Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data.

Eun Yong Kang, Lisa J Martin, Serghei Mangul, Warin Isvilanonda, Jennifer Zou, Eyal Ben-David, Buhm Han, Aldons J Lusis, Sagiv Shifman, Eleazar Eskin
Author Information
  1. Eun Yong Kang: Department of Computer Science, University of California, Los Angeles, California 90095-1596.
  2. Lisa J Martin: Department of Human Genetics, University of California, Los Angeles, California 90095-1596.
  3. Serghei Mangul: Department of Computer Science, University of California, Los Angeles, California 90095-1596.
  4. Warin Isvilanonda: Department of Computer Science, University of California, Los Angeles, California 90095-1596.
  5. Jennifer Zou: Department of Computer Science, University of California, Los Angeles, California 90095-1596.
  6. Eyal Ben-David: Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Israel.
  7. Buhm Han: Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115.
  8. Aldons J Lusis: Department of Human Genetics, University of California, Los Angeles, California 90095-1596.
  9. Sagiv Shifman: Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Israel.
  10. Eleazar Eskin: Department of Computer Science, University of California, Los Angeles, California 90095-1596 eeskin@cs.ucla.edu.

Abstract

The study of the genetics of gene expression is of considerable importance to understanding the nature of common, complex diseases. The most widely applied approach to identifying relationships between genetic variation and gene expression is the expression quantitative trait loci (eQTL) approach. Here, we increased the computational power of eQTL with an alternative and complementary approach based on analyzing allele specific expression (ASE). We designed a novel analytical method to identify cis-acting regulatory variants based on genome sequencing and measurements of ASE from RNA-sequencing (RNA-seq) data. We evaluated the power and resolution of our method using simulated data. We then applied the method to map regulatory variants affecting gene expression in lymphoblastoid cell lines (LCLs) from 77 unrelated northern and western European individuals (CEU), which were part of the HapMap project. A total of 2309 SNPs were identified as being associated with ASE patterns. The SNPs associated with ASE were enriched within promoter regions and were significantly more likely to signal strong evidence for a regulatory role. Finally, among the candidate regulatory SNPs, we identified 108 SNPs that were previously associated with human immune diseases. With further improvements in quantifying ASE from RNA-seq, the application of our method to other datasets is expected to accelerate our understanding of the biological basis of common diseases.

Keywords

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Grants

  1. R01 ES021801/NIEHS NIH HHS
  2. R01 MH101782/NIMH NIH HHS
  3. K25 HL080079/NHLBI NIH HHS
  4. P01 HL028481/NHLBI NIH HHS
  5. U01 DA024417/NIDA NIH HHS
  6. U54 EB020403/NIBIB NIH HHS
  7. R01 ES022282/NIEHS NIH HHS
  8. P01 HL030568/NHLBI NIH HHS
  9. R01 GM083198/NIGMS NIH HHS

MeSH Term

Algorithms
Alleles
Cell Line, Tumor
Europe
Genome-Wide Association Study
HapMap Project
Humans
Immune System Diseases
Polymorphism, Single Nucleotide
Promoter Regions, Genetic
Quantitative Trait Loci
Transcriptome
White People

Word Cloud

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