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Exome chip

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The Exome chip is a laboratory equipment product developed by Illumina. It is designed for targeted sequencing of the human exome, which encompasses the protein-coding regions of the genome. The core function of the Exome chip is to enable efficient and cost-effective analysis of genetic variations within the exome.

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Lab products found in correlation

Omniexpress, by Illumina (5 mentions) Immunochip, by Illumina (5 mentions) Cardio metabochip, by Illumina (3 mentions) Taqman snp genotyping assay, by Thermo Fisher Scientific (2 mentions) Humanexome beadchip, by Illumina (2 mentions) Drug metabolizing enzymes and transporters dmet array, by Illumina (2 mentions) Liaison instrument, by DiaSorin (2 mentions) Genome wide human snp array 6, by Thermo Fisher Scientific (1 mentions) Infinium array, by Illumina (1 mentions) Ms replication chip, by Illumina (1 mentions) Genotyping array, by Illumina (1 mentions) Infinium platform, by Illumina (1 mentions) Snp array 6, by Thermo Fisher Scientific (1 mentions) Metabochip, by Illumina (1 mentions) Coreexome, by Illumina (1 mentions)

22 protocols using exome chip

1

Genotyping APOL1 Risk Variants in Cohorts

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APOL1 risk variants (G1 and G2) were genotyped using TaqMan SNP Genotyping Assays (Applied Biosystems/ThermoFisher Scientific).9 (link),22 (link)APOL1 high-risk status was defined as the presence of 2 risk alleles (G1/G1, G2/G2, or G1/G2) versus the low-risk status, defined as having 1 or 0 risk variants (G1/G0, G2/G0, G0/G0), representing a recessive model. Sickle cell trait (HbS) was genotyped as described in Naik et al.23 (link) In secondary analyses, an additive (0, 1, 2 risk alleles) and dominant model (2 or 1 APOL1 risk alleles versus 0 risk alleles) were assessed. A subset of participants had available genomic array data (Illumina exome chip) to estimate population substructure (n=6714) via principal components generated using EIGENSOFT software.24 (link),25 (link) In sensitivity analyses, principal components were used to adjust for African ancestry in the subset with available data.
Gutiérrez O.M., Irvin M.R., Chaudhary N.S., Cushman M., Zakai N.A., David V.A., Limou S., Pamir N., Reiner A.P., Naik R.P., Sale M.M., Safford M.M., Hyacinth H.I., Judd S.E., Kopp J.B, & Winkler C.A. (2018). APOL1 Nephropathy Risk Variants and Incident Cardiovascular Disease Events in Community-Dwelling Black Adults. Circulation. Genomic and precision medicine, 11(6), e002098.
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2

Genetic and Serologic Factors in MS

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In EIMS and GEMS, HLA-DRB1 and HLA-A alleles were determined at 4-digit resolution. Genotyping was performed on the MS replication chip,15 (link) which is based on an Illumina exome chip, and HLA was then imputed with HLA*IMP:02.16 (link) In EIRA, HLA-DRB1 genotypes were obtained as previously described.13 (link) In EIMS and GEMS, we used multiplex serology to detect immunoglobulin G antibodies against the EBNA-1 peptide segment (aa 385–420),17 (link),18 (link) which has been identified as the primary EBNA-1 fragment associated with MS risk.19 (link) Dual-laser flow-based detection was used to quantify the antibodies in median flourescence intensity (MFI) units. We dichotomized EBNA-1 antibody levels based on the median seroreactivity among controls (5,620 MFI) into groups with high and low EBNA-1 antibody levels.7 (link) To study whether the potential interaction between obesity at age 20 years and high EBNA-1 antibody levels was affected by increasing EBNA-1 levels, we also divided the participants into 4 groups based on the seroreactivity among controls at the 50th, 75th, and 95th percentiles.
Hedström A.K., Brenner N., Butt J., Hillert J., Waterboer T., Olsson T, & Alfredsson L. (2020). Overweight/obesity in young adulthood interacts with aspects of EBV infection in MS etiology. Neurology® Neuroimmunology & Neuroinflammation, 8(1), e912.
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3

Large-Scale Genetic Imputation and Analysis

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All the genetic analyses were performed using a genetic map based on 6602 samples genotyped with 4 Illumina arrays (OmniExpress, ImmunoChip, Cardio-MetaboChip and ExomeChip), as previously described [19 (link)]. Imputation was performed on a genome-wide scale using a Sardinian sequence-based reference panel of 3514 individuals and the software Minimac3 on pre-phased genotypes. After imputation, only markers with imputation quality (RSQR) > 0.3 for estimated minor allele frequency (MAF) ≥ 1% or > 0.6 for MAF < 1% were retained for further analyses, yielding ~22 million variants (20,143,392 SNPs and 1,688,858 indels).
Idda M.L., Zoledziewska M., Urru S.A., McInnes G., Bilotta A., Nuvoli V., Lodde V., Orrù S., Schlessinger D., Cucca F, & Floris M. (2022). Genetic Variation among Pharmacogenes in the Sardinian Population. International Journal of Molecular Sciences, 23(17), 10058.
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4

HLA Typing and Anti-EBNA1 Antibody Assay

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HLA-DRB1 and HLA-A alleles were determined at four-digit resolution. Genotyping was performed on the MS replication chip (11 ), which is based on an Illumina exome chip to which ~90,000 custom markers were added with extra-high density in the HLA region, and HLA alleles were then imputed with HLA*IMP:02 (12 (link)).
A multiplex serological assay using beads loaded with recombinant glutathione s-transferase fusion proteins was used for detection of IgG antibodies against the EBNA1 peptide segment (aa 385–420) (13 (link), 14 (link)), which has been identified as the primary EBNA1 fragment associated with MS risk (9 (link)). Dual-laser flow-based detection was used to quantify the antibodies as units of median flourescence intensity. Anti-EBNA-1 antibody levels were dichotomized based on the median among controls, defining groups with high and low anti-EBNA-1 antibody levels. We also categorized anti-EBNA-1 antibody levels based on the 25th, 50th, and 75th percentiles among controls in order to perform a sensitivity analysis.
Hedström A.K., Huang J., Michel A., Butt J., Brenner N., Hillert J., Waterboer T., Kockum I., Olsson T, & Alfredsson L. (2020). High Levels of Epstein–Barr Virus Nuclear Antigen-1-Specific Antibodies and Infectious Mononucleosis Act Both Independently and Synergistically to Increase Multiple Sclerosis Risk. Frontiers in Neurology, 10, 1368.
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5

Sardinian GWAS for T1D and MS

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The Sardinian T1D and MS GWAS case-control studies were performed using combined data sets where all samples were genotyped with ImmunoChip (Illumina) and subsets of the samples were genotyped with Genome-Wide Human SNP Array 6.0 (Affymetrix) and OmniExpress (Illumina). This combined approach after quality controls brought 883,557 SNPs subjected to the parallel GWAS studies on MS and T1D (Supplementary Figure.1). See Supplementary material for details for GWAS on MS and T1D using the integrated map (ImmunoChip, OmniExpress and Genome-Wide Human SNP Array 6.0).
To study PRF1:p.A91V mutation effect on phenotypes, the association results from the SardiNIA general population were used. The association results are based on 6,521 SardiNIA volunteers genotyped with Illumina arrays (OmniExpress, ImmunoChip, Cardio-MetaboChip and ExomeChip; 890,542 SNPs in total) [13 (link)]. Samples from both cohorts were imputed with Minimac3 on a Next Generation Sequencing based reference panel of 3,514 Sardinian individuals [13 (link)].
Sidore C., Orrù V., Cocco E., Steri M., Inshaw J.R., Pitzalis M., Mulas A., McGurnaghan S., Frau J., Porcu E., Busonero F., Dei M., Lai S., Sole G., Virdis F., Serra V., Poddie F., Delitala A., Marongiu M., Deidda F., Pala M., Floris M., Masala M., Onengut-Gumuscu S., Robertson C.C., Leoni L., Frongia A., Ricciardi M.R., Chessa M., Olla N., Lovicu M., Loizedda A., Maschio A., Mereu L., Ferrigno P., Curreli N., Balaci L., Loi F., Ferreli L.A., Pilia M.G., Pani A., Marrosu M.G., Abecasis G.R., Rich S.S., Colhoun H., Todd J.A., Schlessinger D., Fiorillo E., Cucca F, & Zoledziewska M. (2020). PRF1 mutation alters immune system activation, inflammation and risk of autoimmunity. Multiple sclerosis (Houndmills, Basingstoke, England), 27(9), 1332-1340.
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6

Genotyping APOL1 Alleles in African Americans

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APOL1 alleles (G1 [rs73885319A>G, S342G] and G2 [Rs71785313 TTATAA/– N388Y389/–]) were genotyped in African-American participants using TaqMan SNP Genotyping Assays (Applied Biosystems/ThermoFisher Scientific).1 (link), 12 (link)APOL1 high-risk status was defined as the presence of 2 risk alleles (G1/G1, G2/G2, or G1/G2) versus the low-risk status, defined as having 1 or 0 risk alleles (G1/G0, G2/G0, G0/G0), representing a recessive model. A subset of participants had available genomic array data (Illumina exome chip) to estimate population substructure (n=6,714) via principal components generated using EIGENSOFT software.13 (link), 14 (link)
Gutiérrez O.M., Irvin M.R., Zakai N.A., Naik R.P., Chaudhary N.S., Estrella M.M., Limou S., Judd S.E., Cushman M., Kopp J.B, & Winkler C.A. (2019). APOL1 Nephropathy Risk Alleles and Mortality in African American Adults: A Cohort Study. American journal of kidney diseases : the official journal of the National Kidney Foundation, 75(1), 54-60.
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7

Sardinian Whole-Genome Genotyping and Sequencing

Cited 3 times
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In brief, we genotyped 6,602 individuals from four villages in the Lanusei valley on Sardinia (>60% of the adult population). Each sample was genotyped on four different Illumina Infinium arrays: OmniExpress, Cardio-Metabochip (Voight et al., 2012 (link)), Immunochip (Parkes et al., 2013 (link)), and Exome Chip. We also performed low-depth (~4x coverage) whole-genome sequencing on 3,839 individuals, 2,340 of whom we also genotyped. Study samples, genotyping, sequencing and variant calling have been previously described (Sidore et al., 2015 (link)).
Over 100 traits (e.g. blood lab measurements, anthropometric values) have been measured at 4 to 5 time-points. We looked at 120 traits from the first visit, for which the majority of the individuals have measurements (median number of samples with at least one measurement per trait= 5814, first quartile= 5473, third quartile= 5923). The traits have been previously summarized (Pilia et al., 2006 (link)).
Gagliano S.A., Sengupta S., Sidore C., Maschio A., Cucca F., Schlessinger D, & Abecasis G.R. (2018). Relative impact of indels versus SNPs on complex disease. Genetic Epidemiology, 43(1), 112-117.
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8

Genetic Variants Affecting Rasburicase Response

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DNA was extracted from peripheral blood after patients achieved clinical remission. 645 patients had their genotype assessed using one or more of the following platforms: Affymetrix Drug Metabolizing Enzymes and Transporters (DMET) array (Santa Clara, CA) (n=379) (18 (link), 19 (link)), Illumina HumanExome BeadChip (Exomechip) (San Diego, CA) (n=634) (20 (link)), and WES (n=374) (21 (link)) (Supplemental Fig. S1). The Affymetrix DMET array interrogated 6 nonsynonymous single nucleotide polymorphisms (SNPs) (A (p.N126D), Canton (p.R459L), Chatham (p.A335T), Mediterranean (p.S188F), Sao Borja (p.D113N), 1 with unknown function (p.V77M)). The Illumina Exomechip interrogated 8 nonsynonymous SNPs (A-, 968 (p.L323P), Asahi (p.V68M), Mediterranean (p.S188F), Malaga (p.D181V), Sierra Leone (p.R104H), Seattle (p.D282H), Mira d’Aire (p.D350H), and one with unknown function (p.Q11H)) (Supplemental Fig. S2, Table S2). Nonsynonymous coding SNPs were classified according to the WHO classification, (16 (link)) and patients were assigned a phenotype based on the CPIC guidelines for rasburicase. (6 (link))
Robinson K.M., Yang W., Haidar C.E., Hankins J.S., Jay D.W., Kornegay N., Rubnitz J.E., Broeckel U., Cheng C., Pui C.H., Jeha S, & Relling M.V. (2018). Concordance between glucose-6-phosphate dehydrogenase (G6PD) genotype and phenotype and rasburicase use in patients with hematologic malignancies. The pharmacogenomics journal, 19(3), 305-314.
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9

HLA Allele and EBNA-1 Antibody Analysis in MS

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Blood samples were available for 2059 cases and 2887 controls. HLA-DRB1 and HLA-A alleles were determined at four-digit resolution. Genotyping was performed on the MS replication chip20 which is based on an Illumina exome chip to which approximately 90,000 custom markers were added and HLA was then imputed with HLA*IMP:0221 (link). The reference panel was enriched with 400 Swedish controls for MHC class II alleles derived from sequence of exome 2 in order to improve imputation quality22 (link). Antibodies against the primary EBNA-1 peptide segment associated with MS risk (aa 385–420)23 (link) was detected using a multiplex serological assay, which has been described in more detail elsewhere24 (link). Dual-laser flow-based detection was used to quantify the antibodies as units of median flourescence intensity.
Hedström A.K., Olsson T, & Alfredsson L. (2021). The increased risk of multiple sclerosis associated with HLA-DRB1*15:01 and smoking is modified by alcohol consumption. Scientific Reports, 11, 21237.
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10

Genotyping Blood Samples for MS Research

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In EIMS, genotyped blood samples were available for 1973 cases and 2784 controls
who had answered both questions regarding breastfeeding. Genotyping was
performed on the MS replication chip24 which is based on an Illumina exome chip to which approximately 100,000
custom markers were added and HLA region alleles were imputed with HLA*IMP:02.25 (link) In KPNC, genotyped blood samples were available for 865 cases and 679
controls. Subjects were genotyped as previously described.26 (link)
Hedström A., Adams C., Shao X., Schaefer C., Olsson T., Barcellos L, & Alfredsson L. (2020). Breastfeeding is associated with reduced risk of multiple sclerosis in males, predominantly among HLA-DRB1*15:01 carriers. Multiple Sclerosis Journal - Experimental, Translational and Clinical, 6(2), 2055217320928101.
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