Axiom array

Manufactured by Thermo Fisher Scientific

Sourced in United States

The Axiom array is a high-throughput genotyping platform from Thermo Fisher Scientific. It is designed for comprehensive, cost-effective, and accurate analysis of genetic variations. The Axiom array enables researchers to interrogate a large number of genetic markers in a single experiment, facilitating studies of complex genetic traits and diseases.

Automatically generated - may contain errors

Lab products found in correlation

Spelling variants of this product

Axiom® Customized Biobank Genotyping Arrays (5 citations) Axiom Genome-Wide TWB 2.0 Array Plate (5 citations) Axiom Precision Medicine Research Array (PMRA; (5 citations) Affymetrix Axiom arrays (4 citations) Axiom LAT-1 arrays (3 citations) Axiom® Exome 319 Array (3 citations) GeneChip Human Genome-wide SNP Axiom Arrays (2 citations) Axiom Pig1.4M array plate (2 citations) Axiom Biobank Plus Genotyping Array (2 citations) Axiom® Array of 614 k SNPs (2 citations)

59 protocols using axiom array

Wnt Pathway SNPs Genotyping QC

Check if the same lab product or an alternative is used in the 5 most similar protocols

Genotyping of the selected Wnt pathway SNPs was performed in two batches totaling 6080 samples (including duplicates), as part of a custom Affymetrix Axiom array that contained 45,747 SNPs chosen for several type 2 diabetes projects. The Axiom array data underwent extensive QC procedures carried out by Affymetrix and Slone Epidemiology Center. About 13% of samples were removed due to high missing call rates (defined as >5%), poor reproducibility, or Dish-QC values <0.6. About 17% of SNPs were removed due to poor cluster properties, high missing call rates (defined as >10%), deviation from Hardy-Weinberg equilibrium (p <10⁻⁵ in controls), or high rates of discordant calls across duplicate samples. Only SNPs that passed QC in both sample batches were retained for analyses. After the application of these QC filters and the consolidation of 63 expected and confirmed duplicate sample pairs, the full type 2 diabetes data set contained 5228 subjects (2632 cases and 2596 controls) and 38,008 SNPs, including 3430 SNPs selected for the current analyses of Wnt pathway genes.

Haddad S.A., Palmer J.R., Lunetta K.L., Ng M.C, & Ruiz-Narváez E.A. (2017). A novel TCF7L2 type 2 diabetes SNP identified from fine mapping in African American women. PLoS ONE, 12(3), e0172577.

+ Open protocol

+ Expand

Genetic Determinants of Serum IGF-II

Cited 1 time

Check if the same lab product or an alternative is used in the 5 most similar protocols

The 911 subjects with serum IGF-II measurements were a subset of a population-based study from the Gila River Indian Community (N = 6789) with data on BMI and diabetes status as well as genotypic data derived from a custom Axiom array (Affymetrix, Santa Clara, CA) for a genome-wide association study (GWAS). Data from this array which passed all QCs constituted 515,692 SNPs and captured 92% of all common variation (minor allele frequency ≥0.05; r²≥0.85 in 300-kb windows) across the entire Pima Indian genome (10 (link)). Common variation spanning the IGF2 locus (100 kb up- and down-stream of the gene, Chr11:2050980–2269939, hg19) was captured by 42 tag SNPs on this array. Genotypic data were also available for 64 “established” BMI-associated SNPs which were identified in prior meta-analyses of GWAS for BMI from predominately large population s of European ancestry (11 (link)).

Muller Y.L., Hanson R.L., Mahkee D., Piaggi P., Kobes S., Hsueh W.C., Knowler W.C., Bogardus C, & Baier L.J. (2020). Low Serum Insulin-like Growth Factor-II Levels Correlate with High Body Mass Index in American Indian Adults. Obesity (Silver Spring, Md.), 28(3), 676-682.

+ Open protocol

+ Expand

Genotype Quality Control and Imputation

Cited 1 time

Check if the same lab product or an alternative is used in the 5 most similar protocols

Blood samples were collected from each participant and the derived genomic DNA was genotyped using the Affymetrix Axiom array in the AD and control children and the Affymetrix 5.0 chip (Affymetrix, Santa Clara, Calif) in the adult controls (Table S1). We excluded samples with call rates for autosomal SNPs less than 95%, and excluded SNPs with minor allele frequencies less than 5% or Hardy-Weinberg P values less than 10⁻⁴. Quality control was performed using PLINK 1.07.^{19 (link)} After quality control exclusions, 402,919 SNPs remained in the children and 287,622 SNPs in the adults. The common sets of SNPs were then used for imputation using minimac^{20 (link)} and the 1000 Genomes Asian reference panel.^{21 (link)} The resulting genotype data for 14,598,181 SNPs were subjected to further quality control checks and selected for high imputation accuracy (r² > 0.9) and minor allele frequency > 5%. As a final quality filter, SNPs were excluded if their allele frequencies differed (P ≥ 0.001) between the adult and children control samples. In the end, 2,501,352 autosomal SNPs were used for the GWAS analysis.

Kim K.W., Myers R.A., Lee J.H., Igartua C., Lee K.E., Kim Y.H., Kim E.J., Yoon D., Lee J.S., Hirota T., Tamari M., Takahashi A., Kubo M., Choi J.M., Kim K.E., Nicolae D.L., Ober C, & Sohn M.H. (2015). Genome-wide association study of recalcitrant atopic dermatitis in Korean children. The Journal of allergy and clinical immunology, 136(3), 678-684.e4.

+ Open protocol

+ Expand

Incident Stroke Types: Nested Case-Control

Check if the same lab product or an alternative is used in the 5 most similar protocols

The nested case-control study of incident stroke types included 5,475 IS cases, 4,776 ICH cases, and 6,290 healthy controls. Participants had no prior history of stroke, CHD, cancer, or use of lipid-lowering, antiplatelet, or anticoagulant drug treatment. Controls were selected among those who were free of diagnosis of stroke of any type, or unspecified type, myocardial infarction, or other CHD, by the censoring date. To avoid selecting any individual as a control who later became a case, the cases were ranked by the reverse of the dates on which they developed an ICH event (starting with the most recent, and working backwards to the earliest cases)³⁹. The same controls were used for both IS and ICH cases. Plasma lipid concentrations were measured, with samples randomly ordered by disease status, using AU680 Chemistry Analyzers (Beckman-Coulter), which provided direct homogenous assays for LDL-C and HDL-C, and enzymatic colour assays for total cholesterol and triglycerides. Plasma concentrations of apolipoprotein B, apolipoprotein A1, and lipoprotein (a) were measured by immune turbidimetric assays. Genotyping was carried out using an Affymetrix Axiom^® array, involving 800,000 SNPs, customised for the Chinese population.

Sun L., Clarke R., Bennett D., Guo Y., Walters R., Hill M., Parish S., Millwood I., Bian Z., Chen Y., Yu C., Lv J., Collins R., Chen J., Peto R., Li L, & Chen Z. (2019). Causal associations of blood lipids with risk of ischaemic stroke and intracerebral haemorrhage in Chinese adults. Nature medicine, 25(4), 569-574.

+ Open protocol

+ Expand

Genetic and Hematological Characteristics of Blood Donors

Cited 3 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

The NHLBI REDS-III RBC-Omics Study was a multi-center study enrolling 13,403 blood donors.^{20 (link)} This cohort was gen- otyped using a customized Affymetrix Axiom Array with approximately 879,000 single nucleotide polymorphisms (SNPs).^{22 (link)} Participants were stratified by sex and categorized into six groups that approximated the longitudinal groups (Figure 1B), differing by selection of females with a previous low hemoglobin deferral, instead of frequent first-time females, who could not be defined because of the cross-sectional study design. Groups 1 (2198 females) and 2 (1800 males) consisted of first-time or reactivated donors. Groups 3 (807 females) and 4 (183 males) had at least one low hemoglobin deferral in the previous 2 years. Groups 5 (818 females) and 6 (1652 males) had at least 8 whole blood donations within the prior 2 years. Iron supplement and cigarette use were obtained by survey.^{20 (link)} Ferritin and CBC were performed as described.^{20 (link),23 (link)}

Mast A.E., Langer J.C., Guo Y., Bialkowski W., Spencer B.R., Lee T.H., Kiss J., Cable R.G., Brambilla D., Busch M.P, & Page G.P. (2020). Genetic and behavioral modification of hemoglobin and iron status among first-time and high-intensity blood donors. Transfusion, 60(4), 747-758.

+ Open protocol

+ Expand

Integrated Multiomics Profiling of KORA Cohort

Cited 2 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

The KORA F4 study is a population-based cohort of 3,080 subjects living in the southern Germany. Study participants were recruited between 2006 and 2008 comprising individuals with age ranging from 32 to 81. All KORA participants have given written informed consent and the study was approved by the Ethics Committee of the Bavarian Medical Association. For this study, joint data was available for methylation, proteomics, and genotyping measurements of 944 individuals. The DNA methylation data set was determined using the Infinium HumanMethylation450 BeadChip platform which was described in detail previously^{6 (link)}. Aptamer-based proteomics was done using the SOMAscan platform and has been described in detail elsewhere^{2 (link)}. Genotyping was performed using the Affymetrix Axiom array, also described in detail elsewhere^{2 (link)}.

Zaghlool S.B., Kühnel B., Elhadad M.A., Kader S., Halama A., Thareja G., Engelke R., Sarwath H., Al-Dous E.K., Mohamoud Y.A., Meitinger T., Wilson R., Strauch K., Peters A., Mook-Kanamori D.O., Graumann J., Malek J.A., Gieger C., Waldenberger M, & Suhre K. (2020). Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits. Nature Communications, 11, 15.

+ Open protocol

+ Expand

C282Y Homozygosity Outcomes in HFE

Check if the same lab product or an alternative is used in the 5 most similar protocols

We tested baseline associations between C282Y homozygosity and outcomes using logistic regression models. These were adjusted for covariates including population substructure using the first five principal components generated in Northern European descent participants, genotyping microarray (Affymetrix Axiom array 90% participants, Affymetrix BiLEVE array, sharing >95% content). In our overall analysis, we compared C282Y homozygotes against wild type (homozygous common) as the reference group. We analyzed men and women separately. As iron overload is cumulative over time (and therefore with advancing age), but menstruation reduces iron stores, we have included specific analyses of older (65–70 years) female and male groups, the 60–64 year olds, and the whole group together (60–70). We tested C282Y homozygosity status for interactions with daily alcohol intake and smoking status for the four main outcomes, since these environmental exposures may exacerbate C282Y iron overload and clinical outcomes (6 (link),25 (link)). We considered a p value less than .05 as statistically significant. Analyses used Stata v14.1 (26 ). Figures were generated in R v3.4.1 using package “metaphor” (v2.0) (27 (link)).

Tamosauskaite J., Atkins J.L., Pilling L.C., Kuo C.L., Kuchel G.A., Ferrucci L, & Melzer D. (2019). Hereditary Hemochromatosis Associations with Frailty, Sarcopenia and Chronic Pain: Evidence from 200,975 Older UK Biobank Participants. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 74(3), 337-342.

+ Open protocol

+ Expand

MVP Genotyping and Imputation Protocol

Cited 2 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

We genotyped 468,961 participants who enrolled in MVP between 2011 and 2017 with a customized Affymetrix Axiom array in two batches. The first batch including 359,964 participants and the second batch including 108,997 participants. The genotyping data generated underwent extensive quality control (QC)^{58 (link)}. We initially imputed to the 1000 Genomes phase 3 version 5 reference panel (1000G)^{59 (link)} in each batch of genotyped data separately using EAGLE v2.3^{60 (link)} and Minimac3^{61 (link)} before joint imputation was performed in the two batches combined using EAGLE v2.4 and Minimac4. Prior to imputation, variants that were poorly called (genotype missingness > 5%) or that deviated from their expected allele frequency observed in the reference data (1000G) were excluded. Genotyped SNPs were interpolated into the imputation file.

Tcheandjieu C., Zhu X., Hilliard A.T., Clarke S.L., Napolioni V., Ma S., Lee K.M., Fang H., Chen F., Lu Y., Tsao N.L., Raghavan S., Koyama S., Gorman B.R., Vujkovic M., Klarin D., Levin M.G., Sinnott-Armstrong N., Wojcik G.L., Plomondon M.E., Maddox T.M., Waldo S.W., Bick A.G., Pyarajan S., Huang J., Song R., Ho Y.L., Buyske S., Kooperberg C., Haessler J., Loos R.J., Do R., Verbanck M., Chaudhary K., North K.E., Avery C.L., Graff M., Haiman C.A., Le Marchand L., Wilkens L.R., Bis J.C., Leonard H., Shen B., Lange L.A., Giri A., Dikilitas O., Kullo I.J., Stanaway I.B., Jarvik G.P., Gordon A.S., Hebbring S., Namjou B., Kaufman K.M., Ito K., Ishigaki K., Kamatani Y., Verma S.S., Ritchie M.D., Kember R.L., Baras A., Lotta L.A., Japan B., Kathiresan S., Hauser E.R., Miller D.R., Lee J.S., Saleheen D., Reaven P.D., Cho K., Gaziano J.M., Natarajan P., Huffman J.E., Voight B.F., Rader D.J., Chang K.M., Lynch J.A., Damrauer S.M., Wilson P.W., Tang H., Sun Y.V., Tsao P.S., O’Donnell C.J, & Assimes T.L. (2022). Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nature medicine, 28(8), 1679-1692.

+ Open protocol

+ Expand

UK Biobank Genotyping Protocol

Cited 2 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

Genomic DNA of participants was extracted from stored blood samples collected at United Kingdom Biobank. Genotyping was carried out by the United Kingdom Biobank Axiom^® Array (825,927 SNPs) or the Affymetrix United Kingdom BiLEVE Axiom^® Array (807,411 SNPs) and the methods have been described in detail (Welsh et al., 2017 (link); Bycroft et al., 2018 (link)). In summary, samples call rates for inclusion was >80% and genotyped call rates was >95%. Quality control (QC) of the data was performed centrally at the Wellcome Trust Centre for Human Genetics (WTCHG) and genotype data are available for access to approved researchers, providing a powerful resource to decipher new genetic associations and the genetic bases of traits or complex diseases.

Jiao H., Zhang M., Zhang Y., Wang Y, & Li W.D. (2021). Pathway Association Studies Reveal Gene Loci and Pathway Networks that Associated With Plasma Cystatin C Levels. Frontiers in Genetics, 12, 711155.

+ Open protocol

+ Expand

Genotyping and Quality Control for GWAS

Check if the same lab product or an alternative is used in the 5 most similar protocols

Blood samples were collected from each subject to extract genomic DNA. The DNA was then genotyped using an Affymetrix Axiom array (Affymetrix Inc., Santa Clara, CA, USA). There was originally a total of 600,252 SNPs; after frequency and genotyping pruning using GRCh37/hg19, there were 574,420 autosomal SNPs. During quality control filtration, samples with an autosomal SNP call rate <95% were removed. SNPs with minor allele frequencies <5% or Hardy–Weinberg equilibrium P value <10⁻⁶ were also excluded. Finally, 423,461 SNPs with a genomic inflation factor (λ) of 1.0033 were used for the GWAS (Supplemental Figure S1). Among the finally refined SNPs, we then selected 46 SNPs located ± 500 base pairs around BTNL2 in order to further analyze the BTNL2 region.

Kim S.Y., Kim E.G., Kim M., Hong J.Y., Kim G.E., Jung J.H., Park M., Kim M.J., Kim Y.H., Sohn M.H, & Kim K.W. (2021). Genome-wide association study identifies BTNL2 associated with atopic asthma in children. Medicine, 100(44), e27626.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!