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1.
Hum Mol Genet ; 31(18): 3051-3067, 2022 09 10.
Artigo em Inglês | MEDLINE | ID: mdl-35445712

RESUMO

Asians are underrepresented across many omics databases, thereby limiting the potential of precision medicine in nearly 60% of the global population. As such, there is a pressing need for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in populations of Asian ancestry. Here, we provide the first blood-based multi-omics analysis of Asian pregnant women, constituting high-resolution genotyping (N = 1079), DNA methylation (N = 915) and transcriptome profiling (N = 238). Integrative omics analysis identified 219 154 CpGs associated with cis-DNA methylation QTLs (meQTLs) and 3703 RNAs associated with cis-RNA expression QTLs (eQTLs). Ethnicity was the largest contributor of inter-individual variation across all omics datasets, with 2561 genes identified as hotspots of this variation; 395 of these hotspot genes also contained both ethnicity-specific eQTLs and meQTLs. Gene set enrichment analysis of these ethnicity QTL hotspots showed pathways involved in lipid metabolism, adaptive immune system and carbohydrate metabolism. Pathway validation by profiling the lipidome (~480 lipids) of antenatal plasma (N = 752) and placenta (N = 1042) in the same cohort showed significant lipid differences among Chinese, Malay and Indian women, validating ethnicity-QTL gene effects across different tissue types. To develop deeper insights into the complex traits and benefit future precision medicine research in Asian pregnant women, we developed iMOMdb, an open-access database.


Assuntos
Gestantes , Locos de Características Quantitativas , Povo Asiático/genética , Feminino , Humanos , Lipídeos , Gravidez , Locos de Características Quantitativas/genética , RNA
2.
Neuroimage ; 173: 57-71, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29448075

RESUMO

Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html.


Assuntos
Interpretação Estatística de Dados , Modelos Estatísticos , Neuroimagem/métodos , Simulação por Computador , Estudo de Associação Genômica Ampla/métodos , Humanos , Modelos Lineares , Estudos Longitudinais
3.
BMC Med ; 15(1): 211, 2017 12 05.
Artigo em Inglês | MEDLINE | ID: mdl-29202839

RESUMO

BACKGROUND: Epigenomes are tissue specific and thus the choice of surrogate tissue can play a critical role in interpreting neonatal epigenome-wide association studies (EWAS) and in their extrapolation to target tissue. To develop a better understanding of the link between tissue specificity and neonatal EWAS, and the contributions of genotype and prenatal factors, we compared genome-wide DNA methylation of cord tissue and cord blood, two of the most accessible surrogate tissues at birth. METHODS: In 295 neonates, DNA methylation was profiled using Infinium HumanMethylation450 beadchip arrays. Sites of inter-individual variability in DNA methylation were mapped and compared across the two surrogate tissues at birth, i.e., cord tissue and cord blood. To ascertain the similarity to target tissues, DNA methylation profiles of surrogate tissues were compared to 25 primary tissues/cell types mapped under the Epigenome Roadmap project. Tissue-specific influences of genotype on the variable CpGs were also analyzed. Finally, to interrogate the impact of the in utero environment, EWAS on 45 prenatal factors were performed and compared across the surrogate tissues. RESULTS: Neonatal EWAS results were tissue specific. In comparison to cord blood, cord tissue showed higher inter-individual variability in the epigenome, with a lower proportion of CpGs influenced by genotype. Both neonatal tissues were good surrogates for target tissues of mesodermal origin. They also showed distinct phenotypic associations, with effect sizes of the overlapping CpGs being in the same order of magnitude. CONCLUSIONS: The inter-relationship between genetics, prenatal factors and epigenetics is tissue specific, and requires careful consideration in designing and interpreting future neonatal EWAS. TRIAL REGISTRATION: This birth cohort is a prospective observational study, designed to study the developmental origins of health and disease, and was retrospectively registered on 1 July 2010 under the identifier NCT01174875 .


Assuntos
Metilação de DNA , Sangue Fetal , Estudo de Associação Genômica Ampla , Cordão Umbilical , Ilhas de CpG , DNA , Epigênese Genética , Feminino , Genótipo , Idade Gestacional , Humanos , Recém-Nascido , Masculino , Gravidez , Estudos Prospectivos , Adulto Jovem
5.
Gigascience ; 132024 01 02.
Artigo em Inglês | MEDLINE | ID: mdl-38991852

RESUMO

BACKGROUND: Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions. FINDINGS: The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements. CONCLUSIONS: In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.


Assuntos
Perfilação da Expressão Gênica , Software , Transcriptoma , Análise por Conglomerados , Perfilação da Expressão Gênica/métodos , Humanos , Biologia Computacional/métodos , Aprendizado de Máquina , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de RNA/métodos , Algoritmos
6.
Sci Transl Med ; 10(453)2018 08 08.
Artigo em Inglês | MEDLINE | ID: mdl-30089632

RESUMO

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.


Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Mieloma Múltiplo/tratamento farmacológico , Animais , Protocolos de Quimioterapia Combinada Antineoplásica/farmacologia , Bortezomib/farmacologia , Bortezomib/uso terapêutico , Linhagem Celular Tumoral , Inibidor de Quinase Dependente de Ciclina p21/genética , Inibidor de Quinase Dependente de Ciclina p21/metabolismo , Dano ao DNA , Metilação de DNA/genética , Decitabina/farmacologia , Decitabina/uso terapêutico , Relação Dose-Resposta a Droga , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Resistencia a Medicamentos Antineoplásicos/genética , Sinergismo Farmacológico , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Genes Supressores de Tumor , Ensaios de Triagem em Larga Escala , Humanos , Camundongos , Mitomicina/farmacologia , Mitomicina/uso terapêutico , Mieloma Múltiplo/genética , Mieloma Múltiplo/patologia , Fenótipo , Proteína Tirosina Fosfatase não Receptora Tipo 6/genética , Proteína Tirosina Fosfatase não Receptora Tipo 6/metabolismo , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Carga Tumoral/efeitos dos fármacos
7.
Psychol Rep ; 100(1): 35-46, 2007 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-17451001

RESUMO

To evaluate psychometric properties the Chinese (mainland) version of the 30-item Job Content Questionnaire was administered to 889 employees in 4 industries of PetroChina. A retest at 3 months with 296 randomly chosen employees showed reliabilities ranged from .76 to .93. Cronbach coefficients alpha for the 8 dimensions ranged from .43 to .88, indicating that the Job Insecurity subscale had low internal consistency. Exploratory factor analysis showed 8 meaningful factors corresponding to the 8 theoretical dimensions of this questionnaire. This version has variable a but suitable retest to be a reliable and valid measure of job strain, applicable to the Chinese industrial working population of PetroChina.


Assuntos
Povo Asiático , Indústria Química , Emprego/estatística & dados numéricos , Satisfação no Emprego , Petróleo , Inquéritos e Questionários , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes
8.
PLoS One ; 12(4): e0174925, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28406915

RESUMO

BACKGROUND: Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database. METHODS: In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors. RESULTS: A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class. CONCLUSIONS: Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.


Assuntos
Algoritmos , Esclerose Lateral Amiotrófica/mortalidade , Bases de Dados Factuais , Modelos Biológicos , Idoso , Intervalo Livre de Doença , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Taxa de Sobrevida
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