humanexome beadchip v1, by Illumina - Product details

1

Genotyping and Quality Control for CHARGE Cohorts

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All four CHARGE cohorts were genotyped for the HumanExome BeadChip v1.0 from Illumina (San Diego, CA, USA). Genotype calling and quality control was performed centrally as described previously [30 (link)]. Each study also performed quality control locally (Supporting methods in S1 Text). Post-analysis quality control on the top results included visual inspection of cluster plots (S1 Fig) and re-genotyping of TM2D3 carriers using a TaqMan assay (genotype calls were 100% validated). TM2D3 variant was genotyped in the AGES-followup cohort using the TaqMan assay. In ADGC and GERAD consortia the SKAP2 and TM2D3 variants were gentoyped on the Illumina HumanExome BeadChip v1.0 or v1.1 from Illumina (Supporting methods in S1 Text).

Jakobsdottir J., van der Lee S.J., Bis J.C., Chouraki V., Li-Kroeger D., Yamamoto S., Grove M.L., Naj A., Vronskaya M., Salazar J.L., DeStefano A.L., Brody J.A., Smith A.V., Amin N., Sims R., Ibrahim-Verbaas C.A., Choi S.H., Satizabal C.L., Lopez O.L., Beiser A., Ikram M.A., Garcia M.E., Hayward C., Varga T.V., Ripatti S., Franks P.W., Hallmans G., Rolandsson O., Jansson J.H., Porteous D.J., Salomaa V., Eiriksdottir G., Rice K.M., Bellen H.J., Levy D., Uitterlinden A.G., Emilsson V., Rotter J.I., Aspelund T., O’Donnell C.J., Fitzpatrick A.L., Launer L.J., Hofman A., Wang L.S., Williams J., Schellenberg G.D., Boerwinkle E., Psaty B.M., Seshadri S., Shulman J.M., Gudnason V, & van Duijn C.M. (2016). Rare Functional Variant in TM2D3 is Associated with Late-Onset Alzheimer's Disease. PLoS Genetics, 12(10), e1006327.

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TREM2 Variant Genotyping in Alzheimer's Disease

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Genotyping was performed in subsets at four centers: NorthShore, Miami, WashU, and CHOP (“CHOP” and “ADC7” datasets) on the Illumina HumanExome BeadChip v1.0. One variant rs75932628 (p.R47H) in TREM2 clustered poorly across all ADGC cohorts, and was therefore re-genotyped using a Taqman assay. Data on all samples underwent standard quality control procedures applied to genome-wide association studies (GWAS), including excluding variants with call rates <95%, and then filtering samples with call rate <95%. Variants with MAF>0.01 were evaluated for departure from HWE and any variants for P_HWE<10^-6 were excluded. Population substructure within each of the five subsets (NorthShore, Miami, WashU, CHOP, and ADC7) was examined using PC analysis in EIGENSTRAT⁴, and population outliers (>6 SD) were excluded from further analyses; the first three PCs were adjusted for as covariates in association testing. Prior to analysis we harmonized the alternate and reference alleles over all datasets. See Supplementary Table 3 for an overview of cohort genotype calling and quality control procedures. All sample genotyping and quality control was performed blind to participant’s disease status.

Sims R., van der Lee S.J., Naj A.C., Bellenguez C., Badarinarayan N., Jakobsdottir J., Kunkle B.W., Boland A., Raybould R., Bis J.C., Martin E.R., Grenier-Boley B., Heilmann-Heimbach S., Chouraki V., Kuzma A.B., Sleegers K., Vronskaya M., Ruiz A., Graham R.R., Olaso R., Hoffmann P., Grove M.L., Vardarajan B.N., Hiltunen M., Nöthen M.M., White C.C., Hamilton-Nelson K.L., Epelbaum J., Maier W., Choi S.H., Beecham G.W., Dulary C., Herms S., Smith A.V., Funk C.C., Derbois C., Forstner A.J., Ahmad S., Li H., Bacq D., Harold D., Satizabal C.L., Valladares O., Squassina A., Thomas R., Brody J.A., Qu L., Sanchez-Juan P., Morgan T., Wolters F.J., Zhao Y., Garcia F.S., Denning N., Fornage M., Malamon J., Naranjo M.C., Majounie E., Mosley T.H., Dombroski B., Wallon D., Lupton M.K., Dupuis J., Whitehead P., Fratiglioni L., Medway C., Jian X., Mukherjee S., Keller L., Brown K., Lin H., Cantwell L.B., Panza F., McGuinness B., Moreno-Grau S., Burgess J.D., Solfrizzi V., Proitsi P., Adams H.H., Allen M., Seripa D., Pastor P., Cupples L.A., Price N.D., Hannequin D., Frank-García A., Levy D., Chakrabarty P., Caffarra P., Giegling I., Beiser A.S., Giedraitis V., Hampel H., Garcia M.E., Wang X., Lannfelt L., Mecocci P., Eiriksdottir G., Crane P.K., Pasquier F., Boccardi V., Henández I., Barber R.C., Scherer M., Tarraga L., Adams P.M., Leber M., Chen Y., Albert M.S., Riedel-Heller S., Emilsson V., Beekly D., Braae A., Schmidt R., Blacker D., Masullo C., Schmidt H., Doody R.S., Spalletta G., Longstreth W J.r., Fairchild T.J., Bossù P., Lopez O.L., Frosch M.P., Sacchinelli E., Ghetti B., Sánchez-Juan P., Yang Q., Huebinger R.M., Jessen F., Li S., Kamboh M.I., Morris J., Sotolongo-Grau O., Katz M.J., Corcoran C., Himali J.J., Keene C.D., Tschanz J., Fitzpatrick A.L., Kukull W.A., Norton M., Aspelund T., Larson E.B., Munger R., Rotter J.I., Lipton R.B., Bullido M.J., Hofman A., Montine T.J., Coto E., Boerwinkle E., Petersen R.C., Alvarez V., Rivadeneira F., Reiman E.M., Gallo M., O’Donnell C.J., Reisch J.S., Bruni A.C., Royall D.R., Dichgans M., Sano M., Galimberti D., St George-Hyslop P., Scarpini E., Tsuang D.W., Mancuso M., Bonuccelli U., Winslow A.R., Daniele A., Wu C.K., Peters O., Nacmias B., Riemenschneider M., Heun R., Brayne C., Rubinsztein D.C., Bras J., Guerreiro R., Hardy J., Al-Chalabi A., Shaw C.E., Collinge J., Mann D., Tsolaki M., Clarimón J., Sussams R., Lovestone S., O’Donovan M.C., Owen M.J., Behrens T.W., Mead S., Goate A.M., Uitterlinden A.G., Holmes C., Cruchaga C., Ingelsson M., Bennett D.A., Powell J., Golde T.E., Graff C., De Jager P.L., Morgan K., Ertekin-Taner N., Combarros O., Psaty B.M., Passmore P., Younkin S.G., Berr C., Gudnason V., Rujescu D., Dickson D.W., Dartigues J.F., DeStefano A.L., Ortega-Cubero S., Hakonarson H., Campion D., Boada M., Kauwe J.“., Farrer L.A., Van Broeckhoven C., Ikram M.A., Jones L., Haines J., Tzourio C., Launer L.J., Escott-Price V., Mayeux R., Deleuze J.F., Amin N., Holmans P.A., Pericak-Vance M.A., Amouyel P., van Duijn C.M., Ramirez A., Wang L.S., Lambert J.C., Seshadri S., Williams J, & Schellenberg G.D. (2017). Rare coding variants in PLCG2, ABI3 and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nature genetics, 49(9), 1373-1384.

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3

Whole Exome and SNP Sequencing Protocols

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There are two golden datasets which are used in this paper. In Table 1 the golden dataset consists of 1782 and 2830 whole exome sequenced (WES) data from the AfAm and EuAm ancestry respectively. The WES dataset has been sequenced with 80–100× coverage. The WES gold standard dataset was aligned with the Mercury pipeline [21 (link)] in single sample mode. The second gold standard dataset which we refer to as cSNP, consists of 3533 samples genotyped with HumanExome BeadChip v1.0 (Illumina, Inc., San Diego, CA) querying 247,870 variable sites using standard protocols suggested by the manufacture at the University of Texas Health Science center at Houston [38 (link)]. There are 1683 EuAm samples and 1850 AfAm samples in the gold standard dataset. All true negative sites with missing genotype data are removed from the gold standard. For the sensitivity calculations, all sites common to WGS dataset with greater than 5 % missing genotypes are also removed from further analysis.

Huang Z., Rustagi N., Veeraraghavan N., Carroll A., Gibbs R., Boerwinkle E., Venkata M.G, & Yu F. (2016). A hybrid computational strategy to address WGS variant analysis in >5000 samples. BMC Bioinformatics, 17(1), 361.

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Genotyping and Quality Control for LOAD Samples

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Genotyping was performed at Life and Brain, Bonn, Germany, with the Illumina HumanExome BeadChip v1.0 (N=247,870 variants) or v1.1 (N=242,901 variants). Illumina’s GenTrain version 2.0 clustering algorithm in GenomeStudio or zCall¹ was used for genotype calling. Quality control (QC) filters were implemented for sample call rate excluding samples with >1% missingness, excess autosomal heterozygosity excluding outliers based on <1% and >1% minor allele frequency (MAF) separately, gender discordance, relatedness excluding one of each pair related with IBD ≥ 0.125 (the level expected for first cousins), and population outliers (i.e. non European ancestry). Variants were filtered based on call rate excluding variants with >1% missingness, genotype cluster separation excluding variants with a separation score < 0.4 and Hardy-Weinberg equilibrium (HWE) excluding variants with P_HWE < 1×10^-4. Ten principal components (PCs) were extracted using EIGENSTRAT, including the first three PCs as covariates had the maximum impact on the genomic control inflation factor, λ². After QC 6,000 LOAD cases and 2,974 elderly controls (version 1.0; 4,093 LOAD cases and 1,599 controls, version 1.1; 1,907 LOAD cases and 1,375 controls) remained. The version 1.0 array had 244,412 variants available for analysis and 239,814 remained for the version 1.1 array.

Sims R., van der Lee S.J., Naj A.C., Bellenguez C., Badarinarayan N., Jakobsdottir J., Kunkle B.W., Boland A., Raybould R., Bis J.C., Martin E.R., Grenier-Boley B., Heilmann-Heimbach S., Chouraki V., Kuzma A.B., Sleegers K., Vronskaya M., Ruiz A., Graham R.R., Olaso R., Hoffmann P., Grove M.L., Vardarajan B.N., Hiltunen M., Nöthen M.M., White C.C., Hamilton-Nelson K.L., Epelbaum J., Maier W., Choi S.H., Beecham G.W., Dulary C., Herms S., Smith A.V., Funk C.C., Derbois C., Forstner A.J., Ahmad S., Li H., Bacq D., Harold D., Satizabal C.L., Valladares O., Squassina A., Thomas R., Brody J.A., Qu L., Sanchez-Juan P., Morgan T., Wolters F.J., Zhao Y., Garcia F.S., Denning N., Fornage M., Malamon J., Naranjo M.C., Majounie E., Mosley T.H., Dombroski B., Wallon D., Lupton M.K., Dupuis J., Whitehead P., Fratiglioni L., Medway C., Jian X., Mukherjee S., Keller L., Brown K., Lin H., Cantwell L.B., Panza F., McGuinness B., Moreno-Grau S., Burgess J.D., Solfrizzi V., Proitsi P., Adams H.H., Allen M., Seripa D., Pastor P., Cupples L.A., Price N.D., Hannequin D., Frank-García A., Levy D., Chakrabarty P., Caffarra P., Giegling I., Beiser A.S., Giedraitis V., Hampel H., Garcia M.E., Wang X., Lannfelt L., Mecocci P., Eiriksdottir G., Crane P.K., Pasquier F., Boccardi V., Henández I., Barber R.C., Scherer M., Tarraga L., Adams P.M., Leber M., Chen Y., Albert M.S., Riedel-Heller S., Emilsson V., Beekly D., Braae A., Schmidt R., Blacker D., Masullo C., Schmidt H., Doody R.S., Spalletta G., Longstreth W J.r., Fairchild T.J., Bossù P., Lopez O.L., Frosch M.P., Sacchinelli E., Ghetti B., Sánchez-Juan P., Yang Q., Huebinger R.M., Jessen F., Li S., Kamboh M.I., Morris J., Sotolongo-Grau O., Katz M.J., Corcoran C., Himali J.J., Keene C.D., Tschanz J., Fitzpatrick A.L., Kukull W.A., Norton M., Aspelund T., Larson E.B., Munger R., Rotter J.I., Lipton R.B., Bullido M.J., Hofman A., Montine T.J., Coto E., Boerwinkle E., Petersen R.C., Alvarez V., Rivadeneira F., Reiman E.M., Gallo M., O’Donnell C.J., Reisch J.S., Bruni A.C., Royall D.R., Dichgans M., Sano M., Galimberti D., St George-Hyslop P., Scarpini E., Tsuang D.W., Mancuso M., Bonuccelli U., Winslow A.R., Daniele A., Wu C.K., Peters O., Nacmias B., Riemenschneider M., Heun R., Brayne C., Rubinsztein D.C., Bras J., Guerreiro R., Hardy J., Al-Chalabi A., Shaw C.E., Collinge J., Mann D., Tsolaki M., Clarimón J., Sussams R., Lovestone S., O’Donovan M.C., Owen M.J., Behrens T.W., Mead S., Goate A.M., Uitterlinden A.G., Holmes C., Cruchaga C., Ingelsson M., Bennett D.A., Powell J., Golde T.E., Graff C., De Jager P.L., Morgan K., Ertekin-Taner N., Combarros O., Psaty B.M., Passmore P., Younkin S.G., Berr C., Gudnason V., Rujescu D., Dickson D.W., Dartigues J.F., DeStefano A.L., Ortega-Cubero S., Hakonarson H., Campion D., Boada M., Kauwe J.“., Farrer L.A., Van Broeckhoven C., Ikram M.A., Jones L., Haines J., Tzourio C., Launer L.J., Escott-Price V., Mayeux R., Deleuze J.F., Amin N., Holmans P.A., Pericak-Vance M.A., Amouyel P., van Duijn C.M., Ramirez A., Wang L.S., Lambert J.C., Seshadri S., Williams J, & Schellenberg G.D. (2017). Rare coding variants in PLCG2, ABI3 and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nature genetics, 49(9), 1373-1384.

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5

Exome-wide genotyping protocol for AGES-RS

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Study samples were processed on the exome-wide genotyping array Illumina HumanExome BeadChip v1.0 (San Diego, CA, USA) for all AGES-RS participants at the University of Texas Health Science Center at Houston genotyping center as previously described^{56 (link)}. The exome array was enriched for exonic variants selected from over 12,000 individual exome and whole-genome sequences from different study populations^{38 (link)} and includes as well tags for previously described GWAS hits, ancestry informative markers, mitochondrial SNPs, and human leukocyte antigen tags^{38 (link)}. A total of 244,883 variants were included on the exome array. Genotype call and quality control filters including call rate, heterozygosity, sex discordance, and principal component analysis outliers were performed as previously described^{2 (link),21 (link)}. Variants with call rate <90% or with Hardy–Weinberg P values < 1 × 10⁻⁷ were removed from the study. Totally, 76,891 variants were detected in at least one individual of the AGES-RS cohort. Of these variants, 54,469 had a MAF > 0.001 and were examined for association against each of the 4782 human serum protein measurements (see below).

Emilsson V., Gudmundsdottir V., Gudjonsson A., Jonmundsson T., Jonsson B.G., Karim M.A., Ilkov M., Staley J.R., Gudmundsson E.F., Launer L.J., Lindeman J.H., Morton N.M., Aspelund T., Lamb J.R., Jennings L.L, & Gudnason V. (2022). Coding and regulatory variants are associated with serum protein levels and disease. Nature Communications, 13, 481.

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Genotyping Workflow for Exome Chip

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Samples were genotyped either at the Center for Inherited Disease Research (CIDR) at Johns Hopkins University on the Illumina HumanExome BeadChip v1.0 (247,870 variants), or at the Human Genetics Center at the University of Texas Health Science Center at Houston (Houston) on either v1.0 or v1.1 (242,901 variants) (http://www.chargeconsortium.com/main/exomechip) [48 (link)]. Genotypes were called with Illumina’s GenTrain 1.0 (CIDR) or Gentrain 2.0 (Houston) clustering algorithm from the Illumina GenomeStudio v2011.1 software.

NANDAKUMAR P., LEE D., RICHARD M.A., TEKOLA-AYELE F., TAYO B.O., WARE E., SUNG Y.J., SALAKO B., OGUNNIYI A., GU C.C., GROVE M.L., FORNAGE M., KARDIA S., ROTIMI C., COOPER R.S., MORRISON A.C., EHRET G, & CHAKRAVARTI A. (2017). RARE CODING VARIANTS ASSOCIATED WITH BLOOD PRESSURE VARIATION IN 15,914 INDIVIDUALS OF AFRICAN ANCESTRY. Journal of hypertension, 35(7), 1381-1389.

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7

Genotyping of rs1800437 in Cohorts

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In both cohorts, information on genotype rs1800437 was obtained from genome-wide association study data performed at the Broad genotyping facility using Illumina OmniExpressExome BeadChip v1.0 B (MDC-CC, n = 3344) or Illumina HumanExome BeadChip v1.0 (PPP-Botnia, n = 4905). The call rate was >99.9% and the SNP was in Hardy–Weinberg equilibrium in both cohorts.

Jujić A., Atabaki-Pasdar N., Nilsson P.M., Almgren P., Hakaste L., Tuomi T., Berglund L.M., Franks P.W., Holst J.J., Prasad R.B., Torekov S.S., Ravassa S., Díez J., Persson M., Melander O., Gomez M.F., Groop L., Ahlqvist E, & Magnusson M. (2020). Glucose-dependent insulinotropic peptide and risk of cardiovascular events and mortality: a prospective study. Diabetologia, 63(5), 1043-1054.

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8

Exome Genotyping Quality Control

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Most of the study participants were genotyped using the HumanExome Bead Chip v1.0 (Illumina, Inc. San Diego, CA), and variant calling was performed jointly for AGES, ARIC, CARDIA, CHS, GENOA, and RS at the University of Texas Health Science Center at Houston [19 (link)]. LBC1921, LBC1936, and CROATIA-Korčula were called in Genome Studio (Illumina Inc.) based on the CHARGE Consortium joint calling cluster file. Quality control procedures included checking concordance with previously collected GWAS genotyping data; exclusion of individuals missing more than 5% of genotypes; population clustering outliers; those with high inbreeding coefficients, rates of heterozygosity, or unexpectedly high identity-by-descent; and participants with gender mismatches. All genetic variants were coded additively with respect to the minor allele in the jointly called dataset. GS:SFHS was genotyped using the HumanExome Bead Chip v1_A and variant calling was performed using GenCall. ASPS genotyping was performed at the Helmholtz Zentrum München using the Illumina HumanExome v1.1 chip and Genome Studio Version V2011.1 software. Samples were excluded if there was contamination with other DNA, sex mismatch, cryptic relatedness, excess heterozygosity, duplicates on the chip or low call rate (<95%). SNPs were excluded based on low call rate (<95%) and Hardy–Weinberg equilibrium p value < 10⁻⁶.

Bressler J., Davies G., Smith A.V., Saba Y., Bis J.C., Jian X., Hayward C., Yanek L., Smith J.A., Mirza S.S., Wang R., Adams H.H., Becker D., Boerwinkle E., Campbell A., Cox S.R., Eiriksdottir G., Fawns-Ritchie C., Gottesman R.F., Grove M.L., Guo X., Hofer E., Kardia S.L., Knol M.J., Koini M., Lopez O.L., Marioni R.E., Nyquist P., Pattie A., Polasek O., Porteous D.J., Rudan I., Satizabal C.L., Schmidt H., Schmidt R., Sidney S., Simino J., Smith B.H., Turner S.T., van der Lee S.J., Ware E.B., Whitmer R.A., Yaffe K., Yang Q., Zhao W., Gudnason V., Launer L.J., Fitzpatrick A.L., Psaty B.M., Fornage M., Arfan Ikram M., van Duijn C.M., Seshadri S., Mosley T.H, & Deary I.J. (2021). Association of low-frequency and rare coding variants with information processing speed. Translational Psychiatry, 11, 613.

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9

Quality Control and Population Structure Analysis

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We performed quality control on the jointly-called samples. Individuals were checked for total number of variants, observed number of singletons and doubletons, Ti/Tv ratio, Het/Hom ratio, missingness, contamination with VerifyBamID^{12 (link)}, and non-reference concordance with available genotype data from the Illumina HumanExome BeadChip v1.0. Individuals that were outliers (> ± 3*interquartile range) on at least one metric were excluded (Supplemental Table 1, Supplemental Figure 1). Population structure was assessed using the multi-dimensional scaling (MDS) algorithm in the PLINK software^{13 (link)} and ten principal components of ancestry were obtained (Supplemental Figure 2).

Peloso G.M., Lange L.A., Varga T.V., Nickerson D.A., Smith J.D., Griswold M.E., Musani S., Polfus L.M., Mei H., Gabriel S., Quarells R.C., Altshuler D., Boerwinkle E., Daly M.J., Neale B., Correa A., Reiner A.P., Wilson J.G, & Kathiresan S. (2016). Association of Exome Sequences With Cardiovascular Traits among Blacks in the Jackson Heart Study. Circulation. Cardiovascular genetics, 9(4), 368-374.

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Exome Chip Genotyping and Quality Control

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All four CHARGE cohorts were genotyped for the Illumina HumanExome BeadChip v1.0. To increase the quality of the rare variant genotype calls, the genotypes for all four studies were jointly called with 62,266 samples from 11 studies at the University of Texas HSC at Houston³. Quality control (QC) procedures for the genotype data were performed both centrally at UT Houston and at each study. The central QC procedures have been described previously³. Minimum QC included: 1) Concordance checking with GWAS data and removal of problematic samples, 2) Removal of individuals with low genotype completion rate (<90%), 3) Removal of variants with low genotype call rate (<95%), 4) Removal of individuals with sex-mismatches, 5) Removal of one individual from duplicate pairs, 6) Removal of first-degree relatives based on genetically calculated relatedness (IBS > 0.45), with cases retained over controls, 7) Removal of variants not called in over 5% of the individuals and those that deviated significantly form the expected Hardy-Weinberg Equilibrium proportions (P<1×10^-6).

Sims R., van der Lee S.J., Naj A.C., Bellenguez C., Badarinarayan N., Jakobsdottir J., Kunkle B.W., Boland A., Raybould R., Bis J.C., Martin E.R., Grenier-Boley B., Heilmann-Heimbach S., Chouraki V., Kuzma A.B., Sleegers K., Vronskaya M., Ruiz A., Graham R.R., Olaso R., Hoffmann P., Grove M.L., Vardarajan B.N., Hiltunen M., Nöthen M.M., White C.C., Hamilton-Nelson K.L., Epelbaum J., Maier W., Choi S.H., Beecham G.W., Dulary C., Herms S., Smith A.V., Funk C.C., Derbois C., Forstner A.J., Ahmad S., Li H., Bacq D., Harold D., Satizabal C.L., Valladares O., Squassina A., Thomas R., Brody J.A., Qu L., Sanchez-Juan P., Morgan T., Wolters F.J., Zhao Y., Garcia F.S., Denning N., Fornage M., Malamon J., Naranjo M.C., Majounie E., Mosley T.H., Dombroski B., Wallon D., Lupton M.K., Dupuis J., Whitehead P., Fratiglioni L., Medway C., Jian X., Mukherjee S., Keller L., Brown K., Lin H., Cantwell L.B., Panza F., McGuinness B., Moreno-Grau S., Burgess J.D., Solfrizzi V., Proitsi P., Adams H.H., Allen M., Seripa D., Pastor P., Cupples L.A., Price N.D., Hannequin D., Frank-García A., Levy D., Chakrabarty P., Caffarra P., Giegling I., Beiser A.S., Giedraitis V., Hampel H., Garcia M.E., Wang X., Lannfelt L., Mecocci P., Eiriksdottir G., Crane P.K., Pasquier F., Boccardi V., Henández I., Barber R.C., Scherer M., Tarraga L., Adams P.M., Leber M., Chen Y., Albert M.S., Riedel-Heller S., Emilsson V., Beekly D., Braae A., Schmidt R., Blacker D., Masullo C., Schmidt H., Doody R.S., Spalletta G., Longstreth W J.r., Fairchild T.J., Bossù P., Lopez O.L., Frosch M.P., Sacchinelli E., Ghetti B., Sánchez-Juan P., Yang Q., Huebinger R.M., Jessen F., Li S., Kamboh M.I., Morris J., Sotolongo-Grau O., Katz M.J., Corcoran C., Himali J.J., Keene C.D., Tschanz J., Fitzpatrick A.L., Kukull W.A., Norton M., Aspelund T., Larson E.B., Munger R., Rotter J.I., Lipton R.B., Bullido M.J., Hofman A., Montine T.J., Coto E., Boerwinkle E., Petersen R.C., Alvarez V., Rivadeneira F., Reiman E.M., Gallo M., O’Donnell C.J., Reisch J.S., Bruni A.C., Royall D.R., Dichgans M., Sano M., Galimberti D., St George-Hyslop P., Scarpini E., Tsuang D.W., Mancuso M., Bonuccelli U., Winslow A.R., Daniele A., Wu C.K., Peters O., Nacmias B., Riemenschneider M., Heun R., Brayne C., Rubinsztein D.C., Bras J., Guerreiro R., Hardy J., Al-Chalabi A., Shaw C.E., Collinge J., Mann D., Tsolaki M., Clarimón J., Sussams R., Lovestone S., O’Donovan M.C., Owen M.J., Behrens T.W., Mead S., Goate A.M., Uitterlinden A.G., Holmes C., Cruchaga C., Ingelsson M., Bennett D.A., Powell J., Golde T.E., Graff C., De Jager P.L., Morgan K., Ertekin-Taner N., Combarros O., Psaty B.M., Passmore P., Younkin S.G., Berr C., Gudnason V., Rujescu D., Dickson D.W., Dartigues J.F., DeStefano A.L., Ortega-Cubero S., Hakonarson H., Campion D., Boada M., Kauwe J.“., Farrer L.A., Van Broeckhoven C., Ikram M.A., Jones L., Haines J., Tzourio C., Launer L.J., Escott-Price V., Mayeux R., Deleuze J.F., Amin N., Holmans P.A., Pericak-Vance M.A., Amouyel P., van Duijn C.M., Ramirez A., Wang L.S., Lambert J.C., Seshadri S., Williams J, & Schellenberg G.D. (2017). Rare coding variants in PLCG2, ABI3 and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nature genetics, 49(9), 1373-1384.

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Humanexome beadchip v1

Lab products found in correlation

23 protocols using humanexome beadchip v1

Genotyping and Quality Control for CHARGE Cohorts

TREM2 Variant Genotyping in Alzheimer's Disease

Whole Exome and SNP Sequencing Protocols

Genotyping and Quality Control for LOAD Samples

Exome-wide genotyping protocol for AGES-RS

Genotyping Workflow for Exome Chip

Genotyping of rs1800437 in Cohorts

Exome Genotyping Quality Control

Quality Control and Population Structure Analysis

Exome Chip Genotyping and Quality Control

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