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Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Confounding in Epidemiological Studies

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

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Updated: Jun 23, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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Published on: January 9, 2020

High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent

Yifan Lin, Kevin Z Lin

Biorxiv : the Preprint Server for Biology

|June 22, 2026

Summary

High-dimensional genomics studies face confounding from unmeasured biological processes. Our new sensGAN framework quantifies this confounding, identifying robust disease signals in genomics research.

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Last Updated: Jun 23, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-dimensional genomics studies are susceptible to unmeasured biological processes that obscure disease-specific signals.
Current methods can estimate latent confounders but lack the ability to quantify the robustness of discoveries to hypothetical confounding.

Purpose of the Study:

To introduce sensGAN, a deep-learning adversarial framework for quantitative sensitivity analysis in genomics.
To systematically explore the confounding spectrum and identify confounder-sensitive genes.

Main Methods:

Developed sensGAN, a deep-learning adversarial framework.
Learned "worst-case" latent variables to nullify gene associations under predictive-gain constraints.
Identified minimum confounding strength required to explain observed effects.

Main Results:

sensGAN accurately recovers latent structures in diverse simulations.
Outperformed existing methods in identifying confounder-sensitive genes.
Applied to Alzheimer's disease microglia, it prioritized robust disease pathways and isolated signals from co-occurring pathologies.

Conclusions:

sensGAN enables a formal, quantitative sensitivity analysis for high-dimensional genomics studies.
The framework effectively distinguishes true disease signals from confounding effects.
Provides a robust approach for prioritizing reliable findings in complex biological data.