Coverage and characteristics of the Affymetrix GeneChip Human Mapping 100K SNP set.
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ABSTRACT:
SUBMITTER: Nicolae DL
PROVIDER: S-EPMC1456318 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHigh throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip Mapping 10K Array from Affymetrix is the automated SNP calling algorithm. The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.ResultsA database of 948 individuals genotyped on the GeneChip Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at http://www.tgen.org/neurogenomics/data.ConclusionThe correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip without sacrificing accuracy, thereby increasing the amount of valid data generated.
| S-EPMC1280925 | biostudies-literature
Project description:BackgroundGenome-wide association studies (GWAS) aim to identify genetic variants (usually single nucleotide polymorphisms [SNPs]) across the entire human genome that are associated with phenotypic traits such as disease status and drug response. Highly accurate and reproducible genotype calling are paramount since errors introduced by calling algorithms can lead to inflation of false associations between genotype and phenotype. Most genotype calling algorithms currently used for GWAS are based on multiple arrays. Because hundreds of gigabytes (GB) of raw data are generated from a GWAS, the samples are typically partitioned into batches containing subsets of the entire dataset for genotype calling. High call rates and accuracies have been achieved. However, the effects of batch size (i.e., number of chips analyzed together) and of batch composition (i.e., the choice of chips in a batch) on call rate and accuracy as well as the propagation of the effects into significantly associated SNPs identified have not been investigated. In this paper, we analyzed both the batch size and batch composition for effects on the genotype calling algorithm BRLMM using raw data of 270 HapMap samples analyzed with the Affymetrix Human Mapping 500 K array set.ResultsUsing data from 270 HapMap samples interrogated with the Affymetrix Human Mapping 500 K array set, three different batch sizes and three different batch compositions were used for genotyping using the BRLMM algorithm. Comparative analysis of the calling results and the corresponding lists of significant SNPs identified through association analysis revealed that both batch size and composition affected genotype calling results and significantly associated SNPs. Batch size and batch composition effects were more severe on samples and SNPs with lower call rates than ones with higher call rates, and on heterozygous genotype calls compared to homozygous genotype calls.ConclusionBatch size and composition affect the genotype calling results in GWAS using BRLMM. The larger the differences in batch sizes, the larger the effect. The more homogenous the samples in the batches, the more consistent the genotype calls. The inconsistency propagates to the lists of significantly associated SNPs identified in downstream association analysis. Thus, uniform and large batch sizes should be used to make genotype calls for GWAS. In addition, samples of high homogeneity should be placed into the same batch.
| S-EPMC2537568 | biostudies-literature
Project description:allele call files from analysis of NCI60 cell line DNA on 100K SNP arrays. Keywords = NCI60, SNP array, cancer cell line Keywords: other
2005-07-02 | GSE2520 | GEO
Project description:BackgroundAffymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change between analyzed groups. There are many different preprocessing algorithms for summarizing Affymetrix data. The main goal of these methods is to remove effects of non-specific hybridization, and to optimally combine information from multiple probes annotated to the same transcript. The methods are benchmarked by comparison with reference methods, such as quantitative reverse-transcription PCR (qRT-PCR).ResultsWe present a comprehensive analysis of agreement between Affymetrix GeneChip and qRT-PCR results. We analyzed the influence of filtering by fraction Present calls introduced by J.N. McClintick and H.J. Edenberg (2006) and 2 mapping procedures: updated probe sets definitions proposed by Dai et al. (2005) and our "naive mapping" method. Because of evolution of genome sequence annotations since the time when microarrays were designed, we also studied the effect of the annotation release date. These comparisons were prepared for 6 popular preprocessing algorithms (MAS5, PLIER, RMA, GC-RMA, MBEI, and MBEImm) in the 2 above-mentioned types of studies. We used data sets from 6 independent biological experiments. As a measure of reproducibility of microarray and qRT-PCR values, we used linear and rank correlation coefficients.ConclusionsWe show that filtering by fraction Present calls increased correlations for all 6 preprocessing algorithms. We observed the difference in performance of PM-MM and PM-only methods: using MM probes increased correlations in fold change studies, but PM-only methods proved to perform better in detection of differential expression. We recommend using GC-RMA for detection of differential expression and PLIER for estimation of fold change. The use of the more recent annotation improves the results in both types of studies, encouraging re-analysis of old data.
| S-EPMC2841208 | biostudies-literature
Project description:High-density single nucleotide polymorphism (SNP) arrays were used to investigate genome-wide copy number (CN) alterations and loss of heterozygosity (LOH) in glioblastomas (GBM) patients; our aim focused on the identification and detailed characterization of the genetic alterations of the chromosomes altered in these tumors and the identification of subgroups of GBM with distinct cytogenetic patterns of alteration for the affected chromosomes potentially associated with the behavior of the disease. Overall, gains of chromosome 7, losses of chromosomes 9p and 10 were the most frequent chromosomal alterations. The 7p11.2 region was amplified in several cases. Based on CN alterations for chromosomes 7, 9 and 10, five different cytogenetic patterns with a significant impact on patient survival were identified; noteworthy, cases with EGFR amplification showed a better survival, specifically among patients older than 60 years. In addition, our results provide further evidence about the relevance of the EGFR, CDKN2A/B, MTAP genes, and other genes coded in chromosome 10 in the pathogenesis of GBM. Altogether, our results confirm the cytogenetic heterogeneity of GBM and suggest that stratification of these tumors into genetic subsets based on the combined assessment of cytogenetic alterations involving chromosomes 7, 9 and 10, may contribute to the prognostic evaluation of GBM.
2012-11-30 | GSE42631 | GEO
Project description:BackgroundMicroarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality.ResultsWe begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies.ConclusionsWe find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.
| S-EPMC3097162 | biostudies-literature
Project description:Cytogenetic profiles of 50 meningiomas using high-density GeneChip Mapping 500K set and Genome-Wide Human SNP 6.0 Array in the tumor tissues and in the peripheral blood of the same patients. A total of two hundred 500k arrays (100 tumor samples and 100 blood samples) and 14 SNP6.0 arrays (7 tumour samples and 7 peripheral blood samples) were studied to explore the most common recurrent chromosomal abnormalities (gains and losses) in meningiomas. Our results confirm that del(22q) (52%) and del(1p) (16%) (common deleted regions: 22q11.21-22q13.3. and 1p31.2-p36.33) are the most frequent abnormalities. Additionally, recurrent monosomy 14 (8%), del(6p) (10%), del(7p) (10%) and del(19p) (6%) were also observed, while copy number variation (CNV) patterns consistent with recurrent chromosome gains, gene amplification was absent or rare. Based on their overall SNP profiles meningiomas could be classified into: i) diploid cases, ii) meningiomas with a single chromosome change (e.g. monosomy 22/del(22q) and iii) tumours with ≥2 altered chromosomes.
2012-11-30 | GSE42624 | GEO
Project description:Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.
| S-EPMC3431718 | biostudies-literature
Project description:Cytogenetic profiles of 50 meningiomas using high-density GeneChip Mapping 500K set and Genome-Wide Human SNP 6.0 Array in the tumor tissues and in the peripheral blood of the same patients. A total of two hundred 500k arrays (100 tumor samples and 100 blood samples) and 14 SNP6.0 arrays (7 tumour samples and 7 peripheral blood samples) were studied to explore the most common recurrent chromosomal abnormalities (gains and losses) in meningiomas. Our results confirm that del(22q) (52%) and del(1p) (16%) (common deleted regions: 22q11.21-22q13.3. and 1p31.2-p36.33) are the most frequent abnormalities. Additionally, recurrent monosomy 14 (8%), del(6p) (10%), del(7p) (10%) and del(19p) (6%) were also observed, while copy number variation (CNV) patterns consistent with recurrent chromosome gains, gene amplification was absent or rare. Based on their overall SNP profiles meningiomas could be classified into: i) diploid cases, ii) meningiomas with a single chromosome change (e.g. monosomy 22/del(22q) and iii) tumours with M-bM-^IM-%2 altered chromosomes. 500K SNP mapping set array and Genome-Wide Human SNP 6.0 Array were used to profile 50 meningiomas with matched blood DNA samples. Loss of heterozygosity (LOH) and copy number abnormality (CNA) profiles were derived from each tumour-blood pair. In seven tumors, both types of arrays were assessed.
2012-11-30 | E-GEOD-42624 | biostudies-arrayexpress
Project description:BackgroundMany of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.ResultsWe assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results.ConclusionOur results indicate that a biologically diverse reference database can be used to train a model for estimating probe set intensities of exclusive test sets, while retaining the overall characteristics of the base algorithm. Although the results we present are specific for RMA, similar versions of other multi-array normalization and summarization schemes can be developed.
| S-EPMC1624855 | biostudies-literature