Snp 6.0 microarray

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The SNP 6.0 microarray is a high-throughput genotyping tool designed to interrogate genetic variation across the human genome. It is capable of simultaneously detecting and analyzing hundreds of thousands of single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) in a single experiment.

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Genotyping console, by Thermo Fisher Scientific (3 mentions) Chas 2, by Thermo Fisher Scientific (1 mentions) Hg133 plus 2, by Thermo Fisher Scientific (1 mentions) Cytoscan hd array, by Thermo Fisher Scientific (1 mentions) Ht 12 v3 microarray, by Illumina (1 mentions)

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SNP 6.0 (30 citations) 6.0 microarrays (4 citations) 6.0 SNP microarrays (2 citations) Genome-Wide Human SNP 6.0 microarrays (2 citations) SNP 6.0 microarray platform (2 citations) SNP 6.0 microarray data (2 citations)

14 protocols using snp 6.0 microarray

Cross-Disorder Genetic Etiology Investigation

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This project was a part of a multilateral collaborative project to investigate genetic etiology across four neurodevelopmental disorders: ADHD, ASD, OCD, and SCZ. This study was approved by the Research Ethics Board at The Hospital for Sick Children. A written informed consent was obtained from all participants or substitute decision makers. CNVs were detected on the same high-resolution microarray platform. The criteria for meeting a diagnosis of ASD, ADHD, OCD, or SCZ were detailed in our previous publications^{8 (link),10 (link),13 (link),23 (link),24 (link)} with a few modifications for ADHD (see Supplementary Information). Data from all OCD individuals and 139/435 (32%) of the SCZ cohort had been previously published,^{11 (link),13 (link)} but we included them here for comparative purposes (Supplementary Information). ADHD and ASD samples were not previously published. Additional supportive evidence for cross-disorder associations of selected CNVs came from our previously published schizophrenia cohorts^{11 (link),24 (link)} and an additional ADHD cohort^{10 (link)}; all were genotyped on the Affymetrix SNP 6.0 microarray (Table 3).

Zarrei M., Burton C.L., Engchuan W., Young E.J., Higginbotham E.J., MacDonald J.R., Trost B., Chan A.J., Walker S., Lamoureux S., Heung T., Mojarad B.A., Kellam B., Paton T., Faheem M., Miron K., Lu C., Wang T., Samler K., Wang X., Costain G., Hoang N., Pellecchia G., Wei J., Patel R.V., Thiruvahindrapuram B., Roifman M., Merico D., Goodale T., Drmic I., Speevak M., Howe J.L., Yuen R.K., Buchanan J.A., Vorstman J.A., Marshall C.R., Wintle R.F., Rosenberg D.R., Hanna G.L., Woodbury-Smith M., Cytrynbaum C., Zwaigenbaum L., Elsabbagh M., Flanagan J., Fernandez B.A., Carter M.T., Szatmari P., Roberts W., Lerch J., Liu X., Nicolson R., Georgiades S., Weksberg R., Arnold P.D., Bassett A.S., Crosbie J., Schachar R., Stavropoulos D.J., Anagnostou E, & Scherer S.W. (2019). A large data resource of genomic copy number variation across neurodevelopmental disorders. NPJ Genomic Medicine, 4, 26.

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Admixture Analysis of Native American Populations

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Genotype data from 112 CEU and 106 YRI from 1KG project; 401 NA samples from Moreno-Estrada et al.^{8 (link)} (described in Supplementary Table 8) and 312 additional NA samples (103 Nahuas, 62 Totonacas, 49 Zapotecas, and 98 Mayas) genotyped with SNP 6.0 microarray (Affymetrix), were used for population admixture analysis. A total of 322,098 autosome-wide SNPs shared among these populations were used to generate MDS plots based on genome-wide identity by state pairwise distances as implemented in PLINK^{52 (link)}. Supplementary Figs. 6 and 7 show the estimated individual ancestry proportions for K = 3 to K = 10 in 312 NA samples, continental reference populations, and the 12 NA and 3 Mexican Mestizo whole genomes using ADMIXTURE^{53 (link)}. The fit of different values of K was assessed using cross-validation (CV) procedures, where K = 10 showed the lowest CV error (Supplementary Fig. 8).

Romero-Hidalgo S., Ochoa-Leyva A., Garcíarrubio A., Acuña-Alonzo V., Antúnez-Argüelles E., Balcazar-Quintero M., Barquera-Lozano R., Carnevale A., Cornejo-Granados F., Fernández-López J.C., García-Herrera R., García-Ortíz H., Granados-Silvestre Á., Granados J., Guerrero-Romero F., Hernández-Lemus E., León-Mimila P., Macín-Pérez G., Martínez-Hernández A., Menjivar M., Morett E., Orozco L., Ortíz-López G., Pérez-Villatoro F., Rivera-Morales J., Riveros-McKay F., Villalobos-Comparán M., Villamil-Ramírez H., Villarreal-Molina T., Canizales-Quinteros S, & Soberón X. (2017). Demographic history and biologically relevant genetic variation of Native Mexicans inferred from whole-genome sequencing. Nature Communications, 8, 1005.

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Frequency of KANSL1 Microduplication in Cohorts

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To determine the frequency of KANSL1 microduplication in patients without 22q11.2DS, a second set of data from Affymetrix SNP 6.0 microarray originating from population controls (cohort two) was employed^{19 (link)}. The cardiac phenotype information in these individuals is unavailable. The CNV filter and calling pipeline for this cohort of patients were the same as for 22q11.2DS cohort. The CNVs were identified, and the frequency of KANSL1 microduplication was calculated. In addition, data from a third cohort^{20 (link)} of patients with available cardiac phenotype information was processed using ChAS 2.0 software (Affymetrix, Santa Clara, CA). The association between CHD phenotype and the presence of KANSL1 microduplication was calculated by Fisher’s exact test in the corresponding cohorts.

León L.E., Benavides F., Espinoza K., Vial C., Alvarez P., Palomares M., Lay-Son G., Miranda M, & Repetto G.M. (2017). Partial microduplication in the histone acetyltransferase complex member KANSL1 is associated with congenital heart defects in 22q11.2 microdeletion syndrome patients. Scientific Reports, 7, 1795.

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TCGA Germline Variant Identification

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Germline variants were derived from the Affymetrix SNP6.0 microarray. Raw CEL files for the TCGA cohort were downloaded from FireCloud (https://software.broadinstitute.org/firecloud/) and the GDC legacy archive (https://portal.gdc.cancer.gov/legacy-archive). Probesets with non-unique mapping in the genome or not mapping to the location provided by Affymetrix (NetAffx Annotation Release 35) were removed.

Shahamatdar S., He M.X., Reyna M.A., Gusev A., AlDubayan S.H., Van Allen E.M, & Ramachandran S. (2020). Germline Features Associated with Immune Infiltration in Solid Tumors. Cell reports, 30(9), 2900-2908.e4.

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Comprehensive Molecular Profiling of Tumors

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TCGA level 3 RNA-Seq version 2 RSEM data, segmented SCNA data (minus germline CNV), and TCGA level 3 mutation data of version 2016_01_28 were downloaded from GDC Data Portal. Mutation data were analyzed and summarized using maftools.18 Differential mutations between groups were accessed applying Fisher’s exact tests. KEGG pathway analyses were performed using DAVID 6.8 for differential mutated genes.19 (link),20 (link) SCNA events were detected by Genomic Identification of Significant Targets in Cancer (GISTIC) 2.0 using the segmented Affymetrix SNP 6.0 microarray data.21 (link) SCNAs between groups were compared by Fisher’s exact tests. An R package, edgeR, was utilized to perform differential expression analysis.22 (link) Gene set enrichment analysis (GSEA) was performed by the GSEA desktop application v.3.0 using Molecular Signatures Database (MSigDB) v6.1 with 1,000 permutations.23 (link),24 (link) The estimation of immune cell proportions for each sample was performed by CIBERSORT algorithm on TCGA RNA-Seq data or Affymetrix HG133plus2 microarray data using LM22 as a reference expression signature with 100 permutations.

Mao Y., Feng Q., Zheng P., Yang L., Liu T., Xu Y., Zhu D., Chang W., Ji M., Ren L., Wei Y., He G, & Xu J. (2018). Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer. Cancer Management and Research, 10, 3569-3577.

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Maternal Preeclampsia GWAS Replication

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Directly genotyped lead SNPs or proxy SNPs (r²>0.5 in 1000 Genomes Northern Europeans from Utah (1KGP CEU)) from the 9 lead association signals in our study were interrogated in the previously described SOPHIA Caucasian maternal preeclampsia GWAS (177 preeclampsia cases, 116 normotensive controls) genotyped on the Affymetrix SNP 6.0 microarray^{19 (link)}. These 9 lead association signals (all directly genotyped by whole genome sequencing) were also interrogated in the subset of European cases from the Inova study (35 preeclampsia cases, 340 normotensive controls). Logistic regression was performed for each ancestry group with PLINK v1.90 using the first 10 principal components as covariates (www.cog-genomics.org/plink/1.9)^{37 (link)}.

Gray K.J., Kovacheva V.P., Mirzakhani H., Bjonnes A.C., Almoguera B., DeWan A.T., Triche E.W., Saftlas A.F., Hoh J., Bodian D.L., Klein E., Huddleston K.C., Ingles S.A., Lockwood C.J., Hakonarson H., McElrath T.F., Murray J.C., Wilson M.L., Norwitz E.R., Karumanchi S.A., Bateman B.T., Keating B.J, & Saxena R. (2018). GENE-CENTRIC ANALYSIS OF PREECLAMPSIA IDENTIFIES MATERNAL ASSOCIATION AT PLEKHG1. Hypertension (Dallas, Tex. : 1979), 72(2), 408-416.

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SNP Array Analysis of Primary Tumors

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Details for the SNP6.0 arrays have been previously described [8 (link)]. The SNP6 array data for primary tumors collected at St. Jude Children's Research Hospital were deposited in the dbGaP database (phs000352.vl.pl). Samples were genotyped using Affymetrix SNP 6.0 microarrays according to the manufacturer's instructions. CEL files were generated using GeneChip Command Console Software. SNP calls were generated using Genotyping Console (Affymetrix) and the Birdseed v2 algorithm with default parameters, with at least 50 arrays in each analysis. Array normalization and copy-number inference were performed according to a published workflow[31 (link)],[32 (link)]. Normalized data were viewed in dChip[33 (link)], and regions with abnormal copy number were identified computationally by circular binary segmentation (CBS)[34 (link)] and analyzed as described[31 (link)],[32 (link)]. All calls for gain and loss were manually reviewed as previous described [35 ] using dChip software [33 (link), 36 ].

McEvoy J., Nagahawatte P., Finkelstein D., Richards-Yutz J., Valentine M., Ma J., Mullighan C., Song G., Chen X., Wilson M., Brennan R., Pounds S., Becksfort J., Huether R., Lu C., Fulton R.S., Fulton L.L., Hong X., Dooling D.J., Ochoa K., Mardis E.R., Wilson R.K., Easton J., Zhang J., Downing J.R., Ganguly A, & Dyer M.A. (2014). RB1 gene inactivation by chromothripsis in human retinoblastoma. Oncotarget, 5(2), 438-450.

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Genome-Wide Copy Number Profiling by SNP Microarrays

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Patient copy number aberrations were determined using SNP6.0 microarrays according to manufacturer’s instructions (Affymetrix, Santa Clara, CA, USA). Data was analyzed as previously described(51 (link)) using optimal reference normalization(52 (link)) and circular binary(53 (link),54 (link)) segmentation with Genotyping Console (Affymetrix) and dCHIP (build Apr 2010)(55 (link)). Detection of loss of heterozygosity (LOH) and allelic ratios were performed using Nexus 7.5.2 software (BioDiscovery Inc, Hawthorne, CA, USA). All segments were manually curated.

Dobson S.M., García-Prat L., Vanner R.J., Wintersinger J., Waanders E., Gu Z., McLeod J., Gan O.I., Grandal I., Payne-Turner D., Edmonson M.N., Ma X., Fan Y., Voisin V., Chan-Seng-Yue M., Xie S.Z., Hosseini M., Abelson S., Gupta P., Rusch M., Shao Y., Olsen S.R., Neale G., Chan S.M., Bader G., Easton J., Guidos C.J., Danska J.S., Zhang J., Minden M.D., Morris Q., Mullighan C.G, & Dick J.E. (2020). Relapse fated latent diagnosis subclones in acute B lineage leukaemia are drug tolerant and possess distinct metabolic programs. Cancer discovery, 10(4), 568-587.

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Genome-Wide Chromothripsis Analysis

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Samples (122 unpaired and 15 paired tumor/germline samples) were
genotyped using Affymetrix SNP 6.0 microarrays according to the
manufacturer’s instructions. CEL files were generated using GeneChip
Command Console Software. SNP calls were generated using Genotyping Console
(Affymetrix) and the Birdseed v2 algorithm with default parameters. Array
normalization and copy number inference were performed according to a previously
published workflow^{74 (link)}.
Normalized data were viewed in dChip^{75 (link)} and regions with abnormal copy number identified
computationally by circular binary segmentation (CBS)^{76 (link)} and analyzed as previously
described.^{74 (link)} Evidence
of chromothripsis was defined as the presence of at least ten changes in
segmental copy number between two or three copy number states on an individual
chromosome.^{24 (link)} Inferred
copy number log2 data from chromosomes with chromothripsis were exported from
dChip and visualized in USCS Genome Browser.

Iacobucci I., Wen J., Meggendorfer M., Choi J.K., Shi L., Pounds S.B., Carmichael C.L., Masih K.E., Morris S.M., Lindsley R.C., Janke L.J., Alexander T.B., Song G., Qu C., Li Y., Payne-Turner D., Tomizawa D., Kiyokawa N., Valentine M., Valentine V., Basso G., Locatelli F., Enemark E.J., Kham S.K., Yeoh A.E., Ma X., Zhou X., Sioson E., Rusch M., Ries R.E., Stieglitz E., Hunger S.P., Wei A.H., To L.B., Lewis I.D., D’Andrea R.J., Kile B.T., Brown A.L., Scott H.S., Hahn C.N., Marlton P., Pei D., Cheng C., Loh M.L., Ebert B.L., Meshinchi S., Haferlach T, & Mullighan C.G. (2019). Genomic Subtyping and Therapeutic Targeting of Acute Erythroleukemia. Nature genetics, 51(4), 694-704.

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Profiling Copy Number Variations

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Copy number variation (CNV) analysis was carried out on Affymetrix SNP 6.0 microarrays (n = 10) and CytoScan HD arrays (n = 4; Affymetrix, Santa Clara, CA, USA). Further details are given in the supplementary information.

Johansson P., Klein-Hitpass L., Choidas A., Habenberger P., Mahboubi B., Kim B., Bergmann A., Scholtysik R., Brauser M., Lollies A., Siebert R., Zenz T., Dührsen U., Küppers R, & Dürig J. (2018). SAMHD1 is recurrently mutated in T-cell prolymphocytic leukemia. Blood Cancer Journal, 8(1), 11.

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