Genotyping errors in a calibrated DNA register: implications for identification of individuals

<p>Abstract</p> <p>Background</p> <p>The use of DNA methods for the identification and management of natural resources is gaining importance. In the future, it is likely that DNA registers will play an increasing role in this development. Microsatellite markers have been the primary tool in ecological, medical and forensic genetics for the past two decades. However, these markers are characterized by genotyping errors, and display challenges with calibration between laboratories and genotyping platforms. The Norwegian minke whale DNA register (NMDR) contains individual genetic profiles at ten microsatellite loci for 6737 individuals captured in the period 1997-2008. These analyses have been conducted in four separate laboratories for nearly a decade, and offer a unique opportunity to examine genotyping errors and their consequences in an individual based DNA register. We re-genotyped 240 samples, and, for the first time, applied a mixed regression model to look at potentially confounding effects on genotyping errors.</p> <p>Results</p> <p>The average genotyping error rate for the whole dataset was 0.013 per locus and 0.008 per allele. Errors were, however, not evenly distributed. A decreasing trend across time was apparent, along with a strong within-sample correlation, suggesting that error rates heavily depend on sample quality. In addition, some loci were more error prone than others. False allele size constituted 18 of 31 observed errors, and the remaining errors were ten false homozygotes (i.e., the <it>true </it>genotype was a heterozygote) and three false heterozygotes (i.e., the <it>true </it>genotype was a homozygote).</p> <p>Conclusions</p> <p>To our knowledge, this study represents the first investigation of genotyping error rates in a wildlife DNA register, and the first application of mixed models to examine multiple effects of different factors influencing the genotyping quality. It was demonstrated that DNA registers accumulating data over time have the ability to maintain calibration and genotyping consistency, despite analyses being conducted on different genotyping platforms and in different laboratories. Although errors were detected, it is demonstrated that if the re-genotyping of individual samples is possible, these will have a minimal effect on the database's primary purpose, i.e., to perform individual identification.</p

Development of SNP for the deep-sea fish blue ling, Molva dypterygia (Pennant, 1784) from ddRAD sequencing data

Blue ling is a deep-water species that has been severely fished upon into coastal and offshore fisheries since the early 1970s, thus causing the collapse of the populations in the last two decades. Genetic information is scarce in this species, and molecular markers are therefore needed to provide advice both for management and for rebuilding the stocks. A suite of 103 SNP markers was identified from ddRAD sequencing data. From those, 81 were organized in three multiplex reactions, and tested on 150 individuals from three different sampling locations. Good-quality amplification products were successfully obtained from 70 of the markers. All SNP loci were biallelic, with averaged He per locus ranging between 0.101 and 0.500

Investigating Population Genetic Structure in a Highly Mobile Marine Organism: The Minke Whale Balaenoptera acutorostrata acutorostrata in the North East Atlantic

Inferring the number of genetically distinct populations and their levels of connectivity is of key importance for the sustainable management and conservation of wildlife. This represents an extra challenge in the marine environment where there are few physical barriers to gene-flow, and populations may overlap in time and space. Several studies have investigated the population genetic structure within the North Atlantic minke whale with contrasting results. In order to address this issue, we analyzed ten microsatellite loci and 331 bp of the mitochondrial D-loop on 2990 whales sampled in the North East Atlantic in the period 2004 and 2007–2011. The primary findings were: (1) No spatial or temporal genetic differentiations were observed for either class of genetic marker. (2) mtDNA identified three distinct mitochondrial lineages without any underlying geographical pattern. (3) Nuclear markers showed evidence of a single panmictic population in the NE Atlantic according STRUCTURE's highest average likelihood found at K = 1. (4) When K = 2 was accepted, based on the Evanno's test, whales were divided into two more or less equally sized groups that showed significant genetic differentiation between them but without any sign of underlying geographic pattern. However, mtDNA for these individuals did not corroborate the differentiation. (5) In order to further evaluate the potential for cryptic structuring, a set of 100 in silico generated panmictic populations was examined using the same procedures as above showing genetic differentiation between two artificially divided groups, similar to the aforementioned observations. This demonstrates that clustering methods may spuriously reveal cryptic genetic structure. Based upon these data, we find no evidence to support the existence of spatial or cryptic population genetic structure of minke whales within the NE Atlantic. However, in order to conclusively evaluate population structure within this highly mobile species, more markers will be required

Minke whale_microsatellites

Data collected in the fiel

Minke whale microsatellites.

<p>Summary statistics per year showing total number of alleles, allelic richness (based on minimum sample size of 449 diploid individuals), number of private alleles, observed heterozygosity (average ± SE), unbiased expected heterozygosity (average ± SE), and inbreeding coefficient (F_IS) (average ± SD).</p><p>Minke whale microsatellites.</p

Minke whale mtDNA.

<p>Analyses of population stability (Tajima's D and Fu's F_S tests) and population expansion (sum of squared deviations, SSD and raggedness, rg mismatch distribution tests). Significant values are indicated with boldface type.</p><p>Minke whale mtDNA.</p

Example of comparison between real populations and the simulated panmictic ones.

<p>Bayesian clustering of North East Atlantic minke whale corresponding to year class 2004 (left column) and to a randomly chosen simulated panmictic population (right column). Inferred ancestry of individuals was calculated after averaging ten STRUCTURE runs with CLUMPP for K = 2 (barplots a,b) and K = 3 (barplots c–f). The outgroups were 95 individuals of the Pacific subspecies (<i>B. a. scammoni</i>) and 93 individuals of the Antarctic species (<i>B. bonaerensis</i>).</p

Distribution of females (F) and males (M) per Management Area (Fig. 1) on a per year class basis.

<p>Distribution of females (F) and males (M) per Management Area (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108640#pone-0108640-g001" target="_blank">Fig. 1</a>) on a per year class basis.</p

Geographic distribution of the five International Whaling Commission (IWC) Management Areas: ES (Svalbard-Bear Island area), EB (Eastern Barents Sea), EW (Norwegian Sea and coastal zones off North Norway, including the Lofoten area), EN (North Sea), and CM (Western Norwegian Sea-Jan Mayen area).

<p>Geographic distribution of the five International Whaling Commission (IWC) Management Areas: ES (Svalbard-Bear Island area), EB (Eastern Barents Sea), EW (Norwegian Sea and coastal zones off North Norway, including the Lofoten area), EN (North Sea), and CM (Western Norwegian Sea-Jan Mayen area).</p