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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...

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Sensor wide association studies in digital medicine.

Nico Steckhan^1,2,3, Felix Broghammer⁴, Dylan Powell⁵

¹Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany. nico.steckhan@hpi.de.

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|May 30, 2026

Summary

Sensor-Wide Association Studies (SWAS) enable rigorous analysis of digital health data. This approach facilitates hypothesis generation for digital epidemiology and personalized medicine.

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Area of Science:

Digital health
Genomics
Epidemiology

Background:

Genomics association studies transformed health research by analyzing large feature sets.
Digital medicine generates high-dimensional, longitudinal data from various sensors.

Purpose of the Study:

Introduce Sensor-Wide Association Studies (SWAS) for analyzing digital sensor data.
Establish SWAS as a reproducible foundation for digital epidemiology and personalized medicine.

Main Methods:

Structured, feature-wide scans of sensor-derived features against clinical phenotypes.
Utilize transparent feature documentation and longitudinal modeling.
Implement principled control of multiplicity for robust findings.

Main Results:

SWAS offers a systematic approach to hypothesis generation from digital health data.
Addresses challenges in analyzing high-dimensional, longitudinal sensor data.

Conclusions:

SWAS provides a framework for reproducible digital epidemiology.
Enables personalized medicine through sensor data analysis.