P1091
spliceR: Detecting and Quantifying Allele-Specific-Expression from RNA-Seq Data

Date: Monday, January 13, 2014
Room: Grand Exhibit Hall
Shreyartha Mukherjee , The Samuel Roberts Noble Foundation, Ardmore, OK
Paul Scott , Iowa State University
Richard Johnson , University of Illinois, Urbana, Urbana, IL
Andrew D. Farmer , National Center for Genome Resources (NCGR), Santa Fe, NM
Gregory D. May , Pioneer HiBred, Johnston, IA
William Beavis , Iowa State University, Ames, IA
Allelic specific expression (ASE) is a vital factor in phenotypic variability and for the development of complex traits. Some genes display allelic disparity in gene expression that is transmitted by Mendelian or non-Mendelian inheritance and this discrepancy may be associated to effects like heterosis, variation in yield, uniformity in plants and complex traits and diseases in animals. It is of great interest to study how genetic and epigenetic modifications lead to transcriptional variation and how transcriptional variation affects the phenotype. Differential allele expression may be controlled by changes to the nucleotide sequence and regulatory elements, such as single nucleotide polymorphisms (SNPs), insertions and deletions, and studies indicate that these variations are rampant across the plant genomes and tissues. Such variants in the coding regions of genes may alter the structure and function of the gene product. Recent studies have shown that preferential expression of alleles is widespread in mammals. Non-imprinted autosomal genes exhibit allelic imbalance at the transcript level in mouse hybrids  and humans, and such expression produces proteins associated with diseases. Hence a solid understanding of classification and functional annotation of allele-specifically expressed genes is vital to recognize the extent of functionally important regulatory variation. This will help us identify candidate haplotypes and the correlation between their genetic sequences and heterotic traits. The physiological vigor and variations in general health of an organism is strongly associated with the extent of variation of parental gametes. In our study we combine sequence variants and transcript counts from next-generation sequence data to develop a novel “personal haplotyping” method (and a software pipeline spliceR) in order to study allele-specific expression and identify alleles that are preferentially expressed across genetic backgrounds and levels of inbreeding.