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

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• 作者：Jong Wha J Joo (1)
  Jae Hoon Sul (2)
  Buhm Han (3) (4)
  Chun Ye (5)
  Eleazar Eskin (1) (2) (6)
  
  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鈥檚 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
  
• 刊名：Genome Biology
• 出版年：2014
• 出版时间：April 2014
• 年：2014
• 卷：15
• 期：4
• 全文大小：669 KB
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• 刊物主题：Animal Genetics and Genomics; Human Genetics; Plant Genetics & Genomics; Microbial Genetics and Genomics; Fungus Genetics; Bioinformatics;
• 出版者：BioMed Central
• ISSN：1465-6906

文摘

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.