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Artikel ; Online: Serial Sampling of the Small Airway Epithelium to Identify Persistent Smoking-dysregulated Genes.

Strulovici-Barel, Yael / Rostami, Mahboubeh R / Kaner, Robert J / Mezey, Jason G / Crystal, Ronald G

American journal of respiratory and critical care medicine

2023 Band 208, Heft 7, Seite(n) 780–790

Abstract: Rationale: ...

Abstract	Rationale:
Mesh-Begriff(e)	Humans ; Smoking/adverse effects ; Smoking/genetics ; Smoking/metabolism ; Smoking Cessation ; Tobacco Smoking ; Transcriptome ; Epithelium/metabolism ; Tripartite Motif Proteins ; Ubiquitin-Protein Ligases/genetics ; Ubiquitin-Protein Ligases/metabolism
Chemische Substanzen	TRIM16 protein, human (EC 2.3.2.27) ; Tripartite Motif Proteins ; Ubiquitin-Protein Ligases (EC 2.3.2.27)
Sprache	Englisch
Erscheinungsdatum	2023-08-01
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1180953-x
ISSN	1535-4970 ; 0003-0805 ; 1073-449X
ISSN (online)	1535-4970
ISSN	0003-0805 ; 1073-449X
DOI	10.1164/rccm.202204-0786OC
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes.

Shafquat, Afrah / Crystal, Ronald G / Mezey, Jason G

BMC bioinformatics

2020 Band 21, Heft 1, Seite(n) 178

Abstract: Background: Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well ... ...

Abstract	Background: Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification. Results: Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation. Conclusion: PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.
Mesh-Begriff(e)	Algorithms ; Area Under Curve ; Bayes Theorem ; Bipolar Disorder/genetics ; Computer Simulation ; Databases, Genetic ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Genotype ; Humans ; Phenotype ; Polymorphism, Single Nucleotide ; ROC Curve
Sprache	Englisch
Erscheinungsdatum	2020-05-07
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-020-3387-z
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Predicted deleterious variants in the human genome relevant to gene therapy with adeno-associated virus vectors.

Rostami, Mahboubeh R / Leopold, Philip L / Vasquez, Jenifer M / de Mulder Rougvie, Miguel / Al Shakaki, Alya / Hssain, Ali Ait / Robay, Amal / Hackett, Neil R / Mezey, Jason G / Crystal, Ronald G

Molecular therapy. Methods & clinical development

2023 Band 31, Seite(n) 101136

Abstract: Based on the observation that humans have variable responses of gene expression with the same dose of an adeno-associated vector, we hypothesized that there are deleterious variants in genes coding for processes required for adeno-associated virus (AAV)- ... ...

Abstract	Based on the observation that humans have variable responses of gene expression with the same dose of an adeno-associated vector, we hypothesized that there are deleterious variants in genes coding for processes required for adeno-associated virus (AAV)-mediated gene transfer/expression that may hamper or enhance the effectiveness of AAV-mediated gene therapy. To assess this hypothesis, we evaluated 69,442 whole genome sequences from three populations (European, African/African American, and Qatari) for predicted deleterious variants in 62 genes known to play a role in AAV-mediated gene transfer/expression. The analysis identified 5,564 potentially deleterious mutations of which 27 were classified as common based on an allele frequency ≥1% in at least one population studied. Many of these deleterious variants are predicated to prevent while others enhance effective AAV gene transfer/expression, and several are linked to known hereditary disorders. The data support the hypothesis that, like other drugs, human genetic variability contributes to the person-to-person effectiveness of AAV gene therapy and the screening for genetic variability should be considered as part of future clinical trials.
Sprache	Englisch
Erscheinungsdatum	2023-10-13
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2872938-9
ISSN	2329-0501 ; 2329-0501
ISSN (online)	2329-0501
ISSN	2329-0501
DOI	10.1016/j.omtm.2023.101136
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

Ju, Jin Hyun / Shenoy, Sushila A / Crystal, Ronald G / Mezey, Jason G

PLoS computational biology

2017 Band 13, Heft 5, Seite(n) e1005537

Abstract: Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, ... ...

Abstract	Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.
Mesh-Begriff(e)	Algorithms ; Anxiety/genetics ; Computational Biology/methods ; Databases, Genetic ; Depression/genetics ; Gene Regulatory Networks ; Genome-Wide Association Study/methods ; Humans ; Models, Genetic ; Models, Statistical ; Netherlands ; Quantitative Trait Loci/genetics
Sprache	Englisch
Erscheinungsdatum	2017-05-15
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1005537
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Reply to Sharma and Zeki: Does Vaping Increase Susceptibility to COVID-19?

Zhang, Haijun / Rostamim, Mahboubeh R / Leopold, Philip L / Mezey, Jason G / O'Beirne, Sarah L / Strulovici-Barel, Yael / Crystal, Ronald G

American journal of respiratory and critical care medicine

2020 Band 202, Heft 7, Seite(n) 1056–1057

Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2020-09-01
Erscheinungsland	United States
Dokumenttyp	Letter ; Comment
ZDB-ID	1180953-x
ISSN	1535-4970 ; 0003-0805 ; 1073-449X
ISSN (online)	1535-4970
ISSN	0003-0805 ; 1073-449X
DOI	10.1164/rccm.202006-2351LE
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Expression of the SARS-CoV-2

Zhang, Haijun / Rostami, Mahboubeh R / Leopold, Philip L / Mezey, Jason G / O'Beirne, Sarah L / Strulovici-Barel, Yael / Crystal, Ronald G

American journal of respiratory and critical care medicine

2020 Band 202, Heft 2, Seite(n) 219–229

Abstract: Rationale: ...

Abstract	Rationale:
Mesh-Begriff(e)	Angiotensin-Converting Enzyme 2 ; Betacoronavirus ; COVID-19 ; Case-Control Studies ; Coronavirus Infections/metabolism ; Female ; Humans ; Lung/metabolism ; Male ; Pandemics ; Peptidyl-Dipeptidase A/genetics ; Peptidyl-Dipeptidase A/metabolism ; Pneumonia, Viral/metabolism ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Respiratory Mucosa/metabolism ; SARS-CoV-2 ; Sex Factors ; Smoking/metabolism ; Trachea/metabolism
Chemische Substanzen	RNA, Messenger ; Peptidyl-Dipeptidase A (EC 3.4.15.1) ; ACE2 protein, human (EC 3.4.17.23) ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23)
Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2020-05-19
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	1180953-x
ISSN	1535-4970 ; 0003-0805 ; 1073-449X
ISSN (online)	1535-4970
ISSN	0003-0805 ; 1073-449X
DOI	10.1164/rccm.202003-0541OC
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R.

Hoffman, Gabriel E / Mezey, Jason G / Schadt, Eric E

Bioinformatics (Oxford, England)

2014 Band 30, Heft 21, Seite(n) 3134–3135

Abstract: Unlabelled: The linear mixed model is the state-of-the-art method to account for the confounding effects of kinship and population structure in genome-wide association studies (GWAS). Current implementations test the effect of one or more genetic ... ...

Abstract	Unlabelled: The linear mixed model is the state-of-the-art method to account for the confounding effects of kinship and population structure in genome-wide association studies (GWAS). Current implementations test the effect of one or more genetic markers while including prespecified covariates such as sex. Here we develop an efficient implementation of the linear mixed model that allows composite hypothesis tests to consider genotype interactions with variables such as other genotypes, environment, sex or ancestry. Our R package, lrgpr, allows interactive model fitting and examination of regression diagnostics to facilitate exploratory data analysis in the context of the linear mixed model. By leveraging parallel and out-of-core computing for datasets too large to fit in main memory, lrgpr is applicable to large GWAS datasets and next-generation sequencing data. Availability and implementation: lrgpr is an R package available from lrgpr.r-forge.r-project.org.
Mesh-Begriff(e)	Genome-Wide Association Study/methods ; Genotype ; Linear Models ; Software
Sprache	Englisch
Erscheinungsdatum	2014-07-16
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btu435
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: PUMA: a unified framework for penalized multiple regression analysis of GWAS data.

Hoffman, Gabriel E / Logsdon, Benjamin A / Mezey, Jason G

PLoS computational biology

2013 Band 9, Heft 6, Seite(n) e1003101

Abstract: Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association ... ...

Abstract	Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns's disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn's disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.
Mesh-Begriff(e)	Genome-Wide Association Study ; Humans ; Models, Theoretical ; Regression Analysis
Sprache	Englisch
Erscheinungsdatum	2013-06-27
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1003101
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Smoking shifts human small airway epithelium club cells toward a lesser differentiated population.

Rostami, Mahboubeh R / LeBlanc, Michelle G / Strulovici-Barel, Yael / Zuo, Wulin / Mezey, Jason G / O'Beirne, Sarah L / Kaner, Robert J / Leopold, Philip L / Crystal, Ronald G

NPJ genomic medicine

2021 Band 6, Heft 1, Seite(n) 73

Abstract: The club cell, a small airway epithelial (SAE) cell, plays a central role in human lung host defense. We hypothesized that subpopulations of club cells with distinct functions may exist. The SAE of healthy nonsmokers and healthy cigarette smokers were ... ...

Abstract	The club cell, a small airway epithelial (SAE) cell, plays a central role in human lung host defense. We hypothesized that subpopulations of club cells with distinct functions may exist. The SAE of healthy nonsmokers and healthy cigarette smokers were evaluated by single-cell RNA sequencing, and unsupervised clustering revealed subpopulations of SCGCB1A1
Sprache	Englisch
Erscheinungsdatum	2021-09-08
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2813848-X
ISSN	2056-7944 ; 2056-7944
ISSN (online)	2056-7944
ISSN	2056-7944
DOI	10.1038/s41525-021-00237-1
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Mouse obesity network reconstruction with a variational Bayes algorithm to employ aggressive false positive control

Logsdon Benjamin A / Hoffman Gabriel E / Mezey Jason G

BMC Bioinformatics, Vol 13, Iss 1, p

2012 Band 53

Abstract: Abstract Background We propose a novel variational Bayes network reconstruction algorithm to extract the most relevant disease factors from high-throughput genomic data-sets. Our algorithm is the only scalable method for regularized network recovery that ...

Abstract	Abstract Background We propose a novel variational Bayes network reconstruction algorithm to extract the most relevant disease factors from high-throughput genomic data-sets. Our algorithm is the only scalable method for regularized network recovery that employs Bayesian model averaging and that can internally estimate an appropriate level of sparsity to ensure few false positives enter the model without the need for cross-validation or a model selection criterion. We use our algorithm to characterize the effect of genetic markers and liver gene expression traits on mouse obesity related phenotypes, including weight, cholesterol, glucose, and free fatty acid levels, in an experiment previously used for discovery and validation of network connections: an F2 intercross between the C57BL/6 J and C3H/HeJ mouse strains, where apolipoprotein E is null on the background. Results We identified eleven genes, Gch1, Zfp69, Dlgap1, Gna14, Yy1, Gabarapl1, Folr2, Fdft1, Cnr2, Slc24a3, and Ccl19, and a quantitative trait locus directly connected to weight, glucose, cholesterol, or free fatty acid levels in our network. None of these genes were identified by other network analyses of this mouse intercross data-set, but all have been previously associated with obesity or related pathologies in independent studies. In addition, through both simulations and data analysis we demonstrate that our algorithm achieves superior performance in terms of power and type I error control than other network recovery algorithms that use the lasso and have bounds on type I error control. Conclusions Our final network contains 118 previously associated and novel genes affecting weight, cholesterol, glucose, and free fatty acid levels that are excellent obesity risk candidates.
Schlagwörter	Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Thema/Rubrik (Code)	006
Sprache	Englisch
Erscheinungsdatum	2012-04-01T00:00:00Z
Verlag	BMC
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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