Allele-Specific RNA Expression Modeling Using Finite Mixture Models

• Activity Number: 505
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Rong Lu* and Ryan Smith and Michal Seweryn and Danxin Wang and Amy Webb and Wolfgang Sadee and Grzegorz Rempala
• Companies: The Ohio State University and The Ohio State University and The Ohio State University and The Ohio State University and The Ohio State University and The Ohio State University and The Ohio State University
• Keywords: RNA-seq ; allelic RNA expression imbalance ; Poisson mixture ; folded Skellam Mixture ; human brain tissues

Abstract

Measuring allele-specific RNA expression provides valuable insights into cis-acting genetic and epigenetic regulation of gene expression. Widespread adoption of high-throughput sequencing technologies for studying RNA expression (RNA-Seq) permits measurement of allelic RNA expression at heterozygous SNPs across the entire transcriptome. Factors causing large allelic RNA expression imbalance (AEI), imprinting for example, are easily detectable using RNA-Seq in combination with genomic DNA genotyping. However, modest AEI ratios, such as those caused by common regulatory genetic variants, are less reliably measured because of confounding factors inherent to RNA-seq. We propose a strategy for searching AEI signals from allelic RNA expression ratios at single SNPs, using information from multiple "comparable" SNPs. Under the null hypothesis of no AEI signal, a group of SNPs are assumed to have comparable fluctuations in sequence read differences between reference and variant alleles if the sum of read counts are similar. By applying this methodology to RNA-Seq data from human brain tissues, we identified numerous candidate SNPs with moderate to strong imbalanced allelic RNA expression.