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Predictive long-range allele-specific mapping of regulatory variants and target transcripts
Kibaick Lee1, Seulkee Lee2, Hyoeun Bang1
1Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
Plos One
|April 14, 2017
Summary
This study introduces a novel method for precisely mapping trait-associated variants to their target genes by analyzing allele-specific regulation. This approach enhances sensitivity and requires fewer samples compared to traditional quantitative trait loci (QTL) mapping.
Area of Science:
- Genomics
- Regulatory Genomics
- Bioinformatics
Background:
- Genome-wide association studies (GWASs) identify numerous noncoding variants requiring functional validation.
- Traditional quantitative trait loci (QTL) mapping faces challenges from experimental variation and confounding factors.
Purpose of the Study:
- To develop a sensitive method for mapping causal regulatory variants to distal target genes.
- To overcome limitations of existing QTL mapping techniques.
Main Methods:
- Developed a chromatin structure-based allele-specific pairing method for regulatory variants and transcripts.
- Utilized phased genotypes to compare alleles within heterozygous loci, controlling for confounding factors.
- Employed machine learning to predict allele-specific expression in the absence of heterozygotes.
Main Results:
- The novel method demonstrated approximately two times greater sensitivity than traditional QTL mapping.
- Allele-specific expression was largely explained by long-range allelic cis-regulation.
- High prediction accuracy was achieved with as few as 10 reference samples, with low sampling variation.
Conclusions:
- The new method enables highly sensitive fine-mapping and target gene identification for trait-associated variants.
- The approach is effective even with a small number of reference samples.
- This facilitates a more systematic understanding of noncoding variant function.