Susceptibility of biallelic haplotype and genotype frequencies to genotyping error

With the availability of fast genotyping methods and genomic databases, the search for statistical association of single nucleotide polymorphisms with a complex trait has become an important methodology in medical genetics. However, even fairly rare errors occurring during the genotyping process can lead to spurious association results and decrease in statistical power. We develop a systematic approach to study how genotyping errors change the genotype distribution in a sample. The general M-marker case is reduced to that of a single-marker locus by recognizing the underlying tensor-product structure of the error matrix. Both method and general conclusions apply to the general error model; we give detailed results for allele-based errors of size depending both on the marker locus and the allele present. Multiple errors are treated in terms of the associated diffusion process on the space of genotype distributions. We find that certain genotype and haplotype distributions remain unchanged under genotyping errors, and that genotyping errors generally render the distribution more similar to the stable one. In case-control association studies, this will lead to loss of statistical power for nondifferential genotyping errors and increase in type I error for differential genotyping errors. Moreover, we show that allele-based genotyping errors do not disturb Hardy-Weinberg equilibrium in the genotype distribution. In this setting we also identify maximally affected distributions. As they correspond to situations with rare alleles and marker loci in high linkage disequilibrium, careful checking for genotyping errors is advisable when significant association based on such alleles/haplotypes is observed in association studies.     

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

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

Multiplex PCR-based Alu insertion polymorphisms genotyping for identifying individuals of Japanese ethnicity

Discrimination of Alu insertions is a useful tool for geographic ancestry analysis, and is usually performed by Alu element amplification and agarose gel electrophoresis. Here, we have developed a new fluorescence-based method for multiple Alu genotyping in forensic identification. Allele frequencies were determined in 70 Japanese individuals, and we selected 30 polymorphic Alu insertions. Three primers were designed for each Alu locus to discriminate alleles using the 3-6 bp differences in amplicon sizes. Furthermore, we classified the amplification primers for the 30 loci into three different sets, and PCR using each set of primers provided 10 loci fragments ranging from 50 to 137 bp. Based on population data, the probability of incorrectly assigning a match was 3.7×10(-13). Three independent amplifications and subsequent capillary electrophoresis enabled the sensitive genotyping of small amounts of DNA, indicating that this method is suitable for identifying individuals of Japanese ethnicity.     

Analysis of Genetic Variation at the Prolactin-RsaI (PRL-RsaI) Locus in Indian Native Cattle Breeds (Bos indicus)

This study assessed the distribution pattern of allelic variants at the prolactin-RsaI locus in 23 Indian native cattle breeds (Bos indicus). PCR-RFLP genotyping of a 156?bp fragment of prolactin (PRL) in exon 3 revealed the predominance of the heterozygous AB genotype (mean frequency 0.58) irrespective of utility type (dairy, dual, draft), geographic region (northern, central, southern), and coat color (red, gray) of the breeds analyzed. The overall frequencies of homozygous AA (0.22) and BB (0.20) genotypes were in a similar range. The PRL (A) and PRL (B) alleles exhibited similar gene frequencies (means 0.52 and 0.48, respectively). The existing profile of the PRL-RsaI gene locus in a large set of Indian native cattle breeds was different from that of Bos taurus and cattle breeds of other countries, where either the BB genotype and PRL (B) allele or the AA genotype and PRL (A) allele have been reported to be more prevalent.     

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

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

Estimation of genotyping error rate from repeat genotyping,unintentional recaptures and known parent–offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus)

Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent–offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop‐out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent–offspring pairs and unintentionally recaptured individuals, respectively. By direct‐count error estimates, the recapture and known parent–offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent–offspring comparisons. Furthermore, there was no correlation in locus‐specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus‐specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent–offspring comparisons in parentage studies).     

Identification of internal variation in the pseudoautosomal VNTR DXYS17, with nonrandom distribution of the alleles on the X and the Y chromosomes.

The PCR technique was used to analyze the DXYS17 locus in the pseudoautosomal region of the X and the Y chromosomes. Analysis on an automated DNA sequencer allowed for sensitive and highly accurate typing of 16 different alleles with a size between 480 and 1,100 bp. Two DXYS17 alleles migrated with the same size on agarose or denaturing polyacrylamide gels but with different mobilities on nondenaturing polyacrylamide gels. Sequence analysis showed that, while an identical number of repeats were present in both alleles, differences in the composition of the units were observed. The origin of these differences was found in the 28- and 33-bp units, which only had a specific repeat pattern at the 5' and 3' ends of the region. The genotype distribution for DXYS17 in a Caucasian population did not deviate from the values expected under Hardy-Weinberg equilibrium. However, the frequency of one allele and one genotype was significantly different between males and females. Segregation analysis showed that this difference was the result of a nonrandom distribution of certain alleles on the sex chromosomes in males.     

Locus effects and sources of error in noninvasive genotyping

In spite of more than a decade of research on noninvasive genetic sampling, the low quality and quantity of DNA in noninvasive studies continue to plague researchers. Effects of locus size on error have been documented but are still poorly understood. Further, sources of error other than allelic dropout have been described but are often not well quantified. Here we analyse the effects of locus size on allelic dropout, amplification success and error rates in noninvasive genotyping studies of three species, and quantify error other than allelic dropout.     

Development of a mechanism-based, DNA staining protocol using SYTOX orange nucleic acid stain and DNA fragment sizing flow cytometry

Accurate measurement of single DNA fragments by DNA fragment sizing flow cytometry (FSFC) depends upon precise, stoichiometric DNA staining by the intercalating dye molecules. In this study, we determined the binding characteristics of a commercially available 532 nm wavelength-excitable dye and used this information to develop a universal DNA staining protocol for DNA FSFC using a compact frequency-doubled Nd:YAG laser excitation source. Among twelve 532 nm wavelength-excitable nucleic acid staining dyes tested, SYTOX Orange stain showed the highest fluorescence intensity along with a large fluorescence enhancement upon binding to double-stranded DNA ( approximately 450-fold). Furthermore, using SYTOX Orange stain, accurate fragment-size-distribution histograms were consistently obtained without regard to the staining dye to base pair (dye/bp) ratio. A model describing two binding modes, intercalation (primary, yielding fluorescence) and external binding (secondary, involving fluorescence quenching), was proposed to interpret the performance of the dyes under different dye/bp ratios. The secondary equilibrium dissociation constant was found to be the most critical parameter in determining the sensitivity of each fluorophore to the staining dye/bp ratio. The measurements of both equilibrium dissociation constants provided us with a theoretical framework for developing a universal protocol which was successfully demonstrated over a wide range of DNA concentrations on a compact flow cytometer equipped with a frequency-doubled, diode-pumped, solid-state Nd:YAG laser for rapid and sensitive DNA fragment sizing.     

New Labelling Technology for Molecular Probes Applied to the Ligation Detection Reaction–Universal Array System

The ligation detection reaction (LDR) associated with universal arrays (UA) uses a fluorescently labelled probe (DP) and a Zip Code-extended probe to detect single nucleotide polymorphisms in DNA target sequences. When used for genotyping, the LDR-UA technique uses two DPs, each specific to an allele and labelled with a different fluorophore. The fluorescent signals are processed to calculate the genotype. The uneven decay of fluorophores due to ageing and freezing/thawing cycles and the consequent unequal fluoresce level can lead to erroneous genotype calls. To circumvent this problem, an indirect labelling strategy was developed based on the substitution of the fluorophore with allele-specific 22 bp universal labelling sequences (ULS). Labelling is achieved with fluorescently labelled oligos complementary to the ULS (cULS). The strategy improved the uniformity in probe labelling, and generated results comparable to those using direct-labelled probes, as shown by genotyping 22 polymorphic sites in 70 samples with both strategies. This method can be easily implemented in the routine screening with LDR-UA or other techniques. Moreover, the approach results in a significant cost reduction over traditional direct labelling, and offers the possibility to interchange fluorophores and to increase the fluorescent signal by using multiple-labelled cULS.     

High Resolution Melt Analysis (HRMA); a Viable Alternative to Agarose Gel Electrophoresis for Mouse Genotyping

Most mouse genetics laboratories maintain mouse strains that require genotyping in order to identify the genetically modified animals. The plethora of mutagenesis strategies and publicly available mouse alleles means that any one laboratory may maintain alleles with random or targeted insertions of orthologous or unrelated sequences as well as random or targeted deletions and point mutants. Many experiments require that different strains be cross bred conferring the need to genotype progeny at more than one locus. In contrast to the range of new technologies for mouse mutagenesis, genotyping methods have remained relatively static with alleles typically discriminated by agarose gel electrophoresis of PCR products. This requires a large amount of researcher time. Additionally it is susceptible to contamination of future genotyping experiments because it requires that tubes containing PCR products be opened for analysis. Progress has been made with the genotyping of mouse point mutants because a range of new high-throughput techniques have been developed for the detection of Single Nucleotide Polymorphisms. Some of these techniques are suitable for genotyping point mutants but do not detect insertion or deletion alleles. Ideally, mouse genetics laboratories would use a single, high-throughput platform that enables closed-tube analysis to genotype the entire range of possible insertion and deletion alleles and point mutants. Here we show that High Resolution Melt Analysis meets these criteria, it is suitable for closed-tube genotyping of all allele types and current genotyping assays can be converted to this technology with little or no effort.     

Application of automated DNA sizing technology for genotyping microsatellite loci.

Highly polymorphic microsatellite loci offer great promise for gene mapping studies, but fulfillment of this potential will require substantial improvements in methods for accurate and efficient genotyping. Here, we report a genotyping method based on fluorescently labeled PCR primers and size characterization of PCR products using an automated DNA fragment analyzer. We capitalize on the availability of three distinct fluorescent dyes to label uniquely loci that overlap in size, and this innovation increases by threefold the number of loci that can be analyzed simultaneously. We label size standards with a fourth dye and combine these with the microsatellite PCR products in each gel lane. Computer programs provide very rapid and accurate sizing of microsatellite alleles and efficient data management. In addition, fluorescence signals are linear over a much greater range of intensity than conventional autoradiography. This facilitates multiplexing of loci (since signal intensities often vary greatly) and helps distinguish major peaks from artifacts, thereby improving genotyping accuracy.     

Simultaneously correcting for population stratification and for genotyping error in case-control association studies

In population-based case-control association studies, the regular chi (2) test is often used to investigate association between a candidate locus and disease. However, it is well known that this test may be biased in the presence of population stratification and/or genotyping error. Unlike some other biases, this bias will not go away with increasing sample size. On the contrary, the false-positive rate will be much larger when the sample size is increased. The usual family-based designs are robust against population stratification, but they are sensitive to genotype error. In this article, we propose a novel method of simultaneously correcting for the bias arising from population stratification and/or for the genotyping error in case-control studies. The appropriate corrections depend on sample odds ratios of the standard 2x3 tables of genotype by case and control from null loci. Therefore, the test is simple to apply. The corrected test is robust against misspecification of the genetic model. If the null hypothesis of no association is rejected, the corrections can be further used to estimate the effect of the genetic factor. We considered a simulation study to investigate the performance of the new method, using parameter values similar to those found in real-data examples. The results show that the corrected test approximately maintains the expected type I error rate under various simulation conditions. It also improves the power of the association test in the presence of population stratification and/or genotyping error. The discrepancy in power between the tests with correction and those without correction tends to be more extreme as the magnitude of the bias becomes larger. Therefore, the bias-correction method proposed in this article should be useful for the genetic analysis of complex traits.     

Estimating Relatedness in the Presence of Null Alleles

Studies of genetics and ecology often require estimates of relatedness coefficients based on genetic marker data. However, with the presence of null alleles, an observed genotype can represent one of several possible true genotypes. This results in biased estimates of relatedness. As the numbers of marker loci are often limited, loci with null alleles cannot be abandoned without substantial loss of statistical power. Here, we show how loci with null alleles can be incorporated into six estimators of relatedness (two novel). We evaluate the performance of various estimators before and after correction for null alleles. If the frequency of a null allele is <0.1, some estimators can be used directly without adjustment; if it is >0.5, the potency of estimation is too low and such a locus should be excluded. We make available a software package entitled PolyRelatedness v1.6, which enables researchers to optimize these estimators to best fit a particular data set.     

Development of a high resolution melting method for genotyping of risk HLA-DQA1 and PLA2R1 alleles and ethnic distribution of these risk alleles

Recent studies have demonstrated that alleles at single nucleotide polymorphisms (SNPs) rs2187668 and rs4664308 within genes HLA-DQA1 and PLA2R1, respectively, had a significant impact on the susceptibility to idiopathic membranous nephropathy (IMN). Analysis of the two genomic loci could identify alleles for individuals at risk for IMN. Conventional methods for genotyping are labor intensive, expensive or time consuming. High resolution melting (HRM) is a new technique for genotyping and has the advantages of simplicity, speed, high sensitivity and low cost. Here, we describe genotyping of SNPs rs2187668 and rs4664308 using HRM. In this study, we identified polymorphisms of rs2187668 and rs4664308 in 480 healthy unrelated Chinese volunteers of two ethnic groups from three different geographical areas in China. The two genomic loci were genotyped by HRM using a saturating fluorescent dye SYTO® 9 on 7900 HT and RG 6000 instruments, and were further confirmed by direct DNA sequencing. Three different SNP genotypes were sufficiently distinguished by HRM with mean sensitivity of 98.8% and mean error rate of 1.9%. In addition, the allele frequencies varied greatly based on ethnic or geographic origins. In conclusion, HRM is a rapid, cost efficient, sensitive, suitable technique for genotyping, and simple enough to be readily implemented in a diagnostic laboratory. We believe this will be a valuable technique for determining the genotype of rs2187668 and rs4664308 and for assessing individual susceptibility to IMN.     

Microsatellite standardization and evaluation of genotyping error in a large multi-partner research programme for conservation of Atlantic salmon (Salmo salar L.)

Microsatellite genotyping is a common DNA characterization technique in population, ecological and evolutionary genetics research. Since different alleles are sized relative to internal size-standards, different laboratories must calibrate and standardize allelic designations when exchanging data. This interchange of microsatellite data can often prove problematic. Here, 16 microsatellite loci were calibrated and standardized for the Atlantic salmon, Salmo salar, across 12 laboratories. Although inconsistencies were observed, particularly due to differences between migration of DNA fragments and actual allelic size ('size shifts'), inter-laboratory calibration was successful. Standardization also allowed an assessment of the degree and partitioning of genotyping error. Notably, the global allelic error rate was reduced from 0.05 ± 0.01 prior to calibration to 0.01 ± 0.002 post-calibration. Most errors were found to occur during analysis (i.e. when size-calling alleles; the mean proportion of all errors that were analytical errors across loci was 0.58 after calibration). No evidence was found of an association between the degree of error and allelic size range of a locus, number of alleles, nor repeat type, nor was there evidence that genotyping errors were more prevalent when a laboratory analyzed samples outside of the usual geographic area they encounter. The microsatellite calibration between laboratories presented here will be especially important for genetic assignment of marine-caught Atlantic salmon, enabling analysis of marine mortality, a major factor in the observed declines of this highly valued species.     

Sample size considerations in genetic polymorphism studies.

OBJECTIVES: Molecular studies for genetic polymorphisms are being carried out for a number of different applications, such as genetic disorders in different populations, pharmacogenomics, genetic identification of ethnic groups for forensic and legal applications, genetic identification of breed/stock in animals and plants for commercial applications and conservation of germ plasm. In this paper, for a random sampling scheme, we address two questions: (A) What should be the minimum size of the sample so that, with a prespecified probability, all alleles at a given locus (or haplotypes at a given set of loci) are detected? (B) What should be the sample size so that the allele frequency distribution at a given locus (or haplotype frequency distribution at a given set of loci) is estimated reliably within permissible error limits? METHODS: We have used combinatorial probabilistic arguments and Monte Carlo simulations to answer these questions. RESULTS: We found that the minimum sample size required in case A depends mainly on the prespecified probability of detecting all alleles, while in case B, it varies greatly depending on the permissible error in estimation (which will vary with the application). We have obtained the minimum sample sizes for different degrees of polymorphism at a locus under high stringency, as well as a relaxed level of permissible error. We present a detailed sampling procedure for estimating allele frequencies at a given locus, which will be of use in practical applications. CONCLUSION: Since the sample size required for reliable estimation of allele frequency distribution increases with the number of alleles at the locus, there is a strong case for using biallelic markers (like single nucleotide polymorphisms) when the available sample size is about 800 or less.     

湖羊6号染色体微卫星标记多样性与产羔数的关系

选择位于绵羊6号染色体上FecB基因紧密连锁的6个微卫星标记, 即LSCV043、BMS2508、GC101、300U、Bulge5和471U, 分析其在湖羊中的遗传多样性以及和产羔数的关系。结果表明, 6个微卫星位点均属多态性位点, 共检测到34个等位基因、53种基因型。LSCV043、BMS2508和300U属高度多态位点, 其多态信息含量分别为0.6674、0.6035和0.5615。对不同基因型群体的产羔率进行统计分析, LSCV043基因型为107 bp/123 bp所对应的总体产羔数明显高于110 bp/123 bp基因型群体所对应的产羔数(P<0.05); BMS2508基因型为154 bp/154 bp、154 bp/170 bp和154 bp/200 bp所对应的群体第一胎产羔数明显高于170 bp/170 bp所对应的产羔数(P<0.05), 基因型170 bp/170 bp的群体总体产羔数均低于该位点的其他基因型(P<0.05); 其他位点各基因型之间产羔数均无显著差异。     

微卫星复杂结构对分型的影响：以熊UamD116 和UamB1 为例

目的:微卫星是基因组上的短串联重复序列,具有高度多态性,表现为核心序列中重复单位的重复次数的变化,这种变化造成不同等位基因核心序列的长度不同。因此,其基因型主要依靠PCR扩增片段长度来判定。在各类研究中,人们更倾向于使用4碱基重复的微卫星以减少2碱基微卫星的stutter等问题的影响。但是4碱基微卫星核心序列结构复杂时,就会对分型的正确性产生影响,从而影响到下游分析的正确性。在很多野生动物的研究中,这一问题常常被忽略。本文以亚洲黑熊(Ursus thibetanus)的2个四碱基微卫星位点UamD116和UamB1为例,揭示内部结构对分型的影响。方法:我们选用96份亚洲黑熊样品(包括血液、肌肉组织和毛发等样品)进行微卫星分型研究,通过荧光标记的PCR扩增和毛细管电泳分型,比较了基于扩增片段长度的分型和基于序列核心结构的分型效果的差异。结果:UamD116核心序列结构除了含有多种不同的重复单位外,还在重复单位之间有碱基插入,出现单碱基T、二碱基TC和三碱基AAG插入;并在一类等位基因下游侧翼序列有1个GA缺失。基于序列结构的分型中可以将不同的等位基因分开,而在基于片段长度的分型中,容易将不同的等位基因合并为1个等位基因。在位点UamB1共发现两种类型的等位基因,在一类等位基因中出现一个3bp的插入,使等位基因之间的差异不再是4bp,而是1bp。在仅依据片段长度分型时,相差1bp的等位基因被认定为1个。此外,还有不同等位基因核心序列不同,但是二者长度完全一致。依据片段长度分型共发现8个等位基因,而经过序列分型确定的等位基因数为12个,相应地基因频率及其他遗传学参数都发生相应的改变。结论:对于核心序列结构复杂的微卫星必须通过等位基因测序来矫正片段长度分型的结果,才能得到可靠的群体遗传学结论。     

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.