When cellular traits are measured using high-throughput DNA sequencing, quantitative trait loci (QTLs) manifest as fragment count differences between individuals and allelic differences within individuals. We present RASQUAL (Robust Allele-Specific Quantitation and Quality Control), a new statistical approach for association mapping that models genetic effects and accounts for biases in sequencing data using a single, probabilistic framework. RASQUAL substantially improves fine-mapping accuracy and sensitivity relative to existing methods in RNA-seq, DNase-seq and ChIP-seq data. We illustrate how RASQUAL can be used to maximize association detection by generating the first map of chromatin accessibility QTLs (caQTLs) in a European population using ATAC-seq. Despite a modest sample size, we identified 2,707 independent caQTLs (at a false discovery rate of 10%) and demonstrated how RASQUAL and ATAC-seq can provide powerful information for fine-mapping gene-regulatory variants and for linking distal regulatory elements with gene promoters. Our results highlight how combining between-individual and allele-specific genetic signals improves the functional interpretation of noncoding variation.
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We thank O. Stegle, M. Hemberg, G. Trynka and the three anonymous reviewers for their helpful comments. N.K., A.J.K. and D.J.G. were funded by Wellcome Trust grant 098051.
The authors declare no competing financial interests.
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Kumasaka, N., Knights, A. & Gaffney, D. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat Genet 48, 206–213 (2016). https://doi.org/10.1038/ng.3467
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