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).     

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

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

Detection and Integration of Genotyping Errors in Statistical Genetics

Detection of genotyping errors and integration of such errors in statistical analysis are relatively neglected topics, given their importance in gene mapping. A few inopportunely placed errors, if ignored, can tremendously affect evidence for linkage. The present study takes a fresh look at the calculation of pedigree likelihoods in the presence of genotyping error. To accommodate genotyping error, we present extensions to the Lander-Green-Kruglyak deterministic algorithm for small pedigrees and to the Markov-chain Monte Carlo stochastic algorithm for large pedigrees. These extensions can accommodate a variety of error models and refrain from simplifying assumptions, such as allowing, at most, one error per pedigree. In principle, almost any statistical genetic analysis can be performed taking errors into account, without actually correcting or deleting suspect genotypes. Three examples illustrate the possibilities. These examples make use of the full pedigree data, multiple linked markers, and a prior error model. The first example is the estimation of genotyping error rates from pedigree data. The second-and currently most useful-example is the computation of posterior mistyping probabilities. These probabilities cover both Mendelian-consistent and Mendelian-inconsistent errors. The third example is the selection of the true pedigree structure connecting a group of people from among several competing pedigree structures. Paternity testing and twin zygosity testing are typical applications.     

Effect of genotyping error in model-free linkage analysis using microsatellite or single-nucleotide polymorphism marker maps

Errors while genotyping are inevitable and can reduce the power to detect linkage. However, does genotyping error have the same impact on linkage results for single-nucleotide polymorphism (SNP) and microsatellite (MS) marker maps? To evaluate this question we detected genotyping errors that are consistent with Mendelian inheritance using large changes in multipoint identity-by-descent sharing in neighboring markers. Only a small fraction of Mendelian consistent errors were detectable (e.g., 18% of MS and 2.4% of SNP genotyping errors). More SNP genotyping errors are Mendelian consistent compared to MS genotyping errors, so genotyping error may have a greater impact on linkage results using SNP marker maps. We also evaluated the effect of genotyping error on the power and type I error rate using simulated nuclear families with missing parents under 0, 0.14, and 2.8% genotyping error rates. In the presence of genotyping error, we found that the power to detect a true linkage signal was greater for SNP (75%) than MS (67%) marker maps, although there were also slightly more false-positive signals using SNP marker maps (5 compared with 3 for MS). Finally, we evaluated the usefulness of accounting for genotyping error in the SNP data using a likelihood-based approach, which restores some of the power that is lost when genotyping error is introduced.     

Using DNA pools for genotyping trios

The genotyping of mother–father–child trios is a very useful tool in disease association studies, as trios eliminate population stratification effects and increase the accuracy of haplotype inference. Unfortunately, the use of trios for association studies may reduce power, since it requires the genotyping of three individuals where only four independent haplotypes are involved. We describe here a method for genotyping a trio using two DNA pools, thus reducing the cost of genotyping trios to that of genotyping two individuals. Furthermore, we present extensions to the method that exploit the linkage disequilibrium structure to compensate for missing data and genotyping errors. We evaluated our method on trios from CEPH pedigree 66 of the Coriell Institute. We demonstrate that the error rates in the genotype calls of the proposed protocol are comparable to those of standard genotyping techniques, although the cost is reduced considerably. The approach described is generic and it can be applied to any genotyping platform that achieves a reasonable precision of allele frequency estimates from pools of two individuals. Using this approach, future trio-based association studies may be able to increase the sample size by 50% for the same cost and thereby increase the power to detect associations.     

A Transmission/Disequilibrium Test That Allows for Genotyping Errors in the Analysis of Single-Nucleotide Polymorphism Data

The present study assesses the effects of genotyping errors on the type I error rate of a particular transmission/disequilibrium test (TDT(std)), which assumes that data are errorless, and introduces a new transmission/disequilibrium test (TDT(ae)) that allows for random genotyping errors. We evaluate the type I error rate and power of the TDT(ae) under a variety of simulations and perform a power comparison between the TDT(std) and the TDT(ae), for errorless data. Both the TDT(std) and the TDT(ae) statistics are computed as two times a log-likelihood difference, and both are asymptotically distributed as chi(2) with 1 df. Genotype data for trios are simulated under a null hypothesis and under an alternative (power) hypothesis. For each simulation, errors are introduced randomly via a computer algorithm with different probabilities (called "allelic error rates"). The TDT(std) statistic is computed on all trios that show Mendelian consistency, whereas the TDT(ae) statistic is computed on all trios. The results indicate that TDT(std) shows a significant increase in type I error when applied to data in which inconsistent trios are removed. This type I error increases both with an increase in sample size and with an increase in the allelic error rates. TDT(ae) always maintains correct type I error rates for the simulations considered. Factors affecting the power of the TDT(ae) are discussed. Finally, the power of TDT(std) is at least that of TDT(ae) for simulations with errorless data. Because data are rarely error free, we recommend that researchers use methods, such as the TDT(ae), that allow for errors in genotype data.     

Inferring haplotypes from genotypes on a pedigree with mutations, genotyping errors and missing alleles

Inferring the haplotypes of the members of a pedigree from their genotypes has been extensively studied. However, most studies do not consider genotyping errors and de novo mutations. In this paper, we study how to infer haplotypes from genotype data that may contain genotyping errors, de novo mutations, and missing alleles. We assume that there are no recombinants in the genotype data, which is usually true for tightly linked markers. We introduce a combinatorial optimization problem, called haplotype configuration with mutations and errors (HCME), which calls for haplotype configurations consistent with the given genotypes that incur no recombinants and require the minimum number of mutations and errors. HCME is NP-hard. To solve the problem, we propose a heuristic algorithm, the core of which is an integer linear program (ILP) using the system of linear equations over Galois field GF(2). Our algorithm can detect and locate genotyping errors that cannot be detected by simply checking the Mendelian law of inheritance. The algorithm also offers error correction in genotypes/haplotypes rather than just detecting inconsistencies and deleting the involved loci. Our experimental results show that the algorithm can infer haplotypes with a very high accuracy and recover 65%-94% of genotyping errors depending on the pedigree topology.     

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.     

GEL: a novel genotype calling algorithm using empirical likelihood

MOTIVATION: Preliminary results on the data produced using the Affymetrix large-scale genotyping platforms show that it is necessary to construct improved genotype calling algorithms. There is evidence that some of the existing algorithms lead to an increased error rate in heterozygous genotypes, and a disproportionately large rate of heterozygotes with missing genotypes. Non-random errors and missing data can lead to an increase in the number of false discoveries in genetic association studies. Therefore, the factors that need to be evaluated in assessing the performance of an algorithm are the missing data (call) and error rates, but also the heterozygous proportions in missing data and errors. RESULTS: We introduce a novel genotype calling algorithm (GEL) for the Affymetrix GeneChip arrays. The algorithm uses likelihood calculations that are based on distributions inferred from the observed data. A key ingredient in accurate genotype calling is weighting the information that comes from each probe quartet according to the quality/reliability of the data in the quartet, and prior information on the performance of the quartet. AVAILABILITY: The GEL software is implemented in R and is available by request from the corresponding author at nicolae@galton.uchicago.edu.     

Comparative study of multipoint methods for genotype error detection

Several programs are currently available for the detection of genotyping error that may or may not be Mendelianly inconsistent. However, no systematic study exists that evaluates their performance under varying pedigree structures and sizes, marker spacing, and allele frequencies. Our simulation study compares four multipoint methods: Merlin, Mendel4, SimWalk2, and Sibmed. We look at empirical thresholds, power, and false-positive rates on 7 small pedigree structures that included sibships with and without genotyped parents, and a three-generation pedigree, using 11 microsatellite markers with 3 different map spacings. Simulated data includes 5,000 replicates of each pedigree structure and marker map, with random genotyping errors in about 4% of the middle marker's genotypes. We found that the default thresholds used by these programs provide low power (47-72%). Power is improved more by adding genotyped siblings than by using more closely spaced markers. Some mistyping methods are sensitive to the frequencies of the observed alleles. Siblings of mistyped individuals have elevated false-positive rates, as do markers close to the mistyped marker. We conclude that thresholds should be decided based on the pedigree and marker data and that greater focus should be placed on modeling genotyping error when computing likelihoods, rather than on detecting and eliminating genotyping errors.     

Linkage disequilibrium-based quality control for large-scale genetic studies

Quality control (QC) is a critical step in large-scale studies of genetic variation. While, on average, high-throughput single nucleotide polymorphism (SNP) genotyping assays are now very accurate, the errors that remain tend to cluster into a small percentage of "problem" SNPs, which exhibit unusually high error rates. Because most large-scale studies of genetic variation are searching for phenomena that are rare (e.g., SNPs associated with a phenotype), even this small percentage of problem SNPs can cause important practical problems. Here we describe and illustrate how patterns of linkage disequilibrium (LD) can be used to improve QC in large-scale, population-based studies. This approach has the advantage over existing filters (e.g., HWE or call rate) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors. Applying this LD-based QC procedure to data from The International HapMap Project, we identify over 1,500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes. Our method is implemented in the software package fastPHASE, available from the Stephens Lab website (http://stephenslab.uchicago.edu/software.html).     

Detection rates for genotyping errors in SNPs using the trio design

One well-known approach for the analysis of transmission-disequilibrium is the investigation of single nucleotide polymorphisms (SNPs) in trios consisting of an affected child and its parents. Results may be biased by erroneously given genotypes. Various reasons, among them sample swap or wrong pedigree structure, represent a possible source for biased results. As these can be partly ruled out by good study conditions together with checks for correct pedigree structure by a series of independent markers, the remaining main cause for errors is genotyping errors. Some of the errors can be detected by Mendelian checks whilst others are compatible with the pedigree structure. The extent of genotyping errors can be estimated by investigating the rate of detected genotyping errors by Mendelian checks. In many studies only one SNP of a specific genomic region is investigated by TDT which leaves Mendelian checks as the only tool to control genotyping errors. From the rate of detected errors the true error rate can be estimated. Gordon et al. [Hum Hered 1999;49:65-70] considered the case of genotyping errors that occur randomly and independently with some fixed probability for the wrong ascertainment of an allele. In practice, instead of single alleles, SNP genotypes are determined. Therefore, we study the proportion of detected errors (detection rate) based on genotypes. In contrast to Gordon et al., who reported detection rates between 25 and 30%, we obtain higher detection rates ranging from 39 up to 61% considering likely error structures in the data. We conclude that detection rates are probably substantially higher than those reported by Gordon et al.     

Error detection for genetic data, using likelihood methods.

As genetic maps become denser, the effect of laboratory typing errors becomes more serious. We review a general method for detecting errors in pedigree genotyping data that is a variant of the likelihood-ratio test statistic. It pinpoints individuals and loci with relatively unlikely genotypes. Power and significance studies using Monte Carlo methods are shown by using simulated data with pedigree structures similar to the CEPH pedigrees and a larger experimental pedigree used in the study of idiopathic dilated cardiomyopathy (DCM). The studies show the index detects errors for small values of theta with high power and an acceptable false positive rate. The method was also used to check for errors in DCM laboratory pedigree data and to estimate the error rate in CEPH-chromosome 6 data. The errors flagged by our method in the DCM pedigree were confirmed by the laboratory. The results are consistent with estimated false-positive and false-negative rates obtained using simulation.     

Assessing reliability of microsatellite genotypes from kit fox faecal samples using genetic and GIS analyses

Noninvasive faecal DNA sampling has the potential to provide a wealth of information necessary for monitoring and managing endangered species while eliminating the need to capture, handle or observe rare individuals. However, scoring problems, and subsequent genotyping errors, associated with this monitoring method remain a great concern as they can lead to misidentification of individuals and biased estimates. We examined a kit fox scat data set (353 scats; 80 genotypes) for genotyping errors using both genetic and GIS analyses, and evaluated the feasibility of combining both approaches to assess reliability of the faecal DNA results. We further checked the appropriateness of using faecal genotypes to study kit fox populations by describing information about foxes that we could deduce from the 'acceptable' scat genotypes, and comparing it to information gathered with traditional field techniques. Overall, genetic tests indicated that our data set had a low rate of genotyping error. Furthermore, examination of distributions of scat locations confirmed our data set was relatively error free. We found that analysing information on sex primer consistency and scat locations provided a useful assessment of scat genotype error, and greatly limited the amount of additional laboratory work that was needed to identify potentially 'false' scores. 'Acceptable' scat genotypes revealed information on sex ratio, relatedness, fox movement patterns, latrine use, and size of home range. Results from genetic and field data were consistent, supporting the conclusion that our data set had a very low rate of genotyping error and that this noninvasive method is a reliable approach for monitoring kit foxes.     

Identifying marker typing incompatibilities in linkage analysis.

A common problem encountered in linkage analyses is that execution of the computer program is halted because of genotypes in the data that are inconsistent with Mendelian inheritance. Such inconsistencies may arise because of pedigree errors or errors in typing. In some cases, the source of the inconsistencies is easily identified by examining the pedigree. In others, the error is not obvious, and substantial time and effort are required to identify the responsible genotypes. We have developed two methods for automatically identifying those individuals whose genotypes are most likely the cause of the inconsistencies. First, we calculate the posterior probability of genotyping error for each member of the pedigree, given the marker data on all pedigree members and allowing anyone in the pedigree to have an error. Second, we identify those individuals whose genotypes could be solely responsible for the inconsistency in the pedigree. We illustrate these methods with two examples: one a pedigree error, the second a genotyping error. These methods have been implemented as a module of the pedigree analysis program package MENDEL.     

A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data

The identification of genes contributing to complex diseases and quantitative traits requires genetic data of high fidelity, because undetected errors and mutations can profoundly affect linkage information. The recent emphasis on the use of the sibling-pair design eliminates or decreases the likelihood of detection of genotyping errors and marker mutations through apparent Mendelian incompatibilities or close double recombinants. In this article, we describe a hidden Markov method for detecting genotyping errors and mutations in multilocus linkage data. Specifically, we calculate the posterior probability of genotyping error or mutation for each sibling-pair-marker combination, conditional on all marker data and an assumed genotype-error rate. The method is designed for use with sibling-pair data when parental genotypes are unavailable. Through Monte Carlo simulation, we explore the effects of map density, marker-allele frequencies, marker position, and genotype-error rate on the accuracy of our error-detection method. In addition, we examine the impact of genotyping errors and error detection and correction on multipoint linkage information. We illustrate that even moderate error rates can result in substantial loss of linkage information, given efforts to fine-map a putative disease locus. Although simulations suggest that our method detects 相似文献   

Molecular and statistical approaches to the detection and correction of errors in genotype databases.

Errors in genotyping data have been shown to have a significant effect on the estimation of recombination fractions in high-resolution genetic maps. Previous estimates of errors in existing databases have been limited to the analysis of relatively few markers and have suggested rates in the range 0.5%-1.5%. The present study capitalizes on the fact that within the Centre d'Etude du Polymorphisme Humain (CEPH) collection of reference families, 21 individuals are members of more than one family, with separate DNA samples provided by CEPH for each appearance of these individuals. By comparing the genotypes of these individuals in each of the families in which they occur, an estimated error rate of 1.4% was calculated for all loci in the version 4.0 CEPH database. Removing those individuals who were clearly identified by CEPH as appearing in more than one family resulted in a 3.0% error rate for the remaining samples, suggesting that some error checking of the identified repeated individuals may occur prior to data submission. An error rate of 3.0% for version 4.0 data was also obtained for four chromosome 5 markers that were retyped through the entire CEPH collection. The effects of these errors on a multipoint map were significant, with a total sex-averaged length of 36.09 cM with the errors, and 19.47 cM with the errors corrected. Several statistical approaches to detect and allow for errors during linkage analysis are presented. One method, which identified families containing possible errors on the basis of the impact on the maximum lod score, showed particular promise, especially when combined with the limited retyping of the identified families. The impact of the demonstrated error rate in an established genotype database on high-resolution mapping is significant, raising the question of the overall value of incorporating such existing data into new genetic maps.     

Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms

The purpose of this work is to quantify the effects that errors in genotyping have on power and the sample size necessary to maintain constant asymptotic Type I and Type II error rates (SSN) for case-control genetic association studies between a disease phenotype and a di-allelic marker locus, for example a single nucleotide polymorphism (SNP) locus. We consider the effects of three published models of genotyping errors on the chi-square test for independence in the 2 x 3 table. After specifying genotype frequencies for the marker locus conditional on disease status and error model in both a genetic model-based and a genetic model-free framework, we compute the asymptotic power to detect association through specification of the test's non-centrality parameter. This parameter determines the functional dependence of SSN on the genotyping error rates. Additionally, we study the dependence of SSN on linkage disequilibrium (LD), marker allele frequencies, and genotyping error rates for a dominant disease model. Increased genotyping error rate requires a larger SSN. Every 1% increase in sum of genotyping error rates requires that both case and control SSN be increased by 2-8%, with the extent of increase dependent upon the error model. For the dominant disease model, SSN is a nonlinear function of LD and genotyping error rate, with greater SSN for lower LD and higher genotyping error rate. The combination of lower LD and higher genotyping error rates requires a larger SSN than the sum of the SSN for the lower LD and for the higher genotyping error rate.     

Relationship Inference from Trios of Individuals, in the Presence of Typing Error

Misspecification of relationships and of genotype data can cause problems in linkage analyses based on genome-scan data. Previous reports have focused on pairwise relationships and a simple error model. This article considers the increased information available from the joint analysis of trios of individuals, integrating this analysis with an error model that allows for the most common genotyping errors. Given observed marker phenotypes in a genome scan, computational methods are outlined both for likelihoods of relationships and for the posterior probabilities of underlying genotypes. The methods are applied to examples from two real data sets: one has been previously well analyzed, and, hence, Mendelian inconsistencies have been removed; the other typifies the pedigree and genotype errors encountered in the initial analyses of a study. It is demonstrated that the coupling of relationship inference and error detection is quite effective, that the error model is computationally practical, and that data on a third relative can often clarify relationships.