Megaex

Manufactured by Illumina

Sourced in United States

MEGAEX is a high-throughput, automated DNA extraction system designed for laboratories. It efficiently isolates and purifies DNA samples from a variety of input materials. The MEGAEX system utilizes magnetic bead-based technology to ensure consistent and reliable DNA extraction.

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Lab products found in correlation

Multi ethnic global array, by Illumina (1 mentions) Global screening array, by Illumina (1 mentions) Human660w quad v1 a, by Illumina (1 mentions) 1m duo, by Illumina (1 mentions) Omniexpressexome, by Illumina (1 mentions) Expanded multi ethnic genotyping array, by Illumina (1 mentions) Humanomni1 quad chip, by Illumina (1 mentions) Omni5, by Illumina (1 mentions) Expanded multi ethnic genotyping array megaex, by Illumina (1 mentions)

14 protocols using megaex

BioVU Genetic Cohort Quality Control

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BioVU is Vanderbilt University Medical Center’s DNA biobank, which is linked to de-identified EHR phenotype data^{35 (link)}. A subset of BioVU (n = 62,303) participants of European Ancestry have SNP genotype data acquired on the Illumina MEGA^EX platform. Quality control steps for the BioVU population have been previously described^{36 (link)}. Genotypes were imputed with IMPUTE4^{37 (link)}, version 2.3.0 (University of Oxford), using the 10/2014 release of the 1000 Genomes cosmopolitan reference haplotypes and variants imputation quality scores less than 0.3 were excluded. One participant from each related pair (pi-hat > 0.2) was randomly excluded. Analyses were restricted to subjects of European ancestry, defined by principal components analyses in conjunction with HapMap reference populations. Quality control analyses used PLINK v1.9^{38 (link)}. The use of BioVU and other de-identified data presented in these analyses was approved by the VUMC Institutional Review Board (IRB), in accordance with the informed consent guidelines.

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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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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Genotyping and Imputation of Large Genetic Cohorts

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All subjects were genotyped using either the Illumina MEGA (cohort 1, n=4931) or the Illumina MEGA-EX (cohort 2, n=4428) array (Illumina; San Diego, CA, USA). Each cohort was cleaned, imputed, and analyzed separately to minimize batch effects. We retained subjects with genotyping call rates exceeding 99% and no evidence of relatedness based on identity by descent (IBD).^{26 (link)} We likewise retained any SNPs with call rate of 95% or greater, and Hardy–Weinberg equilibrium P-value>1 × 10⁻¹⁰. Genotypes were next imputed using the Michigan Imputation Server implementing Minimac3, based on all population subsets from 1000 Genomes Phase 3 v5 as reference panel.^{27 (link), 28 , 29} Phasing of haplotypes used SHAPEIT.³⁰We generated principal components, as implemented in PLINK 1.9, to identify the first 10 components of the variance-standardized relationship matrix among genotyped SNPs in each cohort.^{31 (link)} After overlaying HapMap populations, only those individuals falling within the Northern European cluster were included in subsequent analysis.

McCoy T.H., Castro V.M., Snapper L., Hart K., Januzzi J.L., Huffman J.C, & Perlis R.H. (2017). Polygenic loading for major depression is associated with specific medical comorbidity. Translational Psychiatry, 7(9), e1238-.

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SNP Genotyping and Imputation for EHR Data

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SNP genotype data for the EHR populations were acquired on the Illumina Human660W-Quadv1_A, HumanOmni1-Quad, HumanOmni5-Quad, MEGA-EX, Human610, Human550, HumanOmniExpressExome-8v1.2A and MegArray platforms. Quality control (QC) steps for the EHR data sets were performed per previously published protocols.^{12 (link)} QC analyses used PLINK v 1.90β3.^{13 (link)} SNPs were pre-phased using SHAPEIT,^{14 (link)} imputed using IMPUTE2^{15 (link)} in conjunction with the 10/2014 release of the 1000 Genomes cosmopolitan reference haplotypes. Imputed data were filtered for a sample missingness rate<2%, a SNP missingness rate<4% and a SNP deviation from Hardy-Weinberg<10⁻⁶. Principal components were generated using the SNPRelate package.^{16 (link)}

Mosley J.D., Benson M.D., Smith J.G., Melander O., Ngo D., Shaffer C.M., Ferguson J.F., Herzig M.S., McCarty C.A., Chute C.G., Jarvik G.P., Gordon A.S., Palmer M.R., Crosslin D.R., Larson E.B., Carrell D.S., Kullo I.J., Pacheco J.A., Peissig P.L., Brilliant M.H., Kitchner T.E., Linneman J.G., Namjou B., Williams M.S., Ritchie M.D., Borthwick K.M., Kiryluk K., Mentch F.D., Sleiman P.M., Karlson E.W., Verma S.S., Zhu Y., Vasan R.S., Yang Q., Denny J.C., Roden D.M., Gerszten R.E, & Wang T.J. (2018). Probing the virtual proteome to identify novel disease biomarkers. Circulation, 138(22), 2469-2481.

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Pharmacogenomics of Warfarin in African Americans

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WPC is a prospective cohort of first-time warfarin users aged 19 years or older starting anticoagulation for venous thromboembolism, stroke/transient ischemic attacks, atrial fibrillation, myocardial infarction, and/or peripheral arterial disease⁶⁸. The genotype data was generated using Illumina’s MEGA-EX and 1M duo arrays for 599 and 297 participants, respectively (58% females, average age 61 years, 100% African American by self-report). Imputation was performed using the TOPMed r2 reference panel (Freeze 8). More than 99% of the imputed variants with MAF>1% had R² of 0.6 or higher, and genotypes with genotypic probability of 0.9 or higher were retained for PRS calculation. PCA was performed using EIGENSOFT version 6.1.4 based on 44,137 high quality directly genotyped (missingness <5%), common (MAF ≥ 5%), and independent (r2<0.05) SNPs. APOL1 information was obtained from genotypic array data; rs143830837 was used as a proxy for rs71785313 since these SNPs represent the same G2 variant and were recently merged in dbSNP. For this analysis, only participants aged 40 years or older were included, leaving a total of 448 self-identified African-American participants.

Khan A., Turchin M.C., Patki A., Srinivasasainagendra V., Shang N., Nadukuru R., Jones A.C., Malolepsza E., Dikilitas O., Kullo I.J., Schaid D.J., Karlson E., Ge T., Meigs J.B., Smoller J.W., Lange C., Crosslin D.R., Jarvik G.P., Bhatraju P.K., Hellwege J.N., Chandler P., Torvik L.R., Fedotov A., Liu C., Kachulis C., Lennon N., Abul-Husn N.S., Cho J.H., Ionita-Laza I., Gharavi A.G., Chung W.K., Hripcsak G., Weng C., Nadkarni G., Irvin M.R., Tiwari H.K., Kenny E.E., Limdi N.A, & Kiryluk K. (2022). Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nature medicine, 28(7), 1412-1420.

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Large-Scale Genomic Data Quality Control

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Samples were genotyped, imputed, and cleaned at each site individually, as described in detail in the Supplementary Methods section of the online supplement. Quality control procedures at each site followed a similar standard pipeline. DNA from blood samples obtained from biobank participants was assayed using Illumina bead arrays (Omni-Express Exome, Global Screening, MEGA, MEGA^EX, or MEG BeadChips) containing approximately 700,000 to 2 million markers. Samples at each site were genotyped in multiple batches; indicators for genotyping platform and batch were included as covariates in the analyses. As described in the Supplementary Methods section of the online supplement, single-nucleotide polymorphisms (SNPs) were excluded using filters for call rate, minor allele frequency, and heterozygosity at a minimum. Individuals were excluded for excessive missing data or sex errors; a random individual from any pair of related individuals was also excluded (kinship coefficient >0.2). Principal components or self-reported ancestry was used to identify individuals of European ancestry. SNPs that passed the initial phase of quality control were imputed and then converted to best-guess genotypes where only high-quality markers were retained. Ten principal components were generated within the European sample to use as ancestry covariates in all subsequent analyses.

Zheutlin A.B., Dennis J., Linnér R.K., Moscati A., Restrepo N., Straub P., Ruderfer D., Castro V.M., Chen C.Y., Ge T., Huckins L.M., Charney A., Kirchner H.L., Stahl E.A., Chabris C.F., Davis L.K, & Smoller J.W. (2019). Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems. The American journal of psychiatry, 176(10), 846-855.

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Genotyping and Ancestry Determination

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The genotyping of the participants that were in our sample was completed on the Illumina MEGA^EX platform that includes over two million genetic markers. Details on the procedures of quality controls are described elsewhere (Dennis et al., 2021 (link)). We performed genotype imputation using the Michigan Imputation Server (Das et al., 2016 (link)), while as a reference panel we used the Haplotype Reference Consortium (HRC) reference panel. All variants with a low imputation quality (i.e., R² <0.3) were removed. Cryptic relatedness was addressed by removing one individual of each pair for which pihat> 0.2. To define individuals of primarily European ancestry we used FlashPCA2 combined with CEU, YRI, and CHB reference sets from the 1000 genomes project phase 3 spiked in and the Europeans were selected. The cut-off for the European subset was within 30% of the CEU cluster on the CEU-YRI axis and within 40% of the CEU cluster on the CEU-CHB axis.

Niarchou M., Singer E.V., Straub P., Malow B.A, & Davis L.K. (2022). Investigating the genetic pathways of insomnia in Autism Spectrum Disorder. Research in developmental disabilities, 128, 104299.

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Pharmacogenomics of Nephrotoxicity Biomarkers

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The datasets for vancomycin, gentamicin, cyclosporine and tacrolimus were extracted from BioVU subjects previously genotyped on the Illumina MEGA^EX platform as part of a large institutional effort.^{28 (link)} Subjects were selected for a mention of the drug name of interest in their electronic health record at age 18 or older, and at least one measurement of both drug concentration and serum creatinine in their laboratory results, enabling study of both pharmacodynamic (nephrotoxicity, as indicated by peak serum creatinine) and pharmacokinetic (drug concentration) phenotypes. Peak creatinine was defined as the highest serum creatinine value between 1 and 14 days after the first drug concentration measurement. Peak creatinine values were positively skewed, and log₁₀ transformed to follow a normal distribution. Outliers were defined as values less than 3 times the interquartile range below the 25^th percentile or more than 3 times the interquartile range above the 75^th percentile for the log-transformed variables. A review of a random subset of outliers found that they represented biologically implausible values; thus, the decision was made to exclude all outliers.

Muhammad A., Aka I.T., Birdwell K.A., Gordon A.S., Roden D.M., Wei W.Q., Mosley J.D, & Van Driest S.L. (2021). Genome-wide approach to measure variant-based heritability of drug outcome phenotypes. Clinical pharmacology and therapeutics, 110(3), 714-722.

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HLA Typing and DRESS Association Analysis

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High resolution four-digit HLA A B C DP DR DQ typing was performed using either sequence-based typing on 454FLX or Illumina Miseq^{8 (link),9 (link)} or imputed from SNP data from HumanExome BeadChip and GWAS platforms by Expanded Multi-Ethnic Genotyping Array (MEGA^EX, Illumina), HumanOmni-Quad, HumanOmni5-Quad and Human660W-Quad using SNP2HLA as previously described^{10 (link)}. Imputation for HLA-A*32:01 using SNP2HLA has a reported accuracy of 99.46%^{10 (link)}. Associations between DRESS and carriage of HLA-A/B/C/DRB1/DQA1/DQB1/DPB1 alleles at the 4-digit level were assessed by conditional logistic regression to accommodate the matching. Analyses were carried out in R version 3.4.3. (R Core Team (2017)). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/).

Konvinse K.C., Trubiano J.A., Pavlos R., James I., Shaffer C.M., Bejan C.A., Schutte R.J., Ostrov D.A., Pilkinton M.A., Rosenbach M., Zwerner J.P., Williams K.B., Bourke J., Martinez P., Rwandamuriye F., Chopra A., Watson M., Redwood A.J., White K.D., Mallal S.A, & Phillips E.J. (2019). HLA-A*32:01 is strongly associated with vancomycin-induced drug reaction with eosinophilia and systemic symptoms. The Journal of allergy and clinical immunology, 144(1), 183-192.

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Genetic Analysis of Iberian Ancestry

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Genetic analysis was conducted in a subsample of the GCAT cohort (GCATcore) that is fully genotyped by SNP-array and imputation. This subsample included 4988 unrelated participants with Iberian ethnicity, determined by self-described ethnicity, and supervised ancestry inference by Principal Components Analysis (PCA), as previously described by Galván Femenía et al. [39 (link)]. Genotyping was completed using the Infinium Expanded Multi-Ethnic Genotyping Array (MEGAEX) (ILLUMINA, San Diego, CA, USA). Then, we imputed both SNVs and SVs, with IMPUTE2, and using the GCAT|Panel as reference [40 (link)], an ancestry-geographically matched panel generated by whole-genome sequencing. The final core set included a total of 10,216,971 variants with MAF ≥ 0.01 and INFO score ≥ 0.7 (21,620 SVs, and 10,195,351 SNVs and small indels). All data are available at EGA (Study ID EGAS00001003018).

Farré X., Blay N., Cortés B., Carreras A., Iraola-Guzmán S, & de Cid R. (2023). Skin Phototype and Disease: A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy Using PRS in the GCAT Cohort. Genes, 14(1), 149.

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