Human1m duo beadchip array

Manufactured by Illumina

The Human1M-Duo BeadChip array is a high-density genotyping microarray designed by Illumina. It is capable of simultaneously interrogating over 1 million genetic markers across the human genome.

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Lab products found in correlation

Power tools software, by Thermo Fisher Scientific (1 mentions)

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Human1M-Duo BeadChip Illumina Human1M-Duo array Human1M-Duo Human1M BeadChip Human1M array BeadChip arrays Human1M BeadChips

5 protocols using human1m duo beadchip array

Lung eQTL Dataset: Molecular Insights

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The description of the lung eQTL dataset and subject demographics have been published previously [54] (link)–[56] (link). Briefly, non-tumor lung tissues were collected from patients who underwent lung resection surgery at three participating sites: Laval University (Quebec City, Canada), University of Groningen (Groningen, The Netherlands), and University of British Columbia (Vancouver, Canada). Whole-genome gene expression and genotyping data were obtained from these specimens. Gene expression profiling was performed using an Affymetrix custom array (GPL10379) testing 51,627 non-control probe sets and normalized using RMA [57] (link). Genotyping was performed using the Illumina Human1M-Duo BeadChip array. Genotype imputation was undertaken using the 1000G reference panel. Following standard microarray and genotyping quality controls, 1111 patients were available including 409 from Laval, 363 from Groningen, and 339 from UBC. Lung eQTLs were identified to associate with mRNA expression in either cis (within 1 Mb of transcript start site) or in trans (all other eQTLs) and meeting the 10% false discovery rate (FDR) genome-wide significant threshold.

Wain L.V., Sayers I., Soler Artigas M., Portelli M.A., Zeggini E., Obeidat M., Sin D.D., Bossé Y., Nickle D., Brandsma C.A., Malarstig A., Vangjeli C., Jelinsky S.A., John S., Kilty I., McKeever T., Shrine N.R., Cook J.P., Patel S., Spector T.D., Hollox E.J., Hall I.P, & Tobin M.D. (2014). Whole Exome Re-Sequencing Implicates CCDC38 and Cilia Structure and Function in Resistance to Smoking Related Airflow Obstruction. PLoS Genetics, 10(5), e1004314.

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Imputed Genotypes for ENCODE Samples

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Genomic sequencing-based variants calls for GM12878 were generated by the Broad Institute. Illumina Human-1MDuo BeadChip array genotype data generated by the HusdonAlpha Institute of Biotechnology for GM12878 and 52 other ENCODE samples were obtained from the UCSC genome browser [26 ]. Autosomal genotypes for all 53 samples were imputed using MaCH-Admix [27 (link)] with default parameter settings and the reference panel from the 1000 Genomes Project Phase I version 3 (2012-03-14 release). Chromosome X genotype data in the 53 samples were pre-phased using MaCH [28 (link)] with options --states 500 and --rounds 400 and then imputed using minimac [29 (link)] with options --state 10 and --rounds 10. Post-imputation filtering of variants according to Rsq was performed as previously reported [30 (link)].
Common alleles (MAF > 0.05) used to derive the initial custom reference genome were based on 1000 Genomes Phase I version 3 EUR population [31 ].

Buchkovich M.L., Eklund K., Duan Q., Li Y., Mohlke K.L, & Furey T.S. (2015). Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci. BMC Medical Genomics, 8, 43.

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Investigating Lead SCC Variants and mRNA Expression

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To investigate the association between lead SCC variants and mRNA expression, we used three lung eQTL data sets from the Microarray eQTL study. In the Microarray eQTL study[57 (link)], lung tissues for eQTL analysis were obtained from patients who underwent lung surgery at three academic sites: Laval University, the University of British Columbia (UBC) and the University of Groningen. Whole-genome gene expression profiling in the lung was performed on a custom Affymetrix array and is available through GEO (https://www.ncbi.nlm.nih.gov/geo/) accession number GSE23546. Genotyping was carried out on the Illumina Human 1M-Duo BeadChip array, data is accessible in dbGaP (phs001745.v1.p1). Genotypes and gene expression levels were available for 408 (Laval University), 342 (Groningen) and 287 (UBC) patients. Microarray and genotypes preprocessing, quality control and eQTL mapping have been described previously[58 (link)]. We also investigated top aerodigestive SqCC associations in the public GTEx catalog (V8)[26 (link)] for lung and esophageal tissue eQTLs and sQTLs, summary statistics based on RNAseq and genotypes analyses obtained via the GTEx data portal (https://www.gtexportal.org).

Lesseur C., Ferreiro-Iglesias A., McKay J.D., Bossé Y., Johansson M., Gaborieau V., Landi M.T., Christiani D.C., Caporaso N.C., Bojesen S.E., Amos C.I., Shete S., Liu G., Rennert G., Albanes D., Aldrich M.C., Tardon A., Chen C., Triantafillos L., Field J.K., Teare M.D., Kiemeney L.A., Diergaarde B., Ferris R.L., Zienolddiny S., Lam S., Olshan A.F., Weissler M.C., Lacko M., Risch A., Bickeböller H., Ness A.R., Thomas S., Le Marchand L., Schabath M.B., Wünsch-Filho V., Tajara E.H., Andrew A.S., Clifford G.M., Lazarus P., Grankvist K., Johansson M., Arnold S., Melander O., Brunnström H., Boccia S., Cadoni G., Timens W., Obeidat M., Xiao X., Houlston R.S., Hung R.J, & Brennan P. (2021). Genome-wide association meta-analysis identifies pleiotropic risk loci for aerodigestive squamous cell cancers. PLoS Genetics, 17(3), e1009254.

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Genetic Associations in Asthma Susceptibility

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Single SNP analysis was conducted on polymorphisms within the LPHN1 and LPHN3 genes using the Dutch Asthma genome-wide association study (GWAS) (DAG) cohort, a cohort characterised by the presence of a doctor diagnosis for asthma and bronchial hyper-responsiveness.11 (link)
12 (link) Associations between LPHN1 and LPHN3 polymorphisms and the phenotype of asthma (defined by doctor diagnosis) and severity of bronchial hyperresponsiveness BHR of asthmatics (slope of BHR) were conducted.
SNPs significantly associated with risk of asthma were then explored in a large eQTL lung tissue dataset,13 (link) to determine whether they played a functional role in LPHN gene expression. This lung eQTL dataset consisted of individuals with both genome-wide genotyping and microarray expression data from three sites of recruitment, University of Groningen, Laval University, University of British Columbia (UBC) (n=1095). Genome-wide gene expression and genotyping profiles were obtained using a custom Affymetrix array, Gene Expression Omnibus platform GPL10379 (GSE23546), and the Illumina Human1M-Duo BeadChip array, respectively. Standard quality controls were performed as described previously.13 (link)

Faiz A., Donovan C., Nieuwenhuis M.A., van den Berge M., Postma D.S., Yao S., Park C.Y., Hirsch R., Fredberg J.J., Tjin G., Halayko A.J., Rempel K.L., Ward J.P., Lee T., Bossé Y., Nickle D.C., Obeidat M., Vonk J.M., Black J.L., Oliver B.G., Krishnan R., McParland B., Bourke J.E, & Burgess J.K. (2016). Latrophilin receptors: novel bronchodilator targets in asthma. Thorax, 72(1), 74-82.

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Investigating SNP Effects on Gene Expression in Lung Tissue

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For SNPs with evidence of replication, we investigated their effects on the expression of nearby genes (genes within 100 kb up and downstream from the SNP) in lung samples from the Lung QTL consortium. This includes data on 1,111 individuals undergoing lung surgery, recruited at Laval University (n = 409), University of British Columbia (n = 339) and University of Groningen (n = 363)[24 (link)].
Gene expression and genotyping profiles were obtained using a custom Affymetrix array (GEO platform GPL10379) and the Illumina Human1M-Duo BeadChip array, respectively. Expression values were extracted using the Robust Multichip Average method[25 (link)] implemented in the Affymetrix Power Tools software. Expression values were analysed with a robust regression model adjusted for age, sex and smoking status, using the R statistical package MASS (rlm function).
Genetic associations were performed in PLINK 1.9. A fixed-effect meta-analysis was used to pool the results across the three sites.

Minelli C., Dean C.H., Hind M., Alves A.C., Amaral A.F., Siroux V., Huikari V., Soler Artigas M., Evans D.M., Loth D.W., Bossé Y., Postma D.S., Sin D., Thompson J., Demenais F., Henderson J., Bouzigon E., Jarvis D., Järvelin M.R, & Burney P. (2016). Association of Forced Vital Capacity with the Developmental Gene NCOR2. PLoS ONE, 11(2), e0147388.

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