A common allele of HLA is associated with asymptomatic SARS-CoV-2 infection.

Studies have demonstrated that at least 20% of individuals infected with SARS-CoV-2 remain asymptomatic1-4. Although most global efforts have focused on severe illness in COVID-19, examining asymptomatic infection provides a unique opportunity to consider early immunological features that promote rapid viral clearance. Here, postulating that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection, we enrolled 29,947 individuals, for whom high-resolution HLA genotyping data were available, in a smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n = 1,428) comprised unvaccinated individuals who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci with disease course and identified a strong association between HLA-B*15:01 and asymptomatic infection, observed in two independent cohorts. Suggesting that this genetic association is due to pre-existing T cell immunity, we show that T cells from pre-pandemic samples from individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF. The majority of the reactive T cells displayed a memory phenotype, were highly polyfunctional and were cross-reactive to a peptide derived from seasonal coronaviruses. The crystal structure of HLA-B*15:01-peptide complexes demonstrates that the peptides NQKLIANQF and NQKLIANAF (from OC43-CoV and HKU1-CoV) share a similar ability to be stabilized and presented by HLA-B*15:01. Finally, we show that the structural similarity of the peptides underpins T cell cross-reactivity of high-affinity public T cell receptors, providing the molecular basis for HLA-B*15:01-mediated pre-existing immunity.

Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time-to-event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate-dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user-friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood-borne cancer, where heteroskedasticity and nonproportional hazards are likely present.

Multiplicative fitness, rapid haplotype discovery, and fitness decay explain evolution of human MHC.

The major histocompatibility complex (MHC) is a central component of the vertebrate immune system and hence evolves in the regime of a host-pathogen evolutionary race. The MHC is associated with quantitative traits which directly affect fitness and are subject to selection pressure. The evolution of haplotypes at the MHC HLA (HLA) locus is generally thought to be governed by selection for increased diversity that is manifested in overdominance and/or negative frequency-dependent selection (FDS). However, recently, a model combining purifying selection on haplotypes and balancing selection on alleles has been proposed. We compare the predictions of several population dynamics models of haplotype frequency evolution to the distributions derived from 6.59-million-donor HLA typings from the National Marrow Donor Program registry. We show that models that combine a multiplicative fitness function, extremely high haplotype discovery rates, and exponential fitness decay over time produce the best fit to the data for most of the analyzed populations. In contrast, overdominance is not supported, and population substructure does not explain the observed haplotype frequencies. Furthermore, there is no evidence of negative FDS. Thus, multiplicative fitness, rapid haplotype discovery, and rapid fitness decay appear to be the major factors shaping the HLA haplotype frequency distribution in the human population.

Stem cell donor HLA typing improves CPRA in kidney allocation.

The Organ Procurement and Transplantation Network (OPTN) Kidney Allocation System provides a priority to sensitized candidates based on the calculated panel reactive antibody (CPRA) value. The human leukocyte antigen (HLA) haplotype reference panel used for calculation of the CPRA by the United Network for Organ Sharing (UNOS), the OPTN contractor, has limitations. We derived a novel panel from the National Marrow Donor Program HLA haplotype data set and compared the accuracy of CPRA values generated with this panel (NMDP-CPRA) to those generated from the UNOS panel (UNOS-CPRA), using predicted and actual deceased donor kidney offers for a cohort of 24 282 candidates. The overall accuracy for kidney offers was similar using NMDP-CPRA and UNOS-CPRA. Accuracy was slightly higher for NMDP-CPRA than UNOS-CPRA for candidates in several highly sensitized CPRA categories, with deviations in linkage disequilibrium for Caucasians and the smaller size of the UNOS panel as contributing factors. HLA data derived from stem cell donors yields CPRA values that are comparable to those derived from deceased kidney donors while improving upon several problems with the current reference panel. Consideration should be given to using stem cell donors as the reference panel for calculation of CPRA to improve equity in kidney transplant allocation.

Selection of unrelated donors and cord blood units for hematopoietic cell transplantation: guidelines from the NMDP/CIBMTR.

Allogeneic hematopoietic cell transplantation involves consideration of both donor and recipient characteristics to guide the selection of a suitable graft. Sufficient high-resolution donor-recipient HLA match is of primary importance in transplantation with adult unrelated donors, using conventional graft-versus-host disease prophylaxis. In cord blood transplantation, optimal unit selection requires consideration of unit quality, cell dose and HLA-match. In this summary, the National Marrow Donor Program (NMDP) and the Center for International Blood and Marrow Transplant Research, jointly with the NMDP Histocompatibility Advisory Group, provide evidence-based guidelines for optimal selection of unrelated donors and cord blood units.

GRIMM: GRaph IMputation and matching for HLA genotypes.

MOTIVATION: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA alleles equences; therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time. RESULTS: We present here a novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, the matching algorithm runtime is practically not affected by registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm cross-validation dataset. AVAILABILITY AND IMPLEMENTATION: The Python, Perl and Neo4j code is available at https://github.com/nmdp-bioinformatics/grimm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single haplotype admixture models using large scale HLA genotype frequencies to reproduce human admixture.

The human leukocyte antigen (HLA) is the most polymorphic region in humans. Anthropologists use HLA to trace populations' migration and evolution. However, recent admixture between populations can mask the ancestral haplotype frequency distribution. We present a statistical method based on high-resolution HLA haplotype frequencies to resolve population admixture using a non-negative matrix factorization formalism and validated using haplotype frequencies from 56 world populations. The result is a minimal set of source components (SCs) decoding roughly 90% of the total variance in the studied admixtures. These SCs agree with the geographical distribution, phylogenies, and recent admixture events of the studied groups. With the growing population of multi-ethnic individuals, or individuals that do not report race/ethnic information, the HLA matching process for stem-cell and solid organ transplants is becoming more challenging. The presented algorithm provides a framework that facilitates the breakdown of highly admixed populations into SCs, which can be used to better match the rapidly growing population of multi-ethnic individuals worldwide.

Reducing ethnic disparity in access to high-quality HLA-matched cord blood units for transplantation: analysis of the Canadian Blood Services' Cord Blood Bank inventory.

BACKGROUND: Launched in 2013, Canadian Blood Services' Cord Blood Bank (CBS' CBB) has built a high-quality, ethnically diverse cord blood repository that aims to reduce ethnic disparity in accessing suitable units for transplantation. METHODS AND RESULTS: As of December 2016, 2000 units have been banked. The self-reported maternal ethnicity was 58% non-Caucasian. Overall, 26% of units were classified as multi-ethnicity with Caucasian (84%) most frequently observed in combination with Asian, First Nations (predominant indigenous peoples in Canada south of the Arctic Circle), or African ethnicity. Utilization scores that incorporate total nucleated and CD34+ cell counts in the CBS' CBB were associated with greater likelihood of utilization compared with the international inventory of units (p < 0.05). The distribution of utilization scores was similar for Caucasians compared with non-Caucasians (p < 0.05). Using HLA genotypes of cord blood units and their mothers, we determined probable ethnic assignments for each haplotype using HaploStats (National Marrow Donor Program). Significant increases in HLA-match likelihoods are predicted for all ethnicities as the inventory grows to its target of 10,000 units and the gap in HLA-match likelihoods for Caucasian and non-Caucasian patients progressively declines. CONCLUSIONS: The CBS' CBB inventory is predicted to have high HLA-matching likelihoods across a broad spectrum of ethnic groups, improving access to high-quality stem cell products for all patients.

Machine Learning Approach to Predicting Stem Cell Donor Availability.

The success of unrelated donor stem cell transplants depends on not only finding genetically matched donors, but also donor availability. On average 50% of potential donors in the National Marrow Donor Program database are unavailable for a variety of reasons, after initially matching a patient, with significant variations in availability among subgroups (eg, by race or age). Several studies have established univariate donor characteristics associated with availability. Individual consideration of each applicable characteristic is laborious. Extrapolating group averages to the individual-donor level tends to be highly inaccurate. In the current environment with enhanced donor data collection, we can make better estimates of individual donor availability. We propose a machine learning based approach to predict availability of every registered donor, and evaluate the predictive power on a test cohort of 44,544 requests to be .77 based on the area under the receiver-operating characteristic curve. We propose that this predictor should be used during donor selection to reduce the time to transplant.

Stem Cell Transplantation and Informatics: Current Considerations.

Informatics strategies and applications available to stem cell transplant (SCT) programs are diverse and changing rapidly. Although most hospitals have electronic medical records (EMRs), few are equipped with specialized SCT applications. Most EMRs do not contain critical elements to support SCT practice and research. Strategies to optimize information technology resources to support SCT programs are reviewed and technical and workflow support discussed. Guidance and rationale for the use of both SCT applications and EMRs are emphasized.

Development of an Unrelated Donor Selection Score Predictive of Survival after HCT: Donor Age Matters Most.

Donor factors, in addition to HLA matching status, have been associated with recipient survival in unrelated donor (URD) hematopoietic cell transplantation (HCT); however, there is no hierarchical algorithm that weights the characteristics of individual donors against each other in a quantitative manner to facilitate donor selection. The goal of this study was to develop and validate a donor selection score that prioritizes donor characteristics associated with better survival in 8/8 HLA-matched URDs. Two separate patient/donor cohorts, the first receiving HCT between 1999 and 2011 (n = 5952, c1), and the second between 2012 and 2014 (n = 4510, c2) were included in the analysis. Both cohorts were randomly spilt, 2:1, into training and testing sets. Despite studying over 10,000 URD transplants, we were unable to validate a donor selection score. The only donor characteristic associated with better survival was younger age, with 2-year survival being 3% better when a donor 10 years younger is selected. These results support previous studies suggesting prioritization of a younger 8/8 HLA-matched donor. This large dataset also shows that none of the other donor clinical factors tested were reproducibly associated with survival, and hence flexibility in selecting URDs based on other characteristics is justified. These data support a simplified URD selection process and have significant implications for URD registries.

Modeling coverage gaps in haplotype frequencies via Bayesian inference to improve stem cell donor selection.

Regardless of sampling depth, accurate genotype imputation is limited in regions of high polymorphism which often have a heavy-tailed haplotype frequency distribution. Many rare haplotypes are thus unobserved. Statistical methods to improve imputation by extending reference haplotype distributions using linkage disequilibrium patterns that relate allele and haplotype frequencies have not yet been explored. In the field of unrelated stem cell transplantation, imputation of highly polymorphic human leukocyte antigen (HLA) genes has an important application in identifying the best-matched stem cell donor when searching large registries totaling over 28,000,000 donors worldwide. Despite these large registry sizes, a significant proportion of searched patients present novel HLA haplotypes. Supporting this observation, HLA population genetic models have indicated that many extant HLA haplotypes remain unobserved. The absent haplotypes are a significant cause of error in haplotype matching. We have applied a Bayesian inference methodology for extending haplotype frequency distributions, using a model where new haplotypes are created by recombination of observed alleles. Applications of this joint probability model offer significant improvement in frequency distribution estimates over the best existing alternative methods, as we illustrate using five-locus HLA frequency data from the National Marrow Donor Program registry. Transplant matching algorithms and disease association studies involving phasing and imputation of rare variants may benefit from this statistical inference framework.

The distribution of HLA haplotypes in the ethnic groups that make up the Brazilian Bone Marrow Volunteer Donor Registry (REDOME).

The Registries of Bone Marrow Donors around the world include more than 30 million volunteer donors from 57 different countries, and were responsible for over 17,000 hematopoietic stem cell transplants in 2016. The Brazilian Bone Marrow Volunteer Donor Registry (REDOME) was established in 1993 and is the third largest registry in the world with more than 4.3 million donors. We characterized HLA allele and haplotypes frequencies from REDOME comparing them with the donor self-reported race group classification. Five-locus haplotype frequencies (A~C~B~DRB1~DQB1) were estimated for each of the six race groups, resolving phase and allelic ambiguity using the expectation-maximization (EM) algorithm. The top 100 haplotypes in the race groups were separated into eight clusters of haplotypes, based on haplotype similarity, using CLUTO. We present HLA allele and haplotype frequency data from six race groups from 2,938,259 individuals from REDOME. The most frequent haplotype was the same for all groups: A*01:01g~C*07:01g~B*08:01g~DRB1*03:01g~DQB1*02:01g. Some frequent haplotypes such as A*02:01g~C*16:01g~B*44:03~DRB1*07:01g~DQB1*02:01g was not found in people with Preta (Sub-Saharan African descent). A cluster including Branca (European) and Parda or non-informed (admixed) could be distinguished from both Preta (SubSaharan) and Indígena (Amerindian) groups, and from the Amarela (Asian) ones, which clustered with their original population. These results have implications on cross-population matching and can help in donor searches and population-based recruitment strategies.

The effect of donor characteristics on survival after unrelated donor transplantation for hematologic malignancy.

There are >24 million registered adult donors, and the numbers of unrelated donor transplantations are increasing. The optimal strategy for prioritizing among comparably HLA-matched potential donors has not been established. Therefore, the objective of the current analyses was to study the association between donor characteristics (age, sex, parity, cytomegalovirus serostatus, HLA match, and blood group ABO match) and survival after transplantation for hematologic malignancy. The association of donor characteristics with transplantation outcomes was examined using either logistic or Cox regression models, adjusting for patient disease and transplantation characteristics associated with outcomes in 2 independent datasets: 1988 to 2006 (N = 6349; training cohort) and 2007 to 2011 (N = 4690; validation cohort). All donor-recipient pairs had allele-level HLA typing at HLA-A, -B, -C, and -DRB1, which is the current standard for selecting donors. Adjusting for patient disease and transplantation characteristics, survival was better after transplantation of grafts from young donors (aged 18-32 years) who were HLA matched to recipients (P < .001). These findings were validated for transplantations that occurred between 2007 and 2011. For every 10-year increment in donor age, there is a 5.5% increase in the hazard ratio for overall mortality. Increasing HLA disparity was also associated with worsening survival. Donor age and donor-recipient HLA match are important when selecting adult unrelated donors. Other donor characteristics such as sex, parity, and cytomegalovirus serostatus were not associated with survival. The effect of ABO matching on survival is modest and must be studied further before definitive recommendations can be offered.

HLA class I haplotype diversity is consistent with selection for frequent existing haplotypes.

The major histocompatibility complex (MHC) contains the most polymorphic genetic system in humans, the human leukocyte antigen (HLA) genes of the adaptive immune system. High allelic diversity in HLA is argued to be maintained by balancing selection, such as negative frequency-dependent selection or heterozygote advantage. Selective pressure against immune escape by pathogens can maintain appreciable frequencies of many different HLA alleles. The selection pressures operating on combinations of HLA alleles across loci, or haplotypes, have not been extensively evaluated since the high HLA polymorphism necessitates very large sample sizes, which have not been available until recently. We aimed to evaluate the effect of selection operating at the HLA haplotype level by analyzing HLA A~C~B~DRB1~DQB1 haplotype frequencies derived from over six million individuals genotyped by the National Marrow Donor Program registry. In contrast with alleles, HLA haplotype diversity patterns suggest purifying selection, as certain HLA allele combinations co-occur in high linkage disequilibrium. Linkage disequilibrium is positive (Dij'>0) among frequent haplotypes and negative (Dij'<0) among rare haplotypes. Fitting the haplotype frequency distribution to several population dynamics models, we found that the best fit was obtained when significant positive frequency-dependent selection (FDS) was incorporated. Finally, the Ewens-Watterson test of homozygosity showed excess homozygosity for 5-locus haplotypes within 23 US populations studied, with an average Fnd of 28.43. Haplotype diversity is most consistent with purifying selection for HLA Class I haplotypes (HLA-A, -B, -C), and was not inferred for HLA Class II haplotypes (-DRB1 and-DQB1). We discuss our empirical results in the context of evolutionary theory, exploring potential mechanisms of selection that maintain high linkage disequilibrium in MHC haplotype blocks.

Human leucocyte antigen (HLA)-A, -B, -C, -DRB1 and -DQB1 haplotype frequencies from 2491 cord blood units from Tamil speaking population from Tamil Nadu, India.

The Public Cord Blood Bank of Jeevan Stem Cell Foundation was established in 2008 to harvest cord blood units and make them available to treat multiple blood disorders through Hematopoietic Stem Cell Transplants. We studied Human Leucocyte Antigen (HLA)-A, -B, -C, -DRB1 and -DQB1 allele and haplotype diversity in a sample of 2491 unrelated cord-blood units from Jeevan's Public Cord Blood Bank (part of Be The Cure Registry) in the Tamil Nadu state in the Indian Peninsula.

East Meets West-Impact of Ethnicity on Donor Match Rates in the Ezer Mizion Bone Marrow Donor Registry.

HLA haplotype frequencies in a volunteer bone marrow donor registry should reflect the frequencies of potential transplant recipients served by that registry, a challenge in a country with diverse subethnicities of immigrants from Eastern and Western cultures, such as Israel. We evaluated the likelihood of finding suitable donors for hypothetical patients drawn from defined subethnicities in the Ezer Mizion Bone Marrow Donor Registry (EM BMDR) from donors both within and outside the registry now and during the coming decade. On average, bioinformatics modeling predicts that, given current donor recruitment trends, 6/6 high-resolution HLA match rates for Israelis, which currently stand at 40% to 55% for most subethnicities, will rise by up to 1% per year over the next decade. Subethnicities with historically lower rates of interethnic admixture are less likely to find matches outside of their designated group but will benefit from expansion of the registry, whereas ethnically directed drives will enhance matching rates for currently underrepresented subethnicities. Donor searches for the same cohort using a large extramural registry was of only slight benefit for most of the 19 EM BMDR subethnicities evaluated, confirming that local donor registries that reflect the ethnic diversity of the community being served are best equipped to serve the needs of their respective communities. Contemporary trends of an increasingly multiethnic admixture in Israel may impact the effect of ethnic profiling in assessing future match rates for EM BMDR.

Investigating the Association of Genetic Admixture and Donor/Recipient Genetic Disparity with Transplant Outcomes.

Disparities in survival after allogeneic hematopoietic cell transplantation have been reported for some race and ethnic groups, despite comparable HLA matching. Individuals' ethnic and race groups, as reported through self-identification, can change over time because of multiple sociological factors. We studied the effect of 2 measures of genetic similarity in 1378 recipients who underwent myeloablative first allogeneic hematopoietic cell transplantation between 1995 and 2011 and their unrelated 10 of 10 HLA-A, -B, -C, -DRB1, and-DQB1- matched donors. The studied factors were as follows (1) donor and recipient genetic ancestral admixture and (2) pairwise donor/recipient genetic distance. Increased African genetic admixture for either transplant recipients or donors was associated with increased risk of overall mortality (hazard ratio [HR], 2.26; P = .005 and HR, 3.09; P = .0002, respectively) and transplant-related mortality (HR, 3.3; P = .0003 and HR, 3.86; P = .0001, respectively) and decreased disease-free survival (HR, 1.9; P = .02 and HR, 2.46; P = .002 respectively). The observed effect, albeit statistically significant, was relevant to a small subset of the studied population and was notably correlated with self-reported African-American race. We were not able to control for other nongenetic factors, such as access to health care or other socioeconomic factors; however, the results suggest the influence of a genetic driver. Our findings confirm what has been previously reported for African-American recipients and show similar results for donors. No significant association was found with donor/recipient genetic distance.

HLA match likelihoods for hematopoietic stem-cell grafts in the U.S. registry.

BACKGROUND: Hematopoietic stem-cell transplantation (HSCT) is a potentially lifesaving therapy for several blood cancers and other diseases. For patients without a suitable related HLA-matched donor, unrelated-donor registries of adult volunteers and banked umbilical cord-blood units, such as the Be the Match Registry operated by the National Marrow Donor Program (NMDP), provide potential sources of donors. Our goal in the present study was to measure the likelihood of finding a suitable donor in the U.S. registry. METHODS: Using human HLA data from the NMDP donor and cord-blood-unit registry, we built population-based genetic models for 21 U.S. racial and ethnic groups to predict the likelihood of identifying a suitable donor (either an adult donor or a cord-blood unit) for patients in each group. The models incorporated the degree of HLA matching, adult-donor availability (i.e., ability to donate), and cord-blood-unit cell dose. RESULTS: Our models indicated that most candidates for HSCT will have a suitable (HLA-matched or minimally mismatched) adult donor. However, many patients will not have an optimal adult donor--that is, a donor who is matched at high resolution at HLA-A, HLA-B, HLA-C, and HLA-DRB1. The likelihood of finding an optimal donor varies among racial and ethnic groups, with the highest probability among whites of European descent, at 75%, and the lowest probability among blacks of South or Central American descent, at 16%. Likelihoods for other groups are intermediate. Few patients will have an optimal cord-blood unit--that is, one matched at the antigen level at HLA-A and HLA-B and matched at high resolution at HLA-DRB1. However, cord-blood units mismatched at one or two HLA loci are available for almost all patients younger than 20 years of age and for more than 80% of patients 20 years of age or older, regardless of racial and ethnic background. CONCLUSIONS: Most patients likely to benefit from HSCT will have a donor. Public investment in donor recruitment and cord-blood banks has expanded access to HSCT. (Funded by the Office of Naval Research, Department of the Navy, and the Health Resources and Services Administration, Department of Health and Human Services.).