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Axiom

Manufactured by Illumina
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The Axiom is a high-throughput genotyping array platform designed for large-scale genetic studies. It provides comprehensive coverage of genomic regions and supports a wide range of applications, including genome-wide association studies, pharmacogenomics, and population genetics research.

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Lab products found in correlation

Affy60, by Illumina (1 mentions) Humanhap550, by Illumina (1 mentions) Omniexpress, by Illumina (1 mentions) Oncoarray, by Illumina (1 mentions) 1m duo, by Illumina (1 mentions) 550k duo, by Illumina (1 mentions) Omniexpressexome, by Illumina (1 mentions) Metabochip, by Illumina (1 mentions) Immunochip, by Illumina (1 mentions) 1m 1m duo, by Illumina (1 mentions) Omni1, by Illumina (1 mentions) Omni1m, by Illumina (1 mentions)

8 protocols using axiom

1

Genotype Quality Control and Imputation

Cited 1 time
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All participants were genotyped upon consent using common single nucleotide polymorphism (SNP) arrays including Affymetrix Affy60 and Axiom, and Illumina HumanHap550 (v1, v3), Human610-Quad (v1), and HumanOmniExpress. We applied pre-imputation quality control measures to our genotype data, including removing SNPs with minor allele frequencies (MAF) < 5%, Hardy–Weinberg equilibrium p values < 1.0e − 4, and call rates < 95%. We phased and imputed the genotype data using the Haplotype Reference Consortium reference panel version r1.1 2016 available on the Michigan Imputation server [40 (link)]. After imputation, we further removed duplicated and strand-ambiguous SNPs, as well as SNPs with MAF < 0.01 or an imputation quality score below 0.8. Samples genotyped using different arrays were processed in separate batches.
He Q., Keding T.J., Zhang Q., Miao J., Russell J.D., Herringa R.J., Lu Q., Travers B.G, & Li J.J. (2023). Neurogenetic mechanisms of risk for ADHD: Examining associations of polygenic scores and brain volumes in a population cohort. Journal of Neurodevelopmental Disorders, 15, 30.
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2

DNA Extraction and Genotyping of BMWpop

Cited 3 times
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Total genomic DNA was extracted as described by Plaschke et al. [62 (link)]. The BMWpop was genotyped by TraitGenetics GmbH, Germany, using an Illumina® iSelect® 20k single-nucleotide polymorphism (SNP) array, which includes a total of 17,267 SNPs from the 90k iSelect array described by Wang et al. [63 (link)] and the 820 k Axiom® array reported by Winfield et al. [64 (link)]. Physical SNP positions on the RefSeq v1.0 [65 (link)] were provided by TraitGenetics GmbH. A genetic map for QTL analysis in the BMWpop was previously published by Stadlmeier et al. [21 (link)]. The map comprised 5436 markers distributed over 2804 unique loci (Table S3).
Geyer M., Mohler V, & Hartl L. (2022). Genetics of the Inverse Relationship between Grain Yield and Grain Protein Content in Common Wheat. Plants, 11(16), 2146.
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3

Genotyping and Imputation Workflow

Cited 3 times
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Details on genotyping and imputation have been reported previously(17 (link)–19 (link)). In brief, DNA was mostly obtained from blood samples, with some from buccal swabs. Several platforms, including Affymetrix 500K, Affymetrix Axiom, Illumina 1M, 1M duo, Illumina 300K, Illumina 550K, 550Kduo, 610K, Illumina OmniExpress, Illumina OmniExpressExome, Illumina Oncoarray, Illumina Oncoarray+custom iSelect, were used for genotyping(20 (link),21 (link)). Samples were excluded on the basis of sample call rate, heterozygosity, unexpected duplicates or relative pairs, gender discrepancy and principal component analysis (PCA) outliers. SNPs were excluded on the basis of inconsistency across platforms, call rate, and out of Hardy-Weinberg equilibrium (HWE) in controls(20 (link)). SNPs were imputed using Haplotype Reference Consortium (HRC version r1.0) reference panel (22 (link)), and restricted by imputation accuracy (R2>0.3 for SNPs with MAF>1%, R2>0.5 for SNPs with MAF>0.5% and <1%, and R2>0.99 for SNPs with MAF<0.05%).
Wang X., Su Y.R., Petersen P.S., Bien S., Schmit S.L., Drew D.A., Albanes D., Berndt S.I., Brenner H., Campbell P.T., Casey G., Chang-Claude J., Gallinger S.J., Gruber S.B., Haile R.W., Harrison T.A., Hoffmeister M., Jacobs E.J., Jenkins M.A., Joshi A.D., Li L., Lin Y., Lindor N.M., Marchand L.L., Martin V., Milne R., Maclnnis R., Moreno V., Nan H., Newcomb P.A., Potter J.D., Rennert G., Rennert H., Slattery M.L., Thibodeau S.N., Weinstein S.J., Woods M.O., Chan A.T., White E., Hsu L, & Peters U. (2020). Exploratory genome-wide interaction analysis of non-steroidal anti-inflammatory drugs and predicted gene expression on colorectal cancer risk. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 29(9), 1800-1808.
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4

Quality Control of Genetic Data

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Dense single nucleotide polymorphisms (SNP) genotype data set (n = 1,074,163 SNPs) derived by combining and quality controlling—using standard methods of data filtering—from Affymetrix 6.0, Affymetrix Axiom arrays and the custom Illumina Immunochip and Illumina Metabochip was used for verification of gender and ancestry of study individuals. Individuals who showed statistically relevant genetic dissimilarity to the other subjects (population outliers identified by PCA-based mapping against the HapMap III CEU, CHB, JPT and YRI population) or who showed evidence for cryptic relatedness to other study participants (unexpected duplicates, first- or second-degree relatives identified by identity by descent estimated using the R-package SNPRelate (vs. 0.9.19)) were removed. All gender assignments could be verified by reference to the proportion of heterozygous SNPs on the X chromosome. The final data set consisted of 784 samples.
Claussen J.C., Skiecevičienė J., Wang J., Rausch P., Karlsen T.H., Lieb W., Baines J.F., Franke A, & Hütt M.T. (2017). Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. PLoS Computational Biology, 13(6), e1005361.
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5

Germline DNA Genotyping and Imputation

Cited 3 times
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Details of sample collection, genotyping, quality control (QC) and imputation have been reported elsewhere (Schmit et al., 2018 (link)). In brief, genotype data was generated from germline DNA on the Affymetrix Axiom, Illumina 1M/1M-Duo, Omni1 and OncoArray. Standard QC filters were applied to the high-density genotype array data at both the individual participant and SNP levels. Quality-controlled genotype data was imputed to the 1,000 Genomes Project (1KGP) Phase 1 multiethnic reference panel (March 2012 release, N=1,092) using SHAPE-IT/IMPUTE2 (Affymetrix Axiom; Illumina 1M/1M-Duo, and Omni1) (Eyre et al., 2012 (link); Howie, Donnelly, & Marchini, 2009 (link)) or the 1KGP Phase 3 reference panel (Illumina OncoArray) (Amos et al., 2017 (link)). Imputation quality (info score> 0.3) and minor allele frequency filters (MAF ≥ 1%) were imposed on variants prior to the analysis phase. Approximately 44.4% (N=794) of the participants were run on Illumina 1M/1M-Duo while 12.1% (N=217) were on Illumina OncoArray; Affymetrix Axiom and Illumina Omni1 contributed similar proportions (20.5% (N=366) and 23.0% (N=411), respectively).
Wang J., Asante I., Baron J.A., Figueiredo J.C., Haile R., Levine A.J., Newcomb P.A., Templeton A.S., Schumacher F.R., Louie S.G., Casey G, & Conti D.V. (2019). Genome-wide association study of circulating folate one-carbon metabolites. Genetic epidemiology, 43(8), 1030-1045.
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6

Genotyping and Imputation Pipeline for NTR

Cited 1 time
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Genotyping in children (YNTR) and adults (ANTR) was done on multiple platforms over time including Perlegen-Affymetrix, Affymetrix 6.0, Affymetrix Axiom, Illumina Human Quad Bead 660, Illumina Omni 1M and Illumina GSA. Quality control and processing of the genotype data was performed on the complete dataset of all genotyped participants from the NTR. Quality control was carried out and haplotypes were estimated in PLINK. CEU population outliers, based on per platform 1000 Genomes PC projection with the Smartpca software (50 (link)), were excluded. Data were phased per platform using Eagle, and then imputed to 1000 Genomes using Minimac, following the Michigan imputation server protocols. For the polygenic scoring imputed data were converted to best guess genotypes, and filtered to include only ACGT SNPs, SNPs with MAF > 0.01, HWE p > 10−5 and genotype call rate > 0.98, and exclude SNPs with more than 2 alleles. All Mendelian errors were set to missing. Principal components (PCs) were calculated with Smartpca using linkage-disequilibrium-pruned (LD-pruned) 1000 Genomes–imputed SNPs that were also genotyped on at least one platform, had MAF > 0.05 and were not present in the long-range LD regions.
Odintsova V.V., Rebattu V., Hagenbeek F.A., Pool R., Beck J.J., Ehli E.A., van Beijsterveldt C.E., Ligthart L., Willemsen G., de Geus E.J., Hottenga J.J., Boomsma D.I, & van Dongen J. (2021). Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles. Frontiers in Psychiatry, 12, 688464.
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7

Comprehensive Genomic Variant Imputation

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Genotyping was done on several platforms, that is, Affymetrix-Perlegen, Illumina 660, Illumina Omni Express 1M, Affymetrix 6.0, Affimetrix Axiom and Illumina GSA. For criteria on quality control (QC) of the single nucleotide polymorphisms (SNPs) and samples, before and after imputation (see de Zeeuw et al., 2020) , data were cross-platform phased and imputed using Mach-admix with GoNL (Francioli et al., 2014) as the reference panel for all SNPs that were, after QC, present for at least one platform (Boomsma et al., 2014) . The cross-chip imputed data set was used to calculate genetic principal components with the SmartPCA software (Price et al., 2006) . Subsequently, the data set was aligned against the 1000G phase 3 version 5 reference panel and imputed on the Michigan imputation server (Das et al., 2016) . Best guess genotypes were calculated for all SNPs in Plink 1.96 (Purcell et al., 2007) .
de Zeeuw E.L., Voort L., Schoonhoven R., Nivard M.G., Emery T., Hottenga J.J., Willemsen G.A.H.M., Dykstra P.A., Zarrabi N., Kartopawiro J.D, & Boomsma D.I. (2021). Safe Linkage of Cohort and Population-Based Register Data in a Genomewide Association Study on Health Care Expenditure. Twin research and human genetics : the official journal of the International Society for Twin Studies, 24(2).
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8

Genotyping Quality Control and Imputation

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Genotyping was done on several platforms, i.e. Affymetrix-Perlegen, Illumina 660, Illumina Omni Express 1M, Affymetrix 6.0, Affimetrix Axiom and Illumina GSA. For criteria on quality control (QC) of the single nucleotide polymorphisms (SNP) and samples, before and after imputation, see Michigan imputation server 14 . Best guess genotypes were calculated for all SNPs in Plink 1.96 15 .
Zeeuw E.L.d., Voort L., Schoonhoven R., Nivard M.G., Emery T., Hottenga J., Willemsen G., Dykstra P.A., Zarrabi N., Kartopawiro J.D., & Boomsma D.I. (2020). Safe linkage of cohort and population-based register data in a genome-wide association study on health care expenditure.
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