Effect Of Genotyping Errors Research Articles

Genotype phasing in pedigrees using whole-genome sequence data.

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

Finding the right coverage: the impact of coverage and sequence quality on single nucleotide polymorphism genotyping error rates.

Restriction-enzyme-based sequencing methods enable the genotyping of thousands of single nucleotide polymorphism (SNP) loci in nonmodel organisms. However, in contrast to traditional genetic markers, genotyping error rates in SNPs derived from restriction-enzyme-based methods remain largely unknown. Here, we estimated genotyping error rates in SNPs genotyped with double digest RAD sequencing from Mendelian incompatibilities in known mother-offspring dyads of Hoffman's two-toed sloth (Choloepus hoffmanni) across a range of coverage and sequence quality criteria, for both reference-aligned and de novo-assembled data sets. Genotyping error rates were more sensitive to coverage than sequence quality and low coverage yielded high error rates, particularly in de novo-assembled data sets. For example, coverage ≥5 yielded median genotyping error rates of ≥0.03 and ≥0.11 in reference-aligned and de novo-assembled data sets, respectively. Genotyping error rates declined to ≤0.01 in reference-aligned data sets with a coverage ≥30, but remained ≥0.04 in the de novo-assembled data sets. We observed approximately 10- and 13-fold declines in the number of loci sampled in the reference-aligned and de novo-assembled data sets when coverage was increased from ≥5 to ≥30 at quality score ≥30, respectively. Finally, we assessed the effects of genotyping coverage on a common population genetic application, parentage assignments, and showed that the proportion of incorrectly assigned maternities was relatively high at low coverage. Overall, our results suggest that the trade-off between sample size and genotyping error rates be considered prior to building sequencing libraries, reporting genotyping error rates become standard practice, and that effects of genotyping errors on inference be evaluated in restriction-enzyme-based SNP studies.

The effect of genotyping errors on the robustness of composite linkage disequilibrium measures

We conclude that composite linkage disequilibrium (LD) measures be adopted in population-based LD mapping or association mapping studies since it is unaffected by Hardy-Weinberg disequilibrium. Although some properties of composite LD measures have been recently studied, the effects of genotyping errors on composite LD measures have not been examined. In this report, we derived deterministic formulas to evaluate the impact of genotyping errors on the composite LD measures Δ'AB and rAB, and compared the robustness of Δ'AB and rAB in the presence of genotyping errors. The results showed that Δ'AB and rAB depend on the allele frequencies and the assumed error model, and show varying degrees of robustness in the presence of errors. In general, whether there is HWD or not, rAB is more robust than Δ'AB except some special cases and the difference of robustness between Δ'AB and rAB becomes less severe as the difference between the frequencies of two SNP alleles A and B becomes smaller.

Influence of genotyping error in linkage mapping for complex traits – an analytic study

BackgroundDespite the current trend towards large epidemiological studies of unrelated individuals, linkage studies in families are still thoroughly being utilized as tools for disease gene mapping. The use of the single-nucleotide-polymorphisms (SNP) array technology in genotyping of family data has the potential to provide more informative linkage data. Nevertheless, SNP array data are not immune to genotyping error which, as has been suggested in the past, could dramatically affect the evidence for linkage especially in selective designs such as affected sib pair (ASP) designs. The influence of genotyping error on selective designs for continuous traits has not been assessed yet.ResultsWe use the identity-by-descent (IBD) regression-based paradigm for linkage testing to analytically quantify the effect of simple genotyping error models under specific selection schemes for sibling pairs. We show, for example, that in extremely concordant (EC) designs, genotyping error leads to decreased power whereas it leads to increased type I error in extremely discordant (ED) designs. Perhaps surprisingly, the effect of genotyping error on inference is most severe in designs where selection is least extreme. We suggest a genomic control for genotyping errors via a simple modification of the intercept in the regression for linkage.ConclusionThis study extends earlier findings: genotyping error can substantially affect type I error and power in selective designs for continuous traits. Designs involving both EC and ED sib pairs are fairly immune to genotyping error. When those designs are not feasible the simple genomic control strategy that we suggest offers the potential to deliver more robust inference, especially if genotyping is carried out by SNP array technology.

Population substructure in Finland and Sweden revealed by the use of spatial coordinates and a small number of unlinked autosomal SNPs

BackgroundDespite several thousands of years of close contacts, there are genetic differences between the neighbouring countries of Finland and Sweden. Within Finland, signs of an east-west duality have been observed, whereas the population structure within Sweden has been suggested to be more subtle. With a fine-scale substructure like this, inferring the cluster membership of individuals requires a large number of markers. However, some studies have suggested that this number could be reduced if the individual spatial coordinates are taken into account in the analysis.ResultsWe genotyped 34 unlinked autosomal single nucleotide polymorphisms (SNPs), originally designed for zygosity testing, from 2044 samples from Sweden and 657 samples from Finland, and 30 short tandem repeats (STRs) from 465 Finnish samples. We saw significant population structure within Finland but not between the countries or within Sweden, and isolation by distance within Finland and between the countries. In Sweden, we found a deficit of heterozygotes that we could explain by simulation studies to be due to both a small non-random genotyping error and hidden substructure caused by immigration. Geneland, a model-based Bayesian clustering algorithm, clustered the individuals into groups that corresponded to Sweden and Eastern and Western Finland when spatial coordinates were used, whereas in the absence of spatial information, only one cluster was inferred.ConclusionWe show that the power to cluster individuals based on their genetic similarity is increased when including information about the spatial coordinates. We also demonstrate the importance of estimating the size and effect of genotyping error in population genetics in order to strengthen the validity of the results.

Performance of Whole-Genome Amplified DNA Isolated from Serum and Plasma on High-Density Single Nucleotide Polymorphism Arrays

Defining genetic variation associated with complex human diseases requires standards based on high-quality DNA from well-characterized patients. With the development of robust technologies for whole-genome amplification, sample repositories such as serum banks now represent a potentially valuable source of DNA for both genomic studies and clinical diagnostics. We assessed the performance of whole-genome amplified DNA (wgaDNA) derived from stored serum/plasma on high-density single nucleotide polymorphism arrays. Neither storage time nor usage history affected either DNA extraction or whole-genome amplification yields; however, samples that were thawed and refrozen showed significantly lower call rates (73.9 +/- 7.8%) than samples that were never thawed (92.0 +/- 3.3%) (P < 0.001). Genotype call rates did not differ significantly (P = 0.13) between wgaDNA from never-thawed serum/plasma (92.9 +/- 2.6%) and genomic DNA (97.5 +/- 0.3%) isolated from whole blood. Approximately 400,000 genotypes were consistent between wgaDNA and genomic DNA, but the overall discordance rate of 4.4 +/- 3.8% reflected an average of 11,110 +/- 9502 genotyping errors per sample. No distinct patterns of chromosomal clustering were observed for single nucleotide polymorphisms showing discordant genotypes or homozygote conversion. Because the effects of genotyping errors on whole-genome studies are not well defined, we recommend caution when applying wgaDNA from serum/plasma to high-density single nucleotide polymorphism arrays in addition to the use of stringent quality control requirements for the resulting genotype data.

Effects of Differential Genotyping Error Rate on the Type I Error Probability of Case-Control Studies

Objectives: It is well known that genotyping error adversely affects the power of genetic case-control association studies but there is little research on its effects on type I error, and none that has addressed possible differences in genotype error rates between cases and controls. Methods: We used simulations to examine the influence of genotyping error on the type I error probability given by case-control studies. The effect of genotyping error on the magnitude of type I error was explored for a single marker of varying minor allele frequency (MAF), and for haplotypic tests based on two markers with varying MAF and linkage disequilibrium (LD) measure r². Results: We show that even with low genotyping error rates (<0.01), systematic differences in the error rate between samples can result in type I error rates substantially above 0.05. The effect was maximal for markers with small MAF, markers in strong LD, and where a common allele is more frequently misclassified as a rare allele than vice versa. The problem was also exacerbated by the use of large samples. Conclusions: Our results show that small differential genotyping error rates between cases and controls pose significant problems for association analyses. Differential genotyping error rates are particularly likely to arise where genotype data are combined from multiple sites, or where case genotypes are examined against archived reference population cohort genotypes that are being generated in several countries. Although these strategies may be necessary to obtain adequately powered samples, our data show the importance of stringent quality control. Furthermore, associations based on rare haplotypes should be treated with caution.

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.

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%.

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.

Effects of genotyping errors, missing values and segregation distortion in molecular marker data on the construction of linkage maps.

A simulation study was performed to investigate the effects of missing values, typing errors and distorted segregation ratios in molecular marker data on the construction of genetic linkage maps, and to compare the performance of three locus-ordering criteria (weighted least squares, maximum likelihood and minimum sum of adjacent recombination fractions criteria) in the presence of such effects. The study was based upon three linkage groups of 10 loci at 2, 6, and 10 cM spacings simulated from a doubled-haploid population of size 150. Criteria performance were assessed using the number of replicates with correctly estimated orders, the mean rank correlation between the estimated and the true order and the mean total map length. Bootstrap samples from replicates in the maximum likelihood analysis produced a measure of confidence in the estimated locus order. The effects of missing values and/or typing errors in the data are to reduce the proportion of correctly ordered maps, and this problem worsens as the distances between loci decreases. The maximum likelihood criterion is most successful at ordering loci correctly, but gives estimated map lengths, which are substantially inflated when typing errors are present. The presence of missing values in the data produces shorter map lengths for more widely spaced markers, especially under the weighted least-squares criterion. Overall, the presence of segregation distortion has little effect on this population.

The effects of genotyping errors and interference on estimation of genetic distance.

Analysis of linkage data has typically been carried out assuming genotyping errors are absent. Recent studies have shown, however, that the impact of ignoring genotyping errors can be great, especially in dense marker maps [Buetow, Am J Hum Genet 1991; 49:985-994; Lincoln and Lander, Genomics 1992; 14:604-610]. Because most organisms exhibit positive chiasma interference, we use the chi 2 model [Foss et al., Genetics 1993; 144:681-691] to examine the role interference plays in the estimation of genetic distance in the presence of genotyping errors. For simplicity, we confine our analyses to samples of 1,000 fully informative gametes. Our results support previous findings that ignoring errors inflates distance estimates. The larger the error rate, the greater the inflation. For a given error rate, the relative error in estimated genetic distance is greatest when interference is known to be weak or absent. An approximation to relative error which quantifies the relation to distance, error rate, and interference is provided. Robustness of estimation to error misspecification is also investigated. When the assumed error rate is too low, distance is overestimated while interference is underestimated. The situation is reversed when too large an error rate is assumed (interference is overestimated, and distance underestimated). Unfortunately, the joint estimation of distance and interference is not very robust to error misspecification.

Human chromosome 8 linkage map based on short tandem repeat polymorphisms: Effect of genotyping errors

A linkage map consisting of 21 dinucleotide repeat polymorphisms, 1 tetranucleotide repeat polymorphism, and 3 RFLPs was constructed for human chromosome 8. The map spanned most of the chromosome length from near pter to q23-q24 on the distal portion of the long arm. The total 186 cM length of the female map was over two times the 84 cM length of the male map. Cytogenetic mapping of the polymorphisms using a panel of hybrids containing rearranged chromosomes was completely consistent with the linkage map. Special effort was made to remove as many genotyping errors, including parental phase errors, as possible. Removal of errors, in agreement with recent theoretical predictions, led to reduction of the total length of the sex-equal map by 10% from 145 to 130 cM.