Snp array 6

Manufactured by Thermo Fisher Scientific

Sourced in United States

The SNP Array 6.0 is a high-density single nucleotide polymorphism (SNP) genotyping array developed by Thermo Fisher Scientific. It features over 900,000 SNP markers and more than 900,000 additional probes for the detection of copy number variations (CNVs). The SNP Array 6.0 is designed to provide comprehensive genome-wide coverage for a wide range of genetic research applications.

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Lab products found in correlation

Genotyping console 2, by Thermo Fisher Scientific (2 mentions) Omni 2, by Thermo Fisher Scientific (2 mentions) 450k beadchip idat, by Illumina (1 mentions) Human genome u133 plus 2.0 array, by Thermo Fisher Scientific (1 mentions) Agilent 2100 bioanalyzer, by Agilent Technologies (1 mentions) U133 plus 2.0 array, by Thermo Fisher Scientific (1 mentions) Omniexpress, by Illumina (1 mentions) Exome chip, by Illumina (1 mentions) Immunochip, by Illumina (1 mentions) Humanomni1 quad beadchip, by Illumina (1 mentions) Hiseq 2000 platform, by Illumina (1 mentions) Ht 12 v3 beadchip, by Illumina (1 mentions) Genome wide human snp array 6, by Thermo Fisher Scientific (1 mentions) Human snp array 6, by Thermo Fisher Scientific (1 mentions) Qiamp dna mini kit, by Qiagen (1 mentions) Expression beadchip, by Illumina (1 mentions)

27 protocols using snp array 6

Genome-wide association analysis of schizophrenia and bipolar disorder

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The sample information is summarized in Supplementary Table S1. The dbGaP data (http://www.ncbi.nlm.nih.gov/gap)^{30 (link), 31 (link), 32 (link)} were used as the discovery sample, derived from 2416 SZ patients, 592 BD patients and 2393 controls of EA, as well as 998 SZ patients, 121 BD patients and 822 controls of AA (see supplementary for more details). For all the dbGaP data, DNA was extracted from B Lymphoblastoid Cell Lines transformed by Epstein–Barr virus and genotyping was conducted using Affymetrix SNP Array 6.0. The WTCCC data (https://www.ebi.ac.uk/ega/home)^{19 (link), 22 (link), 28 (link), 33 (link)} were used for replication, where the SZ data set (EGAS00000000118) included 2491 controls and 2127 patients, and the BD data set (EGAS00000000001) included 1456 controls and 1845 patients. For both SZ and BD data, DNA was extracted from white blood cells. Regarding genotyping, Affymetrix SNP Array 6.0 was used for the SZ data set, whereas Affymetrix Mapping 500 K was used for the BD data set.

Chen J., Calhoun V.D., Perrone-Bizzozero N.I., Pearlson G.D., Sui J., Du Y, & Liu J. (2016). A pilot study on commonality and specificity of copy number variants in schizophrenia and bipolar disorder. Translational Psychiatry, 6(5), e824-.

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Identifying Genetic Variants Using Computational and Microarray Approaches

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Two methods were used to identify CNVs: computational CNV prediction and microarray CNV detection. CNV prediction was initially performed for the probands. Microarray CNV detection was applied in selected cases where no disease-causing variants were detected after exome sequencing or when CNV prediction data indicated the presence of a relevant CNV (see Supplementary Methods for details). One third of probands had been evaluated via a diagnostic CNV microarray prior to inclusion via 180K aCGH (Agilent technologies, CA, USA) or SNP Array 6.0 (Affymetrix, CA, USA), which did not provide a definitive diagnosis by itself.

Stray-Pedersen A., Sorte H.S., Samarakoon P., Gambin T., Chinn I.K., Akdemir Z.H., Erichsen H.C., Forbes L.R., Gu S., Yuan B., Jhangiani S.N., Muzny D.M., Rødningen O.K., Sheng Y., Nicholas S.K., Noroski L.M., Seeborg F.O., Davis C., Canter D., Mace E.M., Vece T., Allen C.E., Abhyankar H.A., Boone P., Beck C.R., Wiszniewski W.K., Fevang B., Aukrust P., Tjønnfjord G.E., Gedde-Dahl T., Hjorth-Hansen H., Dybedal I., Nordøy I., Jørgensen S.F., Abrahamsen T.G., Øverland T., Bechensteen A.G., Skogen V., Osnes L.T., Kulseth M.A., Prescott T.E., Rustad C.F., Heimdal K.R., Belmont J.W., Rider N., Chinen J., Cao T., Smith E., Caldirola M.S., Bezrodnik L., Reyes S.O., Rosales F.J., Guerrero D., Pedroza L.A., Poli C.M., Franco J.L., Vargas C.M., Becerra J.C., Wright N., Issekutz T.B., Issekutz A.C., Abbott J., Caldwell J.W., Bayer D.K., Chan A.Y., Aiuti A., Cancrini C., Holmberg E., West C., Burstedt M., Karaca E., Yesil G., Artac H., Bayram Y., Atik M.M., Eldomery M.K., Ehlayel M.S., Jolles S., Flatø B., Bertuch A.A., Hanson I.C., Zhang V.W., Wong L.J., Hu J., Walkiewicz M., Yang Y., Eng C., Boerwinkle E., Gibbs R.A., Shearer W.T., Lyle R., Orange J.S, & Lupski J.R. (2016). Primary immunodeficiency diseases – genomic approaches delineate heterogeneous Mendelian disorders. The Journal of allergy and clinical immunology, 139(1), 232-245.

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Comprehensive Molecular Profiling of Tumors

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Molecular classification, MSI and mutational analysis of tumor-associated genes (APC, TP53, KRAS and B-Raf^V600E), as well as DNA methylation in CIMP-sensitive promoters was done as described [12 (link), 13 (link), 15 (link)]. All data including staging information compiled from the clinical charts are summarized in Table 1. To classify CIMP, a combined panel covering eight markers was applied. Analyzed markers resulted from those originally described by [16 (link), 17 (link)]. On a basis of this panel, tumors with 1–5/8 methylated promoters are being classified as CIMP-L and with 6–8/8 methylated promoters as CIMP-H [18 (link)]. Chromosomal instability (CIN) was assessed using SNP Array 6.0 from Affymetrix (Cleveland, OH) according to manufacturer’s instructions.

Maletzki C., Huehns M., Knapp P., Waukosin N., Klar E., Prall F, & Linnebacher M. (2015). Functional Characterization and Drug Response of Freshly Established Patient-Derived Tumor Models with CpG Island Methylator Phenotype. PLoS ONE, 10(11), e0143194.

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Lung Cancer Subtype Profiling Protocol

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Between January 2016 and June 2019, 51 patients diagnosed with LUAD, LUSC, or SCLC were enrolled (Additional file 1: Table S1). Classification was executed according to the 2016 World Health Organization’s guidelines. When available, results were compared with FFPE tissue (n = 39). SBs were mostly taken at primary diagnosis, whilst LBs were sometimes drawn shortly before starting second-line treatment (Additional file 1: Table S2). Negative controls included LBs from healthy subjects (females from routine non-invasive prenatal testing (NIPT) and healthy males; n = 60) and FFPE samples from benign tissue (n = 9). Other than these in-house cases, public segmental copy number data, derived from SNP array 6.0 (Affymetrix, Santa Clara, CA) experiments, complemented with clinical information and a list of significantly aberrant loci per histological subtype, were collected from the supplement of the study of Seidel et al., which presents the collective effort from the consortia “Clinical Lung Cancer Genome Project” (CLCGP) and “Network Genomic Medicine” (NGM) [16 ]. This dataset was filtered on histology (exclusively LUAD, LUSC, and SCLC; n = 843).

Raman L., Van der Linden M., Van der Eecken K., Vermaelen K., Demedts I., Surmont V., Himpe U., Dedeurwaerdere F., Ferdinande L., Lievens Y., Claes K., Menten B, & Van Dorpe J. (2020). Shallow whole-genome sequencing of plasma cell-free DNA accurately differentiates small from non-small cell lung carcinoma. Genome Medicine, 12, 35.

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Mutational Signature Analysis of LUAD and LUSC

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Somatic mutation analysis of LUAD and LUSC tumor samples was based on whole exome sequencing (WES) data. We extracted all the identified mutations and their basic information from the publicly available WES mutation annotation format (MAF) files. R package deconstructSigs (version 1.8.0) was employed to infer the contribution of known mutational signatures to the observed mutation profile within each tumor sample (Rosenthal et al., 2016 (link)). We considered the 30 mutational signatures cataloged at COSMIC (http://cancer.sanger.ac.uk/cosmic/signatures) (Alexandrov et al., 2015 (link)). First, the frequency of mutations observed in the 96 tri-nucleotide contexts was calculated for each sample using the “mut.to.sigs.input” function. Then, weights of the 30 reference mutational signatures were estimated using “whichSignatures” function, with trinucleotide’s occurence normalized from exome to genome level. Based on the initial analysis provided at COSMIC, there are seven major mutational signatures presented in LUAD samples and five major signatures in LUSC, which are possibly associated with smoking, age, APOBEC, etc. To explore the potential role of germline variants in lung cancer immunity development, we acquired SNPs genotyped in TCGA with the Affymetrix SNP Array 6.0 from the GDC Legacy Archive portal (https://portal.gdc.cancer.gov/legacy-archive).

Yu X, & Wang X. (2018). Tumor immunity landscape in non-small cell lung cancer. PeerJ, 6, e4546.

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Comparison of Copy Number Profiling

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A total of 31 kidney cancer samples (KIRC) from the Firehose database on GDC’s TCGA were selected based on the status of their VHL-coding region on the short arm of chromosome 3 (3p). All 31 samples were selected to contain deep 3p deletions associated with clear cell renal cell carcinomas. The corresponding Illumina 450k BeadChip IDAT files were downloaded from TCGA. A further 50 low-grade glioma (LGG), subtype oligodendroglioma, samples were also downloaded from the Firehose database. Twenty-five samples are histologically defined oligodendrogliomas and contained codeletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p/19q), and the other twenty-five samples were astrocyte-like oligodendrogliomas that were copy-number neutral in these regions (Li et al., 2016 (link)). The Affymetrix SNP Array 6.0 (“gold standard”) copy number segmentation data corresponding to the above data was also downloaded from TCGA.
All testing was performed on a Dell Precision 5,820 Tower X-series workstation running Windows 10 Pro 64bit and R 4.1.2. 128 GB of useable RAM and an Intel(R) Core(TM) i9-10900X CPU x64 processor @ 3.70 GHz. All analyses were performed only using a single core as some packages set multiple cores for the analysis; we wanted to ensure equal computational resources were allocated for each routine.

Mariani M.P., Chen J.A., Zhang Z., Pike S.C, & Salas L.A. (2022). MethylMasteR: A Comparison and Customization of Methylation-Based Copy Number Variation Calling Software in Cancers Harboring Large Scale Chromosomal Deletions. Frontiers in Bioinformatics, 2, 859828.

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SNP Analysis of Normal and Tumor Tissues

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For SNP analysis, SNPs were obtained using Affymetrix® SNP Array 6.0 (each has more than 906,600 SNPs). After excluding SNPs with allele frequency <1% (157,703 SNPs) or call rate <90% (123 SNPs), 748,774 SNPs were further analyzed by McNemar-Bowker’s test to examine the difference of genotypes between normal and tumor tissues from the same subject. SNPs were coded according to the number of minor alleles, i.e., AA, Aa and aa, denoted as 0, 1, 2, respectively. The nonparametric McNemar-Bowker’s test was applied to examine the association between SNPs and tissues. The analyses were done in R version 2.9.0.

Lai L.C., Tsai M.H., Chen P.C., Chen L.H., Hsiao J.H., Chen S.K., Lu T.P., Lee J.M., Hsu C.P., Hsiao C.K, & Chuang E.Y. (2014). SNP rs10248565 in HDAC9 as a novel genomic aberration biomarker of lung adenocarcinoma in non-smoking women. Journal of Biomedical Science, 21(1), 24.

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Transcriptome and Genotype Profiling Protocol

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Genomic DNA was extracted using the standard phenol-chloroform extraction protocol [24 (link)]. The Affymetrix SNP array 6.0 (Affymetrix, Santa Clara, CA, USA) was used according to the manufacturer’s instructions. One hundred and six samples were normalized with the Genotyping console 2.0 software (Affymetrix). Total RNAs were extracted as previously described [21 (link)]. RNA quality was checked on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Samples were then analyzed on Human Genome U133 Plus 2.0 array (Affymetrix), according to the manufacturer’s procedures. All expression data obtained are publicly available on https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71118.

Mauduit O., Delcroix V., Lesluyes T., Pérot G., Lagarde P., Lartigue L., Blay J.Y, & Chibon F. (2019). Recurrent DMD Deletions Highlight Specific Role of Dp71 Isoform in Soft-Tissue Sarcomas. Cancers, 11(7), 922.

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MELK Expression Profiles in Gynecological Cancers

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MELK CNA and mRNA expression profiles in breast, ovarian and endometrial cancer cell lines (n = 135) from the CCLE were downloaded from cBioPortal. In this dataset the DNA CNA was detected by Affymetrix SNP Array 6.0, and gene expression levels were detected by Affymetrix U133 Plus 2.0 Arrays. Queries on CNA (GISTIC2 method) and mRNA expression (RNA-seq RSEM) profiles for breast, endometrial, and ovarian cancers were accessed and analyzed using the cBioPortal (http://www.cbioportal.org) as recommended [28 (link)].

Hardeman A.A., Han Y.J., Grushko T.A., Mueller J., Gomez M.J., Zheng Y, & Olopade O.I. (2022). Subtype-specific expression of MELK is partly due to copy number alterations in breast cancer. PLoS ONE, 17(6), e0268693.

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Allele-Specific Copy Number Analysis

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The allele-specific copy number of the diagnostic sample from patient #10793 was obtained using Affymetrix SNP Array 6.0 with 680-bp median intermarker spacing. For this, 500 ng of genomic DNA isolated from the diagnostic and remission samples was used as input. Raw signal intensities were analyzed using Partek Genomics Suite. First, probe intensities were adjusted for a number of properties that are correlated with intensity, including fragment length, GC content, and other sequence-based hybridization bias. After quantile normalization, paired analysis was performed to generate allele-specific copy numbers by comparing the diagnostic sample to the remission sample.

Li Y., Buijs-Gladdines J.G., Canté-Barrett K., Stubbs A.P., Vroegindeweij E.M., Smits W.K., van Marion R., Dinjens W.N., Horstmann M., Kuiper R.P., Buijsman R.C., Zaman G.J., van der Spek P.J., Pieters R, & Meijerink J.P. (2016). IL-7 Receptor Mutations and Steroid Resistance in Pediatric T cell Acute Lymphoblastic Leukemia: A Genome Sequencing Study. PLoS Medicine, 13(12), e1002200.

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