Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets

Abstract

Background Clinical 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. Methods To 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. Results Quality 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. Conclusion We 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.

Drug-target Mendelian randomization analysis supports lowering plasma ANGPTL3, ANGPTL4, and APOC3 levels as strategies for reducing cardiovascular disease risk

“…Therefore, we pursued a meta-analysis of DNA sequencing-based studies that studied the effect of ANGPTL3 , ANGPTL4 , and APOC3 deleterious variants on CAD. The rationale for excluding DNA microarray and exome bead chip-based studies was the potential risk of introducing measurement error for rare variants (50, 51), leading to bias towards the null hypothesis. DNA-sequencing-based substudies from previous papers (3, 46, 52, 53), were extracted and analyzed together with genetic association analyses conducted in the UK Biobank.…”

“…Therefore, we pursued a meta-analysis of DNA sequencing-based studies that studied the effect of ANGPTL3 , ANGPTL4 , and APOC3 LoF variants on CAD. The rationale for excluding DNA microarray and exome bead chip-based studies was that they could potentially introduce measurement error for rare variants (48, 49). This would lead to bias towards the null hypothesis.…”

Drug-target Mendelian randomization analysis supports lowering plasma ANGPTL3, ANGPTL4, and APOC3 levels as strategies for reducing cardiovascular disease risk

“…Therefore, we pursued a meta-analysis of DNA sequencing-based studies that studied the effect of ANGPTL3 , ANGPTL4 , and APOC3 protein-truncating variants on CAD. The rationale for excluding DNA microarray and exome bead chip-based studies was the potential risk of introducing measurement error for rare variants, 51 , 52 leading to bias towards the null hypothesis. DNA-sequencing-based substudies from previous papers, 3 , 47 , 53 , 54 were extracted and analysed together with genetic association analyses conducted in the UK Biobank.…”

Genetically determined alcohol consumption and cancer risk in Korea

“…In order to estimate the GDAC, all subjects needed genetic test data. For 50% of the subjects in this study, a global screening array (GSA) chip was used for testing [ 9 ], while testing for the remaining 50% was conducted using the Korea Biobank Array [ 10 ]. Then, based on 1,000 genomes in an identical way, imputation was performed using IMPUTE 5 to build integrated data.…”

“…In order to estimate the GDAC, all subjects needed genetic test data. For 50% of the subjects in this study, had a global screening array (GSA) chip was used for testing tested [9]. ], while testing for the The remaining 50% took was conducted using the Korea Biobank Array [10].…”