Abstract

BackgroundUse of genomic tools to characterize wildlife populations has increased in recent years. In the past, genetic characterization has been accomplished with more traditional genetic tools (e.g., microsatellites). The explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of increased precision in population genetic parameter estimates and identification of demographically and evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic tools. At present, few empirical comparisons of population genetic parameters and clustering analyses performed with microsatellites and SNPs have been conducted.ResultsHere we used microsatellite and SNP data generated from Gunnison sage-grouse (Centrocercus minimus) samples to evaluate concordance of the results obtained from each dataset for common metrics of genetic diversity (HO, HE, FIS, AR) and differentiation (FST, GST, DJost). Additionally, we evaluated clustering of individuals using putatively neutral (SNPs and microsatellites), putatively adaptive, and a combined dataset of putatively neutral and adaptive loci. We took particular interest in the conservation implications of any differences. Generally, we found high concordance between microsatellites and SNPs for HE, FIS, AR, and all differentiation estimates. Although there was strong correlation between metrics from SNPs and microsatellites, the magnitude of the diversity and differentiation metrics were quite different in some cases. Clustering analyses also showed similar patterns, though SNP data was able to cluster individuals into more distinct groups. Importantly, clustering analyses with SNP data suggest strong demographic independence among the six distinct populations of Gunnison sage-grouse with some indication of evolutionary independence in two or three populations; a finding that was not revealed by microsatellite data.ConclusionWe demonstrate that SNPs have three main advantages over microsatellites: more precise estimates of population-level diversity, higher power to identify groups in clustering methods, and the ability to consider local adaptation. This study adds to a growing body of work comparing the use of SNPs and microsatellites to evaluate genetic diversity and differentiation for a species of conservation concern with relatively high population structure and using the most common method of obtaining SNP genotypes for non-model organisms.

Highlights

  • Use of genomic tools to characterize wildlife populations has increased in recent years

  • Genetic diversity For all diversity metrics, 95% confidence intervals calculated from single nucleotide polymorphism (SNP) were narrower than confidence intervals from microsatellites (Fig. 2; Additional file 1: Table S1)

  • Microsatellite estimates had large confidence intervals in all cases, which resulted in no significant differences among population estimates

Read more

Summary

Introduction

Use of genomic tools to characterize wildlife populations has increased in recent years. The explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of increased precision in population genetic parameter estimates and identification of demographically and evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic tools. Most past genetic studies of wildlife species have been accomplished with relatively few (< 20) highly variable microsatellite loci. Unlike many other types of markers, microsatellites have a high mutation rate that is quite variable across different loci. This mutation rate is the result of slippage during DNA replication, a process that is not well understood [17]. Repeatability of genotyping across laboratories can be challenging [18,19,20,21] largely because allele size calls are somewhat subjective and size determination methods can impact inferred fragment size [22], even with use of automated software [23]

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call