Estimation of Individual’s Risk for Complex Trait Diseases: Methods and Challenges using Allelic Specific Expression and Mapping Cis-variance from NGS Data (RNA and Exome Sequencing Data)

Thursday, November 17, 2011 - 2:00pm - 3:00pm
Keller 3-180
Shipra Agrawal (BioCOS Life Sciences Private Limited)
In current genetic and clinical research, identification of disease specific variations particularly from non-coding RNA and cis-elements is a major bottleneck. Massively parallel sequencing of exome and transcriptome is widely being used to effectively interrogate the key protein-coding and non-coding RNA regions. In such scenarios, the deep sequencing data of exome and transcriptome could be used for estimating levels of allele-specific expression in diseased vs. control samples (case-control cohorts) and hence the identification of disease specific signatures. This provides a functional basis to identify the differentially expressed alleles, mono-allelic expression, imprinting of alleles and allele regulated alternative splicing. All such data and approaches together make a stronger strategy to predict the disease susceptibility alleles and their functional role in disease mechanism.

Our approaches at BioCOS Life Sciences using the Next Generation Sequencing (NGS) data analysis for the precise detection of allele’s differential expression becomes important in identifying causal/susceptibility genes by mapping their variance in both coding and non-coding DNA/RNA regions.

I will present our current research work on developing methods and data processing approaches, which can be applied in identification of the susceptibility alleles using the combined approaches from RNA-Seq and Exome-Seq data as well as directly predicting them from RNA-Seq data. The talk will also discuss the existing bottlenecks in the area and approaches to obtain high quality results with a focus on calling genotypes from RNA-Seq data.