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Genotype-Based Test in Mapping Cis-Regulatory Variants from Allele-Specific Expression Data

Overview of attention for article published in PLOS ONE, June 2012
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Title
Genotype-Based Test in Mapping Cis-Regulatory Variants from Allele-Specific Expression Data
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038667
Pubmed ID
Authors

Jean Francois Lefebvre, Emilio Vello, Bing Ge, Stephen B. Montgomery, Emmanouil T. Dermitzakis, Tomi Pastinen, Damian Labuda

Abstract

Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome-wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript.

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Geographical breakdown

Country Count As %
United States 3 8%
Unknown 35 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 34%
Student > Ph. D. Student 13 34%
Student > Master 3 8%
Other 3 8%
Professor > Associate Professor 2 5%
Other 2 5%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 61%
Engineering 3 8%
Medicine and Dentistry 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Mathematics 2 5%
Other 3 8%
Unknown 2 5%