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A method to improve the reproducibility of findings from epigenome- and transcriptome-wide association studies.

Edwin Jcg van den OordJerry D GuintivanoKarolina A Aberg
Published in: bioRxiv : the preprint server for biology (2023)
Reproducibility is a cornerstone of scientific progress. In epigenome- and transcriptome-wide association studies (E/TWAS) failure to reproduce may be the result of false discoveries. Whereas multiple methods exist to control false discoveries due to sampling error, minimizing false discoveries due to outliers and other data artefacts remains challenging. We propose a robust E/TWAS approach that outperforms alternative methods to improve reproducibility such as split-half replication. Furthermore, robust E/TWAS results in only a minor loss of power if there are no outliers and can in the presence of outliers, likely a more realistic scenario, even be more powerful than regular E/TWAS.
Keyphrases
  • dna methylation
  • genome wide
  • gene expression
  • single cell
  • rna seq
  • case control
  • big data
  • electronic health record
  • deep learning