Development of a hierarchical algorithm to investigate the role of long non coding RNA regions in the etiology of asthma

Asthma is a complex disease caused by a combination of genetic, epigenetic and environmental factors.

Although several studies have attempted to identify the specific genes associated with asthma, the underlying genetic mechanisms are still unclear. Genomic imprinting, an epigenetic phenomenon that occurs early in life whereby only one gene copy is active and the other  parental copy is fully methylated and hence inactive (“parent-of-origin effects”), may be involved.

I performed the analysis of the first large-scale genome-wide association study (GWAS) of parent-of-origin effects in asthma on data collected from three Canadian family-based studies/cohorts. Preliminary results strongly suggest the involvement of long non-coding (lnc) RNA.

lncRNAs are known to be involved in genomic imprinting. I hypothesize that lncRNAs identified from the parent-of-origin effects in asthma are involved in imprinting.

Due to their length and low information density, lncRNA regions are very time- and cost intensive to confirm and study. I will develop a hierarchical algorithm that will automate the selection of lncRNA regions and specific sites to investigate for DNA methylation.

The ability to select important lncRNA regions in an efficient and automated manner will result in increased efficiency for researchers, and will save time, materials and personnel costs. The selection algorithm will be added to our collection of web-based tools on the Genapha website and will be widely used by researchers interested in genomic regulation.

Unraveling the molecular mechanisms for variation in lung function

Lung function measures reflect the physiological state of the lungs, and are essential for the diagnosis and management of obstructive lung diseases such as asthma and chronic obstructive pulmonary disease (COPD), two common diseases with a huge burden on patients and health care systems. The measures are thought to have a genetic component, yet it is not known exactly how genes affect lung function. One mechanism by which genetic variation can influence lung function is through changing the amount of protein produced by that gene. This can be discovered by relating DNA sequence variations to mRNA or protein expression. Hence, to unravel the molecular mechanisms underlying lung function, it is very informative to study the genetic control of lung-specific gene expression.
Dr. Obeidat hypothesizes that a subset of lung function and obstructive lung diseases associated genetic loci act to change the level of expression of their gene product within the lung.
Dr. Obeidat is a member of a team that conducted genome-wide genotyping and gene expression analysis of lung tissue samples from ~1,200 individuals; the worlds largest study of its kind. The study identified ~17,000 loci where variants were related to the level of gene expression.  Variants that are associated with both lung function and gene expression will be prioritized as being potentially causal for variation in lung function and will be investigated further. Further, bioinformatics methods will be used to identify molecular pathways and networks enriched in the genes driving lung function variation.
This approach represents the next frontier in complex diseases genetics and has been successfully implemented in other disease areas. With this integrative genomics method, Dr. Obeidat aims to use the new genetics derived knowledge to develop new therapies to alleviate common respiratory diseases.