Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism panels.
Alina von ThadenCarsten NowakAnnika TiesmeyerTobias E ReinersPaulo C AlvesLeslie A LyonsFederica MattucciEttore RandiMargherita CragnoliniJosé GaliánZsolt HegyeliAndrew C KitchenerClotilde LambinetJosé M LucasThomas MölichLuana RamosVinciane SchockertBerardino CocchiararoPublished in: Molecular ecology resources (2020)
The genomic era has led to an unprecedented increase in the availability of genome-wide data for a broad range of taxa. Wildlife management strives to make use of these vast resources to enable refined genetic assessments that enhance biodiversity conservation. However, as new genomic platforms emerge, problems remain in adapting the usually complex approaches for genotyping of noninvasively collected wildlife samples. Here, we provide practical guidelines for the standardized development of reduced single nucleotide polymorphism (SNP) panels applicable for microfluidic genotyping of degraded DNA samples, such as faeces or hairs. We demonstrate how microfluidic SNP panels can be optimized to efficiently monitor European wildcat (Felis silvestris S.) populations. We show how panels can be set up in a modular fashion to accommodate informative markers for relevant population genetics questions, such as individual identification, hybridization assessment and the detection of population structure. We discuss various aspects regarding the implementation of reduced SNP panels and provide a framework that will allow both molecular ecologists and practitioners to help bridge the gap between genomics and applied wildlife conservation.
Keyphrases
- genome wide
- copy number
- dna methylation
- single cell
- high throughput
- label free
- primary care
- single molecule
- electronic health record
- circulating tumor cells
- clinical practice
- healthcare
- mental health
- circulating tumor
- nucleic acid
- machine learning
- general practice
- high density
- real time pcr
- sensitive detection
- data analysis
- bioinformatics analysis