generated hepatocyte-specific Bis-knockout (Bis-HKO) mice and found no abnormalities in metabolic function and survival. However, depletion of HSPB8 and accumulation of p62 indicated impaired autophagy in Bis-HKO livers. Interestingly, the number of peroxisomes wrapped by phagophore membranes increased as evidenced by transmission electron microscopy analysis, indicating defects in the progression of pexophagy. In addition, increased dihydroethidine intensities and histone H3 K9me3-positive nuclei indicated increased oxidative stress and senescence induction in Bis-HKO livers. Mechanistically, p27 was upregulated in Bis-HKO livers. In SNU368 hepatocellular carcinoma cells, BIS depletion led to p27 upregulation, and increase in histone H3 K9me3 levels and senescence-associated ß-galactosidase staining; therefore, reproducing the in vivo senescence phenotype. Despite the observation of no metabolic abnormalities, BIS depletion led to defective autophagy, increased oxidative stress, and senescence in Bis-HKO livers. Collectively, our results suggest a role for BIS in maintaining liver regeneration potential under pathological conditions.

Inhibitory Effect of Etravirine, a Non-Nucleoside Reverse Transcriptase Inhibitor, via Anterior Gradient Protein 2 Homolog Degradation against Ovarian Cancer Metastasis.

Anterior gradient protein 2 homolog (AGR2), an endoplasmic reticulum protein, is secreted in the tumor microenvironment. AGR2 is a member of the disulfide isomerase family, is highly expressed in multiple cancers, and promotes cancer metastasis. In this study, we found that etravirine, which is a non-nucleoside reverse transcriptase inhibitor, could induce AGR2 degradation via autophagy. Moreover, etravirine diminished proliferation, migration, and invasion in vitro. Moreover, in an orthotopic xenograft mouse model, the combination of etravirine and paclitaxel significantly suppressed cancer progression and metastasis. This drug may be a promising therapeutic agent for the treatment of ovarian cancer.

Protective Effects and Benefits of Olive Oil and Its Extracts on Women's Health.

Women and men share similar diseases; however, women have unique issues, including gynecologic diseases and diseases related to menstruation, menopause, and post menopause. In recent decades, scientists paid more attention to natural products and their derivatives because of their good tolerability and effectiveness in disease prevention and treatment. Olive oil is an essential component in the Mediterranean diet, a diet well known for its protective impact on human well-being. Investigation of the active components in olive oil, such as oleuropein and hydroxytyrosol, showed positive effects in various diseases. Their effects have been clarified in many suggested mechanisms and have shown promising results in animal and human studies, especially in breast cancer, ovarian cancer, postmenopausal osteoporosis, and other disorders. This review summarizes the current evidence of the role of olives and olive polyphenols in women's health issues and their potential implications in the treatment and prevention of health problems in women.

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel.

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary-statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500-subject coming from the 1000 Genome-Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic-mixture based solely on Z-scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary-statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS-studies.

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.

SUMMARY: Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including 'unwanted' variation that needs to be removed in downstream analyses (e.g. batch effects) and 'wanted' or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying 'wanted' variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. AVAILABILITY AND IMPLEMENTATION: The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA. CONTACT: leed13@miamioh.edu or duygu.ucar@jax.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA).

Single cell RNA-sequencing (scRNA-seq) precisely characterizes gene expression levels and dissects variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for batch correction (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden factors even when they are correlated with other sources of variation by identifying a set of genes associated with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.

Amniotic fluid index, single deepest pocket and transvaginal cervical length: Parameter of predictive delivery latency in preterm premature rupture of membranes.

OBJECTIVE: Prediction of delivery latency complicated with preterm premature rupture of membrane (PPROM) is crucial for reducing maternal and neonatal complications. Therefore, we investigated the correlations between latency period and cut-off values of ultrasonographic parameters, ultimately predicting delivery latency. MATERIALS AND METHODS: The retrospective study was performed on 121 PPROM patients enrolled between March 2010 and July 2015. Parameters including amniotic fluid index (AFI), single deepest pocket (SDP) and transvaginal cervical length (TVCL) were measured in 99 singleton pregnancies with PPROM. Latency was defined as the period from sonographic measurements to delivery day. The parameters were analyzed independently by Wilcoxon rank sum test and Fisher's exact test. Cut-off values were determined using a receiver operating characteristic (ROC) curve. RESULTS: In delivery latency within 3 days, AFI and SDP were decreased with significantly shorter TVCL. AFI and SDP had the highest sensitivity (82.2%) and SDP combined with TVCL showed the highest specificity (75.9%) in area under curve (AUC) value. The predicted median latency period was less than 2 days within the cutoff value of parameter (AFI ≤ 7.72, SDP ≤ 3.2 and TVCL ≤ 1.69). CONCLUSION: AFI and SDP combined with TVCL could be useful predictive parameters of the latency interval from PPROM to delivery.

Leptin, leptin receptors and hypoxia-induced factor-1α expression in the placental bed of patients with and without preeclampsia during pregnancy.

The mechanism underlying the pathogenesis of preeclampsia (PE) has been previously investigated but remains to be elucidated. Among numerous biomarkers that are associated with the pathogenesis of PE, leptin is most frequently investigated. Although studies concerning the association between PE and the expression of leptin in the serum and placenta have been conducted, the results are conflicting and inconsistent. Furthermore, the expression of leptin and its receptors in the placental bed and their association with PE, to the best of our knowledge, has not been previously reported. Therefore, to determine the association between the expression of leptin and its receptor, and pathogenesis and onset period of PE, placental bed tissues were obtained from cesarean section deliveries. The mRNA and protein expression levels of leptin and its receptor were investigated in normal pregnancies (n=18), pregnancies complicated with early­onset PE (n=9) and late­onset PE (n=9) by reverse transcription­quantitative polymerase chain reaction and western blotting, respectively. The results demonstrated that the mRNA and protein expression of leptin in the placental bed was significantly increased in the PE groups compared with normal controls and was associated with the onset period of PE. Furthermore, as evidenced by immunostaining, leptin was upregulated in endothelial cells of the placental bed in the PE groups, with a particularly strong upregulation in activated endothelial cells from patients with early­onset PE. The results of the present study indicate that altered expression of leptin in the placental bed may contribute to the pathogenesis of PE.

The Effect of Electrical Muscle Stimulation and In-bed Cycling on Muscle Strength and Mass of Mechanically Ventilated Patients: A Pilot Study.

BACKGROUND: Critically ill patients experience muscle weakness, which leads to functional disability. Both functional electrical stimulation (FES) and in-bed cycling can be an alternative measure for intensive care unit (ICU) patients who are not feasible for active exercise. The aim of this study was to examine whether FES and in-bed cycling have a positive effect on muscle mass in ICU patients. METHODS: Critically ill patients who received mechanical ventilation for at least 24 hours were included. After passive range of motion exercise, in-bed cycling was applied for 20 minutes, and FES was applied for 20 minutes on the left leg. The right leg received in-bed cycling and the left leg received both FES and in-bed cycling. Thigh circumferences and rectus femoris cross-sectional area (CSA) were assessed with ultrasonography before and after the intervention. Muscle strength was assessed by Medical Research Council scale. RESULTS: A total of 10 patients were enrolled in this study as a pilot study. Before and after the intervention, the CSA of right rectus femoris increased from 5.08 ± 1.51 cm2 to 6.01 ± 2.21 cm2 , which was statistically significant (P = 0.003). The thigh circumference was also increased and statistically significant (P = 0.006). There was no difference between left and right in regard to FES application. There is no significant change in muscle strength before and after the intervention (right and left, P = 0.317 and P = 0.368, respectively). CONCLUSIONS: In-bed cycling increased thigh circumferences rectus femoris CSA. Adding FES did not show differences.

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

Motivation: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. Results: We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of P-values. Availability and implementation: https://github.com/Chatzinakos/JEPEGMIX2. Supplementary information: Supplementary data are available at Bioinformatics online.

Tracing neuronal circuits in transgenic animals by transneuronal control of transcription (TRACT).

Understanding the computations that take place in brain circuits requires identifying how neurons in those circuits are connected to one another. We describe a technique called TRACT (TRAnsneuronal Control of Transcription) based on ligand-induced intramembrane proteolysis to reveal monosynaptic connections arising from genetically labeled neurons of interest. In this strategy, neurons expressing an artificial ligand ('donor' neurons) bind to and activate a genetically-engineered artificial receptor on their synaptic partners ('receiver' neurons). Upon ligand-receptor binding at synapses the receptor is cleaved in its transmembrane domain and releases a protein fragment that activates transcription in the synaptic partners. Using TRACT in Drosophila we have confirmed the connectivity between olfactory receptor neurons and their postsynaptic targets, and have discovered potential new connections between neurons in the circadian circuit. Our results demonstrate that the TRACT method can be used to investigate the connectivity of neuronal circuits in the brain.

Methods to investigate the structure and connectivity of the nervous system.

Understanding the computations that take place in neural circuits requires identifying how neurons in those circuits are connected to one another. In addition, recent research indicates that aberrant neuronal wiring may be the cause of several neurodevelopmental disorders, further emphasizing the importance of identifying the wiring diagrams of brain circuits. To address this issue, several new approaches have been recently developed. In this review, we describe several methods that are currently available to investigate the structure and connectivity of the brain, and discuss their strengths and limitations.

A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans.

MOTIVATION: For genetic studies, statistically significant variants explain far less trait variance than 'sub-threshold' association signals. To dimension follow-up studies, researchers need to accurately estimate 'true' effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winner's curse biases, which are reduced only by laborious winner's curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities. RESULTS: WCA of Z-scores shrinks these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values toward one, which corresponds to the zero Z-score value. Thus, WCA on Z-scores scale is a proxy for MTA on P-value scale. Therefore, to estimate Z-score noncentralities for all SNPs in genome scans, we propose F: DR I: nverse Q: uantile T: ransformation (FIQT). It (i) performs the simpler MTA of P-values using FDR and (ii) obtains noncentralities by back-transforming MTA P-values on Z-score scale. When compared to competitors, realistic simulations suggest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude. Practical application of FIQT to Psychiatric Genetic Consortium schizophrenia cohort predicts a non-trivial fraction of sub-threshold signals which become significant in much larger supersamples. CONCLUSIONS: FIQT is a simple, yet accurate, WCA method for Z-scores (and ORs/RRs, via simple transformations). AVAILABILITY AND IMPLEMENTATION: A 10 lines R function implementation is available at https://github.com/bacanusa/FIQT CONTACT: sabacanu@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Meta-analysis of Positive and Negative Symptoms Reveals Schizophrenia Modifier Genes.

BACKGROUND: Evidence suggests that genetic factors may influence both schizophrenia (Scz) and its clinical presentation. In recent years, genome-wide association studies (GWAS) have demonstrated considerable success in identifying risk loci. Detection of "modifier loci" has the potential to further elucidate underlying disease processes. METHODS: We performed GWAS of empirically derived positive and negative symptom scales in Irish cases from multiply affected pedigrees and a larger, independent case-control sample, subsequently combining these into a large Irish meta-analysis. In addition to single-SNP associations, we considered gene-based and pathway analyses to better capture convergent genetic effects, and to facilitate biological interpretation of these findings. Replication and testing of aggregate genetic effects was conducted using an independent European-American sample. RESULTS: Though no single marker met the genome-wide significance threshold, genes and ontologies/pathways were significantly associated with negative and positive symptoms; notably, NKAIN2 and NRG1, respectively. We observed limited overlap in ontologies/pathways associated with different symptom profiles, with immune-related categories over-represented for negative symptoms, and addiction-related categories for positive symptoms. Replication analyses suggested that genes associated with clinical presentation are generalizable to non-Irish samples. CONCLUSIONS: These findings strongly support the hypothesis that modifier loci contribute to the etiology of distinct Scz symptom profiles. The finding that previously implicated "risk loci" actually influence particular symptom dimensions has the potential to better delineate the roles of these genes in Scz etiology. Furthermore, the over-representation of distinct gene ontologies/pathways across symptom profiles suggests that the clinical heterogeneity of Scz is due in part to complex and diverse genetic factors.

JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.

MOTIVATION: To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts. RESULTS: We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia. AVAILABILITY AND IMPLEMENTATION: Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/. CONTACT: donghyung.lee@vcuhealth.org SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.