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

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

Genotyping‐free parentage assignment using RAD‐seq reads

Parentage assignment is defined as the identification of the true parents of one focal offspring among a list of candidates and has been commonly used in zoological, ecological, and agricultural studies. Although likelihood‐based parentage assignment is the preferred method in most cases, it requires genotyping a predefined set of DNA markers and providing their population allele frequencies. In the present study, we proposed an alternative method of parentage assignment that does not depend on genotype data and prior information of allele frequencies. Our method employs the restriction site‐associated DNA sequencing (RAD‐seq) reads for clustering into the overlapped RAD loci among the compared individuals, following which the likelihood ratio of parentage assignment could be directly calculated using two parameters—the genome heterozygosity and error rate of sequencing reads. This method was validated on one simulated and two real data sets with the accurate assignment of true parents to focal offspring. However, our method could not provide a statistical confidence to conclude that the first ranked candidate is a true parent.     

Effects of genotyping errors on parentage exclusion analysis

Genetic markers are widely used to determine the parentage of individuals in studies of mating systems, reproductive success, dispersals, quantitative genetic parameters and in the management of conservation populations. These markers are, however, imperfect for parentage analyses because of the presence of genotyping errors and undetectable alleles, which may cause incompatible genotypes (mismatches) between parents and offspring and thus result in false exclusions of true parentage. Highly polymorphic markers widely used in parentage analyses, such as microsatellites, are especially prone to genotyping errors. In this investigation, I derived the probabilities of excluding a random (related) individual from parentage and the probabilities of Mendelian-inconsistent errors (mismatches) and Mendelian-consistent errors (which do not cause mismatches) in parent-offspring dyads, when a marker having null alleles, allelic dropouts and false alleles is used in a parentage analysis. These probabilities are useful in evaluating the impact of various types of genotyping errors on the information content of a set of markers in and thus the power of a parentage analysis, in determining the threshold number of genetic mismatches that is appropriate for a parentage exclusion analysis and in estimating the rates of genotyping errors and frequencies of null alleles from observed mismatches between known parent-offspring dyads. These applications are demonstrated by numerical examples using both hypothetical and empirical data sets and discussed in the context of practical parentage exclusion analyses.     

p-loci: a computer program for choosing the most efficient set of loci for parentage assignment

Determining how many and which codominant marker loci are required for accurate parentage assignment is not straightforward because levels of marker polymorphism, linkage, allelic distributions among potential parents and other factors produce differences in the discriminatory power of individual markers and sets of markers. p-loci software identifies the most efficient set of codominant markers for assigning parentage at a user-defined level of success, using either simulated or actual offspring genotypes of known parentage. Simulations can incorporate linkage among markers, mating design and frequencies of null alleles and/or genotyping errors. p-loci is available for windows systems at http://marineresearch.oregonstate.edu/genetics/ploci.htm.     

Noninvasive genotyping and Mendelian analysis of microsatellites in African savannah elephants

We obtained fresh dung samples from 202 (133 mother-offspring pairs) savannah elephants (Loxodonta africana) in Samburu, Kenya, and genotyped them at 20 microsatellite loci to assess genotyping success and errors. A total of 98.6% consensus genotypes was successfully obtained, with allelic dropout and false allele rates at 1.6% (n = 46) and 0.9% (n = 37) of heterozygous and total consensus genotypes, respectively, and an overall genotyping error rate of 2.5% based on repeat typing. Mendelian analysis revealed consistent inheritance in all but 38 allelic pairs from mother-offspring, giving an average mismatch error rate of 2.06%, a possible result of null alleles, mutations, genotyping errors, or inaccuracy in maternity assignment. We detected no evidence for large allele dropout, stuttering, or scoring error in the dataset and significant Hardy-Weinberg deviations at only two loci due to heterozygosity deficiency. Across loci, null allele frequencies were low (range: 0.000-0.042) and below the 0.20 threshold that would significantly bias individual-based studies. The high genotyping success and low errors observed in this study demonstrate reliability of the method employed and underscore the application of simple pedigrees in noninvasive studies. Since none of the sires were included in this study, the error rates presented are just estimates.     

The potential costs of accounting for genotypic errors in molecular parentage analyses

Genotypic errors, whether due to mutation or laboratory error, can cause the genotypes of parents and their offspring to appear inconsistent with Mendelian inheritance. As a result, molecular parentage analyses are expected to benefit when allowances are made for the presence of genotypic errors. However, a cost of allowing for genotypic errors might also be expected under some analytical conditions, primarily because parentage analyses that assume nonzero genotypic error rates can neither assign nor exclude parentage with certainty. The goal of this work was therefore to determine whether or not such costs might be important under conditions relevant to parentage analyses, particularly in natural populations. Simulation results indicate that the costs may often outweigh the benefits of accounting for nonzero error rates, except in situations where data are available for many marker loci. Consequently, the most powerful approach to handling genotypic errors in parentage analyses might be to apply likelihood equations with error rates set to values substantially lower than the rates at which genotypic errors occur. When applying molecular parentage analyses to natural populations, we advocate an increased consideration of optimal strategies for handling genotypic errors. Currently available software packages contain procedures that can be used for this purpose.     

Using blocks of linked single nucleotide polymorphisms as highly polymorphic genetic markers for parentage analysis

Single nucleotide polymorphisms (SNPs) are plentiful in most genomes and amenable to high throughput genotyping, but they are not yet popular for parentage or paternity analysis. The markers are bi-allelic, so individually they contain little information about parentage, and in nonmodel organisms the process of identifying large numbers of unlinked SNPs can be daunting. We explore the possibility of using blocks of between three and 26 linked SNPs as highly polymorphic molecular markers for reconstructing male genotypes in polyandrous organisms with moderate (five offspring) to large (25 offspring) clutches of offspring. Haplotypes are inferred for each block of linked SNPs using the programs Haplore and Phase 2.1. Each multi-SNP haplotype is then treated as a separate allele, producing a highly polymorphic, 'microsatellite-like' marker. A simulation study is performed using haplotype frequencies derived from empirical data sets from Drosophila melanogaster and Mus musculus populations. We find that the markers produced are competitive with microsatellite loci in terms of single parent exclusion probabilities, particularly when using six or more linked SNPs to form a haplotype. These markers contain only modest rates of missing data and genotyping or phasing errors and thus should be seriously considered as molecular markers for parentage analysis, particularly when the study is interested in the functional significance of polymorphisms across the genome.     

Genotyping error detection through tightly linked markers

The identification of genotyping errors is an important issue in mapping complex disease genes. Although it is common practice to genotype multiple markers in a candidate region in genetic studies, the potential benefit of jointly analyzing multiple markers to detect genotyping errors has not been investigated. In this article, we discuss genotyping error detections for a set of tightly linked markers in nuclear families, and the objective is to identify families likely to have genotyping errors at one or more markers. We make use of the fact that recombination is a very unlikely event among these markers. We first show that, with family trios, no extra information can be gained by jointly analyzing markers if no phase information is available, and error detection rates are usually low if Mendelian consistency is used as the only standard for checking errors. However, for nuclear families with more than one child, error detection rates can be greatly increased with the consideration of more markers. Error detection rates also increase with the number of children in each family. Because families displaying Mendelian consistency may still have genotyping errors, we calculate the probability that a family displaying Mendelian consistency has correct genotypes. These probabilities can help identify families that, although showing Mendelian consistency, may have genotyping errors. In addition, we examine the benefit of available haplotype frequencies in the general population on genotyping error detections. We show that both error detection rates and the probability that an observed family displaying Mendelian consistency has correct genotypes can be greatly increased when such additional information is available.     

An evaluation of allowing for mismatches as a way to manage genotyping errors in parentage assignment by exclusion

In parentage assignment by exclusion, using multiple and very polymorphic loci, genotyping errors are a major cause of non‐assignment. Using stochastic simulations, we tested the possibility to allow for mismatches at one or more allele as a way to recover assignment power. This was very efficient provided the set of loci used had a high assignment power (> 99%) and the error rate was not too high (below 3–4%). In these cases, most of the theoretical assignment power could be recovered. We also showed the efficiency of the method in a practical experiment with rainbow trout.     

Parentage analysis with few contributing breeders: validation and improvement

Validation of parental allocation using PAPA software (Duchesne P, Godbout MH, Bernatchez L. 2002. PAPA (package for the analysis of parental allocation): a computer program for simulated and real parental allocation. Mol Ecol Notes. 2:191-193.) was investigated under the assumption that only a small proportion of potential breeders contributed to the offspring sample. Inbreeding levels proved to have a large impact on allocation error rate. Consequently, simulations from artificial, unrelated parents may strongly underestimate allocation error, and so, whenever possible, simulations based on the actual parental genotypes should be run. An unexpected and interesting finding was that ambiguity (the highest likelihood is shared by several parental pairs) rates below 10% stood very close to exact allocation error rates (true proportions of wrong allocations). Hence, the ambiguity rate statistic may be viewed as a ready-made indicator of the resolution power of a specific parental allocation run and, if not exceeding 10%, used as an estimate of allocation error rate. It was found that the PAPA simulator, even with few contributing breeders, can be trusted to output reasonably accurate estimates of allocation error as long as those estimates do not exceed 15%. Indeed, most discrepancies between exact and estimated error then stood below 3%. Reproductive success variance had little impact on error estimate discrepancies within the same range. Finally, a (focal set) method was described to correct the estimated family sizes computed directly from parental allocations. Essentially, this method makes use of the detailed structure of the allocation probabilities associated with each parental pair with at least 1 allocated offspring. The allocation probabilities are expressed in matrix form, and the subsequent calculations are run based on standard matrix algebra. On average, this method provided better estimates of family sizes for each investigated combination of parameter values. As the size of offspring samples increased, the corrections improved until a plateau was finally reached. Typically, samples comprising 250, 500, and 1000 offspring would bring corrections in the order of 10-20%, 20-30%, and 30-40%, respectively.     

Using molecular markers for pedigree reconstruction of the greater amberjack (Seriola dumerili) in the absence of parental information

Ensuring appropriate levels of genetic diversity in captive populations is essential to avoid inbreeding and loss of rare alleles by genetic drift. Pedigree reconstruction and parentage analysis in the absence of parental genotypes can be a challenging task that relies in the assignment of sibship relationships among the offspring. Here, we used eight highly variable microsatellite markers and three different assignment methods to reconstruct the most likely genotypes of a parental group of wild Seriola dumerili fish based on the genotypes of six cohorts of their offspring, to assess their relative contributions to the offspring. We found that a combination of the four most variable microsatellites was enough to identify the number of parents and their contribution to the offspring, suggesting that the variability of the markers can be more critical than the number of markers. Estimated effective population sizes were lower than the number of breeders and variable among years. The results suggest unequal parental contribution that should be accounted for breeding programs in the future.     

A High Throughput Single Nucleotide Polymorphism Multiplex Assay for Parentage Assignment in New Zealand Sheep

Accurate pedigree information is critical to animal breeding systems to ensure the highest rate of genetic gain and management of inbreeding. The abundance of available genomic data, together with development of high throughput genotyping platforms, means that single nucleotide polymorphisms (SNPs) are now the DNA marker of choice for genomic selection studies. Furthermore the superior qualities of SNPs compared to microsatellite markers allows for standardization between laboratories; a property that is crucial for developing an international set of markers for traceability studies. The objective of this study was to develop a high throughput SNP assay for use in the New Zealand sheep industry that gives accurate pedigree assignment and will allow a reduction in breeder input over lambing. This required two phases of development- firstly, a method of extracting quality DNA from ear-punch tissue performed in a high throughput cost efficient manner and secondly a SNP assay that has the ability to assign paternity to progeny resulting from mob mating. A likelihood based approach to infer paternity was used where sires with the highest LOD score (log of the ratio of the likelihood given parentage to likelihood given non-parentage) are assigned. An 84 “parentage SNP panel” was developed that assigned, on average, 99% of progeny to a sire in a problem where there were 3,000 progeny from 120 mob mated sires that included numerous half sib sires. In only 6% of those cases was there another sire with at least a 0.02 probability of paternity. Furthermore dam information (either recorded, or by genotyping possible dams) was absent, highlighting the SNP test’s suitability for paternity testing. Utilization of this parentage SNP assay will allow implementation of progeny testing into large commercial farms where the improved accuracy of sire assignment and genetic evaluations will increase genetic gain in the sheep industry.     

Bayesian parentage analysis reliably controls the number of false assignments in natural populations

Parentage analysis in natural populations is a powerful tool for addressing a wide range of ecological and evolutionary questions. However, identifying parent–offspring pairs in samples collected from natural populations is often more challenging than simply resolving the Mendelian pattern of shared alleles. For example, large numbers of pairwise comparisons and limited numbers of genetic markers can contribute to incorrect assignments, whereby unrelated individuals are falsely identified as parent–offspring pairs. Determining which parentage methods are the least susceptible to making false assignments is an important challenge facing molecular ecologists. In a recent paper, Harrison et al. (2013a) address this challenge by comparing three commonly used parentage methods, including a Bayesian approach, in order to explore the effects of varied proportions of sampled parents on the accuracy of parentage assignments. Unfortunately, Harrison et al. made a simple error in using the Bayesian approach, which led them to incorrectly conclude that this method could not control the rate of false assignment. Here, I briefly outline the basic principles behind the Bayesian approach, identify the error made by Harrison et al., and provide detailed guidelines as to how the method should be correctly applied. Furthermore, using the exact data from Harrison et al., I show that the Bayesian approach actually provides greater control over the number of false assignments than either of the other tested methods. Lastly, I conclude with a brief introduction to solomon , a recently updated version of the Bayesian approach that can account for genotyping error, missing data and false matching.     

Parentage and sibship inference from markers in polyploids

Many plants and some animal species are polyploids. Nondisomically inherited markers (e.g. microsatellites) in such species cannot be analysed directly by standard population genetics methods developed for diploid species. One solution is to transform the polyploid codominant genotypes to pseudodiploid‐dominant genotypes, which can then be analysed by standard methods for various purposes such as spatial genetic structure, individual relatedness and relationship. Although this data transformation approach has been used repeatedly in the literature, no systematic study has been conducted to investigate how efficient it is, how much marker information is lost and thus how much analysis accuracy is reduced. More specifically, it is unknown whether or not the transformed data can be used to infer parentage and sibship jointly, and how different sampling schemes (number and polymorphism of markers, number of individuals) and ploidy level affect the inference accuracy. This study analyses both simulated and empirical data to examine the effects of polyploid levels, actual pedigree structures and marker number and polymorphism on the accuracy of joint parentage and sibship assignments in polyploid species. We show that sibship, parentage and selfing rates in polyploids can be inferred accurately from a typical set of microsatellite loci. We also show that inferences can be substantially improved by allowing for a small genotyping error rate to accommodate the distortion in assumed Mendelian inheritance of the converted markers when large sibship groups are involved. The results are discussed in the context of polyploid data analysis in molecular ecology.     

FAP: an exclusion‐based parental assignment program with enhanced predictive functions

In molecular‐based parentage analyses, the accurate prediction of resolving power for a panel of loci is important for the critical assessment of results. A DOS‐based computer program, fap (Family Analysis Program), is described that calculates exclusion‐based family assignment probabilities within family mixtures where all parental genotypes are known. Three levels of hierarchy can be explored (sample, groups of families, individual families). The package also provides useful aids to identify problematic loci/misscoring during actual assignment. fap is available for free download from http://www.aqua.stir.ac.uk/rep‐general/downloads.html .     

Pedigree simulations reveal that maternity assignment is reliable in populations with conspecific brood parasitism,incomplete parental sampling and kin structure

Modern genetic parentage methods reveal that alternative reproductive strategies are common in both males and females. Under ideal conditions, genetic methods accurately connect the parents to offspring produced by extra-pair matings or conspecific brood parasitism. However, some breeding systems and sampling scenarios present significant complications for accurate parentage assignment. We used simulated genetic pedigrees to assess the reliability of parentage assignment for a series of challenging sampling regimes that reflect realistic conditions for many brood-parasitic birds: absence of genetic samples from sires, absence of samples from brood parasites and female kin-structured populations. Using 18 microsatellite markers and empirical allele frequencies from two populations of a conspecific brood parasite, the wood duck (Aix sponsa), we simulated brood parasitism and determined maternity using two widely used programs, cervus and colony . Errors in assignment were generally modest for most sampling scenarios but differed by program: cervus suffered from false assignment of parasitic offspring, whereas colony sometimes failed to assign offspring to their known mothers. Notably, colony was able to accurately infer unsampled parents. Reducing the number of markers (nine loci rather than 18) caused the assignment error to slightly worsen with colony but balloon with cervus . One potential error with important biological implications was rare in all cases—few nesting females were incorrectly excluded as the mother of their own offspring, an error that could falsely indicate brood parasitism. We consider the implications of our findings for both a retrospective assessment of previous studies and suggestions for best practices for future studies.     

Establishment of a quasi-field trial in <Emphasis Type="Italic">Abies nordmanniana</Emphasis>—test of a new approach to forest tree breeding

This study used DNA markers to establish a quasi-field trial within a production Christmas tree stand produced from seed collected in an open-pollinated clonal seed orchard (CSO). A total of 660 offspring from the CSO, which comprised 99 clones of Abies nordmanniana, were genotyped with 12 microsatellites. Parentage was assigned successfully to 93% and 98% of the progeny at 95% and 80% confidence, respectively. The assignment rate declined only to 90% when the number of markers was reduced to 10. The distribution of parentage to the offspring among the CSO clones was highly skewed. The most successful clone was assigned as parent in 7% of the cases, and only 92 of the 119 potential parental genotypes were assigned as parents. The obtained pedigree was used to estimate breeding values for the CSO clones for five characters relevant for Christmas tree breeding. For high-heritability traits, such as flushing, accurate breeding values could be estimated for a considerable proportion of the clones. To estimate breeding values for low-heritability traits, such as Christmas tree quality score, more genotyped offspring will be required. The largest drawback of the method is the highly skewed distribution of parentage among the parents in the seed orchards, making it difficult to calculate breeding values for all clones. The approach seems well suited for tree breeding that puts more emphasis on pure selection of parental genotypes and less on estimating quantitative genetic parameters.     

Parentage testing in alpacas (Vicugna pacos) using semi-automated fluorescent multiplex PCRs with 10 microsatellite markers

The aim of this study was to assess and apply a microsatellite multiplex system for parentage determination in alpacas. An approach for parentage testing based on 10 microsatellites was evaluated in a population of 329 unrelated alpacas from different geographical zones in Perú. All microsatellite markers, which amplified in two multiplex reactions, were highly polymorphic with a mean of 14.5 alleles per locus (six to 28 alleles per locus) and an average expected heterozygosity ( H _E) of 0.8185 (range of 0.698–0.946). The total parentage exclusion probability was 0.999456 for excluding a candidate parent from parentage of an arbitrary offspring, given only the genotype of the offspring, and 0.999991 for excluding a candidate parent from parentage of an arbitrary offspring, given the genotype of the offspring and the other parent. In a case test of parentage assignment, the microsatellite panel assigned 38 (from 45 cases) offspring parentage to 10 sires with LOD scores ranging from 2.19 × 10⁺¹³ to 1.34 × 10⁺¹⁵ and Δ values ranging from 2.80 × 10⁺¹² to 1.34 × 10⁺¹⁵ with an estimated pedigree error rate of 15.5%. The performance of this multiplex panel of markers suggests that it will be useful in parentage testing of alpacas.     

gimlet: a computer program for analysing genetic individual identification data

Growing interest in microsatellite genotyping, combined with noninvasive genetic sampling has led to the increased production of data. New tools to analyse these data are required. gimlet is a user‐friendly software package designed to perform several simple tasks: (i) construction of consensus genotypes from repeated genotyping; (ii) estimation of genotyping error rates; (iii) identification of identical genotypes; (iv) comparison of new genotypes to a set of reference genotypes; (v) determination of the kinship; and (vi) estimation of several population parameters such as allele frequencies, heterozygosity, probability of identity, and population size.     

中国对虾微卫星家系鉴定的模拟分析与应用

本研究基于中国对虾群体所获的微卫星标记等位基因频率进行了计算机模拟分析,并选择5个微卫星标记,就单独养殖家系群体微卫星标记家系鉴定的准确性及混养家系群体微卫星标记家系鉴定的应用价值做了研究.模拟分析表明4个微卫星标记可以鉴定95%的后裔.而单独养殖的家系鉴定准确率达到92.9%,在30个可能的父母对,215尾中国对虾组成的混养家系群体中,90.7%的后裔可以鉴定其父母.本研究结果表明微卫星分子标记可以应用于中国对虾的家系鉴定.模拟分析与实际应用的差异及父母与子代间的错配部分原因是由于无效等位基因的出现,基因分型错误也是一个重要原因.基于父母LOD值的分析可以降低错配的几率.