Sampling for Microsatellite-Based Population Genetic Studies: 25 to 30 Individuals per Population Is Enough to Accurately Estimate Allele Frequencies

<div><p>One of the most common questions asked before starting a new population genetic study using microsatellite allele frequencies is “how many individuals do I need to sample from each population?” This question has previously been answered by addressing how many individuals are needed to detect all of the alleles present in a population (i.e. rarefaction based analyses). However, we argue that obtaining accurate allele frequencies and accurate estimates of diversity are much more important than detecting all of the alleles, given that very rare alleles (i.e. new mutations) are not very informative for assessing genetic diversity within a population or genetic structure among populations. Here we present a comparison of allele frequencies, expected heterozygosities and genetic distances between real and simulated populations by randomly subsampling 5–100 individuals from four empirical microsatellite genotype datasets (<em>Formica lugubris</em>, <em>Sciurus vulgaris</em>, <em>Thalassarche melanophris</em>, and <em>Himantopus novaezelandia</em>) to create 100 replicate datasets at each sample size. Despite differences in taxon (two birds, one mammal, one insect), population size, number of loci and polymorphism across loci, the degree of differences between simulated and empirical dataset allele frequencies, expected heterozygosities and pairwise F_ST values were almost identical among the four datasets at each sample size. Variability in allele frequency and expected heterozygosity among replicates decreased with increasing sample size, but these decreases were minimal above sample sizes of 25 to 30. Therefore, there appears to be little benefit in sampling more than 25 to 30 individuals per population for population genetic studies based on microsatellite allele frequencies.</p> </div

Impact of sample size on the precision of sample allele frequencies.

<p>The range (lines) and mean ± one standard deviation (solid boxes) of allele frequencies of the 100 random replicates at each sample size for one common and one relatively rare allele in A) the ant dataset, B) the squirrel dataset, C) the albatross dataset and D) the kakī dataset. The alleles are: A) allele 159 at locus FE16 (grey boxes, real frequency  = 0.176) and allele 116 at locus FE17 (black boxes, real frequency  = 0.833); B) allele 196 at locus Scv8 (grey boxes, real frequency  = 0.126) and allele 162 at locus Scv23 (black boxes, real frequency  = 0.766); C) allele 187 at locus De35 (grey boxes, real frequency  = 0.147) and allele 165 at locus D5 (black boxes, real frequency  = 0.852); and D) allele 241 at locus Kakī_21 (grey boxes, real frequency  = 0.112) and allele 200 at locus Kakī_27 (black boxes, real frequency  = 0.745).</p

Impact of allele frequency and sample size on the accuracy of mean sample allele frequency.

<p>Mean difference from the real allele frequency for each sample size (of the 100 random replicates per size) for A) the ant dataset, B) the squirrel dataset, C) the albatross dataset and D) the kakī dataset. Black circles represent alleles with a real frequency ≥0.05 (data for the same allele at different sample sizes linked by a line), white circles represent alleles with a real frequency between 0.05 and 0.01, and grey circles represent alleles with a real frequency ≤0.01.</p

Sleeping with the enemy, Hricta and Hfemorata microsatellite, stridulation and location data

Microsatellite data from 8 loci (HR3, HR12, HR13A, HR15, HR34, HR35, Hma03, Hma04) amplified from samples of Hemideina ricta, Hemideina femorata and one known hybrid. Location data, mitochondrial haplotypes and location data are included where possible. -9 indicates missing microsatellite data

Impact of sample size on mean genetic distance between samples and the true population.

<p>Mean pairwise F_ST between the 100 random replicates and the empirical dataset for A) the ant dataset, B) the squirrel dataset, C) the albatross dataset and D) the kakī dataset at each sample size. Error bars are standard deviation.</p

Human-Assisted Spread of a Maladaptive Behavior in a Critically Endangered Bird

<div><p>Conservation management often focuses on counteracting the adverse effects of human activities on threatened populations. However, conservation measures may unintentionally relax selection by allowing the ‘survival of the not-so-fit’, increasing the risk of fixation of maladaptive traits. Here, we report such a case in the critically-endangered Chatham Island black robin (<i>Petroica traversi</i>) which, in 1980, was reduced to a single breeding pair. Following this bottleneck, some females were observed to lay eggs on the rims of their nests. Rim eggs left in place always failed to hatch. To expedite population recovery, rim eggs were repositioned inside nests, yielding viable hatchlings. Repositioning resulted in rapid growth of the black robin population, but by 1989 over 50% of all females were laying rim eggs. We used an exceptional, species-wide pedigree to consider both recessive and dominant models of inheritance over all plausible founder genotype combinations at a biallelic and possibly sex-linked locus. The pattern of rim laying is best fitted as an autosomal dominant Mendelian trait. Using a phenotype permutation test we could also reject the null hypothesis of non-heritability for this trait in favour of our best-fitting model of heritability. Data collected after intervention ceased shows that the frequency of rim laying has strongly declined, and that this trait is maladaptive. This episode yields an important lesson for conservation biology: fixation of maladaptive traits could render small threatened populations completely dependent on humans for reproduction, irreversibly compromising the long term viability of populations humanity seeks to conserve.</p></div

Inferred trajectories of allele A under the simple dominant model (model No. 4 in Table 2) between 1980 and 1989.

<p>The mean trajectory is shown by black line and the 50% and 95% confidence sets of the trajectories are shown in decreasing shades of grey. Twice the population size (total number of alleles) is shown by the dashed and dotted line.</p

Pedigree of all black robins breeding between 1980 and 1989.

<p>All individuals are labelled and descend from one breeding pair (A1 and A2). Males are shown as squares. Females that lay rim eggs are shown as red ircles and females that do not lay rim eggs are shown as blue circles (pedigree was generated using Pedigraph <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079066#pone.0079066-Garbe1" target="_blank">[31]</a>).</p

Model Selection.

<p>Log likelihood and posterior probability of the phenotypes conditional on the pedigree, founder genotypes and model of inheritance. Z is a sex chromosome. The prior probability of each model is uniformly distributed and all other models considered (see text) have zero likelihood.</p

Fitness consequences of rim laying behavior.

<p>Data from 2007–11 (during which rim eggs were not repositioned) shows that females that laid rim eggs had a significantly reduced clutch size (i.e. number of eggs laid inside nests that were incubated), and decreased hatching and breeding success compared to normal-laying females. We obtain p-values from likelihood ratio tests with generalized linear mixed models of data with sample size <i>n</i>.</p