Assessing statistical power of SNPs for population structure and conservation studies

Abstract

Single nucleotide polymorphisms (SNPs) have been proposed by some as the new frontier for population studies, and several papers have presented theoretical and empirical evidence reporting the advantages and limitations of SNPs. As a practical matter, however, it remains unclear how many SNP markers will be required or what the optimal characteristics of those markers should be in order to obtain sufficient statistical power to detect different levels of population differentiation. We use a hypothetical case to illustrate the process of designing a population genetics project, and present results from simulations that address several issues for maximizing statistical power to detect differentiation while minimizing the amount of effort in developing SNPs. Results indicate that (i) while ~30 SNPs should be sufficient to detect moderate (F(ST) = 0.01) levels of differentiation, studies aimed at detecting demographic independence (e.g. F(ST) < 0.005) may require 80 or more SNPs and large sample sizes; (ii) different SNP allele frequencies have little affect on power, and thus, selection of SNPs can be relatively unbiased; (iii) increasing the sample size has a strong effect on power, so that the number of loci can be minimized when sample number is known, and increasing sample size is almost always beneficial; and (iv) power is increased by including multiple SNPs within loci and inferring haplotypes, rather than trying to use only unlinked SNPs. This also has the practical benefit of reducing the SNP ascertainment effort, and may influence the decision of whether to seek SNPs in coding or noncoding regions.

Historical isolation and hydrodynamically constrained gene flow in declining populations of the South-African abalone, Haliotis midae

“…Significant linkage disequilibrium (P \ 0.05) was observed only for pairs of SNPs originating from the same EST sequence. The latter further increased expectations of diminished statistical power compared to the microsatellites but results of Morin et al (2009) demonstrated that although linked loci reduce the coverage of the genome, the statistical power of SNPs for population structure can actually be increased by including multiple SNPs within loci and inferring haplotypes rather than using only unlinked loci. In this study, the very small number of haplotypes inferred with PHASE was unlikely to have resulted in a significant gain in statistical power.…”

Genome-wide single nucleotide polymorphism (SNP) identification and characterization in a non-model organism, the African buffalo (Syncerus caffer), using next generation sequencing

“…Previous studies revealed that four to twelve times more SNPs are needed for population structure inference to match the statistical power of one microsatellite (Liu et al, 2005). For highly dispersive organisms it has been shown that the detection of low levels of differentiation is possible with a minimum of 80 SNPs (Morin et al, 2009;Ryman et al, 2006). Our large set of hundreds of SNPs should thus allow to scale the genetic marker system to the needs of future studies of the interaction between landscape features and microevolutionary processes (Manel et al, 2003).…”

“…Recent technological advances have revolutionized the generation of these genetic resources, allowing DNA-library construction, large-scale sequencing and identification of single nucleotide polymorphism (SNP) genetic markers (Seeb et al, 2011). SNPs were shown to constitute highly informative markers (Morin et al, 2009) and lead to a better inference of population structure than microsatellites (Liu et al, 2005;Santure et al, 2010). Attention has begun to shift toward SNPs as preferred genetic markers due to their increased power of resolution and accuracy for studying fine scale population structure (Schlötterer, 2004).…”

Toward a genome-wide approach for detecting hybrids: informative SNPs to detect introgression between domestic cats and European wildcats (Felis silvestris)

“…Recently, Nussberger et al (2013) developed a diagnostic marker set containing 48 SNPs that allows the identification of wildcats, domestic cats, their hybrids and backcrosses, and have demonstrated their accurate genotyping in single hairs (Nussberger et al, 2014). However, these authors used a restricted set of reference Toward a genome-wide approach for detecting hybrids R Oliveira et al samples, and the choice of highly differentiated traits/loci from a small panel of parental individuals has been considered to possibly overlook population differentiation (Brumfield et al, 2003;Schlötterer, 2004;Morin et al, 2009). This is a concern among European wildcat populations because the genetic partition of the populations is still poorly known, and central European wildcats might not be as fragmented as other regions (Mattucci et al, unpublished).…”

“…Although microsatellites have been the dominant markers in wildcat genetic studies (for example, Beaumont et al, 2001;Randi et al, 2001;Pierpaoli et al, 2003;Lecis et al, 2006;Germain et al, 2008;Eckert et al, 2010;O'Brien et al, 2009), and recently mtDNA diagnostic SNPs have been suggested (Driscoll et al, 2011), the increasing availability and numerous advantages of nuclear SNPs make them an appealing alternative and/or a complement to maternal and paternal lineage markers. SNPs have been attracting a growing interest in a wide range of evolutionary applications and are becoming efficient tools among wildlife conservation-oriented studies (Brumfield et al, 2003;Morin et al, 2004;Seddon et al, 2005;Morin et al, 2009). Offering less variability per locus than STRs, SNPs provide a substantial number of advantages, namely: (i) reduced propensity for homoplasy due to lower mutation rates; (ii) higher density and more uniform distribution in genomes; (iii) suitability for successful high-throughput genotyping and straightforward comparability and transportability across laboratories and detection protocols; and (iv) highly successful application in fragmented DNA samples, for example, noninvasive and historical DNA (see Brumfield et al, 2003;Morin et al, 2004;Garvin et al, 2010 for reviews).…”