ArticleBMC medical genomics2022
Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multitrait analysis of genome-wide association studies expands eosinophilic esophagitis genetic susceptibility and polygenic risk scores.The Journal of allergy and clinical immunology · 2026Article
- Genetic and measured LDL-C: Differential and interactive effects on cardiovascular and stroke subtypes.American journal of preventive cardiology · 2025Article
- Drug-target Mendelian randomization analysis supports lowering plasma ANGPTL3, ANGPTL4, and APOC3 levels as strategies for reducing cardiovascular disease risk.European heart journal open · 2024Article
- Genetically determined alcohol consumption and cancer risk in Korea.Epidemiology and health · 2023Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical use of genotype data requires high positive predictive value (PPV) and thorough understanding of the genotyping platform characteristics. BeadChip arrays, such as the Global Screening Array (GSA), potentially offer a high-throughput, low-cost clinical screen for known variants. We hypothesize that quality assessment and comparison to whole-genome sequence and benchmark data establish the analytical validity of GSA genotyping.
methodsTo test this hypothesis, we selected 263 samples from Coriell, generated GSA genotypes in triplicate, generated whole genome sequence (rWGS) genotypes, assessed the quality of each set of genotypes, and compared each set of genotypes to each other and to the 1000 Genomes Phase 3 (1KG) genotypes, a performance benchmark. For 59 genes (MAP59), we also performed theoretical and empirical evaluation of variants deemed medically actionable predispositions.
resultsQuality analyses detected sample contamination and increased assay failure along the chip margins. Comparison to benchmark data demonstrated that > 82% of the GSA assays had a PPV of 1. GSA assays targeting transitions, genomic regions of high complexity, and common variants performed better than those targeting transversions, regions of low complexity, and rare variants. Comparison of GSA data to rWGS and 1KG data showed > 99% performance across all measured parameters. Consistent with predictions from prior studies, the GSA detection of variation within the MAP59 genes was 3/261.
conclusionWe establish the analytical validity of GSA assays using quality analytics and comparison to benchmark and rWGS data. GSA assays meet the standards of a clinical screen although assays interrogating rare variants, transversions, and variants within low-complexity regions require careful evaluation.
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