Coreexome

Manufactured by Illumina

Sourced in United States

The CoreExome is a DNA microarray analysis tool designed for comprehensive coverage of core genome regions. It provides a high-throughput and cost-effective platform for genotyping a broad set of genetic variants across the human genome.

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Lab products found in correlation

Omniquad, by Illumina (1 mentions) Omniexpress, by Illumina (1 mentions) Exome chip, by Illumina (1 mentions) Psycharray, by Illumina (1 mentions) Psycharray 24 beadchip, by Illumina (1 mentions) Psychchip, by Illumina (1 mentions) Omniexpress beadchip, by Illumina (1 mentions) Omni2, by Illumina (1 mentions)

7 protocols using coreexome

Genetic Data Curation and Quality Control

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Imputed genetic data were obtained from BiB. All samples were genotyped using two chips: the Infinium Global Sequencing Array-24 v.1 (GSA) (~640K SNPs) and the Infinium CoreExome-24 v1.1 BeadChip (~550K SNPs) (21 (link)). Genetic data from the Illumina Global Sequencing Array (GSA) and Illumina CoreExome SNPs were combined. Where SNPs were missing in >5% of individuals, they were excluded (21 (link)). When evaluating imputed data, the R² value can be a measure of quality control as it reflects to the estimated proportion of genetic variation maintained in the imputed data. As a result, SNPs with an R² <0.9 were excluded prior to analysis.

Fuller H., Iles M.M., Moore J.B, & Zulyniak M.A. (2023). Metabolic drivers of dysglycemia in pregnancy: ethnic-specific GWAS of 146 metabolites and 1-sample Mendelian randomization analyses in a UK multi-ethnic birth cohort. Frontiers in Endocrinology, 14, 1157416.

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Emory-Georgia Tech Health Discovery Cohort

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The replication cohort in Atlanta is the Center for Health Discovery and Well Being (CHDWB) cohort of the Emory-Georgia Tech Predictive Health Institute [21 (link)]. Whole genome genotypes were imputed from either Illumina OmniQuad or Illumina Core+Exome genotyping arrays using Impute2 against the 1000 Genomes. Over 200 trait measures are available for the cohort, at baseline and between 3 and 5 subsequent visits over a 4 year period (n = 738). Participants are all employees of Emory University between the ages of 25 and 75 contacted at random and representing a broad cross-section of the geography of the city of Atlanta and socioeconomic diversity. Summarised clinical data for the seven major traits is available (S4 Table). Written informed consent for participation in genetic research was provided under the auspices of the Institutional Review Boards of Georgia Tech and Emory University.

Benton M.C., Lea R.A., Macartney-Coxson D., Hanna M., Eccles D.A., Carless M.A., Chambers G.K., Bellis C., Goring H.H., Curran J.E., Harper J.L., Gibson G., Blangero J, & Griffiths L.R. (2015). A Phenomic Scan of the Norfolk Island Genetic Isolate Identifies a Major Pleiotropic Effect Locus Associated with Metabolic and Renal Disorder Markers. PLoS Genetics, 11(10), e1005593.

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Undiagnosed Individuals Genetic Evaluation

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We prospectively recruited individuals undiagnosed after a negative NGS (panel, ES or GS) result from a single centre, Victorian Clinical Genetics Services (VCGS) in Melbourne, Australia from March 2016 to June 2018. The recruiting clinical geneticist proposed each individual at a clinical review panel at which time the following criteria were assessed: (i) their phenotype was likely to be monogenic; (ii) appropriate testing had been undertaken, including standard resolution CMA (CytoSNP300K, Core Exome, or GSA; Illumina, San Diego USA) and singleton ES; (iii) phenotypically relevant genomic lesions not tractable by ES had been excluded, e.g. FMR1 triplet repeat analysis or methylation studies for imprinting disorders; and (iv) additional family members for sequencing were available if appropriate. Ethics approval was granted by the Royal Children’s Hospital Ethics Committee (HREC 36291A). Written informed consent was obtained from each family.

Cloney T., Gallacher L., Pais L., Tan N.B., Yeung A., Stark Z., Brown N.J., McGillivray G., Delatycki M.B., de Silva M.G., Downie L., Stutterd C.A., Elliott J., Compton A.G., Lovgren A., Oertel R., Francis D., Bell K., Sadedin S., Lim S.C., Helman G., Simons C., MacArthur D.G., Thorburn D.R., O’Donnell-Luria A., Christodoulou J., White S.M, & Tan T.Y. (2021). Lessons learned from multifaceted diagnostic approaches to the first 150 families in Victoria’s Undiagnosed Diseases Program. Journal of medical genetics, 59(8), 748-758.

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Genotyping and Imputation Workflow for GWAS

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The samples were genotyped using genotyping array Illumina OmniExpress, Illumina CoreExome, Affymetrix 6.0, Illumina 610K. Imputation reference panel was SISu v2 with 2690 hcWGS and 5092 WES Finnish genomes. Pre-imputation quality control was done per genotyping batch. Population outliers were removed based on principal components. Pre-phasing done with Eagle 2.3. Imputation with Impute2 (version 2.3.1). Post-imputation quality control consisted of checking chunk integrity (along the chromosome) and minor allele frequency for imputed variants (compared to the reference panel). Quality control for the genome-wide association analysis study (GWAS) results comprised removal of SNPs with Hardy–Weinberg equilibrium P-value ≤10⁻⁷, minor allele frequency <5%, and imputation quality (INFO-value) <0.7, proportion of missings ≥2%. Genotyping batch was used as a covariate in the analyses. Population stratification was controlled by adjusting for the first 10 principal components. Close relatives (PI_HAT > 0.2) were excluded from the analyses.

Geelhoed B., Börschel C.S., Niiranen T., Palosaari T., Havulinna A.S., Fouodo C.J., Scheinhardt M.O., Blankenberg S., Jousilahti P., Kuulasmaa K., Zeller T., Salomaa V, & Schnabel R.B. (2020). Assessment of causality of natriuretic peptides and atrial fibrillation and heart failure: a Mendelian randomization study in the FINRISK cohort. Europace, 22(10), 1463-1469.

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ABO Study Metabolomics and Genetics

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The ABO Study recruited healthy volunteers (non-pregnant/lactating women and men, age 18–50) to a single study visit at the University of Pennsylvania from 2012 to 2014, as described^{53 (link)}. Plasma metabolomics profiling, including measurement of kynurenine, was carried out at Metabolon (Metabolon Inc, Morrisville, NC; global metabolomics platform). Genotyping was performed using the Exome chip (Illumina, CoreExome, N > 540,000 variants, including rs3184504) at the Center for Applied Genomics at the Children's Hospital of Philadelphia. We analyzed data for a subset of individuals with overlapping metabolite and genetic data (N = 66).
The GENE and ABO studies were approved by the IRB of the University of Pennsylvania and Vanderbilt University. All participants provided written informed consent.

Bagheri M., Wang C., Shi M., Manouchehri A., Murray K.T., Murphy M.B., Shaffer C.M., Singh K., Davis L.K., Jarvik G.P., Stanaway I.B., Hebbring S., Reilly M.P., Gerszten R.E., Wang T.J., Mosley J.D, & Ferguson J.F. (2021). The genetic architecture of plasma kynurenine includes cardiometabolic disease mechanisms associated with the SH2B3 gene. Scientific Reports, 11, 15652.

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Genome-wide Genotyping and Imputation

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Genome-wide genotyping of participants within our recruitment pool was previously performed using a range of genotyping arrays including Illumina chips designed using HapMap references (317 K, 370 K, 610 K, 660 K) and more recent Illumina arrays designed using the 1KGP reference (Core + Exome, PsychArray, OmniExpress) as previously detailed (Cuellar-Partida et al., 2015 (link), Medland et al., 2009 (link)). All datasets have been combined with strict quality control procedures and imputed to the Haplotype Reference Consortium (HRC) Release 1 reference panel (McCarthy et al., 2016 (link)).

Lupton M.K., Robinson G.A., Adam R.J., Rose S., Byrne G.J., Salvado O., Pachana N.A., Almeida O.P., McAloney K., Gordon S.D., Raniga P., Fazlollahi A., Xia Y., Ceslis A., Sonkusare S., Zhang Q., Kholghi M., Karunanithi M., Mosley P.E., Lv J., Borne L., Adsett J., Garden N., Fripp J., Martin N.G., Guo C.C, & Breakspear M. (2020). A prospective cohort study of prodromal Alzheimer’s disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA). NeuroImage : Clinical, 29, 102527.

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Imputed Genotypic Data Across Cohorts

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For UKB, imputed genetic data released in 2018 were used. Two custom genotyping arrays were used to cover more than 800,000 markers and were further imputed to Haplotype Reference Consortium (HRC) and UK10K + 1000 Genomes Phase 3 reference panels (27) . In AO50, individuals were genotyped using Illumina single-nucleotide polymorphism (SNP) platforms: 317, 370, 610, 660, Core-Exome, PsychChip, Omni2.5 and OmniExpress and were imputed to HRC.1.1.
In SALT, genotyping was carried out using the Illumina OmniExpress bead chip, further imputed to Hapmap 2 build 36 reference panel. In SATSA, genotyping was carried out using Illumina PsychArray-24 BeadChip, imputed to 1000 Genomes Phase 3 reference panel. For both STR samples, only one twin from each monozygotic (MZ) pair was directly genotyped and genotypes were later imputed to their co-twin.

Daníelsdóttir H.B., Jylhävä J., Hägg S., Lu Y., Colodro-Conde L., Martin N.G., Pedersen N.L., Mosing M.A, & Lehto K. (2019). Neuroticism as a Predictor of Frailty in Old Age: A Genetically Informative Approach. Psychosomatic medicine, 81(9).

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