Mark W. Geraci, M.D.
University of Colorado, Denver
Center for Geonomics
To provide excellence in genomic analysis for CMREF IPAH researchers.
Methods for genomic analysis have proven powerful tools for identification of potential new pathobiology, biomarkers, and drug targets. The field of genomics and its associated technologies is rapidly evolving. Initially, the use of expression arrays became a powerful avenue for hypothesis generation and discovery. More recently, the ability to perform high-throughput SNP analysis has also enabled investigators to explore the genome in disease states.
With such density of SNPs on an array, it is now possible to consider large-scale association studies, such as was done in patients with esophageal cancer. Complex traits can also be examined, as has been performed in inbred murine strains. Indeed, SNP analysis has been shown to increase information content compared to traditional microsatellite approaches.
With the density and content of these arrays increasing, the ability to perform powerful association studies and examining quantitative trait loci should possible. Affymetrix currently has available a 100k SNP set, with plans to release a 500k set within the year. As in the early days of microarray expression analysis, handling such data has proven to be complex, but newer algorithms, some freely available in the public domain, are becoming established.
As a further step to improve statistical power, clusters of SNPs can be evaluated en block, as haplotypes, which reduces the numbers of statistical tests performed and lessens the chance of type I errors due to false positives.
With the “state of the art” being, in fact, a moving target, we propose that establishing a compressive expression and SNP approach may provide the most information for future research. A general schema for the evaluation of tissues and blood by both expression analysis and SNP analysis is shown below in the figure to the left.
By providing a well-characterized control data set (as detailed in Specific Aim 2), annotated with clinical information, expression analysis and large-scale SNP determination, comparison to disease tissue is made much more meaningful and robust.
Micheala A. Aldred, PhD
Genomic Medicine Institute, Cleveland Clinic, Cleveland OH
Center for Geonomics
The major known genetic predisposition to Pulmonary Arterial Hypertension (PAH) is a heterozygous mutation in the bone morphogenetic protein receptor-II gene (BMPR2), a member of the transforming growth factor beta (TGFβ) superfamily. BMPR2 mutations are identifiable in approximately 80% of patients with a family history and about 20% of idiopathic cases. Under the new WHO classification, all such patients are now grouped together as Heritable PAH (HPAH, group 1.2). PAH also occurs in families with hereditary hemorrhagic telangiectasia (HHT), a distinct vascular disorder caused by mutations in ALK1 or endoglin (ENG), two other TGFβ receptors.
ALK1 or ENG mutations are uncommon in PAH patients unless there is a personal or family history of HHT. However, several recent reports of mosaicism associated with these genes highlight the importance of screening them in all PAH patients using sensitive methods that will detect mosaic mutations. The fourth gene recently implicated in PAH is SMAD9, a downstream mediator of BMP signaling. The overall frequency of SMAD9 mutations has not yet been determined.
The goal of the Mutation Analysis Core is to perform comprehensive mutation analysis of these four genes and provide the results to the PHBI network for correlation with other clinical and experimental data. Initially we will perform standard Sanger sequencing of all coding regions and splice sites, with follow-up studies on RNA where needed.
Large genomic rearrangements (deletion or duplication of at least one exon) constitute a significant proportion of BMPR2 mutations and are missed by Sanger sequencing, so we will also perform gene dosage studies to identify copy number changes. Sequencing technology is evolving rapidly and the cost of massively parallel “next-generation” sequencing is reducing.
In the second phase of the project, we will utilize these new technologies to screen a larger panel of candidate genes for germline and somatic mutations.