Expression quantitative locus mapping for identification of hotspots using an empirical Bayes mixture model
by Guanglong Jiang; Yingqiang Fu; Pengyue Zhang; Shirin Ardeshir-Rouhani-Fard; Lijun Cheng; Lang Li; Zhigao Li
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 10, No. 2, 2017

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.

Online publication date: Tue, 25-Apr-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Biology and Drug Design (IJCBDD):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email