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Mapping eQTL by leveraging multiple tissues and DNA methylation.

Chaitanya R AcharyaKouros OwzarAndrew S Allen
Published in: BMC bioinformatics (2017)
Our score test-based approach does not need parameter estimation under the alternative hypothesis. As a result, our model parameters are estimated only once for each mRNA - CpG pair. Our model specifically studies the effects of non-coding regions of DNA (in this case, CpG sites) on mapping eQTLs. However, we can easily model micro-RNAs instead of CpG sites to study the effects of post-transcriptional events in mapping eQTL. Our model's flexible framework also allows us to investigate other genomic events such as alternative gene splicing by extending our model to include gene isoform-specific data.
Keyphrases
  • dna methylation
  • gene expression
  • genome wide
  • high resolution
  • machine learning
  • artificial intelligence
  • single molecule
  • deep learning