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At least 19 recordsLinked to original sources
PubMed2005

A statistical method to detect chromosomal regions with DNA copy number alterations using SNP-array-based CGH data.

Single nucleotide polymorphism (SNP) arrays were used to detect chromosomal regions with DNA copy number alterations. Current statistical methods for microarray-based comparative genomic hybridization (array-CGH) analysis generally assume certain relationships among adjacent markers on the same chromosome, and these assumptions may be questionable. For an SNP-array-based CGH study, multiple normal reference SNP arrays were collected. In order to utilize these normal reference SNP arrays, we derived an empirical distribution of signal ratios for each SNP marker. With an assumed threshold value for the overall error rate control and the defined signal ratio ranges for chromosomal amplification and deletion, we proposed a procedure to identify chromosomal alteration regions based on several bootstrapped one-sample t-tests and the false discovery rate control. When we have multiple arrays for different individuals with the same disease, our method can also be used to detect SNP markers for chromosomal alteration regions that are common among these individuals. We applied our method to a published SNP array data set for breast carcinoma cell lines. For an individual with breast cancer, numerous chromosomal alteration regions were identified. Compared to results of previous studies, our method identified more chromosomal alteration regions, with some being implicated in the literature to harbor genes associated with breast cancer. For multiple cancer arrays, our results suggested the existence of common chromosomal alteration regions. However, a high proportion of false positives also indicated that genetic variations among different individuals with breast cancer can be present.

Breast Neoplasms↗
PubMed2006

Tumor classification based on DNA copy number aberrations determined using SNP arrays.

High-density single nucleotide polymorphism (SNP) array is a recently introduced technology that genotypes more than 10,000 human SNPs on a single array. It has been shown that SNP arrays can be used to determine not only SNP genotype calls, but also DNA copy number (DCN) aberrations, which are common in solid tumors. In the past, effective cancer classification has been demonstrated using microarray gene expression data, or DCN data derived from comparative genomic hybridization (CGH) arrays. However, the feasibility of cancer classification based on DCN aberrations determined by SNP arrays has not been previously investigated. In this study, we address this issue by applying state-of-the-art classification algorithms and feature selection algorithms to the DCN aberration data derived from a public SNP array dataset. Performance was measured via leave-one-out cross-validation (LOOCV) classification accuracy. Experimental results showed that the maximum accuracy was 73.33%, which is comparable to the maximum accuracy of 76.5% based on CGH-derived DCN data reported previously in the literature. These results suggest that DCN aberration data derived from SNP arrays is useful for etiology-based tumor classification.

Algorithms↗
PubMed2005

Genome-wide SNP arrays as a diagnostic tool: clinical description, genetic mapping, and molecular characterization of Salla disease in an Old Order Mennonite population.

An Old Order Mennonite child was evaluated for gross motor delay, truncal ataxia, and slow linear growth. The diagnostic evaluation, which included sub-specialty consultations, neuroimaging, and metabolic testing, was long, costly, and did not yield a diagnosis. Recognition of a similarly affected second cousin prompted a genome-wide homozygosity mapping study using high-density single nucleotide polymorphism (SNP) arrays. SNP genotypes from two affected individuals and their parents were used to localize the disease locus to a 14.9 Mb region on chromosome 6. This region contained 55 genes, including SLC17A5, the gene encoding the lysosomal N-acetylneuraminic acid transport protein. Direct sequencing of SLC17A5 in the proband revealed homozygosity for the 115C --> T (R39C) sequence variant, the common cause of Salla disease in Finland. Three additional affected Mennonite individuals, ages 8 months to 50 years, were subsequently identified by directed molecular genetic testing. This small-scale mapping study was rapid, inexpensive, and analytically simple. In families with shared genetic heritage, genome-wide SNP arrays with relatively high marker density allow disease gene mapping studies to be incorporated into routine diagnostic evaluations.

Agenesis of Corpus Callosum↗
PubMed2005

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.

Algorithms↗
PubMed2007

A procedure for the detection of linkage with high density SNP arrays in a large pedigree with colorectal cancer.

BACKGROUND: The apparent dominant model of colorectal cancer (CRC) inheritance in several large families, without mutations in known CRC susceptibility genes, suggests the presence of so far unidentified genes with strong or moderate effect on the development of CRC. Linkage analysis could lead to identification of susceptibility genes in such families. In comparison to classical linkage analysis with multi-allelic markers, single nucleotide polymorphism (SNP) arrays have increased information content and can be processed with higher throughput. Therefore, SNP arrays can be excellent tools for linkage analysis. However, the vast number of SNPs on the SNP arrays, combined with large informative pedigrees (e.g. >35-40 bits), presents us with a computational complexity that is challenging for existing statistical packages or even exceeds their capacity. We therefore setup a procedure for linkage analysis in large pedigrees and validated the method by genotyping using SNP arrays of a colorectal cancer family with a known MLH1 germ line mutation. METHODS: Quality control of the genotype data was performed in Alohomora, Mega2 and SimWalk2, with removal of uninformative SNPs, Mendelian inconsistencies and Mendelian consistent errors, respectively. Linkage disequilibrium was measured by SNPLINK and Merlin. Parametric linkage analysis using two flanking markers was performed using MENDEL. For multipoint parametric linkage analysis and haplotype analysis, SimWalk2 was used. RESULTS: On chromosome 3, in the MLH1-region, a LOD score of 1.9 was found by parametric linkage analysis using two flanking markers. On chromosome 11 a small region with LOD 1.1 was also detected. Upon linkage disequilibrium removal, multipoint linkage analysis yielded a LOD score of 2.1 in the MLH1 region, whereas the LOD score dropped to negative values in the region on chromosome 11. Subsequent haplotype analysis in the MLH1 region perfectly matched the mutation status of the family members. CONCLUSION: We developed a workflow for linkage analysis in large families using high-density SNP arrays and validated this workflow in a family with colorectal cancer. Linkage disequilibrium has to be removed when using SNP arrays, because it can falsely inflate the LOD score. Haplotype analysis is adequate and can predict the carrier status of the family members.

Adaptor Proteins, Signal Transducing↗
PubMed2005

Comparative linkage analysis and visualization of high-density oligonucleotide SNP array data.

BACKGROUND: The identification of disease-associated genes using single nucleotide polymorphisms (SNPs) has been increasingly reported. In particular, the Affymetrix Mapping 10 K SNP microarray platform uses one PCR primer to amplify the DNA samples and determine the genotype of more than 10,000 SNPs in the human genome. This provides the opportunity for large scale, rapid and cost-effective genotyping assays for linkage analysis. However, the analysis of such datasets is nontrivial because of the large number of markers, and visualizing the linkage scores in the context of genome maps remains less automated using the current linkage analysis software packages. For example, the haplotyping results are commonly represented in the text format. RESULTS: Here we report the development of a novel software tool called CompareLinkage for automated formatting of the Affymetrix Mapping 10 K genotype data into the "Linkage" format and the subsequent analysis with multi-point linkage software programs such as Merlin and Allegro. The new software has the ability to visualize the results for all these programs in dChip in the context of genome annotations and cytoband information. In addition we implemented a variant of the Lander-Green algorithm in the dChipLinkage module of dChip software (V1.3) to perform parametric linkage analysis and haplotyping of SNP array data. These functions are integrated with the existing modules of dChip to visualize SNP genotype data together with LOD score curves. We have analyzed three families with recessive and dominant diseases using the new software programs and the comparison results are presented and discussed. CONCLUSIONS: The CompareLinkage and dChipLinkage software packages are freely available. They provide the visualization tools for high-density oligonucleotide SNP array data, as well as the automated functions for formatting SNP array data for the linkage analysis programs Merlin and Allegro and calling these programs for linkage analysis. The results can be visualized in dChip in the context of genes and cytobands. In addition, a variant of the Lander-Green algorithm is provided that allows parametric linkage analysis and haplotyping.

Family Health↗
PubMed2026

Prenatal SNP-array chromosomal microarray analysis in 3,549 pregnancies: indication-specific yields and clinical implications.

BACKGROUND: SNP-based chromosomal microarray analysis (CMA) is widely used in invasive prenatal diagnosis, yet real-world performance across contemporary referral pathways, especially in the NIPT era, remains incompletely characterized. METHODS: We retrospectively analyzed 3,549 prenatal invasive samples tested by SNP array, and evaluated diagnostic yield overall and by referral indication and ultrasound phenotype. RESULTS: In total, we identified 398 pathogenic or likely pathogenic (P/LP) variants across 386 fetuses, resulting in an overall diagnostic yield of 10.9% (386/3,549). These findings comprised 223 aneuploidies and 175 pathogenic CNVs. In contrast, variants of uncertain significance (VOUS) were detected in 12.0% (426/3,549) of cases. Diagnostic yields were heavily stratified by indication: yields peaked in NIPT high-risk referrals (38.9%) and were intermediate in ultrasound-based cases (~ 11%), but dropped significantly in the advanced maternal age (AMA; 4.2%) and serum screening (~ 5-6%) groups. Conversely, VOUS rates remained remarkably stable across all referral categories. Sub-analysis of ultrasound abnormalities revealed that multisystem anomalies conferred the highest risk (27.3%), driven predominantly by aneuploidies; among soft markers, increased nuchal translucency (NT) emerged as the strongest predictor of chromosomal pathology. CONCLUSIONS: In our cohort, SNP-array identified clinically actionable findings in 10.9% of cases. NIPT enriched diagnostic yields, particularly for aneuploidies, and NT thickness was strongly associated with pathogenic findings. These results support an indication-based approach to genomic testing, with NIPT as a triage tool for aneuploidy and CMA for high-risk populations, while improving VOUS counseling.

Humans↗
PubMed2006

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.

Algorithms↗
PubMed2006

Genome-wide analysis of DNA copy number changes and LOH in CLL using high-density SNP arrays.

Recurrent genomic aberrations are important prognostic parameters in chronic lymphocytic leukemia (CLL). High-resolution 10k and 50k Affymetrix SNP arrays were evaluated as a diagnostic tool for CLL and revealed chromosomal imbalances in 65.6% and 81.5% of 70 consecutive cases, respectively. Among the prognostically important aberrations, the del13q14 was present in 36 (51.4%), trisomy 12 in 9 (12.8%), del11q22 in 9 (12.8%), and del17p13 in 4 cases (5.7%). A prominent clustering of breakpoints on both sides of the MIRN15A/MIRN16-1 genes indicated the presence of recombination hot spots in the 13q14 region. Patients with a monoallelic del13q14 had slower lymphocyte growth kinetics (P=.002) than patients with biallelic deletions. In 4 CLL cases with unmutated VH genes, a common minimal 3.5-Mb gain of 2p16 spanning the REL and BCL11A oncogenes was identified, implicating these genes in the pathogenesis of CLL. Twenty-four large (>10 Mb) copy-neutral regions with loss of heterozygosity were identified in 14 cases. These regions with loss of heterozygosity are not detectable by alternative methods and may harbor novel imprinted genes or loss-of-function alleles that may be important for the pathogenesis of CLL. Genomic profiling with SNP arrays is a convenient and efficient screening method for simultaneous genome-wide detection of chromosomal aberrations.

Adult↗
PubMed2005

Allele-specific amplification in cancer revealed by SNP array analysis.

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ.

Alleles↗
PubMed2004

dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data.

MOTIVATION: Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed. RESULT: Here, we report automated methods for pooling SNP array replicates to make LOH calls, visualizing SNP and LOH data along chromosomes in the context of genes and cytobands, making statistical inference to identify shared LOH regions, clustering samples based on LOH profiles and correlating the clustering results to clinical variables. Application of these methods to prostate and breast cancer datasets generates biologically important results. AVAILABILITY: The software module dChipSNP implementing these methods is available at http://biosun1.harvard.edu/complab/dchip/snp/ SUPPLEMENTARY INFORMATION: The breast cancer data are provided by Andrea L. Richardson, Zhigang C. Wang and James D. Iglehart.

Algorithms↗
PubMed2004

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.

Chromosomes, Human, Pair 6↗
PubMed2006

Visualization of genomic aberrations using Affymetrix SNP arrays.

MOTIVATION: DNA copy number aberrations are frequently found in different types of cancer. Recent developments of microarray-based approaches have broadened the knowledge on number and structure of such aberrations. High-density single nucleotide polymorphism (SNP) microarrays provide an extremely high resolution with up to 500,000 SNPs per genome. Owing to the enormous amount of data the detection of common aberrations in large datasets is a great challenge. We describe a novel open source software tool--IdeogramBrowser--which was specifically designed for use with the Affymetrix SNP arrays. It provides an interactive karyotypic visualization of multiple aberration profiles and direct links to GeneCards. Visualization of consensus regions together with gene representation allows the explorative assessment of the data. AVAILABILITY: IdeogramBrowser and its source code are freely available under a creative commons license and can be obtained from http://www.informatik.uni-ulm.de/ni/staff/HKestler/ideo/. IdeogramBrowser is a platform independent Java application.

Algorithms↗
PubMed2006

Homozygosity mapping with SNP arrays identifies TRIM32, an E3 ubiquitin ligase, as a Bardet-Biedl syndrome gene (BBS11).

The identification of mutations in genes that cause human diseases has largely been accomplished through the use of positional cloning, which relies on linkage mapping. In studies of rare diseases, the resolution of linkage mapping is limited by the number of available meioses and informative marker density. One recent advance is the development of high-density SNP microarrays for genotyping. The SNP arrays overcome low marker informativity by using a large number of markers to achieve greater coverage at finer resolution. We used SNP microarray genotyping for homozygosity mapping in a small consanguineous Israeli Bedouin family with autosomal recessive Bardet-Biedl syndrome (BBS; obesity, pigmentary retinopathy, polydactyly, hypogonadism, renal and cardiac abnormalities, and cognitive impairment) in which previous linkage studies using short tandem repeat polymorphisms failed to identify a disease locus. SNP genotyping revealed a homozygous candidate region. Mutation analysis in the region of homozygosity identified a conserved homozygous missense mutation in the TRIM32 gene, a gene coding for an E3 ubiquitin ligase. Functional analysis of this gene in zebrafish and expression correlation analyses among other BBS genes in an expression quantitative trait loci data set demonstrate that TRIM32 is a BBS gene. This study shows the value of high-density SNP genotyping for homozygosity mapping and the use of expression correlation data for evaluation of candidate genes and identifies the proteasome degradation pathway as a pathway involved in BBS.

Animals↗
PubMed2006

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.

Alleles↗
PubMed2005

Application of genomewide SNP arrays for detection of simulated susceptibility loci.

The prospect of SNP-based genomewide association analysis has been extensively discussed, but practical experiences remain limited. We performed an association study using a recently developed array of 11,555 SNPs distributed throughout the human genome. A total of 104 DNA samples were hybridized to these chips with an average call rate of 97% (range 85.3-98.6%). The resulting genomewide scans were applied to distinguish between carriers and noncarriers of 37 test variants, used as surrogates for monogenic disease traits. The test variants were not contained in the chip and had been determined by other methods. Without adjustment for multiple testing, the procedure detected 24% of the test variants, but the positive predictive value was low (2%). Adjustment for multiple testing eliminated most false-positive associations, but the share of true positive associations decreased to 10-12%. We also simulated fine-mapping of susceptibility loci by restricting testing to the immediate neighborhood of test variants (+/-5 Mb). This increased the proportion of correctly identified test variants to 22-27%. Simulation of a bigenic inheritance reduced the sensitivity to 1%. Similarly adverse effect had reduction of allelic penetrance. In summary, we demonstrate the feasibility and considerable specificity of SNP array-based association studies to detect variants underlying monogenic, highly penetrant traits. The outcome is affected by allelic frequencies of chip SNPs, by the ratio between simulated "cases" and "controls," and by the degree of linkage disequilibrium. A major improvement is expected from raising the density of the SNP array.

Alleles↗
PubMed2004

Molecular karyotyping using an SNP array for genomewide genotyping.

BACKGROUND: Chromosomal imbalances are a major cause of developmental defects as well as cancer and often constitute the key in identification of novel disease related genes. Classical cytogenetic methods are limited in resolution and dependent on highly skilled labour, while methods with higher resolution, based on molecular cytogenetics approaches such as matrix CGH, are not widely available. METHODS: We have developed and evaluated a method we term "molecular karyotyping", using readily available and easy to handle oligonucleotide arrays originally designed for parallel genomewide analysis of over 10,000 SNPs. We show that we can easily and reliably detect unbalanced chromosomal aberrations of various sizes from as little as 250 ng of DNA on a single microarray, based on fluorescence intensity information from clusters of SNPs. RESULTS: We determined the resolution of this method through analysis of 20 trios with 21 previously confirmed subtle aberrations sizing between 0.2 and 13 Mb. Duplications and deletions of at least 5 Mb in size were reliably detectable, but detection of smaller aberrations was dependent on the number of SNPs they contained, thus seven of 10 different deletions analysed, with sizes ranging from 0.2 to 3.7 Mb, were not detectable due to insufficient SNP densitiy in the respective region. CONCLUSIONS: Deduction of reliable cut off levels for array peaks in our series of well characterised patients allows the use of the GeneChip Mapping 10K SNP array for performing rapid molecular karyotyping from small amounts of DNA for the detection of even subtle deletions and duplications with high sensitivity and specificity.

Chromosome Aberrations↗
PubMed2007

SNPchip: R classes and methods for SNP array data.

UNLABELLED: High-density single nucleotide polymorphism microarrays (SNP chips) provide information on a subject's genome, such as copy number and genotype (heterozygosity/homozygosity) at a SNP. While fluorescence in situ hybridization and karyotyping reveal many abnormalities, SNP chips provide a higher resolution map of the human genome that can be used to detect, e.g., aneuploidies, microdeletions, microduplications and loss of heterozygosity (LOH). As a variety of diseases are linked to such chromosomal abnormalities, SNP chips promise new insights for these diseases by aiding in the discovery of such regions, and may suggest targets for intervention. The R package SNPchip contains classes and methods useful for storing, visualizing and analyzing high density SNP data. Originally developed from the SNPscan web-tool, SNPchip utilizes S4 classes and extends other open source R tools available at Bioconductor. This has numerous advantages, including the ability to build statistical models for SNP-level data that operate on instances of the class, and to communicate with other R packages that add additional functionality. AVAILABILITY: The package is available from the Bioconductor web page at www.bioconductor.org. SUPPLEMENTARY INFORMATION: The supplementary material as described in this article (case studies, installation guidelines and R code) is available from http://biostat.jhsph.edu/~iruczins/publications/sm/

Models, Statistical↗