Multi ethnic global array

Manufactured by Illumina

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The Multi-Ethnic Global Array (MEGA) is a high-throughput microarray platform designed for the comprehensive genetic analysis of diverse human populations. It is a robust and versatile tool for researchers to explore genetic variations across a broad range of ancestral backgrounds.

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Lab products found in correlation

Humanomniexpressexome 8 version 1.0 kit, by Illumina (2 mentions) Humanhap550 duo, by Illumina (2 mentions) Human610 quad beadchip, by Illumina (2 mentions) Axiom panafr snp array, by Illumina (2 mentions) Global screening array, by Illumina (1 mentions) Megaex, by Illumina (1 mentions) Axiom panafr array, by Thermo Fisher Scientific (1 mentions) Genomestudio software, by Illumina (1 mentions) Genome wide human snp array 6, by Thermo Fisher Scientific (1 mentions) Genomestudio, by Illumina (1 mentions) Humancoreexome array, by Illumina (1 mentions)

17 protocols using multi ethnic global array

Genetic Diversity of Kenyan Ethnic Groups

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We collected DNA and demographic information from a total of 572 individuals from Turkana, Samburu, Rendille, and Waso Borana and successfully genotyped 376 of these individuals on Illumina’s Multi-Ethnic Global Array (Table 1; Table S1). For each ethnolinguistic group, we sampled individuals from at least two clans. Data collection occurred across northern Kenya from October 2016 – October 2017. For each participant, a DNA sample was taken in the form of saliva or cheek swab; the saliva was collected in an Oragene OG-500 DNA collection kit. In addition to collecting a DNA sample, a questionnaire was administered to each participant to acquire demographic information; this information included, for example, natal and post-marital clan affiliation, and spoken languages. DNA was extracted using a phenol-chloroform extraction method for the samples collected from cheek swabs. DNA for the samples collected with the Oragene OG-500 DNA collection kit was extracted at Yale Center for Genomic Analysis. The extracted DNA was then quantified on both a Qubit and Nanodrop. Each sample’s extracted DNA was then diluted to at least 35 ng/ul in a volume of 40 ul and sent to Langebio-Cinvestav sequencing facility in Mexico for SNP genotyping on Illumina’s Multi-Ethnic Global Array.

Oill A.M., Handley C., Howell E.K., Stone A.C., Mathew S, & Wilson M.A. (2022). Genomic analysis reveals geography rather than culture as the predominant factor shaping genetic variation in northern Kenyan human populations. American journal of biological anthropology, 178(3), 488-503.

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Genotyping and Imputation for Traumatic Brain Injury

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Genotyping was completed at FIMM Technology Center for CENTER-TBI, Cambridge, Turku patients and the Broad Institute for TRACK-TBI, using the Illumina Global Screening Array (GSA-24v2-0 + Multi-Disease). The MGB cohort were genotyped using Illumina's Multi-Ethnic Global array (MEGA) and the pre-releases forms, including MEGA and MEGA-Ex arrays at Illumina at the MGB Translational Genomics Core. A unified quality control procedure was applied for each study cohort and the array-based genotypes were imputed using the Haplotype Reference Consortium¹⁶ (link) panel. Details regarding genotyping and imputation are in the Supplementary Methods.
TBI patients’ ancestry were determined by self-reports and confirmed through principal components (PCs) calculated based on the genotypes of the study population combined with the genotypes of the 1000 Genomes¹⁷ (link) reference data (Supplementary Methods). The final data set contained 4710 individuals of European ancestry. TRACK-TBI patients clustered to the 1000 Genomes Africans (n = 245) and Admixed Americans (n = 313) ethnic groups were included in the trans-ethnic GWAS meta-analysis, allowing us to constitute a multi-ethnic cohort of 5268 individuals. Target sample size was not defined a priori, but was the largest combined cohort of well-phenotyped patients with outcomes, and DNA that satisfied quality control requirements.

Kals M., Kunzmann K., Parodi L., Radmanesh F., Wilson L., Izzy S., Anderson C.D., Puccio A.M., Okonkwo D.O., Temkin N., Steyerberg E.W., Stein M.B., Manley G.T., Maas A.I., Richardson S., Diaz-Arrastia R., Palotie A., Ripatti S., Rosand J, & Menon D.K. (2022). A genome-wide association study of outcome from traumatic brain injury. EBioMedicine, 77, 103933.

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Multi-Ethnic Genome-Wide Association Analysis Pipeline

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For Affymetrix Axiom^® PANAFR array, see https://www.thermofisher.com/order/catalog/product/901788?ICID=search-product; for ClinVar Accession SCV000191187.1, see https://www.ncbi.nlm.nih.gov/clinvar/variation/156804/; for dbGaP, see https://www.ncbi.nlm.nih.gov/gap; for Eugene, see https://genepi.qimr.edu.au/staff/manuelF/eugene/download.html; for GMMAT, see https://content.sph.harvard.edu/xlin/software.html#gmmat; for Illumina Multi-Ethnic Global Array (MEGA), see https://www.illumina.com/science/consortia/human-consortia/multi-ethnic-genotyping-consortium.html; for MetaSoft, see http://genetics.cs.ucla.edu/meta/index.html; for The NHGRI-EBI Catalog of published genome-wide association studies, see https://www.ebi.ac.uk/gwas/; for NCBI Molecular QTL Browser, see https://preview.ncbi.nlm.nih.gov/gap/eqtl/studies/; for OMIM, see http://www.omim.org/; for Sanger Imputation Service, see https://imputation.sanger.ac.uk/.

Adeyemo A.A., Zaghloul N.A., Chen G., Doumatey A.P., Leitch C.C., Hostelley T.L., Nesmith J.E., Zhou J., Bentley A.R., Shriner D., Fasanmade O., Okafor G., Eghan B J.r., Agyenim-Boateng K., Chandrasekharappa S., Adeleye J., Balogun W., Owusu S., Amoah A., Acheampong J., Johnson T., Oli J., Adebamowo C., Collins F., Dunston G, & Rotimi C.N. (2019). ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response. Nature Communications, 10, 3195.

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Genetic Analysis of Thai METH-MEGA Cohort

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Second-stage subjects (N = 3,161; the Thai METH-MEGA sample, Table 1) were recruited from 2015 to 2020 [6 (link)]. DNA samples were genotyped using the Illumina Multi-Ethnic Global Array (MEGA) which includes ~1.78 M SNPs. We removed subjects with sample genotype call rate <0.95, sex mismatch, excess heterozygosity rate, or that were duplicates. SNPs with genotype call rate ≥0.95, or MAF ≥0.01, or HWE p value >10⁻⁶ were retained for imputation as with the Thai METH–GSA sample. The same imputation processes and post-imputation quality controls (QC) were applied. We included 794 cases and 1576 alcohol-exposed controls in the association analysis, which used GEMMA and age, sex and the first ten PCs as covariates.

Zhou H., Kalayasiri R., Sun Y., Nuñez Y.Z., Deng H.W., Chen X.D., Justice A.C., Kranzler H.R., Chang S., Lu L., Shi J., Sanichwankul K., Mutirangura A., Malison R.T, & Gelernter J. (2022). Genome-wide meta-analysis of alcohol use disorder in East Asians. Neuropsychopharmacology, 47(10), 1791-1797.

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Genotyping and Quality Control for GWAS

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The iSAEC samples were genotyped using Illumina HumanOmniExpressExome-8 version 1.0 kit (Illumina, San Diego, CA), which contains ~ 1 million SNPs. The BioVU samples were genotyped using the Illumina Multi-Ethnic Global Array (MEGA; Illumina; ~ 1.7 million SNPs). The other samples were genotyped on Illumina HumanOmni2.5S-8 version 1 (Illumina; ~ 2.5 million SNPs) or Illumina Infinium Global Screening Array version 2 (Illumina; ~ 0.65 million SNPs; Table S2). Quality controls were performed for each of the four datasets separately. SNPs common to all GWAS panels were extracted and the four datasets were then merged. Genotype imputation was performed on the merged dataset using 1000 Genomes phase III version 5 reference panel. The detailed quality control steps for all raw data and the imputation steps were descripted in Supplementary Methods.

Yang G., Singh S., McDonough C.W., Lamba J.K., Hamadeh I., Holliday L.S., Wang D., Katz J., Lakatos P.A., Balla B., Kosa J.P., Pelliccioni G.A., Price D.K., Van Driest S.L., Figg W.D., Langaee T., Moreb J.S, & Gong Y. (2021). Genome‐wide Association Study Identified Chromosome 8 Locus Associated with Medication‐Related Osteonecrosis of the Jaw. Clinical Pharmacology and Therapeutics, 110(6), 1558-1569.

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Glaucoma GWAS in Mass General Biobank

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Mass General Brigham Biobank (formally known as Partners HealthCare Biobank) is a biorepository of samples from consented patients at Mass General Brigham^{40 (link)} (parent organization of Massachusetts General Hospital and Brigham and Women’s Hospital). In this study, cases were defined based on a diagnosis available on electronic health records, and controls were participants without a recorded diagnosis of glaucoma in their electronic health records. In total, 1,415 glaucoma cases and 18,632 controls were genotyped on an Illumina Multi-Ethnic Global Array (MEGA) (Illumina). Participants showing high rates of missingness or those deemed ancestry outliers from the European ancestry population were removed. Genetic variants with high missingness or extreme allele frequencies were removed before imputation using the HRCr1.1 reference panel (Michigan Imputation server)^{41 (link)}. Imputed genotype data in dosage format were used for the analysis. Glaucoma GWAS was conducted using PLINK v.2.00 with a logistic regression model adjusting for age, sex, genetic principal components and genotype batches^{42 (link)}.

Han X., Gharahkhani P., Hamel A.R., Ong J.S., Rentería M.E., Mehta P., Dong X., Pasutto F., Hammond C., Young T.L., Hysi P., Lotery A.J., Jorgenson E., Choquet H., Hauser M., Cooke Bailey J.N., Nakazawa T., Akiyama M., Shiga Y., Fuller Z.L., Wang X., Hewitt A.W., Craig J.E., Pasquale L.R., Mackey D.A., Wiggs J.L., Khawaja A.P., Segrè A.V, & MacGregor S. (2023). Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci. Nature Genetics, 55(7), 1116-1125.

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Genotyping and Imputation of Large-Scale Genomic Datasets

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The iSAEC samples were genotyped using Illumina HumanOmniExpressExome‐8 version 1.0 kit (Illumina, San Diego, CA), which contains ~ 1 million SNPs. The BioVU samples were genotyped using the Illumina Multi‐Ethnic Global Array (MEGA; Illumina; ~ 1.7 million SNPs). The other samples were genotyped on Illumina HumanOmni2.5S‐8 version 1 (Illumina; ~ 2.5 million SNPs) or Illumina Infinium Global Screening Array version 2 (Illumina; ~ 0.65 million SNPs; Table S2). Quality controls were performed for each of the four datasets separately. SNPs common to all GWAS panels were extracted and the four datasets were then merged. Genotype imputation was performed on the merged dataset using 1000 Genomes phase III version 5 reference panel. The detailed quality control steps for all raw data and the imputation steps were descripted in Supplementary Methods.

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Admixed Ancestry Genotyping with MEGA

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Genotyping was performed using Illumina’s Multi-Ethnic Global Array (MEGA) that contains SNP sets tailored towards admixed ancestry^{29 (link)}. MEGA maximizes coverage and captures the genomic architecture of AA population. Genotypes were called using Genetrain2 algorithm in Illumina Genome Studio software. Genotype data were available for both cases and control individuals over 1.43 million variants.

Mack G.J, & Compton D.A. (2001). Analysis of mitotic microtubule-associated proteins using mass spectrometry identifies astrin, a spindle-associated protein. Proceedings of the National Academy of Sciences of the United States of America, 98(25), 14434-14439.

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Genotyping Quality Control and Filtering

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Genotyping in GERA was performed on the Affymetric Axion Genotyping System and underwent standard quality control procedures as previously described.^{8 (link)} Subjects in the MGB Biobank were genotyped on the Illumina Multi-Ethnic Global Array (MEGA), Expanded MEGA (MEGA Ex), and MEGA with imputation to the 1000 Genomes phase 3 reference panel using the Michigan Imputation Server.^{14 (link)} Genotyping was performed using the Illumina HumanHap550-Duo and Human 610-Quad BeadChips in the Rotterdam Study. Single-nucleotide polymorphisms (SNPs) with minor allele frequencies<1% and Hardy-Weinberg equilibrium exact test p<10⁻⁵ were filtered using Plink V.1.9.^{15 (link)} Identity-by-descent estimates were calculated in each cohort, and one subject from each pair with an identity-by-descent estimate >0.1875 was removed. In GERA, the final dataset comprised 8 995 492 and 9 004 920 variants, respectively, for the OCS burst (online supplemental figure 1) and asthma-related exacerbation (online supplemental figure 2) phenotypes.

Wang A.L., Lahousse L., Dahlin A., Edris A., McGeachie M., Lutz S.M., Sordillo J.E., Brusselle G., Lasky-Su J., Weiss S.T., Iribarren C., Lu M.X., Tantisira K.G, & Wu A.C. (2022). Novel genetic variants associated with inhaled corticosteroid treatment response in older adults with asthma. Thorax, 78(5), 432-441.

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Genotyping and Imputation for Genetic Analyses

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Genotyping for the AADM study was performed using either the Affymetrix Axiom PANAFR SNP array or the Illumina’s Multi-Ethnic Global Array (MEGA) [31 (link)]. Quality control was performed for each of the arrays separately, resulting in a sample level genotype call rate of at least 0.95 for all samples. For the replication cohorts, genotyping was performed using the Affymetrix Genome-Wide Human SNP Array 6.0 [39 (link)]. For all cohorts, the SNP datasets were filtered for missingness per marker (> 0.05), minor allele frequency (< 0.01), and Hardy-Weinberg equilibrium (P value ≤ 1 × 10⁻⁶). Imputation for all cohorts was performed using the African Genome Resources Haplotype Reference Panel via the Sanger imputation Service [40 ]. Quality of imputation was evaluated using INFO scores and only SNPs with INFO scores > 0.3 were retained. After filtering, 18,199,418 variants remained in the final dataset for AADM and 18,093,757 variants for replication cohorts. We checked for population stratification using the “epacts-pca-plot” function in the EPACTS software package (version 3.2.6) [41 ] and identified three significant principal components (PCs) for AADM, one for HUFS, and two for the other replication cohorts [42 ].

Meeks K.A., Bentley A.R., Gouveia M.H., Chen G., Zhou J., Lei L., Adeyemo A.A., Doumatey A.P, & Rotimi C.N. (2021). Genome-wide analyses of multiple obesity-related cytokines and hormones informs biology of cardiometabolic traits. Genome Medicine, 13, 156.

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