Importance of a pilot study for non-invasive genetic sampling: genotyping errors and population size estimation in red deer

Population size information is critical for managing endangered or harvested populations. Population size can now be estimated from non-invasive genetic sampling. However, pitfalls remain such as genotyping errors (allele dropout and false alleles at microsatellite loci). To evaluate the feasibility of non-invasive sampling (e.g., for population size estimation), a pilot study is required. Here, we present a pilot study consisting of (i) a genetic step to test loci amplification and to estimate allele frequencies and genotyping error rates when using faecal DNA, and (ii) a simulation step to quantify and minimise the effects of errors on estimates of population size. The pilot study was conducted on a population of red deer in a fenced natural area of 5440 ha, in France. Twelve microsatellite loci were tested for amplification and genotyping errors. The genotyping error rates for microsatellite loci were 0–0.83 (mean=0.2) for allele dropout rates and 0–0.14 (mean=0.02) for false allele rates, comparable to rates encountered in other non-invasive studies. Simulation results suggest we must conduct 6 PCR amplifications per sample (per locus) to achieve approximately 97% correct genotypes. The 3% error rate appears to have little influence on the accuracy and precision of population size estimation. This paper illustrates the importance of conducting a pilot study (including genotyping and simulations) when using non-invasive sampling to study threatened or managed populations.     

Two-step multiplex polymerase chain reaction improves the speed and accuracy of genotyping using DNA from noninvasive and museum samples

Many studies in molecular ecology rely upon the genotyping of large numbers of low‐quantity DNA extracts derived from noninvasive or museum specimens. To overcome low amplification success rates and avoid genotyping errors such as allelic dropout and false alleles, multiple polymerase chain reaction (PCR) replicates for each sample are typically used. Recently, two‐step multiplex procedures have been introduced which drastically increase the success rate and efficiency of genotyping. However, controversy still exists concerning the amount of replication needed for suitable control of error. Here we describe the use of a two‐step multiplex PCR procedure that allows rapid genotyping using at least 19 different microsatellite loci. We applied this approach to quantified amounts of noninvasive DNAs from western chimpanzee, western gorilla, mountain gorilla and black and white colobus faecal samples, as well as to DNA from ~100‐year‐old gorilla teeth from museums. Analysis of over 45 000 PCRs revealed average success rates of > 90% using faecal DNAs and 74% using museum specimen DNAs. Average allelic dropout rates were substantially reduced compared to those obtained using conventional singleplex PCR protocols, and reliable genotyping using low (< 25 pg) amounts of template DNA was possible. However, four to five replicates of apparently homozygous results are needed to avoid allelic dropout when using the lowest concentration DNAs (< 50 pg/reaction), suggesting that use of protocols allowing routine acceptance of homozygous genotypes after as few as three replicates may lead to unanticipated errors when applied to low‐concentration DNAs.     

Maximum-likelihood estimation of allelic dropout and false allele error rates from microsatellite genotypes in the absence of reference data

The importance of quantifying and accounting for stochastic genotyping errors when analyzing microsatellite data is increasingly being recognized. This awareness is motivating the development of data analysis methods that not only take errors into consideration but also recognize the difference between two distinct classes of error, allelic dropout and false alleles. Currently methods to estimate rates of allelic dropout and false alleles depend upon the availability of error-free reference genotypes or reliable pedigree data, which are often not available. We have developed a maximum-likelihood-based method for estimating these error rates from a single replication of a sample of genotypes. Simulations show it to be both accurate and robust to modest violations of its underlying assumptions. We have applied the method to estimating error rates in two microsatellite data sets. It is implemented in a computer program, Pedant, which estimates allelic dropout and false allele error rates with 95% confidence regions from microsatellite genotype data and performs power analysis. Pedant is freely available at http://www.stats.gla.ac.uk/ approximately paulj/pedant.html.     

TECHNICAL ADVANCES: Effects of genotyping protocols on success and errors in identifying individual river otters (Lontra canadensis) from their faeces

In noninvasive genetic sampling, when genotyping error rates are high and recapture rates are low, misidentification of individuals can lead to overestimation of population size. Thus, estimating genotyping errors is imperative. Nonetheless, conducting multiple polymerase chain reactions (PCRs) at multiple loci is time-consuming and costly. To address the controversy regarding the minimum number of PCRs required for obtaining a consensus genotype, we compared consumer-style the performance of two genotyping protocols (multiple-tubes and 'comparative method') in respect to genotyping success and error rates. Our results from 48 faecal samples of river otters (Lontra canadensis) collected in Wyoming in 2003, and from blood samples of five captive river otters amplified with four different primers, suggest that use of the comparative genotyping protocol can minimize the number of PCRs per locus. For all but five samples at one locus, the same consensus genotypes were reached with fewer PCRs and with reduced error rates with this protocol compared to the multiple-tubes method. This finding is reassuring because genotyping errors can occur at relatively high rates even in tissues such as blood and hair. In addition, we found that loci that amplify readily and yield consensus genotypes, may still exhibit high error rates (7-32%) and that amplification with different primers resulted in different types and rates of error. Thus, assigning a genotype based on a single PCR for several loci could result in misidentification of individuals. We recommend that programs designed to statistically assign consensus genotypes should be modified to allow the different treatment of heterozygotes and homozygotes intrinsic to the comparative method.     

A Maximum-Likelihood Method to Correct for Allelic Dropout in Microsatellite Data with No Replicate Genotypes

Allelic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one or both allelic copies at a locus fail to be amplified by the polymerase chain reaction. Especially for samples with poor DNA quality, this problem causes a downward bias in estimates of observed heterozygosity and an upward bias in estimates of inbreeding, owing to mistaken classifications of heterozygotes as homozygotes when one of the two copies drops out. One general approach for avoiding allelic dropout involves repeated genotyping of homozygous loci to minimize the effects of experimental error. Existing computational alternatives often require replicate genotyping as well. These approaches, however, are costly and are suitable only when enough DNA is available for repeated genotyping. In this study, we propose a maximum-likelihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropout rates and allele frequencies when only one set of nonreplicated genotypes is available. Our method considers estimates of allelic dropout caused by both sample-specific factors and locus-specific factors, and it allows for deviation from Hardy–Weinberg equilibrium owing to inbreeding. Using the estimated parameters, we correct the bias in the estimation of observed heterozygosity through the use of multiple imputations of alleles in cases where dropout might have occurred. With simulated data, we show that our method can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (2) correctly estimate model parameters, including sample-specific dropout rates, locus-specific dropout rates, and the inbreeding coefficient; and (3) successfully correct the downward bias in estimating the observed heterozygosity. We find that our method is fairly robust to violations of model assumptions caused by population structure and by genotyping errors from sources other than allelic dropout. Because the data sets imputed under our model can be investigated in additional subsequent analyses, our method will be useful for preparing data for applications in diverse contexts in population genetics and molecular ecology.     

Estimation of genotyping error rate from repeat genotyping,unintentional recaptures and known parent–offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus)

Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent–offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop‐out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent–offspring pairs and unintentionally recaptured individuals, respectively. By direct‐count error estimates, the recapture and known parent–offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent–offspring comparisons. Furthermore, there was no correlation in locus‐specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus‐specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent–offspring comparisons in parentage studies).     

Assessing allelic dropout and genotype reliability using maximum likelihood.

A growing number of population genetic studies utilize nuclear DNA microsatellite data from museum specimens and noninvasive sources. Genotyping errors are elevated in these low quantity DNA sources, potentially compromising the power and accuracy of the data. The most conservative method for addressing this problem is effective, but requires extensive replication of individual genotypes. In search of a more efficient method, we developed a maximum-likelihood approach that minimizes errors by estimating genotype reliability and strategically directing replication at loci most likely to harbor errors. The model assumes that false and contaminant alleles can be removed from the dataset and that the allelic dropout rate is even across loci. Simulations demonstrate that the proposed method marks a vast improvement in efficiency while maintaining accuracy. When allelic dropout rates are low (0-30%), the reduction in the number of PCR replicates is typically 40-50%. The model is robust to moderate violations of the even dropout rate assumption. For datasets that contain false and contaminant alleles, a replication strategy is proposed. Our current model addresses only allelic dropout, the most prevalent source of genotyping error. However, the developed likelihood framework can incorporate additional error-generating processes as they become more clearly understood.     

Identification, Inheritance, and Variation of Microsatellite Markers in the Black Scallop Mimachlamys varia

Five polymorphic microsatellite loci were identified in the black scallop Mimachlamys varia after construction of a genomic library enriched for (GT)n. To examine the transmission pattern of microsatellite alleles, several families were created and genotypes scored for three loci. The expected Mendelian ratios were found in 12 of 14 segregations examined. Unexpected segregations may be explained by a genotyping error (allelic dropout), given that when a specific allele was treated as dominant, the phenotypic ratios conformed to Mendelian expectations. The five loci were also examined in two samples from the Spanish coast. The two localities displayed similar mean values for the number of alleles per locus (7.2-8.4), allelic richness (7.2-7.9), and observed (0.389-0.484) and expected heterozygosity (0.545-0.618). Significant Hardy-Weinberg deviations were observed at three loci, with heterozygote deficiency occurring in all cases. Global multilocus θ value and allele frequencies at one locus revealed significant differentiation between the two localities.     

Microsatellite genotyping errors: detection approaches, common sources and consequences for paternal exclusion

Microsatellite genotyping errors will be present in all but the smallest data sets and have the potential to undermine the conclusions of most downstream analyses. Despite this, little rigorous effort has been made to quantify the size of the problem and to identify the commonest sources of error. Here, we use a large data set comprising almost 2000 Antarctic fur seals Arctocephalus gazella genotyped at nine hypervariable microsatellite loci to explore error detection methods, common sources of error and the consequences of errors on paternal exclusion. We found good concordance among a range of contrasting approaches to error-rate estimation, our range being 0.0013 to 0.0074 per single locus PCR (polymerase chain reaction). The best approach probably involves blind repeat-genotyping, but this is also the most labour-intensive. We show that several other approaches are also effective at detecting errors, although the most convenient alternative, namely mother-offspring comparisons, yielded the lowest estimate of the error rate. In total, we found 75 errors, emphasizing their ubiquitous presence. The most common errors involved the misinterpretation of allele banding patterns (n = 60, 80%) and of these, over a third (n = 22, 36.7%) were due to confusion between homozygote and adjacent allele heterozygote genotypes. A specific test for whether a data set contains the expected number of adjacent allele heterozygotes could provide a useful tool with which workers can assess the likely size of the problem. Error rates are also positively correlated with both locus polymorphism and product size, again indicating aspects where extra effort at error reduction should be directed. Finally, we conducted simulations to explore the potential impact of genotyping errors on paternity exclusion. Error rates as low as 0.01 per allele resulted in a rate of false paternity exclusion exceeding 20%. Errors also led to reduced estimates of male reproductive skew and increases in the numbers of pups that matched more than one candidate male. Because even modest error rates can be strongly influential, we recommend that error rates should be routinely published and that researchers make an attempt to calculate how robust their analyses are to errors.     

Incorporating Genotype Uncertainty into Mark–Recapture-Type Models For Estimating Abundance Using DNA Samples

Summary .  Sampling DNA noninvasively has advantages for identifying animals for uses such as mark–recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark–recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger ( Meles meles ).     

gemini: software for testing the effects of genotyping errors and multitubes approach for individual identification

Improvements in the determination of individual genotypes from samples with low DNA quantity and quality are of prime importance in molecular ecology and conservation for reliable genetic individual identification (molecular tagging using microsatllites loci). Thus, errors (e.g. allelic dropout and false allele) appearing during samples genotyping must be monitored and eliminated as far as possible. The multitubes approach is a very effective but a costly and time‐consuming solution. In this paper, we present a simulation software that allows evaluation of the effect of genotyping errors on genetic identification of individuals and the effectiveness of a multitubes approach to correct these errors.     

Allelic dropout from a high-quality DNA source

Allelic dropouts are an important source of genotyping error, particularly in studies using non-invasive sampling techniques. This has important implications for conservation biology, as an increasing number of studies are now using non-invasive techniques to study rare species or endangered populations. Previously, allelic dropout has typically been associated with PCR amplification of low quality/quantity template DNA. However, in this study we recorded high levels of allelic dropout (21–57%) at specific loci amplified from a high quality DNA (63.1 ± 7.8 ng/μl) source in the red fox (Vulpes vulpes). We designed a series of experiments to identify the sources of error. Whilst we were able to show that the best method to identify allelic dropout was the dilution of template DNA prior to PCR amplification, our data also showed two specific patterns: (1) allelic dropouts occurred at specific loci; (2) allelic dropouts occurred at specific pair-wise combinations of alleles. These patterns suggest that mechanisms other than low quantity template DNA are responsible for allelic dropout. Further research on the causes of these patterns in this and other studies would further our understanding of genotyping errors and would aid future studies where allelic dropout may be a serious issue.     

A methodological approach for non-invasive sampling for population size estimates in wild boars (Sus scrofa)

Composite microsatellite genotypes were determined at five loci from 35 tissue-sampled wild boars and used as reference genotypes to estimate both allelic drop-out rate and false allele rate in comparison to genotypes from scats and hair strands of the same animals. These rates allow to assess the genotyping reliability when only non-invasively collected material is available. Polymerase chain reaction (PCR) amplification from scats was often corrupted by inhibitors and worked poorly, whereas genotyping success in hair samples was high. Body region of hair origin had no influence on PCR suitability, whereas the type of hair had. We recommend the use of bristles. PCR conditions were optimized for single-hair (bristle) genotyping.     

Assessment of genotyping accuracy in a non-invasive DNA-based population survey of Asiatic black bears (Ursus thibetanus): lessons from a large-scale pilot study in Iwate prefecture,northern Japan

Non-invasive DNA genotyping using hair samples has become a common method in population surveys of Asiatic black bears (Ursus thibetanus) in Japan; however, the accuracy of the genotyping data has rarely been discussed in empirical studies. Therefore, we conducted a large-scale pilot study to examine genotyping accuracy and sought an efficient way of error-checking hair-trapping data. We collected 2,067 hair samples, successfully determined the genotypes of 1,245 samples, and identified 295 individuals. The genotyping data were further divided into 3 subsets of data according to the number of hairs used for DNA extraction in each sample (1–4, 5–9, and ≥10 hairs), and the error rates of allelic dropout and false alleles were estimated for each subset using a maximum likelihood method. The genotyping error rates in the samples with ≥10 hairs were found to be lower than those in the samples with 1–4 and 5–9 hairs. The presence of erroneous genotypes among the identified individuals was further checked using a post hoc goodness-of-fit test that determined the match between the expected and observed frequencies of individual homozygotes at 0–6 loci. The results indicated the presence of erroneous genotypes, possibly as a result of allelic dropout, in the samples. Therefore, for improved accuracy, it is recommended that samples containing ≥10 hairs should be used for genotyping and a post hoc goodness-of-fit test should be performed to exclude erroneous genotypes before proceeding with downstream analysis such as capture-mark-recapture estimation.     

Fast and cost‐effective single nucleotide polymorphism (SNP) detection in the absence of a reference genome using semideep next‐generation Random Amplicon Sequencing (RAMseq)

Biodiversity has suffered a dramatic global decline during the past decades, and monitoring tools are urgently needed providing data for the development and evaluation of conservation efforts both on a species and on a genetic level. However, in wild species, the assessment of genetic diversity is often hampered by the lack of suitable genetic markers. In this article, we present Random Amplicon Sequencing (RAMseq), a novel approach for fast and cost‐effective detection of single nucleotide polymorphisms (SNPs) in nonmodel species by semideep sequencing of random amplicons. By applying RAMseq to the Eurasian otter (Lutra lutra), we identified 238 putative SNPs after quality filtering of all candidate loci and were able to validate 32 of 77 loci tested. In a second step, we evaluated the genotyping performance of these SNP loci in noninvasive samples, one of the most challenging genotyping applications, by comparing it with genotyping results of the same faecal samples at microsatellite markers. We compared (i) polymerase chain reaction (PCR) success rate, (ii) genotyping errors and (iii) Mendelian inheritance (population parameters). SNPs produced a significantly higher PCR success rate (75.5% vs. 65.1%) and lower mean allelic error rate (8.8% vs. 13.3%) than microsatellites, but showed a higher allelic dropout rate (29.7% vs. 19.8%). Genotyping results showed no deviations from Mendelian inheritance in any of the SNP loci. Hence, RAMseq appears to be a valuable tool for the detection of genetic markers in nonmodel species, which is a common challenge in conservation genetic studies.     

micro‐checker: software for identifying and correcting genotyping errors in microsatellite data

DNA degradation, low DNA concentrations and primer‐site mutations may result in the incorrect assignment of microsatellite genotypes, potentially biasing population genetic analyses. micro ‐checker is windows ®‐based software that tests the genotyping of microsatellites from diploid populations. The program aids identification of genotyping errors due to nonamplified alleles (null alleles), short allele dominance (large allele dropout) and the scoring of stutter peaks, and also detects typographic errors. micro ‐checker estimates the frequency of null alleles and, importantly, can adjust the allele and genotype frequencies of the amplified alleles, permitting their use in further population genetic analysis. micro ‐checker can be freely downloaded from http://www.microchecker.hull.ac.uk/ .     

DNA sampling from eggshell swabbing is widely applicable in wild bird populations as demonstrated in 23 species

There is increasing interest in noninvasive DNA sampling techniques. In birds, there are several methods proposed for sampling DNA, and of these, the use of eggshell swabbing is potentially applicable to a wide range of species. We estimated the effectiveness of this method in the wild by sampling the eggs of 23 bird species. Sampling of eggs was performed twice per nest, soon after the clutch was laid and again at the end of egg incubation. We genotyped DNA samples using a set of five conserved microsatellite markers, which included a Z-linked locus and a sex-typing marker. We successfully collected avian DNA from the eggs of all species tested and from 88.48% of the samples. In most of the cases, the DNA concentration was low (ca. 10 ng/μL). The number of microsatellite loci amplified per sample (0-5) was used as a measure of the genotyping success of the sample. On average, we genotyped 3.01 ± 0.12 loci per sample (mean ± SE), and time of sampling did not seem to have an effect; however, genotyping success differed among species and was greater in those species that used feather material for lining their nest cups. We also checked for the occurrence of possible genotyping errors derived from using samples with very low DNA quantities (i.e. allelic dropout or false alleles) and for DNA contamination from individuals other than the mother, which appeared at a moderate rate (in 44% of the PCR replicates and in 17.36% of samples, respectively). Additionally, we investigated whether the DNA on eggshells corresponded to maternal DNA by comparing the genotypes obtained from the eggshells to those obtained from blood samples of all the nestlings for six nests of magpies. In five of the six magpie nests, we found evidence that the swab genotypes were a mixture of genotypes from both parents and this finding was independent of the time of incubation. Thus, our results broadly confirm that the swabbing of eggshells can be used as a noninvasive method for obtaining DNA and is applicable across a wide range of bird species. Nonetheless, genotyping errors should be properly estimated for each species by using a suite of highly polymorphic loci. These errors may be resolved by sampling only recently laid eggs (to avoid non-maternal DNA contamination) or by performing several PCR replicates per sample (to avoid allelic dropout and false alleles) and/or by increasing the amount of DNA used in the PCR through increasing the volume of the PCR or increasing the concentration of template DNA.     

Incorporating Genotyping Error Into Non-Invasive DNA-Based Mark—Recapture Population Estimates

ABSTRACT Use of non-invasive sources of DNA, such as hair or scat, to obtain a genetic mark for population estimates is becoming commonplace. Unfortunately, with such marks, potentials for genotyping errors and for the shadow effect have resulted in use of many loci and amplification of each specimen many times at each locus, drastically increasing time and cost of obtaining a population estimate. We proposed a method, the Genotyping Uncertainty Added Variance Adjustment (GUAVA), which statistically adjusts for genotyping errors and the shadow effect, thereby allowing use of fewer loci and one amplification of each specimen per locus. Using allele frequencies and estimates of genotyping error rates, we determined, for each pair of specimens, the probability that the pair was obtained from the same individual, whether or not their observed genotypes match. Using these probabilities, we reconstructed possible capture history matrices and used this distribution to obtain a population estimate. With simulated data, we consistently found our estimates had lower bias and smaller variance than estimates based on single amplifications in which genotyping error was ignored and that were comparable to estimates based on data free of genotyping errors. We also demonstrated the method on a fecal DNA data set from a population of red wolves (Canis rufus). The GUAVA estimate based on only one amplification genotypes compares favorably to the estimate based on consensus genotypes. A program to conduct the analysis is available from the first author for UNIX or Windows platforms. Application of GUAVA may allow for increased accuracy in population estimates at reduced cost.     

Locus effects and sources of error in noninvasive genotyping

In spite of more than a decade of research on noninvasive genetic sampling, the low quality and quantity of DNA in noninvasive studies continue to plague researchers. Effects of locus size on error have been documented but are still poorly understood. Further, sources of error other than allelic dropout have been described but are often not well quantified. Here we analyse the effects of locus size on allelic dropout, amplification success and error rates in noninvasive genotyping studies of three species, and quantify error other than allelic dropout.     

A New Method for Noninvasive Genetic Sampling of Saliva in Ecological Research

Noninvasive samples for genetic analyses have become essential to address ecological questions. Popular noninvasive samples such as faeces contain degraded DNA which may compromise genotyping success. Saliva is an excellent alternative DNA source but scarcity of suitable collection methods makes its use anecdotal in field ecological studies. We develop a noninvasive method of collection that combines baits and porous materials able to capture saliva. We report its potential in optimal conditions, using confined dogs and collecting saliva early after deposition. DNA concentration in saliva extracts was generally high (mean 14 ng μl^-1). We correctly identified individuals in 78% of samples conservatively using ten microsatellite loci, and 90% of samples using only eight loci. Consensus genotypes closely matched reference genotypes obtained from hair DNA (99% of identification successes and 91% of failures). Mean genotyping effort needed for identification using ten loci was 2.2 replicates. Genotyping errors occurred at a very low frequency (allelic dropout: 2.3%; false alleles: 1.5%). Individual identification success increased with duration of substrate handling inside dog’s mouth and the volume of saliva collected. Low identification success was associated with baits rich in DNA-oxidant polyphenols and DNA concentrations <1 ng μl^-1. The procedure performed at least as well as other noninvasive methods, and could advantageously allow detection of socially low-ranked individuals underrepresented in sources of DNA that are involved in marking behaviour (faeces or urine). Once adapted and refined, there is promise for this technique to allow potentially high rates of individual identification in ecological field studies requiring noninvasive sampling of wild vertebrates.