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

• Published in: PloS one Vol. 12; no. 4; p. e0175768
• Main Authors: Lee, Kibaick, Lee, Seulkee, Bang, Hyoeun, Choi, Jung Kyoon
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.04.2017; Public Library of Science (PLoS)
• Subjects: Accessibility; Alignment; Alleles; Allergic diseases; Analysis; Artificial intelligence; Autoimmune diseases; Binding; Bioinformatics; Biological effects; Biology and Life Sciences; Biotechnology; Brain; Burrows; Cell lines; Chromatin; Chromatin - genetics; Chromosome Mapping - methods; Chromosome Mapping - standards; Coding; Comparative analysis; Computer and Information Sciences; Cyclin-dependent kinase inhibitor p27; Data acquisition; Data processing; Deoxyribonuclease; Deoxyribonucleic acid; Differentiation; Disease; DNA; DNA sequencing; Engineering; Environmental effects; Feedback control; Gene mapping; Genes; Genetic variation; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genotypes; Haplotypes; Hinges; Humans; Immune system; Immunoproliferative diseases; Knowledge representation; Learning algorithms; Lymphocytes B; Lymphoma; Machine Learning; Mapping; Medical science; Polymorphism, Single Nucleotide; Power; Quantitative genetics; Quantitative Trait Loci; Regression analysis; Research and Analysis Methods; Ribonucleic acid; RNA; RNA, Messenger - genetics; Samples; Sensitivity; Splice junctions; Statistical analysis; Statistics; Transcription factors; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0175768

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 trait-associated variants based on a small number of reference samples.