Genotyping Error Rates Research Articles

Where are the beachmasters? Unexpectedly weak polygyny among southern elephant seals on a South Shetland Island

AbstractIntraspecific variation in animal mating systems can have important implications for ecological, evolutionary and demographic processes in wild populations. For example, patterns of mating can impact social structure, dispersal, effective population size and inbreeding. However, few species have been studied in sufficient detail to elucidate mating system plasticity and its dependence on ecological and demographic factors. Southern elephant seals (Mirounga leonina) have long been regarded as a textbook example of a polygynous mating system, with dominant ‘beachmaster’ males controlling harems of up to several hundred females. However, behavioural and genetic studies have uncovered appreciable geographic variation in the strength of polygyny among elephant seal populations. We, therefore, used molecular parentage analysis to investigate patterns of parentage in a small satellite colony of elephant seals at the South Shetland Islands. We hypothesised that dominant males would be able to successfully monopolise the relatively small numbers of females present in the colony, leading to relatively high levels of polygyny. A total of 424 individuals (comprising 33 adult males, 101 adult females and 290 pups) sampled over 8 years were genotyped at 20 microsatellites and reproductive success was analysed by genetically assigning parents. Paternity could only be assigned to 31 pups (10.7%), despite our panel of genetic markers being highly informative and the genotyping error rate being very low. The strength of inferred polygyny was weak in comparison to previous genetic studies of the same species, with the most successful male fathering only seven pups over the entire course of our study. Our results show that, even in a species long regarded as a model for extreme polygyny, male reproductive skew can vary substantially among populations.

Noninvasive genetic sampling for flying foxes: a valuable method for monitoring demographic parameters

AbstractEstablishing effective wildlife conservation measures requires accurate demographic information such as population size and survival probability: parameters that can be extremely difficult to obtain. This is especially the case for threatened species, which are often rare and sometimes occupy inaccessible areas. While noninvasive genetic sampling (NIGS) techniques are promising tools for providing demographic data, these methods may be unreliable in certain situations. For instance, fecal samples of frugivorous species in tropical areas degrade rapidly, affecting the usability of the genetic material. In this study, we compared (1) NIGS capture–mark–recapture (NIGS‐CMR) with conventional CMR to determine their potential in estimating demographic parameters of fruit bats, and (2) the precision of these demographic parameters and the associated costs given varying sampling designs through simulations. Using Livingstone’s fruit bats (Pteropus livingstonii) fecal samples, microsatellite markers were tested and genotyping success and error rates were assessed. The average genotyping success rate was 77%, and the total genotyping error rate for all loci was low (allelic dropout rate = 0.089, false alleles rate = 0.018). Our results suggested that five loci were required to identify individuals. Simulations showed that monitoring the species over a 9‐yr period with a recapture rate of 0.20 or over a 6‐yr period with a recapture rate of 0.30 seems appropriate to obtain valuable demographic parameters. Overall, in comparison to conventional CMR, NIGS‐CMR offers a better method for estimating demographic parameters and subsequently for conducting long‐term population monitoring in flying foxes due to the fact that (1) sample collection is easy and the level of genotyping errors in the laboratory is low and (2) it is cheaper, less time‐consuming, and less disturbing to individual animals. We strongly advocate an approach that couples a pilot study with simulations as done in this study in order to choose the most efficient monitoring method for a given species or context.

Cross-amplification of ungulate microsatellite markers in the endemic Indian antelope or blackbuck (Antilope cervicapra) for population monitoring and conservation genetics studies in south Asia

The Indian antelope or blackbuck (Antilope cervicapra) is endemic to the Indian subcontinent, inhabiting scrublands and dry grasslands. Most of the blackbuck populations are small, isolated, and threatened by habitat fragmentation and degradation. Management of such disjunct populations requires genetic characterization, which is critical for assessing hazards of stochastic events and inbreeding. Addressing the scarcity of such information on the blackbuck, we describe a novel panel of microsatellite markers that could be used to monitor blackbuck demography and population genetic parameters using non-invasive faecal sampling. We screened microsatellites (n = 40) that had been reported to amplify in bovid and cervid species using faecal samples of the blackbuck collected from Kaimoor Wildlife Sanctuary, Uttar Pradesh, India and its vicinities. We selected 12 markers for amplification using faecal DNA extracts (n = 140) in three multiplex reactions. We observed a mean amplification success rate of 72.4% across loci (92.1-25.7%) with high allele diversity (mean number of alleles/locus = 8.67 ± 1.03). Mean genotyping error rates across the markers were low to moderate (allelic drop-out rate = 0.09; false allele rate = 0.11). The proportions of first- and second-order relatives in the study population were 0.69% and 6.21%, respectively. Based on amplification success, genotyping error rates and the probability of identity (PID), we suggest (i) a panel of five microsatellite markers (cumulative PID = 1.24 × 10-5) for individual identification and population monitoring and (ii) seven additional markers for conservation genetics studies. This study provides essential tools capable of augmenting blackbuck conservation strategies at the landscape level, integral to protecting the scrubland-grassland ecosystem.

Performing parentage analysis for polysomic inheritances based on allelic phenotypes.

Polyploidy poses several problems for parentage analysis. We present a new polysomic inheritance model for parentage analysis based on genotypes or allelic phenotypes to solve these problems. The effects of five factors are simultaneously accommodated in this model: (1) double-reduction, (2) null alleles, (3) negative amplification, (4) genotyping errors and (5) self-fertilization. To solve genotyping ambiguity (unknown allele dosage), we developed a new method to establish the likelihood formulas for allelic phenotype data and to simultaneously include the effects of our five chosen factors. We then evaluated and compared the performance of our new method with three established methods by using both simulated data and empirical data from the cultivated blueberry (Vaccinium corymbosum). We also developed and compared the performance of two additional estimators to estimate the genotyping error rate and the sample rate. We make our new methods freely available in the software package polygene, at http://github.com/huangkang1987/polygene.

Microsatellite Techniques in Guamandrsquo;s Specific-Pathogen-Free Penaeus vannamei Stock: Genetic Variance and Parentage Identification

A microsatellite DNA marker technique was used to monitor specific-pathogen-free Penaeus vannamei (white shrimp) stock in the hatchery of the University of Guam, mainly for assessment of genetic diversity and identification of the parentage for two consecutive generations. A panel of 16 loci was selected to analyze 36 families of P. vannamei, comprising of a total of 1,152 individual shrimp samples. The families showed high genetic variation. The average number of alleles per locus was 10.625 for the parents and 10.052 for their offspring. The average observed heterozygosity declined slightly from 0.891 in the parents to 0.813 in the offspring. Similarly the expected heterozygosity was 0.804 among the parents and 0.792 for their progeny. The inbreeding coefficient was -0.107 for the parents but -0.026 for the offspring. These two generations did not differ significantly in any foregoing measures (p>0.05). CERVUS and COLONY confirmed that any 12 loci from the panel could deliver 100% correct parentage identification when the genotyping error rate was set as 0.01.

Linkage mapping and QTL analysis of flowering time using ddRAD sequencing with genotype error correction in Brassica napus

BackgroundBrassica napus is an important oilseed crop cultivated worldwide. During domestication and breeding of B. napus, flowering time has been a target of selection because of its substantial impact on yield. Here we use double digest restriction-site associated DNA sequencing (ddRAD) to investigate the genetic basis of flowering in B. napus. An F2 mapping population was derived from a cross between an early-flowering spring type and a late-flowering winter type.ResultsFlowering time in the mapping population differed by up to 25 days between individuals. High genotype error rates persisted after initial quality controls, as suggested by a genotype discordance of ~ 12% between biological sequencing replicates. After genotype error correction, a linkage map spanning 3981.31 cM and compromising 14,630 single nucleotide polymorphisms (SNPs) was constructed. A quantitative trait locus (QTL) on chromosome C2 was detected, covering eight flowering time genes including FLC.ConclusionsThese findings demonstrate the effectiveness of the ddRAD approach to sample the B. napus genome. Our results also suggest that ddRAD genotype error rates can be higher than expected in F2 populations. Quality filtering and genotype correction and imputation can substantially reduce these error rates and allow effective linkage mapping and QTL analysis.

Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding

Genotyping-by-Sequencing (GBS) is a low-cost, high-throughput genotyping method that relies on restriction enzymes to reduce genome complexity. GBS is being widely used for various genetic and breeding applications. In the present study, 2240 individuals from eight maize populations, including two association populations (AM), backcross first generation (BC1), BC1F2, F2, double haploid (DH), intermated B73 × Mo17 (IBM), and a recombinant inbred line (RIL) population, were genotyped using GBS. A total of 955,120 of raw data for SNPs was obtained for each individual, with an average genotyping error of 0.70%. The rate of missing genotypic data for these SNPs was related to the level of multiplex sequencing: ~ 25% missing data for 96-plex and ~ 55% for 384-plex. Imputation can greatly reduce the rate of missing genotypes to 12.65% and 3.72% for AM populations and bi-parental populations, respectively, although it increases total genotyping error. For analysis of genetic diversity and linkage mapping, unimputed data with a low rate of genotyping error is beneficial, whereas, for association mapping, imputed data would result in higher marker density and would improve map resolution. Because imputation does not influence the prediction accuracy, both unimputed and imputed data can be used for genomic prediction. In summary, GBS is a versatile and efficient SNP discovery approach for homozygous materials and can be effectively applied for various purposes in maize genetics and breeding.

Development of microsatellite loci and optimization of a multiplex assay for Latibulus argiolus (Hymenoptera: Ichneumonidae), the specialized parasitoid of paper wasps

Microsatellite loci are commonly used markers in population genetic studies. In this study, we present 40 novel and polymorphic microsatellite loci elaborated for the ichneumonid parasitoid Latibulus argiolus (Rossi, 1790). Reaction condition optimisation procedures allowed 14 of these loci to be co-amplified in two PCRs and loaded in two multiplex panels onto a genetic analyser. The assay was tested on 197 individuals of L. argiolus originating from ten natural populations obtained from the host nests of paper wasps. The validated loci were polymorphic with high allele numbers ranging from eight to 27 (average 17.6 alleles per locus). Both observed and expected heterozygosity values were high, ranging between 0.75 and 0.92 for HO (mean 0.83) and from 0.70 to 0.90 for HE (mean 0.85). The optimized assay showed low genotyping error rate and negligible null allele frequency. The designed multiplex panels could be successfully applied in relatedness analyses and genetic variability studies of L. argiolus populations, which would be particularly interesting considering the coevolutionary context of this species with its social host.

Marker genotyping error effects on genomic predictions under different genetic architectures.

This study aimed to determine the effect of different rates of marker genotyping error on the accuracy of genomic prediction that was examined under distinct marker and quantitative trait loci (QTL) densities and different heritability estimates using a stochastic simulation approach. For each scenario of simulation, a reference population with phenotypic and genotypic records and a validation population with only genotypic records were considered. Marker effects were estimated in the reference population, and then their genotypic records were used to predict genomic breeding values in the validation population. The prediction accuracy was calculated as the correlation between estimated and true breeding values. The prediction bias was examined by computing the regression of true genomic breeding value on estimated genomic breeding value. The accuracy of the genomic evaluation was the highest in a scenario with no marker genotyping error and varied from 0.731 to 0.934. The accuracy of the genomic evaluation was the lowest in a scenario with marker genotyping error equal to 20% and changed from 0.517 to 0.762. The unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value were obtained in the reference and validation populations when the rate of marker genotyping error was equal to zero. The results showed that marker genotyping error can reduce the accuracy of genomic evaluations. Moreover, marker genotyping error can provide biased estimates of genomic breeding values. Therefore, for obtaining accurate results it is recommended to minimize the marker genotyping errors to zero in genomic evaluation programs.

Genome-wide screening of microsatellites in golden snub-nosed monkey (Rhinopithecus roxellana), for the development of a standardized genetic marker system

Golden snub-nosed monkey (Rhinopithecus roxellana) is an endangered primate endemic to China. The lack of standardized genetic markers limits its conservation works. In the present study, a total of 1,400,552 perfect STRs was identified in the reference genome of R. roxellana. By comparing it with the 12 resequencing genomes of four geographical populations, a total of 1,927 loci were identified as perfect tetranucleotides and shared among populations. We randomly selected 74 loci to design primer pairs. By using a total of 64 samples from the Chengdu Zoo captive population and the Pingwu wild population, a set of 14 novel STR loci were identified with good polymorphism, strong stability, high repeatability, low genotyping error rate that were suitable for non-invasive samples. These were used to establish a standardized marker system for golden snub-nosed monkeys. The genetic diversity analysis showed the average HO, HE, and PIC was 0.477, 0.549, and 0.485, respectively, in the Chengdu Zoo population; and 0.516, 0.473, and 0.406, respectively, in Pingwu wild population. Moreover, an individual identification method was established, which could effectively distinguish individuals with seven markers. The paternity tests were conducted on seven offspring with known mothers from two populations, and their fathers were determined with high confidence. A genotyping database for the captive population in the Chengdu Zoo (n = 25) and wild population in Pingwu country (n = 8) was acquired by using this marker system.

Population genomic SNPs from epigenetic RADs: Gaining genetic and epigenetic data from a single established next‐generation sequencing approach

Abstract Epigenetics is increasingly recognized as an important molecular mechanism underlying phenotypic variation. To study DNA methylation in ecological and evolutionary contexts, epiRADseq is a cost‐effective next‐generation sequencing (NGS) technique based on reduced representation sequencing of genomic regions surrounding non‐/methylated sites. EpiRADseq for genome‐wide methylation abundance and ddRADseq for genome‐wide single‐nucleotide polymorphism (SNP) genotyping follow very similar library and sequencing protocols, but to date these two types of dataset have been handled separately. Here we test the performance of using epiRADseq data to generate SNPs for population genomic analyses. We tested the robustness of using epiRADseq data for population genomics with two independent datasets: a newly generated single‐end dataset for the European whitefish Coregonus lavaretus, and a re‐analysis of publicly available, previously published paired‐end data on corals. Using standard bioinformatic pipelines with a reference genome and without (i.e. de novo catalogue loci), we compared the number of SNPs retained, population genetic summary statistics and population genetic structure between data drawn from ddRADseq and epiRADseq library preparations. We found that SNPs drawn from epiRADseq are similar in number to those drawn from ddRADseq, with 55%–83% of SNPs being identified by both methods. Genotyping error rate was <5% in both approaches. EpiRADseq‐specific allele dropout was low (~1%). For summary statistics, such as heterozygosity and nucleotide diversity, there is a strong correlation between methods (Spearman's rho > 0.88). Furthermore, identical patterns of population genetic structure were recovered using SNPs from epiRADseq and ddRADseq approaches. We show that SNPs obtained from epiRADseq are highly similar to those from ddRADseq and are equivalent for estimating genetic diversity and population structure. This finding is particularly relevant to researchers interested in genetics and epigenetics on the same individuals because using a single epigenomic approach to generate two datasets greatly reduces the time and financial costs compared to using these techniques separately. It also efficiently enables correction of epigenetic estimates with population genetic data. Many studies will benefit from a combinatorial approach with genetic and epigenetic markers and this demonstrates a single, efficient method to do so.

Molecular Genetic Identification of Apple Cultivars Based on Microsatellite DNA Analysis. I.\xa0The Database of 600 Validated Profiles

Apple (Malus × domestica Borkh.) is the most widely grown permanent fruit crop of temperate climates. Although commercial apple growing is based on a small number of globally spread cultivars, its diversity is much larger and there are estimates about the existence of more than 10,000 documented varieties. The varietal diversity can be described and determined based on phenotypic characters of the external and internal traits of fruit, which, however, can be modulated by environmental factors. Consequently, molecular methods have become an important alternative means for the characterisation of apple cultivar diversity. In order to use multilocus microsatellite data for determination of unidentified or misidentified apple varieties, a database with molecular genetic fingerprints of well-determined reference cultivars needs to be available. The objective of the present work was to establish such a database that could be applied for the molecular genetic determination of a large number of historic and modern, diploid and triploid apple cultivars. Based on the analysis of more than 1600 accessions of apple trees sampled in 37 public and private cultivar collections in different European countries at 14 variable microsatellite loci, a database with 600 molecular genetic profiles was finally obtained. The key criterion for considering a molecular genetic profile as confirmed and for including it into the reference database was that at least two accessions of the same cultivar of different provenances generated an identical result, which was achieved for 98% of the apple cultivars present in the database. For the remaining genotypes, the cultivar assignment was supported by a parentage analysis or by comparison to molecular genetic profiles available in published works. The database is composed of 574 scion cultivars, 24 rootstock genotypes and two species of crab apples. Of the 574 scion cultivars, 61% were derived from historic or old cultivars, many of which were grown in Central Europe in the past. The remaining scion cultivars are currently grown or available in testing programmes and may gain importance in the future. In order to validate the genotyping data, parentage analysis was performed involving cultivars and rootstocks that arose after 1900, for which information about at least one parent cultivar was available from pomological and scientific literature and the molecular genetic profiles of the assumed parent(s) were also present in our database. This analysis revealed the presence of null alleles at locus COL, however, when excluding this locus, a mean genotyping error rate of only 0.28% per locus was revealed, which points to a high reliability of the dataset. The datasets with 14 and 13 loci (excluding locus COL) showed a high degree of discrimination power, with a combined non-exclusion probability of identity of 2.6 × 10−20 and 3.4 × 10−19. Five of the microsatellite loci analysed in the present study overlapped with another published dataset and after the application of conversion values, it was possible to align the allele lengths and compare the molecular genetic profiles of 20 randomly derived cultivars, which were analysed in both studies. This comparison evidenced an exact correspondence of the microsatellite profiles contained in the two datasets, further pointing to the accuracy of our database. Apart from its application to characterise genetic resources or to manage germplasm collections, the here presented database could serve as an important tool for quality control or as a useful instrument in breeding programmes.

Error, noise and bias in de novo transcriptome assemblies.

De novo transcriptome assembly is a powerful tool, and has been widely used over the last decade for making evolutionary inferences. However, it relies on two implicit assumptions: that the assembled transcriptome is an unbiased representation of the underlying expressed transcriptome, and that expression estimates from the assembly are good, if noisy approximations of the relative abundance of expressed transcripts. Using publicly available data for model organisms, we demonstrate that, across assembly algorithms and data sets, these assumptions are consistently violated. Bias exists at the nucleotide level, with genotyping error rates ranging from 30% to 83%. As a result, diversity is underestimated in transcriptome assemblies, with consistent underestimation of heterozygosity in all but the most inbred samples. Even at the gene level, expression estimates show wide deviations from map-to-reference estimates, and positive bias at lower expression levels. Standard filtering of transcriptome assemblies improves the robustness of gene expression estimates but leads to the loss of a meaningful number of protein-coding genes, including many that are highly expressed. We demonstrate a computational method, length-rescaled CPM, to partly alleviate noise and bias in expression estimates. Researchers should consider ways to minimize the impact of bias in transcriptome assemblies.

Estimating and accounting for genotyping errors in RAD‐seq experiments

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.

Parentage and relatedness reconstruction in Pinus sylvestris using genotyping-by-sequencing

Estimating kinship is fundamental for studies of evolution, conservation, and breeding. Genotyping-by-sequencing (GBS) and other restriction based genotyping methods have become widely applied in these applications in non-model organisms. However, sequencing errors, depth, and reproducibility between library preps could potentially hinder accurate genetic inferences. In this study, we tested different sets of parameters in data filtering, different reference populations and eight estimation methods to obtain a robust procedure for relatedness estimation in Scots pine (Pinus sylvestris L.). We used a seed orchard as our study system, where candidate parents are known and pedigree reconstruction can be compared with theoretical expectations. We found that relatedness estimates were lower than expected for all categories of kinship estimated if the proportion of shared SNPs was low. However, estimates reached expected values if loci showing an excess of heterozygotes were removed and genotyping error rates were considered. The genetic variance-covariance matrix (G-matrix) estimation, however, performed poorly in kinship estimation. The reduced relatedness estimates are likely due to false heterozygosity calls. We analyzed the mating structure in the seed orchard and identified a selfing rate of 3% (including crosses between clone mates) and external pollen contamination of 33.6%. Little genetic structure was observed in the sampled Scots pine natural populations, and the degree of inbreeding in the orchard seed crop is comparable to natural stands. We illustrate that under our optimized data processing procedure, relatedness, and genetic composition, including level of pollen contamination within a seed orchard crop, can be established consistently by different estimators.

Genotype phasing in pedigrees using whole-genome sequence data.

Phasing is the process of inferring haplotypes from genotype data. Efficient algorithms and associated software for accurate phasing in pedigrees are needed, especially for populations lacking reference panels of sequenced individuals. We present a novel method for phasing genotypes from whole-genome sequence data in pedigrees, called PULSAR (Phasing Using Lineage Specific Alleles/Rare variants). The method is based on the property that alleles specific to a single founding chromosome within a pedigree are highly informative for identifying haplotypes that are shared identical by descent. Simulation studies are used to assess the performance of PULSAR with various pedigree sizes and structures, and the effect of genotyping errors and the presence of nonsequenced individuals is investigated. In pedigrees with complete sequencing and realistic genotyping error rates, PULSAR correctly phases >99.9% of heterozygous genotypes, excluding sites at which all individuals are heterozygous, and does so with a switch error rate frequently below 10-4. PULSAR is highly accurate, capable of genotype error correction and imputation, and computationally competitive with alternative phasing software applicable to pedigrees. Our method has the significant advantage of not requiring reference panels that are essential for other population-based phasing algorithms. A software implementation of PULSAR is freely available.

Ancestry-agnostic estimation of DNA sample contamination from sequence reads.

Detecting and estimating DNA sample contamination are important steps to ensure high-quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes. We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele frequencies projected from reference genotypes onto principal component coordinates. Our method can also be used for estimating genetic ancestries, similar to LASER or TRACE, but simultaneously accounting for potential contamination. We demonstrate that our method robustly estimates contamination rates and genetic ancestries across populations and contamination scenarios. We further demonstrate that, in the presence of contamination, genetic ancestry inference can be substantially biased with existing methods that ignore contamination, while our method corrects for such biases.

Assessment of sire contribution and breed-of-origin of alleles in a three-way crossbred broiler dataset

ABSTRACTBroiler breeding programs rely on crossbreeding. With genomic selection, widespread use of crossbred performance in breeding programs comes within reach. Commercial crossbreds, however, may have unknown pedigrees and their genomes may include DNA from 2 to 4 different breeds. Our aim was, for a broiler dataset with a limited number of sires having both purebred and crossbred offspring generated using natural mating, to rapidly derive parentage, assess the distribution of the sire contribution to the offspring generation, and to assess breed-of-origin of alleles in crossbreds. The dataset contained genotypes for 56,075 SNPs for 5,882 purebred and 10,943 3-way crossbred offspring generated by natural mating of 164 purebred sires to 1,016 purebred and 1,386 F1 crossbred hens. Using our algorithm FindParents, joint parentage derivation for the offspring and parent generations required only 1 m 29 s to retrieve parentage for 20,253 animals considering 4,504 possible parents. FindParents was similarly accurate as a maximum likelihood based method, apart from situations where settings of FindParents did not match the genotyping error rate in the data. Numbers of offspring per sire had a very skewed distribution, ranging from 1 to 270 crossbreds and 1 to 154 purebreds. Derivation of breed-of-origin of alleles relied on phasing all genotypes, including 8,205, 372, and 720 animals from the 3 pure lines involved, and allocating haplotypes in the crossbreds to purebred lines based on observed frequencies in the purebred lines. Breed-of-origin could be derived for 96.94% of the alleles of the 1,386 F1 crossbred hens and for 91.88% of the alleles of the 10,943 3-way crossbred offspring, of which 49.49% to the sire line. The achieved percentage of assignment to the sire line was sufficient to proceed with subsequent analyses requiring only the breed-of-origin of the paternal alleles to be known. Although required number of animals may be population dependent, to increase the total percentage of assigned alleles, it seems advisable to use at least approx. 1,000 genotyped purebred animals for each of the lines involved.

Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)

The use of microsatellite markers provides a window into the evolutionary processes of a given species. As such, these markers are widely used in scientific and applied research and are praised for their practicality and ease of use; however, the unavoidable incidence of genotyping deviations has been broadly neglected in the literature. Therefore, the present study aimed to estimate the rate of null alleles, mutations, and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles, and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.

Consideration of sample source for establishing reliable genetic microsatellite data from mammalian carnivore specimens held in natural history collections

AbstractSpecimens from natural history collections (NHCs) are increasingly being used for genetic studies and can provide information on extinct populations, facilitate comparisons of historical and contemporary populations, produce baseline data before environmental changes, and elucidate patterns of change. Destructive sampling for DNA may be in disagreement with NHC goals of long-term care and maintenance. Differentiating quality among sample sources can direct destructive sampling to the source predicted to yield the highest quality DNA and most reliable data, potentially reducing damage to specimens, laboratory costs, and genotyping errors. We used the kit fox (Vulpes macrotis) as a model species and evaluated the quality and reliability of genetic data obtained from carnivoran specimens via three different sample sources: cranial bones, nasal bones, and toepads. We quantified variation in microsatellite amplification success and genotyping error rates and assessed the reliability of source-specific genic data. Toepads had the highest amplification success rates and lowest genotyping error rates. Shorter loci had higher amplification success and lower allelic dropout rates than longer loci. There were substantial differences in the reliability of resulting multilocus genotypes. Toepads produced the most reliable data, required the fewest replicates, and therefore, had the lowest costs to achieve reliable data. Our results demonstrate that the quality of DNA obtained from specimens varies by sample source and can inform NHCs when evaluating requests for destructive sampling. Our results suggest that prior to large-scale specimen sampling, researchers should conduct pilot studies to differentiate among source-specific data reliability, identify high performing loci, reduce costs of analyses, and minimize destructive sampling.