Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation

Casey Hanson, Junmei Cairns, Liewei M Wang, Saurabh Sinha

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype'that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.

Original languageEnglish (US)
Pages (from-to)1207-1216
Number of pages10
JournalGenome Research
Volume28
Issue number8
DOIs
StatePublished - Aug 1 2018

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Regulator Genes
Transcription Factors
Quantitative Trait Loci
Phenotype
Gene Expression
Pharmaceutical Preparations
Cell Line
Precision Medicine
Statistical Models
DNA Methylation
Methylation
Binding Sites
Genotype
DNA
Research
Genes
Neoplasms
Therapeutics

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. / Hanson, Casey; Cairns, Junmei; Wang, Liewei M; Sinha, Saurabh.

In: Genome Research, Vol. 28, No. 8, 01.08.2018, p. 1207-1216.

Research output: Contribution to journalArticle

Hanson, Casey ; Cairns, Junmei ; Wang, Liewei M ; Sinha, Saurabh. / Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. In: Genome Research. 2018 ; Vol. 28, No. 8. pp. 1207-1216.
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