Allelic imbalance (AI) indicates the presence of functional variation in cis regulatory regions. Detecting cis regulatory differences using AI is widespread, yet there is no formal statistical methodology that tests whether AI differs between conditions. Here a team led by researchers at the University of Texas present a novel model and formally test differences in AI across conditions using Bayesian credible intervals. The approach tests AI by environment (GxE) interactions and can be used to test AI between environments, genotypes, sex, and any other condition. The researchers incorporate bias into the modeling process. Bias is allowed to vary between conditions, making the formulation of the model general. As gene expression affects power for detection of AI, and as expression may vary between conditions, the model explicitly takes coverage into account. The proposed model has low type I and II error under several scenarios, and is robust to large differences in coverage between conditions. They reanalyze RNA-seq data from a Drosophila melanogaster population panel, with F1 genotypes, to compare levels of AI between mated and virgin female flies and we show that AI*genotype interactions can also be tested. To demonstrate the use of the model to test genetic differences and interactions, a formal test between two F1’s was performed, showing the expected 20% difference in AI. The proposed model allows a formal test of GxE and GxG and reaffirms a previous finding, that cis regulation is robust between environments.
Reads aligning to the maternal allele (m) are purple, those aligning to the paternal allele (p) are green, and those aligning equally well to both alleles (unassigned) are grey. Expected values according to the environmental model are given in Table 1 and are in black type above. The baseline model uses information of the reads aligning to paternal or maternal alleles only (red boxes), while the environmental model additionally incorporates information about unassigned reads (blue boxes).
Availability – Supplemental material includes an implementation of the model in R and a toy data set – FileS2.zip