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Related Experiment Video

Updated: Dec 12, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology.

Hongkai Li1,2, Zhi Geng3, Xiaoru Sun4,5

  • 1Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China. lihongkaiyouxiang@163.com.

BMC Genetics
|August 11, 2020
PubMed
Summary

We developed a new statistic, path-specific effect (PSE), to identify differing biological pathways in complex diseases. PSE accurately detects these differential pathways, offering new insights into disease mechanisms.

Keywords:
Causal diagram modelCausal inferenceIdentificationPath-specific effect

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Area of Science:

  • Genomics
  • Systems Biology
  • Biostatistics

Background:

  • Complex diseases like cancer arise from multifactorial causes, including genetic mutations and pathway dysregulation.
  • Understanding biological pathways is crucial for disease occurrence, development, and recovery.

Purpose of the Study:

  • To introduce a novel path-specific effect (PSE) statistic for detecting differential biological pathways between two conditions.
  • To validate the PSE statistic's performance through theoretical analysis, simulations, and real-world data application.

Main Methods:

  • The PSE statistic is calculated by assessing the average causal effect of directed edges within a path, adjusting for parent nodes.
  • Permutation tests are employed with the PSE statistic for robust Type I error control.
  • The method was validated using simulated data and applied to Glioblastoma Multiforme (GBM) patient data.

Main Results:

  • Simulation studies demonstrated that PSE with permutation tests maintains stable Type I error rates and accurately detects differential pathways compared to other methods.
  • The power of PSE increases with larger path-specific effects and their differences between conditions, showing robustness to variations in connected nodes.
  • Application to GBM data identified 14 positive specific pathways within the mTOR pathway influencing patient survival.

Conclusions:

  • The proposed PSE statistic effectively identifies differential specific pathways implicated in complex diseases.
  • PSE offers a valuable tool for uncovering disease mechanisms and potentially guiding therapeutic strategies.