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Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets
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....
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919546/ https://www.ncbi.nlm.nih.gov/pubmed/35287663 http://dx.doi.org/10.1186/s12920-022-01199-8 |
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author | Cherukuri, Praveen F. Soe, Melissa M. Condon, David E. Bartaria, Shubhi Meis, Kaitlynn Gu, Shaopeng Frost, Frederick G. Fricke, Lindsay M. Lubieniecki, Krzysztof P. Lubieniecka, Joanna M. Pyatt, Robert E. Hajek, Catherine Boerkoel, Cornelius F. Carmichael, Lynn |
author_facet | Cherukuri, Praveen F. Soe, Melissa M. Condon, David E. Bartaria, Shubhi Meis, Kaitlynn Gu, Shaopeng Frost, Frederick G. Fricke, Lindsay M. Lubieniecki, Krzysztof P. Lubieniecka, Joanna M. Pyatt, Robert E. Hajek, Catherine Boerkoel, Cornelius F. Carmichael, Lynn |
author_sort | Cherukuri, Praveen F. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01199-8. |
format | Online Article Text |
id | pubmed-8919546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89195462022-03-16 Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets Cherukuri, Praveen F. Soe, Melissa M. Condon, David E. Bartaria, Shubhi Meis, Kaitlynn Gu, Shaopeng Frost, Frederick G. Fricke, Lindsay M. Lubieniecki, Krzysztof P. Lubieniecka, Joanna M. Pyatt, Robert E. Hajek, Catherine Boerkoel, Cornelius F. Carmichael, Lynn BMC Med Genomics Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01199-8. BioMed Central 2022-03-14 /pmc/articles/PMC8919546/ /pubmed/35287663 http://dx.doi.org/10.1186/s12920-022-01199-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Cherukuri, Praveen F. Soe, Melissa M. Condon, David E. Bartaria, Shubhi Meis, Kaitlynn Gu, Shaopeng Frost, Frederick G. Fricke, Lindsay M. Lubieniecki, Krzysztof P. Lubieniecka, Joanna M. Pyatt, Robert E. Hajek, Catherine Boerkoel, Cornelius F. Carmichael, Lynn Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets |
title | Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets |
title_full | Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets |
title_fullStr | Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets |
title_full_unstemmed | Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets |
title_short | Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets |
title_sort | establishing analytical validity of beadchip array genotype data by comparison to whole-genome sequence and standard benchmark datasets |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919546/ https://www.ncbi.nlm.nih.gov/pubmed/35287663 http://dx.doi.org/10.1186/s12920-022-01199-8 |
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