A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing
Abstract Background Head-to-head comparison of BeadChip and WGS/WES genotyping techniques for their precision is far from straightforward. A tool for validation of high-throughput genotyping calls such as Sanger sequencing is neither scalable nor practical for large-scale DNA processing. Here we rep...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-09-01
|
| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12864-020-06919-x |
| _version_ | 1818932544301694976 |
|---|---|
| author | Kirill A. Danilov Dimitri A. Nikogosov Sergey V. Musienko Ancha V. Baranova |
| author_facet | Kirill A. Danilov Dimitri A. Nikogosov Sergey V. Musienko Ancha V. Baranova |
| author_sort | Kirill A. Danilov |
| collection | DOAJ |
| description | Abstract Background Head-to-head comparison of BeadChip and WGS/WES genotyping techniques for their precision is far from straightforward. A tool for validation of high-throughput genotyping calls such as Sanger sequencing is neither scalable nor practical for large-scale DNA processing. Here we report a cross-validation analysis of genotyping calls obtained via Illumina GSA BeadChip and WGS (Illumina HiSeq X Ten) techniques. Results When compared to each other, the average precision and accuracy of BeadChip and WGS genotyping techniques exceeded 0.991 and 0.997, respectively. The average fraction of discordant variants for both platforms was found to be 0.639%. A sliding window approach was utilized to explore genomic regions not exceeding 500 bp encompassing a maximal amount of discordant variants for further validation by Sanger sequencing. Notably, 12 variants out of 26 located within eight identified regions were consistently discordant in related calls made by WGS and BeadChip. When Sanger sequenced, a total of 16 of these genotypes were successfully resolved, indicating that a precision of WGS and BeadChip genotyping for this genotype subset was at 0.81 and 0.5, respectively, with accuracy values of 0.87 and 0.61. Conclusions We conclude that WGS genotype calling exhibits higher overall precision within the selected variety of discordantly genotyped variants, though the amount of validated variants remained insufficient. |
| first_indexed | 2024-12-20T04:34:10Z |
| format | Article |
| id | doaj.art-faf99612e66942c8a14bce83293c2a65 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-20T04:34:10Z |
| publishDate | 2020-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-faf99612e66942c8a14bce83293c2a652022-12-21T19:53:19ZengBMCBMC Genomics1471-21642020-09-0121S711110.1186/s12864-020-06919-xA comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencingKirill A. Danilov0Dimitri A. Nikogosov1Sergey V. Musienko2Ancha V. Baranova3Atlas Biomed Group LimitedAtlas Biomed Group LimitedAtlas Biomed Group LimitedSchool of Systems Biology, George Mason UniversityAbstract Background Head-to-head comparison of BeadChip and WGS/WES genotyping techniques for their precision is far from straightforward. A tool for validation of high-throughput genotyping calls such as Sanger sequencing is neither scalable nor practical for large-scale DNA processing. Here we report a cross-validation analysis of genotyping calls obtained via Illumina GSA BeadChip and WGS (Illumina HiSeq X Ten) techniques. Results When compared to each other, the average precision and accuracy of BeadChip and WGS genotyping techniques exceeded 0.991 and 0.997, respectively. The average fraction of discordant variants for both platforms was found to be 0.639%. A sliding window approach was utilized to explore genomic regions not exceeding 500 bp encompassing a maximal amount of discordant variants for further validation by Sanger sequencing. Notably, 12 variants out of 26 located within eight identified regions were consistently discordant in related calls made by WGS and BeadChip. When Sanger sequenced, a total of 16 of these genotypes were successfully resolved, indicating that a precision of WGS and BeadChip genotyping for this genotype subset was at 0.81 and 0.5, respectively, with accuracy values of 0.87 and 0.61. Conclusions We conclude that WGS genotype calling exhibits higher overall precision within the selected variety of discordantly genotyped variants, though the amount of validated variants remained insufficient.http://link.springer.com/article/10.1186/s12864-020-06919-xWGSWESWhole genome sequencingMicroarray genotypingGenotype concordanceSanger sequencing |
| spellingShingle | Kirill A. Danilov Dimitri A. Nikogosov Sergey V. Musienko Ancha V. Baranova A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing BMC Genomics WGS WES Whole genome sequencing Microarray genotyping Genotype concordance Sanger sequencing |
| title | A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing |
| title_full | A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing |
| title_fullStr | A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing |
| title_full_unstemmed | A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing |
| title_short | A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing |
| title_sort | comparison of beadchip and wgs genotyping outputs using partial validation by sanger sequencing |
| topic | WGS WES Whole genome sequencing Microarray genotyping Genotype concordance Sanger sequencing |
| url | http://link.springer.com/article/10.1186/s12864-020-06919-x |
| work_keys_str_mv | AT kirilladanilov acomparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing AT dimitrianikogosov acomparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing AT sergeyvmusienko acomparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing AT anchavbaranova acomparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing AT kirilladanilov comparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing AT dimitrianikogosov comparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing AT sergeyvmusienko comparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing AT anchavbaranova comparisonofbeadchipandwgsgenotypingoutputsusingpartialvalidationbysangersequencing |