Inferring biased allele expression across the genome Kimberly C. Olney1, Line Sko?e2, Ramus Nielsen3, Melissa Wilson Sayres1,4 1School of Life Sciences, Arizona State University, Tempe, Arizona 2Bioinforma<cs Center, Department of Biology, University of Copenhagen, Copenhagen 3Departments of Sta<s<cs and Integra<ve Biology, University of California, Berkeley, Berkeley, CA 4Center for Evolu<on and Medicine, Arizona State University, Tempe, Arizona Background When an individual’s two copies of an autosomal gene are expressed in unequal propor<ons the gene is said to be subject to biased allele expression. Biased allele expression has been shown to play a role in diseases like breast and ovarian cancer1. The ability to infer if a gene is subject to biased allele expression can improve our understanding of the molecular basis of disease. When modeling biased allele expression from RNA-­‐Seq data it is important to take into account several sources of bias2. Factors that may aﬀect the analysis of biased allele expression include: 1) reference mapping; 2) SNP type bias; and, 3) over-­‐dispersion of read counts. Biased allele analysis pipeline References 1. Rijlaarsdam MA, et al. PLoS ONE. 2015;10: e0122146..0122146 2. Lappalainen, T. et al. Nature 501, 506–511 (2013). Acknowledgments Startup funds from the School of Life Sciences and The Biodesign Ins<tute to MWS Simulated data Figure 2. Method for inferring allele-­‐speciﬁc expression. We test three diﬀerent methods, and variable ﬁltering criteria, for their speciﬁcity and sensi<vity to capture biased allele-­‐expression. Figure 3. Simulated quanWle-­‐quanWle plots. Minus log10 of the simulated and expected p-­‐values. Expected values assume no biased allele expression. 300 genes, 30 depth coverage, and 5 SNPs. In order the: 1. SNP-­‐wise binomial test are shown in red; 2. binomial-­‐based logisWc regression test are shown in orange; and, 3. binomial-­‐based logisWc regression test with over-­‐dispersion corrected for are shown in green. Figure 3. Autosomes of 89 CEU populaWon data and various ﬁltering Minus log 10 of the observed p-­‐values are plo_ed against the expected corresponding quan<les under the null hypothesis of no allelic imbalance. The p-­‐values for the SNP-­‐wise binomial test for the ﬁltered heterozygous sites in 89 CEU samples are shown in red. P-­‐values for our model are shown in green. Figure 4. StaWsWcal power. We test power to detect biased allele expression under (A) variable depth of coverage per gene and (B) variable number of SNPs per gene. Plo_ed numbers of the frac<on of p-­‐ values smaller than 0.05 divided by the number of tests to obtain Bonferroni correc<on for mul<ple tes<ng. Based on simulated count data for 300 genes, with 150 genes subject to balanced expression and 150 under biased allele expression. Figure 1. Biased allele expression. The diﬀerence in read counts could be biologically meaningful or could be due to technical variance in sequencing and mapping. hg19 reference 89 Europeans (CEU) Methods 3. LogisWc regression with over-­‐dispersion Filtering 2. LogisWc regression without over-­‐dispersion 1. SNP-­‐wise binomial test SNPs per gene 2 3 4 Sequencing depth 20x 30x 40x Mapability ﬁle from UCSC Protein coding gene annotaWon hg19 Genotype 1000Genomes Transcriptome Geuvadis 89 CEU individuals ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 Logistic regression with overdispersion Expected −log10(p−value) Observed −log10(p−value) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●