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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

BLESS: bagged logistic regression for biomarker identification.

Kyle Gardiner¹, Xuekui Zhang², Li Xing¹

¹Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, SK, Canada.

Frontiers in Genetics

|September 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel machine learning method to improve biomarker discovery in genome-wide association studies (GWAS). The new approach effectively identifies significant single nucleotide polymorphisms (SNPs) linked to cognitive function, outperforming traditional methods.

Keywords:

bagging (bootstrap aggregation)cognitive function ensemble learning genome-wide association study genomics biomarker

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Area of Science:

Genetics
Bioinformatics
Machine Learning

Background:

Traditional genome-wide association studies (GWAS) use a single nucleotide polymorphism (SNP)-wise approach.
This method examines each SNP individually, which can lack the statistical power to detect significant biomarkers.
Adjustments for multiple testing in SNP-wise analyses can further reduce the ability to find true associations.

Purpose of the Study:

To develop and evaluate an ensemble machine learning approach for enhanced biomarker identification in high-dimensional genomic data.
To compare the performance of the novel approach against the traditional SNP-wise method using a real-world GWAS dataset.

Main Methods:

An ensemble machine learning strategy was designed, aggregating results from logistic regression models built on multiple data subsamples.
The methods were applied to analyze genome-wide association study data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Main Results:

The traditional SNP-wise analysis failed to identify any significant signals associated with cognitive function.
The proposed ensemble machine learning approach successfully identified and ranked SNPs associated with cognitive functions of interest.

Conclusions:

Ensemble machine learning offers a more powerful and effective strategy for biomarker discovery in GWAS compared to traditional SNP-wise methods.
This novel approach can successfully identify significant genetic associations in high-dimensional genomic datasets, such as those from ADNI.