scholarly journals High-Dimensional Mediation Analysis with Applications to Causal Gene Identification

2021 ◽  
Author(s):  
Qi Zhang
Keyword(s):  
Mediation Analysis ◽  
Gene Identification ◽  
High Dimensional ◽  
Causal Gene

scholarly journals High Dimensional Mediation Analysis with Applications to Causal Gene Identification

2018 ◽  
Author(s):  
Qi Zhang
Keyword(s):  
Mediation Analysis ◽  
Mixed Model ◽  
Adaptive Lasso ◽  
Gene Identification ◽  
Tuning Parameter ◽  
High Dimensional ◽  
Size Estimation ◽  
Mediation Model ◽  
Causal Gene ◽  
Model Based

AbstractMediation analysis has been a popular framework for elucidating the mediating mechanism of the exposure effect on the outcome. Previous literature in causal mediation primarily focused on the classical settings with univariate exposure and univariate mediator, with recent growing interests in high dimensional mediator. In this paper, we study the mediation model with high dimensional exposure and high dimensional mediator, and introduce two procedures for mediator selection, MedFix and MedMix. MedFix is our new application of adaptive lasso with one additional tuning parameter. MedMix is a novel mediation model based on high dimensional linear mixed model, for which we also develop a new variable selection algorithm. Our study is motivated by the causal gene identification problem, where causal genes are defined as the genes that mediate the genetic effect. For this problem, the genetic variants are the high dimensional exposure, the gene expressions the high dimensional mediator, and the phenotype of interest the outcome. We evaluate the proposed methods using a mouse f2 dataset for diabetes study, and extensive real data driven simulations. We show that the mixed model based approach leads to higher accuracy in mediator selection and mediation effect size estimation, and is more reproducible across independent measurements of the response and more robust against model misspecification. The source R code will be made available on Githubhttps://github.com/QiZhangStat/highMedupon the publication of this paper.

scholarly journals The mediation effect of serum metabolites on the relationship between long-term smoking exposure and esophageal squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mengke Wei ◽  
Lihong Zhao ◽  
Jiali Lv ◽  
Xia Li ◽  
Guangshuai Zhou ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cigarette Smoking ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cholic Acid ◽  
Mediation Analysis ◽  
Mediation Effect ◽  
High Dimensional ◽  
Smoking Exposure ◽  
Smoking Index

Abstract Background Long-term smoking exposure will increase the risk of esophageal squamous cell carcinoma (ESCC), whereas the mechanism is still unclear. We conducted a cross-sectional study to explore whether serum metabolites mediate the occurrence of ESCC caused by cigarette smoking. Methods Serum metabolic profiles and lifestyle information of 464 participants were analyzed. Multiple logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of smoking exposure to ESCC risk. High-dimensional mediation analysis and univariate mediation analysis were performed to screen potential intermediate metabolites of smoking exposure for ESCC. Results Ever smoking was associated with a 3.11-fold increase of ESCC risk (OR = 3.11, 95% CI 1.63–6.05), and for each cigarette-years increase in smoking index, ESCC risk increased by 56% (OR = 1.56, 95% CI 1.18–2.13). A total of 5 metabolites were screened as mediators by high-dimensional mediation analysis. In addition, glutamine, histidine, and cholic acid were further proved existing mediation effects according to univariate mediation analysis. And the proportions of mediation of histidine and glutamine were 40.47 and 30.00%, respectively. The mediation effect of cholic acid was 8.98% according to the analysis of smoking index. Conclusions Our findings suggest that cigarette smoking contributed to incident ESCC, which may be mediated by glutamine, histidine and cholic acid.

scholarly journals Identifying neural signatures mediating behavioral symptoms and psychosis onset: High-dimensional whole brain functional mediation analysis

NeuroImage ◽  
2021 ◽  
Vol 226 ◽  
pp. 117508
Author(s):  
Oliver Y. Chén ◽  
Hengyi Cao ◽  
Huy Phan ◽  
Guy Nagels ◽  
Jenna M. Reinen ◽  
...  
Keyword(s):  
Mediation Analysis ◽  
Behavioral Symptoms ◽  
High Dimensional ◽  
Whole Brain ◽  
Neural Signatures

scholarly journals High-Dimensional Mediation Analysis Based on Additive Hazards Model for Survival Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Yidan Cui ◽  
Chengwen Luo ◽  
Linghao Luo ◽  
Zhangsheng Yu
Keyword(s):  
Indirect Effect ◽  
Mediation Analysis ◽  
Survival Data ◽  
Cox Model ◽  
The Cancer Genome Atlas ◽  
High Dimensional ◽  
Additive Hazards Model ◽  
Additive Hazards ◽  
Hazards Model ◽  
Positive Rate

Mediation analysis has been extensively used to identify potential pathways between exposure and outcome. However, the analytical methods of high-dimensional mediation analysis for survival data are still yet to be promoted, especially for non-Cox model approaches. We propose a procedure including “two-step” variable selection and indirect effect estimation for the additive hazards model with high-dimensional mediators. We first apply sure independence screening and smoothly clipped absolute deviation regularization to select mediators. Then we use the Sobel test and the BH method for indirect effect hypothesis testing. Simulation results demonstrate its good performance with a higher true-positive rate and accuracy, as well as a lower false-positive rate. We apply the proposed procedure to analyze DNA methylation markers mediating smoking and survival time of lung cancer patients in a TCGA (The Cancer Genome Atlas) cohort study. The real data application identifies four mediate CpGs, three of which are newly found.

scholarly journals An integrative approach for causal gene identification and gene regulatory pathway inference

2006 ◽  
Vol 22 (14) ◽  
pp. e489-e496 ◽  
Author(s):  
Z. Tu ◽  
L. Wang ◽  
M. N. Arbeitman ◽  
T. Chen ◽  
F. Sun
Keyword(s):  
Gene Identification ◽  
Integrative Approach ◽  
Regulatory Pathway ◽  
Causal Gene ◽  
Pathway Inference ◽  
Gene Regulatory

scholarly journals Application of a novel analytical pipeline for high-dimensional multivariate mediation analysis of environmental data

2020 ◽  
Author(s):  
Max T. Aung ◽  
Yanyi Song ◽  
Kelly K. Ferguson ◽  
David E. Cantonwine ◽  
Lixia Zeng ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Confidence Interval ◽  
Dimension Reduction ◽  
Gestational Age ◽  
Mediation Analysis ◽  
Environmental Data ◽  
High Dimensional ◽  
Environmental Toxicants ◽  
Bayesian Shrinkage ◽  
The Impact

ABSTRACTDiverse toxicological mechanisms may mediate the impact of environmental toxicants (phthalates, phenols, polycyclic aromatic hydrocarbons, and metals) on pregnancy outcomes. In this study we introduce an analytical pipeline for high-dimensional mediation analysis to identify mediation pathways (q = 63 mediators) in the relationship between environmental toxicants (p = 38 analytes) and gestational age at delivery. Our analytical pipeline included: (1) conducting pairwise mediation for unique exposure-mediator combinations, (2) subjecting mediators to Bayesian shrinkage mediation analysis and population value decomposition, and (3) exposure dimension reduction by estimating environmental risk scores. Dimension reduction demonstrated that a one unit increase in phthalate risk score was associated with a total effect of 1.09 lower gestational age (in weeks) at delivery (95% confidence interval: 1.78 – 0.36) and eicosanoids from the cytochrome p450 pathway mediated 24.5% of this effect (95% confidence interval: 4%-66%). Eicosanoid products derived from the cytochrome p450 pathway may be important mediators of phthalate toxicity.

High-dimensional Causal Mediation Analysis with a Large Number of Mediators Clumping at Zero to Assess the Contribution of the Microbiome to the Risk of Bacterial Pathogen Colonization in Older Adults

2020 ◽  
Vol 15 (7) ◽  
pp. 671-696
Author(s):  
Wei Liu ◽  
John P. Haran ◽  
Arlene S. Ash ◽  
Jeroan J. Allison ◽  
Shangyuan Ye ◽  
...  
Keyword(s):  
Older Adults ◽  
Mediation Analysis ◽  
Bacterial Pathogen ◽  
Outcome Variable ◽  
High Dimensional ◽  
Modeling Framework ◽  
Causal Mediation ◽  
Causal Pathways ◽  
Causal Mediation Analysis ◽  
Pathogen Colonization

Background: Causal mediation analysis is conducted in biomedical research with the goal of investigating causal mechanisms that consist of both direct causal pathways between the treatment and outcome variables and intermediate causal pathways through mediators. Recently, this type of analysis has been applied in the context of bioinformatics; however, it encounters the obstacle of high-dimensional and semi-continuous mediators with clumping at zero. Methods: In this article, we develop a methodology to conduct high-dimensional causal mediation analysis with a modeling framework that involves (i) a nonlinear model for the outcome variable, (ii) two-part models for semi-continuous mediators with clumping at zero, and (iii) sophisticated variable-selection techniques using machine learning. We conducted simulations and investigated the performance of the proposed method. It is shown that the proposed method can provide reliable statistical information on the causal effects with high-dimensional mediators. The method is adopted to assess the contribution of the intestinal microbiome to the risk of bacterial pathogen colonization in older adults from US nursing homes. Conclusions: The proposed high-dimensional causal mediation analysis with nonlinear models is an innovative and reliable approach to conduct causal inference with high-dimensional mediators.

Genetic analysis and causal gene identification of maize viviparous mutant vp-like8

2019 ◽  
Vol 45 (5) ◽  
pp. 656
Author(s):  
Rui WANG ◽  
Yang-Song CHEN ◽  
Ming-Hao SUN ◽  
Xiu-Yan ZHANG ◽  
Yi-Cong DU ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Gene Identification ◽  
Causal Gene

scholarly journals Fine‐mapping and QTL tissue‐sharing information improves the reliability of causal gene identification

2020 ◽  
Vol 44 (8) ◽  
pp. 854-867
Author(s):  
Alvaro N. Barbeira ◽  
Owen J. Melia ◽  
Yanyu Liang ◽  
Rodrigo Bonazzola ◽  
Gao Wang ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Gene Identification ◽  
Causal Gene
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