Analysis of high dimensional gene data combining correlation principal component regression and additive risk model

2008 ◽  
Author(s):  
Yichuan Zhao ◽  
Guoshen Wang
Keyword(s):  
Risk Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
High Dimensional ◽  
Additive Risk Model ◽  
Additive Risk ◽  
Gene Data

ADDITIVE RISK ANALYSIS OF MICROARRAY GENE EXPRESSION DATA VIA CORRELATION PRINCIPAL COMPONENT REGRESSION

2010 ◽  
Vol 08 (04) ◽  
pp. 645-659 ◽  
Author(s):  
YICHUAN ZHAO ◽  
GUOSHEN WANG
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Risk Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Data Set ◽  
Additive Risk

In order to predict future patients' survival time based on their microarray gene expression data, one interesting question is how to relate genes to survival outcomes. In this paper, by applying a semi-parametric additive risk model in survival analysis, we propose a new approach to conduct a careful analysis of gene expression data with the focus on the model's predictive ability. In the proposed method, we apply the correlation principal component regression to deal with right censoring survival data under the semi-parametric additive risk model frame with high-dimensional covariates. We also employ the time-dependent area under the receiver operating characteristic curve and root mean squared error for prediction to assess how well the model can predict the survival time. Furthermore, the proposed method is able to identify significant genes, which are significantly related to the disease. Finally, the proposed useful approach is illustrated by the diffuse large B-cell lymphoma data set and breast cancer data set. The results show that the model fits the data sets very well.

A Partly Parametric Additive Risk Model

1993 ◽  
Author(s):  
Ian W. McKeague ◽  
Peter D. Sasieni
Keyword(s):  
Risk Model ◽  
Additive Risk Model ◽  
Additive Risk

scholarly journals A Likelihood Ratio Test for Gene-Environment Interaction Based on the Trend Effect of Genotype Under an Additive Risk Model Using the Gene-Environment Independence Assumption

2020 ◽  
Vol 190 (1) ◽  
pp. 129-141
Author(s):  
Matthieu de Rochemonteix ◽  
Valerio Napolioni ◽  
Nilotpal Sanyal ◽  
Michaël E Belloy ◽  
Neil E Caporaso ◽  
...  
Keyword(s):  
Apolipoprotein E ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Risk Model ◽  
Ratio Test ◽  
Independence Assumption ◽  
Additive Risk Model ◽  
Gene Environment ◽  
Genome Wide ◽  
Additive Risk

Abstract Several statistical methods have been proposed for testing gene-environment (G-E) interactions under additive risk models using data from genome-wide association studies. However, these approaches have strong assumptions from underlying genetic models, such as dominant or recessive effects that are known to be less robust when the true genetic model is unknown. We aimed to develop a robust trend test employing a likelihood ratio test for detecting G-E interaction under an additive risk model, while incorporating the G-E independence assumption to increase power. We used a constrained likelihood to impose 2 sets of constraints for: 1) the linear trend effect of genotype and 2) the additive joint effects of gene and environment. To incorporate the G-E independence assumption, a retrospective likelihood was used versus a standard prospective likelihood. Numerical investigation suggests that the proposed tests are more powerful than tests assuming dominant, recessive, or general models under various parameter settings and under both likelihoods. Incorporation of the independence assumption enhances efficiency by 2.5-fold. We applied the proposed methods to examine the gene-smoking interaction for lung cancer and gene–apolipoprotein E $\varepsilon$4 interaction for Alzheimer disease, which identified 2 interactions between apolipoprotein E $\varepsilon$4 and loci membrane-spanning 4-domains subfamily A (MS4A) and bridging integrator 1 (BIN1) genes at genome-wide significance that were replicated using independent data.

The Risk of Neck, Shoulder and Upper Limb Musculoskeletal Disorders due to Interactions between Physical and Psychosocial Work Risk Factors

2000 ◽  
Vol 44 (30) ◽  
pp. 5-597-5-597 ◽  
Author(s):  
J. J. Devereux ◽  
P.W. Buckle
Keyword(s):  
Risk Factors ◽  
Upper Limb ◽  
Risk Model ◽  
Psychosocial Risk Factors ◽  
Psychosocial Risk ◽  
Additive Risk Model ◽  
Additive Risk ◽  
Upper Limb Disorders ◽  
Customer Services ◽  
Self Administered Questionnaire

Objectives - To investigate the possible interactions between physical and psychosocial risk factors in the workplace that may be associated with self-reported neck and upper-limb musculoskeletal disorder symptoms. Methods - 891 of 1514 manual handlers, delivery drivers, technicians, customer services computer operators and general office staff reported physical and psychosocial working conditions and neck and upper-limb disorder symptoms using a self-administered questionnaire (59% return rate). Of the 869 valid questionnaire respondents, 564 individual workers were classified in to one of four exposure groups: high physical - high psychosocial, high physical - low psychosocial, low physical - high psychosocial and low physical - low psychosocial. Results - The highest increase in risk was found in the high physical - high psychosocial exposure group for upper limb disorders. In the analyses, a departure from an additive risk model was observed for the upper-limb outcome definitions but not for those of the neck. Conclusions - This study suggests that an interaction effect between physical and psychosocial risk factors in the workplace may exist to increase the risk of self-reported upper-limb disorders.

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