Detection Of Genotyping Errors Research Articles

Genotyping Error Detection and Customised Filtration for SNP Datasets.

A major challenge in analysing single-nucleotide polymorphism (SNP) genotype datasets is detecting and filtering errors that bias analyses and misinterpret ecological and evolutionary processes. Here, we present a comprehensive method to estimate and minimise genotyping error rates (deviations from the 'true' genotype) in any SNP datasets using triplicates (three repeats of the same sample) in a four-step filtration pipeline. The approach involves: (1) SNP filtering by missing data; (2) SNP filtering by error rates; (3) sample filtering by missing data and (4) detection of recaptured individuals by using estimated SNP error rates. The modular pipeline is provided in an R script that allows customised adjustments. We demonstrate the applicability of the method using non-invasive sampling from the Asiatic wild ass (Equus hemionus) population in Israel. We genotyped 756 samples using 625 SNPs, of which 255 were triplicates of 85 samples. The average SNP error rate, calculated based on the number of mismatching genotypes across triplicates before filtration, was 0.0034 and was reduced to 0.00174 following filtration. Evaluating genetic distance (GD) and relatedness (r) between triplicates before and after filtration (expected to be at the minimum and maximum respectively) showed a significant reduction in the average GD, from 58.1 to 25.3 (p = 0.0002) and a significant increase in relatedness, from r = 0.98 to r = 0.991 (p = 0.00587). We demonstrate how error rate estimation enhances recapture detection and improves genotype quality.

Detection of Mendelian Consistent Genotyping Errors in Pedigrees

Detection of genotyping errors is a necessary step to minimize false results in genetic analysis. This is especially important when the rate of genotyping errors is high, as has been reported for high-throughput sequence data. To detect genotyping errors in pedigrees, Mendelian inconsistent (MI) error checks exist, as do multi-point methods that flag Mendelian consistent (MC) errors for sparse multi-allelic markers. However, few methods exist for detecting MC genotyping errors, particularly for dense variants on large pedigrees. Here, we introduce an efficient method to detect MC errors even for very dense variants (e.g., SNPs and sequencing data) on pedigrees that may be large. Our method first samples inheritance vectors (IVs) using a moderately sparse but informative set of markers using a Markov chain Monte Carlo-based sampler. Using sampled IVs, we considered two test statistics to detect MC genotyping errors: the percentage of IVs inconsistent with observed genotypes (A1) or the posterior probability of error configurations (A2). Using simulations, we show that this method, even with the simpler A1 statistic, is effective for detecting MC genotyping errors in dense variants, with sensitivity almost as high as the theoretical best sensitivity possible. We also evaluate the effectiveness of this method as a function of parameters, when including the observed pattern for genotype, density of framework markers, error rate, allele frequencies, and number of sampled inheritance vectors. Our approach provides a line of defense against false findings based on the use of dense variants in pedigrees.

Haplotype phasing after joint estimation of recombination and linkage disequilibrium in breeding populations

A novel method for haplotype phasing in families after joint estimation of recombination fraction and linkage disequilibrium is developed. Results from Monte Carlo computer simulations show that the newly developed E.M. algorithm is accurate if true recombination fraction is 0 even for single families of relatively small sizes. Estimates of recombination fraction and linkage disequilibrium were 0.00 (SD 0.00) and 0.19 (SD 0.03) for simulated recombination fraction and linkage disequilibrium of 0.00 and 0.20, respectively. A genome fragmentation phasing strategy was developed and used for phasing haplotypes in a sire and 36 progeny using the 50 k Illumina BeadChip by: a) estimation of the recombination fraction and LD in consecutive SNPs using family information, b) linkage analyses between fragments, c) phasing of haplotypes in parents and progeny and in following generations. Homozygous SNPs in progeny allowed determination of paternal fragment inheritance, and deduction of SNP sequence information of haplotypes from dams. The strategy also allowed detection of genotyping errors. A total of 613 recombination events were detected after linkage analysis was carried out between fragments. Hot and cold spots were identified at the individual (sire level). SNPs for which the sire and calf were heterozygotes became informative (over 90%) after the phasing of haplotypes. Average of regions of identity between half-sibs when comparing its maternal inherited haplotypes (with at least 20 SNP) in common was 0.11 with a maximum of 0.29 and a minimum of 0.05. A Monte-Carlo simulation of BTA1 with the same linkage disequilibrium structure and genetic linkage as the cattle family yielded a 99.98 and 99.94% of correct phases for informative SNPs in sire and calves, respectively.

The Hardy-Weinberg principle and its applications in modern population genetics

The discovery of the Hardy-Weinberg principle marked the beginning of the field of population genetics. Over the past hundred years, it has provided a starting point for many population genetic investigations. In this review, the Hardy-Weinberg principle, its statistical testing, and several of its applications in various modern population genetic research areas, including allelic variability and selection in the human leukocyte antigen region, microsatellite genotyping error detection, and accuracy of haplotype estimation, are discussed.

Parametric model‐based statistics for possible genotyping errors and sample stratification in sibling‐pair SNP data

The detection of genotyping errors, based on apparent Mendelian incompatibilities in a sample of sib-pairs, is a complicated problem. In the case of a single marker and unknown parental genotypes, all combinations of sib-pair genotypes are self-consistent. Moreover, the observed deviation from equilibrium genotype frequencies may result from genotyping errors as well as from the sample's stratification. This in turn, may profoundly affect the results of association and linkage analyses, and therefore an estimation of these factors should be done beforehand. Here we present several parametric models, and using likelihood ratio statistics, we suggest a method of combined analysis of genotyping errors and a sample stratification for randomly ascertained sib-pair single nucleotide polymorphism (SNP) data. Specifically, we implemented two models of genotyping errors in either heterozygotes or homozygotes, and two models of sample stratification resulting from either the presence of families of different ethnic origin (e.g., a population admixture) or from a different ethnic origin of the parents in the family (e.g., intermarriage). The power of this method was established by Monte Carlo data simulation. The results clearly suggest that the proposed method is most efficient for detecting genotyping errors in heterozygotes, a common error caused by incorrect SNP data interpretation. We also provide an example of its application to real data.

Genotyping Error Detection in Samples of Unrelated Individuals without Replicate Genotyping

Objective: Identifying genotyping errors is an important issue in genetic research, yet it has been relatively less studied in samples consisting of unrelated individuals. In this article, we consider several models of genotyping errors, which were originally proposed for pedigree data, for unrelated population samples with single nucleotide polymorphism (SNP) genotype data. The mathematical constraints are investigated for detecting genotyping errors without resampling replicates or genotyping relatives. Methods: For the various proposed genotyping error models, we unveil the conditions under which the parameters are identifiable. These results are verified through applications to simulated and real SNP data. Results: We show that, with constraints, two particular models provide both identifiable error rate and allele frequencies of an SNP for unrelated population data. The simulation study shows that these two models present unbiased estimates for the allele frequencies. One of the models also gives an unbiased estimate for the genotyping error rate. Conclusion: While the Hardy-Weinberg equilibrium test can be used to detect genotyping errors, a key advantage of these models is the explicit estimates of genotyping error rates and allele frequencies. This work may help researchers to estimate error rates and to use the estimates in their analysis to increase power and decrease bias, without the extra work of genotyping family members or replicates.

Construction of a 10,000-Marker Ultradense Genetic Recombination Map of Potato: Providing a Framework for Accelerated Gene Isolation and a Genomewide Physical Map

An ultradense genetic linkage map with >10,000 AFLP loci was constructed from a heterozygous diploid potato population. To our knowledge, this is the densest meiotic recombination map ever constructed. A fast marker-ordering algorithm was used, based on the minimization of the total number of recombination events within a given marker order in combination with genotyping error-detection software. This resulted in "skeleton bin maps," which can be viewed as the most parsimonious marker order. The unit of distance is not expressed in centimorgans but in "bins." A bin is a position on the genetic map with a unique segregation pattern that is separated from adjacent bins by a single recombination event. Putative centromeres were identified by a strong clustering of markers, probably due to cold spots for recombination. Conversely, recombination hot spots resulted in large intervals of up to 15 cM without markers. The current level of marker saturation suggests that marker density is proportional to physical distance and independent of recombination frequency. Most chromatids (92%) recombined once or never, suggesting strong chiasma interference. Absolute chiasma interference within a chromosome arm could not be demonstrated. Two examples of contig construction and map-based cloning have demonstrated that the marker spacing was in accordance with the expected physical distance: approximately one marker per BAC length. Currently, the markers are used for genetic anchoring of a physical map of potato to deliver a sequence-ready minimal tiling path of BAC contigs of specific chromosomal regions for the potato genome sequencing consortium (http://www.potatogenome.net).

Detection of genotyping errors and pseudo-SNPs via deviations from Hardy-Weinberg equilibrium.

Genotype error can greatly reduce the power of a genetic study. For family data, genotype error can be assessed by examining marker data for non-Mendelian inconsistencies, closely linked markers for double recombination events, and consistency of duplicate genotypes. For case-control data, duplicate samples are genotyped, and controls are tested for deviations from Hardy-Weinberg equilibrium (HWE). Duplicate samples can provide accurate estimates of genotyping error rates, unless systematic genotyping errors have occurred. Although genotyping errors can cause deviations from HWE, these deviations are usually small, and the power to detect them is low except for high rates of genotyping error and/or large sample sizes. An additional problem is that even when deviations from HWE are detected for marker loci, without additional experimentation it is not possible to unequivocally implicate genotyping error as the cause. The power and sample sizes necessary to detect deviations from HWE for single-nucleotide polymorphism (SNP) data are examined for a variety of genotyping error and pseudo-SNP models. For the majority of genotyping models examined, the power is poor to detect deviations from HWE. For example, for 1,000 controls, if an allele with a frequency of 0.1 fails to amplify for 28% of the heterozygous genotypes producing a sample error rate of 0.05, the power is 0.51 to detect a deviation from HWE at an alpha level of 0.05. On the other hand, the detection of deviations from HWE for pseudo-SNPs (paralogous and ectopic sequence variants) for the majority of models examined produces a power of >0.8 for sample sizes as small as 50 individuals.

Detection of genotyping errors by Hardy-Weinberg equilibrium testing.

Genotyping data sets may contain errors that, in some instances, lead to false conclusions. Deviation from Hardy-Weinberg equilibrium (HWE) in random samples may be indicative of problematic assays. This study has analysed 107,000 genotypes generated by TaqMan, RFLP, sequencing or mass spectrometric methods from 443 single-nucleotide polymorphisms (SNPs). These SNPs are distributed both within genes and in intergenic regions. Genotype distributions for 36 out of 313 assays (11.5%) whose minor allele frequencies were >0.05 deviated from HWE (P<0.05). Some of the possible reasons for this deviation were explored: assays for five SNPs proved nonspecific, and genotyping errors were identified in 21 SNPs. For the remaining 10 SNPs, no reasons for deviation from HWE were identified. We demonstrate the successful identification of a proportion of nonspecific assays, and assays harbouring genotyping error. Consequently, our current high-throughput genotyping system incorporates tests for both assay specificity and deviation from HWE, to minimise the genotype error rate and therefore improve data quality.

The impact of genotyping error on haplotype reconstruction and frequency estimation.

The choice of genotyping families vs unrelated individuals is a critical factor in any large-scale linkage disequilibrium (LD) study. The use of unrelated individuals for such studies is promising, but in contrast to family designs, unrelated samples do not facilitate detection of genotyping errors, which have been shown to be of great importance for LD and linkage studies and may be even more important in genotyping collaborations across laboratories. Here we employ some of the most commonly-used analysis methods to examine the relative accuracy of haplotype estimation using families vs unrelateds in the presence of genotyping error. The results suggest that even slight amounts of genotyping error can significantly decrease haplotype frequency and reconstruction accuracy, that the ability to detect such errors in large families is essential when the number/complexity of haplotypes is high (low LD/common alleles). In contrast, in situations of low haplotype complexity (high LD and/or many rare alleles) unrelated individuals offer such a high degree of accuracy that there is little reason for less efficient family designs. Moreover, parent-child trios, which comprise the most popular family design and the most efficient in terms of the number of founder chromosomes per genotype but which contain little information for error detection, offer little or no gain over unrelated samples in nearly all cases, and thus do not seem a useful sampling compromise between unrelated individuals and large families. The implications of these results are discussed in the context of large-scale LD mapping projects such as the proposed genome-wide haplotype map.

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.

Probability of Detection of Genotyping Errors and Mutations as Inheritance Inconsistencies in Nuclear-Family Data

Gene-mapping studies routinely rely on checking for Mendelian transmission of marker alleles in a pedigree, as a means of screening for genotyping errors and mutations, with the implicit assumption that, if a pedigree is consistent with Mendel's laws of inheritance, then there are no genotyping errors. However, the occurrence of inheritance inconsistencies alone is an inadequate measure of the number of genotyping errors, since the rate of occurrence depends on the number and relationships of genotyped pedigree members, the type of errors, and the distribution of marker-allele frequencies. In this article, we calculate the expected probability of detection of a genotyping error or mutation as an inheritance inconsistency in nuclear-family data, as a function of both the number of genotyped parents and offspring and the marker-allele frequency distribution. Through computer simulation, we explore the sensitivity of our analytic calculations to the underlying error model. Under a random-allele-error model, we find that detection rates are 51%-77% for multiallelic markers and 13%-75% for biallelic markers; detection rates are generally lower when the error occurs in a parent than in an offspring, unless a large number of offspring are genotyped. Errors are especially difficult to detect for biallelic markers with equally frequent alleles, even when both parents are genotyped; in this case, the maximum detection rate is 34% for four-person nuclear families. Error detection in families in which parents are not genotyped is limited, even with multiallelic markers. Given these results, we recommend that additional error checking (e.g., on the basis of multipoint analysis) be performed, beyond routine checking for Mendelian consistency. Furthermore, our results permit assessment of the plausibility of an observed number of inheritance inconsistencies for a family, allowing the detection of likely pedigree-rather than genotyping-errors in the early stages of a genome scan. Such early assessments are valuable in either the targeting of families for resampling or discontinued genotyping.

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 </=50% of genotyping errors, it generally flags those errors that have the largest impact on linkage results. For high-resolution genetic maps, removal of the errors identified by our method restores most or nearly all the lost linkage information and can be accomplished without generating false evidence for linkage by removing incorrectly identified errors.