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1

Genotyping and Sequencing of Icelandic and British Cohorts

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In the Icelandic data set, those with ICV measurements and with others phenotypes/traits participated in the number of projects at deCODE genetics. The preparation, chip-genotyping and or whole-genome sequencing of these samples was carried out at deCODE genetics.^{30 (link),31 (link)} Using Graphtyper^{32 (link)} on WGS (using GAIIx, HiSeq, HiSeqX and NovaSeq Illumina technology to a mean depth of at least ×17.8), data of 61 205 Icelanders, 42.9 million high quality sequence variants were identified. Along with WGS set, deCODE genetics has also chip-genotyped 155 250 Icelanders using one of the Illumina genotyping arrays. The genotype calls (including SNP and insertions/deletions) based on WGS set were imputed into chip-genotyped subjects and long-range phased by using haplotype sharing and genealogical information.^{33 (link)}In the UKB, the samples were genotyped on two Affymetrix arrays. Initial set of 50 000 samples was chip-genotyped using the Affymetrix UK BiLEVE Axiom array.^{34 (link)} The additional set of 450 000 samples were chip-genotyped for 850 000 sequence variants using the Affymetrix UKB Axiom® array.^{34 (link)} Both arrays target 95% of same variants.^{34 (link)} The chip-genotypes were used to impute for additional markers by using 1000 Genomes phase 3,^{35 (link)} UK10K^{36 (link)} and HRC reference panels.

Nawaz M.S., Einarsson G., Bustamante M., Gisladottir R.S., Walters G.B., Jonsdottir G.A., Skuladottir A.T., Bjornsdottir G., Magnusson S.H., Asbjornsdottir B., Unnsteinsdottir U., Sigurdsson E., Jonsson P.V., Palmadottir V.K., Gudjonsson S.A., Halldorsson G.H., Ferkingstad E., Jonsdottir I., Thorleifsson G., Holm H., Thorsteinsdottir U., Sulem P., Gudbjartsson D.F., Stefansson H., Thorgeirsson T.E., Ulfarsson M.O, & Stefansson K. (2022). Thirty novel sequence variants impacting human intracranial volume. Brain Communications, 4(6), fcac271.

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Genome-wide SNP Genotyping and Imputation

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Genome‐wide single nucleotide polymorphism (SNP) genotyping was performed within each study using Illumina or Affymetrix genotyping arrays. SNP quality control (QC) was performed prior to imputation using PLINK, Birdseed v1.33, or Illumina GenomeStudio. QC measures removed (a) samples with genotyping success rate < 95%, (b) SNPs failing genotyping call rate thresholds, between 90% and 99%, (c) monomorphic SNPs, (d) SNPs that mapped to several loci in the human genome, and (e) SNPs with minor allele frequency (MAF) <1%. Other QC filters included removing SNPs (a) with Mendelian inconsistencies (for cohorts with family data), and (b) those with significant deviation from Hardy–Weinberg equilibrium with p‐value < 10⁻⁶ in JHS and HyperGEN or <10⁻⁵ in CHS. A combined YRI and CEU reference panel from HapMap phase 2 (build 36 release 22) was used for imputation in each of the five cohorts, as the African‐American population is admixed with ~17%–19% European ancestry (Zhu et al., 2005). More details of genotyping, QC, and imputation for each study are provided in Table S1.

Do A.N., Zhao W., Baldridge A.S., Raffield L.M., Wiggins K.L., Shah S.J., Aslibekyan S., Tiwari H.K., Limdi N., Zhi D., Sitlani C.M., Taylor K.D., Psaty B.M., Sotoodehnia N., Brody J.A., Rasmussen‐Torvik L.J., Lloyd‐Jones D., Lange L.A., Wilson J.G., Smith J.A., Kardia S.L., Mosley T.H., Vasan R.S., Arnett D.K, & Irvin M.R. (2019). Genome‐wide meta‐analysis of SNP and antihypertensive medication interactions on left ventricular traits in African Americans. Molecular Genetics & Genomic Medicine, 7(10), e00788.

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Genome-wide Genotyping and Imputation

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Genotyping was performed by each participating study using genotyping arrays from either Illumina (San Diego, CA, USA) or Affymetrix (Santa Clara, CA, USA). Each study conducted imputation using various software. The cosmopolitan reference panel from the 1000 Genomes Project Phase I Integrated Release Version 3 Haplotypes (2010–11 data freeze, 2012–03-14 haplotypes) was specified for imputation and used by most studies, with some using the HapMap Phase II reference panel instead. Only variants on the autosome and with MAF of at least 0.01 were considered. Specific details of each participating study’s genotyping platform and imputation software are described (Supplementary Tables 3 and 6). Genotype was coded as the dosage of the imputed genetic variant, coded additively (0,1,2).

Bentley A.R., Sung Y.J., Brown M.R., Winkler T.W., Kraja A.T., Ntalla I., Schwander K., Chasman D.I., Lim E., Deng X., Guo X., Liu J., Lu Y., Cheng C.Y., Sim X., Vojinovic D., Huffman J.E., Musani S.K., Li C., Feitosa M.F., Richard M.A., Noordam R., Baker J., Chen G., Aschard H., Bartz T.M., Ding J., Dorajoo R., Manning A.K., Rankinen T., Smith A.V., Tajuddin S.M., Zhao W., Graff M., Alver M., Boissel M., Chai J.F., Chen X., Divers J., Evangelou E., Gao C., Goel A., Hagemeijer Y., Harris S.E., Hartwig F.P., He M., Horimoto A.R., Hsu F.C., Hung Y.J., Jackson A.U., Kasturiratne A., Komulainen P., Kühnel B., Leander K., Lin K.H., Luan J., Lyytikäinen L.P., Matoba N., Nolte I.M., Pietzner M., Prins B., Riaz M., Robino A., Said M.A., Schupf N., Scott R.A., Sofer T., Stančáková A., Takeuchi F., Tayo B.O., van der Most P.J., Varga T.V., Wang T.D., Wang Y., Ware E.B., Wen W., Xiang Y.B., Yanek L.R., Zhang W., Zhao J.H., Adeyemo A., Afaq S., Amin N., Amini M., Arking D.E., Arzumanyan Z., Aung T., Ballantyne C., Barr R.G., Bielak L.F., Boerwinkle E., Bottinger E.P., Broeckel U., Brown M., Cade B.E., Campbell A., Canouil M., Charumathi S., Chen Y.D., Christensen K., Concas M.P., Connell J.M., de las Fuentes L., de Silva H.J., de Vries P.S., Doumatey A., Duan Q., Eaton C.B., Eppinga R.N., Faul J.D., Floyd J.S., Forouhi N.G., Forrester T., Friedlander Y., Gandin I., Gao H., Ghanbari M., Gharib S.A., Gigante B., Giulianini F., Grabe H.J., Gu C.C., Harris T.B., Heikkinen S., Heng C.K., Hirata M., Hixson J.E., Ikram M.A., Consortium E.I., Jia Y., Joehanes R., Johnson C., Jonas J.B., Justice A.E., Katsuya T., Khor C.C., Kilpeläinen T.O., Koh W.P., Kolcic I., Kooperberg C., Krieger J.E., Kritchevsky S.B., Kubo M., Kuusisto J., Lakka T.A., Langefeld C.D., Langenberg C., Launer L.J., Lehne B., Lewis C.E., Li Y., Liang J., Lin S., Liu C.T., Liu J., Liu K., Loh M., Lohman K.K., Louie T., Luzzi A., Mägi R., Mahajan A., Manichaikul A.W., McKenzie C.A., Meitinger T., Metspalu A., Milaneschi Y., Milani L., Mohlke K.L., Momozawa Y., Morris A.P., Murray A.D., Nalls M.A., Nauck M., Nelson C.P., North K.E., O'Connell J.R., Palmer N.D., Papanicolau G.J., Pedersen N.L., Peters A., Peyser P.A., Polasek O., Poulter N., Raitakari O.T., Reiner A.P., Renström F., Rice T.K., Rich S.S., Robinson J.G., Rose L.M., Rosendaal F.R., Rudan I., Schmidt C.O., Schreiner P.J., Scott W.R., Sever P., Shi Y., Sidney S., Sims M., Smith J.A., Snieder H., Starr J.M., Strauch K., Stringham H.M., Tan N.Y., Tang H., Taylor K.D., Teo Y.Y., Tham Y.C., Tiemeier H., Turner S.T., Uitterlinden A.G., van Heemst D., Waldenberger M., Wang H., Wang L., Wang L., Wei W.B., Williams C.A., Wilson G S.r., Wojczynski M.K., Yao J., Young K., Yu C., Yuan J.M., Zhou J., Zonderman A.B., Becker D.M., Boehnke M., Bowden D.W., Chambers J.C., Cooper R.S., de Faire U., Deary I.J., Elliott P., Esko T., Farrall M., Franks P.W., Freedman B.I., Froguel P., Gasparini P., Gieger C., Horta B.L., Juang J.M., Kamatani Y., Kammerer C.M., Kato N., Kooner J.S., Laakso M., Laurie C.C., Lee I.T., Lehtimäki T., Magnusson P.K., Oldehinkel A.J., Penninx B.W., Pereira A.C., Rauramaa R., Redline S., Samani N.J., Scott J., Shu X.O., van der Harst P., Wagenknecht L.E., Wang J.S., Wang Y.X., Wareham N.J., Watkins H., Weir D.R., Wickremasinghe A.R., Wu T., Zeggini E., Zheng W., Bouchard C., Evans M.K., Gudnason V., Kardia S.L., Liu Y., Psaty B.M., Ridker P.M., van Dam R.M., Mook-Kanamori D.O., Fornage M., Province M.A., Kelly T.N., Fox E.R., Hayward C., van Duijn C.M., Tai E.S., Wong T.Y., Loos R.J., Franceschini N., Rotter J.I., Zhu X., Bierut L.J., Gauderman W.J., Rice K., Munroe P.B., Morrison A.C., Rao D.C., Rotimi C.N, & Cupples L.A. (2019). Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nature genetics, 51(4), 636-648.

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4

Genotyping and Annotation of SLC30A8 Variants

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Study participants were previously genotyped for approximately 995,321 SNP markers using several Illumina genotyping arrays, including the HumanHap550v3, HumanExon510Sv1, Human1Mv1, and Human1M-Duov3. Details of the data cleaning and imputing steps for the genotypic data have been detailed elsewhere [24 (link)]. We used all the SNPs within the SLC30A8 gene as well those within 50 kb upstream and downstream of this gene. A total of 118 SNPs were found in this region. Detailed characteristics along with genomic locations of these 118 SNPs are provided in Supplementary Table 1 in Supplementary Material available online at http://dx.doi.org/10.1155/2016/6463214. The variants were annotated using ANNOVAR [25 (link)] that used human genome Build 19 and SNP version 138 databases for annotation.

Kulkarni H., Mamtani M., Peralta J.M., Diego V., Dyer T.D., Goring H., Almasy L., Mahaney M.C., Williams-Blangero S., Duggirala R., Curran J.E, & Blangero J. (2016). Lack of Association between SLC30A8 Variants and Type 2 Diabetes in Mexican American Families. Journal of Diabetes Research, 2016, 6463214.

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5

Genotyping and Imputation Protocol

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Genotyping was performed by each participating study locally using genotyping arrays from either Illumina (San Diego, CA, USA) or Affymetrix (Santa Clara, CA, USA). Each study conducted imputation using various software programmes and with local cleaning thresholds for call rates (usually > 98%) and Hardy–Weinberg equilibrium (usually P-value < 1e⁻⁵). The cosmopolitan reference panel from the 1000 Genomes Project Phase I Integrated Release Version 3 Haplotypes (2010–11 data freeze, 2012-03-14 haplotypes) was specified for imputation. Only SNPs on the autosomal chromosomes with a minor allele frequency of at least 0.01 were considered in the analyses. Specific details of each participating study’s genotyping platform and imputation software are described (Supplementary Tables 3 and 6).

Noordam R., Bos M.M., Wang H., Winkler T.W., Bentley A.R., Kilpeläinen T.O., de Vries P.S., Sung Y.J., Schwander K., Cade B.E., Manning A., Aschard H., Brown M.R., Chen H., Franceschini N., Musani S.K., Richard M., Vojinovic D., Aslibekyan S., Bartz T.M., de las Fuentes L., Feitosa M., Horimoto A.R., Ilkov M., Kho M., Kraja A., Li C., Lim E., Liu Y., Mook-Kanamori D.O., Rankinen T., Tajuddin S.M., van der Spek A., Wang Z., Marten J., Laville V., Alver M., Evangelou E., Graff M.E., He M., Kühnel B., Lyytikäinen L.P., Marques-Vidal P., Nolte I.M., Palmer N.D., Rauramaa R., Shu X.O., Snieder H., Weiss S., Wen W., Yanek L.R., Adolfo C., Ballantyne C., Bielak L., Biermasz N.R., Boerwinkle E., Dimou N., Eiriksdottir G., Gao C., Gharib S.A., Gottlieb D.J., Haba-Rubio J., Harris T.B., Heikkinen S., Heinzer R., Hixson J.E., Homuth G., Ikram M.A., Komulainen P., Krieger J.E., Lee J., Liu J., Lohman K.K., Luik A.I., Mägi R., Martin L.W., Meitinger T., Metspalu A., Milaneschi Y., Nalls M.A., O’Connell J., Peters A., Peyser P., Raitakari O.T., Reiner A.P., Rensen P.C., Rice T.K., Rich S.S., Roenneberg T., Rotter J.I., Schreiner P.J., Shikany J., Sidney S.S., Sims M., Sitlani C.M., Sofer T., Strauch K., Swertz M.A., Taylor K.D., Uitterlinden A.G., van Duijn C.M., Völzke H., Waldenberger M., Wallance R.B., van Dijk K.W., Yu C., Zonderman A.B., Becker D.M., Elliott P., Esko T., Gieger C., Grabe H.J., Lakka T.A., Lehtimäki T., North K.E., Penninx B.W., Vollenweider P., Wagenknecht L.E., Wu T., Xiang Y.B., Zheng W., Arnett D.K., Bouchard C., Evans M.K., Gudnason V., Kardia S., Kelly T.N., Kritchevsky S.B., Loos R.J., Pereira A.C., Province M., Psaty B.M., Rotimi C., Zhu X., Amin N., Cupples L.A., Fornage M., Fox E.F., Guo X., Gauderman W.J., Rice K., Kooperberg C., Munroe P.B., Liu C.T., Morrison A.C., Rao D.C., van Heemst D, & Redline S. (2019). Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. Nature Communications, 10, 5121.

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6

Genotyping and Data Cleaning Protocol

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Study participants were previously genotyped for approximately one million single nucleotide polymorphism markers using several Illumina genotyping arrays, including the HumanHap550v3, HumanExon510Sv1, Human1Mv1, and Human1M-Duov3. The Infinium Whole-Genome Genotyping Assay was employed according to manufacturers’ instructions. Details of the data cleaning and imputing steps for this genotypic data have been described previously [20 (link)]. Out of a total of 995,320 SNPS genotyped, we incorporated 759,809 SNPs into the association analyses, which had ≥97 % call rate, a minor allele frequency ≥5 % and a Hardy-Weinberg significance value ≥0.001.

Mamtani M., Kulkarni H., Dyer T.D., Göring H.H., Neary J.L., Cole S.A., Kent J.W., Kumar S., Glahn D.C., Mahaney M.C., Comuzzie A.G., Almasy L., Curran J.E., Duggirala R., Blangero J, & Carless M.A. (2016). Genome- and epigenome-wide association study of hypertriglyceridemic waist in Mexican American families. Clinical Epigenetics, 8, 6.

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7

Genome-Wide Genotyping and Imputation

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Genome-wide SNP genotyping was performed within each cohort using Illumina or Affymetrix genotyping arrays. Follow-up (“Stage II”) genotyping of selected variants was performed using a custom content on the Illumina Human Exome array. Details of genotyping and QC are provided in Supplementary Material (Section C in S1 File); these procedures generally involved exclusion of participants on the basis of sex mismatches and duplicate samples; limitation to unrelated individuals in all cohorts except the family-based FHS; exclusion of samples with genotyping success rate <95%; and exclusion of SNPs failing genotyping call rate thresholds, typically between 95% and 99%.
To increase coverage and facilitate evaluation of the same SNPs across cohorts, SNPs passing quality control were used to impute to the HapMap Phase 2 reference panels using MaCH [17 (link)], BEAGLE, [18 (link)] or BIMBAM [19 (link)].

Bis J.C., Sitlani C., Irvin R., Avery C.L., Smith A.V., Sun F., Evans D.S., Musani S.K., Li X., Trompet S., Krijthe B.P., Harris T.B., Quibrera P.M., Brody J.A., Demissie S., Davis B.R., Wiggins K.L., Tranah G.J., Lange L.A., Sotoodehnia N., Stott D.J., Franco O.H., Launer L.J., Stürmer T., Taylor K.D., Cupples L.A., Eckfeldt J.H., Smith N.L., Liu Y., Wilson J.G., Heckbert S.R., Buckley B.M., Ikram M.A., Boerwinkle E., Chen Y.D., de Craen A.J., Uitterlinden A.G., Rotter J.I., Ford I., Hofman A., Sattar N., Slagboom P.E., Westendorp R.G., Gudnason V., Vasan R.S., Lumley T., Cummings S.R., Taylor HA J.r., Post W., Jukema J.W., Stricker B.H., Whitsel E.A., Psaty B.M, & Arnett D. (2015). Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease: A Pharmacogenomics Study from the CHARGE Consortium. PLoS ONE, 10(10), e0140496.

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Genetic Determinants of Ocular Biometrics

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All studies included in this meta-analysis are part of the International Glaucoma Genetics Consortium (IGGC). A description of the details of all cohorts participating in this study can be found in Supplementary Note and Supplementary Tables 1–6. The mean IOP, VCDR, CCT, CA, and DA of both eyes was used for the analyses. In case of missing or unreliable data for one eye, the measurement of the other eye was used instead. For subjects who received IOP-lowering medication, the measured IOP was multiplied by a factor of 1.3. The total number of individuals in the meta-analysis was 24,493 for CA, 24,509 for DA, 25,180 for VCDR, 31,269 for IOP, and 16,204 for CCT. All studies were performed with the approval of the local institutional review board (Supplementary Note) and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.
Genotyping was performed using commercially available Affymetrix or Illumina genotyping arrays (Supplementary Table 7). Quality control was executed independently for each study. To facilitate meta-analysis, each cohort performed genotype imputation using either the Sanger imputation service (https://imputation.sanger.ac.uk) or the Michigan imputation server (https://imputationserver.sph.umich.edu) with reference to the HRC panel, version 1 or 1.1^{34 (link)}.

Bonnemaijer P.W., Leeuwen E.M., Iglesias A.I., Gharahkhani P., Vitart V., Khawaja A.P., Simcoe M., Höhn R., Cree A.J., Igo R.P., Gerhold-Ay A., Nickels S., Wilson J.F., Hayward C., Boutin T.S., Polašek O., Aung T., Khor C.C., Amin N., Lotery A.J., Wiggs J.L., Cheng C.Y., Hysi P.G., Hammond C.J., Thiadens A.A., MacGregor S., Klaver C.C, & Duijn C.M. (2019). Multi-trait genome-wide association study identifies new loci associated with optic disc parameters. Communications Biology, 2, 435.

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9

Genome-wide Genotyping and Imputation

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Genotyping was performed by each participating study using genotyping arrays from either Illumina (San Diego, CA, USA) or Affymetrix (Santa Clara, CA, USA). Each study conducted imputation using various software. The cosmopolitan reference panel from the 1000 Genomes Project Phase I Integrated Release Version 3 Haplotypes (2010–11 data freeze, 2012–03-14 haplotypes) was specified for imputation and used by most studies, with some using the HapMap Phase II reference panel instead. Only variants on the autosome and with MAF of at least 0.01 were considered. Specific details of each participating study’s genotyping platform and imputation software are described (Supplementary Tables 3 and 6). Genotype was coded as the dosage of the imputed genetic variant, coded additively (0,1,2).

Bentley A.R., Sung Y.J., Brown M.R., Winkler T.W., Kraja A.T., Ntalla I., Schwander K., Chasman D.I., Lim E., Deng X., Guo X., Liu J., Lu Y., Cheng C.Y., Sim X., Vojinovic D., Huffman J.E., Musani S.K., Li C., Feitosa M.F., Richard M.A., Noordam R., Baker J., Chen G., Aschard H., Bartz T.M., Ding J., Dorajoo R., Manning A.K., Rankinen T., Smith A.V., Tajuddin S.M., Zhao W., Graff M., Alver M., Boissel M., Chai J.F., Chen X., Divers J., Evangelou E., Gao C., Goel A., Hagemeijer Y., Harris S.E., Hartwig F.P., He M., Horimoto A.R., Hsu F.C., Hung Y.J., Jackson A.U., Kasturiratne A., Komulainen P., Kühnel B., Leander K., Lin K.H., Luan J., Lyytikäinen L.P., Matoba N., Nolte I.M., Pietzner M., Prins B., Riaz M., Robino A., Said M.A., Schupf N., Scott R.A., Sofer T., Stančáková A., Takeuchi F., Tayo B.O., van der Most P.J., Varga T.V., Wang T.D., Wang Y., Ware E.B., Wen W., Xiang Y.B., Yanek L.R., Zhang W., Zhao J.H., Adeyemo A., Afaq S., Amin N., Amini M., Arking D.E., Arzumanyan Z., Aung T., Ballantyne C., Barr R.G., Bielak L.F., Boerwinkle E., Bottinger E.P., Broeckel U., Brown M., Cade B.E., Campbell A., Canouil M., Charumathi S., Chen Y.D., Christensen K., Concas M.P., Connell J.M., de las Fuentes L., de Silva H.J., de Vries P.S., Doumatey A., Duan Q., Eaton C.B., Eppinga R.N., Faul J.D., Floyd J.S., Forouhi N.G., Forrester T., Friedlander Y., Gandin I., Gao H., Ghanbari M., Gharib S.A., Gigante B., Giulianini F., Grabe H.J., Gu C.C., Harris T.B., Heikkinen S., Heng C.K., Hirata M., Hixson J.E., Ikram M.A., Consortium E.I., Jia Y., Joehanes R., Johnson C., Jonas J.B., Justice A.E., Katsuya T., Khor C.C., Kilpeläinen T.O., Koh W.P., Kolcic I., Kooperberg C., Krieger J.E., Kritchevsky S.B., Kubo M., Kuusisto J., Lakka T.A., Langefeld C.D., Langenberg C., Launer L.J., Lehne B., Lewis C.E., Li Y., Liang J., Lin S., Liu C.T., Liu J., Liu K., Loh M., Lohman K.K., Louie T., Luzzi A., Mägi R., Mahajan A., Manichaikul A.W., McKenzie C.A., Meitinger T., Metspalu A., Milaneschi Y., Milani L., Mohlke K.L., Momozawa Y., Morris A.P., Murray A.D., Nalls M.A., Nauck M., Nelson C.P., North K.E., O'Connell J.R., Palmer N.D., Papanicolau G.J., Pedersen N.L., Peters A., Peyser P.A., Polasek O., Poulter N., Raitakari O.T., Reiner A.P., Renström F., Rice T.K., Rich S.S., Robinson J.G., Rose L.M., Rosendaal F.R., Rudan I., Schmidt C.O., Schreiner P.J., Scott W.R., Sever P., Shi Y., Sidney S., Sims M., Smith J.A., Snieder H., Starr J.M., Strauch K., Stringham H.M., Tan N.Y., Tang H., Taylor K.D., Teo Y.Y., Tham Y.C., Tiemeier H., Turner S.T., Uitterlinden A.G., van Heemst D., Waldenberger M., Wang H., Wang L., Wang L., Wei W.B., Williams C.A., Wilson G S.r., Wojczynski M.K., Yao J., Young K., Yu C., Yuan J.M., Zhou J., Zonderman A.B., Becker D.M., Boehnke M., Bowden D.W., Chambers J.C., Cooper R.S., de Faire U., Deary I.J., Elliott P., Esko T., Farrall M., Franks P.W., Freedman B.I., Froguel P., Gasparini P., Gieger C., Horta B.L., Juang J.M., Kamatani Y., Kammerer C.M., Kato N., Kooner J.S., Laakso M., Laurie C.C., Lee I.T., Lehtimäki T., Magnusson P.K., Oldehinkel A.J., Penninx B.W., Pereira A.C., Rauramaa R., Redline S., Samani N.J., Scott J., Shu X.O., van der Harst P., Wagenknecht L.E., Wang J.S., Wang Y.X., Wareham N.J., Watkins H., Weir D.R., Wickremasinghe A.R., Wu T., Zeggini E., Zheng W., Bouchard C., Evans M.K., Gudnason V., Kardia S.L., Liu Y., Psaty B.M., Ridker P.M., van Dam R.M., Mook-Kanamori D.O., Fornage M., Province M.A., Kelly T.N., Fox E.R., Hayward C., van Duijn C.M., Tai E.S., Wong T.Y., Loos R.J., Franceschini N., Rotter J.I., Zhu X., Bierut L.J., Gauderman W.J., Rice K., Munroe P.B., Morrison A.C., Rao D.C., Rotimi C.N, & Cupples L.A. (2019). Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nature genetics, 51(4), 636-648.

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10

Genotyping and Ancestry Analysis of CLL

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Genotyping of the study cohort was done using Illumina genotyping arrays and genotypes were called using Illumina GenomeStudio software. Extensive quality control metrics were utilized including removing monomorphic SNPs, SNPs with call rates<95%, or SNPs with extreme Hardy-Weinberg disequilibrium (P<1.0×10⁻⁵). We also dropped individuals with call rates <90%, gender discordance, or those who had a relative genotyped. Duplicates showed >99% concordance. From these data, we pulled the 41 SNPs previously found to be associated with CLL (Supplemental Table 2-3). Using ADMIXTURE^{26 (link)}, we determined genetic ancestry for each individual using the HapMAP as the reference. Individuals with percent of African ancestry ≥50% were considered AA, and individuals with >80% Caucasian ancestry were considered EA. We correlated MAF between EA and AA across the 41 CLL SNPs using the 1000 genomes project data^{27 (link)}.

Kleinstern G., Weinberg J.B., Parikh S.A., Braggio E., Achenbach S.J., Robinson D.P., Norman A.D., Rabe K.G., Boddicker N.J., Vachon C.M., Lesnick C.E., Call T.G., Brander D.M., Rassenti L.Z., Kipps T.J., Olson J.E., Cerhan J.R., Kay N.E., Furman R.R., Hanson C.A., Shanafelt T.D, & Slager S.L. (2021). Polygenic risk score and risk of monoclonal B-cell lymphocytosis in caucasians and risk of chronic lymphocytic leukemia (CLL) in African Americans. Leukemia, 36(1), 119-125.

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Genotyping array

Lab products found in correlation

21 protocols using genotyping array

Genotyping and Sequencing of Icelandic and British Cohorts

Genome-wide SNP Genotyping and Imputation

Genome-wide Genotyping and Imputation

Genotyping and Annotation of SLC30A8 Variants

Genotyping and Imputation Protocol

Genotyping and Data Cleaning Protocol

Genome-Wide Genotyping and Imputation

Genetic Determinants of Ocular Biometrics

Genome-wide Genotyping and Imputation

Genotyping and Ancestry Analysis of CLL

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