Genotype Calls Research Articles

High-throughput genotyping in citrus accessions using an SNP genotyping array

We developed a 384 multiplexed SNP array, named CitSGA-1, for the genotyping of Citrus cultivars, and evaluated the performance and reliability of the genotyping. SNPs were surveyed by direct sequence comparison of the sequence tagged site (STS) fragment amplified from genomic DNA of cultivars representing the genetic diversity of citrus breeding in Japan. Among 1497 SNPs candidates, 384 SNPs for a high-throughput genotyping array were selected based on physical parameters of Illumina’s bead array criteria. The assay using CitSGA-1 was applied to a hybrid population of 88 progeny and 103 citrus accessions for breeding in Japan, which resulted in 73,726 SNP calls. A total of 351 SNPs (91 %) could call different genotypes among the DNA samples, resulting in a success rate for the assay comparable to previously reported rates for other plant species. To confirm the reliability of SNP genotype calls, parentage analysis was applied, and it indicated that the number of reliable SNPs and corresponding STSs were 276 and 213, respectively. The multiplexed SNP genotyping array reported here will be useful for the efficient construction of linkage map, for the detection of markers for marker-assisted breeding, and for the identification of cultivars.

Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data

Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.

The Effect of Input DNA Copy Number on Genotype Call and Characterising SNP Markers in the Humpback Whale Genome Using a Nanofluidic Array

Recent advances in nanofluidic technologies have enabled the use of Integrated Fluidic Circuits (IFCs) for high-throughput Single Nucleotide Polymorphism (SNP) genotyping (GT). In this study, we implemented and validated a relatively low cost nanofluidic system for SNP-GT with and without Specific Target Amplification (STA). As proof of principle, we first validated the effect of input DNA copy number on genotype call rate using well characterised, digital PCR (dPCR) quantified human genomic DNA samples and then implemented the validated method to genotype 45 SNPs in the humpback whale, Megaptera novaeangliae, nuclear genome. When STA was not incorporated, for a homozygous human DNA sample, reaction chambers containing, on average 9 to 97 copies, showed 100% call rate and accuracy. Below 9 copies, the call rate decreased, and at one copy it was 40%. For a heterozygous human DNA sample, the call rate decreased from 100% to 21% when predicted copies per reaction chamber decreased from 38 copies to one copy. The tightness of genotype clusters on a scatter plot also decreased. In contrast, when the same samples were subjected to STA prior to genotyping a call rate and a call accuracy of 100% were achieved. Our results demonstrate that low input DNA copy number affects the quality of data generated, in particular for a heterozygous sample. Similar to human genomic DNA, a call rate and a call accuracy of 100% was achieved with whale genomic DNA samples following multiplex STA using either 15 or 45 SNP-GT assays. These calls were 100% concordant with their true genotypes determined by an independent method, suggesting that the nanofluidic system is a reliable platform for executing call rates with high accuracy and concordance in genomic sequences derived from biological tissue.

Improving sequence-based genotype calls with linkage disequilibrium and pedigree information

Whole and targeted sequencing of human genomes is a promising, increasingly feasible tool for discovering genetic contributions to risk of complex diseases. A key step is calling an individual's genotype from the multiple aligned short read sequences of his DNA, each of which is subject to nucleotide read error. Current methods are designed to call genotypes separately at each locus from the sequence data of unrelated individuals. Here we propose likelihood-based methods that improve calling accuracy by exploiting two features of sequence data. The first is the linkage disequilibrium (LD) between nearby SNPs. The second is the Mendelian pedigree information available when related individuals are sequenced. In both cases the likelihood involves the probabilities of read variant counts given genotypes, summed over the unobserved genotypes. Parameters governing the prior genotype distribution and the read error rates can be estimated either from the sequence data itself or from external reference data. We use simulations and synthetic read data based on the 1000 Genomes Project to evaluate the performance of the proposed methods. An R-program to apply the methods to small families is freely available at http://med.stanford.edu/epidemiology/PHGC/.

Association Testing for Next‐Generation Sequencing Data Using Score Statistics

The advances in sequencing technology have made large-scale sequencing studies for large cohorts feasible. Often, the primary goal for large-scale studies is to identify genetic variants associated with a disease or other phenotypes. Even when deep sequencing is performed, there will be many sites where there is not enough data to call genotypes accurately. Ignoring the genotype classification uncertainty by basing subsequent analyses on called genotypes leads to a loss in power. Additionally, using called genotypes can lead to spurious association signals. Some methods taking the uncertainty of genotype calls into account have been proposed; most require numerical optimization which for large-scale data is not always computationally feasible. We show that using a score statistic for the joint likelihood of observed phenotypes and observed sequencing data provides an attractive approach to association testing for next-generation sequencing data. The joint model accounts for the genotype classification uncertainty via the posterior probabilities of the genotypes given the observed sequencing data, which gives the approach higher power than methods based on called genotypes. This strategy remains computationally feasible due to the use of score statistics. As part of the joint likelihood, we model the distribution of the phenotypes using a generalized linear model framework, which works for both quantitative and discrete phenotypes. Thus, the method presented here is applicable to case-control studies as well as mapping of quantitative traits. The model allows additional covariates that enable correction for confounding factors such as population stratification or cohort effects.

Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs

BackgroundWe explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.MethodsThe sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n = 1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy.ResultsThe single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers.ConclusionsThe program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.

Development of pipeline for exome sequencing data analysis

Motivations. Exome sequencing the targeted sequencing of the subset of the protein coding human genome is a powerful and cost-effective new tool for dissecting the genetic basis of diseases and traits that have proved to be intractable to conventional gene-discovery strategies. Until now many algorithms have been produced, each of them addressing a different task in the downstream analysis of next-generation sequencing (NGS) data. The aim of this work is to combine these algorithms into an analysis pipeline for the detection of SNP and deletion/insertion polymorphisms within DNA sequences obtained by whole exome sequencing. The pipeline tested with data obtained from SRA (http://www.ncbi.nlm.nih.gov/sra), will then be applied to studies undergoing in our laboratory. Methods. Starting from raw sequence data, we first performed quality statistics and filtering of sequence reads and then aligned them to a reference genome. To this end, BWA was used to align both single- and paired-end reads for its computational efficiency and multi-platform compatibility. Post-alignment analysis, including removal of duplicate reads and quality score recalibration, was carried out using GATK, which takes into account several covariates such as machine cycle and dinucleotide context. Next, SNP calling was done using GATK UnifiedGenotyper, that uses a Bayesian model to estimate the most likely genotypes and allele frequency in a population of N samples, giving an annotated VCF file as output. Subsequently, variant quality score was recalibrated to estimate the probability of each variant being a true polymorphism, rather than a sequencer, alignment or data processing artifact, and finally filtered to improve the accuracy of genotype and SNP calling. Results. The results obtained support the accuracy of our pipeline to identify SNP and short indels, to provide a global and quantitative catalog of nucleotide variants in the exome. The next step will be to apply this pipeline to samples sequenced in our laboratory.

Abstract 3965: An algorithm to detect somatic mutations in cancer specimens using mass spectrometric genotyping

Abstract Although advances in technology and the biological understanding of cancer pathogenesis have brought the goal of personalized cancer medicine closer, important challenges remain in several areas. One such challenge is the reliable and sensitive detection of somatic mutations in clinical tumor specimens. Somatic mutation detection in clinical tumor specimens is confounded by biological variables such as the percentage of tumor cells in a specimen, stromal contamination, sequence quality (particularly in DNA derived from formalin-fixed clinical samples), tumor heterogeneity and ploidy considerations. These and other experimental factors can lead to allele signal ratios very different from the 1:0, 1:1 or 0:1 ratio expected for a classically-defined SNP. Germline genotyping using the Sequenom MALDI-TOF mass spectrometry platform has been used to reliably detect single-nucleotide polymorphisms (SNPs) in a wide variety of samples. The automated genotype calling algorithm included with the Sequenom platform falters, however, when the ratio of allele peak signals for a given assay in a specific sample do not approximate the native algorithm's expectations of frequency, confounding accurate genotype calling by the system. We report here a cancer-specific algorithm for sensitive genotype calling of somatic mutations, based on an independent clustering of raw allele peak data for each assay run, and the identification and stratification of outliers from an empirical background (‘wildtype’) cluster of genotypes. Outliers from this cluster are then evaluated using a number of criteria to determine the quality and performance of the overall assay run, as well as the individual call; these factors include the relative signal-to-noise ratio (SNR) of each allele peak, the ratio of variant allele peak height to peak area, the percentage of signal lost in unextended probe (UEP) and relative distance of a putative mutation call from the wildtype cluster In a scatter plot of peak area values. Based on these criteria, a confidence value is assigned to each candidate outlier, allowing for stratification and prioritization of these candidates for validation. We applied the algorithm to samples run against the our OncoMap genotyping panel, comprised of 460 assays interrogating mutations in 33 cancer-associated genes. Candidate mutations produced by this algorithm correlate strongly with those called by users manually, and use of the algorithm increases the sensitivity and reliability of the OncoMap platform to detect somatic mutations in clinical tumor samples, particularly in FFPE-derived material. The algorithm code and documentation will be made publicly available in a form compatible with most Sequenom genotyping platform installations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3965. doi:1538-7445.AM2012-3965

Abstract LB-407: Global differential allele-specific expression (DASE) analysis in mammary epithelial transcriptome

Abstract The significant mortality associated with breast cancer (BCa) suggests a clear need to improve current research strategies to identify new genes that predispose women to breast cancer. Differential allele-specific expression (DASE) has been shown to contribute to phenotypic variables in human and recently to the pathogenesis of cancer. We previously reported that nonsense-mediated mRNA decay (NMD) could lead to DASE of BRCA1/2 which is associated with elevated susceptibility to BCa. In addition to truncation mutations, multiple genetic and epigenetic factors, such as mutations in promoter regions, alteration in microRNA binding sites on 3′UTRs and changes in chromatin methylation status can contribute to DASE. Therefore, we hypothesize that DASE is a functional index for cis-acting variants and pathogenic mutations and global profiling of DASE in BCa precursor tissues can be used to identify novel causative alleles for BCa susceptibility. To test our hypothesis, we employed the Illumina® Omni1-Quad BeadChip in paired genomic DNA (gDNA) and double-stranded cDNA (ds-cDNA) samples prepared from of eight BCa patient-derived normal mammary epithelial lines (HMEC). We filtered original array data according to heterozygous genotype calls from 86,267 female transcriptome SNPs covered by the BeadChip and calculated DASE values using the Log ratio of cDNA allele intensity, which was normalized to the corresponding gDNA intensity to limit the potential technical variation cause by the design of oligo probes. We have developed two statistical methods, SNP- and gene-based approaches, which allowed us to identify 90 and 143 genes with a significant DASE&amp;gt;2 (P &amp;lt;= 0.01, FDR &amp;lt;= 0.05), respectively. Importantly, 60 candidate loci are identified by both approaches. Ingenuity Pathway Analysis of the 34 coding genes in top 60 candidates revealed two major interaction networks, which involves a variety of biological functions, including cell death and cell-to-cell signaling. One of the networks includes known cancer causative gene ZNF331 (DASE = 2.31, P = 0.0018, FDR = 0.040), USP6 (DASE = 4.80, P = 0.0013, FDR = 0.013) and breast cancer causative gene DMBT1 (DASE=2.03, P = 0.0017, FDR = 0.014). Sequencing of the 5′UTR of DMBT1 identified a genetic variant, rs2981745, which has been reported previously as a causative variant for breast cancer risk in DMBT1 locus. Further sequencing of 5′ RACE product of DMBT1 confirmed rs2981745 as the causal variant leading to DASE in DMBT1. This finding indicates that abnormalities in phenotypes of DASE reflect pathogenic genotypes. In summary, our study clearly demonstrated that global DASE profiling is a powerful new approach to identify BCa risk alleles. This work was kindly supported by the Susan G. Komen for the Cure (KG100274) and Eileen Stein Jacoby Fund. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-407. doi:1538-7445.AM2012-LB-407

A Low-Cost Exon Capture Method Suitable for Large-Scale Screening of Genetic Deafness by the Massively-Parallel Sequencing Approach

Current major barriers for using next-generation sequencing (NGS) technologies in genetic mutation screening on an epidemiological scale appear to be the high accuracy demanded by clinical applications and high per-sample cost. How to achieve high efficiency in enriching targeted disease genes while keeping a low cost/sample is a key technical hurdle to overcome. We validated a cDNA-probe-based approach for capturing exons of a group of genes known to cause deafness. Polymerase chain reaction amplicons were made from cDNA clones of the targeted genes and used as bait probes in hybridization for capturing human genomic DNA (gDNA) fragments. The cDNA library containing the clones of targeted genes provided a readily available, low-cost, and regenerable source for producing capture probes with standard molecular biology equipment. Captured gDNA fragments by our method were sequenced by the Illumina NGS platform. Results demonstrated that targeted exons captured by our approach achieved specificity, multiplexicity, uniformity, and depth of coverage suitable for accurate sequencing applications by the NGS systems. Reliable genotype calls for various homozygous and heterozygous mutations were achieved. The results were confirmed independently by conventional Sanger sequencing. The method validated here could be readily expanded to include all-known deafness genes for applications such as genetic hearing screening in newborns. The high coverage depth and cost benefits of the cDNA-probe-based exon capture approach may also facilitate widespread applications in clinical practices beyond screening mutations in deafness genes.

Incorporating Genotype Uncertainties Into the Genotypic TDT for Main Effects and Gene‐Environment Interactions

Genotype imputation has become a standard option for researchers to expand their genotype datasets to improve signal precision and power in tests of genetic association with disease. In imputations for family-based studies however, subjects are often treated as unrelated individuals: currently, only BEAGLE allows for simultaneous imputation for trios of parents and offspring; however, only the most likely genotype calls are returned, not estimated genotype probabilities. For population-based SNP association studies, it has been shown that incorporating genotype uncertainty can be more powerful than using hard genotype calls. We here investigate this issue in the context of case-parent family data. We present the statistical framework for the genotypic transmission-disequilibrium test (gTDT) using observed genotype calls and imputed genotype probabilities, derive an extension to assess gene-environment interactions for binary environmental variables, and illustrate the performance of our method on a set of trios from the International Cleft Consortium. In contrast to population-based studies, however, utilizing the genotype probabilities in this framework (derived by treating the family members as unrelated) can result in biases of the test statistics toward protectiveness for the minor allele, particularly for markers with lower minor allele frequencies and lower imputation quality. We further compare the results between ignoring relatedness in the imputation and taking family structure into account, based on hard genotype calls. We find that by far the least biased results are obtained when family structure is taken into account and currently recommend this approach in spite of its intense computational requirements.

ParentChecker: a computer program for automated inference of missing parental genotype calls and linkage phase correction

BackgroundAccurate genetic maps are the cornerstones of genetic discovery, but their construction can be hampered by missing parental genotype information. Inference of parental haplotypes and correction of phase errors can be done manually on a one by one basis with the aide of current software tools, but this is tedious and time consuming for the high marker density datasets currently being generated for many crop species. Tools that help automate the process of inferring parental genotypes can greatly speed the process of map building. We developed a software tool that infers and outputs missing parental genotype information based on observed patterns of segregation in mapping populations. When phases are correctly inferred, they can be fed back to the mapping software to quickly improve marker order and placement on genetic maps.ResultsParentChecker is a user-friendly tool that uses the segregation patterns of progeny to infer missing genotype information of parental lines that have been used to construct a mapping population. It can also be used to automate correction of linkage phase errors in genotypic data that are in ABH format.ConclusionParentChecker efficiently improves genetic mapping datasets for cases where parental information is incomplete by automating the process of inferring missing genotypes of inbred mapping populations and can also be used to correct linkage phase errors in ABH formatted datasets.

Genotype calling from next-generation sequencing data using haplotype information of reads

Low coverage sequencing provides an economic strategy for whole genome sequencing. When sequencing a set of individuals, genotype calling can be challenging due to low sequencing coverage. Linkage disequilibrium (LD) based refinement of genotyping calling is essential to improve the accuracy. Current LD-based methods use read counts or genotype likelihoods at individual potential polymorphic sites (PPSs). Reads that span multiple PPSs (jumping reads) can provide additional haplotype information overlooked by current methods. In this article, we introduce a new Hidden Markov Model (HMM)-based method that can take into account jumping reads information across adjacent PPSs and implement it in the HapSeq program. Our method extends the HMM in Thunder and explicitly models jumping reads information as emission probabilities conditional on the states of adjacent PPSs. Our simulation results show that, compared to Thunder, HapSeq reduces the genotyping error rate by 30%, from 0.86% to 0.60%. The results from the 1000 Genomes Project show that HapSeq reduces the genotyping error rate by 12 and 9%, from 2.24% and 2.76% to 1.97% and 2.50% for individuals with European and African ancestry, respectively. We expect our program can improve genotyping qualities of the large number of ongoing and planned whole genome sequencing projects. dzhi@ms.soph.uab.edu; kzhang@ms.soph.uab.edu The software package HapSeq and its manual can be found and downloaded at www.ssg.uab.edu/hapseq/. Supplementary data are available at Bioinformatics online.

Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias.

BackgroundHigh-density genotyping arrays that measure hybridization of genomic DNA fragments to allele-specific oligonucleotide probes are widely used to genotype single nucleotide polymorphisms (SNPs) in genetic studies, including human genome-wide association studies. Hybridization intensities are converted to genotype calls by clustering algorithms that assign each sample to a genotype class at each SNP. Data for SNP probes that do not conform to the expected pattern of clustering are often discarded, contributing to ascertainment bias and resulting in lost information - as much as 50% in a recent genome-wide association study in dogs.ResultsWe identified atypical patterns of hybridization intensities that were highly reproducible and demonstrated that these patterns represent genetic variants that were not accounted for in the design of the array platform. We characterized variable intensity oligonucleotide (VINO) probes that display such patterns and are found in all hybridization-based genotyping platforms, including those developed for human, dog, cattle, and mouse. When recognized and properly interpreted, VINOs recovered a substantial fraction of discarded probes and counteracted SNP ascertainment bias. We developed software (MouseDivGeno) that identifies VINOs and improves the accuracy of genotype calling. MouseDivGeno produced highly concordant genotype calls when compared with other methods but it uniquely identified more than 786000 VINOs in 351 mouse samples. We used whole-genome sequence from 14 mouse strains to confirm the presence of novel variants explaining 28000 VINOs in those strains. We also identified VINOs in human HapMap 3 samples, many of which were specific to an African population. Incorporating VINOs in phylogenetic analyses substantially improved the accuracy of a Mus species tree and local haplotype assignment in laboratory mouse strains.ConclusionThe problems of ascertainment bias and missing information due to genotyping errors are widely recognized as limiting factors in genetic studies. We have conducted the first formal analysis of the effect of novel variants on genotyping arrays, and we have shown that these variants account for a large portion of miscalled and uncalled genotypes. Genetic studies will benefit from substantial improvements in the accuracy of their results by incorporating VINOs in their analyses.

Highly Parallel and Short-Acting Amplification with Locus-Specific Primers to Detect Single Nucleotide Polymorphisms by the DigiTag2 Assay

The DigiTag2 assay enables analysis of a set of 96 SNPs using Kapa 2GFast HotStart DNA polymerase with a new protocol that has a total running time of about 7 hours, which is 6 hours shorter than the previous protocol. Quality parameters (conversion rate, call rate, reproducibility and concordance) were at the same levels as when genotype calls were acquired using the previous protocol. Multiplex PCR with 192 pairs of locus-specific primers was available for target preparation in the DigiTag2 assay without the optimization of reaction conditions, and quality parameters had the same levels as those acquired with 96-plex PCR. The locus-specific primers were able to achieve sufficient (concentration of target amplicon ≥5 nM) and specific (concentration of unexpected amplicons <2 nM) amplification within 2 hours, were also able to achieve detectable amplifications even when working in a 96-plex or 192-plex form. The improved DigiTag2 assay will be an efficient platform for screening an intermediate number of SNPs (tens to hundreds of sites) in the replication analysis after genome-wide association study. Moreover, highly parallel and short-acting amplification with locus-specific primers may thus facilitate widespread application to other PCR-based assays.

The Population Genomics of a Fast Evolver: High Levels of Diversity, Functional Constraint, and Molecular Adaptation in the Tunicate Ciona intestinalis

Phylogenomics has revealed the existence of fast-evolving animal phyla in which the amino acid substitution rate, averaged across many proteins, is consistently higher than in other lineages. The reasons for such differences in proteome-wide evolutionary rates are still unknown, largely because only a handful of species offer within-species genomic data from which molecular evolutionary processes can be deduced. In this study, we use next-generation sequencing technologies and individual whole-transcriptome sequencing to gather extensive polymorphism sequence data sets from Ciona intestinalis. Ciona is probably the best-characterized member of the fast-evolving Urochordata group (tunicates), which was recently identified as the sister group of the slow-evolving vertebrates. We introduce and validate a maximum-likelihood framework for single-nucleotide polymorphism and genotype calling, based on high-throughput short-read typing. We report that the C. intestinalis proteome is characterized by a high level of within-species diversity, efficient purifying selection, and a substantial percentage of adaptive amino acid substitutions. We conclude that the increased rate of amino acid sequence evolution in tunicates, when compared with vertebrates, is the consequence of both a 2–6 times higher per-year mutation rate and prevalent adaptive evolution.

Quality Requirements for Genomic DNA Preparations and Storage Conditions for a High-Density Oligonucleotide Microarray

High-density oligonucleotide microarrays are widely used in genome-wide association studies. The purpose of this study was to assess the influence of various factors during the preparation of DNA on genotype calling for the Affymetrix high-density oligonucleotide microarray 250K GeneChip. DNA was extracted from peripheral whole blood by solution-based and silica-membrane-based methods. Blood was stored at 4°C or 25°C for 4 or 24 h, followed by DNA extraction. To examine the effects of freeze-thaw cycles, blood and DNA were also subjected to 5 and 10 or 20 of freeze-thaw cycles, respectively. The suitability of variously DNA preparations for the array was assessed by the call rate resulting from genotyping. All DNA samples showed mean call rates of more than 0.99, which passed the quality criteria for genotyping (greater than 0.95). The results indicated that the solution-based method and the silica-membrane-based DNA extraction method could provide DNA of sufficient quality for genotyping. In addition, DNA quality suitable for high-density oligonucleotide microarrays is not strongly dependent on the preparation conditions under standard procedures.

Pitfall of genome-wide association studies: Sources of inconsistency in genotypes and their effects

Personalized medicine will improve heath outcomes and patient satisfaction. However, implementing personalized medicine based on individuals’ biological information is far from simple, requiring genetic biomarkers that are mainly developed and used by the pharmaceutical companies for selecting those patients who benefit more, or have less risk of adverse drug reactions, from a particular drug. Genome-wide Association Studies (GWAS) aim to identify genetic variants across the human genome that might be utilized as genetic biomarkers for diagnosis and prognosis. During the last several years, high-density genotyping SNP arrays have facilitated GWAS that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. The replication studies demonstrated that only a small portion of associated loci in the initial GWAS can be replicated, even within the same populations. Given the complexity of GWAS, multiple sources of Type I (false positive) and Type II (false negative) errors exist. The inconsistency in genotypes that caused either by the genotypeing experiment or by genotype calling process is a major source of the false GWAS findings. Accurate and reproducible genotypes are paramount as inconsistency in genotypes can lead to an inflation of false associations. This article will review the sources of inconsistency in genotypes and discuss its effect in GWAS findings.

M3: an improved SNP calling algorithm for Illumina BeadArray data

Genotype calling from high-throughput platforms such as Illumina and Affymetrix is a critical step in data processing, so that accurate information on genetic variants can be obtained for phenotype-genotype association studies. A number of algorithms have been developed to infer genotypes from data generated through the Illumina BeadStation platform, including GenCall, GenoSNP, Illuminus and CRLMM. Most of these algorithms are built on population-based statistical models to genotype every SNP in turn, such as GenCall with the GenTrain clustering algorithm, and require a large reference population to perform well. These approaches may not work well for rare variants where only a small proportion of the individuals carry the variant. A fundamentally different approach, implemented in GenoSNP, adopts a single nucleotide polymorphism (SNP)-based model to infer genotypes of all the SNPs in one individual, making it an appealing alternative to call rare variants. However, compared to the population-based strategies, more SNPs in GenoSNP may fail the Hardy-Weinberg Equilibrium test. To take advantage of both strategies, we propose a two-stage SNP calling procedure, named the modified mixture model (M(3)), to improve call accuracy for both common and rare variants. The effectiveness of our approach is demonstrated through applications to genotype calling on a set of HapMap samples used for quality control purpose in a large case-control study of cocaine dependence. The increase in power with M(3) is greater for rare variants than for common variants depending on the model. M(3) algorithm: http://bioinformatics.med.yale.edu/group. name@bio.com; hongyu.zhao@yale.edu Supplementary data are available at Bioinformatics online.

A NEW GENOTYPE CALLING METHOD FOR AFFYMETRIX SNP ARRAYS

Current genotype-calling methods such as Robust Linear Model with Mahalanobis Distance Classifier (RLMM) and Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) provide accurate calling results for Affymetrix Single Nucleotide Polymorphisms (SNP) chips. However, these methods are computationally expensive as they employ preprocess procedures, including chip data normalization and other sophisticated statistical techniques. In the small sample case the accuracy rate may drop significantly. We develop a new genotype calling method for Affymetrix 100 k and 500 k SNP chips. A two-stage classification scheme is proposed to obtain a fast genotype calling algorithm. The first stage uses unsupervised classification to quickly discriminate genotypes with high accuracy for the majority of the SNPs. And the second stage employs a supervised classification method to incorporate allele frequency information either from the HapMap data or from a self-training scheme. Confidence score is provided for every genotype call. The overall performance is shown to be comparable to that of CRLMM as verified by the known gold standard HapMap data and is superior in small sample cases. The new algorithm is computationally simple and standalone in the sense that a self-training scheme can be used without employing any other training data. A package implementing the calling algorithm is freely available at http://www.sfs.ecnu.edu.cn/teachers/xuj_en.html.