Dr Elise Furlan1, Dr Bernd Gruber1
1University Of Canberra, Bruce, Australia
Biography:
Dr Elise Furlan is an ARC DECRA Fellow and environmental DNA researcher based at the University of Canberra. Her research focuses on developing practical genetic and genomic approaches for wildlife monitoring and management. Dr Furlan has led numerous projects across Australia involving threatened species monitoring, invasive species detection and biodiversity assessment, and has published extensively on the application of molecular tools to conservation and environmental management.
Professor Bernd Gruber is a quantitative ecologist and conservation genomicist at the University of Canberra's Institute for Applied Ecology. His research focuses on population genomics, landscape genomics and the development of analytical tools that support evidence-based conservation management. He is a lead developer of the open-source dartRverse software platform, which provides accessible genomic analysis tools for researchers and practitioners worldwide. Professor Gruber has made significant contributions to advancing the application of population genomics in conservation, with particular interests in translating complex genomic data into practical management outcomes.
Abstract:
Reduced SNP panels are transforming conservation genetics by enabling affordable, standardised genetic monitoring. Their importance is growing as genetic analyses expand beyond tissue samples to eDNA and other trace-DNA sources, where DNA is often degraded, present at low concentrations and unsuitable for routine genome-wide genotyping. Despite this, most SNP panels continue to be developed using relatively simple approaches that optimise individual loci rather than overall panel performance, with little guidance on matching panel design to the intended application.
This presentation introduces a new open-source optimisation framework that helps researchers design SNP panels that are genuinely fit for purpose. Rather than relying on a single measure of marker performance, the framework evaluates alternative panel designs against multiple conservation objectives and validates how well reduced panels capture information from genome-wide datasets. It also enables researchers to explore trade-offs between competing objectives and assess how panel performance is likely to change over time.
Simulation studies demonstrate that optimising SNPs collectively produces more informative panels than conventional rank-based approaches, particularly when only small panels are feasible—an important consideration for eDNA and other degraded DNA applications. By providing an objective and transparent approach to panel design, this framework bridges the gap between genome-wide discovery datasets and practical genetic monitoring, making targeted SNP panels more accessible, defensible and effective for conservation management.