Allelic imbalance analysis of oral tongue squamous cell carcinoma by high-density single nucleotide polymorphism arrays using whole-genome amplified DNA

Multiple displacement-based whole-genome DNA amplification is a promising tool to obtain sufficient DNA from small tissue specimens for various genetic analyses, such as SNP array-based analysis. Using Affymetrix 10 K and 100 K SNP mapping array, we evaluated the performance of the Phi29 DNA polymerase-based genome amplification. Greater than 99% concordance in genotyping calls were achieved between amplified and non-amplified DNAs for both arrays. By utilizing the Affymetrix GeneChip Chromosome Copy Number Tool, the allelic imbalance profiles for the advanced stage oral tongue squamous cell carcinoma (OTSCC) were generated based on 10 K and 100 K SNP mapping array results. The results from these two array platforms agree closely, but more precise allelic imbalance patterns can be revealed from the 100 K SNP mapping array data. Furthermore, our data suggested a frequent loss at 3p11–p12 for advanced stage OTSCC.     

Genotyping and annotation of Affymetrix SNP arrays

In this paper we develop a new method for genotyping Affymetrix single nucleotide polymorphism (SNP) array. The method is based on (i) using multiple arrays at the same time to determine the genotypes and (ii) a model that relates intensities of individual SNPs to each other. The latter point allows us to annotate SNPs that have poor performance, either because of poor experimental conditions or because for one of the alleles the probes do not behave in a dose–response manner. Generally, our method agrees well with a method developed by Affymetrix. When both methods make a call they agree in 99.25% (using standard settings) of the cases, using a sample of 113 Affymetrix 10k SNP arrays. In the majority of cases where the two methods disagree, our method makes a genotype call, whereas the method by Affymetrix makes a no call, i.e. the genotype of the SNP is not determined. By visualization it is indicated that our method is likely to be correct in majority of these cases. In addition, we demonstrate that our method produces more SNPs that are in concordance with Hardy–Weinberg equilibrium than the method by Affymetrix. Finally, we have validated our method on HapMap data and shown that the performance of our method is comparable to other methods.     

Challenges of interpreting copy number variation in syndromic and non-syndromic congenital heart defects

Array comparative genomic hybridization (aCGH) has led to an increased detection of causal chromosomal imbalances in individuals with congenital heart defects (CHD). The introduction of aCGH as a diagnostic tool in a clinical cardiogenetic setting entails numerous challenges. Based on our own experience as well as those of others described in the literature, we outline the state of the art and attempt to answer a number of outstanding questions such as the detection frequency of causal imbalances in different patient populations, the added value of higher-resolution arrays, and the existence of predictive factors in syndromic cases. We introduce a step-by-step approach for clinical interpretation of copy number variants (CNV) detected in CHD, which is primarily based on gene content and overlap with known chromosomal syndromes, rather than on CNV inheritance and size. Based on this algorithm, we have reclassified the detected aberrations in aCGH studies for their causality for syndromic and non-syndromic CHD. From this literature overview, supplemented with own investigations in a cohort of 46 sporadic patients with severe non-syndromic CHD, it seems clear that the frequency of causal CNVs in non-syndromic CHD populations is lower than that in syndromic CNV populations (3.6 vs. 19%). Moreover, causal CNVs in non-syndromic CHD mostly involve imbalances with a moderate effect size and reduced penetrance, whereas the majority of causal imbalances in syndromic CHD consistently affects human development and significantly reduces reproductive fitness.     

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.     

Estimation and assessment of raw copy numbers at the single locus level

MOTIVATION: Although copy-number aberrations are known to contribute to the diversity of the human DNA and cause various diseases, many aberrations and their phenotypes are still to be explored. The recent development of single-nucleotide polymorphism (SNP) arrays provides researchers with tools for calling genotypes and identifying chromosomal aberrations at an order-of-magnitude greater resolution than possible a few years ago. The fundamental problem in array-based copy-number (CN) analysis is to obtain CN estimates at a single-locus resolution with high accuracy and precision such that downstream segmentation methods are more likely to succeed. RESULTS: We propose a preprocessing method for estimating raw CNs from Affymetrix SNP arrays. Its core utilizes a multichip probe-level model analogous to that for high-density oligonucleotide expression arrays. We extend this model by adding an adjustment for sequence-specific allelic imbalances such as cross-hybridization between allele A and allele B probes. We focus on total CN estimates, which allows us to further constrain the probe-level model to increase the signal-to-noise ratio of CN estimates. Further improvement is obtained by controlling for PCR effects. Each part of the model is fitted robustly. The performance is assessed by quantifying how well raw CNs alone differentiate between one and two copies on Chromosome X (ChrX) at a single-locus resolution (27kb) up to a 200kb resolution. The evaluation is done with publicly available HapMap data. AVAILABILITY: The proposed method is available as part of an open-source R package named aroma.affymetrix. Because it is a bounded-memory algorithm, any number of arrays can be analyzed.     

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.     

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 detection of chromosomal imbalances in tumors using BAC microarrays

Chromosomal imbalances such as deletions and amplifications are common rearrangements in most tumors. Specific rearrangements are consistently associated with specific tumor types or stages, implicating the role of the genes in a region of chromosomal imbalance in tumor initiation and progression. The development of comparative genomic hybridization (CGH) has obviated the need to obtain metaphase spreads from tumors, so that the chromosomal imbalances in many solid tumors may be revealed using an extracted genomic DNA sample. However, the resolution of the cytogenetic method remains and the extreme technical difficulty of CGH has restricted its use. Conceptually, DNA microarray-based CGH is an obvious solution to all of the limitations of conventional CGH. Although arrays have been used for CGH studies, their success has been limited by poor specific signal-to-noise ratios. Here we demonstrate a microarray-based CGH method that allows reliable detection of chromosomal deletions and amplifications with high resolution. Our microarray system is fundamentally different from most current microarray technologies in that activated DNA is printed on natural glass surfaces while other systems almost exclusively focus on activating the surfaces, a strategy that invariably introduces hybridization backgrounds. The concept of using pre-modification may be generally applied for making arrays of other biological materials, as modifying the substrates will be more controllable in solution than on surfaces.     

Assessment of the functionality of genome‐wide canine SNP arrays and implications for canine disease association studies

Domestic dogs share a wide range of important disease conditions with humans, including cancers, diabetes and epilepsy. Many of these conditions have similar or identical underlying pathologies to their human counterparts and thus dogs represent physiologically relevant natural models of human disorders. Comparative genomic approaches whereby disease genes can be identified in dog diseases and then mapped onto the human genome are now recognized as a valid method and are increasing in popularity. The majority of dog breeds have been created over the past few hundred years and, as a consequence, the dog genome is characterized by extensive linkage disequilibrium (LD), extending usually from hundreds of kilobases to several megabases within a breed, rather than tens of kilobases observed in the human genome. Genome‐wide canine SNP arrays have been developed, and increasing success of using these arrays to map disease loci in dogs is emerging. No equivalent of the human HapMap currently exists for different canine breeds, and the LD structure for such breeds is far less understood than for humans. This study is a dedicated large‐scale assessment of the functionalities (LD and SNP tagging performance) of canine genome‐wide SNP arrays in multiple domestic dog breeds. We have used genotype data from 18 breeds as well as wolves and coyotes genotyped by the Illumina 22K canine SNP array and Affymetrix 50K canine SNP array. As expected, high tagging performance was observed with most of the breeds using both Illumina and Affymetrix arrays when multi‐marker tagging was applied. In contrast, however, large differences in population structure, LD coverage and pairwise tagging performance were found between breeds, suggesting that study designs should be carefully assessed for individual breeds before undertaking genome‐wide association studies (GWAS).     

The hyper-IgE syndrome is not caused by a microdeletion syndrome

The hyper-immunoglobulin E syndrome (HIES) is a rare primary immunodeficiency characterized by recurrent infections, elevated serum IgE-levels, and involvement of the soft- and bony tissues. We speculated that this complex disease may be caused by a microdeletion syndrome. We therefore analyzed 30 sporadic HIES patients for the presence of chromosomal imbalances using Affymetrix 50k XbaI and 23 of the 30 patients with the higher-resolution 250k StyI SNP mapping arrays. We detected only eight different copy number alterations in six patients with the 50k approach, and seven of these presented known polymorphic regions not associated with disease. However, one patient showed a unique gain on chromosome 20p. 250k array analysis identified this gain as a rare polymorphism segregating in the patient’s family, but not associated with the HIES phenotype. In addition, 265 known and novel copy number variants (CNVs) were identified with the 250k arrays, but no recurrent imbalances reminescent of a microdeletion syndrome were found. We aligned the identified CNVs with loci that have been associated with HIES or phenotypically overlapping syndromes. Doing so, a 2-Mb deletion spanning the PEPD gene on 19q13.11 was identified on one allele of one patient. Homozygous mutations in PEPD are responsible for the autosomal-recessive prolidase deficiency which resembles HIES in some aspects. Sequencing of the healthy allele, however, revealed a wild-type sequence. In summary, our results suggest that HIES is not likely to be a microdeletion syndrome. Dietmar Pfeifer and Cristina Woellner contributed equally to the work and are considered aequo loco.     

Match-Only Integral Distribution (MOID) Algorithm for high-density oligonucleotide array analysis

Background

High-density oligonucleotide arrays have become a valuable tool for high-throughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics.

Results

A new algorithm, Match-Only Integral Distribution (MOID), was developed to analyze high-density oligonucleotide arrays. Using known data from both spiking experiments and no-change experiments performed with Affymetrix GeneChip^® arrays, MOID and the Affymetrix algorithm implemented in Microarray Suite 4.0 (MAS4) were compared. While MOID gave similar performance to MAS4 in the spiking experiments, better performance was observed in the no-change experiments. MOID also provides a set of alternative statistical analysis tools to MAS4. There are two main features that distinguish MOID from MAS4. First, MOID uses continuous P values for the likelihood of gene presence, while MAS4 resorts to discrete absolute calls. Secondly, MOID uses heuristic confidence intervals for both gene expression levels and fold change values, while MAS4 categorizes the significance of gene expression level changes into discrete fold change calls.

Conclusions

The results show that by using MOID, Affymetrix GeneChip^® arrays may need as little as ten probes per gene without compromising analysis accuracy.     

GPHMM: an integrated hidden Markov model for identification of copy number alteration and loss of heterozygosity in complex tumor samples using whole genome SNP arrays

There is an increasing interest in using single nucleotide polymorphism (SNP) genotyping arrays for profiling chromosomal rearrangements in tumors, as they allow simultaneous detection of copy number and loss of heterozygosity with high resolution. Critical issues such as signal baseline shift due to aneuploidy, normal cell contamination, and the presence of GC content bias have been reported to dramatically alter SNP array signals and complicate accurate identification of aberrations in cancer genomes. To address these issues, we propose a novel Global Parameter Hidden Markov Model (GPHMM) to unravel tangled genotyping data generated from tumor samples. In contrast to other HMM methods, a distinct feature of GPHMM is that the issues mentioned above are quantitatively modeled by global parameters and integrated within the statistical framework. We developed an efficient EM algorithm for parameter estimation. We evaluated performance on three data sets and show that GPHMM can correctly identify chromosomal aberrations in tumor samples containing as few as 10% cancer cells. Furthermore, we demonstrated that the estimation of global parameters in GPHMM provides information about the biological characteristics of tumor samples and the quality of genotyping signal from SNP array experiments, which is helpful for data quality control and outlier detection in cohort studies.     

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.     

Calibrating the performance of SNP arrays for whole-genome association studies

To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N=359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level.     

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.     

Common copy number variations in fifty radiosensitive cell lines

Hypersensitivity to radiation exposure is a major challenge to radiotherapy in the treatment of cancer patients. Copy number variations (CNVs) are believed to identify genomic regions of functional significance for radiosensitivity (RS) but have yet to be systematically investigated. We used Affymetrix 6.0 SNP arrays to survey common CNVs in a cohort of 50 radiosensitive lymphoblastoid cell lines (RS-LCLs) derived from patients with undiagnosed diseases. A total of 317 CNVs that were present in at least 10% of the studied cell lines were identified. Three hundred and eight CNVs overlapped with polymorphic CNVs, 13 of which were significantly enriched in the RS-LCLs compared to the reference. The remaining 9 CNVs were novel. The majority of these enriched and novel CNVs were chromosomal gains. The dominance of the chromosomal gains over losses is inconsistent with the traditional concept of molecular basis of RS and suggests more complex genetic mechanisms for RS.     

Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data

Background

Genomic deletions and duplications are important in the pathogenesis of diseases, such as cancer and mental retardation, and have recently been shown to occur frequently in unaffected individuals as polymorphisms. Affymetrix GeneChip whole genome sampling analysis (WGSA) combined with 100 K single nucleotide polymorphism (SNP) genotyping arrays is one of several microarray-based approaches that are now being used to detect such structural genomic changes. The popularity of this technology and its associated open source data format have resulted in the development of an increasing number of software packages for the analysis of copy number changes using these SNP arrays.

Results

We evaluated four publicly available software packages for high throughput copy number analysis using synthetic and empirical 100 K SNP array data sets, the latter obtained from 107 mental retardation (MR) patients and their unaffected parents and siblings. We evaluated the software with regards to overall suitability for high-throughput 100 K SNP array data analysis, as well as effectiveness of normalization, scaling with various reference sets and feature extraction, as well as true and false positive rates of genomic copy number variant (CNV) detection.

Conclusion

We observed considerable variation among the numbers and types of candidate CNVs detected by different analysis approaches, and found that multiple programs were needed to find all real aberrations in our test set. The frequency of false positive deletions was substantial, but could be greatly reduced by using the SNP genotype information to confirm loss of heterozygosity.     

CGcgh: a tool for molecular karyotyping using DNA microarray-based comparative genomic hybridization (array-CGH)

Microarray-based comparative genomic hybridization (array-CGH) is a technique by which variations in copy numbers between two genomes can be analyzed using DNA microarrays. Array CGH has been used to survey chromosomal amplifications and deletions in fetal aneuploidies or cancer tissues. Herein we report a user-friendly, MATLAB-based, array CGH analyzing program, Chang Gung comparative genomic hybridization (CGcgh), as a standalone PC version. The analyzed chromosomal data are displayed in a graphic interface, and CGcgh allows users to launch a corresponding G-banding ideogram. The abnormal DNA copy numbers (gains and losses) can be identified automatically using a user defined window size (default value is 50 probes) and sequential student t-tests with sliding windows along with chromosomes. CGcgh has been tested in multiple karyotype-confirmed human samples, including five published cases and trisomies 13, 18, 21 and X from our laboratories, and 18 cases of which microarray data are available publicly. CGcgh can be used to detect the copy number changes in small genomic regions, which are commonly encountered by clinical geneticists. CGcgh works well for the data from cDNA microarray, spotted oligonucleotide microarrays, and Affymetrix Human Mapping Arrays (10K, 100K, 500K Array Sets). The program can be freely downloaded from . Y. S. Lee and A. Chao contributed equally to this work.     

Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference

DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.