Utilization of genome data

We make extensive use the genome data from blood donors available from the Blood Service Biobank. In particular, the FinnGen project has returned genome data from over 58,000 blood donors to the Blood Service Biobank. We are now utilizing these data for the benefits of blood service activities. We also are active participants in the immunogenetics studies of FinnGen¹.

We have developed a range of bioinformatics tools for predicting major blood cell alloantigens from genome‑wide array data. We make all software tools publicly available under open-source licenses via Blood Service GitHub repositories. These include tools for:

• Extended blood groups (Hyvärinen et al.²)
• Platelet HPA types (Gleadall et al.³)
• KIR gene content (Ritari et al.⁴)
• Classical HLA alleles (Ritari et al.⁵)
• HLA‑E, HLA‑F, HLA‑G and MICA and MICB alleles (Tammi et al.⁶)

These tools are now used in development projects within the Blood Service to identify blood donors with rare blood and HLA types. This enables us to increase the critical number of appropriately matched blood donors matched for patients in need of rare blood groups or for leukemia patients in need of HLA-typed thrombocytes. The tools have been utilized in dissecting the genetic structure of our blood donor biobank^7,8.

We utilize these tools in collaborative FinnGen studies. We systematically screened the roles of KIR and HLA systems over all FinnGen phenotypes^9,10 . We have participated in collaborative fine mapping studies on diseases such as lichen planus¹¹, autoimmune hypothyroidism¹², type I diabetes¹³, parodontitis¹⁴, and infertility¹⁵, all producing high-impact publications.

We are members of The Blood transfusion Genomics Consortium (BGC), an international initiative launched by Professor Willem Ouwehand (University of Cambridge, UK). BGC has developed an SNP array integrated with interpretive software that can be used to type all relevant blood groups and HLA alleles relevant in transfusion medicine. The flagship publication describing the results was published in the Blood journal³. The typing kit including both the array and interpretation software is now available through ThermoFisher.

Our key publications

1. Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–519 (2023).
2. Hyvärinen, K. et al. A machine-learning method for biobank-scale genetic prediction of blood group antigens. PLoS Comput. Biol. 20, e1011977 (2024).
3. Gleadall, N. S. et al. Array genotyping of transfusion-relevant blood cell antigens in 6946 ancestrally diverse study participants. Blood 146, 1511–1524 (2025).
4. Ritari, J., Hyvärinen, K., Partanen, J. & Koskela, S. KIR gene content imputation from single-nucleotide polymorphisms in the Finnish population. PeerJ 10, e12692 (2022).
5. Ritari, J. et al. Increasing accuracy of HLA imputation by a population-specific reference panel in a FinnGen biobank cohort. NAR Genom. Bioinform. 2, e12692 (2020).
6. Tammi, S. et al. Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data. PLoS Comput. Biol. 20, e1011718 (2024).
7. Clancy, J. et al. Blood donor biobank and HLA imputation as a resource for HLA homozygous cells for therapeutic and research use. Stem Cell Res. Ther. 13, 1–11 (2022).
8. Clancy, J. et al. Blood donor biobank as a resource in personalised biomedical genetic research. Eur J Hum Genet 2024 1–9 (2024) doi:10.1038/s41431-023-01528-0.
9. Ritari, J., Koskela, S., Hyvärinen, K. & Partanen, J. HLA-disease association and pleiotropy landscape in over 235,000 Finns. Hum. Immunol. https://doi.org/10.1016/j.humimm.2022.02.003 (2022).
10. Ritari, J. et al. Disease associations of natural killer (NK) cell KIR gene content variation in 352,783 Finns. Hum. Immunol. 85, 111177 (2024).
11. Reeve, M. P. et al. Oral and non-oral lichen planus show genetic heterogeneity and differential risk for autoimmune disease and oral cancer. Am. J. Hum. Genet. 111, 1047–1060 (2024).
12. Reeve, M. P. et al. Genome-wide association analyses of autoimmune hypothyroidism reveal autoimmune and thyroid-specific contributions and an inverse relationship with cancer risk. Nature Genet 2026 1–10 (2026) doi:10.1038/s41588-026-02521-1.
13. Wang, F. et al. Effects of parental autoimmune diseases on type 1 diabetes in offspring can be partially explained by HLA and non-HLA polymorphisms. Cell Genomics 100854 (2025) doi:10.1016/J.XGEN.2025.100854.
14. Salminen, A. et al. Genome-wide association study of pulpal and apical diseases. Nature Comm 2025 16:1 16, 6774- (2025).
15. Ruotsalainen, S. et al. Inherited infertility: Mapping loci associated with impaired female reproduction. Am. J. Hum. Genet. 111, 2789–2798 (2024).
16. Partanen, J. et al. Potilaan ja luovuttajan välinen kudossopivuus elinten ja kantasolujen siirroissa. Duodecim 1517–1524 (2024).
17. Helanterä, I., Markkinen, S., Partanen, J. & Hyvärinen, K. Novel Aspects of Immunogenetics and Post-Transplant Events in Kidney Transplantation. Transplant Int 37, 13317 (2024).
18. McCarroll, S. A. et al. Donor-recipient mismatch for common gene deletion polymorphisms in graft-versus-host disease. Nat. Genet. 41, (2009).
19. Markkinen, S. et al. Mismatches in Gene Deletions and Kidney-related Proteins as Candidates for Histocompatibility Factors in Kidney Transplantation. Kidney Int. Rep. 7, 2484–2494 (2022).
20. Semenova, M. et al. The Impact of Genome-wide Histocompatibility on Liver Transplantation Outcomes. Transplantation https://doi.org/10.1097/TP.0000000000005475 (2025)
21. Spierings, E. et al. Multicenter Analyses Demonstrate Signi fi cant Clinical Effects of Minor Histocompatibility Antigens on GvHD and GvL after HLA-Matched Related and Unrelated. Biol Blood Marrow Transplant 19, 1244–1253 (2013).
22. Ritari, J. et al. Computational Analysis of HLA-presentation of Non-synonymous Recipient Mismatches Indicates Effect on the Risk of Chronic Graft-vs.-Host Disease after Allogeneic HSCT. Front. Immunol. 10, (2019).
23. Collins, K. E. et al. Donor genetic burden for cerebrovascular risk and kidney transplant outcome. J. Nephrol. 1–10 (2024) doi:10.1007/S40620-024-01973-0/FIGURES/3.
24. Collins, K. E. et al. Donor and Recipient Polygenic Risk Scores Influence Kidney Transplant Function. Transplant Int 38, 14171 (2025).
25. Collins, K. E. et al. Polygenic risk scores for eGFR are associated with age at kidney failure. J. Nephrol. 1–10 (2025) doi:10.1007/S40620-025-02207-7/TABLES/4.
26. Nihtilä, J. et al. Donor genetic determinant of thymopoiesis, rs2204985, and stem cell transplantation outcome in a multipopulation cohort. Hum. Immunol. 85, 110791 (2024).
27. Nihtilä, J. et al. Impact of MICA-129 Mismatch on Hematopoietic Stem Cell Transplantation Outcomes: Evidence from a Large European Cohort and Meta-Analysis. Transplant. Cell. Ther. 31, 954.e1-954.e4 (2025).
28. Nihtilä, J. et al. Effect of NK cell receptor genetic variation on allogeneic stem cell transplantation outcome and in vitro NK cell cytotoxicity. Sci Rep 2024 14:1 14, 1–12 (2024).
29. Impola, U. et al. Donor haplotype B of NK KIR receptor reduces the relapse risk in HLA-identical sibling hematopoietic stem cell transplantation of AML patients. Front. Immunol. 5, 1–5 (2014).
30. Hyvärinen, K. et al. Meta-analysis of genome-wide association and gene expression studies implicates donor T cell function and cytokine pathways in acute GvHD. Front. Immunol. 11, (2020).