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ABLeS Participant: Queensland Research Centre for Peripheral Vascular Disease, AITHM, James Cook University

Project title

Exploiting mouse genetics to identify genomic biomarkers that drive abdominal aortic aneurysm risk, prediction and diagnosis

Contact(s)

Project description and aims

  • Background and context: Abdominal aortic aneurysm (AAA) occurs due to progressive weakening and expansion of the abdominal aorta. It affects 4-5% of the population 1,2, with males >60 years particularly at risk 3. Despite decades of pre-clinical research aimed at identifying diagnostic biomarkers and drug interventions that limit or prevent AAA growth, the most effective clinical strategies remain early detection and imaging surveillance of small, asymptomatic AAAs. Surgical repair is performed only for large or symptomatic AAA 3. Even with emergency surgical repair, the mortality rate of ruptured AAA remains 76-90% 4–6. These realities underscore the need for new approaches that can advance our mechanistic understanding of AAA and guide therapeutic development. In this project we will perform a comparative genetic modifier analysis to identify new AAA biomarkers that can be used for disease risk and development prediction.

  • Aim: Identify genetic modifiers of AAA susceptibility using mouse strain–specific SNPs and assess their potential as human biomarkers. Twin studies estimate that genetic factors account for ~70% of AAA risk in human cohorts3, yet the strong environmental influence on this disease has made identifying modifier genes difficult. Though genome wide association studies have implicated 24 loci in AAA development, efforts have struggled to validate these as biomarkers. To overcome this limitation, we will exploit naturally occurring strain-specific genetic variation in inbred mouse lines to identify novel modifiers of AAA susceptibility with direct translational potential. A landmark study in 20067 compared the susceptibility of eight inbred mouse strains to elastase-induced AAA and demonstrated that FVB and C57BL/6 backgrounds were highly permissive to aneurysm formation, whereas SvEv, SvJ, and CBA strains were resistant. Consequently, most subsequent AAA studies have relied on the C57BL/6 background to ensure consistent disease induction. Despite this long-standing recognition of strain-dependent susceptibility, the underlying genetic determinants distinguishing permissive from resistant phenotypes remain uncharacterized. This project will integrate strain-specific single nucleotide polymorphisms (SNP) data to pinpoint candidate modifier genes, which will then be cross-referenced with human genetic and expression data to identify conserved biomarkers of AAA risk.

  • Aim: High-quality SNP and variant calls for the resistant and permissive inbred strains will be obtained from the Mouse Genomes Project or JAX, or generated by whole-genome sequencing if required. Variant files will be normalised, quality-filtered, and harmonised to a common reference genome. To identify genetic modifiers of AAA susceptibility, we will computationally extract loci where alleles are identical within the AAA-permissive strains and within the resistant strains, but differ between the two groups. The resulting variants will be annotated for predicted functional impact, genomic context and regulatory datasets to prioritise biologically meaningful loci. Candidate biomarkers will then be validated in vitro using genetic material from AAA patients stored in the Queensland Research Centre for Peripheral Vascular Disease Biobank.

The analysis pipeline requires access to R and RStudio, with the ability to install packages and Bioconductor dependencies. Some steps may be more efficiently run in or require Python for analysis, requiring Ubuntu OS and pip also.

How is ABLeS supporting this work?

This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation.

Expected outputs enabled by participation in ABLeS

The analysis will also uncover candidate genomic and transcriptomic biomarkers to improve risk prediction, diagnosis, and inform therapeutic development.


These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above.