A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology
Abstract Background Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. Results We propose a path-s...
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doaj-73234b4a1d974d66857ecaf7caf0fe252020-11-25T03:51:28ZengBMCBMC Genetics1471-21562020-08-0121111210.1186/s12863-020-00876-wA novel path-specific effect statistic for identifying the differential specific paths in systems epidemiologyHongkai Li0Zhi Geng1Xiaoru Sun2Yuanyuan Yu3Fuzhong Xue4Institute for Medical Dataology, Cheeloo College of Medicine, Shandong UniversitySchool of Mathematical Sciences, Peking UniversityInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong UniversityInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong UniversityInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong UniversityAbstract Background Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. Results We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials. Conclusion The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.http://link.springer.com/article/10.1186/s12863-020-00876-wCausal diagram modelCausal inferenceIdentificationPath-specific effect |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongkai Li Zhi Geng Xiaoru Sun Yuanyuan Yu Fuzhong Xue |
spellingShingle |
Hongkai Li Zhi Geng Xiaoru Sun Yuanyuan Yu Fuzhong Xue A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology BMC Genetics Causal diagram model Causal inference Identification Path-specific effect |
author_facet |
Hongkai Li Zhi Geng Xiaoru Sun Yuanyuan Yu Fuzhong Xue |
author_sort |
Hongkai Li |
title |
A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology |
title_short |
A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology |
title_full |
A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology |
title_fullStr |
A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology |
title_full_unstemmed |
A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology |
title_sort |
novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology |
publisher |
BMC |
series |
BMC Genetics |
issn |
1471-2156 |
publishDate |
2020-08-01 |
description |
Abstract Background Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. Results We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials. Conclusion The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms. |
topic |
Causal diagram model Causal inference Identification Path-specific effect |
url |
http://link.springer.com/article/10.1186/s12863-020-00876-w |
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