Dataset Information

Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers.

ABSTRACT: BACKGROUND:Environmental Enteropathy (EE) is a subclinical condition caused by constant fecal-oral contamination and resulting in blunting of intestinal villi and intestinal inflammation. Of primary interest in the clinical research is to evaluate the association between non-invasive EE biomarkers and malnutrition in a cohort of Bangladeshi children. The challenges are that the number of biomarkers/covariates is relatively large, and some of them are highly correlated. METHODS:Many variable selection methods are available in the literature, but which are most appropriate for EE biomarker selection remains unclear. In this study, different variable selection approaches were applied and the performance of these methods was assessed numerically through simulation studies, assuming the correlations among covariates were similar to those in the Bangladesh cohort. The suggested methods from simulations were applied to the Bangladesh cohort to select the most relevant biomarkers for the growth response, and bootstrapping methods were used to evaluate the consistency of selection results. RESULTS:Through simulation studies, SCAD (Smoothly Clipped Absolute Deviation), Adaptive LASSO (Least Absolute Shrinkage and Selection Operator) and MCP (Minimax Concave Penalty) are the suggested variable selection methods, compared to traditional stepwise regression method. In the Bangladesh data, predictors such as mother weight, height-for-age z-score (HAZ) at week 18, and inflammation markers (Myeloperoxidase (MPO) at week 12 and soluable CD14 at week 18) are informative biomarkers associated with children's growth. CONCLUSIONS:Penalized linear regression methods are plausible alternatives to traditional variable selection methods, and the suggested methods are applicable to other biomedical studies. The selected early-stage biomarkers offer a potential explanation for the burden of malnutrition problems in low-income countries, allow early identification of infants at risk, and suggest pathways for intervention. TRIAL REGISTRATION:This study was retrospectively registered with ClinicalTrials.gov, number NCT01375647, on June 3, 2011.

SUBMITTER: Lu M

PROVIDER: S-EPMC5345248 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers.

Lu Miao M Zhou Jianhui J Naylor Caitlin C Kirkpatrick Beth D BD Haque Rashidul R Petri William A WA Ma Jennie Z JZ

Biomarker research 20170309

<h4>Background</h4>Environmental Enteropathy (EE) is a subclinical condition caused by constant fecal-oral contamination and resulting in blunting of intestinal villi and intestinal inflammation. Of primary interest in the clinical research is to evaluate the association between non-invasive EE biomarkers and malnutrition in a cohort of Bangladeshi children. The challenges are that the number of biomarkers/covariates is relatively large, and some of them are highly correlated.<h4>Methods</h4>Man ...[more]

PMID: 28293424

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Project description:Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whom expression levels were associated with both SNPs and GPGs. Importantly, we replicated results for 36 (75%) of them in an independent data set (TCGA). We checked the performance of the proposed method with a simulation study and further supported our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexibly and easy to implement when analyzing several omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complexity of disease genetic mechanisms. Gene expression data were obtained from 44 tumor samples using the Affymetrix DNA Microarray Human Gene 1.0 ST Array with 32,321 probes.

Project description:Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whom expression levels were associated with both SNPs and GPGs. Importantly, we replicated results for 36 (75%) of them in an independent data set (TCGA). We checked the performance of the proposed method with a simulation study and further supported our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexibly and easy to implement when analyzing several omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complexity of disease genetic mechanisms. Bisulphite modification of 46 tumor DNA samples using EZ-96 DNA METHYLATIONGOLD KIT (Zymo Research, Irvin, CA, USA), CpG methylation data was generated using the Infinum Human Methylation 27 BeadChip Kit that detected the CpG sites with two probes, one designed against the unmethylated site (signal U) and the other against the methylated site (signal M).

Dataset Information

Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers.

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Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers.

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