Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays

We present a genotyping method for simultaneously scoring 116,204 SNPs using oligonucleotide arrays. At call rates >99%, reproducibility is >99.97% and accuracy, as measured by inheritance in trios and concordance with the HapMap Project, is >99.7%. Average intermarker distance is 23.6 kb, and 92% of the genome is within 100 kb of a SNP marker. Average heterozygosity is 0.30, with 105,511 SNPs having minor allele frequencies >5%.     

A model for genetic complementation controlling the chromosomal abnormalities and loss of heterozygosity formation in cancer

The relationship between the apparently random chromosomal changes found in aneuploidy and the genetic instability driving the progression of cancer is not clear. We report a test of the hypothesis that aneuploid chromosomal abnormalities might be selected to preserve cell-survival genes during loss of heterozygosity (LOH) formations which eliminate tumor suppressor genes. The LOHs and structurally abnormal chromosomes present in the aneuploid LoVo (colon), A549 (lung), SUIT-2 (pancreas), and LN-18 (glioma) cancer cell lines were identified by single nucleotide polymorphisms (SNPs) and Spectral Karyotyping (SKY). The Mann-Whitney U and chi square tests were used to evaluate possible differences in chromosome numbers and abnormalities between the cell lines, with two-tailed P values of <0.01 being considered significant. The cell lines differed significantly in chromosome numbers and frequency of structurally abnormal chromosomes. The SNP analysis revealed that each cell line contained at least a haploid set of somatic chromosomes, consistent with our hypothesis that cell-survival genes are widely scattered throughout the genome. Further, over 90% of the chromosomal abnormalities seemed to be selected, often after LOH formation, for gene-dosage compensation or to provide heterozygosity for specific chromosomal regions. These results suggest that the chromosomal changes of aneuploidy are not random, but may be selected to provide gene-dosage compensation and/or retain functional alleles of cell-survival genes during LOH formation.     

Loss-of-heterozygosity analysis of small-cell lung carcinomas using single-nucleotide polymorphism arrays

Human cancers arise by a combination of discrete mutations and chromosomal alterations. Loss of heterozygosity (LOH) of chromosomal regions bearing mutated tumor suppressor genes is a key event in the evolution of epithelial and mesenchymal tumors. Global patterns of LOH can be understood through allelotyping of tumors with polymorphic genetic markers. Simple sequence length polymorphisms (SSLPs, or microsatellites) are reliable genetic markers for studying LOH, but only a modest number of SSLPs are used in LOH studies because the genotyping procedure is rather tedious. Here, we report the use of a highly parallel approach to genotype large numbers of single-nucleotide polymorphisms (SNPs) for LOH, in which samples are genotyped for nearly 1,500 loci by performing 24 polymerase chain reactions (PCR), pooling the resulting amplification products and hybridizing the mixture to a high-density oligonucleotide array. We characterize the results of LOH analyses on human small-cell lung cancer (SCLC) and control DNA samples by hybridization. We show that the patterns of LOH are consistent with those obtained by analysis with both SSLPs and comparative genomic hybridization (CGH), whereas amplifications rarely are detected by the SNP array. The results validate the use of SNP array hybridization for tumor studies.     

Allelic imbalance analysis by high-density single-nucleotide polymorphic allele (SNP) array with whole genome amplified DNA

Besides their use in mRNA expression profiling, oligonucleotide microarrays have also been applied to single-nucleotide polymorphism (SNP) and loss of heterozygosity (LOH) or allelic imbalance studies. In this report, we evaluate the reliability of using whole genome amplified DNA for analysis with an oligonucleotide microarray containing 11 560 SNPs to detect allelic imbalance and chromosomal copy number abnormalities. Whole genome SNP analyses were performed with DNA extracted from osteosarcoma tissues and patient-matched blood. SNP calls were then generated by Affymetrix® GeneChip® DNA Analysis Software. In two osteosarcoma cases, using unamplified DNA, we identified 793 and 1070 SNP loci with allelic imbalance, respectively. In a parallel experiment with amplified DNA, 78% and 83% of these SNP loci with allelic imbalance was detected. The average false-positive rate is 13.8%. Furthermore, using the Affymetrix® GeneChip® Chromosome Copy Number Tool to analyze the SNP array data, we were able to detect identical chromosomal regions with gain or loss in both amplified and unamplified DNA at cytoband resolution.     

Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium

Single-nucleotide polymorphisms (SNPs) determined based on SNP arrays from the international HapMap consortium (HapMap) and the genetic variants detected in the 1000 genomes project (1KGP) can serve as two references for genomewide association studies (GWAS). We conducted comparative analyses to provide a means for assessing concerns regarding SNP array-based GWAS findings as well as for realistically bounding expectations for next generation sequencing (NGS)-based GWAS. We calculated and compared base composition, transitions to transversions ratio, minor allele frequency and heterozygous rate for SNPs from HapMap and 1KGP for the 622 common individuals. We analysed the genotype discordance between HapMap and 1KGP to assess consistency in the SNPs from the two references. In 1KGP, 90.58% of 36,817,799 SNPs detected were not measured in HapMap. More SNPs with minor allele frequencies less than 0.01 were found in 1KGP than HapMap. The two references have low discordance (generally smaller than 0.02) in genotypes of common SNPs, with most discordance from heterozygous SNPs. Our study demonstrated that SNP array-based GWAS findings were reliable and useful, although only a small portion of genetic variances were explained. NGS can detect not only common but also rare variants, supporting the expectation that NGS-based GWAS will be able to incorporate a much larger portion of genetic variance than SNP arrays-based GWAS.     

Comparative study of the linkage disequilibrium of an ENCODE region, chromosome 7p15, in Korean, Japanese, and Han Chinese samples

The extent and pattern of linkage disequilibrium (LD) in the human genome provide important information for disease gene mapping. Previous studies have shown that LDs vary depending on chromosomal regions and populations. As the Asian samples of the International HapMap Project consisted of Japanese and Chinese populations, it was of interest whether we could use the HapMap data as a reference to carry out association studies of common complex diseases in a closely related population, such as Koreans. We have compared the LD and recombination patterns defined by single-nucleotide polymorphisms (SNPs) in ENCODE region ENm010, chromosome 7p15.2, in Korean, Japanese, and Chinese samples and further tested the robustness of tagSNPs among the Asian samples. We genotyped 792 SNPs in 500 kb (chromosome 7: 26699793-27199792, NCBI build 34) from 90 unrelated Koreans by fluorescence polarization detection and compared the data with Asian data from the HapMap project. Despite some differences in the position of high LD region boundaries, the overall patterns of LD were remarkably similar across the three samples, reflecting strong genetic affinities among them. Furthermore, the haplotype tag SNP transferability across the three samples was greater than 90%. Our results support the initial suggestion that the populations genotyped in the HapMap project might serve as reference populations for the selection of tagSNPs in association studies.     

Identification of somatic chromosomal abnormalities in hypothalamic hamartoma tissue at the GLI3 locus

Hypothalamic hamartomas (HH) are rare, benign congenital tumors associated with intractable epilepsy. Most cases are sporadic and nonsyndromic. Approximately 5% of HH cases are associated with Pallister-Hall syndrome (PHS), which is caused by haploinsufficiency of GLI3. We have investigated the possibility that HH pathogenesis in sporadic cases is due to a somatic (tumor-only) mutation in GLI3. We isolated genomic DNA from peripheral blood and surgically resected HH tissue in 55 patients with sporadic HH and intractable epilepsy. A genome-wide screen for loss of heterozygosity (LOH) and chromosomal abnormalities was performed with parallel analysis of blood and HH tissue with Affymetrix 10K SNP microarrays. Additionally, resequencing and fine mapping with SNP genotyping were completed for the GLI3 gene with comparisons between peripheral blood and HH tissue pairs. By analyzing chromosomal copy-number data for paired samples on the Affymetrix 10K array, we identified a somatic chromosomal abnormality on chromosome 7p in one HH tissue sample. Resequencing of GLI3 did not identify causative germline mutations but did identify LOH within the GLI3 gene in the HH tissue samples of three patients. Further genotyping of 28 SNPs within and surrounding GLI3 identified five additional patients exhibiting LOH. Together, these data provide evidence that the development of chromosomal abnormalities within GLI3 is associated with the pathogenesis of HH lesions in sporadic, nonsyndromic patients with HH and intractable epilepsy. Chromosomal abnormalities including the GLI3 locus were seen in 8 of 55 (15%) of the resected HH tissue samples. These somatic mutations appear to be highly variable.     

Genome-wide single-nucleotide polymorphism map for Candida albicans

Single-nucleotide polymorphisms (SNPs) are essential tools for studying a variety of organismal properties and processes, such as recombination, chromosomal dynamics, and genome rearrangement. This paper describes the development of a genome-wide SNP map for Candida albicans to study mitotic recombination and chromosome loss. C. albicans is a diploid yeast which propagates primarily by clonal mitotic division. It is the leading fungal pathogen that causes infections in humans, ranging from mild superficial lesions in healthy individuals to severe, life-threatening diseases in patients with suppressed immune systems. The SNP map contains 150 marker sequences comprising 561 SNPs and 9 insertions-deletions. Of the 561 SNPs, 437 were transition events while 126 were transversion events, yielding a transition-to-transversion ratio of 3:1, as expected for a neutral accumulation of mutations. The average SNP frequency for our data set was 1 SNP per 83 bp. The map has one marker placed every 111 kb, on average, across the 16-Mb genome. For marker sequences located partially or completely within coding regions, most contained one or more nonsynonymous substitutions. Using the SNP markers, we identified a loss of heterozygosity over large chromosomal fragments in strains of C. albicans that are frequently used for gene manipulation experiments. The SNP map will be useful for understanding the role of heterozygosity and genome rearrangement in the response of C. albicans to host environments.     

Inferring loss-of-heterozygosity from unpaired tumors using high-density oligonucleotide SNP arrays

Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples.     

Allelic spectrum of the natural variation in CRP

With the recent completion of the International HapMap Project, many tools are in hand for genetic association studies seeking to test the common variant/common disease hypothesis. In contrast, very few tools and resources are in place for genotype–phenotype studies hypothesizing that rare variation has a large impact on the phenotype of interest. To create these tools for rare variant/common disease studies, much interest is being generated towards investing in re-sequencing either large sample sizes of random chromosomes or smaller sample sizes of patients with extreme phenotypes. As a case study for rare variant discovery in random chromosomes, we have re-sequenced ~1,000 chromosomes representing diverse populations for the gene C-reactive protein (CRP). CRP is an important gene in the fields of cardiovascular and inflammation genetics, and its size (~2 kb) makes it particularly amenable medical or deep re-sequencing. With these data, we explore several issues related to the present-day candidate gene association study including the benefits of complete SNP discovery, the effects of tagSNP selection across diverse populations, and completeness of dbSNP for CRP. Also, we show that while deep re-sequencing uncovers potentially medically relevant coding SNPs, these SNPs are fleetingly rare when genotyped in a population-based survey of 7,000 Americans (NHANES III). Collectively, these data suggest that several different types re-sequencing and genotyping approaches may be required to fully understand the complete spectrum of alleles that impact human phenotypes.Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

A genomewide single-nucleotide-polymorphism panel for Mexican American admixture mapping

For admixture mapping studies in Mexican Americans (MAM), we define a genomewide single-nucleotide-polymorphism (SNP) panel that can distinguish between chromosomal segments of Amerindian (AMI) or European (EUR) ancestry. These studies used genotypes for >400,000 SNPs, defined in EUR and both Pima and Mayan AMI, to define a set of ancestry-informative markers (AIMs). The use of two AMI populations was necessary to remove a subset of SNPs that distinguished genotypes of only one AMI subgroup from EUR genotypes. The AIMs set contained 8,144 SNPs separated by a minimum of 50 kb with only three intermarker intervals >1 Mb and had EUR/AMI FST values >0.30 (mean FST = 0.48) and Mayan/Pima FST values <0.05 (mean FST < 0.01). Analysis of a subset of these SNP AIMs suggested that this panel may also distinguish ancestry between EUR and other disparate AMI groups, including Quechuan from South America. We show, using realistic simulation parameters that are based on our analyses of MAM genotyping results, that this panel of SNP AIMs provides good power for detecting disease-associated chromosomal segments for genes with modest ethnicity risk ratios. A reduced set of 5,287 SNP AIMs captured almost the same admixture mapping information, but smaller SNP sets showed substantial drop-off in admixture mapping information and power. The results will enable studies of type 2 diabetes, rheumatoid arthritis, and other diseases among which epidemiological studies suggest differences in the distribution of ancestry-associated susceptibility.     

Comparative assessment of the association information captured by SNP tagging

Exploiting the association between single nucleotide polymorphisms (SNP) can potentially reduce the costs of association mapping of common disease genes. Different methods have been proposed for defining subsets of SNPs as proxies (or tagSNPs) for other SNPs, some of which rely upon a model of haplotype blocks. Other approaches only consider the pair-wise correlation between markers as a basis for selecting tagSNPs. Yet another, recently proposed model-based method takes marker heterozygosity and genetic distance into account in order to maximize the expected utility of a marker set to map frequent, but unobserved genetic variants. We compared these tagging approaches with regard to their ability to correlate tagSNPs and bi-allelic, potentially disease-causing genetic variants. We used the CEU sample of chromosome 19 from the HapMap project for an initial comparison, and demonstrated a comparable performance of both approaches but a difference in terms of tagSNPs selected and variants captured. In any case, we conclude that a considerable loss of information appears to be inherent to any type of SNP tagging, even when dense marker sets are available for SNP selection.     

Genome-wide detection of LOH in prostate cancer using human SNP microarray technology

Loss of heterozygosity (LOH) of chromosomal regions is crucial in tumor progression. In this study we assessed the potential of the Affymetrix GeneChip HuSNP mapping assay for detecting genome-wide LOH in prostate tumors. We analyzed two human prostate cell lines, P69SV40Tag (P69) and its tumorigenic subline, M12, and 11 prostate cancer cases. The M12 cells showed LOH in chromosomes 3p12.1-p22.1, 11q22.1-q24.2, 19p13.12, and 19q13.42. All of the prostate cases with informative single-nucleotide polymorphism (SNP) markers showed LOH in 1p31.2, 10q11.21, 12p13.1, 16q23.1-q23.2, 17p13.3, 17q21.31, and 21q21.2. Additionally, a high percentage of cases showed LOH at 6p25.1-p25.3 (75%), 8p22-p23.2, and 10q22.1 (70%). Several tumor suppressor genes (TSGs) have been mapped in these loci. These results demonstrate that the HuSNP mapping assay can serve as an alternative to comparative genomic hybridization for assessing genome-wide LOH and can identify chromosomal regions harboring candidate TSGs implicated in prostate cancer.     

Single nucleotide polymorphism (SNP)-based loss of heterozygosity (LOH) testing by real time PCR in patients suspect of myeloproliferative disease

During tumor development, loss of heterozygosity (LOH) often occurs. When LOH is preceded by an oncogene activating mutation, the mutant allele may be further potentiated if the wild-type allele is lost or inactivated. In myeloproliferative neoplasms (MPN) somatic acquisition of JAK2V617F may be followed by LOH resulting in loss of the wild type allele. The occurrence of LOH in MPN and other proliferative diseases may lead to a further potentiating the mutant allele and thereby increasing morbidity. A real time PCR based SNP profiling assay was developed and validated for LOH detection of the JAK2 region (JAK2LOH). Blood of a cohort of 12 JAK2V617F-positive patients (n=6 25-50% and n=6>50% JAK2V617F) and a cohort of 81 patients suspected of MPN was stored with EDTA and subsequently used for validation. To generate germ-line profiles, non-neoplastic formalin-fixed paraffin-embedded tissue from each patient was analyzed. Results of the SNP assay were compared to those of an established Short Tandem Repeat (STR) assay. Both assays revealed JAK2LOH in 1/6 patients with 25-50% JAK2V617F. In patients with >50% JAK2V617F, JAK2LOH was detected in 6/6 by the SNP assay and 5/6 patients by the STR assay. Of the 81 patients suspected of MPN, 18 patients carried JAK2V617F. Both the SNP and STR assay demonstrated the occurrence of JAK2LOH in 5 of them. In the 63 JAK2V617F-negative patients, no JAK2LOH was observed by SNP and STR analyses. The presented SNP assay reliably detects JAK2LOH and is a fast and easy to perform alternative for STR analyses. We therefore anticipate the SNP approach as a proof of principle for the development of LOH SNP-assays for other clinically relevant LOH loci.     

Sample size determination for classifiers based on single-nucleotide polymorphisms

Single-nucleotide polymorphisms (SNPs), believed to determine human differences, are widely used to predict risk of diseases. Typically, clinical samples are limited and/or the sampling cost is high. Thus, it is essential to determine an adequate sample size needed to build a classifier based on SNPs. Such a classifier would facilitate correct classifications, while keeping the sample size to a minimum, thereby making the studies cost-effective. For coded SNP data from 2 classes, an optimal classifier and an approximation to its probability of correct classification (PCC) are derived. A linear classifier is constructed and an approximation to its PCC is also derived. These approximations are validated through a variety of Monte Carlo simulations. A sample size determination algorithm based on the criterion, which ensures that the difference between the 2 approximate PCCs is below a threshold, is given and its effectiveness is illustrated via simulations. For the HapMap data on Chinese and Japanese populations, a linear classifier is built using 51 independent SNPs, and the required total sample sizes are determined using our algorithm, as the threshold varies. For example, when the threshold value is 0.05, our algorithm determines a total sample size of 166 (83 for Chinese and 83 for Japanese) that satisfies the criterion.     

Conditional random pattern algorithm for LOH inference and segmentation

Motivation: Loss of heterozygosity (LOH) is one of the mostimportant mechanisms in the tumor evolution. LOH can be detectedfrom the genotypes of the tumor samples with or without pairednormal samples. In paired sample cases, LOH detection for informativesingle nucleotide polymorphisms (SNPs) is straightforward ifthere is no genotyping error. But genotyping errors are alwaysunavoidable, and there are about 70% non-informative SNPs whoseLOH status can only be inferred from the neighboring informativeSNPs. Results: This article presents a novel LOH inference and segmentationalgorithm based on the conditional random pattern (CRP) model.The new model explicitly considers the distance between twoneighboring SNPs, as well as the genotyping error rate and theheterozygous rate. This new method is tested on the simulatedand real data of the Affymetrix Human Mapping 500K SNP arrays.The experimental results show that the CRP method outperformsthe conventional methods based on the hidden Markov model (HMM). Availability: Software is available upon request. Contact: xzhou{at}tmhs.org Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Alex Bateman     

Evaluation of the SNP tagging approach in an independent population sample—array-based SNP discovery in Sami

Significant efforts have been made to determine the correlation structure of common SNPs in the human genome. One method has been to identify the sets of tagSNPs that capture most of the genetic variation. Here, we evaluate the transferability of tagSNPs between populations using a population sample of Sami, the indigenous people of Scandinavia. Array-based SNP discovery in a 4.4 Mb region of 28 phased copies of chromosome 21 uncovered 5,132 segregating sites, 3,188 of which had a minimum minor allele frequency (mMAF) of 0.1. Due to the population structure and consequently high LD, the number of tagSNPs needed to capture all SNP variation in Sami is much lower than that for the HapMap populations. TagSNPs identified from the HapMap data perform only slightly better in the Sami than choosing tagSNPs at random from the same set of common SNPs. Surprisingly, tagSNPs defined from the HapMap data did not perform better than selecting the same number of SNPs at random from all SNPs discovered in Sami. Nearly half (46%) of the Sami SNPs with a mMAF of 0.1 are not present in the HapMap dataset. Among sites overlapping between Sami and HapMap populations, 18% are not tagged by the European American (CEU) HapMap tagSNPs, while 43% of the SNPs that are unique to Sami are not tagged by the CEU tagSNPs. These results point to serious limitations in the transferability of common tagSNPs to capture random sequence variation, even between closely related populations, such as CEU and Sami. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Power to detect risk alleles using genome-wide tag SNP panels

Advances in high-throughput genotyping and the International HapMap Project have enabled association studies at the whole-genome level. We have constructed whole-genome genotyping panels of over 550,000 (HumanHap550) and 650,000 (HumanHap650Y) SNP loci by choosing tag SNPs from all populations genotyped by the International HapMap Project. These panels also contain additional SNP content in regions that have historically been overrepresented in diseases, such as nonsynonymous sites, the MHC region, copy number variant regions and mitochondrial DNA. We estimate that the tag SNP loci in these panels cover the majority of all common variation in the genome as measured by coverage of both all common HapMap SNPs and an independent set of SNPs derived from complete resequencing of genes obtained from SeattleSNPs. We also estimate that, given a sample size of 1,000 cases and 1,000 controls, these panels have the power to detect single disease loci of moderate risk (λ ~ 1.8–2.0). Relative risks as low as λ ~ 1.1–1.3 can be detected using 10,000 cases and 10,000 controls depending on the sample population and disease model. If multiple loci are involved, the power increases significantly to detect at least one locus such that relative risks 20%–35% lower can be detected with 80% power if between two and four independent loci are involved. Although our SNP selection was based on HapMap data, which is a subset of all common SNPs, these panels effectively capture the majority of all common variation and provide high power to detect risk alleles that are not represented in the HapMap data.     

Concurrent analysis of loss of heterozygosity (LOH) and copy number abnormality (CNA) for oral premalignancy progression using the Affymetrix 10K SNP mapping array

Like most human cancers, oral squamous cell carcinoma (SCC) is characterized by genetic instabilities. In this study, a single platform (Affymetrix 10K SNP mapping array) was used to generate both loss of heterozygosity (LOH) and DNA copy number abnormality (CNA) read-outs for precise and high-resolution genetic alteration profiles. As a proof of principle, we performed this concordant analysis on a panel of deletion and trisomy cell lines with known chromosomal alterations and the precise LOH and CNA regions were detected as expected. Using a previously described oral SCC progression model system, we identified a set of genomic regions that may be associated with the malignancy progression, including chromosome regions 3pter–3p35.3, 3p14.1–3p13, 11p, 11q14.3–11q22.2, and 11q13.5–11q14.1. These data show that it is feasible to utilize high-density SNP arrays to generate concordant LOH and CNA profiles at high resolution.     

Single nucleotide polymorphisms (SNPs) that map to gaps in the human SNP map

An international effort is underway to generate a comprehensive haplotype map (HapMap) of the human genome represented by an estimated 300000 to 1 million ‘tag’ single nucleotide polymorphisms (SNPs). Our analysis indicates that the current human SNP map is not sufficiently dense to support the HapMap project. For example, 24.6% of the genome currently lacks SNPs at the minimal density and spacing that would be required to construct even a conservative tag SNP map containing 300 000 SNPs. In an effort to improve the human SNP map, we identified 140 696 additional SNP candidates using a new bioinformatics pipeline. Over 51 000 of these SNPs mapped to the largest gaps in the human SNP map, leading to significant improvements in these regions. Our SNPs will be immediately useful for the HapMap project, and will allow for the inclusion of many additional genomic intervals in the final HapMap. Nevertheless, our results also indicate that additional SNP discovery projects will be required both to define the haplotype architecture of the human genome and to construct comprehensive tag SNP maps that will be useful for genetic linkage studies in humans.