Omniexpressexome beadchip

Manufactured by Illumina

Sourced in Japan

The OmniExpressExome BeadChip is a high-density microarray designed for comprehensive genome-wide genotyping and exome analysis. It provides coverage of common and rare genetic variants across the entire genome, including the protein-coding regions, known as the exome.

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Lab products found in correlation

Humanexome beadchip, by Illumina (2 mentions) Omniexpress beadchip, by Illumina (2 mentions) Cnvpartition v3, by Illumina (2 mentions) Genomestudio software, by Illumina (2 mentions) Humanomni1 quad, by Illumina (2 mentions) Human1m duo beadchip, by Illumina (1 mentions) Coreexome chip, by Illumina (1 mentions) Spss v22, by IBM (1 mentions) Humanmethylation450 array, by Illumina (1 mentions) Oragene dna self collection kit, by DNA Genotek (1 mentions) Abi prism 6100 nucleic acid prepstation, by Thermo Fisher Scientific (1 mentions) Qiaamp dna blood kit, by Qiagen (1 mentions) Humanomniexpressexome beadchip, by Illumina (1 mentions) Humanomniexpress, by Illumina (1 mentions)

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BeadChips (68 citations) OmniExpressExome (14 citations) Infinium OmniExpressExome-8 v1.4 BeadChip (3 citations) OmniExpressExome v1.6 BeadChip genotyping array (2 citations) OmniExpressExome-8 BeadChip (2 citations) OmniExpressExome BeadChip 8v1.1 (2 citations) Human OmniExpressExome-8v1 BeadChip (2 citations) Infinium OmniExpressExome-8 BeadChip (2 citations) Infinium OmniExpressExome BeadChip (2 citations) OmniExpressExome BeadChip array (1 citations)

11 protocols using omniexpressexome beadchip

Genome-Wide Genotyping and Imputation

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Genome‐wide single nucleotide polymorphism (SNP) genotype data on PEAR participants were obtained through genotyping on the Illumina Human 1M‐duo beadchip (Illumina, San Diego, CA). All genome‐wide genotypes, including autosomal and sex chromosome variants, underwent quality control steps in PLINK.22 To confirm participants' self‐identified race, Principal Components Analysis for genetic ancestry was performed on a linkage disequilibrium pruned data set using the EIGENSTRAT method.23 Genotypes of variants that passed quality control underwent haplotype phasing in Markov Chain Haplotyping (MaCH) software24 followed by imputation to 1000 Genomes phase III reference panels using minimac/minimac2.25, 26 Variants with imputation quality r²<0.30 and minor allele frequency <3% were excluded.
INVEST DNA samples were genotyped on the Illumina OmniExpressExome Beadchip (Illumina, San Diego, CA). The 1000 Genomes phase III imputed genome‐wide variants data were used for replication analysis. Imputation was performed using a similar strategy as in PEAR. Principal Components Analysis for genetic ancestry was performed on a data set of linkage disequilibrium–pruned high‐quality SNPs using EIGENSTRAT.23 Race/ethnicity was genetically classified as white or Hispanic based on Principal Components Analysis.

Magvanjav O., Gong Y., McDonough C.W., Chapman A.B., Turner S.T., Gums J.G., Bailey K.R., Boerwinkle E., Beitelshees A.L., Tanaka T., Kubo M., Pepine C.J., Cooper‐DeHoff R.M, & Johnson J.A. (2017). Genetic Variants Associated With Uncontrolled Blood Pressure on Thiazide Diuretic/β‐Blocker Combination Therapy in the PEAR (Pharmacogenomic Evaluation of Antihypertensive Responses) and INVEST (International Verapamil‐SR Trandolapril Study) Trials. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 6(11), e006522.

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Genome-Wide Genotyping and Imputation

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Each respective cohort performed genome-wide genotyping on the Illumina CoreExome chip, the Illumina OmniExpressExome BeadChip, or the Affymetrix 6 array. Case and control subjects from each study center were matched on the same genotyping chip to reduce batch effects. Standard post-genotyping quality control was performed, including sample exclusions for ambiguous gender, call rate <95%, and any duplicate or related individuals (pi_hat ≥0.2), and single nucleotide polymorphism (SNP) exclusions for monomorphic SNPs, SNPs with minor allele frequency <0.05, and SNPs with missingness rate >0.05. The Haplotype Reference Consortium imputation service (Michigan imputation server, https://imputationserver.sph.umich.edu/index.html) was used to perform imputation for autosomal SNPs.

Cousminer D.L., Ahlqvist E., Mishra R., Andersen M.K., Chesi A., Hawa M.I., Davis A., Hodge K.M., Bradfield J.P., Zhou K., Guy V.C., Åkerlund M., Wod M., Fritsche L.G., Vestergaard H., Snyder J., Højlund K., Linneberg A., Käräjämäki A., Brandslund I., Kim C.E., Witte D., Sørgjerd E.P., Brillon D.J., Pedersen O., Beck-Nielsen H., Grarup N., Pratley R.E., Rickels M.R., Vella A., Ovalle F., Melander O., Harris R.I., Varvel S., Grill V.E., Hakonarson H., Froguel P., Lonsdale J.T., Mauricio D., Schloot N.C., Khunti K., Greenbaum C.J., Åsvold B.O., Yderstræde K.B., Pearson E.R., Schwartz S., Voight B.F., Hansen T., Tuomi T., Boehm B.O., Groop L., Leslie R.D, & Grant S.F. (2018). First Genome-Wide Association Study of Latent Autoimmune Diabetes in Adults Reveals Novel Insights Linking Immune and Metabolic Diabetes. Diabetes Care, 41(11), 2396-2403.

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Isle of Wight Birth Cohort DNA Methylation

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In 1989, a whole population birth cohort was recruited on the Isle of Wight (IoW) to assess the impact of heredity and environment on the development of allergic disorders and allergen sensitization. The IoW 1989 birth cohort has been described in detail previously (29 (link)). Exact age at 18-year follow-up was calculated from the date of blood sample collection for the 18-year follow-up and the date of birth. BMI was calculated based on height and weight at the 18-year follow-up. DNA methylation was profiled in peripheral blood samples collected at the 18-year follow-up, using Illumina’s HumanMethylation450 array in a subset (n = 367) of subjects. DNA methylation data were preprocessed using IMA (30 (link)) and batch corrected using ComBat (31 (link)) as described previously (32 (link)). Genotyping was performed in a subset of cohort subjects with DNA methylation data (n = 87) using Illumina's OmniExpressExome BeadChip (v1.2). Potential mQTLs were modeled using generalized linear models for the effect of genotype (additive model) on logit-transformed DNA methylation, adjusting for sex and exact age at 18-year follow-up. All analyses used SPSS (v22.0).

Elliott H.R., Shihab H.A., Lockett G.A., Holloway J.W., McRae A.F., Smith G.D., Ring S.M., Gaunt T.R, & Relton C.L. (2017). Role of DNA Methylation in Type 2 Diabetes Etiology: Using Genotype as a Causal Anchor. Diabetes, 66(6), 1713-1722.

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Genetic Variants Influencing Vertebral Fracture Risk

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A large-scale meta-analysis of previous GWASs identified 56 BMD loci and revealed 14 loci associated with fracture risk (Estrada et al., 2012 (link)). To select SNPs for this study, those within or close to the 56 BMD loci were evaluated, as well as those on the Illumina HumanExome BeadChip (Grove et al., 2013 ) (Illumina, Inc., San Diego, CA, USA). A total of 979 (858 nonsynonymous and 121 silent) SNPs in 74 genes were identified (Supplementary Table 1) and evaluated for their association with the incidence of vertebral fracture among the 441 cases. The genotyping data for the 979 SNPs of the study subjects were provided from the Biobank Japan genotyping database generated using Illumina OmniExpressExome BeadChip version 1.2 (Illumina, Inc.) with call rates of > 0.99 during the process of genotyping.

Zhou H., Mori S., Ishizaki T., Takahashi A., Matsuda K., Koretsune Y., Minami S., Higashiyama M., Imai S., Yoshimori K., Doita M., Yamada A., Nagayama S., Kaneko K., Asai S., Shiono M., Kubo M, & Ito H. (2016). Genetic risk score based on the prevalence of vertebral fracture in Japanese women with osteoporosis. Bone Reports, 5, 168-172.

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Genotyping of Genomic Variants

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Genotyping was performed at John P. Hussman Institute for Human Genomics, University of Miami using Illumina (San Diego, CA) OmniExpress BeadChip or Illumina OmniExpressExome BeadChip. Genotype calls were generated with Illumina GenomeStudio software. Overlapping markers from 2 genotyping platforms were combined for data quality control (QC); single nucleotide polymorphisms (SNPs) were excluded if they had call rate < 95%, were monomorphic, or deviated from Hardy-Weinberg equilibrium (p < 0.00001). Samples were excluded if they showed call rate < 95%, had sex mismatch, had unconfirmed relatedness, or did not carry the classic 1.5 Mb duplication on chromosome 17p. Copy number variation (CNV) was checked using Illumina cnvPartition v3.2.1. Population stratification was assessed using EigenStrat.^{15 (link)} All other QC procedures were performed with PLINK v1.07.^{16 (link)} The combined dataset after QC included 699,650 markers and 903 samples.

Tao F., Beecham G.W., Rebelo A.P., Blanton S.H., Moran J.J., Lopez-Anido C., Svaren J., Morrow J.M., Abreu L., Rizzo D., Kirk C.A., Wu X., Feely S., Verhamme C., Saporta M.A., Herrmann D.N., Day J.W., Sumner C.J., Lloyd T.E., Li J., Yum S.W., Taroni F., Baas F., Choi B.O., Pareyson D., Scherer S.S., Reilly M.M., Shy M.E, & Züchner S. (2019). Variation in SIPA1L2 Is Correlated with Phenotype Modification in Charcot– Marie– Tooth Disease Type 1A. Annals of neurology, 85(3), 316-330.

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Genomic DNA Isolation and Genotyping

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The genomic DNA was isolated from blood samples by using QIAamp DNA Blood Kits
(Qiagen, Valencia, CA, USA) or the ABI PRISM 6100 Nucleic Acid PrepStation
(Applied Biosystems, Foster City, CA, USA) or from saliva using Oragene DNA Self
Collection Kits (DNA Genotek, Ottawa, ON, Canada). Genotyping was performed
using Illumina’s OmniExpressExome BeadChip (v1.2). Genotypes were extracted from
image data using GenomeStudio software (Illumina), and technical failure of
genotyping assays was determined by significant deviation of genotype
frequencies from expectations under Hardy-Weinberg equilibrium
(P < .01, χ² tests). In total, genome-wide
SNP data of 139 participants of IoWF2 (third generation) were included in the
study.

Rathod A., Duan J., Zhang H., Holloway J.W., Ewart S., Arshad S.H, & Karmaus W. (2020). Interweaving Between Genetic and Epigenetic Studies on Childhood Asthma. Epigenetics Insights, 13, 2516865720923395.

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Epigenomic Analysis of Multiple Sclerosis in Women

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Data from this study were both gathered and analyzed independently by the UC Berkeley investigators. Whole blood samples from 208 self-identified white, female MS patients were used for the study (Table 1). Genome-wide DNA methylation was profiled using Infinium MethylationEPIC BeadChips. Methylation data were analyzed using Bioconductor minfi package^{28 (link)}. Background dye correction and quantile normalization were performed, followed by the removal of batch effects using the ComBat^{44 (link)} function of R sva package. Ancestry and cell-type heterogeneity were estimated using GLINT^{37 (link)}, and methylation M values were adjusted for ancestry and cell type components as well as for age at sampling. Genome-wide genotyping was performed using Illumina Infinium 660 K OmniExpress or OmniExpressExome BeadChip arrays. Merged 660 K and Omni Express genotyping dataset (273,906 SNPs) were phased using SHAPEIT2, and imputed against reference haplotypes from Phase 3 of the 1000 Genomes Project using IMPUTE4. Linear regression models were used to study the association between adjusted M values and genotypes, while covarying for MS treatment status (0/1, never/ever treated). MHC imputation was not available for this study.

Roostaei T., Klein H.U., Ma Y., Felsky D., Kivisäkk P., Connor S.M., Kroshilina A., Yung C., Kaskow B.J., Shao X., Rhead B., Ordovás J.M., Absher D.M., Arnett D.K., Liu J., Patsopoulos N., Barcellos L.F., Weiner H.L, & De Jager P.L. (2021). Proximal and distal effects of genetic susceptibility to multiple sclerosis on the T cell epigenome. Nature Communications, 12, 7078.

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Genotyping and CNV Detection Protocol

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Genotyping was performed at the Keck Biotechnology Resource Laboratory at Yale University School of Medicine and at RUCDR Infinite Biologics® at Rutgers University using the Illumina HumanOmni1-Quad or OmniExpressExome BeadChip (Illumina, San Diego, CA). Samples from each pedigree were genotyped on the same platform. Genotype calling and CNV detection were performed as previously described ^{40 (link)} (see Supplementary Methods for detail). A pCNV threshold of <=0.05 was used for initial selection of CNVs. CNV annotation was performed by CNVision, AnnotSV ^{41 (link)}, and a custom program for inheritance pattern analysis. CNVs that are smaller than 1,000 base pairs or larger than 2 million base pairs were excluded.

Cao X., Zhang Y., Abdulkadir M., Deng L., Fernandez T.V., Garcia-Delgar B., Hagstrøm J., Hoekstra P.J., King R.A., Koesterich J., Kuperman S., Morer A., Nasello C., Plessen K.J., Thackray J.K., Zhou L., Dietrich A., Tischfield J.A., Heiman G.A, & Xing J. (2021). Whole exome sequencing identifies genes associated with Tourette’s Disorder in multiplex families. Molecular psychiatry, 26(11), 6937-6951.

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Genotyping Quality Control for Genetic Association Studies

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All the case samples and the non-BBJ control samples were genotyped using Illumina Infinium CoreExome Array or a combination of Illumina Infinium Core Array and Illumina Infinium Exome Array. BBJ samples were genotyped using Illumina Human OmniExpress Exome BeadChip or a combination of Illumina HumanOmniExpress and HumanExome BeadChips (CoreExome Array and OmniExpress Exome BeadChip are not exome arrays, but genome-wide arrays).
The genotype QC criteria were set as follows and variants that did not meet any of these criteria were excluded from further analyses; genotyping call rate ≥99%, Hardy–Weinberg equilibrium p-values (HWE-P) > 1.0 × 10⁻⁶, allele frequency difference from those of the imputation panel <3.0%, and MAF > 0.01.
The subjects who met any of the following criteria were excluded from the analyses; subjects with genotyping call rate <0.98, those who were in a high degree of relatedness showing PiHAT > 0.25 estimated by PLINK1.9^{72 (link)}, or those who were outliers of East Asian (EAS) in the principal component analysis (PCA) (Supplementary Fig. 1, 2). For the PCA, we extracted variants shared between our datasets (Set1 and Set2) and those in the HapMap project.

Ishikawa Y., Tanaka N., Asano Y., Kodera M., Shirai Y., Akahoshi M., Hasegawa M., Matsushita T., Saito K., Motegi S.I., Yoshifuji H., Yoshizaki A., Kohmoto T., Takagi K., Oka A., Kanda M., Tanaka Y., Ito Y., Nakano K., Kasamatsu H., Utsunomiya A., Sekiguchi A., Niiro H., Jinnin M., Makino K., Makino T., Ihn H., Yamamoto M., Suzuki C., Takahashi H., Nishida E., Morita A., Yamamoto T., Fujimoto M., Kondo Y., Goto D., Sumida T., Ayuzawa N., Yanagida H., Horita T., Atsumi T., Endo H., Shima Y., Kumanogoh A., Hirata J., Otomo N., Suetsugu H., Koike Y., Tomizuka K., Yoshino S., Liu X., Ito S., Hikino K., Suzuki A., Momozawa Y., Ikegawa S., Tanaka Y., Ishikawa O., Takehara K., Torii T., Sato S., Okada Y., Mimori T., Matsuda F., Matsuda K., Amariuta T., Imoto I., Matsuo K., Kuwana M., Kawaguchi Y., Ohmura K, & Terao C. (2024). GWAS for systemic sclerosis identifies six novel susceptibility loci including one in the Fcγ receptor region. Nature Communications, 15, 319.

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Genotyping and Quality Control for GWAS

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Genotyping and data QC have been described previously in [32, 33 ]. Patient DNA samples were genotyped using Illumina OmniExpress beadchip or Illumina OmniExpress Exome beadchip. Standard GWAS QC was performed using PLINK v1.07 [10 (link)] to remove monomorphic SNPs and SNPs with low call rate (<95%) or deviating from Hardy-Weinberg equilibrium (HWE P < 0.00001). Samples were checked for the classic 1.5 Mb duplication on chromosome 17p using Illumina cnvPartition v3.2.1. Population stratification was assessed by principal component analysis (PCA) using EigenStrat [11 (link)].

Tao F., Beecham G.W., Rebelo A.P., Blanton S.H., Moran J.J., Lopez-Anido C., Svaren J., Abreu L., Rizzo D., Kirk C.A., Wu X., Feely S., Verhamme C., Saporta M.A., Herrmann D.N., Day J.W., Sumner C.J., Lloyd T.E., Li J., Yum S.W., Taroni F., Baas F., Choi B.O., Pareyson D., Scherer S.S., Reilly M.M., Shy M.E, & Züchner S. (2019). Modifier Gene Candidates in Charcot-Marie-Tooth Disease Type 1A: A Case-Only Genome-Wide Association Study. Journal of Neuromuscular Diseases, 6(2), 201-211.

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