AUTHOR=Gardiner Kyle , Zhang Xuekui , Xing Li 

TITLE=BLESS: bagged logistic regression for biomarker identification

JOURNAL=Frontiers in Genetics

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1336891

DOI=10.3389/fgene.2024.1336891

ISSN=1664-8021

ABSTRACT=The traditional single nucleotide polymorphism (SNP)-wise approach in genome-wide association studies is focused on examining the marginal association between each SNP with the outcome separately and applying multiple testing adjustments to the resulting p-values to reduce false positives. However, the approach suffers a lack of power in identifying biomarkers.We design an ensemble machine learning approach to aggregate results from logistic regression models based on multiple sub-samples, which helps to identify biomarkers from high-dimensional genomic data. We employ different methods to analyze a genome-wide association study from the Alzheimer's Disease Neuroimaging Initiative. The SNP-wise approach does not identify any significant signal, while our novel approach provides a list of ranked SNPs associated with the cognitive functions of interests.