Predictive long-range allele-specific mapping of regulatory variants and target transcripts

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Genome-wide association studies (GWASs) have identified a large number of noncoding associations, calling for systematic mapping to causal regulatory variants and their distal target genes. A widely used method, quantitative trait loci (QTL) mapping for chromatin or expression traits, suffers from sample-to-sample experimental variation and trans-acting or environmental effects. Instead, alleles at heterozygous loci can be compared within a sample, thereby controlling for those confounding factors. Here we introduce a method for chromatin structure-based allele-specific pairing of regulatory variants and target transcripts. With phased genotypes, much of allele-specific expression could be explained by paired allelic cis-regulation across a long range. This approach showed approximately two times greater sensitivity than QTL mapping. There are cases in which allele imbalance cannot be tested because heterozygotes are not available among reference samples. Therefore, we employed a machine learning method to predict missing positive cases based on various features shared by observed allele-specific pairs. We showed that only 10 reference samples are sufficient to achieve high prediction accuracy with a low sampling variation. In conclusion, our method enables highly sensitive fine mapping and target identification for traitassociated variants based on a small number of reference samples.
Publisher
PUBLIC LIBRARY SCIENCE
Issue Date
2017-04
Language
English
Article Type
Article
Keywords

DNA-SEQUENCING DATA; EXPRESSION VARIATION; CHROMATIN STATES; ANALYSIS TOOLKIT; GENETIC-CONTROL; HUMAN-CELLS; DISEASE; GENOME; HUMANS; UPDATE

Citation

PLOS ONE, v.12, no.4

ISSN
1932-6203
DOI
10.1371/journal.pone.0175768
URI
http://hdl.handle.net/10203/223849
Appears in Collection
BiS-Journal Papers(저널논문)
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