Presence Of Genotyping Errors Research Articles

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.)

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

Parent-offspring inference in inbred populations.

Genealogical relationships are fundamental components of genetic studies. However, it is often challenging to infer correct and complete pedigrees even when genome-wide information is available. For example, inbreeding can obscure genetic differences between individuals, making it difficult to even distinguish first-degree relatives such as parent-offspring from full siblings. Similarly, genotyping errors can interfere with the detection of genetic similarity between parents and their offspring. Inbreeding is common in natural, domesticated, and experimental populations and genotyping of these populations often has more errors than in human data sets, so efficient methods for building pedigrees under these conditions are necessary. Here, we present a new method for parent-offspring inference in inbred pedigrees called specific parent-offspring relationship estimation (spore). spore is vastly superior to existing pedigree-inference methods at detecting parent-offspring relationships, in particular when inbreeding is high or in the presence of genotyping errors, or both. spore therefore fills an important void in the arsenal of pedigree inference tools.

The potential of probabilistic graphical models in linkage map construction

Key messageProbabilistic graphical models show great potential for robust and reliable construction of linkage maps. We show how to use probabilistic graphical models to construct high-quality linkage maps in the face of data perturbations caused by genotyping errors and reciprocal translocations.It has been shown that linkage map construction can be hampered by the presence of genotyping errors and chromosomal rearrangements such as inversions and translocations. Here, we report a novel method for linkage map construction using probabilistic graphical models. The method is proven, both theoretically and practically, to be effective in filtering out markers that contain genotyping errors. In particular, it carries out marker filtering and ordering simultaneously, and is therefore superior to the standard post hoc filtering using nearest-neighbour stress. Furthermore, we demonstrate empirically that the proposed method offers a promising solution to linkage map construction in the case of a reciprocal translocation.

LINKPHASE3: an improved pedigree-based phasing algorithm robust to genotyping and map errors.

Many applications in genetics require haplotype reconstruction. We present a phasing program designed for large half-sibs families (as observed in plant and animals) that is robust to genotyping and map errors. We demonstrate that it is more efficient than previous versions and other programs, particularly in the presence of genotyping errors.

Assessing the Impact of Differential Genotyping Errors on Rare Variant Tests of Association

Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential genotyping errors, whereas genotyping errors that occur with different processes in the cases and controls are known as differential genotype errors. For single marker tests, non-differential genotyping errors reduce power, while differential genotyping errors increase the type I error rate. However, little is known about the behavior of the new generation of rare variant tests of association in the presence of genotyping errors. In this manuscript we use a comprehensive simulation study to explore the effects of numerous factors on the type I error rate of rare variant tests of association in the presence of differential genotyping error. We find that increased sample size, decreased minor allele frequency, and an increased number of single nucleotide variants (SNVs) included in the test all increase the type I error rate in the presence of differential genotyping errors. We also find that the greater the relative difference in case-control genotyping error rates the larger the type I error rate. Lastly, as is the case for single marker tests, genotyping errors classifying the common homozygote as the heterozygote inflate the type I error rate significantly more than errors classifying the heterozygote as the common homozygote. In general, our findings are in line with results from single marker tests. To ensure that type I error inflation does not occur when analyzing next-generation sequencing data careful consideration of study design (e.g. use of randomization), caution in meta-analysis and using publicly available controls, and the use of standard quality control metrics is critical.

Relative accuracy of three common methods of parentage analysis in natural populations

Parentage studies and family reconstructions have become increasingly popular for investigating a range of evolutionary, ecological and behavioural processes in natural populations. However, a number of different assignment methods have emerged in common use and the accuracy of each may differ in relation to the number of loci examined, allelic diversity, incomplete sampling of all candidate parents and the presence of genotyping errors. Here, we examine how these factors affect the accuracy of three popular parentage inference methods (colony, famoz and an exclusion-Bayes' theorem approach by Christie (Molecular Ecology Resources, 2010a, 10, 115) to resolve true parent-offspring pairs using simulated data. Our findings demonstrate that accuracy increases with the number and diversity of loci. These were clearly the most important factors in obtaining accurate assignments explaining 75-90% of variance in overall accuracy across 60 simulated scenarios. Furthermore, the proportion of candidate parents sampled had a small but significant impact on the susceptibility of each method to either false-positive or false-negative assignments. Within the range of values simulated, colony outperformed FaMoz, which outperformed the exclusion-Bayes' theorem method. However, with 20 or more highly polymorphic loci, all methods could be applied with confidence. Our results show that for parentage inference in natural populations, careful consideration of the number and quality of markers will increase the accuracy of assignments and mitigate the effects of incomplete sampling of parental populations.

Three-point appraisal of genetic linkage maps

This paper develops a simple diagnostic for the investigation of uncertainty within genetic linkage maps using a Bayesian procedure. The method requires only the genotyping data and the proposed genetic map, and calculates the posterior probability for the possible orders of any set of three markers, accounting for the presence of genotyping error (mistyping) and for missing genotype data. The method uses a Bayesian approach to give insight into conflicts between the order in the proposed map and the genotype scores. The method can also be used to assess the accuracy of a genetic map at different genomic scales and to assess alternative potential marker orders. Simulation and two case studies were used to illustrate the method. In the first case study, the diagnostic revealed conflicts in map ordering for short inter-marker distances that were resolved at a distance of 8-12 cM, except for a set of markers at the end of the linkage group. In the second case study, the ordering did not resolve as distances increase, which could be attributed to regions of the map where many individuals were untyped.

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.

Association of XPC polymorphisms with breast cancer risk

To the Editor, We read with great interest the article by Zheng W et al. [1], which assessed the association between Xeroderma Pigmentosum group C (XPC) polymorphisms and breast cancer risk with a meta-analysis. The results suggested that there was no significant association between either Lys939Gln or Ala499Val polymorphism and breast cancer risk in any genetic model or any subgroup based on ethnicity/source of controls. However, a significant association was observed for PAT ± polymorphism under a recessive model (odds ratio 1.41, 95% confidence interval 1.05–1.89). After carefully reading the article, we noted several methodological issues which should be considered. First, in the ‘‘Publication search’’, they did not state whether there was language restriction for the study identification. In addition, in our opinion, ‘‘Xeroderma Pigmentosum group C’’, the full name of ‘‘XPC’’ also should be used as one of the search terms, to search all relevant potential studies. Second, they did not assess Hardy–Weinberg equilibrium (HWE) in controls of each study. Evidence suggested that deviation from HWE might reflect the presence of genotyping errors, population stratification, and selection bias in the controls [2]. Thus, we assessed HWE in controls of included studies [3–10] in Table 1. Third, in the Table 1 of their study, it is better to present the genotype frequencies of cases and controls to be more readable. In addition, I do not know why they categorized the population in the study by Jorgensen et al. [5] as Caucasian. Indeed, the ethnicity was unknown for this study. In the table 2 of the study, it is also better to provide the number of studies for each subgroup analysis. Fourth, there was only one study with 583 cases and 583 controls, which investigated the association between PAT ± polymorphism and breast cancer risk [10]. Thus, the positive conclusion should be made with caution. At last, we excluded two studies which deviated from HWE in controls for Lys939Gln polymorphism and reassessed the association under a dominant genetic model based on ethnicity (Figs. 1, 2). For Lys939Gln polymorphism, there were 4,064 cases and 4,025 controls; for Ala499Val polymorphism, there were 1,838 cases and 2,007 controls. The association was not statically significant for either Lys939Gln or Ala499Val polymorphism. Since the sample size was relative small, especially in subgroups, further well-designed studies with enough statistical power are required to assess the associations. F. Xu D. Zhong (&) Z. Tang M. Zhou Department of Breast Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410007, China e-mail: zhong_dewu@163.com

Limited evidence supported the association of HRAS1 polymorphism with breast cancer risk

We read with great interest the recent meta-analysis by Zhang et al. [1], which investigated the association between HRAS1 rare alleles and breast cancer risk. This study was very important as it clarified the controversial results among previous individual studies. Their metaanalysis indicated that HRAS1 rare alleles could increased the risk of breast cancer (odds ratio (OR) = 2.03, 95% confidence interval (CI) = 1.34–3.10). However, several methodological issues should be addressed in the study. First, the authors did not evaluate Hardy–Weinberg equilibrium (HWE) in the controls of included studies. Many evidences have indicated that not in HWE might reflect the presence of genotyping errors, population stratification, and selection bias in the controls [2–4]. Therefore, we suggested that HWE in controls should be assessed before the meta-analysis was performed. Second, as reported by the authors, publication bias existed among all the individual studies included (Egger test, P = 0.02). Publication bias usually indicated that positive studies were more likely to be published than the negative ones. In addition, as the meta-analysis included only published studies, it would generate the false positive result [3, 4]. Third, we noted that the meta-analysis was based on the majority of studies with small sample size in breast cancer cases (n \ 200) except for two most recent studies with relative large sample size in cases (n C 200). The studies with small sample size usually overestimate the true association, comparing to those with large sample size; moreover a large study with either finding might reflect the true association as it had sufficient statistical power [5]. Therefore, we performed the further meta-analysis by sample size in cases, and to re-assess the association between HRAS1 rare alleles and breast cancer risk (Fig. 1). The results suggested that HRAS1 rare alleles were not associated with the risk of breast cancer among relative large sample size in cases (OR = 0.93, 95% CI = 0.77–1.13). In summary, the conclusion should be made with caution for the association of HRAS1 polymorphism with breast cancer risk. Further well-designed studies with large sample size are needed to investigate the association between HRAS1 polymorphism with breast cancer.

Model-Based Multifactor Dimensionality Reduction for detecting epistasis in case-control data in the presence of noise

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

FRANz: reconstruction of wild multi-generation pedigrees

Summary: We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov Chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical dataset with known pedigree. The parentage inference is robust even in the presence of genotyping errors.Availability: The C source code of FRANz can be obtained under the GPL from http://www.bioinf.uni-leipzig.de/Software/FRANz/.Contact: markus@bioinf.uni-leipzig.de

Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

The presence of genotyping errors can invalidate statistical tests for linkage and disease association, particularly for methods based on haplotype analysis. Becker et al. have recently proposed a simple likelihood ratio approach for detecting errors in trio genotype data. Under this approach, a SNP genotype is flagged as a potential error if the likelihood associated with the original trio genotype data increases by a multiplicative factor exceeding a user selected threshold when the SNP genotype under test is deleted. In this article we give improved error detection methods using the likelihood ratio test approach in conjunction with likelihood functions that can be efficiently computed based on a Hidden Markov Model of haplotype diversity in the population under study. Experimental results on both simulated and real datasets show that proposed methods have highly scalable running time and achieve significantly improved detection accuracy compared to previous methods.

Assessing Genuine Parents-Offspring Trios for Genetic Association Studies

Objectives: Family-based association tests such as the transmission disequilibrium test (TDT) are dependent on the successful ascertainment of true nuclear family trios. Relationship misspecification inevitably occurs in a proportion of trios collected for genotyping which undetected can lead to a loss of power and increased Type I error due to biases in over-transmission of common alleles. Here, we introduce a method for evaluating the authenticity of nuclear family trios. Methods: Operating in a Bayesian framework, our approach assesses the extent of pedigree inconsistent genotype configurations in the presence of genotyping errors. Unlike other approaches, our method: (i) utilizes information from three individuals collectively (the whole trio) rather than consider two independent pairwise relationships; (ii) down-weighs SNPs with poor performance; (iii) does not require the user to pre-define a rate of genotyping error, which is often unknown to the user and seldom fixed across the different SNPs considered which available methods unrealistically assumed. Results: Simulation studies and comparisons with a real set of data showed that our approach is more likely to correctly identify the presence of true and misspecified trios compared to available software, accurately infers the extent of relationship misspecification in a trio and accurately estimates the genotyping error rates. Conclusions: Assessing relationship misspecification depends on the fidelity of the genotype data used. Available algorithms are not optimised for genotyping technology with varying rates of errors across the markers. Through our comparison studies, our approach is shown to outperform available methods for assessing relationship misspecifications.

Genetic Mapping in the Presence of Genotyping Errors

Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.

The Impact of Missing and Erroneous Genotypes on Tagging SNP Selection and Power of Subsequent Association Tests

Objective: Single nucleotide polymorphisms (SNPs) serve as effective markers for localizing disease susceptibility genes, but current genotyping technologies are inadequate for genotyping all available SNP markers in a typical linkage/association study. Much attention has recently been paid to methods for selecting the minimal informative subset of SNPs in identifying haplotypes, but there has been little investigation of the effect of missing or erroneous genotypes on the performance of these SNP selection algorithms and subsequent association tests using the selected tagging SNPs. The purpose of this study is to explore the effect of missing genotype or genotyping error on tagging SNP selection and subsequent single marker and haplotype association tests using the selected tagging SNPs. Methods: Through two sets of simulations, we evaluated the performance of three tagging SNP selection programs in the presence of missing or erroneous genotypes: Clayton’s diversity based program htstep, Carlson’s linkage disequilibrium (LD) based program ldSelect, and Stram’s coefficient of determination based program tagsnp.exe. Results: When randomly selected known loci were relabeled as ‘missing’, we found that the average number of tagging SNPs selected by all three algorithms changed very little and the power of subsequent single marker and haplotype association tests using the selected tagging SNPs remained close to the power of these tests in the absence of missing genotype. When random genotyping errors were introduced, we found that the average number of tagging SNPs selected by all three algorithms increased. In data sets simulated according to the haplotype frequecies in the CYP19 region, Stram’s program had larger increase than Carlson’s and Clayton’s programs. In data sets simulated under the coalescent model, Carlson’s program had the largest increase and Clayton’s program had the smallest increase. In both sets of simulations, with the presence of genotyping errors, the power of the haplotype tests from all three programs decreased quickly, but there was not much reduction in power of the single marker tests. Conclusions: Missing genotypes do not seem to have much impact on tagging SNP selection and subsequent single marker and haplotype association tests. In contrast, genotyping errors could have severe impact on tagging SNP selection and haplotype tests, but not on single marker tests.

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.

Does kin selection influence fostering behaviour in Antarctic fur seals (Arctocephalus gazella)?

A recent observation that female Antarctic fur seals foster pups born to related females raises the fascinating possibility that kin selection may promote altruistic behaviour even on a crowded breeding beach, where individual interactions are frequent and complex. However, the use of genetic markers to identify small numbers of unusually highly related individuals is fraught with difficulty due to the likely presence of genotyping errors and related problems. Consequently, we examined an enlarged dataset where errors had been reduced to an absolute minimum by a combination of close scrutiny and repeat genotyping. We find no support for the idea that females preferentially suckle pups born to female relatives. Instead, the previously reported pattern can be explained by a combination of genotyping errors and de novo mutations. Our study emphasizes the need for caution when interpreting rare events that occur at a rate approaching that expected for normal genotyping errors.

Bayesian and maximum likelihood estimation of genetic maps

There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods that makes the Bayesian method applicable to large data sets. We present an extensive simulation study examining the statistical properties of the method and comparing it with the likelihood method implemented in Mapmaker. We show that the Maximum A Posteriori (MAP) estimator of the genetic distances, corresponding to the maximum likelihood estimator, performs better than estimators based on the posterior expectation. We also show that while the performance is similar between Mapmaker and the MCMC-based method in the absence of genotyping errors, the MCMC-based method has a distinct advantage in the presence of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances.