Donor genetic determinant of thymopoiesis, rs2204985, and stem cell transplantation outcome in a multipopulation cohort.

BACKGROUND: A genetic polymorphism, rs2204985, has been reported to be associated with the diversity of T-cell antigen receptor repertoire and TREC levels, reflecting the function of the thymus. As the thymus function can be assumed to be an important factor regulating the outcome of stem cell transplantation (SCT), it was of great interest that rs2204985 showed a genetic association to disease-free and overall survival in a German SCT donor cohort. Tools to predict the outcome of SCT more accurately would help in risk assessment and patient safety. OBJECTIVE: To evaluate the general validity of the original genetic association found in the German cohort, we determined genetic associations between rs2204985 and the outcome of SCT in 1,473 SCT donors from four different populations. STUDY DESIGN: Genetic associations between rs2204985 genotype AA versus AG/GG and overall survival (OS) and disease-free survival (DFS) in 1,473 adult, allogeneic SCT from Finland, the United Kingdom, Spain, and Poland were performed using the Kaplan-Meier analysis and log-rank tests. We adjusted the survival models with covariates using Cox regression. RESULTS: In unrelated SCT donors (N = 425), the OS of genotype AA versus AG/GG had a trend for a similar association (p = 0.049, log-rank test) as previously reported in the German cohort. The trend did not remain significant in the Cox regression analysis with covariates. No other associations were found. CONCLUSION: Weak support for the genetic association between rs2204985, previously also associated with thymus function, and the outcome of SCT could be found in a cohort from four populations.

A machine-learning method for biobank-scale genetic prediction of blood group antigens.

A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9-100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1-99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1-99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank-scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors.

Blood donor biobank as a resource in personalised biomedical genetic research.

Health questionnaires and donation criteria result in accumulation of highly selected individuals in a blood donor population. To understand better the usefulness of a blood donor-based biobank in personalised disease-associated genetic studies, and for possible personalised blood donation policies, we evaluated the occurrence and distributions of common and rare disease-associated genetic variants in Finnish Blood Service Biobank. We analysed among 31,880 blood donors the occurrence and geographical distribution of (i) 53 rare Finnish-enriched disease-associated variants, (ii) mutations assumed to influence blood donation: four Bernard-Soulier syndrome and two hemochromatosis mutations, (iii) type I diabetes risk genotype HLA-DQ2/DQ8. In addition, we analysed the level of consanguinity in Blood Service Biobank. 80.3% of blood donors carried at least one (range 0-9 per donor) of the rare variants, many in homozygous form, as well. Donors carrying multiple rare variants were enriched in Eastern Finland. Haemochromatosis mutation HFE C282Y homozygosity was 43.8% higher than expected, whereas mutations leading to Bernard-Soulier thrombocytopenia were rare. The frequency of HLA-DQ2/DQ8 genotype was slightly lower than expected. First-degree consanguinity was higher in Blood Service Biobank than in the general population. We demonstrate that despite donor selection, the Blood Service Biobank is a valuable resource for personalised medical research and for genotype-selected samples from unaffected individuals. The geographical genetic substructure of Finland enables efficient recruitment of donors carrying rare variants. Furthermore, we show that blood donor biobank material can be utilised for personalised blood donation policies.

Development of mesothelioma-specific oncolytic immunotherapy enabled by immunopeptidomics of murine and human mesothelioma tumors.

Malignant pleural mesothelioma (MPM) is an aggressive tumor with a poor prognosis. As the available therapeutic options show a lack of efficacy, novel therapeutic strategies are urgently needed. Given its T-cell infiltration, we hypothesized that MPM is a suitable target for therapeutic cancer vaccination. To date, research on mesothelioma has focused on the identification of molecular signatures to better classify and characterize the disease, and little is known about therapeutic targets that engage cytotoxic (CD8+) T cells. In this study we investigate the immunopeptidomic antigen-presented landscape of MPM in both murine (AB12 cell line) and human cell lines (H28, MSTO-211H, H2452, and JL1), as well as in patients' primary tumors. Applying state-of-the-art immuno-affinity purification methodologies, we identify MHC I-restricted peptides presented on the surface of malignant cells. We characterize in vitro the immunogenicity profile of the eluted peptides using T cells from human healthy donors and cancer patients. Furthermore, we use the most promising peptides to formulate an oncolytic virus-based precision immunotherapy (PeptiCRAd) and test its efficacy in a mouse model of mesothelioma in female mice. Overall, we demonstrate that the use of immunopeptidomic analysis in combination with oncolytic immunotherapy represents a feasible and effective strategy to tackle untreatable tumors.

DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning.

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

MICA and MICB allele assortment in Finland.

Genetic variation in the MICA and MICB genes located within the major histocompatibility complex region has been reported to be associated with transplantation outcome and susceptibility to autoimmune diseases and infections. Only limited data of polymorphism in these genes in different populations are available. We here report allelic variation at 2-field resolution and the haplotypes of the MICA and MICB genes in Finland (n = 1032 individuals), a north European population with historical bottleneck and founder effects. Altogether 24 MICA and 18 MICB alleles were found, forming 70 estimated MICA-MICB haplotypes. As compared to other populations frequency differences were found, for example, MICA*010:01 was found to be at an allele frequency of 0.133 in Finland which is higher than in other European populations (0.021-0.077), but close to Asian populations (0.151-0.220). Three novel alleles with amino acid change are described. The results demonstrate a relatively high level of polymorphism and population differences in MICA and MICB allele distribution.

Clinically relevant germline variants in allogeneic hematopoietic stem cell transplant recipients.

Allogeneic hematopoietic stem cell transplantation (HSCT) provides patients with severe hematologic disease a well-established potential for curation. Incorporation of germline analyses in the workup of HSCT patients is not a common practice. Recognizing rare harmful germline variants may however affect patients' pre-transplantation care, choice of the stem cell donor, and complication risks. We analyzed a population-based series of germline exome data of 432 patients who had undergone HSCT. Our aim was to identify clinically relevant variants that may challenge the outcome of the HSCT. We focused on genes predisposing to hematological diseases, or solid tumors, and genes included in the American College of Medical Genetics secondary findings list v3.0. As population-specific controls, we used GnomAD non-cancer Finns (n = 10,816). We identified in our population-based analysis rare harmful germline variants in disease-predisposing or actionable toxicity-increasing genes in 17.8% of adult and pediatric patients that have undergone HSCT (15.1% and 22.9%, respectively). More than half of the patients with a family member as a donor had not received genetic diagnosis prior to the HSCT. Our results encourage clinicians to incorporate germline genetic testing in the HSCT protocol in the future in order to reach optimal long-term outcome for the patients.

Mismatches in Gene Deletions and Kidney-related Proteins as Candidates for Histocompatibility Factors in Kidney Transplantation.

Introduction: The genomic mismatch level between donor and recipient may be associated with the risk of rejection and graft survival. We determined the association of genome-level matching with acute rejection in deceased-donor kidney transplantation. Methods: The study cohort consists of 1025 recipient-donor pairs transplanted in a single center from 2007 to 2017 in Helsinki. The associations between the sums of whole-genome missense variant mismatches and missense mismatches in transmembrane, secretory, and kidney-related proteins, with acute rejection were estimated using Cox model. In addition, we analyzed 40 deletion-tagging variants using Cox model. Results: The association analysis between mismatch sums of kidney-related proteins and acute rejection resulted in an unadjusted hazard ratio (HR) of 1.15 (95% confidence interval [CI], 1.01-1.30; P = 0.029) and adjusted HR of 1.13 (95% CI, 0.99-1.28; P = 0.071). In deletion analysis, a mismatch in rs7542235 genotype GG tagging a homozygous deletion at the complement factor H-related (CFHR), proteins locus, predisposed to acute rejection with an unadjusted HR of 3.10 (95% CI, 1.53-6.29; P = 0.002) and adjusted HR of 2.97 (95% CI, 1.46-6.05; P = 0.003). Conclusion: In conclusion, analyses of genome-level mismatches may be useful tools in prediction of transplantation outcome. The relative importance differs between populations, because we found evidence for CFHR deletion but could not replicate the finding of previously reported LIMS1 deletion.

Blood donor biobank and HLA imputation as a resource for HLA homozygous cells for therapeutic and research use.

BACKGROUND: Allogeneic therapeutic cells may be rejected if they express HLA alleles not found in the recipient. As finding cell donors with a full HLA match to a recipient requires vast donor pools, the use of HLA homozygous cells has been suggested as an alternative. HLA homozygous cells should be well tolerated by those who carry at least one copy of donor HLA alleles. HLA-A-B homozygotes could be valuable for HLA-matched thrombocyte products. We evaluated the feasibility of blood donor biobank and HLA imputation for the identification of potential cell donors homozygous for HLA alleles. METHODS: We imputed HLA-A, -B, -C, -DRB1, -DQA1, -DQB1 and -DPB1 alleles from genotypes of 20,737 Finnish blood donors in the Blood Service Biobank. We confirmed homozygosity by sequencing HLA alleles in 30 samples and by examining 36,161 MHC-located polymorphic DNA markers. RESULTS: Three hundred and seventeen individuals (1.5%), representing 41 different haplotypes, were found to be homozygous for HLA-A, -B, -C, -DRB1, -DQA1 and -DQB1 alleles. Ten most frequent haplotypes homozygous for HLA-A to -DQB1 were HLA-compatible with 49.5%, and three most frequent homozygotes to 30.4% of the Finnish population. Ten most frequent HLA-A-B homozygotes were compatible with 75.3%, and three most frequent haplotypes to 42.6% of the Finnish population. HLA homozygotes had a low level of heterozygosity in MHC-located DNA markers, in particular in HLA haplotypes enriched in Finland. CONCLUSIONS: The present study shows that HLA imputation in a blood donor biobank of reasonable size can be used to identify HLA homozygous blood donors suitable for cell therapy, HLA-typed thrombocytes and research. The homozygotes were HLA-compatible with a large fraction of the Finnish population. Regular blood donors reported to have positive attitude to research donation appear a good option for these purposes. Differences in population frequencies of HLA haplotypes emphasize the need for population-specific collections of HLA homozygous samples.

HLA allele-specific expression: Methods, disease associations, and relevance in hematopoietic stem cell transplantation.

Varying HLA allele-specific expression levels are associated with human diseases, such as graft versus host disease (GvHD) in hematopoietic stem cell transplantation (HSCT), cytotoxic T cell response and viral load in HIV infection, and the risk of Crohn's disease. Only recently, RNA-based next generation sequencing (NGS) methodologies with accompanying bioinformatics tools have emerged to quantify HLA allele-specific expression replacing the quantitative PCR (qPCR) -based methods. These novel NGS approaches enable the systematic analysis of the HLA allele-specific expression changes between individuals and between normal and disease phenotypes. Additionally, analyzing HLA allele-specific expression and allele-specific expression loss provide important information for predicting efficacies of novel immune cell therapies. Here, we review available RNA sequencing-based approaches and computational tools for NGS to quantify HLA allele-specific expression. Moreover, we explore recent studies reporting disease associations with differential HLA expression. Finally, we discuss the role of allele-specific expression in HSCT and how considering the expression quantification in recipient-donor matching could improve the outcome of HSCT.

Sperm Physiological Response to Female Serum-Potential New Insights into the Reproductive Incompatibility Diagnostics.

Infertility is assumed to arise exclusively from male- and female-dependent pathological factors. However, recent studies have indicated that reproductive failure may also result from the reproductive incompatibility of the partners. Selection against such incompatibilities likely occurs via female-derived reproductive secretions, including follicular fluid (FF), that mediate gamete-level mate choice towards the sperm of specific males. To facilitate potential development of diagnostic tests for human reproductive incompatibility, we examined whether sperm physiological response to female serum indicate male-female compatibility in the presence of FF. We performed a full-factorial experiment, in which the sperm of 10 males were treated with the FF and serum of 6 healthy females. We found that sperm motility and viability in both biofluids were highly similar and that in 70% of the males, sperm serum treatment predicted male-female compatibility. We also identified male human leucocyte antigen (HLA) alleles and female (FF and serum) anti-HLA antibodies and tested whether the number of allele-antibody matches predict sperm physiological response to female fluids. However, no association was found between measured sperm traits and the number of allele-antibody matches. Overall, the present results may open novel possibilities for the future development of reproductive incompatibility tests and may pave the way towards more accurate infertility diagnostics and treatments.

HLA-disease association and pleiotropy landscape in over 235,000 Finns.

The human leukocyte antigen (HLA) system is the single most important genetic susceptibility factor for many autoimmune diseases and immunological traits. For systematic population-level analysis of HLA-phenotype association landscape we imputed the alleles of classical HLA genes in a discovery cohort of 146,630 and replication cohort of 89,340 Finns of whom SNP genotype data and 3,355 disease phenotypes were available as part of the FinnGen project. In total, 3,649 statistically significant single HLA allele associations in 368 phenotypes were found. Known susceptibility associations clearly dominated the landscape but we discovered also a few previously poorly established HLA associations such as DQA1*01:03 and DQB1*06:03 with mental and behavioural disorders due to cannabinoids (p-value = 10-5; beta = 0.6). As certain HLAs were found to be involved in both autoimmune and infectious diseases, we studied further the independence of their associations and statistical pleiotropy. We found that altogether 11 shared HLA alleles were associated independently with both autoimmune and infectious diseases. The most prominent of these were DQA1*03:01 and DQB1*03:02 both of which associated with three infectious and three autoimmune phenotypes. All the shared HLAs showed risk effects in both disease groups, suggesting that infections can increase the risk for autoimmune diseases. The population-level landscape analysis is an excellent resource for estimating the contribution and genetic models of HLA genes in many different phenotypes and for fine-mapping primary associations.

How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior: an Observational Follow-up Study.

BACKGROUND: Prediction tools that combine polygenic risk scores with clinical factors provide a new opportunity for improved prediction and prevention of atherosclerotic cardiovascular disease, but the clinical utility of polygenic risk score has remained unclear. METHODS: We collected a prospective cohort of 7342 individuals (64% women, mean age 56 years) and estimated their 10-year risk for atherosclerotic cardiovascular disease both by a traditional risk score and a composite score combining the effect of a polygenic risk score and clinical risk factors. We then tested how returning the personal risk information with an interactive web-tool impacted on the participants' health behavior. RESULTS: When reassessed after 1.5 years by a clinical visit and questionnaires, 20.8% of individuals at high (>10%) 10-year atherosclerotic cardiovascular disease risk had seen a doctor, 12.4% reported weight loss, 14.2% of smokers had quit smoking, and 15.4% had signed up for health coaching online. Altogether, 42.6% of persons at high risk had made one or more health behavioral changes versus 33.5% of persons at low/average risk such that higher baseline risk predicted a favorable change (OR [CI], 1.53 [1.37-1.72] for persons at high risk versus the rest, P<0.001), with both high clinical (P<0.001) and genomic risk (OR [CI], 1.10 [1.03-1.17], P=0.003) contributing independently. CONCLUSIONS: Web-based communication of personal atherosclerotic cardiovascular disease risk-data including polygenic risk to middle-aged persons motivates positive changes in health behavior and the propensity to seek care. It supports integration of genomic information into clinical risk calculators as a feasible approach to enhance disease prevention.

KIR gene content imputation from single-nucleotide polymorphisms in the Finnish population.

The killer cell immunoglobulin-like receptor (KIR) gene cluster on chromosome 19 encodes cell surface glycoproteins that bind class I human leukocyte antigen (HLA) molecules as well as some other ligands. Through regulation of natural killer (NK) cell activity KIRs participate in tumour surveillance and clearing viral infections. KIR gene gene copy number variation associates with the outcome of transplantations and susceptibility to immune-mediated diseases. Inferring KIR gene content from genetic variant data is therefore desirable for immunogenetic analysis, particularly in the context of growing biobank genome data collections that rely on genotyping by microarray. Here we describe a stand-alone and freely available gene content imputation for 12 KIR genes. The models were trained using 807 Finnish biobank samples genotyped for 5900 KIR-region SNPs and analysed for KIR gene content with targeted sequencing. Cross-validation results demonstrate a high mean overall accuracy of 98.5% (95% CI [97.0-99.2]%) which compares favourably with previous methods including short-read sequencing based approaches.

PeptiCHIP: A Microfluidic Platform for Tumor Antigen Landscape Identification.

Identification of HLA class I ligands from the tumor surface (ligandome or immunopeptidome) is essential for designing T-cell mediated cancer therapeutic approaches. However, the sensitivity of the process for isolating MHC-I restricted tumor-specific peptides has been the major limiting factor for reliable tumor antigen characterization, making clear the need for technical improvement. Here, we describe our work from the fabrication and development of a microfluidic-based chip (PeptiCHIP) and its use to identify and characterize tumor-specific ligands on clinically relevant human samples. Specifically, we assessed the potential of immobilizing a pan-HLA antibody on solid surfaces via well-characterized streptavidin-biotin chemistry, overcoming the limitations of the cross-linking chemistry used to prepare the affinity matrix with the desired antibodies in the immunopeptidomics workflow. Furthermore, to address the restrictions related to the handling and the limited availability of tumor samples, we further developed the concept toward the implementation of a microfluidic through-flow system. Thus, the biotinylated pan-HLA antibody was immobilized on streptavidin-functionalized surfaces, and immune-affinity purification (IP) was carried out on customized microfluidic pillar arrays made of thiol-ene polymer. Compared to the standard methods reported in the field, our methodology reduces the amount of antibody and the time required for peptide isolation. In this work, we carefully examined the specificity and robustness of our customized technology for immunopeptidomics workflows. We tested this platform by immunopurifying HLA-I complexes from 1 × 106 cells both in a widely studied B-cell line and in patients-derived ex vivo cell cultures, instead of 5 × 108 cells as required in the current technology. After the final elution in mild acid, HLA-I-presented peptides were identified by tandem mass spectrometry and further investigated by in vitro methods. These results highlight the potential to exploit microfluidics-based strategies in immunopeptidomics platforms and in personalized immunopeptidome analysis from cells isolated from individual tumor biopsies to design tailored cancer therapeutic vaccines. Moreover, the possibility to integrate multiple identical units on a single chip further improves the throughput and multiplexing of these assays with a view to clinical needs.

Structural dissimilarity of partners' immune genes increases sperm viability in women's reproductive tract.

Human leucocyte antigen (HLA) genes appear to mediate pre- and post-copulatory mate choice towards HLA-dissimilar ('compatible') partners. However, since genetically distinct alleles often have similar immunogenic properties, genetic dissimilarity is not necessarily an accurate predictor of the functional compatibility of HLA alleles and, hence, may not reflect partners' true compatibility. Furthermore, it has remained unclear whether other genes of the immune system could also play a role in male-female compatibility. We studied whether the immunoglobulin binding regions (eplets) of HLA molecules and the immunoglobulin structural dissimilarity of the partners affect their gamete-level compatibility. We exposed sperm of multiple men to follicular fluid or cervical mucus of multiple women and tested whether sperm viability in these reproductive secretions was influenced by HLA eplet and immunoglobulin structural dissimilarity between partners. We found that eplet dissimilarity positively affects sperm viability in follicular fluid, whereas immunoglobulin dissimilarity enhanced sperm viability in cervical mucus. Together, these findings indicate that structural characteristics of both HLA alleles and immunoglobulins may facilitate cryptic female choice towards immunologically compatible partners. Our results, thus, indicate that partners' genetic compatibility may have wider immunological basis than traditionally has been assumed. Relative contribution of different immunogenetic factors to overall compatibility of the reproductive partners needs to be clarified in future studies.

Low ferritin levels appear to be associated with worsened health in male repeat blood donors.

BACKGROUND AND OBJECTIVES: Frequent blood donation depletes iron stores of blood donors. Iron depletion may lead to anaemia, but the health effects of iron depletion without anaemia in healthy blood donors are not well understood. We studied in the FinDonor cohort whether worsening of self-rated health of blood donors during the study period was associated with biomarkers for iron levels or other self-reported changes in lifestyle. MATERIALS AND METHODS: We included 1416 participants from the cohort who answered an 89-item questionnaire on their health and lifestyle during their enrolment visit and again at the end of the study. We performed multivariate logistic regression to test if blood donation-related factors affected the probability of reporting worsened health. To set these findings into a more holistic context of health, we subsequently analysed all other questionnaire items with a data-driven exploratory analysis. RESULTS: We found that donation frequency in men and post-menopausal women and ferritin level only in men was associated negatively with worsened health between questionnaires. In the exploratory analysis, stable physical condition was the only questionnaire item that was associated negatively with worsened health in both women and men. CONCLUSION: Our results suggest that low ferritin level is associated with worsened health even in non-anaemic repeat donors, although we find that when health is analysed more holistically, ferritin and other factors primarily related to blood donation lose their importance.

HLA RNA Sequencing With Unique Molecular Identifiers Reveals High Allele-Specific Variability in mRNA Expression.

The HLA gene complex is the most important single genetic factor in susceptibility to most diseases with autoimmune or autoinflammatory origin and in transplantation matching. Most studies have focused on the vast allelic variation in these genes; only a few studies have explored differences in the expression levels of HLA alleles. In this study, we quantified mRNA expression levels of HLA class I and II genes from peripheral blood samples of 50 healthy individuals. The gene- and allele-specific mRNA expression was assessed using unique molecular identifiers, which enabled PCR bias removal and calculation of the number of original mRNA transcripts. We identified differences in mRNA expression between different HLA genes and alleles. Our results suggest that HLA alleles are differentially expressed and these differences in expression levels are quantifiable using RNA sequencing technology. Our method provides novel insights into HLA research, and it can be applied to quantify expression differences of HLA alleles in various tissues and to evaluate the role of this type of variation in transplantation matching and susceptibility to autoimmune diseases.

Targeted RNA-Based Oxford Nanopore Sequencing for Typing 12 Classical HLA Genes.

Identification of human leukocyte antigen (HLA) alleles from next-generation sequencing (NGS) data is challenging because of the high polymorphism and mosaic nature of HLA genes. Owing to the complex nature of HLA genes and consequent challenges in allele assignment, Oxford Nanopore Technologies' (ONT) single-molecule sequencing technology has been of great interest due to its fitness for sequencing long reads. In addition to the read length, ONT's advantages are its portability and possibility for a rapid real-time sequencing, which enables a simultaneous data analysis. Here, we describe a targeted RNA-based method for HLA typing using ONT sequencing and SeqNext-HLA SeqPilot software (JSI Medical Systems GmbH). Twelve classical HLA genes were enriched from cDNA of 50 individuals, barcoded, pooled, and sequenced in 10 MinION R9.4 SpotON flow cell runs producing over 30,000 reads per sample. Using barcoded 2D reads, SeqPilot assigned HLA alleles to two-field typing resolution or higher with the average read depth of 1750x. Sequence analysis resulted in 99-100% accuracy at low-resolution level (one-field) and in 74-100% accuracy at high-resolution level (two-field) with the expected alleles. There are still some limitations with ONT RNA sequencing, such as noisy reads, homopolymer errors, and the lack of robust algorithms, which interfere with confident allele assignment. These issues need to be inspected carefully in the future to improve the allele call rates. Nevertheless, here we show that sequencing of multiplexed cDNA amplicon libraries on ONT MinION can produce accurate high-resolution typing results of 12 classical HLA loci. For HLA research, ONT RNA sequencing is a promising method due to its capability to sequence full-length HLA transcripts. In addition to HLA genotyping, the technique could also be applied for simultaneous expression analysis.

Review of Genetic Variation as a Predictive Biomarker for Chronic Graft-Versus-Host-Disease After Allogeneic Stem Cell Transplantation.

Chronic graft-versus-host disease (cGvHD) is one of the major complications of allogeneic stem cell transplantation (HSCT). cGvHD is an autoimmune-like disorder affecting multiple organs and involves a dermatological rash, tissue inflammation and fibrosis. The incidence of cGvHD has been reported to be as high as 30% to 60% and there are currently no reliable tools for predicting the occurrence of cGvHD. There is therefore an important unmet clinical need for predictive biomarkers. The present review summarizes the state of the art for genetic variation as a predictive biomarker for cGvHD. We discuss three different modes of action for genetic variation in transplantation: genetic associations, genetic matching, and pharmacogenetics. The results indicate that currently, there are no genetic polymorphisms or genetic tools that can be reliably used as validated biomarkers for predicting cGvHD. A number of recommendations for future studies can be drawn. The majority of studies to date have been under-powered and included too few patients and genetic markers. Like in all complex multifactorial diseases, large collaborative genome-level studies are now needed to achieve reliable and unbiased results. Some of the candidate genes, in particular, CTLA4, HSPE, IL1R1, CCR6, FGFR1OP, and IL10, and some non-HLA variants in the HLA gene region have been replicated to be associated with cGvHD risk in independent studies. These associations should now be confirmed in large well-characterized cohorts with fine mapping. Some patients develop cGvHD despite very extensive immunosuppression and other treatments, indicating that the current therapeutic regimens may not always be effective enough. Hence, more studies on pharmacogenetics are also required. Moreover, all of these studies should be adjusted for diagnostic and clinical features of cGvHD. We conclude that future studies should focus on modern genome-level tools, such as machine learning, polygenic risk scores and genome-wide association study-transcription meta-analyses, instead of focusing on just single variants. The risk of cGvHD may be related to the summary level of immunogenetic differences, or whole genome histocompatibility between each donor-recipient pair. As the number of genome-wide analyses in HSCT is increasing, we are approaching an era where there will be sufficient data to incorporate these approaches in the near future.