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At least 37 records · Page 2Linked to original sources
PubMed2005

ALOHOMORA: a tool for linkage analysis using 10K SNP array data.

SUMMARY: ALOHOMORA is a software tool designed to facilitate genome-wide linkage studies performed with high-density single nucleotide polymorphism (SNP) marker panels such as the Affymetrix GeneChip(R) Human Mapping 10K Array. Genotype data are converted into appropriate formats for a number of common linkage programs and subjected to standard quality control routines before linkage runs are started. ALOHOMORA is written in Perl and may be used to perform state-of-the-art linkage scans in small and large families with any genetic model. Options for using different genetic maps or ethnicity-specific allele frequencies are implemented. Graphic outputs of whole-genome multipoint LOD score values are provided for the entire dataset as well as for individual families. AVAILABILITY: ALOHOMORA is available free of charge for non-commercial research institutions. For more details, see http://gmc.mdc-berlin.de/alohomora/

Algorithms↗
PubMed2005

Whole genome SNP arrays using DNA derived from formalin-fixed, paraffin-embedded ovarian tumor tissue.

Array-based genotyping platforms, such as the Affymetrix mapping array, have been validated as reliable methods for obtaining high-resolution copy number and allele status information when using DNA derived from fresh tissue sources. However, the suitability of such systems for the examination of DNA derived from formalin-fixed, paraffin-embedded (FFPE) tissues has not been tested. Therefore, we analyzed DNA derived from five matching fresh frozen and FFPE ovarian tumors for gene copy number changes and loss of heterozygosity using the Affymetrix GeneChip Human Mapping 10 K Array Xba 131. The data was analyzed using Affymetrix proprietary software, GeneChip DNA Analysis Software, and Chromosome Copy Number Tool. The average SNP call rate (rate of successful genotype identification) of the fresh samples was 89.44% (range 78.72-96.22%, median 92.72%) and was only slightly lower for the matching FFPE samples at 83.48% (range 76.93-93.17%, median 82.60%). The average concordance (rate of agreement between successful genotype calls) between the fresh and matching FFPE samples was 97.06% (range 92.70-99.41%, median 97.52%). Loss of heterozygosity (LOH) profiles of the fresh and FFPE samples were essentially identical across all chromosomes. Copy number data was also comparable, although the quantification of copy number changes appears overstated in the FFPE samples. In conclusion, we have shown that it is possible to achieve high-performance outcomes using FFPE-derived DNA in the Affymetrix 10 K mapping array. This advance will open up vast archival tissue resources to high-resolution genetic analysis and unlock a wealth of biological information.

DNA↗
PubMed2006

PLASQ: a generalized linear model-based procedure to determine allelic dosage in cancer cells from SNP array data.

Human cancer is largely driven by the acquisition of mutations. One class of such mutations is copy number polymorphisms, comprised of deviations from the normal diploid two copies of each autosomal chromosome per cell. We describe a probe-level allele-specific quantitation (PLASQ) procedure to determine copy number contributions from each of the parental chromosomes in cancer cells from single-nucleotide polymorphism (SNP) microarray data. Our approach is based upon a generalized linear model that takes advantage of a novel classification of probes on the array. As a result of this classification, we are able to fit the model to the data using an expectation-maximization algorithm designed for the purpose. We demonstrate a strong model fit to data from a variety of cell types. In normal diploid samples, PLASQ is able to genotype with very high accuracy. Moreover, we are able to provide a generalized genotype in cancer samples (e.g. CCCCT at an amplified SNP). Our approach is illustrated on a variety of lung cancer cell lines and tumors, and a number of events are validated by independent computational and experimental means. An R software package containing the methods is freely available.

Algorithms↗
PubMed2006

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

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

Algorithms↗
PubMed2003

Identification of novel targets in scleroderma: update on population studies, cDNA arrays, SNP analysis, and mutations.

PURPOSE OF REVIEW: Systemic sclerosis, or scleroderma, is an uncommon autoimmune connective tissue disease that results in systemic fibrosis. Its etiologic basis remains unclear. The pathogenesis of systemic sclerosis involves a proliferative and obliterative vasculopathy resulting from endothelial cell dysfunction, extensive fibrosis secondary to fibroblast activation, and autoimmunity as demonstrated by the presence of disease-specific autoantibodies. Although there is no clear and convincing evidence for an environmental trigger in most cases, accumulating data emphasize the role of genetic factors in systemic sclerosis. As in other complex human diseases, multiple genes likely contribute to disease susceptibility and the clinical manifestations of systemic sclerosis. This review will cover the application of genomics to the complex genetics of systemic sclerosis. RECENT FINDINGS: The following review is an update on novel targets identified in scleroderma based on published reports (May 2000-May 2003) of mutation/polymorphism analysis (using SNP and haplotyping), the results from a recent genome-wide scan on a Native American population with systemic sclerosis, and gene expression studies (microarrays). SUMMARY: The use of genomics has revealed novel targets and genetic associations that may contribute to the cause, the onset, and the subsequent pathologic changes that constitute systemic sclerosis. The identification of potential candidates for gene therapy or disease-specific targets amenable to pharmacologic intervention will benefit patients with systemic sclerosis who are currently being treated for their symptoms and not the disease process itself.

Autoantibodies↗
PubMed2006

Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data.

In most microarray technologies, a number of critical steps are required to convert raw intensity measurements into the data relied upon by data analysts, biologists, and clinicians. These data manipulations, referred to as preprocessing, can influence the quality of the ultimate measurements. In the last few years, the high-throughput measurement of gene expression is the most popular application of microarray technology. For this application, various groups have demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of the gene expression measurements, relative to ad hoc procedures introduced by designers and manufacturers of the technology. Currently, other applications of microarrays are becoming more and more popular. In this paper, we describe a preprocessing methodology for a technology designed for the identification of DNA sequence variants in specific genes or regions of the human genome that are associated with phenotypes of interest such as disease. In particular, we describe a methodology useful for preprocessing Affymetrix single-nucleotide polymorphism chips and obtaining genotype calls with the preprocessed data. We demonstrate how our procedure improves existing approaches using data from 3 relatively large studies including the one in which large numbers of independent calls are available. The proposed methods are implemented in the package oligo available from Bioconductor.

Algorithms↗
PubMed2003

Genome-wide loss of heterozygosity analysis from laser capture microdissected prostate cancer using single nucleotide polymorphic allele (SNP) arrays and a novel bioinformatics platform dChipSNP.

Oligonucleotide arrays that detect single nucleotide polymorphisms were used to generate genome-wide loss of heterozygosity (LOH) maps from laser capture microdissected paraffin-embedded samples using as little as 5 ng of DNA. The allele detection rate from such samples was comparable with that obtained with standard amounts of DNA prepared from frozen tissues. A novel informatics platform, dChipSNP, was used to automate the definition of statistically valid regions of LOH, assign LOH genotypes to prostate cancer samples, and organize by hierarchical clustering prostate cancers based on the pattern of LOH. This organizational strategy revealed apparently distinct genetic subsets of prostate cancer.

Alleles↗
PubMed2007

Deletion mapping in Xp21 for patients with complex glycerol kinase deficiency using SNP mapping arrays.

Infantile or complex glycerol kinase deficiency (cGKD) is a contiguous gene deletion syndrome caused by a loss of GK (MIM# 300474), along with its neighboring genes, Duchenne muscular dystrophy (DMD; MIM# 300377) and/or Nuclear Receptor Subfamily 0, Group B, Member 1 (NR0B1; MIM# 300473). Patients with cGKD present with glyceroluria and hyperglycerolemia in association with DMD and/or adrenal hypoplasia congenita (AHC). The purpose of these investigations was to determine whether the Affymetrix GeneChip Mapping Array (SNP chip) could be utilized to detect and map breakpoints in patients with cGKD. Genomic DNAs from several primary lymphoblastoid cell lines from patients with cGKD were analyzed on the Affymetrix platform. The Affymetrix SNP chip is a high-density oligonucleotide array that allows a standardized, parallel interrogation of thousands of SNPs across the entire genome (except for the Y chromosome). Analysis of the array features' hybridization intensities enabled clear delineation of the patient deletions with a high degree of confidence. Many of these patient deletions had been mapped by PCR and their breakpoints confirmed by sequencing. This study demonstrates the utility of the Affymetrix Mapping GeneChips for molecular cytogenetic analysis, beyond the SNP genotyping for which the arrays were initially designed. With one out of 160 live births (approximately 25,000 U.S. neonates annually) reported to have cytogenetic disorders, we envision a significant need for such a standardized platform to carry out rapid, high-throughput, genomic analyses for molecular cytogenetics applications.

Cells, Cultured↗
PubMed2006

Free energy of DNA duplex formation on short oligonucleotide microarrays.

DNA/DNA duplex formation is the basic mechanism that is used in genome tiling arrays and SNP arrays manufactured by Affymetrix. However, detailed knowledge of the physical process is still lacking. In this study, we show a free energy analysis of DNA/DNA duplex formation these arrays based on the positional-dependent nearest-neighbor (PDNN) model, which was developed previously for describing DNA/RNA duplex formation on expression microarrays. Our results showed that the two ends of a probe contribute less to the stability of the duplexes and that there is a microarray surface effect on binding affinities. We also showed that free energy cost of a single mismatch depends on the bases adjacent to the mismatch site and obtained a comprehensive table of the cost of a single mismatch under all possible combination of adjacent bases. The mismatch costs were found to be correlated with those determined in aqueous solution. We further demonstrate that the DNA copy number estimated from the SNP array correlates negatively with the target length; this is presumably caused by inefficient PCR amplification for long fragments. These results provide important insights into the molecular mechanisms of microarray technology and have implications for microarray design and the interpretation of observed data.

Base Pair Mismatch↗
PubMed2004

Genomewide linkage searches for Mendelian disease loci can be efficiently conducted using high-density SNP genotyping arrays.

Genomewide linkage searches aimed at identifying disease susceptibility loci are generally conducted using 300-400 microsatellite markers. Genotyping bi-allelic single nucleotide polymorphisms (SNPs) provides an alternative strategy. The availability of dense SNP maps coupled with recent technological developments in highly paralleled SNP genotyping makes it practical to now consider the use of these markers for whole-genome genetic linkage analyses. Here, we report the findings from three successful genomewide linkage analyses of families segregating autosomal recessively inherited neonatal diabetes, craniosynostosis and dominantly inherited renal dysplasia using the Affymetrix 10K SNP array. A single locus was identified for each disease state, two of which are novel. The performance of the SNP array, both in terms of efficiency and precision, indicates that such platforms will become the dominant technology for performing genomewide linkage searches.

Amino Acid Sequence↗
PubMed2004

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.

Carcinoma, Squamous Cell↗
PubMed2005

Rapid array-based genomic characterization of a subtle structural abnormality: a patient with psychosis and der(18)t(5;18)(p14.1;p11.23).

Array-based copy number analysis has recently emerged as a rapid means of mapping complex and/or subtle chromosomal abnormalities. We have compared two such techniques, using bacterial artificial chromosome (BAC) and single nucleotide polymorphism (SNP) arrays in the evaluation of a 45-year-old woman with dysmorphic features, mental retardation, psychosis, and an unbalanced derivative chromosome 18, (46,XX, der(18)t(18;?)(p12;?)). Both array-based methods demonstrated that the additional material on chromosome 18 was of 5p origin. The 5p duplication mapped telomeric to 25.320 Mb (BAC array) and 25.607 Mb (SNP array), corresponding to the band 5p14.1. Both BAC and SNP arrays also showed a deletion involving chromosome 18p extending telomeric from 8.437 Mb (BAC array) and 8.352 Mb (SNP array), corresponding to the band 18p11.23. Molecular cytogenetic mapping using fluorescence in situ hybridization (FISH) supported the array findings and further refined the breakpoint regions, confirming that the BAC and SNP chips were both useful in this regard. Both case reports and linkage analyses have implicated these chromosomal intervals in psychosis. The array-based experiments were completed over the course of several days. While these methods do not eliminate the requirement for traditional fine-mapping, they provide an efficient approach to identifying the origin and extent of deleted and duplicated material in chromosomal rearrangements.

Chromosome Banding↗
PubMed2007

Single nucleotide polymorphism array analysis of cancer.

PURPOSE OF REVIEW: Classifying tumors and identifying therapeutic targets requires a description of the genetic changes underlying cancer. Single nucleotide polymorphism (SNP) arrays provide a high-resolution platform for describing several types of genetic changes simultaneously. With the resolution of these arrays increasing exponentially, they are becoming increasingly powerful tools for describing the genetic events underlying cancer. RECENT FINDINGS: The ability to map loss of heterozygosity (LOH) and overall copy number variations using SNP arrays is known. Techniques have recently been developed to map LOH at high resolution in the absence of paired normal data. Copy number variations described by SNP array studies are now reaching resolutions enabling the identification of novel oncogenes and tumor suppressor genes. The ability to determine allele-specific copy number changes has only recently been described. Moreover, SNP arrays offer a high-throughput platform for large-scale association studies that are likely to lead to the identification of multiple germline variants that predispose to cancer. SUMMARY: SNP arrays are an ideal platform for identifying both somatic and germline genetic variants that lead to cancer. They provide a basis for DNA-based cancer classification and help to define the genes being modulated, improving understanding of cancer genesis and potential therapeutic targets.

Chromosome Aberrations↗
PubMed2007

High-resolution analysis of allelic imbalance in neuroblastoma cell lines by single nucleotide polymorphism arrays.

Genomic copy number changes are detectable in many malignancies, including neuroblastoma, using techniques such as comparative genomic hybridization (CGH), microsatellite analysis, conventional karyotyping, and fluorescence in situ hybridization (FISH). We report the use of 10K single nucleotide polymorphism (SNP) microarrays to detect copy number changes and allelic imbalance in six neuroblastoma cell lines (IMR32, SHEP, NBL-S, SJNB-1, LS, and SKNBE2c). SNP data were generated using the GeneChip DNA Analysis and GeneChip chromosome copy number software (Affymetrix). SNP arrays confirmed the presence of all previously reported cytogenetic abnormalities in the cell lines, including chromosome 1p deletion, MYCN amplification, gain of 17q and 11q, and 14q deletions. In addition, the SNP arrays revealed several chromosome gains and losses not detected by CGH or karyotyping; these included gain of 8q21.1 approximately 24.3 and gain of chromosome 12 in IMR-32 cells; loss at 4p15.3 approximately 16.1 and loss at 16p12.3 approximately 13.2, 11q loss with loss of heterozygosity (LOH) at 11q14.3 approximately 23.3 in SJNB-1 cells; and loss at 8p21.2 approximately 23.3 and 9p21.3 approximately 22.1 with corresponding LOH in SHEP cells. The SNP arrays refined the mapping of the 2p amplicons in LS, BE2c, and IMR-32 cell lines, the 12q amplicon in LS cells, and also identified an 11q13 amplicon in LS cells. There was good concordance among SNP arrays, CGH, and karyotyping. SNP array analysis is a powerful tool for the detection of allelic imbalance in neuroblastoma and also allows identification of LOH without changes in copy number (uniparental disomy).

Allelic Imbalance↗
PubMed2004

High-resolution single-nucleotide polymorphism array and clustering analysis of loss of heterozygosity in human lung cancer cell lines.

Chromosomal loss of heterozygosity (LOH) is a common mechanism for the inactivation of tumor suppressor genes in human epithelial cancers. Hybridization to single-nucleotide polymorphism (SNP) arrays is an efficient method to detect genome-wide cancer LOH. Here, we survey LOH patterns in a panel of 33 human lung cancer cell lines using SNP array hybridization containing 1500 SNPs. We compared the LOH patterns generated by SNP array hybridization to those previously obtained by 399 microsatellite markers and find a high degree of concordance between the two methods. A novel informatics platform, dChipSNP, was used to perform hierarchical tumor clustering based on genome-wide LOH patterns. We demonstrate that this method can separate non-small-cell and small-cell lung cancer samples based on their shared LOH. Furthermore, we analysed seven human lung cancer cell lines using a novel 10 000 SNP array and demonstrate that this is an efficient and reliable method of high-density allelotyping. Using this array, we identified small regions of LOH that were not detected by lower density SNP arrays or by standard microsatellite marker panels.

Biomarkers, Tumor↗
PubMed2004

An integrated view of copy number and allelic alterations in the cancer genome using single nucleotide polymorphism arrays.

Changes in DNA copy number contribute to cancer pathogenesis. We now show that high-density single nucleotide polymorphism (SNP) arrays can detect copy number alterations. By hybridizing genomic representations of breast and lung carcinoma cell line and lung tumor DNA to SNP arrays, and measuring locus-specific hybridization intensity, we detected both known and novel genomic amplifications and homozygous deletions in these cancer samples. Moreover, by combining genotyping with SNP quantitation, we could distinguish loss of heterozygosity events caused by hemizygous deletion from those that occur by copy-neutral events. The simultaneous measurement of DNA copy number changes and loss of heterozygosity events by SNP arrays should strengthen our ability to discover cancer-causing genes and to refine cancer diagnosis.

Alleles↗