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1m duo

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The 1M-Duo is a high-throughput genetic analysis tool designed for genomic research. It provides simultaneous interrogation of up to 1 million genetic markers in a single experiment, enabling efficient and comprehensive genome-wide association studies and other large-scale genetic analyses.

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Lab products found in correlation

Humanhap300, by Illumina (4 mentions) Humanhap610q, by Illumina (4 mentions) Omni2, by Illumina (2 mentions) Humanht 12 v3 beadchip, by Illumina (2 mentions) Genome wide human snp array 6, by Illumina (1 mentions) Taqman assay, by Thermo Fisher Scientific (1 mentions) Omniexpress, by Illumina (1 mentions) Oncoarray, by Illumina (1 mentions) 550k duo, by Illumina (1 mentions) Omniexpressexome, by Illumina (1 mentions) Axiom, by Illumina (1 mentions) Megaex, by Illumina (1 mentions) Humanhap 650y, by Illumina (1 mentions) Humanomniexpress platform, by Illumina (1 mentions)

12 protocols using 1m duo

1

Genotyping and Imputation of AD-related SNPs

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Blood samples were genotyped using the Affymetrix® Genome-Wide Human SNP Array 6.0 or the Illumina 1M Duo. Samples and SNPs with a call rate <95%, samples with mismatched sex, and duplicate samples were removed. Genotypes were imputed using the 1000 Genomes Project phase I integrated variant set (v.3) (Hg19, released in March 2012). Of the six SNPs of interest identified from the existing literature (rs3764647, rs3764650, rs115550680, rs3752246, rs3752239, and rs4147929), five had high imputation quality (r2 > 0.7), and one (rs3752239) was excluded due to low imputation quality (r2 = 0.49). SNPs were coded as the dosage of the corresponding AD risk allele, as specified in the previous literature. Genetic principal components were calculated from genotyped SNPs and included in regression models to control for population stratification. In order to evaluate confounding and/or effect modification by APOE isoforms known to influence dementia risk, we measured rs7412 (to capture the APOE ε2 allele) and rs429359 (to capture the APOE ε4 allele) using a TaqMan assay and ABI Prism© Sequence Detection (Applied Biosystems, Foster City, CA, USA) in 1544 participants. Participants were classified as having 0, 1, or 2 copies of ε2 (represented by the rs7412 T allele) and/or ε4 (represented by the rs429359 C allele).
Chaar D.L., Nguyen K., Wang Y.Z., Ratliff S.M., Mosley T.H., Kardia S.L., Smith J.A, & Zhao W. (2022). SNP-by-CpG Site Interactions in ABCA7 Are Associated with Cognition in Older African Americans. Genes, 13(11), 2150.
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2

Genotyping and Imputation Workflow

Cited 3 times
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Details on genotyping and imputation have been reported previously(17 (link)–19 (link)). In brief, DNA was mostly obtained from blood samples, with some from buccal swabs. Several platforms, including Affymetrix 500K, Affymetrix Axiom, Illumina 1M, 1M duo, Illumina 300K, Illumina 550K, 550Kduo, 610K, Illumina OmniExpress, Illumina OmniExpressExome, Illumina Oncoarray, Illumina Oncoarray+custom iSelect, were used for genotyping(20 (link),21 (link)). Samples were excluded on the basis of sample call rate, heterozygosity, unexpected duplicates or relative pairs, gender discrepancy and principal component analysis (PCA) outliers. SNPs were excluded on the basis of inconsistency across platforms, call rate, and out of Hardy-Weinberg equilibrium (HWE) in controls(20 (link)). SNPs were imputed using Haplotype Reference Consortium (HRC version r1.0) reference panel (22 (link)), and restricted by imputation accuracy (R2>0.3 for SNPs with MAF>1%, R2>0.5 for SNPs with MAF>0.5% and <1%, and R2>0.99 for SNPs with MAF<0.05%).
Wang X., Su Y.R., Petersen P.S., Bien S., Schmit S.L., Drew D.A., Albanes D., Berndt S.I., Brenner H., Campbell P.T., Casey G., Chang-Claude J., Gallinger S.J., Gruber S.B., Haile R.W., Harrison T.A., Hoffmeister M., Jacobs E.J., Jenkins M.A., Joshi A.D., Li L., Lin Y., Lindor N.M., Marchand L.L., Martin V., Milne R., Maclnnis R., Moreno V., Nan H., Newcomb P.A., Potter J.D., Rennert G., Rennert H., Slattery M.L., Thibodeau S.N., Weinstein S.J., Woods M.O., Chan A.T., White E., Hsu L, & Peters U. (2020). Exploratory genome-wide interaction analysis of non-steroidal anti-inflammatory drugs and predicted gene expression on colorectal cancer risk. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 29(9), 1800-1808.
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3

Ancestry Analysis of G34S Variant

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The 5 individuals (03C16092, 03C16094, SSC02696, SSC03276, NA19020) containing the G34S variant were assessed for ancestry by Principal Component Analysis. A set of ~6,000 autosomal SNPs genotyped in common in all 5 samples (Affymetrix 5.0, Affymetrix 5.0, Illumina 1MDuo, Illumina 1MDuo, Illumina OMNI 2.5) were analyzed using the Eigenstrat program. Genotypes from reference populations came from the CEU, YRI, and CHB/JPT populations.
Turner T.N., Sharma K., Oh E.C., Liu Y.P., Collins R.L., Sosa M.X., Auer D.R., Brand H., Sanders S.J., Moreno-De-Luca D., Pihur V., Plona T., Pike K., Soppet D.R., Smith M.W., Cheung S.W., Martin C.L., State M.W., Talkowski M.E., Cook E., Huganir R., Katsanis N, & Chakravarti A. (2015). Loss of delta catenin function in severe autism. Nature, 520(7545), 51-56.
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4

Genetic Associations in Flucloxacillin and Co-Amoxiclav DILI

Cited 1 time
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DILI datasets were from case-control studies of individuals taking flucloxacillin (77 cases and 288 population controls)2 (link) and individuals taking co-amoxiclav (201 cases and 532 population controls)13 (link). The genotyping data were generated using the Illumina 1M-duo described previously2 (link)13 (link).
Overby C.L., Hripcsak G, & Shen Y. (2014). Estimating heritability of drug-induced liver injury from common variants and implications for future study designs. Scientific Reports, 4, 5762.
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5

Comprehensive Genotype Imputation and QC

Cited 2 times
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Genotypes were available for 48 individuals in the discovery sample and for 47 individuals in the follow-up sample. Genotypes were obtained on a combination of Illumina platforms (HumanHap300, HumanHap610Q, 1M-Duo and 1.2MDuo 1M custom arrays) and stringent QC checks were applied to these data as previously described35 (link). HapMap genotypes were imputed using Impute (v2 (ref. 60 (link))) with two reference panels, P0 (HapMap2, rel 22, combined CEU, YRI and ASN panels) and P1 (610K+, including the combined HumanHap610K and 1M array). Altogether, there were 2,028,354 (discovery) and 2,054,344 (follow-up) directly genotyped and imputed autosomal single-nucleotide polymorphisms (SNPs), which were further filtered for subsequent analyses.
Bell J.T., Loomis A.K., Butcher L.M., Gao F., Zhang B., Hyde C.L., Sun J., Wu H., Ward K., Harris J., Scollen S., Davies M.N., Schalkwyk L.C., Mill J., Williams F.M., Li N., Deloukas P., Beck S., McMahon S.B., Wang J., John S.L, & Spector T.D. (2014). Differential methylation of the TRPA1 promoter in pain sensitivity. Nature Communications, 5, 2978.
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6

Pharmacogenomics of Warfarin in African Americans

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WPC is a prospective cohort of first-time warfarin users aged 19 years or older starting anticoagulation for venous thromboembolism, stroke/transient ischemic attacks, atrial fibrillation, myocardial infarction, and/or peripheral arterial disease68 . The genotype data was generated using Illumina’s MEGA-EX and 1M duo arrays for 599 and 297 participants, respectively (58% females, average age 61 years, 100% African American by self-report). Imputation was performed using the TOPMed r2 reference panel (Freeze 8). More than 99% of the imputed variants with MAF>1% had R2 of 0.6 or higher, and genotypes with genotypic probability of 0.9 or higher were retained for PRS calculation. PCA was performed using EIGENSOFT version 6.1.4 based on 44,137 high quality directly genotyped (missingness <5%), common (MAF ≥ 5%), and independent (r2<0.05) SNPs. APOL1 information was obtained from genotypic array data; rs143830837 was used as a proxy for rs71785313 since these SNPs represent the same G2 variant and were recently merged in dbSNP. For this analysis, only participants aged 40 years or older were included, leaving a total of 448 self-identified African-American participants.
Khan A., Turchin M.C., Patki A., Srinivasasainagendra V., Shang N., Nadukuru R., Jones A.C., Malolepsza E., Dikilitas O., Kullo I.J., Schaid D.J., Karlson E., Ge T., Meigs J.B., Smoller J.W., Lange C., Crosslin D.R., Jarvik G.P., Bhatraju P.K., Hellwege J.N., Chandler P., Torvik L.R., Fedotov A., Liu C., Kachulis C., Lennon N., Abul-Husn N.S., Cho J.H., Ionita-Laza I., Gharavi A.G., Chung W.K., Hripcsak G., Weng C., Nadkarni G., Irvin M.R., Tiwari H.K., Kenny E.E., Limdi N.A, & Kiryluk K. (2022). Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nature medicine, 28(7), 1412-1420.
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7

Genome-wide Analysis of HIV Controllers

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The Vanderbilt Institutional Review Boards and the ACTG approved this use of DNA. Genome-wide polymorphism data was obtained as part of the International HIV Controllers study as described [35 (link)]. Briefly, samples were genotyped on either the Illumina HumanHap 650Y or 1M-Duo platform. Data quality control included removal of population outliers, highly related individuals and individuals with highly missing genotype data. Polymorphisms were excluded for low frequency (<1%), high missingness (>5%) or deviation from Hardy-Weinberg expectation (Fisher’s exact test p<5e-6) using PLINK version 1.07 [36 (link)]. Genetic ancestry was determined using principal components analysis in EIGENSTRAT [37 (link)]. Based on comparison with HapMap phase 3 populations, data were clustered into Black, White and Hispanic groups for analysis. After quality control, unobserved genotypes were imputed using the 1,000 Genomes Project Phase I release integrated polymorphisms and indels (March 2012) and BEAGLE software version 4 [38 (link)]. After imputation, >8 million high-quality polymorphisms were tested for association.
Lehmann D.S., Ribaudo H.J., Daar E.S., Gulick R.M., Haubrich R.H., Robbins G.K., de Bakker P.I., Haas D.W, & McLaren P.J. (2015). Genome-wide Association Study of Virologic Response with Efavirenz- or Abacavir-containing Regimens in AIDS Clinical Trials Group Protocols. Pharmacogenetics and genomics, 25(2), 51-59.
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8

Exon-level ciseQTL Analysis of RSPO3

Cited 1 time
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The TwinsUK samples were genotyped on a combination of Illumina chips, namely the HumanHap300, HumanHap610Q, 1M-Duo and 1.2MDuo Illumina arrays. Samples were imputed using the Haplotype Reference Consortium (HRC) reference panel (http://www.haplotype-reference-consortium.org), using Minimac 3 on the Michigan Imputation Server (https://imputationserver.sph.umich.edu). Genotype data were filtered to exclude SNVs with HWE P < 1 × 10−6, imputation R2 < 0.8, or a minor allele frequency (MAF) < 0.01. RNA-seq expression data from subcutaneous adipose tissue were available for 766 twins. RNA-seq data were generated, quantified and normalised68 (link). Expression data were adjusted for family structure (family and zygosity) and 50 PEER factors using mixed effects models fitted using the lmer function from the lme4 package in R, and expression residuals used for ciseQTL analysis. Exon-level ciseQTL analysis at the RSPO3 gene was conducted within a 2 Mb window surrounding the transcription start site using the MatrixeQTL package in R version 3.5.0, which employs a standard additive linear model, with BMI, age and RNA extraction batch included as covariates.
Loh N.Y., Minchin J.E., Pinnick K.E., Verma M., Todorčević M., Denton N., Moustafa J.E., Kemp J.P., Gregson C.L., Evans D.M., Neville M.J., Small K.S., McCarthy M.I., Mahajan A., Rawls J.F., Karpe F, & Christodoulides C. (2020). RSPO3 impacts body fat distribution and regulates adipose cell biology in vitro. Nature Communications, 11, 2797.
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9

Integrative eQTL Analysis of Twin Adipose Tissue

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We used Genevar software, which allows an integrative analysis and visualization of SNP–gene associations in expression quantitative trait loci (eQTL) studies. We queried eQTL data from adipose tissue collected from 856 healthy female twins (one-third monozygotic and two-thirds dizygotic from the TwinsUK adult registry) of the MuTHER (Multiple Tissue Human Expression Resource) (33 (link)). In this study, expression profiling of the samples was performed using Illumina HumanHT-12 v3 BeadChips, whereas genotyping was performed with a combination of Illumina HumanHap300, HumanHap610Q, 1M-Duo, and 1.2M-Duo 1M chips (33 (link)). We queried the data set for any eQTL associations with rs4251961 and rs6759676.
Herder C., Nuotio M.L., Shah S., Blankenberg S., Brunner E.J., Carstensen M., Gieger C., Grallert H., Jula A., Kähönen M., Kettunen J., Kivimäki M., Koenig W., Kristiansson K., Langenberg C., Lehtimäki T., Luotola K., Marzi C., Müller C., Peters A., Prokisch H., Raitakari O., Rathmann W., Roden M., Salmi M., Schramm K., Swerdlow D., Tabak A.G., Thorand B., Wareham N., Wild P.S., Zeller T., Hingorani A.D., Witte D.R., Kumari M., Perola M, & Salomaa V. (2014). Genetic Determinants of Circulating Interleukin-1 Receptor Antagonist Levels and Their Association With Glycemic Traits. Diabetes, 63(12), 4343-4359.
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10

Ancestry Analysis of G34S Variant

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The 5 individuals (03C16092, 03C16094, SSC02696, SSC03276, NA19020) containing the G34S variant were assessed for ancestry by Principal Component Analysis. A set of ~6,000 autosomal SNPs genotyped in common in all 5 samples (Affymetrix 5.0, Affymetrix 5.0, Illumina 1MDuo, Illumina 1MDuo, Illumina OMNI 2.5) were analyzed using the Eigenstrat program. Genotypes from reference populations came from the CEU, YRI, and CHB/JPT populations.
Turner T.N., Sharma K., Oh E.C., Liu Y.P., Collins R.L., Sosa M.X., Auer D.R., Brand H., Sanders S.J., Moreno-De-Luca D., Pihur V., Plona T., Pike K., Soppet D.R., Smith M.W., Cheung S.W., Martin C.L., State M.W., Talkowski M.E., Cook E., Huganir R., Katsanis N, & Chakravarti A. (2015). Loss of delta catenin function in severe autism. Nature, 520(7545), 51-56.
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