# BLESS: bagged logistic regression for biomarker identification

## TL;DR

This paper introduces BLESS, a new method for identifying biomarkers in genomic data that outperforms traditional approaches in finding significant associations with cognitive functions.

## Contribution

BLESS is a novel ensemble machine learning method that improves biomarker identification in genome-wide association studies.

## Key findings

- The SNP-wise approach failed to identify significant signals in the Alzheimer's study.
- BLESS successfully ranked SNPs associated with cognitive functions.
- The method shows improved power for biomarker discovery in high-dimensional genomic data.

## 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 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.