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Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies

Jong Wha J Joo1, Jae Hoon Sul2, Buhm Han34, Chun Ye5 and Eleazar Eskin126*

Author Affiliations

1 Bioinformatics IDP, University of California, Los Angeles, CA, USA

2 Computer Science Department, University of California, Los Angeles, CA, USA

3 Division of Genetics, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA

4 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

5 Broad Institute, 7 Cambridge Center, Cambridge, MA, USA

6 Department of Human Genetics, University of California, Los Angeles, CA, USA

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Genome Biology 2014, 15:r61  doi:10.1186/gb-2014-15-4-r61

Published: 7 April 2014

Abstract

Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods.