Biology subjects

Majumdar, A.

Publications and source records attributed to Majumdar, A..

72 records · Page 4Linked to original sources

bioRxiv2020

Computational prediction of Drug-Disease association based on Graph-regularized one bit Matrix completion

MotivationInvestigation of existing drugs is an effective alternative to discovery of new drugs for treating diseases. This task of drug re-positioning can be assisted by various kinds of computational methods to predict the best indication for a drug given the open-source biological datasets. Owing to the fact that similar drugs tend to have common pathways and disease indications, the association matrix is assumed to be of low-rank structure. Hence, the problem of drug-disease association prediction can been modelled as a low-rank matrix-completion problem. ResultsIn this work, we propose a novel matrix completion framework which makes use of the sideinformation associated with drugs/diseases for the prediction of drug-disease indications modelled as neighborhood graph: Graph regularized 1-bit matrix compeltion (GR1BMC). The algorithm is specially designed for binary data and uses parallel proximal algorithm to solve the aforesaid minimization problem taking into account all the constraints including the neighborhood graph incorporation and restricting predicted scores within the specified range. The results of the proposed algorithm have been validated on two standard drug-disease association databases (Fdataset and Cdataset) by evaluating the AUC across the 10-fold cross validation splits. The usage of the method is also evaluated through a case study where top 5 indications are predicted for novel drugs and diseases, which then are verified with the CTD database. The results of these experiments demonstrate the practical usage and superiority of the proposed approach over the benchmark methods. Contactaanchalm@iiitd.ac.in

bioinformatics↗

bioRxiv2020

The properties of buried ion pairs are governed by the propensity of proteins to reorganize

Charges are incompatible with the hydrophobic interior of proteins, yet proteins use buried charges, often in pairs or networks, to drive energy transduction processes, catalysis, pH-sensing, and ion transport. The structural adaptations necessary to accommodate interacting charges in the protein interior are not well understood. According to continuum electrostatic calculations, the Coulomb interaction between two buried charges cannot offset the highly unfavorable penalty of dehydrating two charges. This was investigated experimentally with two variants of staphylococcal nuclease (SNase) with Glu:Lys or Lys:Glu pairs introduce at internal i, i+4 positions on an -helix. Contrary to expectations from previous theoretical and experimental studies, the proteins tolerated the charged ion pairs in both orientations. Crystal structures and NMR spectroscopy studies showed that in both variants, side chains or backbone are reorganized. This leads to the exposure of at least one of the two buried groups to water. Comparison of these ion pairs with a highly stable buried ion pair in SNase shows that the location and the amplitude of structural reorganization can vary dramatically between ion pairs buried in the same general region of the protein. The propensity of the protein to populate alternative conformation states in which internal charges can contact water appears to be the factor that governs the magnitude of electrostatic effects in hydrophobic environments. The net effect of structural reorganization is to weaken the Coulomb interactions between charge pairs; however, the reorganized protein no longer has to pay the energetic penalty for burying charges. These results provide the framework necessary to understand the interplay between the dehydration of charges, Coulomb interactions and protein reorganization that tunes the functional properties of proteins.

biophysics↗

bioRxiv2020

Electrostatic effects in proteins are governed by pH-redistribution of the conformational ensemble

The imperative for charges to be hydrated is one of the most important organizing principles in biology, responsible for the general architecture of biological macromolecules and for energy storage in the form of electrochemical gradients. Paradoxically, many functional sites in proteins have buried ionizable groups1. These groups are tolerated because they are usually buried in the neutral state2. However, when they become charged they can drive structural transitions to open states in which the charge can be stabilized, mostly through interactions with water3. This coupling between the ionization of a buried group and conformational reorganization is precisely the mechanism used by proteins to perform energy transduction4,5,6. By applying this principle to a family of 25 variants of staphylococcal nuclease with internal Lys residues, it was possible to characterize in detail the range of localized partial unfolding events that even a highly stable protein that unfolds cooperatively can undergo in response to H+-binding. Conformational states that constitute vanishingly small populations of the equilibrium native state ensemble of this protein were identified by correlation of structural and thermodynamic data, providing a map of the conformational landscape of this protein with unprecedented detail. The data demonstrate that the apparent pKa values of buried ionizable residues are not determined by the properties of their microenvironment but by the intrinsic propensity of the protein to populate open states in which internal charged residues can be hydrated. The role of buried residues in functional sites in proteins relies on their ability to tune the conformational ensemble for redistribution in response to small changes in pH. These results provide the physical framework necessary for understanding the role of pH-driven conformational changes in driving biological energy transduction4, the identification of pH-sensing proteins in nature7, and for the engineering of pH-sensitive dynamics and function in de novo designed proteins8.

biophysics↗

bioRxiv2019

SurA is a "Groove-y" Chaperone That Expands Unfolded Outer Membrane Proteins

The periplasmic chaperone network ensures the biogenesis of bacterial outer membrane proteins (OMPs) and has recently been identified as a promising target for antibiotics. SurA is the most important member of this network both due to its genetic interaction with the {beta}-barrel assembly machinery complex as well as its ability to prevent unfolded OMP (uOMP) aggregation. Using only binding energy, the mechanism by which SurA carries out these two functions is not well understood. Here we use a combination of photo-crosslinking, mass spectrometry, solution scattering, and molecular modeling techniques to elucidate the key structural features that define how SurA solubilizes uOMPs. Our experimental data support a model in which SurA binds uOMPs in a groove formed between the core and P1 domains. This binding event results in a drastic expansion of the rest of the uOMP, which has many biological implications. Using these experimental data as restraints, we adopted an integrative modeling approach to create a sparse ensemble of models of a SurA*uOMP complex. We validated key structural features of the SurA*uOMP ensemble using independent scattering and chemical crosslinking data. Our data suggest that SurA utilizes three distinct binding modes to interact with uOMPs and that more than one SurA can bind a uOMP at a time. This work demonstrates that SurA operates in a distinct fashion compared to other chaperones in the OMP biogenesis network. Significance StatementOuter membrane proteins play critical roles in bacterial physiology and increasingly are exploited as antibiotic targets. SurA is the most important chaperone in the OMP biogenesis network and is thought to initiate their folding through an interaction with the BAM complex. We observe an unprecedented expansion of unfolded outer membrane proteins when bound to SurA. This expansion suggests a potential mechanism by which SurA can deliver uOMPs to the BAM complex. In addition, this study highlights the use of an integrative/hybrid structural biology approach and emerging methods to map highly heterogeneous structural ensembles such as that of an unfolded protein bound to a chaperone.

biophysics↗

bioRxiv2019

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

Drug discovery is an important field in the pharmaceutical industry with one of its crucial chemogenomic process being drug-target interaction prediction. This interaction determination is expensive and laborious, which brings the need for alternative computational approaches which could help reduce the search space for biological experiments. This paper proposes a novel framework for drug-target interaction (DTI) prediction: Multi-Graph Regularized Deep Matrix Factorization (MGRDMF). The proposed method, motivated by the success of deep learning, finds a low-rank solution which is structured by the proximities of drugs and targets (drug similarities and target similarities) using deep matrix factorization. Deep matrix factorization is capable of learning deep representations of drugs and targets for interaction prediction. It is an established fact that drug and target similarities incorporation preserves the local geometries of the data in original space and learns the data manifold better. However, there is no literature on which the type of similarity matrix (apart from the standard biological chemical structure similarity for drugs and genomic sequence similarity for targets) could best help in DTI prediction. Therefore, we attempt to take into account various types of similarities between drugs/targets as multiple graph Laplacian regularization terms which take into account the neighborhood information between drugs/targets. This is the first work which has leveraged multiple similarity/neighborhood information into the deep learning framework for drug-target interaction prediction. The cross-validation results on four benchmark data sets validate the efficacy of the proposed algorithm by outperforming shallow state-of-the-art computational methods on the grounds of AUPR and AUC.

bioinformatics↗

bioRxiv2019

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power.

bioinformatics↗

bioRxiv2019

A role of cellular translation regulation associated with toxic Huntingtin protein

Huntingtons disease (HD) is a severe neurodegenerative disorder caused by poly Q repeat expansion in the Huntingtin (Htt) gene. While the Htt amyloid aggregates are known to affect many cellular processes, its role in translation is not addressed. Here we report pathogenic Htt expression causes protein synthesis deficit in cells. We find a functional prion-like protein, the translation regulator Orb2 to be sequestered by Htt aggregates. Coexpression of Orb2 can partially rescue the lethality associated with poly Q expanded Htt. These findings can be relevant for HD as human homologs of Orb2 also can be sequestered by pathogenic Htt aggregates. Our work suggests that translation dysfunction could be one of the contributors in the pathogenesis of HD and new therapies targeting protein synthesis pathways might help alleviate disease symptoms.

cell biology↗

bioRxiv2019

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

bioRxiv2019

Lysosomal enzyme tripeptidyl peptidase 1 plays a role in degradation of beta amyloid fibrils

Alzheimers disease (AD) is characterized by the accumulation of amyloid plaques surrounded by microglia. In cell culture, microglia internalize fibrillar {beta}-amyloid but do not degrade it efficiently. Unactivated microglia have a relatively high lysosomal pH, which impairs the activity of lysosomal proteases. Previous studies showed that activation of microglia with macrophage colony stimulating factor decreases lysosomal pH and enhances fibrillar {beta}-amyloid degradation. We investigated the role of the lysosomal protease tripeptidyl peptidase 1 (TPP1) in cell culture and in a mouse model of Alzheimers disease. Increased levels of TPP1 in unactivated microglia enhanced fibrillar {beta}-amyloid degradation. Conversely, reduction of TPP1 led to decreased fibrillar {beta}-amyloid degradation in activated microglia, macrophages, and other cells that degrade fibrillar {beta}-amyloid efficiently. Reduction of TPP1 in an AD model mouse using a gene-targeted hypomorphic Tpp1 allele increased plaque burden. These results suggest that decreased TPP1 potentiates AD pathogenesis and that strategies to increase TPP1 activity may have therapeutic value. Highlights*In microglia, TPP1 is important for the degradation of fibrillar {beta}-amyloid. *Increased TPP1 in microglia results in enhanced fibrillar {beta}-amyloid degradation. *In an AD mouse model, reduction of TPP1 led to increased amyloid plaque deposition.

cell biology↗

bioRxiv2019

Liquid-Liquid Phase Separation is Driven by Large-Scale Conformational Unwinding and Fluctuations of Intrinsically Disordered Protein Molecules

Liquid-liquid phase separation occurs via a multitude of transient, non-covalent, intermolecular interactions resulting in phase transition of intrinsically disordered proteins/regions (IDPs/IDRs) and other biopolymers into mesoscopic, dynamic, non-stoichiometric, supramolecular condensates. IDPs resemble associative polymers possessing stereospecific \"stickers\" and flexible \"spacers\" that govern the transient chain-chain interactions and fluidity in phase-separated liquid droplets. However, the fundamental molecular origin of phase separation remains elusive. Here we present a unique case to demonstrate that unusual conformational expansion events coupled with solvation and fluctuations drive phase separation of tau, an IDP associated with Alzheimers disease. Using intramolecular excimer emission as a powerful proximity readout, we show the unraveling of polypeptide chains within the protein-rich interior environment that can promote critical interchain contacts. Using highly-sensitive picosecond time-resolved fluorescence depolarization measurements, we directly capture rapid large-amplitude torsional fluctuations in the extended chains that can control the relay of making-and-breaking of noncovalent intermolecular contacts maintaining the internal fluidity. Our observations, together with the existing polymer theories, suggest that such an orchestra of concerted molecular shapeshifting events involving chain expansion, solvation, and fluctuations can provide additional favorable free energies to overcome the entropy of mixing term during phase separation. The interplay of these key molecular parameters can also be of prime importance in modulating the mesoscale material property of liquid-like condensates and their maturation of into pathological gel-like and solid-like aggregates.

biophysics↗

bioRxiv2019

Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture

The proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-heritability reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.

genomics↗

bioRxiv2018

Drug-target interaction prediction using Doubly Graph Regularized Matrix Completion

The identification of interactions between drugs and target proteins is crucial in pharmaceutical sciences. The experimental validation of interactions in genomic drug discovery is laborious and expensive; hence, there is a need for efficient and accurate in-silico techniques which can predict potential drug-target interactions to narrow down the search space for experimental verification.\n\nIn this work, we propose a new framework, namely, Multi Graph Regularized Nuclear Norm Minimization, which predicts the interactions between drugs and proteins from three inputs: known drug-target interaction network, similarities over drugs and those over targets. The proposed method focuses on finding a low-rank interaction matrix that is structured by the proximities of drugs and targets encoded by graphs. Previous works on Drug Target Interaction (DTI) prediction have shown that incorporating drug and target similarities helps in learning the data manifold better by preserving the local geometries of the original data. But, there is no clear consensus on which kind and what combination of similarities would best assist the prediction task. Hence, we propose to use various multiple drug-drug similarities and target-target similarities as multiple graph Laplacian (over drugs/targets) regularization terms to capture the proximities exhaustively.\n\nExtensive cross-validation experiments on four benchmark datasets using standard evaluation metrics (AUPR and AUC) show that the proposed algorithm improves the predictive performance and outperforms recent state-of-the-art computational methods by a large margin.\n\nAuthor summaryThis work introduces a computational approach, namely Multi-Graph Regularized Nuclear Norm Minimization (MGRNNM), to predict potential interactions between drugs and targets. The novelty of MGRNNM lies in structuring drug-target interactions by multiple proximities of drugs and targets. There have been previous works which have graph regularized Matrix factorization and Matrix completion algorithms to incorporate the standard chemical structure drug similarity and genomic sequence target protein similarity, respectively. We introduce multiple drug-graph laplacian and target-graph laplacian regularization terms to the standard matrix completion framework to predict the missing values in the interaction matrix. The graph Laplacian terms are constructed from various kinds and combinations of similarities over drugs and targets (computed from the interaction matrix itself). In addition to this, we further improve the prediction accuracy by sparsifying the drug and target similarity matrices, respectively. For performance evaluation, we conducted extensive experiments on four benchmark datasets. The experimental results demonstrated that MGRNNM clearly outperforms recent state-of-the-art methods under three different cross-validation settings, in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPR).

bioinformatics↗

bioRxiv2018

deepMc: deep Matrix Completion for imputation of single cell RNA-seq data

Single cell RNA-seq has fueled discovery and innovation in medicine over the past few years and is useful for studying cellular responses at individual cell resolution. But, due to paucity of starting RNA, the data acquired is highly sparse. To address this, We propose a deep matrix factorization based method, deepMc, to impute missing values in gene-expression data. For the deep architecture of our approach, We draw our motivation from great success of deep learning in solving various Machine learning problems. In this work, We support our method with positive results on several evaluation metrics like clustering of cell populations, differential expression analysis and cell type separability.

bioinformatics↗

bioRxiv2018

McImpute: Matrix completion based imputation for single cell RNA-seq data

MotivationSingle cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression analysis at single cell resolution, provides a window into dynamics of cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified.\n\nResultsWe introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, performance of dimensionality reduction techniques for cell visualization and gene distribution.\n\nAvailability and Implementationhttps://github.com/aanchalMongia/McImpute_scRNAseq

bioinformatics↗

bioRxiv2018

A non-canonical lysosome biogenesis pathway generates Golgi-associated lysosomes during epidermal differentiation

Keratinocytes maintain epidermis integrity and function including physical and antimicrobial barrier through cellular differentiation. This process is predicted to be controlled by calcium ion gradient and nutritional stress. Keratinocytes undergo proteome changes during differentiation, which enhances the intracellular organelle digestion to sustain the stress conditions. However, the molecular mechanism between epidermal differentiation and organelle homeostasis is poorly understood. Here, we used primary neonatal human epidermal keratinocytes to study the link between cellular differentiation, signaling pathways and organelle turnover. Upon addition of calcium chloride (2 mM) to the culture medium, keratinocytes increased their cell size and the expression of differentiation markers. Moreover, differentiated keratinocytes showed enhanced lysosome biogenesis that was dependent on ATF6-arm of UPR signaling but independent of mTOR-MiT/TFE transcription factors. Furthermore, chemical inhibition of mTOR has increased keratinocyte differentiation and relocalized the MiT/TFE TFs to the lysosome membranes, indicating that autophagy activation promotes the epidermal differentiation. Interestingly, differentiation of keratinocytes resulted in dispersal of fragmented Golgi and lysosomes, and the later organelles showed colocalization with Golgi-tethering proteins, suggesting that these lysosomes possibly originated from Golgi, hence named as Golgi-associated lysosomes (GALs). Consistent to this prediction, inhibition of Golgi function using brefeldin A completely abolished the formation of GALs and the keratinocyte differentiation. Thus, ER stress regulates the biogenesis of GALs, which maintains keratinocyte differentiation and epidermal homeostasis.

cell biology↗

bioRxiv2018

The action of a cosmetic hair treatment on follicle function

OBJECTIVEHuman hair changes with age: fibre diameter and density decrease, hair growth slows and shedding increases. This series of controlled studies examined the effect on hair growth parameters of a new leave-on hair treatment (LOT) formulated with DynagenTM (containing hydrolysed yeast protein) and zinc salts.\n\nMETHODSHair growth data were collected from healthy women aged 18-65 years. The LOTs effect on hair growth was measured in a randomized double-blind study and in hair samples; its effect on follicle-cell proliferation was assessed by quantifying Ki67 expression in scalp biopsies. The LOTs effect on plucking force was determined in an ex vivo model. Dynagens effect on the expression of the tight-junction marker claudin-1 was analysed in cultured follicles. The effect on protease activity of zinc salts used in the LOT was examined in vitro.\n\nRESULTSHair growth rate decreased with increasing subject age. The LOT significantly increased hair growth rate, fibre diameter, bundle cross-sectional area, Ki67 expression and the plucking force required to remove hair. Dynagen significantly increased claudin-1 expression in cultured follicles. Protease activity was reduced by zinc salts.\n\nCONCLUSIONThe Dynagen-based LOT increases hair-fibre diameter, strengthens the follicular root structure and increases hair growth rate.

physiology↗

bioRxiv2018

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

neuroscience↗

bioRxiv2017

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes produces a substantially better accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected nine independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis, and Peripheral Vascular Disease. The GERA cohort analysis suggests that CPBayes is more powerful than ASSET with respect to detecting independent pleiotropic variants. We provide an R-package CPBayes implementing the proposed method.\n\nAuthor SummaryGenome-wide association studies (GWASs) have highlighted shared genetic susceptibility to various human diseases (pleiotropy). We propose a Bayesian meta-analysis method CPBayes that simultaneously evaluates the evidence of aggregate-level pleiotropic association and selects an optimal subset of associated traits underlying a pleiotropic signal. CPBayes analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate GWASs with or without shared subjects, cohort study for multiple traits. It performs a fully Bayesian analysis and offers various flexibilities in the inference. In addition to parameters of primary interest (e.g., the measures of overall pleiotropic association, the optimal subset of associated traits), it provides additional interesting insights into a pleiotropic signal (e.g., the trait-specific posterior probability of association, the credible interval of unknown true genetic effects). Using computer simulations and a real data application to the large Kaiser GERA cohort, we demonstrate that CPBayes offers substantially better accuracy while selecting the non-null traits compared to a well known subset-based meta analysis ASSET. In the GERA cohort analysis, CPBayes detected a larger number of independent pleiotropic variants than ASSET. We provide a user-friendly R-package CPBayes for general use.

genetics↗