Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies.

ABSTRACT:

Background

DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors.

Results

Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent con

SUBMITTER: Ori APS 

PROVIDER: S-EPMC9590227 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature