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Hardy–Weinberg proportions (HWP) are often explored to evaluate the assumption of random mating. However, in autopolyploids, organisms with more than two sets of homologous chromosomes, HWP and random mating are different hypotheses that require different statistical testing approaches. Currently, the only available methods to test for random mating in autopolyploids (i) heavily rely on asymptotic approximations and (ii) assume genotypes are known, ignoring genotype uncertainty. Furthermore, these approaches are all frequentist, and so do not carry the benefits of Bayesian analysis, including ease of interpretability, incorporation of prior information, and consistency under the null. Here, we present Bayesian approaches to test for random mating, bringing the benefits of Bayesian analysis to this problem. Our Bayesian methods also (i) do not rely on asymptotic approximations, being appropriate for small sample sizes, and (ii) optionally account for genotype uncertainty via genotype likelihoods. We validate our methods in simulations and demonstrate on two real datasets how testing for random mating is more useful for detecting genotyping errors than testing for HWP (in a natural population) and testing for Mendelian segregation (in an experimental S1 population). Our methods are implemented in Version 2.0.2 of the hwep R package on the Comprehensive R Archive Network https://cran.r‐project.org/package=hwep. [ABSTRACT FROM AUTHOR]

Abstract Background Important information on movement pathways and introduction routes of invasive parasites can be obtained by comparing the genetic makeup of an invader with its spatial genetic structure in other distribution areas. Sometimes, the population genetic structure of the host might be more informative than that of the parasite itself, and it is important to collect tissue samples of both host and parasite. However, host tissue samples are frequently not available for analysis. We aimed to test whether it is possible to generate reliable microsatellite profiles of host individuals by amplifying DNA extracted from a nematode parasite, using the raccoon (Procyon lotor) and the raccoon roundworm (Baylisascaris procyonis) as a test case. Methods Between 2020 and 2021, we collected tissue as well as a single roundworm each from 12 raccoons from central Germany. Both the raccoon and the roundworm DNA extracts were genotyped using 17 raccoon-specific microsatellite loci. For each roundworm DNA extract, we performed at least eight amplification reactions per microsatellite locus. Results We extracted amplifiable raccoon DNA from all 12 roundworms. We obtained at least two amplification products for 186 of the 204 possible genotypes. Altogether 1077 of the 1106 genotypes (97.4%) matched the host-DNA derived reference genotypes and thus did not contain genotyping errors. Nine of the 12 roundworm-derived genetic profiles matched the reference profiles from the raccoon hosts, with one additional genetic profile containing genotyping errors at a single locus. The remaining two genetic profiles were deemed unsuitable for downstream analysis because of genotyping errors and/or a high proportion of missing data. Conclusions We showed that reliable microsatellite-based genetic profiles of host individuals can be obtained by amplifying DNA extracted from a parasitic nematode. Specifically, the approach can be applied to reconstruct invasion pathways of roundworms when samples of the raccoon hosts are lacking. Further research should assess whether this method can be replicated in smaller species of parasitic nematodes and other phyla of parasites more generally. Graphical Abstract

Background: Important information on movement pathways and introduction routes of invasive parasites can be obtained by comparing the genetic makeup of an invader with its spatial genetic structure in other distribution areas. Sometimes, the population genetic structure of the host might be more informative than that of the parasite itself, and it is important to collect tissue samples of both host and parasite. However, host tissue samples are frequently not available for analysis. We aimed to test whether it is possible to generate reliable microsatellite profiles of host individuals by amplifying DNA extracted from a nematode parasite, using the raccoon (Procyon lotor) and the raccoon roundworm (Baylisascaris procyonis) as a test case. Methods: Between 2020 and 2021, we collected tissue as well as a single roundworm each from 12 raccoons from central Germany. Both the raccoon and the roundworm DNA extracts were genotyped using 17 raccoon-specific microsatellite loci. For each roundworm DNA extract, we performed at least eight amplification reactions per microsatellite locus. Results: We extracted amplifiable raccoon DNA from all 12 roundworms. We obtained at least two amplification products for 186 of the 204 possible genotypes. Altogether 1077 of the 1106 genotypes (97.4%) matched the host-DNA derived reference genotypes and thus did not contain genotyping errors. Nine of the 12 roundworm-derived genetic profiles matched the reference profiles from the raccoon hosts, with one additional genetic profile containing genotyping errors at a single locus. The remaining two genetic profiles were deemed unsuitable for downstream analysis because of genotyping errors and/or a high proportion of missing data. Conclusions: We showed that reliable microsatellite-based genetic profiles of host individuals can be obtained by amplifying DNA extracted from a parasitic nematode. Specifically, the approach can be applied to reconstruct invasion pathways of roundworms when samples of the raccoon hosts are lacking. Further research should assess whether this method can be replicated in smaller species of parasitic nematodes and other phyla of parasites more generally. [ABSTRACT FROM AUTHOR]

Background: Two individuals with a first-degree relationship share about 50 percent of their alleles. Parent–offspring relationships cannot be homozygous for alternative alleles (genetic exclusion). Methods: Applying the concept of genetic exclusion to HD arrays typed in animals for experimental purposes or genomic selection allows estimation of the rate of rejection of first-degree relationships as the rate at which two individuals typed for a large number of Single Nucleotide Polymorphisms (SNPs) do not share at least one allele. An Expectation–Maximization algorithm is applied to estimate parentage. In addition, genotyping errors are estimated in true parent–offspring relationships. Samples from nine candidate Duroc sires and 55 Iberian dams producing 214 Duroc × Iberian barrows were typed for the HD porcine Affymetrix array. Results: We were able to establish paternity and maternity of 75 and 85 piglets, respectively. Rate of rejection in true parent–offspring relationships was estimated as 0.000735. This is a lower bound of the genotyping error since rate of rejection depends on allele frequencies. After accounting for allele frequencies, our estimate of the genotyping error is 0.6%. A total of 7,744 SNPs were rejected in five or more true parent–offspring relationships facilitating identification of “problematic” SNPs with inconsistent inheritance in multiple parent–offspring relationships. Conclusions: This study shows that animal experiments and routine genotyping in genomic selection allow to establish or to verify first-degree relationships as well as to estimate genotyping errors for each batch of animals or experiment. [ABSTRACT FROM AUTHOR]

Genetic non-invasive sampling has been a popular tool in obtaining insight into species' ecology and biology, including demographic parameters, at a large landscape level. The possibility of inflated population estimates due to inherent genotyping errors has been established in the literature. However, the persistence of false 'ghost' individuals, despite widely used remedial measures, may influence population estimates and hence, requires re-examination and validation. Therefore, we used blind sub-samples of known individuals and aimed to (i) provide the extent of correct identification from faecal DNA while following standard protocols, (ii) quantify the error and (iii) suggest remedial measures using Asian elephants as a case study. We used six empirical sub-samples each from eight elephant individuals, resulting in 48 faecal DNA extracts, and genotyped across eight microsatellite markers in four replicates each to obtain consensus data. We tallied the known individual identities with the obtained genotypes of the sub-samples under several marker combinations. We observed that errors remained even after deriving consensus genotypes using a multi-tube approach. The loci-wise proportions of aberrant genotypes ranged up to 23.9%. The overall accuracy of individual assignment ranged from 18.1 to 95.2% under different marker combinations and mismatch thresholds. The combination of five markers allowing one-locus mismatch could group the highest number of empirical sub-samples (n = 28; 84.8%) correctly to their respective known genotypes. Therefore, we suggest undertaking pilot studies to screen microsatellite panels for their accuracy in assigning the identities of faecal DNA samples from known individuals using a blind test approach. Such empirical studies before undertaking large scale molecular census hold the potential to provide accurate demographic estimates. [ABSTRACT FROM AUTHOR]

Genealogical relationships are fundamental components of genetic studies. However, it is often challenging to infer correct and complete pedigrees even when genome‐wide information is available. For example, inbreeding can obscure genetic differences between individuals, making it difficult to even distinguish first‐degree relatives such as parent‐offspring from full siblings. Similarly, genotyping errors can interfere with the detection of genetic similarity between parents and their offspring. Inbreeding is common in natural, domesticated, and experimental populations and genotyping of these populations often has more errors than in human data sets, so efficient methods for building pedigrees under these conditions are necessary. Here, we present a new method for parent‐offspring inference in inbred pedigrees called specific parent‐offspring relationship estimation (spore). spore is vastly superior to existing pedigree‐inference methods at detecting parent‐offspring relationships, in particular when inbreeding is high or in the presence of genotyping errors, or both. spore therefore fills an important void in the arsenal of pedigree inference tools. [ABSTRACT FROM AUTHOR]

In non‐model organisms, evolutionary questions are frequently addressed using reduced representation sequencing techniques due to their low cost, ease of use, and because they do not require genomic resources such as a reference genome. However, evidence is accumulating that such techniques may be affected by specific biases, questioning the accuracy of obtained genotypes, and as a consequence, their usefulness in evolutionary studies. Here, we introduce three strategies to estimate genotyping error rates from such data: through the comparison to high quality genotypes obtained with a different technique, from individual replicates, or from a population sample when assuming Hardy‐Weinberg equilibrium. Applying these strategies to data obtained with Restriction site Associated DNA sequencing (RAD‐seq), arguably the most popular reduced representation sequencing technique, revealed per‐allele genotyping error rates that were much higher than sequencing error rates, particularly at heterozygous sites that were wrongly inferred as homozygous. As we exemplify through the inference of genome‐wide and local ancestry of well characterized hybrids of two Eurasian poplar (Populus) species, such high error rates may lead to wrong biological conclusions. By properly accounting for these error rates in downstream analyses, either by incorporating genotyping errors directly or by recalibrating genotype likelihoods, we were nevertheless able to use the RAD‐seq data to support biologically meaningful and robust inferences of ancestry among Populus hybrids. Based on these findings, we strongly recommend carefully assessing genotyping error rates in reduced representation sequencing experiments, and to properly account for these in downstream analyses, for instance using the tools presented here. [ABSTRACT FROM AUTHOR]

Strategic management of brushtail possum populations in New Zealand is presently dependent on the use of a standardized trap-catch procedure for monitoring population trends. Where this has been used in the first few months after control, calculated rates of increase often far exceed known reproductive and dispersal rates, suggesting that trapping-based population indices immediately following control are biased low. We are investigating the problem by genotyping DNA extracted from possum fecal pellets and using matching genotypes in the ear tissue of trapped possums as a measure of trappability. We have used quantitative polymerase chain reaction (PCR) to determine the threshold of possum DNA required from fecal samples in order to obtain an accurate genotype (>99%) from field samples. This has enabled the removal of “allelic dropout” as a source of error in obtaining accurate genotypes from such noninvasive DNA samples. Validation tests were conducted on fecal samples collected from caged (i.e., identifiable) possums, and fecal pellets and ear tissue from trapped possums in the field. The tests confirmed that the sample collection and preservation procedures used resulted in accurate identification of possums from both types of sample material, although fecal pellets older than about 7 days were unlikely to yield sufficient DNA for amplification and genotyping. Genotyping of a large quantity of sample material collected before control, and at 1, 4, and 9 months after control, in 2 replicated field trials is proceeding and revealing information on the trappability of possums that survive control, and the contribution of immigration to the populations after control.

The recovery of genealogy in both natural and captive populations is critical for any decision in the management of genetic resources. It allows for the estimation of genetic parameters such as heritability and genetic correlations, as well as defining an optimal mating design that maintains a large effective population size. We utilised “genotyping-by-sequencing” (GBS) in combination with bioinformatics tools developed specifically for GBS data to recover genetic relatedness, with a focus on parent-offspring relationships in a Eucalyptus nitens breeding population as well as recognition of individuals representing other Eucalyptus species and putative hybrids. We found a clear advantage on using tools specifically designed for data of highly variable sequencing quality when recovering genetic relatedness. The parent-offspring relatedness showed a significant response to data filtering from 0.05 to 0.3 when the standard approach (G1) was used, while it oscillated around 0.4 when the specifically designed method (G5) was implemented. Additionally, comparisons with commonly used tools demonstrated vulnerability of the relatedness estimates to incorrect imputation of missing data when shallow sequencing information and genetically distant individuals are present in the population. In turn, these biased imputed genotypes negatively affected the estimation of genetic relatedness between parents and offspring. Careful filtering for both genetic outliers and shallowly sequenced markers led to improvements in estimations of genetic relatedness. Alternatively, a method that avoided missing data imputation and took sequence depth into consideration improved the accuracy of parent-offspring relationship coefficients where sequencing data quality was highly variable.

Summary: SNP arrays are widely used in genetic research and agricultural genomics applications, and the quality of SNP genotyping data is of paramount importance. In the present study, SNP genotyping concordance and discordance were evaluated for commercial bovine SNP arrays based on two types of quality assurance (QA) samples provided by Neogen GeneSeek. The genotyping discordance rates (GDRs) between chips were on average between 0.06% and 0.37% based on the QA type I data and between 0.05% and 0.15% based on the QA type II data. The average genotyping error rate (GER) pertaining to single SNP chips, based on the QA type II data, varied between 0.02% and 0.08% per SNP and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome SNPs had considerably elevated GDRs and GERs compared to the SNPs on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele 'dropout' (i.e. from AB to AA or BB). Simultaneous allotyping errors on both alleles (e.g. mistaking AA for BB or vice versa) were relatively rare. Finally, a list of SNPs with a GER greater than 1% is provided. Interpretation of association effects of these SNPs, for example in genome‐wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine SNP arrays. [ABSTRACT FROM AUTHOR]

Highlights • We illustrate the potential of dense sets of SNP markers to resolve distant relationships. • We study the impact of linkage disequilibrium, genotyping errors and assuming inappropriate population. • We conclude that denser sets of genetic markers is well suited for inference of distant relationships. • We explore four real cases and illustrate that these may be resolved using dense marker sets. Abstract With the advent of high density SNP arrays and the progress of next generation sequencing, demands for new methods to handle the subsequent data analysis have exploded. Forensic laboratories are generally hesitant to implement new methods in casework unless they are thoroughly tested and validated. This is particularly true when a third party contractor is involved in the analysis. In this paper we explore data from dense sets of SNP markers and study how different errors could potentially affect the results. Particularly, we study the effects of genotyping errors, linkage disequilibrium as well as the use of inappropriate population frequencies. We demonstrate that ignoring these concepts may lead to false conclusions for some different relationship cases and outline solutions to mitigate these problems. [ABSTRACT FROM AUTHOR]

Abstract Background Schistosomiasis japonica remains a major public health problem in China. Integrating molecular analyses, such as population genetic analyses, of the parasite into the on-going surveillance programs is helpful in exploring the factors causing the persistence and/or spread of Schistosoma japonicum. However, genotyping errors can seriously affect the results of such studies, unless accounted for in the analyses. Methods We assessed the genotyping errors (missing alleles or false alleles) of seven S. japonicum microsatellites, using a pedigree data approach for schistosome miracidia, which were stored on Whatman FTA cards. Results Among 107 schistosome miracidia successfully genotyped, resulting in a total of 715 loci calls, a total of 31 genotyping errors were observed with 25.2 % of the miracidia having at least one error. The error rate per locus differed among loci, which ranged from 0 to 9.8 %, with the mean error rate 4.3 % over loci. With the parentage analysis software Cervus, the assignment power with these seven markers was estimated to be 89.5 % for one parent and 99.9 % for a parent pair. One locus was inferred to have a high number of null alleles and a second with a high mistyping rate. Conclusion To the authors’ knowledge, this is the first time that S. japonicum pedigrees have been used in an assessment of genotyping errors of microsatellite markers. The observed locus-specific error rate will benefit downstream epidemiological or ecological analyses of S. japonicum with the markers.

Abstract: This study introduces the NMπ computer program designed for estimation of plant mating system and seed and pollen dispersal kernels. NMπ is a re‐implementation of the NM+ program and provides new features such as support for multicore processors, explicit treatment of dioecy, the possibility of incorporating uniparentally cytoplasmic markers, the possibility of assessing assortative mating due to phenotypic similarity and inference about offspring genealogies. The probability model of parentage (the neighbourhood model) accounts for missing data and genotyping errors, which can be estimated along with regular parameters of the mating system. The program has virtually no restrictions with respect to a number of individuals, markers or phenotypic characters. A console version of NMπ can be run under a wide variety of operating systems, including Windows, Linux or Mac OS. For Windows users, a graphical user interface is provided to facilitate operating the software. The program, user manual and example data are available on http://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/. [ABSTRACT FROM AUTHOR]

Genetic data are in widespread use in ecological research, and an understanding of this type of data and its uses and interpretations will soon be an imperative for ecological statisticians. Here, we provide an introduction to the subject, intended for statisticians who have no previous knowledge of genetics. Although there are numerous types of genetic data, we restrict attention to multilocus genotype data from microsatellite loci. We look at two application areas in wide use: investigating population structure using genetic assignment and related techniques; and using genotype data in capture-recapture studies for estimating population size and demographic parameters. In each case, we outline the conceptual framework and draw attention to both the strengths and weaknesses of existing approaches to analysis and interpretation. [ABSTRACT FROM AUTHOR]

The study is focused on addressing the problem of building genetic maps in the presence of ~10³-104 of markers per chromosome. We consider a spectrum of situations with intrachromosomal heterogeneity of recombination rate, different level of genotyping errors, and missing data. In the ideal scenario of the absence of errors and missing data, the majority of markers should appear as groups of cosegregating markers ("twins") representing no challenge for map construction. The central aspect of the proposed approach is to take into account the structure of the marker space, where each twin group (TG) and singleton markers are represented as points of this space. The confounding effect of genotyping errors and missing data leads to reduction of TG size, but upon a low level of these effects surviving TGs can still be used as a source of reliable skeletal markers. Increase in the level of confounding effects results in a considerable decrease in the number or even disappearance of usable TGs and, correspondingly, of skeletal markers. Here, we show that the paucity of informative markers can be compensated by detecting kernels of markers in the marker space using a clustering procedure, and demonstrate the utility of this approach for high-density genetic map construction on simulated and experimentally obtained genotyping datasets. [ABSTRACT FROM AUTHOR]

Microsatellite-based genetic assignment is used broadly to monitor contemporary effective dispersal among populations. The need to investigate the robustness of this method to common genotyping errors was emphasized more than a decade ago, but it remains unaddressed., We evaluate here for the first time the effect of mistaken and null alleles on estimates of contemporary seed and pollen migration rates obtained with genetic assignment methods. We also introduce a novel Bayesian approach to jointly estimate seed and pollen migration rates, genotyping error rates and null allele frequencies, not requiring independent reference or duplicate genotypic data., Unaccounted-for mistaken alleles caused positive bias and increased the root mean square error ( RMSE) of pollen migration rate estimates, whereas seed migration rate estimates were weakly sensitive to mistyping. Jointly estimating mistyping rates minimized the bias and RMSE they introduce on pollen migration estimates, while yielding seed migration rate estimates with similar or slightly larger bias and RMSE than those obtained when ignoring mistyping., Ignoring genotyping errors can be especially problematic when there is no actual migration, because it can lead to the wrong conclusion that there is statistically significant exchange of pollen and/or seeds among populations that are actually isolated., Unaccounted-for null alleles are problematic when among-population pollen dispersal is present, leading to underestimation of pollen migration rates and overestimation of seed migration rates. Jointly estimating null allele frequencies minimized these two biases, reduced the RMSE of seed migration rate estimates and produced relatively small changes in the RMSE of pollen dispersal estimates., Synthesis. Disregarding genotyping errors and null alleles can produce biased and less accurate estimates of the rates at which present-day plant populations are exchanging seed and pollen. An approach is proposed here to minimize the effect of genotyping problems on contemporary migration rate estimates, which should help avoiding erroneous migration inference, monitoring and management, especially when dealing with low migration rates and their associated uncertainty. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms (SNPs) which represent the most widespread source of sequence variation in genomes, are becoming a routine application in several fields such as forensics, ecology and conservation genetics. Their use, requiring short amplifications, may allow a more efficient genotyping of degraded DNA. We provide the first application of SNP genotyping in an Italian non-invasive genetic monitoring project of the wolf. We compared three different techniques for genotyping SNPs: pyrosequencing, SNaPshot® and TaqMan® Probe Assay in Real-Time PCR. We successively genotyped nine SNPs using the TaqMan Probe Assay in 51 Italian wolves, 57 domestic dogs, 15 wolf x dog hybrids and 313 wolf scats collected in the northern Apennines. The obtained results were used to estimate genetic variability and PCR error rates in SNP genotyping protocols compared to standard microsatellite analysis. We evaluated the cost, laboratory effort and reliability of these different markers and discuss the possible future use of VeraCode, SNPlex and Fluidigm EP1 system in wild population monitoring.

Estimation of selection response and genealogical tracing in family mixtures require an appropriate tool for parentage analysis. In this study, we tested 19 marker loci for parentage analysis allocation in Mediterranean mussel ( Mytilus galloprovincialis). To this aim, we reared families in tanks isolated from wild mussel seed, analysed them using the 19 marker loci and characterized their performances based on Mendelian rules. Probabilities of exclusion of a false parent were estimated for different groups of loci and contrasted to the real paternity assignment. Based on this, we chose nine microsatellites with the highest exclusion probabilities and a real paternity assignment of 99.6%. Next, we analysed 600 individuals reared as in the usual production process, where contamination from wild seed is likely. We obtained a real assignment of 94.7% and were able to identify individuals from the wild as the most likely hypothesis to explain the observed incompatibilities with candidate parents. This information was used to evaluate parental contribution in offspring obtained from gamete mixtures of several parents, which bestowed results of interest for future breeding programs of Mediterranean mussel. [ABSTRACT FROM AUTHOR]

Null alleles are alleles that for various reasons fail to amplify in a PCR assay. The presence of null alleles in microsatellite data is known to bias the genetic parameter estimates. Thus, efficient detection of null alleles is crucial, but the methods available for indirect null allele detection return inconsistent results. Here, our aim was to compare different methods for null allele detection, to explain their respective performance and to provide improvements. We applied several approaches to identify the 'true' null alleles based on the predictions made by five different methods, used either individually or in combination. First, we introduced simulated 'true' null alleles into 240 population data sets and applied the methods to measure their success in detecting the simulated null alleles. The single best-performing method was ML- Null Freq_frequency. Furthermore, we applied different noise reduction approaches to improve the results. For instance, by combining the results of several methods, we obtained more reliable results than using a single one. Rule-based classification was applied to identify population properties linked to the false discovery rate. Rules obtained from the classifier described which population genetic estimates and loci characteristics were linked to the success of each method. We have shown that by simulating 'true' null alleles into a population data set, we may define a null allele frequency threshold, related to a desired true or false discovery rate. Moreover, using such simulated data sets, the expected null allele homozygote frequency may be estimated independently of the equilibrium state of the population. [ABSTRACT FROM AUTHOR]

Accurate genetic data are important prerequisite of performing genetic linkage test or association test. Currently, most analytical methods assume that the observed genotypes are correct. However, due to the constraint at the technical level, most of the genetic data that people used so far contain errors. In this paper, we considered the problem of QTL mapping based on biological data with genotyping errors. By analysing all possible genotypes of each individual in framework of multipleinterval mapping, we proposed an algorithm of inferring all model parameters through the expectation-maximization (EM) algorithm and discussed the hypothesis testing of the existence of QTL. We carried out extensive simulation studies to assess the proposed method. Simulation results showed that the new method outperforms the method that does not take the genotyping errors into account, and therefore it can decrease the impact of genotyping errors on QTL mapping. The proposed method was also applied to analyse a real barley dataset. [ABSTRACT FROM AUTHOR]

Tamandua tetradactyla (Pilosa), the lesser anteater, is a medium-size mammal from South America. Its wide distribution through different landscapes, solitary and nocturnal habits, and the difficulty to capture and contain specimens limit the amount of individuals and populations sampled during fieldworks. These features along with the lack of specific molecular markers for the lesser anteater might be the causes for paucity in population genetic studies for the species. Historical samples from museum specimens, such as skins, and non-invasive samples, such as plucked hair, can be supplementary sources of DNA samples. However, the DNA quantity and quality of these samples may be limiting factors in molecular studies. In this study, we describe nine microsatellite loci for T. tetradactyla and test the amplification success, data reliability and estimate errors on both historical and non-invasive sample sets. We tested nine polymorphic microsatellites and applied the quality index approach to evaluate the relative performance in genotype analysis of 138 historical samples (study skin) and 19 non-invasive samples (plucked hair). The observed results show a much superior DNA quality of non-invasive over historical samples and support the quality index analysis as a practical tool to exclude samples with doubtful performance in genetic studies. We also found a relationship between the age of non-invasive samples and DNA quality, but lack of evidence of this pattern for historical samples. [ABSTRACT FROM AUTHOR]

We evaluate the effect of genotyping errors on the type-I error of a general association test based on genotypes, showing that, in the presence of errors in the case and control samples, the test statistic asymptotically follows a scaled non-central $\chi ^2$ distribution. We give explicit formulae for the scaling factor and non-centrality parameter for the symmetric allele-based genotyping error model and for additive and recessive disease models. They show how genotyping errors can lead to a significantly higher false-positive rate, growing with sample size, compared with the nominal significance levels. The strength of this effect depends very strongly on the population distribution of the genotype, with a pronounced effect in the case of rare alleles, and a great robustness against error in the case of large minor allele frequency. We also show how these results can be used to correct $p$-values. [ABSTRACT FROM AUTHOR]

Microsatellite loci are widely used in population genetic studies, but the presence of null alleles may lead to biased results. Here, we assessed five methods that indirectly detect null alleles and found large inconsistencies among them. Our analysis was based on 20 microsatellite loci genotyped in a natural population of Microtus oeconomus sampled during 8 years, together with 1200 simulated populations without null alleles, but experiencing bottlenecks of varying duration and intensity, and 120 simulated populations with known null alleles. In the natural population, 29% of positive results were consistent between the methods in pairwise comparisons, and in the simulated data set, this proportion was 14%. The positive results were also inconsistent between different years in the natural population. In the null-allele-free simulated data set, the number of false positives increased with increased bottleneck intensity and duration. We also found a low concordance in null allele detection between the original simulated populations and their 20% random subsets. In the populations simulated to include null alleles, between 22% and 42% of true null alleles remained undetected, which highlighted that detection errors are not restricted to false positives. None of the evaluated methods clearly outperformed the others when both false-positive and false-negative rates were considered. Accepting only the positive results consistent between at least two methods should considerably reduce the false-positive rate, but this approach may increase the false-negative rate. Our study demonstrates the need for novel null allele detection methods that could be reliably applied to natural populations. [ABSTRACT FROM AUTHOR]

Population and conservation genetic studies of endangered species increasingly rely on non-invasively collected samples like hair and faeces. The extant genotyping errors in such samples can be further aggravated in studies on sympatric, closely related species like tiger (Panthera tigris) and leopard (Panthera pardus), where mistakes in species identification can lead to erroneous population estimation, structure and genetic diversity. In this study we assessed possible errors in identification of tiger/leopard faecal samples with published species identification markers. We further analysed a panel of cross-species, polymorphic microsatellite loci for individual identification of both tigers and leopards with an aim to understand the level of allelic overlaps between the two species and to identify a subset of loci useful in eliminating errors in species identification. Two out of four species-specific primers show cross-species amplification. The suite of microsatellite markers used in this study is informative enough to identify individuals and establish genetic diversity in both species. However, 11 out of 13 microsatellites loci exhibit overlapping alleles in tigers and leopards. One locus (Fca96) does not amplify in leopard, whereas only one locus (F124) shows different allelic ranges in the two species. Prior to taking up non-invasive genetic studies on one/multiple sympatric species, it is important to accurately identify the origin of samples. Further, we recommend correct identification of private alleles in each species which will help in eliminating errors in species identification. [ABSTRACT FROM AUTHOR]

Several options are available to the scientific community for genetic map construction but few are simple to install and use. Available programs either lack intuitive interface or are commercial, expensive for many laboratories. We present MapDisto, a free, user-friendly and powerful program for constructing genetic maps from experimental segregating populations. MapDisto is freely available at . Current version: 1.7.5. [ABSTRACT FROM AUTHOR]

Optimal collection and preservation protocols for fecal DNA genotyping are not firmly established. We evaluated 3 factors that influence microsatellite genotyping success of fecal DNA extracted from coyote (Canis latrans) scats: 1) age of scat, 2) preservative, and 3) diet content. We quantified genotyping success by comparing rates of allelic dropout, false alleles, and failed amplifications among consensus genotypes. We used a panel of 6 microsatellite loci to genotype 20 scat samples, each of which was subjected to 3 age (1 day, 5 days, and 10 days post-deposition) and 3 preservation (DET buffer, 95% ethanol [EtOH], and lysis buffer) treatments. Both sample age and storage buffer had a significant effect on success and reliability. Ethanol and DET buffer preserved fecal samples with similar efficiency, and both were superior to lysis buffer. Our analysis of DNA degradation rates revealed that samples collected as early as 5 days of age yielded DNA that was highly degraded relative to samples collected on day 1. We tested the influence of dietary remains on microsatellite genotyping by using scat samples consisting predominantly of insect prey (n = 5), mammalian prey (n = 9), or the remains of juniper (Juniperus spp.) berries (n = 6) and compared EtOH and DET buffer preservation efficacy. We observed a significant interaction effect between storage buffer and diet for the probability of a false allele in a polymerase chain reaction (PCR), suggesting that the optimal preservation technique depended on the food remains comprising the scat. Scats comprised of juniper berry remains were more reliably genotyped when preserved in DET than EtOH. Mammalian prey-based scats were more reliable when stored in EtOH than DET buffer. Insect-predominant scats were preserved in EtOH and DET buffer with similar efficiency. Although accurate and reliable results can be obtained from scats collected at =5 days of age, we suggest sampling design to include collection of scats <5 days of age to minimize field and laboratory expenses. We suggest EtOH preservation for scats of obligate carnivores and of facultative carnivores with a diet consisting primarily of mammals. We suggest DET buffer preservation for animals with a diet consisting of plant-derived foods. Lysis buffer protocols that we employed should not be used for fecal DNA preservation. [ABSTRACT FROM AUTHOR]

Abstract: Genotyping individual larval stages and eggs of natural parasite populations is complicated by the difficulty of obtaining reliable genotypes from low quantity DNA template. A suitable storage and extraction protocol, together with a thorough quantification of genotyping errors are therefore crucial for molecular epidemiological studies. Here we test the robustness, handling time, ease of use, cost effectiveness and success rate of various fixation (Whatman FTA® Classic and Elute Cards, 70% EtOH and RNAlater ®) and subsequent DNA extraction methods (commercial kits and proteinase K protocol). None of these methods require a cooling chain and are therefore suitable for field collection. Based on a multiplex microsatellite PCR with nine loci the success and reliability of each technique is evaluated by the proportion of samples with at least eight scored loci and the proportion of genotyping errors. If only the former is taken into account, FTA® Elute is recommended (83% success; 44% genotyping error; 0.2€/sample; 1h 20m handling time). However, when also considering the genotyping errors, handling time and ease of use, we opt for 70% EtOH with the 96-well plate technology followed by a simple proteinase K extraction (73% success; 0% genotyping error; 0.2€/sample; 15m handling time). For eggs we suggest (1) to pool all eggs per person in 1.5ml tubes filled with 70% EtOH for transport and (2) to identify each egg to species level prior to genotyping. To this end we extended the Rapid diagnostic PCR developed by with a S. mansoni-specific primer to discriminate between S. mansoni, S. haematobium and S. bovis in a single PCR reaction. The success rate of genotyping eggs was 75% (0% genotyping error). This is the first study to incorporate genotyping errors through re-amplification for the evaluation of schistosome sampling protocols and the identification of error-prone loci. [Copyright &y& Elsevier]

For many applications in population genetics, codominant simple sequence repeats (SSRs) may have substantial advantages over dominant anonymous markers such as amplified fragment length polymorphisms (AFLPs). In high polyploids, however, allele dosage of SSRs cannot easily be determined and alleles are not easily attributable to potentially diploidized loci. Here, we argue that SSRs may nonetheless be better than AFLPs for polyploid taxa if they are analyzed as effectively dominant markers because they are more reliable and more precise. We describe the transfer of SSRs developed for diploid Mercurialis huetii to the clonal dioecious M. perennis. Primers were tested on a set of 54 male and female plants from natural decaploid populations. Eight of 65 tested loci produced polymorphic fragments. Binary profiles from 4 different scoring routines were used to define multilocus lineages (MLLs). Allowing for fragment differences within 1 MLL, all analyses revealed the same 14 MLLs without conflicting with merigenet, sex, or plot assignment. For semiautomatic scoring, a combination of as few as 2 of the 4 most polymorphic loci resulted in unambiguous discrimination of clones. Our study demonstrates that microsatellite fingerprinting of polyploid plants is a cost efficient and reliable alternative to AFLPs, not least because fewer loci are required than for diploids. [ABSTRACT FROM PUBLISHER]

Conventional methods for monitoring cougar, Puma concolor, populations involve capture, tagging, and radio-collaring, but these methods are time-consuming, expensive, and logistically challenging. For difficult-to-study species such as cougars, noninvasive genetic sampling (NGS) may be a useful alternative. The ability to identify individuals from samples collected through NGS methods provides many opportunities for developing population-monitoring tools, but the utility of these survey methods is dependent upon collection of samples and accurate genotyping of those samples. In January 2003, we initiated a 3-yr evaluation of NGS methods for cougars using a radio-collared population in Yellowstone National Park (YNP), USA. Our goals were to: 1) determine which DNA collection method, hair snares or snow tracking, provided a better method for obtaining samples for genetic analysis, 2) evaluate reliability of the genetic data derived from hair samples collected in the field, and 3) evaluate the potential of NGS for demographic monitoring of cougar populations. Snow tracking yielded more hair samples and was more cost effective than snagging hair with rub pads. Samples collected from bed sites and natural hair snags (e.g., branch tips, thorn bushes) while snow tracking accurately identified and sexed 22 individuals (9 F, 13 M). The ratio of the count from snow tracking to the count from radio-telemetry was 15:24 in winter 2004, 13:12 in 2005, and 22:29 for both years combined. Annual capture probabilities for obtaining DNA from snow tracking varied considerably between years for females (0.42 in 2004 and 0.88 in 2005) but were more consistent for males (0.77 in 2004 and 0.88 in 2005). Our results indicate that snow tracking can be an efficient, reliable NGS method for cougars in YNP and has potential for estimating demographic and genetic parameters of other carnivore populations in similar climates. [ABSTRACT FROM AUTHOR]

Noninvasive genetic monitoring of animal populations has become a widely used method in animal conservation and wildlife management due to its known advantages in sample availability of endangered or elusive species. A variety of methods have been suggested to overcome the difficulties of collecting reliable genetic data despite poor DNA quality and quantity of samples. We used quantitative real-time polymerase chain reaction (qPCR) to quantify DNA contents and preselect extracts suitable for microsatellite genotyping of noninvasive samples from 2 carnivore species, wolf (Canis lupus) and Eurasian otter (Lutra lutra). We tested 2 concentration thresholds for DNA extracts containing either 5 pg/μL or 25 pg/μL at minimum and evaluated the effect of excluding samples from genotyping falling below either of these DNA concentrations. Depending on species and threshold concentration applied, we reduced the genotyping effort by 21% to 47% and genotyping errors by 7% to 45%, yet we could still detect 82% to 99% of available genotypes. Thus, qPCR may potentially reduce genotyping effort and enhance data reliability in noninvasive genetic studies. Genetic laboratories working on noninvasive population genetic studies could transfer this approach to other species, streamline genetic analyses and, thus, more efficiently provide wildlife managers with reliable genetic data of wild populations. [ABSTRACT FROM AUTHOR]

Background/Aims: There is a growing interest regarding the effect of differential misclassification on power and type I error rate in genome-wide association studies. We present an extension of a previously published test statistic: the likelihood ratio test allowing for errors (LRTAE). This test uses double-sample information on a subset of individuals to increase power for genetic association in the presence of nondifferential misclassification. Methods:We extend the original LRTAE by allowing for differential genotype misclassification between case and control populations. We label this new statistic as LRTDAME . We test the performance of this statistic with data simulated under differential misclassification specifications and two different types of genetic models: null and power. For simulations using the null model, we specify that there is no difference between case and control genotype frequencies before the introduction of errors. For simulations under power, we consider three modes of inheritance: dominant, multiplicative, and recessive. Results: We show that the LRTDAME , with p values computed using permutation, maintains a correct type I error rate under the null model after the introduction of differential genotyping errors. Also, we find that as little as 10 to 15% of double-sampled genotype data is needed to achieve this effect. Aside from a few situations (particularly recessive mode of inheritance simulations) the LRTDAME version that calculates p values through permutation requires 15 to 20% double sampling to maintain an 80% power for a 0.05 significance level and approximately 20% double sampling for a 0.01 significance level. Copyright © 2010 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

We investigated the influence of sampling location within a faeces on DNA quality by sampling from both the outside and inside of 25 brown bear ( Ursus arctos) scats and the side and the tip of 30 grey wolf ( Canis lupus) scats. The outside of the bear scat and side of the wolf scat had significantly lower nuclear DNA microsatellite allelic dropout error rates ( U. arctos: P = 0.017; C. lupus: P = 0.025) and significantly higher finalized genotyping success rates ( U. arctos: P = 0.017; C. lupus: P = 0.012) than the tip and inside of the scat. A review of the faecal DNA literature indicated that <45% of studies report the sampling location within a faeces indicating that this methodological consideration is currently underappreciated. Based on our results, we recommend sampling from the side of canid scats and the outside portion of ursid scats to obtain higher quality DNA samples. The sampling location within a faeces should be carefully considered and reported as it can directly influence laboratory costs and efficiency, as well as the ability to obtain reliable genotypes. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

ConGenR (available at ) is an R based conservation genetics script that facilitates rapid determination of consensus genotypes from replicated samples, determines overall (successful amplifications/amplification attempted) and individual sample level (proportion of samples with successful amplifications at n loci) amplification success rates, and quantifies genotyping error rates. ConGenR is intended for use with codominant, multilocus microsatellite data generated primarily through noninvasive genetic sampling and processed with a multi-tubes approach. ConGenR handles input that can be easily exported from GENEMAPPER, a program commonly used to score allele sizes. Amplification success and genotyping error rates can be evaluated by sample class (i.e., any identifiable and meaningful subdivision of samples; e.g., sex, season, region, or sample condition), offering insights into processes driving amplification success and genotyping error rates. Additionally, amplification success and genotyping error rates are calculated by locus, expediting the identification of problematic loci during pilot studies. [ABSTRACT FROM AUTHOR]

Two-stage design is a cost effective approach for identifying disease genes in genetic studies and it has received much attention recently. In general, there are two types of two-stage designs that differ on the methods and samples used to measure allele frequencies in the first stage: (1) Individual genotyping is used in the first stage; (2) DNA pooling is used in the first stage. In this paper, we focus on the latter. Zuo et al. (2006) investigated statistical power of such a design, among other things, but the cost of the study was not taken into account. The purpose of this paper is to study the optimal design under the given overall cost. We investigate how to allocate the resources to the two stages. Note that in addition to the measurement errors associated with DNA pooling, genotyping errors are also unavoidable with individual genotyping. Therefore, we discuss the optimal design combining genotyping errors associated with individual genotyping. The joint statistical distributions of test statistics in the first and second stages are derived. For a fixed cost, our results show that the optimal design requires no additional samples in the second stage but only that the samples in the first stage be re-used. When the second stage uses an entirely independent sample, however, the optimal design under a given cost depends on the population allele frequency and allele frequency difference between the case and control groups. For the current genotyping costs, we can roughly allocate 1/3 to 1/2 of the total sample size to the first stage for screening. [ABSTRACT FROM AUTHOR]

We introduce a simple and yet scientifically objective criterion for identifying SNPs with genotyping errors due to poor clustering. This yields a metric for assessing the stability of the assigned genotypes by evaluating the extent of discordance between the calls made with the unperturbed and perturbed intensities. The efficacy of the metric is evaluated by: (1) estimating the extent of over-dispersion of the Hardy-Weinberg equilibrium chi-square test statistics; (2) an interim case-control study, where we investigated the efficacy of the introduced metric and standard quality control filters in reducing the number of SNPs with evidence of phenotypic association which are attributed to genotyping errors; (3) investigating the call and concordance rates of SNPs identified by perturbation analysis which have been genotyped on both Affymetrix and Illumina platforms. Removing SNPs identified by the extent of discordance can reduce the degree of over-dispersion of the HWE test statistic. Sensible use of perturbation analysis in an association study can correctly identify SNPs with problematic genotyping, reducing the number required for visual inspection. SNPs identified by perturbation analysis had lower call and concordance rates, and removal of these SNPs significantly improved the performance for the remaining SNPs. [ABSTRACT FROM AUTHOR]

Abstract: Schistosomiasis is one of the major unconquered infectious diseases afflicting people of developing countries, particularly in Africa. A deeper understanding of the epidemiology of schistosomes is complicated by the intravascular location of adult worms which makes them routinely unavailable for study. Their progeny, miracidia, which are hatched from eggs that are passed in feces, are available and can provide valuable insights about human infections, but they are small in size, hindering robust molecular analyses. Here we present a new high-throughput technique to assess the genotypes at 21 previously published microsatellite loci for individual miracidia of S. mansoni. The 21 loci can be amplified in four multiplexed PCR reactions; however, enough template is produced for approximately six PCR reactions, which allows for additional PCR reactions for resampling or obtaining additional data. We validated this technique using a pedigree study employing laboratory crosses of S. mansoni from Kenya to obtain sets of parents and offspring. Of 23 loci examined, 21 loci were found to be reliable: false alleles were rare and missing alleles due to allelic dropout occurred at only two loci in approximately 5% of the offspring. The latter type of error can be further reduced by reamplification which is possible with our method. This technique is amenable to a 96-well format thus facilitating analysis of larger samples of miracidia, allowing more robust molecular epidemiological studies of S. mansoni to infer population size, population structure, gene flow, mating systems, speciation, and host race formation. [Copyright &y& Elsevier]

One limitation of the existing tagging SNP selection algorithms is that they assume the reported genotypes are error free. However, genotyping errors are often unavoidable in practice. Many tagging SNP selection methods depend heavily on the estimated haplotype frequencies. Recent studies have demonstrated that even slight genotyping errors can lead to serious consequences with regard to haplotype reconstruction and frequency estimation. Here we present a tagging SNP selection method that allows for genotyping errors. Our method is a modification of the pair-wise r2 tagging SNP selection algorithm proposed by Carlson et al. (2004) . We have replaced the standard EM algorithm in Carlson's method with an EM that accounts for genotyping errors, in an attempt to obtain better estimates of the haplotype frequencies and r2 measure. Through simulation studies we compared the performance of our modified algorithm with that of the original algorithm. We found that the number of tags selected by both methods increased with increasing genotyping errors, though our method led to smaller increase. The power of haplotype association tests using the selected tags decreased dramatically with increasing genotyping errors. The power of single marker tests also decreased, but the reduction was not as much as the reduction in power of haplotype tests. When restricting the mean number of tags selected by both methods to be similar to the baseline number, Carlson's method and our method led to similar power for the subsequent haplotype and single marker tests. Our results showed that, by accounting for random genotyping errors, our method can select tagging SNPs more efficiently than Carlson's method. The computer program that implements our modified tagging SNP selection algorithm is available at our web site: . [ABSTRACT FROM AUTHOR]

Noninvasive population genetics has found many applications in ecology and conservation biology. However, the technical difficulties inherent to the analysis of low quantities of DNA generally tend to limit the efficiency of this approach. The nature of samples and loci used in noninvasive population genetics are important factors that may help increasing the potential success of case studies. Here we reviewed the effects of the source of DNA (hair vs. faeces), the diet of focal species, the length of mitochondrial DNA fragments, and the length and repeat motif of nuclear microsatellite loci on genotyping success (amplification success and rate of allelic dropout). Locus-specific effects appeared to have the greatest impact, amplification success decreasing with both mitochondrial and microsatellite fragments' length, while error rates increase with amplicons' length. Dinucleotides showed best amplification success and lower error rates compared to longer repeat units. Genotyping success did not differ between hair- versus faeces-extracted DNA, and success in faeces-based analyses was not consistently influenced by the diet of focal species. While the great remaining variability among studies implies that other unidentified parameters are acting, results show that the careful choice of genetic markers may allow optimizing the success of noninvasive approaches. [ABSTRACT FROM AUTHOR]

Previously, sequencing of mitochondrial DNA (mtDNA) from non-invasively collected faecal material (scat) has been used to help manage hybridization in the wild red wolf (Canis rufus) population. This method is limited by the maternal inheritance of mtDNA and the inability to obtain individual identification. Here, we optimize the use of nuclear DNA microsatellite markers on red wolf scat DNA to distinguish between individuals and detect hybrids. We develop a data filtering method in which scat genotypes are compared to known blood genotypes to reduce the number of PCR amplifications needed. We apply our data filtering method and the more conservative maximum likelihood ratio method (MLR) of Miller et al. (2002 Genetics 160:357-366) to a scat dataset previously screened for hybrids by sequencing of mtDNA. Using seven microsatellite loci, we obtained genotypes for 105 scats, which were matched to 17 individuals. The PCR amplification success rate was 50% and genotyping error rates ranged from 6.6% to 52.1% per locus. Our data filtering method produced comparable results to the MLR method, and decreased the time and cost of analysis by 25%. Analysis of this dataset using our data filtering method verified that no hybrid individuals were present in the Alligator River National Wildlife Refuge, North Carolina in 2000. Our results demonstrate that nuclear DNA microsatellite analysis of red wolf scats provides an efficient and accurate approach to screen for new individuals and hybrids. [ABSTRACT FROM AUTHOR]