title: Detection for pathway effect contributing to disease in systems epidemiology with a case-control design.
abstract: OBJECTIVE
  Identification of pathway effects responsible for specific diseases has been one of the essential tasks in systems epidemiology. Despite some advance in procedures for distinguishing specific pathway (or network) topology between different disease status, statistical inference at a population level remains unsolved and further development is still needed. To identify the specific pathways contributing to diseases, we attempt to develop powerful statistics which can capture the complex relationship among risk factors.
  
  
  METHODS
  Acute myeloid leukaemia (AML) data obtained from 133 adults (98 patients and 35 controls; 47% female).
  
  
  RESULTS
  Simulation studies indicated that the proposed Pathway Effect Measures (PEM) were stable; bootstrap-based methods outperformed the others, with bias-corrected bootstrap CI method having the highest power. Application to real data of AML successfully identified the specific pathway (Treg→TGFβ→Th17) effect contributing to AML with p values less than 0.05 under various methods and the bias-corrected bootstrap CI (-0.214 to -0.020). It demonstrated that Th17-Treg correlation balance was impaired in patients with AML, suggesting that Th17-Treg imbalance potentially plays a role in the pathogenesis of AML.
  
  
  CONCLUSIONS
  The proposed bootstrap-based PEM are valid and powerful for detecting the specific pathway effect contributing to disease, thus potentially providing new insight into the underlying mechanisms and ways to study the disease effects of specific pathways more comprehensively.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Jiadong
authors.initials: J
authors.lastname: Ji
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Zhongshang
authors.initials: Z
authors.lastname: Yuan
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Xiaoshuai
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Neurology, Capital Medical University, Xuanwu Hospital, Beijing, China.
authors.forename: Fangyu
authors.initials: F
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Xu
authors.affiliation: Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden.
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Hongkai
authors.initials: H
authors.lastname: Li
authors.affiliation: School of Mathematics, Shandong University, Jinan, China.
authors.forename: Jia
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China.
authors.forename: Fuzhong
authors.initials: F
authors.lastname: Xue
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000209:etiology* / Q000276:immunology / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012307:Risk Factors; D015398:Signal Transduction / Q000502:physiology*; D050378:T-Lymphocytes, Regulatory / Q000378:metabolism*; D058504:Th17 Cells / Q000378:metabolism*
keywords: EPIDEMIOLOGY; STATISTICS & RESEARCH METHODS