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Human1m duo

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The Human1M-Duo is a high-throughput genotyping array designed for genome-wide association studies and genetic analysis. It features comprehensive coverage of genetic variation across the human genome, enabling researchers to conduct large-scale genetic studies. The core function of the Human1M-Duo is to provide a reliable platform for the simultaneous interrogation of hundreds of thousands of genetic markers in a single experiment.

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Lab products found in correlation

Humanomni1 quad, by Illumina (4 mentions) Genome wide human snp array 6, by Thermo Fisher Scientific (1 mentions) Humanhap 650y, by Illumina (1 mentions) Human610 quad, by Illumina (1 mentions) Genomestudio v2011, by Illumina (1 mentions) Humanomni2, by Illumina (1 mentions) Humanhap550, by Illumina (1 mentions) Genome wide human snp array 6.0 platform, by Illumina (1 mentions) Human660w quad beadchip, by Illumina (1 mentions) Cytoscan hd, by Thermo Fisher Scientific (1 mentions) Psycharray, by Illumina (1 mentions) Infinium psycharray platform, by Illumina (1 mentions) Genomestudio 2011, by Illumina (1 mentions) Humanomni1 quad beadchip, by Illumina (1 mentions)

Spelling variants of this product

Human1M-Duo BeadChip (22 citations) Human1M (10 citations) Human1M-Duo DNA Analysis BeadChip (7 citations) Human1M-Duo BeadChip array (5 citations) Human1M-Duo DNA BeadChip (2 citations) Human1M–Duo Genotyping BeadChip (2 citations) Infinium Bead Chip Human1M-Duo-v3 (2 citations) Illumina Human1M-Duo array (2 citations)

11 protocols using human1m duo

1

Genotypic Data Quality Control and Imputation

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We used the genotype data (Illumina Infinium Omni2.5) from 1442 participants from the Bambui Study in the context of the EPIGEN project39 (link). For the ARIC and MESA studies, we used the genotype data (Affymetrix Genome-Wide Human SNP Array 6.0) from 12,773 and 6814 participants, respectively. For the HABC study, we used the genotype data (Illumina Human1M-Duo) from 2870 individuals. We split all genotype study datasets by ethnicity (i.e., African Americans, European Americans, Hispanic Americans, and Chinese Americans) before quality control, phasing, imputation, and downstream analyses. The Brazilian Bambui Study could not be meaningfully subdivided by ethnicity because this population is highly admixed39 (link).
For all genotype datasets, we applied standard data quality control and imputation procedures. Quality control was performed using PLINK software44 (link) by applying the following filters: minor allele frequency (–maf 0.01), missing rate per variant (–geno 0.05), missing rate per individual (–mind 0.05), and Hardy–Weinberg equilibrium (–hwe 0.000001).
Gouveia M.H., Bentley A.R., Leonard H., Meeks K.A., Ekoru K., Chen G., Nalls M.A., Simonsick E.M., Tarazona-Santos E., Lima-Costa M.F., Adeyemo A., Shriner D, & Rotimi C.N. (2021). Trans-ethnic meta-analysis identifies new loci associated with longitudinal blood pressure traits. Scientific Reports, 11, 4075.
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2

Characterizing SNP Regulatory Potential

Cited 1 time
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To further characterize SNP regulatory potential, we utilized genome-wide SNP genotype (Illumina Human1M–Duo [version 3] and HumanHap650Y [version 3]) and gene expression (Illumina Human 49K Oligo) data made available to the scientific community by the BrainCloud project and dbGaP (accession number phs000417.v2.p1).(Colantuoni et al., 2011 (link)) These data enabled us to implicate expression quantitative trait loci (eQTLs) by testing SNP associations with mRNA expression levels. Post-mortem dorsolateral prefrontal cortex samples were retrieved, as previously described (Lipska et al., 2006 (link)), from 113 AAs and 110 EAs, ranging in age from 0 to 78 years old, who had no neuropathological or neuropsychiatric diagnoses. Their drug abuse histories are unknown. We did not analyze the fetal samples, given the observed differences in gene expression patterns between fetal and postnatal periods.(Colantuoni et al., 2011 (link))
We used the BrainCloud software application to test additive SNP genotypes for association with expression level separately by ancestry using the best fit procedure under a general linear model, as described at http://braincloud.jhmi.edu/. SNP-expression associations were tested for the most proximally located gene (cis-acting effect) and distally located genes of interest (trans-acting effect).
Johnson E.O., Hancock D.B., Levy J.L., Gaddis N.C., Page G.P., Glasheen C., Saccone N.L., Bierut L.J, & Kral A.H. (2015). KAT2B Polymorphism Identified for Drug Abuse in African Americans with Regulatory Links to Drug Abuse Pathways in Human Prefrontal Cortex. Addiction biology, 21(6), 1217-1232.
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3

Genotype Imputation Using 1000 Genomes

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Cases were genotyped using the Illumina Human670Quad BeadArray, the Australian controls on Illumina Human610Quad and the UK controls on Illumina Human1M-Duo. The genotype data, including the autosomes and chromosome X, were imputed to the latest 1000 Genomes Phase three reference panel (October 2014). Pre-phasing and imputation were performed using SHAPEIT2 (Delaneau et al., 2014 (link)) and IMPUTE2 (Howie et al., 2009 (link)) softwares, respectively.
Uimari O., Rahmioglu N., Nyholt D.R., Vincent K., Missmer S.A., Becker C., Morris A.P., Montgomery G.W, & Zondervan K.T. (2017). Genome-wide genetic analyses highlight mitogen-activated protein kinase (MAPK) signaling in the pathogenesis of endometriosis. Human Reproduction (Oxford, England), 32(4), 780-793.
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4

Genome-Wide SNP Analysis of Samples

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The SNP-A analyses and original SNP data have previously been reported[4 (link), 22 (link)]. The SNP-A systems HumanOmni1-Quad or Human1M-Duo (Illumina, San Diego, CA, USA), covering >1,000,000 SNPs, were used. The analyses were performed according to the manufacturer’s instructions and the B-allele frequencies and the log2 ratios were ascertained by the Genome studio v2011.1 software (Illumina), extracting probe positions from the GRCh37 genome build.
Gunnarsson R., Dilorenzo S., Lundin-Ström K.B., Olsson L., Biloglav A., Lilljebjörn H., Rissler M., Wahlberg P., Lundmark A., Castor A., Behrendtz M., Fioretos T., Paulsson K., Isaksson A, & Johansson B. (2018). Mutation, methylation, and gene expression profiles in dup(1q)-positive pediatric B-cell precursor acute lymphoblastic leukemia. Leukemia, 32(10), 2117-2125.
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5

Genome-wide Genotyping and SNP Marker Selection

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DNA was extracted from all eligible subjects and genome-wide genotyping was performed using the Illumina HumanHap550, 610Q, the Human1M-Duo, Human Omni1-Quad, or the Human Omni 2.5 platforms. We identified a set of single nucleotide polymorphism (SNP) markers common to all platforms and used this as the genome-wide marker set. To avoid inflation of linkage statistics due to linkage disequilibrium and false positive results, the marker set was pruned by eliminating SNPs in high linkage disequilibrium. The 25,436 markers selected had a minimum spacing of 0.1 cM, a minimum heterozygosity of 0.3, and a maximum r2 of 0.16 over a sliding 500,000 basepair window in the publically available HapMap CEPH/Utah (CEU) data, and exceeded an individual call rate of 98% for genotyped subjects.
ALLEN-BRADY K., CANNON-ALBRIGHT L.A., FARNHAM J.M, & NORTON P.A. (2014). Evidence for Pelvic Organ Prolapse Predisposition Genes on Chromosomes 10 and 17. American journal of obstetrics and gynecology, 212(6), 771.e1-771.e7.
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6

Genome-Wide Association Study Protocol

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GENOA African American samples were genotyped on the Affymetrix® Genome-Wide Human SNP Array 6.0 platform, Illumina® Human1M-Duo, or Human660W-Quad BeadChips. Participants were excluded if they had an overall SNP call rate < 95% or sex mismatch between genotypic and phenotypic measurement. SNPs were excluded if they had unknown chromosomal location, < 95% call rate, or minor allele frequency (MAF) less than 0.01. Imputation was performed separately by chip (Affymetrix or Illumina) using the 1000G Phase3 v5 reference panel, and post-imputation comparison between the two groups revealed no substantial differences in genotype and allele frequencies. SNPs with imputation quality less than 0.8 were removed before MR analysis.
Kho M., Zhao W., Ratliff S.M., Ammous F., Mosley T.H., Shang L., Kardia S.L., Zhou X, & Smith J.A. (2020). Epigenetic loci for blood pressure are associated with hypertensive target organ damage in older African Americans from the genetic epidemiology network of Arteriopathy (GENOA) study. BMC Medical Genomics, 13, 131.
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7

SNP Array Analysis of Diagnostic Samples

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SNP array analysis was done on DNA extracted from diagnostic bone marrow or peripheral blood samples on the Human1M-Duo, Human-Omni1-Quad, Human-Omni5-4v (Illumina, San Diego, CA), or CytoScan HD platforms (Applied Biosystems, Thermo Fisher); data have been published previously57 (link).
Yang M., Vesterlund M., Siavelis I., Moura-Castro L.H., Castor A., Fioretos T., Jafari R., Lilljebjörn H., Odom D.T., Olsson L., Ravi N., Woodward E.L., Harewood L., Lehtiö J, & Paulsson K. (2019). Proteogenomics and Hi-C reveal transcriptional dysregulation in high hyperdiploid childhood acute lymphoblastic leukemia. Nature Communications, 10, 1519.
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8

Genotyping of Schizophrenia Samples

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SD cases were genotyped using the Infinium PsychArray platform (Illumina, Inc., San Diego, CA, Supplementary Method S2). Janssen’s SA cases and control samples were genotyped using either PsychArray, Human1M-Duo, or HumanOmni5Exome (Illumina, Inc., San Diego, CA). QC was performed initially by a local QC pipeline by genotyping batch, while the combined data were QC’ed again using RICOPILI [46 (link)] pipeline. Additional details on QC, principal component analysis (PCA), case–control matching, and imputation can be found in Supplementary Method S3.
Li Q.S., Shabalin A.A., DiBlasi E., Gopal S., Canuso C.M., Palotie A., Drevets W.C., Docherty A.R, & Coon H. (2022). Genome-wide association study meta-analysis of suicide death and suicidal behavior. Molecular Psychiatry, 28(2), 891-900.
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9

SNP Array Analysis of BCP-ALL

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SNP array analysis was performed on DNA extracted from bone marrow or peripheral blood at diagnosis for 156 BCP ALL cases. The analysis was performed using HumanOmni1-Quad and Human1M- Duo array systems (Illumina) with data analysis using Genomestudio 2011.1 (Illumina). The SNP array data has been previously published3 (link).
Lilljebjörn H., Henningsson R., Hyrenius-Wittsten A., Olsson L., Orsmark-Pietras C., von Palffy S., Askmyr M., Rissler M., Schrappe M., Cario G., Castor A., Pronk C.J., Behrendtz M., Mitelman F., Johansson B., Paulsson K., Andersson A.K., Fontes M, & Fioretos T. (2016). Identification of ETV6-RUNX1-like and DUX4-rearranged subtypes in paediatric B-cell precursor acute lymphoblastic leukaemia. Nature Communications, 7, 11790.
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10

Genetic Markers for Lapatinib-Induced DILI

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GWAS SNP data were generated for all subjects who consented to genetic analysis using the Illumina Human1M-Duo or the Illumina HumanOmni1-Quad BeadChip arrays by Expression Analysis (Durham, NC, USA). Classical 4-digit HLA genotyping was performed by Laboratory Corporation (Burlington, NC, USA) and Histogenetics (Ossining, NY, USA). UGT1A1*28 was assessed using GeneScan fragment analysis by sequencing at GlaxoSmithKline (Philadelphia, PA, USA).
Candidate gene variants included the following four categories: (i) classical HLA alleles for HLA-A, -B, -C, -DQA1,-DQB1, -DPB1 and -DRB1, (ii) functional variants from DILI genes with potential lapatinib metabolite interaction (ABCB11 and UGT1A1), (iii) functional variants from lapatinib metabolism and disposition genes (CYP3A4, CYP3A5, CYP2C8, CYP2C19, SLCO1B1, ABCB1, ABCG2, NR1l2 and NR1l3) and (iv) 213 autoimmune disease associated markers with reported P-values at genome-wide significance levels from the literature.5 (link) The variants in categories (ii) to (iv) were present as SNPs on the GWAS platform.
Parham L.R., Briley L.P., Li L., Shen J., Newcombe P.J., King K.S., Slater A.J., Dilthey A., Iqbal Z., McVean G., Cox C.J., Nelson M.R, & Spraggs C.F. (2015). Comprehensive genome-wide evaluation of lapatinib-induced liver injury yields a single genetic signal centered on known risk allele HLA-DRB1*07:01. The Pharmacogenomics Journal, 16(2), 180-185.
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