A fair fight between molecular marker types in a seascape genetics setting

Area of Science:

• Population genetics
• Genomic sequencing
• Molecular ecology

Background:

• Population genetics has evolved through various genetic data types, including allozymes, microsatellites, and single nucleotide polymorphisms (SNPs).
• High-throughput genomic sequencing, particularly whole genome sequencing and reduced representation library (RRL) sequencing, has become a primary method for generating SNP data.
• Comparisons between emerging and existing data types often use different gold standards, potentially affecting resolution and power assessments.

Purpose of the Study:

• To compare the effectiveness of different genetic data types in discerning population structure at fine scales.
• To evaluate the performance of high-throughput SNP data against traditional microsatellites and nuclear loci.
• To assess the impact of data down-sampling on the results of population genetics analyses.

Main Methods:

• Application of high-throughput sequencing to microsatellites, nuclear loci, and SNPs using a reduced representation library (RRL) method.
• Implementation of a spatially dense sampling scheme.
• Conducting a comprehensive suite of population genetics analyses across different data types, including down-sampled datasets.

Main Results:

• Single nucleotide polymorphism (SNP) data demonstrated slightly greater sensitivity to fine-scale genetic structure.
• SNP data results were more resilient to down-sampling compared to microsatellites and nonrepetitive nuclear loci.
• Modest additions to microsatellite and nuclear loci datasets could provide analytical power comparable to SNPs for fine-scale structure delineation.

Conclusions:

• While SNP data offer advantages in sensitivity and resilience, they do not render traditional data types obsolete.
• Combining traditional genetic markers (microsatellites, nuclear loci) with SNP data can enhance the power of population structure analysis.
• Well-designed sampling schemes remain crucial for robust population genetics inferences, regardless of the data type used.