Expression quantitative locus mapping for identification of hotspots using an empirical Bayes mixture model

Guanglong Jiang, Yingqiang Fu, Pengyue Zhang, Shirin Ardeshir-Rouhani-Fard, Lijun Cheng, Lang Li, Zhigao Li

Research output: Contribution to journalArticle

Abstract

Identification of genomic regions that regulate gene expression can help our understanding of the mechanisms underlying genetic contributions to phenotypic variations. Hence, we consider a mixture model to locate candidate genomic regions that are more frequently associated with gene expression traits. A modified two-sample t-statistic was used, and singlenucleotide polymorphisms (SNPs) with P-values <10-5 were considered for a subsequent two-component negative binomial mixture model. An expectationmaximisation algorithm was adopted to identify the parameters involved in the model. The SNPs were then ranked based on their false discovery rate (FDR) values. Any SNP with a FDR value <1% was considered as a potential hotspot. Three independent datasets were used to replicate the findings. A number of common hotspots were identified, and many hotspots have annotated function as the binding site of transcription factors or histone proteins.

Original languageEnglish (US)
Pages (from-to)108-122
Number of pages15
JournalInternational Journal of Computational Biology and Drug Design
Volume10
Issue number2
DOIs
StatePublished - 2017

Keywords

  • Empirical Bayes
  • Expression quantitative trait loci
  • Gene expression
  • Genome-wide association studies
  • Genotype
  • Mixture model
  • Transcription factor

ASJC Scopus subject areas

  • Computer Science Applications
  • Drug Discovery

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