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2.
Clin Epigenetics ; 11(1): 122, 2019 Aug 23.
Artigo em Inglês | MEDLINE | ID: mdl-31443688

RESUMO

BACKGROUND: Although epigenetic mechanisms are important risk factors for allergic disease, few studies have evaluated DNA methylation differences associated with atopic dermatitis (AD), and none has focused on AD with eczema herpeticum (ADEH+). We will determine how methylation varies in AD individuals with/without EH and associated traits. We modeled differences in genome-wide DNA methylation in whole blood cells from 90 ADEH+, 83 ADEH-, and 84 non-atopic, healthy control subjects, replicating in 36 ADEH+, 53 ADEH-, and 55 non-atopic healthy control subjects. We adjusted for cell-type composition in our models and used genome-wide and candidate-gene approaches. RESULTS: We replicated one CpG which was significantly differentially methylated by severity, with suggestive replication at four others showing differential methylation by phenotype or severity. Not adjusting for eosinophil content, we identified 490 significantly differentially methylated CpGs (ADEH+ vs healthy controls, genome-wide). Many of these associated with severity measures, especially eosinophil count (431/490 sites). CONCLUSIONS: We identified a CpG in IL4 associated with serum tIgE levels, supporting a role for Th2 immune mediating mechanisms in AD. Changes in eosinophil level, a measure of disease severity, are associated with methylation changes, providing a potential mechanism for phenotypic changes in immune response-related traits.

4.
Nat Commun ; 10(1): 880, 2019 02 20.
Artigo em Inglês | MEDLINE | ID: mdl-30787307

RESUMO

Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations.


Assuntos
Afro-Americanos/genética , Asma/genética , Predisposição Genética para Doença/genética , Asma/epidemiologia , Cromossomos Humanos Par 12/genética , Cromossomos Humanos Par 17/genética , Cromossomos Humanos Par 8/genética , Loci Gênicos , Estudo de Associação Genômica Ampla , Hispano-Americanos/genética , Humanos , Polimorfismo de Nucleotídeo Único/genética , Estados Unidos/epidemiologia
6.
Nat Genet ; 2018 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-30455414

RESUMO

We used a deeply sequenced dataset of 910 individuals, all of African descent, to construct a set of DNA sequences that is present in these individuals but missing from the reference human genome. We aligned 1.19 trillion reads from the 910 individuals to the reference genome (GRCh38), collected all reads that failed to align, and assembled these reads into contiguous sequences (contigs). We then compared all contigs to one another to identify a set of unique sequences representing regions of the African pan-genome missing from the reference genome. Our analysis revealed 296,485,284 bp in 125,715 distinct contigs present in the populations of African descent, demonstrating that the African pan-genome contains ~10% more DNA than the current human reference genome. Although the functional significance of nearly all of this sequence is unknown, 387 of the novel contigs fall within 315 distinct protein-coding genes, and the rest appear to be intergenic.

7.
PLoS One ; 12(4): e0175860, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28437440

RESUMO

Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85-100%). We further implement Synthesize in an open source web application, Synthesizer (https://github.com/lisagandy/synthesizer). The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Armazenamento e Recuperação da Informação/métodos , Software , Algoritmos , Bases de Dados Factuais
8.
Bioinformatics ; 30(19): 2757-63, 2014 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-24907368

RESUMO

MOTIVATION: Sample source, procurement process and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intragroup biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch-corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori. RESULTS: Therefore, we assess the extent to which various batch correction algorithms remove true biological heterogeneity. We also introduce an algorithm, permuted-SVA (pSVA), using a new statistical model that is blind to biological covariates to correct for technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin-fixed and frozen samples. When applied to predict Human Papillomavirus (HPV) status, pSVA improved cross-study validation even if the sample batches were highly confounded with HPV status in the training set. AVAILABILITY AND IMPLEMENTATION: All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.


Assuntos
Algoritmos , Genômica/métodos , Neoplasias de Cabeça e Pescoço/genética , Infecções por Papillomavirus/diagnóstico , Artefatos , Biologia Computacional/métodos , Neoplasias de Cabeça e Pescoço/virologia , Humanos , Modelos Estatísticos , Reprodutibilidade dos Testes , Software
9.
Bioinformation ; 7(2): 76-81, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21938209

RESUMO

Antigenic drift and shift involving the surface proteins of Influenza virus gave rise to new strains that caused epidemics affecting millions of people worldwide over the last hundred years. Variations in the membrane proteins like Hemagglutinin (HA) and Neuraminidase (NA) necessitates new vaccine strains to be updated frequently and poses challenge to effective vaccine design. Though the HA protein, the primary target of the human immune system, has been well studied, reports on the antigenic variability in the other membrane protein NA are sparse. In this paper we investigate the molecular basis of antigenic drift in the NA protein of the Influenza A/H3N2 vaccine strains between 1968 and 2009 and proceed to establish correlation between antigenic drift and antigen-antibody interactions. Sequence alignments and phylogenetic analyses were carried out and the antigenic variability was evaluated in terms of antigenic distance. To study the effects of antigenic drift on the protein structures, 3D structure of NA from various strains were predicted. Also, rigid body docking protocol has been used to study the interactions between these NA proteins and antibody Mem5, a 1998 antibody.

10.
Bioinformation ; 6(7): 266-70, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21738327

RESUMO

The emergence of new strains of Influenza virus have caused several pandemics over the last hundred years with the latest being the H1N1 Swine flu pandemic of 2009. The Hemagglutinin (HA) protein of the Influenza virus is the primary target of human immune system and is responsible for generation of protective antibodies in humans. Mutations in this protein results in change in antigenic regions (antigenic drift) which consequently leads to loss of immunity in hosts even in vaccinated population (herd immunity). This necessitates periodic changes in the Influenza vaccine composition. In this paper, we investigate the molecular basis of the reported loss of herd immunity in vaccinated population (vaccine component: Influenza A/X-31/1968 (H3N2)) which resulted in the outbreak due to strain Influenza A/Port Chalmers/1/1973 (H3N2). Also, the effects of antigenic drift in HA protein (H3N2 vaccine strains 1968-2007) on the 3D structures as well as interactions with BH151, a 1968 antibody, has been studied. Rigid body molecular docking protocol has been used to study the antigen-antibody interactions. We believe that the present study will help in better understanding of host-pathogen interactions at the molecular level.

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