A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays

MOTIVATION: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. RESULTS: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. AVAILABILITY: R functions are available upon request from the authors.     

Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays

MOTIVATION: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample. RESULTS: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. AVAILABILITY: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website.     

A genotype calling algorithm for the Illumina BeadArray platform

MOTIVATION: Large-scale genotyping relies on the use of unsupervised automated calling algorithms to assign genotypes to hybridization data. A number of such calling algorithms have been recently established for the Affymetrix GeneChip genotyping technology. Here, we present a fast and accurate genotype calling algorithm for the Illumina BeadArray genotyping platforms. As the technology moves towards assaying millions of genetic polymorphisms simultaneously, there is a need for an integrated and easy-to-use software for calling genotypes. RESULTS: We have introduced a model-based genotype calling algorithm which does not rely on having prior training data or require computationally intensive procedures. The algorithm can assign genotypes to hybridization data from thousands of individuals simultaneously and pools information across multiple individuals to improve the calling. The method can accommodate variations in hybridization intensities which result in dramatic shifts of the position of the genotype clouds by identifying the optimal coordinates to initialize the algorithm. By incorporating the process of perturbation analysis, we can obtain a quality metric measuring the stability of the assigned genotype calls. We show that this quality metric can be used to identify SNPs with low call rates and accuracy. AVAILABILITY: The C++ executable for the algorithm described here is available by request from the authors.     

Evaluating the effective numbers of independent tests and significant <Emphasis Type="Italic">p</Emphasis>-value thresholds in commercial genotyping arrays and public imputation reference datasets

Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M _e) for the adjustment of multiple testing, but current methods of calculation for M _e are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M _e. Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M _e, and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10⁻⁷ as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10⁻⁸ for current or merged commercial genotyping arrays, ~10⁻⁸ for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10⁻⁸ for the common SNPs only within genes.     

Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates

Array based DNA pooling techniques facilitate genome-wide scale genotyping of large samples. We describe a structured analysis method for pooled data using internal replication information in large scale genotyping sets. The method takes advantage of information from single nucleotide polymorphisms (SNPs) typed in parallel on a high density array to construct a test statistic with desirable statistical properties. We utilize a general linear model to appropriately account for the structured multiple measurements available with array data. The method does not require the use of additional arrays for the estimation of unequal hybridization rates and hence scales readily to accommodate arrays with several hundred thousand SNPs. Tests for differences between cases and controls can be conducted with very few arrays. We demonstrate the method on 384 endometriosis cases and controls, typed using Affymetrix Genechip© HindIII 50 K arrays. For a subset of this data there were accurate measures of hybridization rates available. Assuming equal hybridization rates is shown to have a negligible effect upon the results. With a total of only six arrays, the method extracted one-third of the information (in terms of equivalent sample size) available with individual genotyping (requiring 768 arrays). With 20 arrays (10 for cases, 10 for controls), over half of the information could be extracted from this sample.     

A genotype calling algorithm for affymetrix SNP arrays

MOTIVATION: A classification algorithm, based on a multi-chip, multi-SNP approach is proposed for Affymetrix SNP arrays. Current procedures for calling genotypes on SNP arrays process all the features associated with one chip and one SNP at a time. Using a large training sample where the genotype labels are known, we develop a supervised learning algorithm to obtain more accurate classification results on new data. The method we propose, RLMM, is based on a robustly fitted, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variance is reduced through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as across thousands of SNPs for accurate classification. In this paper, we apply RLMM to Affymetrix 100 K SNP array data, present classification results and compare them with genotype calls obtained from the Affymetrix procedure DM, as well as to the publicly available genotype calls from the HapMap project.     

Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Estimating genotyping error rates from Mendelian errors in SNP array genotypes and their impact on inference

A simple method of inferring the genotyping error rate of SNP arrays and similar high-throughput genotyping methods from Mendelian errors is described. Application to genotypes from small families using the Affymetrix GeneChip Human Mapping 50 k Array indicates an error rate of about 0.1%, and this rate can be reduced by increasing the quality criterion for calls, though at the cost of a reduced genotype call rate, which limits the benefit available. Simulated data are used to show that the number of SNPs on this array is sufficient for such a low error rate to have little impact on identical by descent-based inference for disease linkage in sib-pair studies.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays

MOTIVATION: The technology to genotype single nucleotide polymorphisms (SNPs) at extremely high densities provides for hypothesis-free genome-wide scans for common polymorphisms associated with complex disease. However, we find that some errors introduced by commonly employed genotyping algorithms may lead to inflation of false associations between markers and phenotype. RESULTS: We have developed a novel SNP genotype calling program, SNiPer-High Density (SNiPer-HD), for highly accurate genotype calling across hundreds of thousands of SNPs. The program employs an expectation-maximization (EM) algorithm with parameters based on a training sample set. The algorithm choice allows for highly accurate genotyping for most SNPs. Also, we introduce a quality control metric for each assayed SNP, such that poor-behaving SNPs can be filtered using a metric correlating to genotype class separation in the calling algorithm. SNiPer-HD is superior to the standard dynamic modeling algorithm and is complementary and non-redundant to other algorithms, such as BRLMM. Implementing multiple algorithms together may provide highly accurate genotyping calls, without inflation of false positives due to systematically miss-called SNPs. A reliable and accurate set of SNP genotypes for increasingly dense panels will eliminate some false association signals and false negative signals, allowing for rapid identification of disease susceptibility loci for complex traits. AVAILABILITY: SNiPer-HD is available at TGen's website: http://www.tgen.org/neurogenomics/data.     

Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays

Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms (SNPs) from Affymetrix microarrays. We extend and validate the algorithm CRLMM, which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed). Also, we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.     

High concordance of bovine single nucleotide polymorphism genotypes generated using two independent genotyping strategies

Single nucleotide polymorphisms (SNPs) represent the most common form of DNA sequence variation in mammalian livestock genomes. While the past decade has witnessed major advances in SNP genotyping technologies, genotyping errors caused, in part, by the biochemistry underlying the genotyping platform used, can occur. These errors can distort project results and conclusions and can result in incorrect decisions in animal management and breeding programs; hence, SNP genotype calls must be accurate and reliable. In this study, 263 Bos spp. samples were genotyped commercially for a total of 16 SNPs. Of the total possible 4,208 SNP genotypes, 4,179 SNP genotypes were generated, yielding a genotype call rate of 99.31% (standard deviation?±?0.93%). Between 110 and 263 samples were subsequently re-genotyped by us for all 16 markers using a custom-designed SNP genotyping platform, and of the possible 3,819 genotypes a total of 3,768 genotypes were generated (98.70% genotype call rate, SD?±?1.89%). A total of 3,744 duplicate genotypes were generated for both genotyping platforms, and comparison of the genotype calls for both methods revealed 3,741 concordant SNP genotype call rates (99.92% SNP genotype concordance rate). These data indicate that both genotyping methods used can provide livestock geneticists with reliable, reproducible SNP genotypic data for in-depth statistical analysis.     

ALG: automated genotype calling of Luminex assays

Single nucleotide polymorphisms (SNPs) are the most commonly used polymorphic markers in genetics studies. Among the different platforms for SNP genotyping, Luminex is one of the less exploited mainly due to the lack of a robust (semi-automated and replicable) freely available genotype calling software. Here we describe a clustering algorithm that provides automated SNP calls for Luminex genotyping assays. We genotyped 3 SNPs in a cohort of 330 childhood leukemia patients, 200 parents of patient and 325 healthy individuals and used the Automated Luminex Genotyping (ALG) algorithm for SNP calling. ALG genotypes were called twice to test for reproducibility and were compared to sequencing data to test for accuracy. Globally, this analysis demonstrates the accuracy (99.6%) of the method, its reproducibility (99.8%) and the low level of no genotyping calls (3.4%). The high efficiency of the method proves that ALG is a suitable alternative to the current commercial software. ALG is semi-automated, and provides numerical measures of confidence for each SNP called, as well as an effective graphical plot. Moreover ALG can be used either through a graphical user interface, requiring no specific informatics knowledge, or through command line with access to the open source code. The ALG software has been implemented in R and is freely available for non-commercial use either at http://alg.sourceforge.net or by request to mathieu.bourgey@umontreal.ca.     

High Concordance of Bovine Single Nucleotide Polymorphism Genotypes Generated Using Two Independent Genotyping Strategies

Single nucleotide polymorphisms (SNPs) represent the most common form of DNA sequence variation in mammalian livestock genomes. While the past decade has witnessed major advances in SNP genotyping technologies, genotyping errors caused, in part, by the biochemistry underlying the genotyping platform used, can occur. These errors can distort project results and conclusions and can result in incorrect decisions in animal management and breeding programs; hence, SNP genotype calls must be accurate and reliable. In this study, 263 Bos spp. samples were genotyped commercially for a total of 16 SNPs. Of the total possible 4,208 SNP genotypes, 4,179 SNP genotypes were generated, yielding a genotype call rate of 99.31% (standard deviation ± 0.93%). Between 110 and 263 samples were subsequently re-genotyped by us for all 16 markers using a custom-designed SNP genotyping platform, and of the possible 3,819 genotypes a total of 3,768 genotypes were generated (98.70% genotype call rate, SD ± 1.89%). A total of 3,744 duplicate genotypes were generated for both genotyping platforms, and comparison of the genotype calls for both methods revealed 3,741 concordant SNP genotype call rates (99.92% SNP genotype concordance rate). These data indicate that both genotyping methods used can provide livestock geneticists with reliable, reproducible SNP genotypic data for in-depth statistical analysis.     

Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions

ABSTRACT: BACKGROUND: Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome. RESULTS: Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents. CONCLUSIONS: The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.     

High fidelity of whole-genome amplified DNA on high-density single nucleotide polymorphism arrays

Current microarray technology allows researchers to genotype a large number of SNPs with relatively small amounts of DNA. Nevertheless, researchers and clinicians still frequently face the problem of acquiring enough high-quality DNA for analysis. Whole-genome amplification (WGA) methods offer a solution for this problem, and earlier studies have shown that WGA samples perform reasonably well in small-scale genetic analyses (e.g. Affymetrix 10K array). To determine the performance of WGA products on a large-scale genotyping array, we compared the Affymetrix 250K array genotyping results of genomic DNA and their WGA products from four individuals. Our results indicate that WGA product performs well on the 250K array compared to genomic DNA, especially when using the BRLMM calling algorithm. WGA samples have high call rates (97.5% on average, compared to 99.4% for genomic DNA) and excellent concordance rates with their corresponding genomic DNA samples (98.7% on average). In addition, no apparent systematic genomic amplification bias can be detected. This study demonstrates that, although there is a slight decrease in the total call rates, WGA methods provide a reliable approach for increasing the amount of DNA samples for use with a common SNP genotyping array.     

Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy

A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of genotyped SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality.     

Multiplexing schemes for generic SNP genotyping assays.

Association studies in populations relate genomic variation among individuals with medical condition. Key to these studies is the development of efficient and affordable genotyping techniques. Generic genotyping assays are independent of the target SNPs and offer great flexibility in the genotyping process. Efficient use of such assays calls for identifying sets of SNPs that can be interrogated in parallel under constraints imposed by the genotyping technology. In this paper, we study problems arising in the design of genotyping experiments using generic assays. Our problem formulation deals with two main factors that affect the genotyping cost: the number of assays used and the number of PCR reactions required for sample preparation. We prove that the resulting computational problems are hard, but provide approximate and heuristic solutions to these problems. Our algorithmic approach is based on recasting the multiplexing problems as partitioning and packing problems on a bipartite graph. We tested our algorithmic approaches on an extensive collection of synthetic data and on data that was simulated using real SNP sequences. Our results show that the algorithms achieve near-optimal designs in many cases and demonstrate the applicability of generic assays to SNP genotyping.     

Ultrahigh-Density Linkage Map for Cultivated Cucumber (Cucumis sativus L.) Using a Single-Nucleotide Polymorphism Genotyping Array

Genotyping arrays are tools for high-throughput genotyping, which is beneficial in constructing saturated genetic maps and therefore high-resolution mapping of complex traits. Since the report of the first cucumber genome draft, genetic maps have been constructed mainly based on simple-sequence repeats (SSRs) or on combinations of SSRs and sequence-related amplified polymorphism (SRAP). In this study, we developed the first cucumber genotyping array consisting of 32,864 single-nucleotide polymorphisms (SNPs). These markers cover the cucumber genome with a median interval of ~2 Kb and have expected genotype calls in parents/F₁ hybridizations as a training set. The training set was validated with Fluidigm technology and showed 96% concordance with the genotype calls in the parents/F₁ hybridizations. Application of the genotyping array was illustrated by constructing a 598.7 cM genetic map based on a ‘9930’ × ‘Gy14’ recombinant inbred line (RIL) population comprised of 11,156 SNPs. Marker collinearity between the genetic map and reference genomes of the two parents was estimated at R² = 0.97. We also used the array-derived genetic map to investigate chromosomal rearrangements, regional recombination rate, and specific regions with segregation distortions. Finally, 82% of the linkage-map bins were polymorphic in other cucumber variants, suggesting that the array can be applied for genotyping in other lines. The genotyping array presented here, together with the genotype calls of the parents/F₁ hybridizations as a training set, should be a powerful tool in future studies with high-throughput cucumber genotyping. An ultrahigh-density linkage map constructed by this genotyping array on RIL population may be invaluable for assembly improvement, and for mapping important cucumber QTLs.