Haplotype frequency estimation error analysis in the presence of missing genotype data

Background  

Increasingly researchers are turning to the use of haplotype analysis as a tool in population studies, the investigation of linkage disequilibrium, and candidate gene analysis. When the phase of the data is unknown, computational methods, in particular those employing the Expectation-Maximisation (EM) algorithm, are frequently used for estimating the phase and frequency of the underlying haplotypes. These methods have proved very successful, predicting the phase-known frequencies from data for which the phase is unknown with a high degree of accuracy. Recently there has been much speculation as to the effect of unknown, or missing allelic data – a common phenomenon even with modern automated DNA analysis techniques – on the performance of EM-based methods. To this end an EM-based program, modified to accommodate missing data, has been developed, incorporating non-parametric bootstrapping for the calculation of accurate confidence intervals.     

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.     

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.     

Linkage analysis in the presence of errors II: marker-locus genotyping errors modeled with hypercomplex recombination fractions

It is well known that genotyping errors lead to loss of power in gene-mapping studies and underestimation of the strength of correlations between trait- and marker-locus genotypes. In two-point linkage analysis, these errors can be absorbed in an inflated recombination-fraction estimate, leaving the test statistic quite robust. In multipoint analysis, however, genotyping errors can easily result in false exclusion of the true location of a disease-predisposing gene. In a companion article, we described a "complex-valued" extension of the recombination fraction to accommodate errors in the assignment of trait-locus genotypes, leading to a multipoint LOD score with the same robustness to errors in trait-locus genotypes that is seen with the conventional two-point LOD score. Here, a further extension of this model to "hypercomplex-valued" recombination fractions (hereafter referred to as "hypercomplex recombination fractions") is presented, to handle random and systematic sources of marker-locus genotyping errors. This leads to a multipoint method (either "model-based" or "model-free") with the same robustness to marker-locus genotyping errors that is seen with conventional two-point analysis but with the advantage that multiple marker loci can be used jointly to increase meiotic informativeness. The cost of this increased robustness is a decrease in fine-scale resolution of the estimated map location of the trait locus, in comparison with traditional multipoint analysis. This probability model further leads to algorithms for the estimation of the lower bounds for the error rates for genomewide and locus-specific genotyping, based on the null-hypothesis distribution of the LOD-score statistic in the presence of such errors. It is argued that those genome scans in which the LOD score is 0 for >50% of the genome are likely to be characterized by high rates of genotyping errors in general.     

Multiplex genotyping and allele frequency estimation in pooled DNAs using non-gel capillary electrophoresis

We present a novel approach of single-nucleotide polymorphism (SNP) analysis in which allele-specific oligonucleotide hybridization is followed by non-gel capillary electrophoresis (ASOH-NGCE) in conjunction with laser-induced fluorescence (LIF). This allows rapid multiplex allelotyping and allele frequency estimation. This method, based on site separation of the hybridization duplexes, retains the simplicity and specificity of ASOH and the homogeneous feature of NGCE with poly(N,N-dimethylacrylamide) (PDMA) as a sieving medium. ASOH-NGCE can be applied to multiplex SNP loci genotyping with excellent separation of hybridization mixtures. Average relative standard deviations (RSDs) were low for within-day (1.10%) and between-day (2.41%) reproducibility. Moreover, the allele frequencies in pooled DNAs were accurately determined from peak areas and equilibrium dissociation constants. Our method was highly sensitive in detecting alleles with frequency as low as 1% and in distinguishing allele frequencies differing by 1% between pools. The average value of differences between real and estimated frequencies (accuracy) was only 0.004.     

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.     

Importance of a pilot study for non-invasive genetic sampling: genotyping errors and population size estimation in red deer

Population size information is critical for managing endangered or harvested populations. Population size can now be estimated from non-invasive genetic sampling. However, pitfalls remain such as genotyping errors (allele dropout and false alleles at microsatellite loci). To evaluate the feasibility of non-invasive sampling (e.g., for population size estimation), a pilot study is required. Here, we present a pilot study consisting of (i) a genetic step to test loci amplification and to estimate allele frequencies and genotyping error rates when using faecal DNA, and (ii) a simulation step to quantify and minimise the effects of errors on estimates of population size. The pilot study was conducted on a population of red deer in a fenced natural area of 5440 ha, in France. Twelve microsatellite loci were tested for amplification and genotyping errors. The genotyping error rates for microsatellite loci were 0–0.83 (mean=0.2) for allele dropout rates and 0–0.14 (mean=0.02) for false allele rates, comparable to rates encountered in other non-invasive studies. Simulation results suggest we must conduct 6 PCR amplifications per sample (per locus) to achieve approximately 97% correct genotypes. The 3% error rate appears to have little influence on the accuracy and precision of population size estimation. This paper illustrates the importance of conducting a pilot study (including genotyping and simulations) when using non-invasive sampling to study threatened or managed populations.     

Effects of genotyping errors on parentage exclusion analysis

Genetic markers are widely used to determine the parentage of individuals in studies of mating systems, reproductive success, dispersals, quantitative genetic parameters and in the management of conservation populations. These markers are, however, imperfect for parentage analyses because of the presence of genotyping errors and undetectable alleles, which may cause incompatible genotypes (mismatches) between parents and offspring and thus result in false exclusions of true parentage. Highly polymorphic markers widely used in parentage analyses, such as microsatellites, are especially prone to genotyping errors. In this investigation, I derived the probabilities of excluding a random (related) individual from parentage and the probabilities of Mendelian-inconsistent errors (mismatches) and Mendelian-consistent errors (which do not cause mismatches) in parent-offspring dyads, when a marker having null alleles, allelic dropouts and false alleles is used in a parentage analysis. These probabilities are useful in evaluating the impact of various types of genotyping errors on the information content of a set of markers in and thus the power of a parentage analysis, in determining the threshold number of genetic mismatches that is appropriate for a parentage exclusion analysis and in estimating the rates of genotyping errors and frequencies of null alleles from observed mismatches between known parent-offspring dyads. These applications are demonstrated by numerical examples using both hypothetical and empirical data sets and discussed in the context of practical parentage exclusion analyses.     

Detection rates for genotyping errors in SNPs using the trio design

One well-known approach for the analysis of transmission-disequilibrium is the investigation of single nucleotide polymorphisms (SNPs) in trios consisting of an affected child and its parents. Results may be biased by erroneously given genotypes. Various reasons, among them sample swap or wrong pedigree structure, represent a possible source for biased results. As these can be partly ruled out by good study conditions together with checks for correct pedigree structure by a series of independent markers, the remaining main cause for errors is genotyping errors. Some of the errors can be detected by Mendelian checks whilst others are compatible with the pedigree structure. The extent of genotyping errors can be estimated by investigating the rate of detected genotyping errors by Mendelian checks. In many studies only one SNP of a specific genomic region is investigated by TDT which leaves Mendelian checks as the only tool to control genotyping errors. From the rate of detected errors the true error rate can be estimated. Gordon et al. [Hum Hered 1999;49:65-70] considered the case of genotyping errors that occur randomly and independently with some fixed probability for the wrong ascertainment of an allele. In practice, instead of single alleles, SNP genotypes are determined. Therefore, we study the proportion of detected errors (detection rate) based on genotypes. In contrast to Gordon et al., who reported detection rates between 25 and 30%, we obtain higher detection rates ranging from 39 up to 61% considering likely error structures in the data. We conclude that detection rates are probably substantially higher than those reported by Gordon et al.     

Haplotype estimation from fuzzy genotypes using penalized likelihood

The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain ("fuzzy") genotypes, like those resulting from AFLP scores, can be handled by adding an extra layer to the model. We describe the model and the estimation algorithm. We apply it to a data set of accurate human single nucleotide polymorphism (SNP) and to a data set of fuzzy tomato AFLP scores.     

Optimal Step Length EM Algorithm (OSLEM) for the estimation of haplotype frequency and its application in lipoprotein lipase genotyping

Background  

Haplotype based linkage disequilibrium (LD) mapping has become a powerful and cost-effective method for performing genetic association studies, particularly in the search for genetic markers in linkage disequilibrium with complex disease loci. Various methods (e.g. Monte-Carlo (Gibbs sampling); EM (expectation maximization); and Clark's method) have been used to estimate haplotype frequencies from routine genotyping data.     

Inorganic phosphate estimation in the presence of dithiothreitol

Dithiothreitol (Cleland's reagent) is widely used as a sulfhydryl protective reagent in biochemical systems in vitro. For example, dithiothreitol has been used to achieve maximal rates of enzyme activity for protein phosphokinase reactions (1–4) as well as for phosphoprotein phosphatese assays (1,5). Meisler and Langan (5) have utilized ³²P-labeled histone phosphoprotein as a substrate to examine the protein phosphatase activity of a rat liver cytosol enzyme preparation. However, if one is dealing with a phosphoprotein substrate which may not be labeled with ³²P, it would be desirable to measure the phosphatase activity using a sensitive chemical analysis, e.g., the method of Berenblum and Chain (6) as modified by Martin and Doty (7). We have been interested in examining the protein phosphatase activity associated with prostatic chromatin and the androgenic influences thereupon, using nonhistone and histone phosphoproteins and phosvitin as substrates (Ahmed and Davis, unpublished data). In designing these experiments, 1–3 mm dithiothreitol was added in the reaction medium; this subsequently resulted in interference of P_i analysis using the Berenblum and Chain procedure (6,7). We have, therefore, systematically examined the conditions which may be used to assay P_i when dithiothreitol is present in the sample. The following report deseribes these observations.     

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.