AUTHOR=Gardiner Kyle , Zhang Xuekui , Xing Li 

TITLE=BLESS: bagged logistic regression for biomarker identification

JOURNAL=Frontiers in Genetics

VOLUME=15

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=<p>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 <italic>p</italic>-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 subsamples, which helps to identify biomarkers from high-dimensional genomic data. We use 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.</p>