Chris Harvey, Gregory A Moyebrailean, Omar Davis, Xiaoquan Wen, Francesca Luca, Roger Pique-Regi
Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression and have been crucial to enable a better understanding of the functional role of non-coding sequences. However, eQTL studies are generally quite expensive, requiring a large sample size and genome-wide genotyping. On the other hand, allele specific expression (ASE) is becoming a very popular approach to detect the effect of a genetic variant on gene expression, even with a single individual. This is typically achieved by counting the number of RNA-seq reads for each allele at heterozygous sites and rejecting the null hypothesis of 1:1 ratio. When genotype information is not readily available it could be inferred from the RNA-seq reads directly, but there are no methods available that can incorporate the uncertainty on the genotype call with the ASE inference step. Here, we present QuASAR, Quantitative Allele Specific Analysis of Reads, a novel statistical learning method for jointly detecting heterozygote genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high quality genotypes are available. Results on an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available.