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Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits.
Gamazon ER, Zwinderman AH, Cox NJ, Denys D, Derks EM
(2019) Nat Genet 51: 933-940
MeSH Terms: Algorithms, Computational Biology, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Mental Disorders, Organ Specificity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Transcriptome
Show Abstract · Added July 17, 2019
The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
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In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers.
Cai C, Fang J, Guo P, Wang Q, Hong H, Moslehi J, Cheng F
(2018) J Chem Inf Model 58: 943-956
MeSH Terms: Antineoplastic Agents, Cardiovascular System, Computational Biology, Computer Simulation, Drug Discovery, Drug-Related Side Effects and Adverse Reactions, Humans, Molecular Targeted Therapy, Myocytes, Cardiac, Pluripotent Stem Cells, Product Surveillance, Postmarketing, Safety
Show Abstract · Added October 1, 2018
Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.
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12 MeSH Terms
Integrating linear optimization with structural modeling to increase HIV neutralization breadth.
Sevy AM, Panda S, Crowe JE, Meiler J, Vorobeychik Y
(2018) PLoS Comput Biol 14: e1005999
MeSH Terms: Algorithms, Amino Acid Motifs, Antibodies, Neutralizing, Computational Biology, Epitopes, HIV Antibodies, HIV Infections, HIV-1, Humans, Linear Models, Machine Learning, Regression Analysis, Software, Support Vector Machine
Show Abstract · Added March 14, 2018
Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.
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14 MeSH Terms
Local ancestry transitions modify snp-trait associations.
Fish AE, Crawford DC, Capra JA, Bush WS
(2018) Pac Symp Biocomput 23: 424-435
MeSH Terms: Adult, African Continental Ancestry Group, Chromosomes, Human, Computational Biology, Epistasis, Genetic, European Continental Ancestry Group, Evolution, Molecular, Gene Frequency, Genetics, Population, Genome-Wide Association Study, Haplotypes, Humans, Linear Models, Models, Genetic, Polymorphism, Single Nucleotide, Recombination, Genetic
Show Abstract · Added March 14, 2018
Genomic maps of local ancestry identify ancestry transitions - points on a chromosome where recent recombination events in admixed individuals have joined two different ancestral haplotypes. These events bring together alleles that evolved within separate continential populations, providing a unique opportunity to evaluate the joint effect of these alleles on health outcomes. In this work, we evaluate the impact of genetic variants in the context of nearby local ancestry transitions within a sample of nearly 10,000 adults of African ancestry with traits derived from electronic health records. Genetic data was located using the Metabochip, and used to derive local ancestry. We develop a model that captures the effect of both single variants and local ancestry, and use it to identify examples where local ancestry transitions significantly interact with nearby variants to influence metabolic traits. In our most compelling example, we find that the minor allele of rs16890640 occuring on a European background with a downstream local ancestry transition to African ancestry results in significantly lower mean corpuscular hemoglobin and volume. This finding represents a new way of discovering genetic interactions, and is supported by molecular data that suggest changes to local ancestry may impact local chromatin looping.
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Amyloid Accumulation Drives Proteome-wide Alterations in Mouse Models of Alzheimer's Disease-like Pathology.
Savas JN, Wang YZ, DeNardo LA, Martinez-Bartolome S, McClatchy DB, Hark TJ, Shanks NF, Cozzolino KA, Lavallée-Adam M, Smukowski SN, Park SK, Kelly JW, Koo EH, Nakagawa T, Masliah E, Ghosh A, Yates JR
(2017) Cell Rep 21: 2614-2627
MeSH Terms: Alzheimer Disease, Amyloid beta-Peptides, Animals, Apolipoproteins E, Brain, Calcium Channels, Computational Biology, Female, Mass Spectrometry, Mice, Mice, Inbred C57BL, Proteome
Show Abstract · Added March 21, 2018
Amyloid beta (Aβ) peptides impair multiple cellular pathways and play a causative role in Alzheimer's disease (AD) pathology, but how the brain proteome is remodeled by this process is unknown. To identify protein networks associated with AD-like pathology, we performed global quantitative proteomic analysis in three mouse models at young and old ages. Our analysis revealed a robust increase in Apolipoprotein E (ApoE) levels in nearly all brain regions with increased Aβ levels. Taken together with prior findings on ApoE driving Aβ accumulation, this analysis points to a pathological dysregulation of the ApoE-Aβ axis. We also found dysregulation of protein networks involved in excitatory synaptic transmission. Analysis of the AMPA receptor (AMPAR) complex revealed specific loss of TARPγ-2, a key AMPAR-trafficking protein. Expression of TARPγ-2 in hAPP transgenic mice restored AMPA currents. This proteomic database represents a resource for the identification of protein alterations responsible for AD.
Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
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12 MeSH Terms
Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records.
Robinson JR, Denny JC, Roden DM, Van Driest SL
(2018) Clin Transl Sci 11: 112-122
MeSH Terms: Biological Variation, Population, Computational Biology, Drug Discovery, Drug Repositioning, Drug-Related Side Effects and Adverse Reactions, Electronic Health Records, Genome, Genome-Wide Association Study, Humans, Molecular Targeted Therapy, Pharmacogenetics, Phenotype, Polymorphism, Single Nucleotide, Treatment Outcome
Added March 14, 2018
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14 MeSH Terms
PyDREAM: high-dimensional parameter inference for biological models in python.
Shockley EM, Vrugt JA, Lopez CF
(2018) Bioinformatics 34: 695-697
MeSH Terms: Algorithms, Calibration, Computational Biology, Markov Chains, Models, Biological, Monte Carlo Method, Software, Uncertainty
Show Abstract · Added March 14, 2018
Summary - Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.
Availability and implementation - PyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM.
Contact - c.lopez@vanderbilt.edu.
Supplementary information - Supplementary data are available at Bioinformatics online.
© The Author(s) 2017. Published by Oxford University Press.
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8 MeSH Terms
Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.
Bejan CA, Angiolillo J, Conway D, Nash R, Shirey-Rice JK, Lipworth L, Cronin RM, Pulley J, Kripalani S, Barkin S, Johnson KB, Denny JC
(2018) J Am Med Inform Assoc 25: 61-71
MeSH Terms: Adverse Childhood Experiences, Child, Computational Biology, Data Mining, Electronic Health Records, Homeless Persons, Humans, Social Determinants of Health
Show Abstract · Added March 14, 2018
Objective - Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository.
Materials and Methods - We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE.
Results - word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC = 0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC = 0.95) than ACE (AUPRC = 0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being "father" (21.8%) and "mother" (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%-47.6%).
Conclusion - We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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Estimating relative mitochondrial DNA copy number using high throughput sequencing data.
Zhang P, Lehmann BD, Samuels DC, Zhao S, Zhao YY, Shyr Y, Guo Y
(2017) Genomics 109: 457-462
MeSH Terms: Breast Neoplasms, Cell Line, Tumor, Computational Biology, DNA Copy Number Variations, DNA, Mitochondrial, Data Mining, Databases, Genetic, Female, Genes, Essential, High-Throughput Nucleotide Sequencing, Humans, Mitochondria, Real-Time Polymerase Chain Reaction, Sequence Analysis, DNA, Sequence Analysis, RNA, Whole Exome Sequencing
Show Abstract · Added March 21, 2018
We hypothesize that the relative mitochondria copy number (MTCN) can be estimated by comparing the abundance of mitochondrial DNA to nuclear DNA reads using high throughput sequencing data. To test this hypothesis, we examined relative MTCN across 13 breast cancer cell lines using the RT-PCR based NovaQUANT Human Mitochondrial to Nuclear DNA Ratio Kit as the gold standard. Six distinct computational approaches were used to estimate the relative MTCN in order to compare to the RT-PCR measurements. The results demonstrate that relative MTCN correlates well with the RT-PCR measurements using exome sequencing data, but not RNA-seq data. Through analysis of copy number variants (CNVs) in The Cancer Genome Atlas, we show that the two nuclear genes used in the NovaQUANT assay to represent the nuclear genome often experience CNVs in tumor cells, questioning the accuracy of this gold-standard method when it is applied to tumor cells.
Copyright © 2017 Elsevier Inc. All rights reserved.
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16 MeSH Terms
Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.
Wei WQ, Bastarache LA, Carroll RJ, Marlo JE, Osterman TJ, Gamazon ER, Cox NJ, Roden DM, Denny JC
(2017) PLoS One 12: e0175508
MeSH Terms: Computational Biology, Electronic Health Records, Genome-Wide Association Study, Genomics, Humans, Phenotype, Polymorphism, Single Nucleotide, Software
Show Abstract · Added October 27, 2017
OBJECTIVE - To compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, the Agency for Healthcare Research and Quality Clinical Classification Software for ICD-9-CM (CCS), and manually curated "phecodes" designed to facilitate phenome-wide association studies (PheWAS) in EHRs.
METHODS AND MATERIALS - We selected 100 disease phenotypes and compared the ability of each coding system to accurately represent them without performing additional groupings. The 100 phenotypes included 25 randomly-chosen clinical phenotypes pursued in prior genome-wide association studies (GWAS) and another 75 common disease phenotypes mentioned across free-text problem lists from 189,289 individuals. We then evaluated the performance of each coding system to replicate known associations for 440 SNP-phenotype pairs.
RESULTS - Out of the 100 tested clinical phenotypes, phecodes exactly matched 83, compared to 53 for ICD-9-CM and 32 for CCS. ICD-9-CM codes were typically too detailed (requiring custom groupings) while CCS codes were often not granular enough. Among 440 tested known SNP-phenotype associations, use of phecodes replicated 153 SNP-phenotype pairs compared to 143 for ICD-9-CM and 139 for CCS. Phecodes also generally produced stronger odds ratios and lower p-values for known associations than ICD-9-CM and CCS. Finally, evaluation of several SNPs via PheWAS identified novel potential signals, some seen in only using the phecode approach. Among them, rs7318369 in PEPD was associated with gastrointestinal hemorrhage.
CONCLUSION - Our results suggest that the phecode groupings better align with clinical diseases mentioned in clinical practice or for genomic studies. ICD-9-CM, CCS, and phecode groupings all worked for PheWAS-type studies, though the phecode groupings produced superior results.
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8 MeSH Terms