An open‐source python library for detection of known and novel Kell, Duffy and Kidd variants from exome sequencing. Issue 4 (10th February 2021)

• Record Type: Journal Article
• Authors: Montemayor, Celina; Simone, Alexandra; Long, James; Montemayor, Oscar; Delvadia, Bhavesh; Rivera, Robert; Lewis, Katie L; Shahsavari, Shahin; Gandla, Divya; Dura, Katherine; Krishnan, Uma S; Wendzel, Nena C; Elavia, Nasha; Grissom, Spencer; Karagianni, Panagiota; Bueno, Marina; Loy, Debrean; Cacanindin, Rizaldi; McLaughlin, Steven; Tynuv, Maxim; Brunker, Patricia A R; Roback, John; Adams, Sharon; Smith, Harold; Biesecker, Leslie; Klein, Harvey G
• Is Part Of: Vox sanguinis. Volume 116:Issue 4(2021)
• Page Start: 451
• Page End: 463
• Publication Date: 2021-02-10
• Subjects: blood groups -- genomics -- high throughput testing -- RBC antigens; Blood -- Periodicals; Blood -- Transfusion -- Periodicals; Immunohematology -- Periodicals; Immunopathology -- Periodicals; 615.39
• DOI: 10.1111/vox.13035
• Languages: English
• ISSNs: 0042-9007

Abstract

Abstract: Background and objectives: Next generation sequencing (NGS) has promising applications in transfusion medicine. Exome sequencing (ES) is increasingly used in the clinical setting, and blood group interpretation is an additional value that could be extracted from existing data sets. We provide the first release of an open‐source software tailored for this purpose and describe its validation with three blood group systems. Materials and methods: The DTM‐Tools algorithm was designed and used to analyse 1018 ES NGS files from the ClinSeq ® cohort. Predictions were correlated with serology for 5 antigens in a subset of 108 blood samples. Discrepancies were investigated with alternative phenotyping and genotyping methods, including a long‐read NGS platform. Results: Of 116 genomic variants queried, those corresponding to 18 known KEL, FY and JK alleles were identified in this cohort. 596 additional exonic variants were identified KEL, ACKR1 and SLC14A1, including 58 predicted frameshifts. Software predictions were validated by serology in 108 participants; one case in the FY blood group and three cases in the JK blood group were discrepant. Investigation revealed that these discrepancies resulted from (1) clerical error, (2) serologic failure to detect weak antigenic expression and (3) a frameshift variant absent in blood group databases. Conclusion: DTM‐Tools can be employed for rapid Kell, Duffy and Kidd blood group antigen prediction from existing ES data sets; for discrepancies detected in the validation data set, software predictions proved accurate. DTM‐Tools is open‐source and in continuous development.