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Related Experiment Video

Updated: Sep 7, 2025

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

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Published on: June 21, 2018

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping.

Marie Courbariaux¹, Kylliann De Santiago¹, Cyril Dalmasso¹

¹Université Paris-Saclay, CNRS, Université d'Évry, Laboratoire de Mathématiques et Modélisation d'Évry, Évry-Courcouronnes, France.

Frontiers in Genetics

|June 23, 2022

Summary

This summary is machine-generated.

This study introduces a novel method for identifying complex disease subtypes using clinical and genetic data. The approach enhances disease subtyping by integrating diverse data without transformation, aiding in the discovery of genetic associations.

Keywords:

Parkinson’s disease clinical data disease subtyping genotyping high dimension longitudinal data mixture of experts model variable selection

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Area of Science:

Genetics and Bioinformatics
Computational Biology
Disease Subtyping

Background:

Complex diseases often exhibit missing heritability, potentially due to distinct subtypes with unique genetic factors.
Current disease subtyping methods using clinical and genetic data have limitations, including data transformation and post-processing interpretation.

Purpose of the Study:

To propose an original, interpretable method for disease subtyping using longitudinal clinical variables and high-dimensional genetic markers.
To address limitations of existing methods by avoiding data transformation and enabling direct interpretation of subtypes.

Main Methods:

A sparse mixture-of-regressions model was developed for concomitant clustering of clinical and genetic data.
The method incorporates a variable selection step to handle high-dimensional genetic data and improve scalability.

Main Results:

The proposed method was successfully validated through simulations.
Analysis of a Parkinson's disease cohort identified distinct disease subtypes and associated genetic variants.

Conclusions:

The developed method offers an interpretable and scalable approach for disease subtyping.
It facilitates the identification of genetic associations relevant to disease heterogeneity.