Humanomniexpressexome 8 v1

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The HumanOmniExpressExome-8 v1.0 is a high-density genotyping array designed for genome-wide association studies and other genetic research applications. The array contains probes for over 4.6 million genetic variants, including exonic, regulatory, and intergenic regions, providing comprehensive coverage of the human genome.

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Humanomniexpress 12v1, by Illumina (3 mentions) Humanexome beadchip, by Illumina (2 mentions) Humancytosnp 12 beadchip, by Illumina (1 mentions) Immunochip, by Illumina (1 mentions) Kaspar assay, by LGC (1 mentions) Applied biosystems 7500 fast real time pcr system, by Thermo Fisher Scientific (1 mentions) Qiacube automated system, by Qiagen (1 mentions) Gentra puregene blood kit, by Qiagen (1 mentions) Humancoreexome12 v1, by Illumina (1 mentions) Human genotyping arrays, by Illumina (1 mentions) Genechip 6, by Illumina (1 mentions)

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HumanOmniExpressExome (12 citations) HumanOmniExpressExome‐8 v1.2 BeadChip (6 citations) HumanOmniExpressExome-8 v1.2 array (4 citations) HumanOmniExpressExome-8-v1.2 BeadArray (2 citations)

26 protocols using humanomniexpressexome 8 v1

Interleukin-38 Regulation in Trained Immunity

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The study was performed in a cohort of ±200 healthy individuals of Western European ancestry from the Human Functional Genomics Project (200FG cohorts, see www.humanfunctionalgenomics.org). PBMCs were isolated from consenting healthy donors as described before.²¹ The preparation of plasma for IL‐38 ELISA and in vitro stimulations of primary monocyte stimulations in this cohort were described elsewhere.²² Monocytes were trained with β‐glucan for 24 h, washed with warm PBS, and rested in RPMI for 6 d.²³ Then, cells were stimulated with 10 ng/ml LPS. After 24 h, supernatants were collected and stored at −20°C until IL‐6 and TNFα were measured by ELISA. Plasma IL‐38 levels were determined by ELISA. The status of IL‐38 SNP rs58965312 was assessed as described in the methods of Li et al.²² by the commercially available SNP chip, Illumina HumanOmniExpressExome‐8 v1.0.

de Graaf D.M., Teufel L.U., van de Veerdonk F.L., Joosten L.A., Netea M.G., Dinarello C.A, & Arts R.J. (2021). IL‐38 prevents induction of trained immunity by inhibition of mTOR signaling. Journal of Leukocyte Biology, 110(5), 907-915.

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Trained Monocyte Immune Response Assay

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The study was performed in a cohort of ±200 healthy individuals of Western European ancestry from the Human Functional Genomics Project (200FG cohorts, see www.humanfunctionalgenomics.org). PBMCs were isolated from consenting healthy donors as described before [20 (link)]. The preparation of plasma for ELISA, and in vitro stimulations of primary monocyte stimulations in this cohort were described elsewhere [21 (link)]. Monocytes were trained with β-glucan for 24h, washed with warm PBS, and rested in RPMI for 6 days [22 (link)]. Then, cells were stimulated with 10 ng/mL LPS. After 24h, supernatants were collected and stored at –20 °C until IL-6 and TNFα were measured by ELISA. Plasma IL-38 levels were determined by ELISA. The status of IL-38 SNP rs58965312 was assessed as described in the methods of [21 (link)] by the commercially available SNP chip, Illumina HumanOmniExpressExome-8 v1.0.

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Genome-wide association of cytokine tolerance

Cited 1 time

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Individuals of 200FG cohort were genotyped using the Illumina HumanOmniExpressExome-8 v1.0 and the data was imputed, as previously described [26 (link)]. Raw cytokine and lactate levels were first log-transformed. Then, the ratio of cytokine/lactate measurements between the second and the first stimulation was computed to capture the induction of tolerance. The ratio of log-transformed cytokine/lactate data was mapped to genotype data using a linear model with age and gender as covariates. The genome-wide significance was based on a threshold of P value <5 × 10⁻⁸.

Kerstholt M., van de Schoor F.R., Oosting M., Moorlag S.J., Li Y., Jaeger M., van der Heijden W.A., Tunjungputri R.N., dos Santos J.C., Kischkel B., Vrijmoeth H.D., Baarsma M.E., Kullberg B.J., Lupse M., Hovius J.W., van den Wijngaard C.C., Netea M.G., de Mast Q, & Joosten L.A. (2022). Identifying platelet-derived factors as amplifiers of B. burgdorferi-induced cytokine production. Clinical and Experimental Immunology, 210(1), 53-67.

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Genotyping and Imputation of 500FG and Lifelines Deep Cohorts

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The procedures for genotyping, genetic data filtering and genotype imputation of the 500FG cohort had been previously described (6 (link)). Extracted DNA was genotyped using the commercially available SNP chip, Illumina HumanOmniExpressExome-8 v1.0. Following pre-imputation filtering steps for both markers and individuals, the remaining dataset SNP genotypes were imputed with GoNL as reference panel (16 ).
For Lifelines Deep cohort, genotyping and imputation was performed as previously described (17 ). Both the HumanCytoSNP-12 BeadChip and the ImmunoChip platforms (Illumina, San Diego, CA, USA) were used to genotype the isolated DNA. Independent markers quality control was performed for both platforms and subsequently merged into one dataset. After merging, genotype SNPs were imputed using IMPUTE2 (18 ) against the GoNL reference panel.

Boahen C.K., Oelen R., Le K., Netea M.G., Franke L., van der Wijst M.G, & Kumar V. (2023). Integration of Candida albicans-induced single-cell gene expression data and secretory protein concentrations reveal genetic regulators of inflammation. Frontiers in Immunology, 14, 1069379.

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Genotyping, Phasing, and Imputation of DNA Samples

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DNA samples of 112 individuals were genotyped using the commercially available SNP chip, Illumina HumanOmniExpressExome-8 v1.0. The genotype calling was performed using Opticall 0.7.0^{46 (link)} using the default settings. Four samples with a call rate ≤ 0.99 were excluded from the dataset as were variants with a HWE ≤ 0.0001, call rate ≤ 0.99 and MAF ≤ 0.001. Two samples were excluded as potential ethnic outliers identified by multi-dimensional scaling plots of samples merged with 1000 Genome data (Supplementary Fig. 14). This resulted in a dataset of 106 samples containing genotype information of 282,382 variants for further imputation. The strands and variant-identifiers were aligned to the reference Genome of The Netherlands (GoNL)^{18 (link)} dataset using Genotype Harmonizer^{47 (link)}. The data was phased using SHAPEIT2 v2.r644^{48 (link)} using the GoNL as a reference panel. Finally this data was imputed using IMPUTE2^{49 (link)} with the GoNL as the reference panel^{50 (link)}. Post imputation provided 7512899 variants. We selected 3959389 SNPs that showed MAF ≥ 5%, INFO score ≥ 0.8 and 3 samples per genotype for downstream cytokine QTL mapping.

Li Y., Oosting M., Deelen P., Ricaño-Ponce I., Smeekens S., Jaeger M., Matzaraki V., Swertz M.A., Xavier R.J., Franke L., Wijmenga C., Joosten L.A., Kumar V, & Netea M.G. (2016). Inter-individual variability and genetic influences on cytokine responses against bacterial and fungal pathogens. Nature medicine, 22(8), 952-960.

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Genotyping of PFKFB3 SNPs in 500FG Cohort

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Genomic DNA was isolated from whole blood using the QIAcube automated system (Qiagen). SNPs were selected based on their putative role as cytokine QTLs in the HFGP study and on their ability to tag surrounding variants with a pairwise correlation coefficient r² of at least 0.80 and a minor allele frequency of ≥5% using publicly available sequencing data from Pilot 1 of the 1000 Genomes Project for the CEU population. Genotyping of rs674430 and rs646564 SNPs in PFKFB3 was performed using KASPar assays (LGC Genomics) in an Applied Biosystems 7500 fast real-time PCR system (Thermo Fisher Scientific), according to the manufacturer’s instructions. The DNA samples of individuals from the 500FG cohort were genotyped using the commercially available chip Illumina HumanOmniExpressExome-8 v1.0. Quality control and imputation were performed as described previously (43 (link)).

Gonçalves S.M., Antunes D., Leite L., Mercier T., ter Horst R., Vieira J., Espada E., Pinho Vaz C., Branca R., Campilho F., Freitas F., Ligeiro D., Marques A., van de Veerdonk F.L., Joosten L.A., Lagrou K., Maertens J., Netea M.G., Lacerda J.F., Campos A J.r., Cunha C, & Carvalho A. (2021). Genetic Variation in PFKFB3 Impairs Antifungal Immunometabolic Responses and Predisposes to Invasive Pulmonary Aspergillosis. mBio, 12(3), e00369-21.

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Genetic Ancestry Filtering in Scottish Family Health Study

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We used data from Generation Scotland: Scottish Family Health Study (GS) [50 (link),51 (link)]. Individuals were genotyped with the Illumina HumanOmniExpressExome-8 v1.0 or v1.2. We used PLINK version 1.9b2c [52 (link)] to exclude SNPs that had a missingness > 2% and a Hardy-Weinberg Equilibrium test P < 10⁻⁶. Markers with a minor allele frequency (MAF) smaller than 0.05 were discarded. Duplicate samples, individuals with gender discrepancies and those with more than 2% missing genotypes were also removed. The resulting data set was merged with the 1092 individuals of the 1000 Genomes population [53 (link)] and a principal component analysis was performed using GCTA [54 (link)]. Individuals more than 6 standard deviations away from the mean of principal component 1 and principal component 2 were removed as potentially having African/Asian ancestry as shown in Amador et al. [55 (link)]. After quality control, individuals had genotypes for 519,819 common SNP spread over the 22 autosomes. Of the ~24,000 individuals in GS, the number of individuals with complete information for smoking and other measures included in the models was 18,522 so we used this core set of samples for the analyses in order to allow comparisons between the models, we refer to this set of samples as GS18K.

Amador C., Zeng Y., Barber M., Walker R.M., Campbell A., McIntosh A.M., Evans K.L., Porteous D.J., Hayward C., Wilson J.F., Navarro P, & Haley C.S. (2021). Genome-wide methylation data improves dissection of the effect of smoking on body mass index. PLoS Genetics, 17(9), e1009750.

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Genetic Effects on Trained Immunity

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Raw cytokine levels were first log-transformed. Then, the ratio of cytokine measurements between the second and the first stimulation was computed to capture the enhanced trained response or tolerant response. Individuals of 200FG cohort were genotyped using the Illumina HumanOmniExpressExome-8 v1.0 and the data was imputed, as previously described. In total there were 122 samples with both DNA and cytokine measurements. We focused on the genetic effect of polymorphisms within a window of 250kb around PYCARD, NLRP3, CASPASE1 and IL-1RAP genes on the oxLDL-induced trained immunity. The ratio of log-transformed cytokine data was mapped to genotype data using a linear model with age and gender as covariates.

Christ A., Günther P., Lauterbach M.A., Duewell P., Biswas D., Pelka K., Scholz C.J., Oosting M., Haendler K., Baßler K., Klee K., Schulte-Schrepping J., Ulas T., Moorlag S.J., Kumar V., Park M.H., Joosten L.A., Groh L.A., Riksen N.P., Espevik T., Schlitzer A., Li Y., Fitzgerald M.L., Netea M.G., Schultze J.L, & Latz E. (2018). Western Diet Triggers NLRP3-Dependent Innate Immune Reprogramming. Cell, 172(1-2), 162-175.e14.

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Genetic Analysis of Vitamin D Pathway

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The deoxyribonucleic acid samples of the participants were genotyped with a commercially available SNP chip, Illumina HumanOmniExpressExome-8 v.1.0, methods previously reported by Li et al. 27 In short, genotype calling was performed using Optical 0.7.0. Call rates less than 0.99 were excluded from the dataset, as were samples with a Hardy-Weinberg equilibrium 0.0001, call rate 0.99, and minor allele frequency 0.001. A total of 483 samples were left for the genetic analysis, as described previously. 27 Of the 39 SNPs involved in the vitamin D pathway that were identified from literature, 10,28-30 31 SNPs were available in our dataset, that is, rs10741657, rs10877012, rs2134095, rs2282679, rs3829251, rs10766197, rs218174, rs1155563, rs12785878, rs12794714, rs2762933, rs7041, rs6599638, rs10500804, rs7975232, rs4588, rs6055987, rs7116978, rs3755967, rs12800438, rs1562902, rs17467825, rs3794060, rs1993116, rs7968585, rs4945008, rs2060793, rs222020, rs4944957, rs2298849, and rs1801222.

Aleva F.E., Tunjungputri R.N., van der Vorm L.N., Li Y., Heijdra Y.F., Oosting M., Smeekens S.P., Jaeger M., Joosten L.A.B., de Groot P.G., Netea M.G., van der Ven A.J.A.M, & de Mast Q. (2020). Platelet Integrin αIIbβ3 Activation is Associated with 25-Hydroxyvitamin D Concentrations in Healthy Adults. Thrombosis and haemostasis, 120(5).

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Genotyping and QC of DNA Samples

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DNA samples of individuals (n = 241) were genotyped using the commercially available SNP chip, Illumina HumanOmniExpressExome‐8 v1.0. Opticall 0.7.0 with default settings was used for genotype calling [42 (link)]. Quality control steps and imputation of the genetic data were done similarly as for the 300BCG cohort. We identified and excluded seven samples as ethnic outliers based on (MDS) (Supporting Information Fig. S6B). We selected 4,296,378 SNPs with MAF 5% for follow‐up QTL mapping.

Moorlag S.J., Matzaraki V., van Puffelen J.H., van der Heijden C., Keating S., Groh L., Röring R.J., Bakker O.B., Mourits V.P., Koeken V.A., de Bree L.C., Smeekens S.P., Oosting M., Gamboa R.A., Riksen N.P., Xavier R.J., Wijmenga C., Kumar V., van Crevel R., Novakovic B., Joosten L.A., Li Y, & Netea M.G. (2021). An integrative genomics approach identifies KDM4 as a modulator of trained immunity. European Journal of Immunology, 52(3), 431-446.

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