Human610 quad

Manufactured by Illumina

Sourced in United States

The Human610-Quad is a high-density genotyping microarray that can be used to interrogate over 610,000 genetic variants in a single assay. It is designed to provide comprehensive coverage of common genetic variation across the human genome.

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30 protocols using human610 quad

Genotyping and Imputation Protocol

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Genotyping was performed using the Illumina Human610-Quad and Core+Exome SNP chips. Quality control included inspection of pedigree, sex, Mendelian errors, and ancestry, as well as filtering for genotyping quality (GenCall <0.7); SNP and individual call rates (<0.95); Hardy-Weinberg equilibrium failure (P <10–6); and minor allele frequency (<0.01). Subjects were imputed to the Haplotype Reference Consortium (HRC.1.1)⁵³ on the Michigan Imputation Server (https://imputationserver.sph.umich.edu/i). Imputation was carried out in two separate waves for the Illumina Human610-Quad and the Core+Exome SNP chips. To account for population stratification (i.e. allele frequency differences between subjects due to systematic ancestry differences) in the PRS analysis, genetic principal components (PC) reflecting the respective ancestry were calculated using EigenSoft 6.0.1 (http://www.hsph.harvard.edu/alkes-price/software/). Further details on genotyping, quality control, and imputation are provided in the Supp. Text.

Rietschel L., Streit F., Zhu G., McAloney K., Frank J., Couvy-Duchesne B., Witt S.H., Binz T.M., McGrath J., Hickie I.B., Hansell N.K., Wright M.J., Gillespie N.A., Forstner A.J., Schulze T.G., Wüst S., Nöthen M.M., Baumgartner M.R., Walker B.R., Crawford A.A., Colodro-Conde L., Medland S.E., Martin N.G, & Rietschel M. (2017). Hair Cortisol in Twins: Heritability and Genetic Overlap with Psychological Variables and Stress-System Genes. Scientific Reports, 7, 15351.

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Genotyping Quality Control Procedures

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Genotyping was performed at Cedars-Sinai Medical Center using Illumina whole genome arrays (Human610-Quad; HumanOmniExpress) and the Immuno-BeadChip array as previously described^{16 (link), 18 (link)}. For the Immuno-BeadChip, average genotyping call rate for samples that passed quality control (QC) was 99.98%. Average concordance rate across 83 samples genotyped in replicate was 99.99%. Single-nucleotide polymorphisms (SNPs) underwent methodological review and were evaluated using several SNP statistic parameters, including SNP call frequency, cluster separation, replicate and heritability error rates, heterozygous excess, theta mean and deviation, and R intensity mean. A total of 135,252 autosomal SNPs passed genotyping QC measures and were common across datasets. For the Human OmniExpress and Human610-Quad, average genotyping call rates for samples that passed QC were 99.85% and 99.83%, respectively. Average concordance rate across 19 samples genotyped in replicate was >99.99%. Genotyping of three control trios yielded a heritability frequency of 99.53%. Optimal allele-calling was verified by manual review of top associated SNPs.

Kaur M., Panikkath D., Yan X., Liu Z., Berel D., Li D., Vasiliauskas E.A., Ippoliti A., Dubinsky M., Shih D.Q., Melmed G.Y., Haritunians T., Fleshner P., Targan S.R, & McGovern D.P. (2016). Perianal Crohn’s Disease is associated with distal colonic disease, stricturing disease behavior, IBD-associated serologies and genetic variation in the JAK-STAT pathway. Inflammatory bowel diseases, 22(4), 862-869.

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Genetic Factors Influencing Simvastatin Response

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This study uses genome-wide genotype and lymphoblastoid cell line (LCL) transcriptomic data derived from 412 of 944 Cholesterol and Pharmacogenetics (CAP) 40 mg/day 6 week simvastatin clinical trial participants (ClinicalTrials.gov ID: NCT00451828) [9 (link)]. Demographic and phenotypic characteristics of this participant subset are shown in Table 5. Self-reported white CAP participants were genotyped as previously described on one or more (Illumina HumanHap300, Human610-Quad, custom iSelect and Cardio-Metabochip) platforms [50 (link), 51 (link)], and self-reported black participants were genotyped on the Illumina HumanOmni2.5Exome and, for the majority of participants, the Cardio-Metabochip and Immunochip.

Table 5

Characteristics of European ancestry and African American ancestry CAP participants used in eQTL analyses

	African Americans	European Americans
N	153	259
Gender	53.6% Female	47.9% Female
Age (years)	53.7 ± 12.8	54.2 ± 12.0
BMI	30.1 ± 6.0	27.9 ± 5.7
Smoker	28.8%	10.8%
^aTotal Cholesterol (mg/dl)	204 ± 36	214 ± 37
^aLDL Cholesterol (mg/dl)	129 ± 35	135 ± 33
^aHDL Cholesterol (mg/dl)	55 ± 17	54 ± 17
^aTriglycerides (mg/dl)	103 ± 49	125 ± 67
% change TC	-26 ± 10%	−28 ± 9%
% change LDLC	−40 ± 13%	−43 ± 11%
% change HDLC	2 ± 11%	5 ± 11%
% change TG	−14 ± 26%	−17 ± 24%

Values are mean ± SD. ^aValues are prior to the start of statin treatment

Theusch E., Chen Y.D., Rotter J.I., Krauss R.M, & Medina M.W. (2020). Genetic variants modulate gene expression statin response in human lymphoblastoid cell lines. BMC Genomics, 21, 555.

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Genome-wide Genotyping and Segmentation

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We genotyped 914 patient tumor samples using Illumina SNP arrays (HumanHap550, Human610-Quad, and HumanOmniExpress). Intensities were analyzed using GenomeStudio to obtain Log R Ratio (LRR) and B-allele frequencies (BAF). GC content bias correction was applied for the common set of 316,210 SNPs. Segmentation was obtained using the SNPRank algorithm in Nexus Copy Number version 8.0 (BioDiscovery), which implements circular binary segmentation (CBS) version 3.0.

Lopez G., Conkrite K.L., Doepner M., Rathi K.S., Modi A., Vaksman Z., Farra L.M., Hyson E., Noureddine M., Wei J.S., Smith M.A., Asgharzadeh S., Seeger R.C., Khan J., Auvil J.G., Gerhard D.S., Maris J.M, & Diskin S.J. (2020). Somatic structural variation targets neurodevelopmental genes and identifies SHANK2 as a tumor suppressor in neuroblastoma. Genome Research, 30(9), 1228-1242.

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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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GWAS Data Processing and Imputation

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We downloaded the GWAS data from the ADNI database on February 20, 2020, Genotyping was performed with the platforms of Illumina Human610-Quad, Illumina Human OmniExpress, and Illumina Omni 2.5M on 1,674 MCI participants from ADNI 1, GO, and ADNI 2 phases, respectively. We then used SHAPEIT for phasing and performed imputation with minimac4 on the Michigan imputation server (https://imputationserver.sph.umich.edu) with the HRC reference panel (Version r1.1 2016) consisting of 64,940 haplotypes of predominantly European ancestry. For imputation, a set of high-quality SNPs were used: MAF > 0.01; call rate > 95%, Hardy-Weinberg equilibrium test p > 10⁻⁶; allele frequency difference ≤ 0.20 between the sample data and the reference panel. The genotyping data was processed by using PLINK 1.90/2.0.
For NACC, we requested the GWAS data for the 10,256 subjects in NACC AD Centers 1-7, of which genotyping was performed with the platforms of Human660W-Quad_v1_A, HumanOmni Express-12v1_A/H, and humanomniexpressexome-8v1-2_a, respectively (https://www.alz.washington.edu/ADGC/GENOtype.html) [20 (link)]. We then conducted quality control and imputation with the same procedures of ADNI.

Liu H., Lutz M, & Luo S. (2021). Association Between Polygenic Risk Score and the Progression from Mild Cognitive Impairment to Alzheimer’s Disease. Journal of Alzheimer's disease : JAD, 84(3), 1323-1335.

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Genotyping and Imputation of Finnish Samples

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NFID samples were genotyped in seven batches on the Illumina Infinium CoreExome or Global Screening Array DNA microarray chips. FINRISK population controls were genotyped on the Illumina Infinium CoreExome, Global Screening Array, Human610-Quad, OmniExpress, or Affymetrix GeneChip chips. Health2000–2011 controls were genotyped in three batches on the Illumina Infinium CoreExome, Human610-Quad, or G4L chips. Samples were imputed using the Sequencing Initiative Suomi (SISU) (www.sisuproject.fi) v3 imputation panel containing 3775 high coverage (25–30x) whole genome sequences of Finnish individuals. Detailed information on DNA array data quality control and imputation can be found in the Supplementary Methods.

Urpa L., Kurki M.I., Rahikkala E., Hämäläinen E., Salomaa V., Suvisaari J., Keski-Filppula R., Rauhala M., Korpi-Heikkilä S., Komulainen-Ebrahim J., Helander H., Vieira P., Uusimaa J., Moilanen J.S., Körkkö J., Singh T., Kuismin O., Pietiläinen O., Palotie A, & Daly M.J. (2024). Evidence for the additivity of rare and common variant burden throughout the spectrum of intellectual disability. European Journal of Human Genetics, 32(5), 576-583.

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Genotyping Quality Control for GWAS

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Study participants were genotyped for 620,901 markers on the Illumina Human610-Quad (Illumina Inc, San Diego, CA, USA). All individuals had a genotyping rate greater than 90% and therefore were included in the analysis. Deviations from Hardy-Weinberg equilibrium assessed in PLINK[29 (link)] found 622 markers with significant deviation from expectation in founder controls (p ≤ 1e-06). An additional 47,687 SNPs were eliminated because of high genotype failure rate (≥10%). An exclusion criterion of a minor allele frequency <5% in founders removed 105,758 SNPs. After removing non-autosomal SNPs a total of 457,969 SNPs remained for the analyses reported here. To further explore a putative association found on chromosome 12 (see results below), we imputed 270,467 SNPs on this chromosome using IMPUTE2 software[48 (link)] and the 1000 Genomes Project as the reference sample (http://browser.1000genomes.org). The genotypes and phenotypes for the Guatemalan study population are available in dbGaP (http://www.ncbi.nih.gov/gap), accession number phs000440.v1.p1.

Wolf Z.T., Brand H.A., Shaffer J.R., Leslie E.J., Arzi B., Willet C.E., Cox T.C., McHenry T., Narayan N., Feingold E., Wang X., Sliskovic S., Karmi N., Safra N., Sanchez C., Deleyiannis F.W., Murray J.C., Wade C.M., Marazita M.L, & Bannasch D.L. (2015). Genome-Wide Association Studies in Dogs and Humans Identify ADAMTS20 as a Risk Variant for Cleft Lip and Palate. PLoS Genetics, 11(3), e1005059.

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Genotyping and Imputation Pipeline

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After phenotype and genotype quality control, data were available for 1101 participants who were mainly twins aged 16 years. All participants, and where appropriate their parent or guardian, gave informed consent, and all studies were approved by the QIMR Berghofer Human Research Ethics Committee. Participants were genotyped on the Illumina Human610-Quad and Core+Exome SNP chips. Genotype data were screened for genotyping quality (GenCall < 0.7), SNP and individual call rates (<0.95), HWE failure (p<1e-6) and MAF (<0.01). Data were checked for pedigree, sex and Mendelian errors and for non-European ancestry. Imputation was performed on the Michigan Imputation Server using the SHAPEIT/minimac Pipeline according to the standard protocols.

Xiong Z., Dankova G., Howe L.J., Lee M.K., Hysi P.G., de Jong M.A., Zhu G., Adhikari K., Li D., Li Y., Pan B., Feingold E., Marazita M.L., Shaffer J.R., McAloney K., Xu S.H., Jin L., Wang S., de Vrij F.M., Lendemeijer B., Richmond S., Zhurov A., Lewis S., Sharp G.C., Paternoster L., Thompson H., Gonzalez-Jose R., Bortolini M.C., Canizales-Quinteros S., Gallo C., Poletti G., Bedoya G., Rothhammer F., Uitterlinden A.G., Ikram M.A., Wolvius E., Kushner S.A., Nijsten T.E., Palstra R.J., Boehringer S., Medland S.E., Tang K., Ruiz-Linares A., Martin N.G., Spector T.D., Stergiakouli E., Weinberg S.M., Liu F, & Kayser M. (2019). Novel genetic loci affecting facial shape variation in humans. eLife, 8, e49898.

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PanScan Cohorts Genome-Wide Association Study

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PanScan 1 and PanScan 2 data were obtained from dbGAP^{60 (link),61 (link)} (dbGaP study accession: phs000206.v4.p3). Data from all participating sites apart from Group Health (which required a separate data sharing agreement) were included in the analysis. Previously published PanScan 1^{9 (link)} and PanScan 2^{10 (link)} studies used the Illumina HumanHap550 and Illumina Human 610-Quad chips respectively. Quality control was performed as described above for PanC4. Forty-five unexpected duplicates between PanScan 1, PanScan 2, and PanC4 were identified and removed from analyses of the PanScan datasets. After data cleaning, 528,179 SNPs and 3,746 individuals (1,856 cases, 1,890 controls) remained in PanScan 1, and 557,555 SNPs and 3,300 individuals (1,618 cases and 1,682 controls) remained in PanScan 2 (See Supplementary Table 4).

Childs E.J., Mocci E., Campa D., Bracci P.M., Gallinger S., Goggins M., Li D., Neale R., Olson S.H., Scelo G., Amundadottir L.T., Bamlet W.R., Bijlsma M.F., Blackford A., Borges M., Brennan P., Brenner H., Bueno-de-Mesquita H.B., Canzian F., Capurso G., Cavestro G.M., Chaffee K.G., Chanock S.J., Cleary S.P., Cotterchio M., Foretova L., Fuchs C., Funel N., Gazouli M., Hassan M., Herman J.M., Holcatova I., Holly E.A., Hoover R.N., Hung R.J., Janout V., Key T.J., Kupcinskas J., Kurtz R.C., Landi S., Lu L., Malecka-Panas E., Mambrini A., Mohelnikova-Duchonova B., Neoptolemos J.P., Oberg A.L., Orlow I., Pasquali C., Pezzilli R., Rizzato C., Saldia A., Scarpa A., Stolzenberg-Solomon R.Z., Strobel O., Tavano F., Vashist Y.K., Vodicka P., Wolpin B.M., Yu H., Petersen G.M., Risch H.A, & Klein A.P. (2015). Common variation at 2p13.3, 3q29, 7p13 and 17q25.1 associated with susceptibility to pancreatic cancer. Nature genetics, 47(8), 911-916.

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