Allele Frequency Estimates Research Articles

Brief communication: Admixture analysis with forensic microsatellites in Minas Gerais, Brazil: The ongoing evolution of the capital and of an African‐derived community

We report the estimated allele frequencies for 13 and 14 microsatellite loci in two populations of Minas Gerais, Brazil as follows: Belo Horizonte (the capital) and Marinhos (an African-derived community). Analysis of the African, Amerindian, and European genetic contributions to both populations, together with historical information, revealed distinct differences between the two populations. Estimates for Belo Horizonte revealed a higher-European (66%) than African (32%) contribution, and a minimal Amerindian contribution. These results are consistent with the peopling of the city mainly by people from the Minas Gerais hinterland, a people highly admixed but with more European ancestry. Estimates for Marinhos confirmed the high-African component of the population. However, a temporal analysis of two datasets-CURRENT (representing the population living in Marinhos today) and ORIGINAL (representing families, who have lived in Marinhos since the onset of the 20th century),-identified a diminishing of the population's African ancestry from 92% in the ORIGINAL group to 67% in the CURRENT group. This change is here interpreted as a consequence of the growing migration into the village of people with more European ancestry and subsequent admixture with the local population.

Missing call bias in high-throughput genotyping

BackgroundThe advent of high-throughput and cost-effective genotyping platforms made genome-wide association (GWA) studies a reality. While the primary focus has been invested upon the improvement of reducing genotyping error, the problems associated with missing calls are largely overlooked.ResultsTo probe into the effect of missing calls on GWAs, we demonstrated experimentally the prevalence and severity of the problem of missing call bias (MCB) in four genotyping technologies (Affymetrix 500 K SNP array, SNPstream, TaqMan, and Illumina Beadlab). Subsequently, we showed theoretically that MCB leads to biased conclusions in the subsequent analyses, including estimation of allele/genotype frequencies, the measurement of HWE and association tests under various modes of inheritance relationships. We showed that MCB usually leads to power loss in association tests, and such power change is greater than what could be achieved by equivalent reduction of sample size unbiasedly. We also compared the bias in allele frequency estimation and in association tests introduced by MCB with those by genotyping errors. Our results illustrated that in most cases, the bias can be greatly reduced by increasing the call-rate at the cost of genotyping error rate.ConclusionThe commonly used 'no-call' procedure for the observations of borderline quality should be modified. If the objective is to minimize the bias, the cut-off for call-rate and that for genotyping error rate should be properly coupled in GWA. We suggested that the ongoing QC cut-off for call-rate should be increased, while the cut-off for genotyping error rate can be reduced properly.

Viability of in-house datamarting approaches for population genetics analysis of SNP genotypes

BackgroundDatabases containing very large amounts of SNP (Single Nucleotide Polymorphism) data are now freely available for researchers interested in medical and/or population genetics applications. While many of these SNP repositories have implemented data retrieval tools for general-purpose mining, these alone cannot cover the broad spectrum of needs of most medical and population genetics studies.ResultsTo address this limitation, we have built in-house customized data marts from the raw data provided by the largest public databases. In particular, for population genetics analysis based on genotypes we have built a set of data processing scripts that deal with raw data coming from the major SNP variation databases (e.g. HapMap, Perlegen), stripping them into single genotypes and then grouping them into populations, then merged with additional complementary descriptive information extracted from dbSNP. This allows not only in-house standardization and normalization of the genotyping data retrieved from different repositories, but also the calculation of statistical indices from simple allele frequency estimates to more elaborate genetic differentiation tests within populations, together with the ability to combine population samples from different databases.ConclusionThe present study demonstrates the viability of implementing scripts for handling extensive datasets of SNP genotypes with low computational costs, dealing with certain complex issues that arise from the divergent nature and configuration of the most popular SNP repositories. The information contained in these databases can also be enriched with additional information obtained from other complementary databases, in order to build a dedicated data mart. Updating the data structure is straightforward, as well as permitting easy implementation of new external data and the computation of supplementary statistical indices of interest.

A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals

We present methods for imputing data for ungenotyped markers and for inferring haplotype phase in large data sets of unrelated individuals and parent-offspring trios. Our methods make use of known haplotype phase when it is available, and our methods are computationally efficient so that the full information in large reference panels with thousands of individuals is utilized. We demonstrate that substantial gains in imputation accuracy accrue with increasingly large reference panel sizes, particularly when imputing low-frequency variants, and that unphased reference panels can provide highly accurate genotype imputation. We place our methodology in a unified framework that enables the simultaneous use of unphased and phased data from trios and unrelated individuals in a single analysis. For unrelated individuals, our imputation methods produce well-calibrated posterior genotype probabilities and highly accurate allele-frequency estimates. For trios, our haplotype-inference method is four orders of magnitude faster than the gold-standard PHASE program and has excellent accuracy. Our methods enable genotype imputation to be performed with unphased trio or unrelated reference panels, thus accounting for haplotype-phase uncertainty in the reference panel. We present a useful measure of imputation accuracy, allelic R(2), and show that this measure can be estimated accurately from posterior genotype probabilities. Our methods are implemented in version 3.0 of the BEAGLE software package.

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.

Accuracy of allele frequency estimates in pool DNA analyzed by high-density Illumina Human 610-Quad microarray

Purpose: DNA pooling method has been previously proven successful for allele frequency estimation using earlier versions of Illumina Beadchip and Affymetrix GeneChip microarrays. The aim of this study was to evaluate the accuracy of the DNA pooling using the High Density Illumina Infinium microarray system.Patients & methods: The accuracy of three different methods of allele frequency estimation on the High Definition Infinium II Beadchip (Human 610-Quad) was tested using the DNA samples obtained from saliva collected from seven Caucasian subjects who had been individually genotyped. Our first approach was to calculate estimated allelic frequency (expressed as B allele frequency) from pooled DNA sample based on the correlation coefficient obtained for individual SNPs, and to correlate it with the true (real) allele frequency computed using individually genotyped samples. The second approach was to estimate SNP frequency in the pooled DNA sample as calculated automatically by the proprietary Beadstudio software, which is based on the internal calibration and normalization procedures. The third approach was to study the pool allele frequency calculated using the estimation of correlation coefficient from microarray SNP sub-pools.Results and conclusions: All analyzed methods of allelic frequency estimates from pooled DNA produced similar results in respect to the calculated correlation coefficient (R) (R ranging from 0.947 to 0.958 with R>0.99 for correlation between each three of them). All three methods of calculations seem to be sufficient and equally effective for calculation of individual allelic frequency for the given SNPs from the pooled DNA.

Impact and Quantification of the Sources of Error in DNA Pooling Designs

The analysis of genome wide variation offers the possibility of unravelling the genes involved in the pathogenesis of disease. Genome wide association studies are also particularly useful for identifying and validating targets for therapeutic intervention as well as for detecting markers for drug efficacy and side effects. The cost of such large-scale genetic association studies may be reduced substantially by the analysis of pooled DNA from multiple individuals. However, experimental errors inherent in pooling studies lead to a potential increase in the false positive rate and a loss in power compared to individual genotyping. Here we quantify various sources of experimental error using empirical data from typical pooling experiments and corresponding individual genotyping counts using two statistical methods. We provide analytical formulas for calculating these different errors in the absence of complete information, such as replicate pool formation, and for adjusting for the errors in the statistical analysis. We demonstrate that DNA pooling has the potential of estimating allele frequencies accurately, and adjusting the pooled allele frequency estimates for differential allelic amplification considerably improves accuracy. Estimates of the components of error show that differential allelic amplification is the most important contributor to the error variance in absolute allele frequency estimation, followed by allele frequency measurement and pool formation errors. Our results emphasise the importance of minimising experimental errors and obtaining correct error estimates in genetic association studies.

Diversity of equine major histocompatiblity complex class II DRA locus in Posavina and Croatian Coldblood horse: a new polymorphism detected

Domestic equidae display polymorphism within ELA-DRA locus which is not characteristic for other species. We characterised sequence polymorphism present at ELA-DRA locus exon 2 and estimated allele frequencies in two autochthonous breeds, Posavina and Croatian Coldblood. In 88 horses, four different alleles were found, one of them not reported before in horses. The new allele shows non-synonymous mutation at position 65 (T→A) causing amino acid change (Phe→Tyr) in antigen binding site and synonymous mutation at position 105 (C→T). Our findings emphasize the importance of DRA polymorphism among equids and some specific DRA frequency pattern potentially specific in draught horses.

High, clustered, nucleotide diversity in the genome of Anopheles gambiae revealed through pooled-template sequencing: implications for high-throughput genotyping protocols

BackgroundAssociation mapping approaches are dependent upon discovery and validation of single nucleotide polymorphisms (SNPs). To further association studies in Anopheles gambiae we conducted a major resequencing programme, primarily targeting regions within or close to candidate genes for insecticide resistance.ResultsUsing two pools of mosquito template DNA we sequenced over 300 kbp across 660 distinct amplicons of the An. gambiae genome. Comparison of SNPs identified from pooled templates with those from individual sequences revealed a very low false positive rate. False negative rates were much higher and mostly resulted from SNPs with a low minor allele frequency. Pooled-template sequencing also provided good estimates of SNP allele frequencies. Allele frequency estimation success, along with false positive and negative call rates, improved significantly when using a qualitative measure of SNP call quality. We identified a total of 7062 polymorphic features comprising 6995 SNPs and 67 indels, with, on average, a SNP every 34 bp; a high rate of polymorphism that is comparable to other studies of mosquitoes. SNPs were significantly more frequent in members of the cytochrome p450 mono-oxygenases and carboxy/cholinesterase gene-families than in glutathione-S-transferases, other detoxification genes, and control genomic regions. Polymorphic sites showed a significantly clustered distribution, but the degree of SNP clustering (independent of SNP frequency) did not vary among gene families, suggesting that clustering of polymorphisms is a general property of the An. gambiae genome.ConclusionThe high frequency and clustering of SNPs has important ramifications for the design of high-throughput genotyping assays based on allele specific primer extension or probe hybridisation. We illustrate these issues in the context of the design of Illumina GoldenGate assays.

Evaluation of allele frequencies of inherited disease genes in subgroups of American Quarter Horses

To estimate allele frequencies of the hyperkalaemic periodic paralysis (HYPP), lethal white foal syndrome (LWFS), glycogen branching enzyme deficiency (GBED), hereditary equine regional dermal asthenia (HERDA), and type 1 polysaccharide storage myopathy (PSSM) genes in elite performance subgroups of American Quarter Horses (AQHs). Prospective genetic survey. 651 elite performance AQHs, 200 control AQHs, and 180 control American Paint Horses (APHs). Elite performance AQHs successful in 7 competitive disciplines (barrel racing, cutting, halter, racing, reining, western pleasure, and working cow horse) were geno- typed for 5 disease-causing alleles. Age-matched control AQHs and APHs were used to establish comparative whole-breed estimates of allele frequencies. Highest allele frequencies among control AQHs were for type 1 PSSM (0.055) and GBED (0.054), whereas HERDA (0.021) and HYPP (0.008) were less prevalent. Control APHs uniquely harbored LWFS (0.107) and had high prevalence of HYPP (0.025), relative to AQHs. Halter horse subgroups had significantly greater allele frequencies for HYPP (0.299) and PSSM (0.155). Glycogen branching enzyme deficiency, HERDA, and PSSM were found broadly throughout subgroups; cutting subgroups were distinct for HERDA (0.142), and western pleasure subgroups were distinct for GBED (0.132). Racing and barrel racing subgroups had the lowest frequencies of the 5 disease genes. Accurate estimates of disease-causing alleles in AQHs and APHs may guide use of diagnostic genetic testing, aid management of genetic diseases, and help minimize production of affected foals.

An unbiased estimator of gene diversity in samples containing related individuals.

Gene diversity is sometimes estimated from samples that contain inbred or related individuals. If inbred or related individuals are included in a sample, then the standard estimator for gene diversity produces a downward bias caused by an inflation of the variance of estimated allele frequencies. We develop an unbiased estimator for gene diversity that relies on kinship coefficients for pairs of individuals with known relationship and that reduces to the standard estimator when all individuals are noninbred and unrelated. Applying our estimator to data simulated based on allele frequencies observed for microsatellite loci in human populations, we find that the new estimator performs favorably compared with the standard estimator in terms of bias and similarly in terms of mean squared error. For human population-genetic data, we find that a close linear relationship previously seen between gene diversity and distance from East Africa is preserved when adjusting for the inclusion of close relatives.

Genotyping Error Detection in Samples of Unrelated Individuals without Replicate Genotyping

Objective: Identifying genotyping errors is an important issue in genetic research, yet it has been relatively less studied in samples consisting of unrelated individuals. In this article, we consider several models of genotyping errors, which were originally proposed for pedigree data, for unrelated population samples with single nucleotide polymorphism (SNP) genotype data. The mathematical constraints are investigated for detecting genotyping errors without resampling replicates or genotyping relatives. Methods: For the various proposed genotyping error models, we unveil the conditions under which the parameters are identifiable. These results are verified through applications to simulated and real SNP data. Results: We show that, with constraints, two particular models provide both identifiable error rate and allele frequencies of an SNP for unrelated population data. The simulation study shows that these two models present unbiased estimates for the allele frequencies. One of the models also gives an unbiased estimate for the genotyping error rate. Conclusion: While the Hardy-Weinberg equilibrium test can be used to detect genotyping errors, a key advantage of these models is the explicit estimates of genotyping error rates and allele frequencies. This work may help researchers to estimate error rates and to use the estimates in their analysis to increase power and decrease bias, without the extra work of genotyping family members or replicates.

Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model

BackgroundHigh throughput genotyping of single nucleotide polymorphisms (SNPs) for genome-wide association requires technologies for generating millions of genotypes with relative ease but also at a reasonable cost and with high accuracy. In this work, we have developed a theoretical approach to estimate allele frequency in pooled DNA samples, based on the physical principles of DNA immobilization and hybridization on solid surface using the Langmuir kinetic model and quantitative analysis of the allelic signals.ResultsThis method can successfully distinguish allele frequencies differing by 0.01 in the actual pool of clinical samples, and detect alleles with a frequency as low as 2%. The accuracy of measuring known allele frequencies is very high, with the strength of correlation between measured and actual frequencies having an r2 = 0.9992. These results demonstrated that this method could allow the accurate estimation of absolute allele frequencies in pooled samples of DNA in a feasible and inexpensive way.ConclusionWe conclude that this novel strategy for quantitative analysis of the ratio of SNP allelic sequences in DNA pools is an inexpensive and feasible alternative for detecting polymorphic differences in candidate gene association studies and genome-wide linkage disequilibrium scans.

Genetic diversity and differentiation of Sclerotinia sclerotiorum populations in sunflower

Ninety-six isolates of sunflower Sclerotinia sclerotiorum (Lib.) de Bary from Inner Mongolia (IM) in China, from Canada and the United Kingdom (UK) were sampled to investigate the genetic diversity and structure using Sequence-Related Amplified Polymorphism. A total of 123 polymorphic bands were obtained, ranging in size from 100 to 500 base pairs. The five populations of S. sclerotiorum isolated from the three countries showed various levels of genetic variability. The percentage of polymorphic loci varied from 30.89% in the UK population to 97.56% in the Middle IM population. The values of Shannon index (i) varied from 0.1876 in the UK population to 0.5301 in the West IM population. The heterozygosity of the five geographic populations obtained by estimating allele frequency varied from 12.91% in the UK population to 35.44% in the West IM population. The genetic identity, as indicated by the Nei unbiased identity index, ranged from 0.9744 between populations from Canada and East IM to 0.6477 between populations from West IM and UK. UPGMA cluster analysis using Nei’s genetic distance gave distances ranging from 0.0259 to 0.4343. The rates of gene flow among five geographic populations ranged from 1.5406 between West IM and UK populations to 18.4149 between West IM and Middle IM populations. The four populations from West IM, Middle IM, East IM and Canada were clustered into one subgroup in which the isolates from West and Middle IM belonged to one population, whereas those from East IM and Canada essentially were another population. The isolates from the UK formed a population that was significantly distinct from other populations.

Efficient Methods to Compute Genomic Predictions

Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.

Abstract CN07-03: Discriminatory accuracy from Single Nucleotide Polymorphisms in models of absolute breast cancer risk

Abstract CN07-03 One purpose for seeking single nucleotide polymorphisms (SNPs) that are associated with disease is to improve models for projecting individualized disease risk. Seven SNPs have recently been confirmed to be associated with breast cancer. I used published estimates of relative risks and allele frequencies to calculate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574 ) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute’s Breast Cancer Risk Assessment Tool (BCRAT), which is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven SNPs improve the discriminatory accuracy of BCRAT only modestly. I used other criteria to compare BCRAT with a model that added the seven SNPs to BCRAT. These criteria included the proportion of cases in the portion of the population with the highest 90% of risk, the expected losses when using risk models to decide which women should be given a mammogram, and the expected losses when using a risk model to decide which women should take tamoxifen to prevent breast cancer. The seven SNPs provided very little improvement over BCRAT for any of these applications. Hundreds of SNPs would be required to achieve high discriminatory accuracy. Citation Information: Cancer Prev Res 2008;1(7 Suppl):CN07-03.

Simultaneous Estimation of Null Alleles and Inbreeding Coefficients

Although microsatellites are a very efficient tool for many population genetics applications, they may occasionally produce "null" alleles, which, when present in high proportion, may affect estimates of key parameters such as inbreeding and relatedness coefficients or measures of genetic differentiation. In order to account for the presence of null alleles, it is first necessary to estimate their frequency within studied populations. However, the commonly used null allele frequency estimators are not of general applicability because they can produce upwardly biased estimates when a population under study experiences some inbreeding. In such a case, 2 formerly described approaches, population inbreeding model and individual inbreeding model, can be applied for simultaneous estimation of null allele frequencies and of the inbreeding coefficient. In this study, we demonstrate the properties and utility of these 2 methods and show that they outperform the commonly used approaches in the estimation of null allele frequencies based on genotypic data. The methods are applied to empirical data from a natural population of European beech (Fagus sylvatica L.), and results are briefly discussed. The methods presented in this paper are implemented in the Windows-based user-friendly INEST computer program (available free of charge at http://genetyka.ukw.edu.pl/INEst10_setup.exe).

Prevalence in the United States of selected candidate gene variants: Third National Health and Nutrition Examination Survey, 1991-1994.

Population-based allele frequencies and genotype prevalence are important for measuring the contribution of genetic variation to human disease susceptibility, progression, and outcomes. Population-based prevalence estimates also provide the basis for epidemiologic studies of gene–disease associations, for estimating population attributable risk, and for informing health policy and clinical and public health practice. However, such prevalence estimates for genotypes important to public health remain undetermined for the major racial and ethnic groups in the US population. DNA was collected from 7,159 participants aged 12 years or older in Phase 2 (1991–1994) of the Third National Health and Nutrition Examination Survey (NHANES III). Certain age and minority groups were oversampled in this weighted, population-based US survey. Estimates of allele frequency and genotype prevalence for 90 variants in 50 genes chosen for their potential public health significance were calculated by age, sex, and race/ethnicity among non-Hispanic whites, non-Hispanic blacks, and Mexican Americans. These nationally representative data on allele frequency and genotype prevalence provide a valuable resource for future epidemiologic studies in public health in the United States.

Applying polynomial standard curve method to correct bias encountered in estimating allele frequencies using DNA pooling strategy

DNA pooling approach is a cost-saving strategy which is crucial for multiple-SNP association study and particularly for laboratories with limited budget. However, the biased allele frequency estimates cannot be completely abolished by κ correction. Using the SNaPshot™, we systematically examined the relations between actual minor allele frequencies (AMiAFs) levels and estimates obtained from the pooling process for all six types of SNPs. We applied principle of polynomial standard curves method (PSCM) to produce allele frequency estimates in pooled DNA samples and compared it with the κ method. The results showed that estimates derived from the PSCM were in general closer to AMiAFs than those from the κ method, particularly for C/G and G/T polymorphisms at the range of AMiAF between 20–40%. We demonstrated that applying PSCM in the SNaPshot™ platform is suitable for multiple-SNP association study using pooling strategy, due to its cost effectiveness and estimation accuracy.

The SNPMaP package for R: a framework for genome-wide association using DNA pooling on microarrays

Summary: Large-scale genome-wide association (GWA) studies using thousands of high-density SNP microarrays are becoming an essential tool in the search for loci related to heritable variation in many phenotypes. However, the cost of GWA remains beyond the reach of many researchers. Fortunately, the majority of statistical power can still be obtained by estimating allele frequencies from DNA pools, reducing the cost to that of tens, rather than thousands of arrays. We present a set of software tools for processing SNPMaP (SNP microarrays and pooling) data from CEL files to Relative Allele Scores in the rich R statistical computing environment.Availability: The SNPMaP package is available from http://cran.r-project.org/ under the GNU General Public License version 3 or later.Contact: snpmap@iop.kcl.ac.ukSupplementary information: Additional resources and test datasets are available at http://sgdp.iop.kcl.ac.uk/snpmap/