Expression quantitative locus mapping for identification of hotspots using an empirical Bayes mixture model. (2017)

• Record Type: Journal Article
• Authors: Jiang, Guanglong; Fu, Yingqiang; Zhang, Pengyue; Ardeshir-Rouhani-Fard, Shirin; Cheng, Lijun; Li, Lang; Li, Zhigao
• Is Part Of: International journal of computational biology and drug design. Volume 10:Number 2(2017)
• Page Start: 108
• Page End: 122
• Publication Date: 2017
• Subjects: genotype -- gene expression -- expression quantitative trait loci -- genome-wide association studies -- empirical Bayes -- mixture model -- transcription factor; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• Languages: English
• ISSNs: 1756-0756

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 single-nucleotide polymorphisms (SNPs) with P-values <10 -5 were considered for a subsequent two-component negative binomial mixture model. An expectation-maximisation 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.