Cardio metabo dna analysis beadchip metabochip

DNA was extracted from blood specimens obtained at the baseline visit using organic solvents and was genotyped according to Illumina protocol^{78 (link)} using the Illumina Cardio-Metabo DNA Analysis BeadChip (MetaboChip), which contains 196,725 markers. These markers were selected based on a large-scale meta-analysis for cardiometabolic traits such as coronary artery disease and type 2 diabetes. Markers included in this analysis have been genotyped previously using strict QC methods⁷⁹ and were selected based on their role in OCM. Single nucleotide polymorphisms (SNPs) were only included if minor allele frequencies were >2%. For genes with multiple SNPs available, SNPs that had been previously associated with arsenic metabolism or diabetes-related outcomes (or SNPs in perfect LD with these SNPs) were selected. SNPs in the following genes were included: rs4646371 in phosphatidylethanolamine N-methyltransferase (PEMT), rs3818239 and rs17751556 in methylenetetrahydrofolate dehydrogenase (MTHFD1), rs12952556 and rs2273027 in serine hydroxymethyltransferase 1 (SHMT1), rs1801131, rs1801133 and rs2274976 in methylenetetrahydrofolate reductase (MTHFR), rs10495387 in methionine synthase (MTR), rs3776464 and rs16879334 in methionine synthase reductase (MTRR) and rs12482221 in cystathionine β-synthase (CBS).

Baseline samples were used for single nucleotide polymorphism (SNP)
genotyping using the Illumina Cardio-Metabo DNA Analysis BeadChip (MetaboChip),
a multiplex panel of approximately 200,000 SNPs. The SNPs were selected to
contain all common variants from previous GWAS studies of diabetes, obesity, and
cardio-metabolic diseases and also less common variants not covered on GWAS
chips (MacArthur et al. 2017 ). In
addition, we had available data from a separately genotyped custom panel
(GoldenGate, genotyping panel, Illumina) of 1152 SNPs related to metal toxicity
including scavengers and other genes involved in metal metabolism and
transport.
Detailed information about genotyping quality control and SNP exclusion
criteria can be found elsewhere (Balakrishnan et
al. 2017). Briefly, poorly-performing DNA samples, genotyping
inconsistencies (mendelian errors) were excluded from the analysis. SNPs not
meeting the quality criteria of call rate and minor allele frequency were also
removed for the analyses. Genetic principal components were estimated to address
population stratification by using a subset of unlinked and ancestry informative
markers in unrelated individuals (Price et al.
2006) that were included in subsequent association analyses.