We as part of the EAGLE study accessed all DNA samples and data from non-European Americans within BioVU as of 2011 for genotyping. These data are collectively referred to here as “EAGLE BioVU” [9 (link)]. A total of 15,863 samples were targeted for Illumina Metabochip genotyping. The Illumina Metabochip is a 200,000 variant array designed for replicating genome-wide association study findings (index variants) and for fine mapping select GWAS findings for cardiovascular and metabolic traits and outcomes [13 (link)]. The EAGLE BioVU dataset was generated by the Vanderbilt DNA Resources Core, and genotype calls and quality control were performed by the Population Architecture using Genomic and Epidemiology (PAGE) Coordinating Center as previously described [9 (link), 14 (link)].
Metabochip
Manufactured by Illumina
Sourced in United States, United Kingdom
The Metabochip is a custom-designed microarray from Illumina that enables the measurement of genetic variants associated with metabolic and cardiovascular traits. The Metabochip focuses on genomic regions and single nucleotide polymorphisms (SNPs) previously implicated in these phenotypes, allowing for efficient and targeted genotyping.
Automatically generated - may contain errors
Lab products found in correlation
Omniexpress, by Illumina (3 mentions)
Human exon 1.0 st genechip platform, by Thermo Fisher Scientific (2 mentions)
550k array, by Thermo Fisher Scientific (2 mentions)
Xevo g2 q tof ms, by Waters Corporation (2 mentions)
Iselect genotyping array, by Illumina (2 mentions)
Kaspar assay, by LGC (2 mentions)
Genomestudio software, by Illumina (2 mentions)
Acquity ultra performance liquid chromatography, by Waters Corporation (1 mentions)
Immunochip, by Illumina (1 mentions)
Omniexpress chip, by Illumina (1 mentions)
Omniexpressexome, by Illumina (1 mentions)
Hiscan system, by Illumina (1 mentions)
Exome chip, by Illumina (1 mentions)
Hiscan, by Illumina (1 mentions)
30 protocols using metabochip
1
Genotyping of Non-European Americans
2
Genetic Profiling of Cardiometabolic Traits
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DNA was extracted from whole‐blood samples and was stored at −80°C as a part of the database study. Metabolism‐related SNPs were analyzed using the Metabochip (consortium version; Illumina, San Diego, CA).(25 ) Briefly, the Metabochip was designed to enable cost‐effective replication and fine‐mapping of 217,965 loci previously associated with cardiometabolic phenotypes including type 2 diabetes, coronary artery disease, and myocardial infarction, as well as related traits like BMI, glucose and insulin levels, lipid levels, and blood pressure. We chose to focus our analysis on these loci, as well as SNPs in the major histocompatibility complex, with minor allele frequency >5% and Hardy‐Weinberg equilibrium P value > 10−6 due to the limited size of our sample. As such, 98,359 SNPs remained in our targeted analysis. Only SNPs on autosomal chromosomes were included in this analysis. GenomeStudio software was used to determine SNP genotypes. Samples were required to have a call rate >99% and no gender discrepancy to pass initial quality control. Identity by state analysis was performed using PLINK(26 ); duplicate samples and first‐degree relatives were subsequently removed.
3
Genotype, Expression, and Metabolomic Data Protocols
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In FHS, SNP data were obtained from the Affymetrix 550K Array (Affymetrix, Santa Clara, CA) and imputed to 1000 Genomes SNPs (phase 1 release), as previously reported.23 (link) The FHS genotype data are available at Database of Genotypes and Phenotypes under the accession number phs000342.v13.p9. In PIVUS, individuals were genotyped using the Illumina OmniExpress and Illumina Metabochip microarrays. Data were imputed to 1000G (version: March 2012) using Impute v.2.2.2.24 (link) Gene expression profiles in blood, obtained using the Affymetrix Human Exon 1.0 ST GeneChip platform, were available for 2246 participants in the FHS. Untargeted metabolomic profiles in serum were available for 785 PIVUS participants also included in the lipid-association analyses. Acquity Ultra Performance Liquid Chromatography coupled to a Xevo G2 Q-TOFMS (Waters Corporation, Milford, MA) was used in metabolomic profiling. Only annotated metabolites (n=229) were used in analysis in relation to DNA methylation. Further details are available in Methods in the Data Supplement .
4
Genetic Risk Factors for HDL-C in African Americans
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A total of 15,863 DNA samples from mostly non-European descent subjects were genotyped on the Illumina Metabochip, including 11,519 African Americans, by Vanderbilt University Center for Human Genetics Research DNA Resources Core. The Illumina Metabochip is a custom array of approximately 200,000 variants chosen as GWAS-identified index variants or GWAS-identified regions for fine-mapping based on data from the first iteration of the 1000 Genomes Project [17 (link)]. Quality control of the Illumina Metabochip data for EAGLE BioVU followed the quality control procedures outlined in Buyske et al. [22 (link)].
Based on a previous fine-mapping study of HDL-C using Metabochip [23 (link)], seven of the 22 fine-mapped HDL-C loci exhibited evidence of association at p < 1x10−4 in African Americans. The seven index SNPs from these seven associated HDL-C loci were selected for use in calculating the genetic risk score (GRS, Table2 ).
Based on a previous fine-mapping study of HDL-C using Metabochip [23 (link)], seven of the 22 fine-mapped HDL-C loci exhibited evidence of association at p < 1x10−4 in African Americans. The seven index SNPs from these seven associated HDL-C loci were selected for use in calculating the genetic risk score (GRS, Table
| SNP | Gene of interest | Effect Allele | Effect on HDL-C†(mg/dl) |
|---|---|---|---|
| rs247617 | CETP | C | −0.111 |
| rs1077834 | LIPC | A | −0.033 |
| rs10096633 | LPL | G | −0.042 |
| rs189069311 | APOA5 | A | −0.080 |
| rs255054 | LCAT | A | −0.042 |
| rs6601299 | PPP1R3B | A | −0.063 |
| rs4810479 | PLTP | G | −0.029 |
†Beta coefficients were drawn from meta-analysis results of PAGE African Americans [23 (link)].
5
DNA Extraction and Genotyping Protocol
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DNA samples were extracted from whole blood using the salting-out method [18] (link). Genotyping was previously performed using the Illumina Metabochip (Illumina, USA) at the DNA Technologies Core of the University of California Davis (California, USA), and genotypes for the two single nucleotide polymorphisms (SNPs) of interest were extracted from data files. All details regarding the genotyping experiment and quality control of the genotypic data have been described previously [19] (link).
6
Genetic Risk Scores for Insulin Resistance and Secretion
7
Cardiometabolic Disease Genetic Risk Profiling
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Blood samples from participants at age 53 years were genotyped using MetaboChip, a custom Illumina iSelectarray (San Diego, CA, USA) that includes ~200,000 SNPs and covers the loci identified by genome-wide association studies (GWASs) in cardiometabolic diseases, including rare variants identified by the 1000 Genomes Project [27 (link)]. Quality control analysis of genotyped samples has been previously reported [28 (link)].
Three PRSs were calculated. A type 2 diabetes PRS has previously been derived for NSHD study members [29 (link)]. In brief, a genetic risk score was computed using the published coefficients for 65 SNPs identified by a prior GWAS for type 2 diabetes [30 (link), 31 ]. We additionally derived a PRS for insulin resistance using 17 previously demonstrated genome-wide significant SNPs [32 (link)] and for hyperglycaemia using ten previously demonstrated SNPs [32 (link)] using PRSice [33 (link)]. This calculates the sum of the number of risk alleles (unweighted score) carried by each person, and weights it based on previously published coefficients (weighted score). As is standard practice, SNPs with a minor allele frequency <0.01 were excluded.
Three PRSs were calculated. A type 2 diabetes PRS has previously been derived for NSHD study members [29 (link)]. In brief, a genetic risk score was computed using the published coefficients for 65 SNPs identified by a prior GWAS for type 2 diabetes [30 (link), 31 ]. We additionally derived a PRS for insulin resistance using 17 previously demonstrated genome-wide significant SNPs [32 (link)] and for hyperglycaemia using ten previously demonstrated SNPs [32 (link)] using PRSice [33 (link)]. This calculates the sum of the number of risk alleles (unweighted score) carried by each person, and weights it based on previously published coefficients (weighted score). As is standard practice, SNPs with a minor allele frequency <0.01 were excluded.
8
Integrating Genomic and Metabolomic Data
9
Genotyping Blood Samples using Metabochip
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DNA, extracted from blood using the salting out method [8 (link)], was normalized to 50 ng.ul−1 prior to genotyping at the UC Davis Genome Centre (California, USA) using the Metabochip (Illumina, San Diego, CA, USA). The Metabochip is a custom genotyping array that allows for the genotyping of almost 200,000 single nucleotide polymorphisms (SNPs) known to influence cardiometabolic traits [9 ]. The DNA samples were genotyped in two separate batches (participants and caregivers) and duplicate samples from each batch (nine in total) were sent with the unique samples to assess genotyping consistency. Genotypes were called using GenomeStudio Software for Illumina (v2011.1) and a custom DNAtech cluster file and final output was provided as final reports in the forward strand orientation.
10
Genotyping Using Open Array and MetaboChip
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Genotyping was performed by the Open Array SNP Genotyping System and the MetaboChip (Illumina Inc., San Diego, CA, USA) array, and SNP proxies were used for 12 out of the 31 SNPs. Information regarding SNPs used in GLACIER is provided in Supplementary Table 2 . The average genotyping success rate was 96.0% and all SNPs were in Hardy–Weinberg equilibrium (P>0.001).
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