Genotyping Error Rates Research Articles

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.

Assessment of ancestry probabilities in the presence of genotyping errors

This paper extends an approach for estimating the ancestry probability, the probability that an inbred line is an ancestor of a given hybrid, to account for genotyping errors. The effect of such errors on ancestry probability estimates is evaluated through simulation. The simulation study shows that if misclassification is ignored, then ancestry probabilities may be slightly overestimated. The sensitivity of ancestry probability calculations to the assumed genotyping error rate is also assessed.

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.

A test of the multiplex pre-amplification approach in microsatellite genotyping of wolverine faecal DNA

Recently, a two-step PCR approach, referred to as multiplex pre-amplification, was proposed to improve microsatellite amplification from non-invasive samples such as faecal DNA. Here, we compare this new approach to standard PCR with respect to amplification success and genotyping error rates in microsatellite analysis (18 markers) of wolverine faecal DNA (48 extracts initially shown to contain amplifiable DNA). The multiplex pre-amplification approach was clearly advantageous both in terms of successful PCR amplifications (91% vs. 80%) and allelic dropout rate (2.4% vs. 12.5%). However, dropouts were to a high extent repeated in all second-step amplifications following multiplex pre-amplification, indicative of being generated during the initial PCR. Analysing more than one PCR from the initial multiplex PCR product may thus be of limited value. We instead suggest to perform two initial multiplex PCRs and to analyse a single second-step PCR from each of them. This was tested for 22 extracts at 18 loci and proved to be an effective way to obtaining a correct genotype.

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.

Estimation of the Rate of SNP Genotyping Errors From DNA Extracted From Different Tissues

High density single nucleotide polymorphism (SNP) genotyping panels provide an alternative to microsatellite markers for genome scans. However, genotype errors have a major impact on power to detect linkage or association and are difficult to detect for SNPs. We estimated error rates with the Affymetrix GeneChip SNP platform in samples from a family with a mixed set of monozygotic (MZ) and dizygotic (DZ) triplets using lymphocyte, buccal DNA and samples from whole genome amplification using the multiple displacement amplification (MDA) technique. The average call rate from 58,960 SNPs for five genomic samples was 99.48%. Comparison of results for the MZ twins showed only three discordant genotypes (concordance rate 99.995%). The mean concordance rate for comparisons of samples from lymphocyte and buccal DNA was 99.97%. Mendelian inconsistencies were identified in 46 SNPs with errors in one or more family members, a rate of 0.022%. Observed genotype concordance rates between parents, between parents and children, and among siblings were consistent with previously reported allele frequencies and Hardy-Weinberg equilibrium. Using the MDA technique, results for two samples had equivalent high accuracy to results with genomic samples. However, the SNP call rate for the remaining seven samples varied from 72.5% to 99.5%, with an average of 86.11%. Quality of the DNA sample following the MDA reaction appears to be the critical factor in SNP call rate for MDA samples. Our results demonstrate highly accurate and reproducible genotyping for the Affymetrix GeneChip Human Mapping Set in lymphocyte and buccal DNA samples.

Estimation of the Rate of SNP Genotyping Errors From DNA Extracted From Different Tissues

AbstractHigh density single nucleotide polymorphism (SNP) genotyping panels provide an alternative to microsatellite markers for genome scans. However, genotype errors have a major impact on power to detect linkage or association and are difficult to detect for SNPs. We estimated error rates with the Affymetrix GeneChip® SNP platform in samples from a family with a mixed set of monozygotic (MZ) and dizygotic (DZ) triplets using lymphocyte, buccal DNA and samples from whole genome amplification using the multiple displacement amplification (MDA) technique. The average call rate from 58,960 SNPs for five genomic samples was 99.48%. Comparison of results for the MZ twins showed only three discordant genotypes (concordance rate 99.995%). The mean concordance rate for comparisons of samples from lymphocyte and buccal DNA was 99.97%. Mendelian inconsistencies were identified in 46 SNPs with errors in one or more family members, a rate of 0.022%. Observed genotype concordance rates between parents, between parents and children, and among siblings were consistent with previously reported allele frequencies and Hardy–Weinberg equilibrium. Using the MDA technique, results for two samples had equivalent high accuracy to results with genomic samples. However, the SNP call rate for the remaining seven samples varied from 72.5% to 99.5%, with an average of 86.11%. Quality of the DNA sample following the MDA reaction appears to be the critical factor in SNP call rate for MDA samples. Our results demonstrate highly accurate and reproducible genotyping for the Affymetrix GeneChip® Human Mapping Set in lymphocyte and buccal DNA samples.

Evaluating Whole Genome Amplification via Multiply-Primed Rolling Circle Amplification for SNP Genotyping of Samples With Low DNA Yield

The amount of available DNA is often a limiting factor in pursuing genetic analyses of large-scale population cohorts. An association between higher DNA yield from blood and several phenotypes associated with inflammatory states has recently been demonstrated, suggesting that exclusion of samples with very low DNA yield may lead to biased results in statistical analyses. Whole genome amplification (WGA) could present a solution to the DNA concentration-dependent sample selection. The aim was to thoroughly assess WGA for samples with low DNA yield, using the multiply-primed rolling circle amplification method. Fifty-nine samples were selected with the lowest DNA yield (less than 7.5 microg) among 799 samples obtained for one population cohort. The genotypes obtained from two replicate WGA samples and the original genomic DNA were compared by typing 24 single nucleotide polymorphisms (SNPs). Multiple genotype discrepancies were identified for 13 of the 59 samples. The largest portion of discrepancies was due to allele dropout in heterozygous genotypes in WGA samples. Pooling the WGA DNA replicates prior to genotyping markedly improved genotyping reproducibility for the samples, with only 7 discrepancies identified in 4 samples. The nature of discrepancies was mostly homozygote genotypes in the genomic DNA and heterozygote genotypes in the WGA sample, suggesting possible allele dropout in the genomic DNA sample due to very low amounts of DNA template. Thus, WGA is applicable for low DNA yield samples, especially if using pooled WGA samples. A higher rate of genotyping errors requires that increased attention be paid to genotyping quality control, and caution when interpreting results.

Evaluating Whole Genome Amplification via Multiply-Primed Rolling Circle Amplification for SNP Genotyping of Samples With Low DNA Yield

AbstractThe amount of available DNA is often a limiting factor in pursuing genetic analyses of large-scale population cohorts. An association between higher DNA yield from blood and several phenotypes associated with inflammatory states has recently been demonstrated, suggesting that exclusion of samples with very low DNA yield may lead to biased results in statistical analyses. Whole genome amplification (WGA) could present a solution to the DNA concentration-dependent sample selection. The aim was to thoroughly assess WGA for samples with low DNA yield, using the multiply-primed rolling circle amplification method. Fifty-nine samples were selected with the lowest DNA yield (less than 7.5µg) among 799 samples obtained for one population cohort. The genotypes obtained from two replicate WGA samples and the original genomic DNA were compared by typing 24 single nucleotide polymorphisms (SNPs). Multiple genotype discrepancies were identified for 13 of the 59 samples. The largest portion of discrepancies was due to allele dropout in heterozygous genotypes in WGA samples. Pooling the WGA DNA replicates prior to genotyping markedly improved genotyping reproducibility for the samples, with only 7 discrepancies identified in 4 samples. The nature of discrepancies was mostly homozygote genotypes in the genomic DNA and heterozygote genotypes in the WGA sample, suggesting possible allele dropout in the genomic DNA sample due to very low amounts of DNA template. Thus, WGA is applicable for low DNA yield samples, especially if using pooled WGA samples. A higher rate of genotyping errors requires that increased attention be paid to genotyping quality control, and caution when interpreting results.

The effect of genotyping error in sib-pair genomewide linkage scans depends crucially upon the method of analysis

In genomewide linkage scans for complex diseases involving many loci with small genetic effects, it may be the case that no loci reach conventional statistical significance. A complementary method of evaluating linkage results, locus counting, may provide evidence for the existence of a number of genetic loci in these cases. Sib-pair study designs are often used in genomewide linkage scans, but because all genotype configurations are consistent with Mendelian inheritance, genotyping error will go largely undetected. Previous work on the effect of genotyping error has focused on a single disease locus. We considered the effect of two levels of genotyping error on genomewide evidence for linkage by using the simulated GAW 13 data. For affected sib-pair and non-parametric quantitative trait study designs, a 0.5% genotyping error rate reduced the number of independent linkage regions towards that expected under the null hypothesis of no linkage. A 2% genotyping error rate yielded less independent linkage regions than expected under the null hypothesis of no linkage. For a quantitative trait analysed using a parametric regression-based method, there was very little erosion of the linkage signal, even for error rates as high as 2%.

Replication of Autism Linkage: Fine-Mapping Peak at 17q21

Autism is a heritable but genetically complex disorder characterized by deficits in language and in reciprocal social interactions, combined with repetitive and stereotypic behaviors. As with many genetically complex disorders, numerous genome scans reveal inconsistent results. A genome scan of 345 families from the Autism Genetic Resource Exchange (AGRE) (AGRE_1), gave the strongest evidence of linkage at 17q11-17q21 in families with no affected females. Here, we report a full-genome scan of an independent sample of 91 AGRE families with 109 affected sibling pairs (AGRE_2) that also shows the strongest evidence of linkage to 17q11-17q21 in families with no affected females. Taken together, these samples provide a replication of linkage to this chromosome region that is, to our knowledge, the first such replication in autism. Fine mapping at 2-centimorgan (cM) intervals in the combined sample of families with no affected females reveals a linkage peak at 66.85 cM, which places this locus at 17q21.

Haplotype sharing transmission/disequilibrium tests that allow for genotyping errors

The present study introduces new Haplotype Sharing Transmission/Disequilibrium Tests (HS-TDTs) that allow for random genotyping errors. We evaluate the type I error rate and power of the new proposed tests under a variety of scenarios and perform a power comparison among the proposed tests, the HS-TDT and the single-marker TDT. The results indicate that the HS-TDT shows a significant increase in type I error when applied to data in which either Mendelian inconsistent trios are removed or Mendelian inconsistent markers are treated as missing genotypes, and the magnitude of the type I error increases both with an increase in sample size and with an increase in genotyping error rate. The results also show that a simple strategy, that is, merging each rare haplotype to a most similar common haplotype, can control the type I error inflation for a wide range of genotyping error rates, and after merging rare haplotypes, the power of the test is very similar to that without merging the rare haplotypes. Therefore, we conclude that a simple strategy may make the HS-TDT robust to genotyping errors. Our simulation results also show that this strategy may also be applicable to other haplotype-based TDTs.

Estimating haplotype frequencies in pooled DNA samples when there is genotyping error

BackgroundMaximum likelihood estimates of haplotype frequencies can be obtained from pooled DNA using the expectation maximization (EM) algorithm. Through simulation, we investigate the effect of genotyping error on the accuracy of haplotype frequency estimates obtained using this algorithm. We explore model parameters including allele frequency, inter-marker linkage disequilibrium (LD), genotyping error rate, and pool size.ResultsPool sizes of 2, 5, and 10 individuals achieved comparable levels of accuracy in the estimation procedure. Common marker allele frequencies and no inter-marker LD result in less accurate estimates. This pattern is observed regardless of the amount of genotyping error simulated.ConclusionGenotyping error slightly decreases the accuracy of haplotype frequency estimates. However, the EM algorithm performs well even in the presence of genotyping error. Overall, pools of 2, 5, and 10 individuals yield similar accuracy of the haplotype frequency estimates, while reducing costs due to genotyping.

Effect of Genotyping Error on Type-I Error Rate of Affected Sib Pair Studies with Genotyped Parents

Objective: In affected sib pair studies without genotyped parents the effect of genotyping error is generally to reduce the type I error rate and power of tests for linkage. The effect of genotyping error when parents have been genotyped is unknown. We investigated the type I error rate of the single-point Mean test for studies in which genotypes of both parents are available. Methods: Datasets were simulated assuming no linkage and one of five models for genotyping error. In each dataset, Mendelian-inconsistent families were either excluded or regenotyped, and then the Mean test applied. Results: We found that genotyping errors lead to an inflated type I error rate when inconsistent families are excluded. Depending on the genotyping-error model assumed, regenotyping inconsistent families has one of several effects. It may produce the same type I error rate as if inconsistent families are excluded; it may reduce the type I error, but still leave an anti-conservative test; or it may give a conservative test. Departures of the type I error rate from its nominal level increase with both the genotyping error rate and sample size. Conclusion: We recommend that markers with high error rates either be excluded from the analysis or be regenotyped in all families.

Quantifying genotyping errors in noninvasive population genetics.

The use of noninvasively collected samples greatly expands the range of ecological issues that may be investigated through population genetics. Furthermore, the difficulty of obtaining reliable genotypes with samples containing low quantities of amplifiable DNA may be overcome by designing optimal genotyping schemes. Such protocols are mainly determined by the rates of genotyping errors caused by false alleles and allelic dropouts. These errors may not be avoided through laboratory procedure and hence must be quantified. However, the definition of genotyping error rates remains elusive and various estimation methods have been reported in the literature. In this paper we proposed accurate codification for the frequencies of false alleles and allelic dropouts. We then reviewed other estimation methods employed in hair- or faeces-based population genetics studies and modelled the bias associated with erroneous methods. It is emphasized that error rates may be substantially underestimated when using an erroneous approach. Genotyping error rates may be important determinants of the outcome of noninvasive studies and hence should be carefully computed and reported.

How to track and assess genotyping errors in population genetics studies.

Genotyping errors occur when the genotype determined after molecular analysis does not correspond to the real genotype of the individual under consideration. Virtually every genetic data set includes some erroneous genotypes, but genotyping errors remain a taboo subject in population genetics, even though they might greatly bias the final conclusions, especially for studies based on individual identification. Here, we consider four case studies representing a large variety of population genetics investigations differing in their sampling strategies (noninvasive or traditional), in the type of organism studied (plant or animal) and the molecular markers used [microsatellites or amplified fragment length polymorphisms (AFLPs)]. In these data sets, the estimated genotyping error rate ranges from 0.8% for microsatellite loci from bear tissues to 2.6% for AFLP loci from dwarf birch leaves. Main sources of errors were allelic dropouts for microsatellites and differences in peak intensities for AFLPs, but in both cases human factors were non-negligible error generators. Therefore, tracking genotyping errors and identifying their causes are necessary to clean up the data sets and validate the final results according to the precision required. In addition, we propose the outline of a protocol designed to limit and quantify genotyping errors at each step of the genotyping process. In particular, we recommend (i) several efficient precautions to prevent contaminations and technical artefacts; (ii) systematic use of blind samples and automation; (iii) experience and rigor for laboratory work and scoring; and (iv) systematic reporting of the error rate in population genetics studies.

Low genotyping error rates in wild ungulate faeces sampled in winter

AbstractWe show that Alpine ibex (Capra ibex) and Corsican mouflon (Ovis musimon) faeces yield useful DNA for microsatellite analysis, however, we detected higher genotyping error rates for spring faeces than for winter faeces. We quantified the genotyping error rate by repeatedly genotyping four microsatellites. Respectively, 99 and 95% of mouflon and ibex genotyping repetitions provided a correct genotype using winter samples, whereas spring samples provided only 52 and 59% correct genotypes. Thus, before starting a noninvasive study, we recommend that researchers conduct a pilot study to quantify genotyping error rates for each season, population and species to be studied.

Algorithms for inferring haplotypes.

Haplotype phase information in diploid organisms provides valuable information on human evolutionary history and may lead to the development of more efficient strategies to identify genetic variants that increase susceptibility to human diseases. Molecular haplotyping methods are labor-intensive, low-throughput, and very costly. Therefore, algorithms based on formal statistical theories were shown to be very effective and cost-efficient for haplotype reconstruction. This review covers 1) population-based haplotype inference methods: Clark's algorithm, expectation-maximization (EM) algorithm, coalescence-based algorithms (pseudo-Gibbs sampler and perfect/imperfect phylogeny), and partition-ligation algorithm implemented by a fully Bayesian model (Haplotyper) or by EM (PLEM); 2) family-based haplotype inference methods; 3) the handling of genotype scoring uncertainties (i.e., genotyping errors and raw two-dimensional genotype scatterplots) in inferring haplotypes; and 4) haplotype inference methods for pooled DNA samples. The advantages and limitations of each algorithm are discussed. By using simulations based on empirical data on the G6PD gene and TNFRSF5 gene, I demonstrate that different algorithms have different degrees of sensitivity to various extents of population diversities and genotyping error rates. Future development of statistical algorithms for addressing haplotype reconstruction will resort more and more to ideas based on combinatorial mathematics, graphical models, and machine learning, and they will have profound impacts on population genetics and genetic epidemiology with the advent of the human HapMap.

Estimation of genotype error rate using samples with pedigree information—an application on the GeneChip Mapping 10K array

Currently, most analytical methods assume all observed genotypes are correct; however, it is clear that errors may reduce statistical power or bias inference in genetic studies. We propose procedures for estimating error rate in genetic analysis and apply them to study the GeneChip Mapping 10K array, which is a technology that has recently become available and allows researchers to survey over 10,000 SNPs in a single assay. We employed a strategy to estimate the genotype error rate in pedigree data. First, the “dose–response” reference curve between error rate and the observable error number were derived by simulation, conditional on given pedigree structures and genotypes. Second, the error rate was estimated by calibrating the number of observed errors in real data to the reference curve. We evaluated the performance of this method by simulation study and applied it to a data set of 30 pedigrees genotyped using the GeneChip Mapping 10K array. This method performed favorably in all scenarios we surveyed. The dose–response reference curve was monotone and almost linear with a large slope. The method was able to estimate accurately the error rate under various pedigree structures and error models and under heterogeneous error rates. Using this method, we found that the average genotyping error rate of the GeneChip Mapping 10K array was about 0.1%. Our method provides a quick and unbiased solution to address the genotype error rate in pedigree data. It behaves well in a wide range of settings and can be easily applied in other genetic projects. The robust estimation of genotyping error rate allows us to estimate power and sample size and conduct unbiased genetic tests. The GeneChip Mapping 10K array has a low overall error rate, which is consistent with the results obtained from alternative genotyping assays.

Improved noninvasive genotyping method: application to brown bear (Ursus arctos) faeces

AbstractWe redesigned new microsatellite primers and one sex‐specific primer for amplification of faecal DNA from brown bears (Ursus arctos). We also combined a semi‐nested polymerase chain reaction (PCR) with a newly developed multiplex preamplification method in order to increase the quality of the amplified DNA fragments. In comparison with a conventional PCR approach, the genotyping error rate was substantially reduced and the amplification rate was increased. This new approach could be transposed to other species where conventional PCR methods experience low success due to limited DNA concentration and/or quality.