.

Project Leaders:

• Elissaveta Naumova  naumovaej@gmail.com
• Katarzyna Bogunia-Kubik katarzyna.bogunia-kubik@hirszfeld.pl
• Fatma Oguz oguzsf@gmail.com   

Detailed Project Description: 

Deterioration of the immune system with aging is associated with an increased susceptibility to infectious diseases, cancer, and autoimmune disorders. Many studies have focused on age-associated changes in immune functions, which might contribute to these pathologies. It has been demonstrated that aging is associated with chronic, low-grade inflammatory activity. The aging process is very complex, and longevity is a multifactorial trait determined by genetic and environmental factors, as well as the interaction of “disease” processes with “intrinsic” aging processes. It is hypothesized that genes governing immune functions play a pivotal role in shaping the level of immune response and, potentially, longevity itself. The diversity of these immune-related genes is believed to exert an influential role in promoting successful aging and extending the human lifespan by modulating an individual’s response to life-threatening disorders. Moreover, emerging evidence suggests that non-immune-related biomarkers may serve as indicators of the biological age of the immune system. However, available data do not currently allow us to clarify the role of these genes due to major methodological challenges, such as the typing approach focusing on single loci, limited sample sizes, different inclusion criteria and age limits, inappropriate control matching, and neglect of considerations related to sex-related effects and the different genetic makeup of studied populations.  

The aim of the ‘Immunogenetics of Aging’ component is to identify new biomarkers for successful aging and an increased capacity to reach the extreme limits of life span through the analysis of immune response genes, as well as genes that directly or indirectly regulate the immune system. Within the framework of 19th IHIW, we plan to confirm the possible biomarkers defined in previous workshops in a larger cohort of healthy elderly individuals, elderly patients with age-associated diseases, and controls (young and middle-aged) from different populations. Furthermore we would also like to study molecular biomarkers for chronological age and age-related neurodegenerative diseases.  

The study will include unrelated elderly individuals (octogenarians and nonagenarians) and families with long-lived members. Comparisons with young and middle-aged controls will be conducted. The project will focus on the following candidate biomarkers: classical HLA loci (HLA-A, -B, -C, -DRB1/3/4/5, -DQB1, -DQA1, -DPA1, -DPB1), genes in the extended MHC region such as MICA and MICB, KIR, NKG2D, ELOVL2, HIF, and telomere length. Linkage and association analyses, as well as correlations with functional parameters defining immune risk profiles, will be performed. 

Milestones in years: 

• 2024: Creation of uniform way to enter HLA, KIR, and MIC typing data, data for other genetic systems:  NKG2D, ELOVL2, HIF, as well as data for telomeres length. Inclusion of many laboratories to cover different European and non-European populations. Start of samples and data collection.
• 2025: Continuing sample and data collection and analyses
• 2026: Finalizing analyses and summarising results. 

Patient/sample description: 

The study focuses on unrelated elderly individuals (octogenarians and nonagenarians). Ethnically matched unrelated young and middle-aged individuals will be included in the study as controls. 

Elderly individuals selected should ideally be characterized according to the SENIEUR or nearly – SENIEUR protocols. 

Unrelated controls should ideally be characterized according to JUNIER protocol.  

Data Required: 

• Second filed HLA typing for HLA-A, -B, -C, -DRB1, -DQB1, -DQA1, -DPB1 loci 
• Allele level MICA and MICB genotype 
• Allele level KIR genotypes 
• NKG2D 
• ELOVL2 
• HIF 
• Viral status: CMV, EBV, HCV, HBV 
• Data on date of birth, ethnicity, place of birth, health status 

Samples required: 

We expect to collect samples/data from several European and non-European populations. The minimal requirements would be at least 100 elderly subjects and 200 controls (100 young and 100 middle aged) from each population. 

Reagents/additional assays required: 

For a proportion of laboratories we expect to require additional HLA, KIR, and MIC NGS analyses, as well as genetic test for some of the other immunogenetic markers.  

Data infrastructure required: 

Uniform way to enter data irrespective of the vendor. 

Project Name:  

NGS of Full-length HLA genes of Reference Cell Lines 

Project Leaders:  

Gonzalo Montero-Martin  – HLA Assistant Director, Vitalant HLA Laboratory, USA (gmonteromartin@vitalant.org) 

Brian J. Franz  –  HLA Director, Vitalant HLA Laboratory, USA (bfranz@vitalant.org) 

Vinicius N. Stelet  –  Senior Laboratory Technologist, National Cancer Institute (INCA), Brazil (vinicius.stelet@inca.gov.br) 

Ian Scott  –  Senior Laboratory Technologist, Vitalant HLA Laboratory, USA (iscott@vitalant.org) 

Detailed Project Description:  

International Histocompatibility Working Group (IHWG) DNA sample panels and their corresponding B-lymphoblastoid cell lines (B-LCLs) have been collected and maintained since initially established for previous International Histocompatibility and Immunogenetics Workshops (IHIWS). These cell panels cover a broad range of ethnic groups displaying unique HLA alleles in both sequence and haplotype linkage. As a result, these cell lines represent a very well characterized and comprehensive source material for common HLA reference sequences. Moreover, they can also serve as unambiguous references for commercial or in-house developed methodologies already in use or for the evaluation of new emerging molecular HLA genotyping technologies. More recently, these and other additional cell lines have been sequenced and genotyped during both 17th IHIWS (Creary et. al. (2019) (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599558/)) and 18th IHIWS (manuscript in preparation) workshops by multiple International Histocompatibility and Immunogenetics (H&I) laboratory groups using various NGS-based HLA genotyping approaches. While previous studies have described NGS-based full-length sequencing of both HLA class I and class II genes, certain non-coding regions have not been fully sequenced. This limitation has prevented the assignment of a completely unambiguous 4-field allelic level typing as well as the submission of the full-length genomic sequence to the IPD-IMGT/HLA Database. Therefore, the main goal of the present 19th IHIWS NGS of Full-length HLA genes of Reference Cell Lines project is to further characterize the HLA class I and II coding and non-coding regions of diverse panels of these IHWG B-LCLs, with the specific purpose of significantly improving sequence coverage and full-length characterization by using the most novel short-read/2nd NGS and/or long-read/3rd NGS based HLA genotyping methods that are available.  

Milestones in Years: 

2023-2024: 

1. To identify available IHWG Cell Lines of interest. This will be done in coordination with Fred Hutchinson Cancer Center – Specimen Processing/Research Cell Bank (https://www.fredhutch.org/en/research/institutes-networks-ircs/international-histocompatibility-working-group.html). 
2. To grow selected cell lines in culture and to perform DNA extraction. This will be done in coordination with Fred Hutchinson Cancer Center – Specimen Processing/Research Cell Bank. 
3. Creation of cell line DNA sample panels (typically, 100 ng/uL concentration and 2 ug amount per sample in each ordered panel). Two categories of panels with de-identified or coded DNA samples will be made available to participants: (1) Two different 24-sample Quality Control (“QC”) cell line panels with consensus NGS typing obtained during the 17th and 18th IHIWS; and (2) Four different 24-sample Unknown (“UNK”) cell line panels. QC panels may be also used for accreditation by the various international certification H&I boards. This will be done in coordination with Fred Hutchinson Cancer Center – Specimen Processing/Research Cell Bank.  
4. Order Forms for 19th IHIWS DNA Cell Lines panel requests will be made available to all confirmed participants of this project. 
5. Participants will have the opportunity to order all 6 panels (2 QC + 4 UNK) or a combination of QC and UNK panels. For all participants, it is required to order at least one QC panel.  In the event that a participating group would like to order only one single set of QC/UNK panels, the specific panel(s) of interest will be selected by the co-leaders of this project in coordination with Fred Hutch and sent to the participant in a blinded fashion to ensure that each individual QC/UNK panel is equally tested  across several different participating groups as well different sequencing methodologies (i.e. different assays, instruments, etc.) Pricing and instructions to be finalized. This will be done in coordination with Fred Hutchinson Cancer Center – Specimen Processing/Research Cell Bank.
6. In coordination with the 19th IHIWS database organizers, to finalize timelines and guidelines for data generation, data submission, data analysis and participants’ feedback. 
7. Notification to the International HLA H&I community via future 19th IHIWS e-newsletters regarding the goals of the project, information about logistics/guidelines and progress that has been made. 
8. In coordination with the 19th IHIWS database organizers, to identify database infrastructure requirements: 

• IPD-IMGT/HLA Database version reference to establish consensus analysis of HLA genotyping data submitted by participants. 
• To describe specific instructions to upload sequencing and/or HLA genotyping data for this project into the 19th IHIWS Database. 
• To identify mechanism to submit and to store fastq or any other raw sequencing data files to the Workshop database if needed. 
• To define the format (HML) requirements for HLA typing data and data submission process for upload in the 19th IHIWS database. 
• To establish levels of concordance, discrepancy, ambiguity and maximum discordance expectation to qualify as proficient in the HLA genotyping data (as previously described during 17th IHIWS in Osoegawa et. al. (2019) (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6446570/#SD1)).  

2024-2025: 

1. Those HLA H&I lab groups interested to participate, will email project leaders. Please, include the following information in any email communication: the intended number of 19th IHIWS IHWG Fred Hutchinson Cell Lines DNA Panels to be tested, the type of panel to be tested (QC, UNK), NGS sequencing platform(s) to be used, NGS HLA genotyping kit(s) reagents to be used (including primers’ coverage of each HLA locus if using Targeted Amplicon Sequencing (TAS) approach), NGS HLA genotyping software analysis program(s) to be used and version, IPD-HLA/IMGT Database version to be used (which needs to be the same IMGT database version for all participants in this 19th IHIWS project). 

1. Confirmed and registered (early in 2024 as a tentative deadline and with a maximum number of groups to be established) participating HLA H&I laboratories in this project will be responsible to order and to purchase 19th IHIWS DNA Cell Lines panels and then to generate, review, and upload corresponding NGS genotyping data into the 19th IHIWS database according to the provided guidelines. 

2025-2026: 

1. All uploaded HLA genotyping data (by August 2025 as a tentative deadline) will be collected from the 19th IHIWS Database and respective analyses will be performed (as previously described during 17th IHIWS in Osoegawa et. al. (2019) (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6446570/#SD1)) to establish NGS HLA genotyping/sequence consensus and final QC and UNK panels 4-field HLA results.  
2. Co-authored 19th IHIWS Report manuscript of this project will be written and submitted to related 19th IHIWS peer-reviewed journal. 
3. Also, any respective new full-length genomic sequences will be submitted to the IPD-IMGT/HLA Database and all new unambiguous 4-field allelic level genotyping will be also made publicly available in Fred Hutchinson Cancer Center – Specimen Processing/Research Cell Bank website (https://www.fredhutch.org/en/research/institutes-networks-ircs/international-histocompatibility-working-group.html).   

Data Required (number, type of data, inclusion/exclusion criteria): 

From registered participating HLA H&I laboratory groups: 

1. Confirmed information sent via email to project leaders in relation to the number and type (QC, UNK) 19th IHIWS IHWG Fred Hutchinson Cell Lines DNA Panels that have been finally ordered, NGS sequencing platform(s) used, NGS HLA genotyping kit(s) reagents used (including primers’ coverage of each HLA locus if using Targeted Amplicon Sequencing (TAS) approach), NGS HLA genotyping software analysis program(s) used and version, IPD-HLA/IMGT Database version used (which needs to be the same IMGT database version for all participants in this 19th IHIWS project). 
2. Submission in the 19th IHIWS Database (by August 2025 as a tentative deadline) of 4-field HLA genotyping data (showing any remaining ambiguities per allele/locus) of 19th IHIW Cell Lines Panels tested on an excel spreadsheet as well as 4-field HLA genotyping data (showing any remaining ambiguities per allele/locus) of 19th IHIW Cell Lines Panels in the HML format. HML is Histocompatibility Markup Language, an XML-style document used to communicate HLA Genotyping results (as previously described in Matern, B.M. et al. (2021). Standard reference sequences for submission of HLA genotyping for the 18th International HLA and Immunogenetics Workshop. HLA, 97(6), pp.512-519)(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251737/). 

Only data deemed proficient (initially, 90% concordance rate of respective QC panel results submitted) from each participant will be included in the final consensus analysis and the 19th IHIWS Workshop report to be published. 

Samples Required (if applicable, number, type of samples, inclusion/exclusion criteria): 

Minimum of one complete (24 samples) QC panel tested to be able to participate in this 19th IHIWS NGS of Full-length HLA genes of Reference Cell Lines project. 

Reagents/Additional Assays Required: 

Participants are required to use one of the most novel short-read/2nd NGS (new primers allowing extended sequence coverage) and/or long-read/3rd NGS based HLA genotyping methods that can be available and which allow for testing of at least HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQB1, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DRB3, HLA-DRB4 and HLA-DRB5 genes per sample. 

Data Infrastructure Required:  

19th IHIWS International Workshop Database as developed in previous IHIWS Workshop editions (https://www.ihiw18.org/data/)(https://17ihiw.org/17th-ihiw-ngs-hla-data/) allowing participants to upload sequencing and/or HLA genotyping data (excel spreadsheet and XML-style document) of these various Cell Lines DNA panels. Then, leaders of this project will need to be able to collect and manage uploaded data to perform posterior consensus and other related analyses. 

Additional Information: 

In addition to these minimum abovementioned requirements to participate in this 19th IHIWS NGS of Full-length HLA genes of Reference Cell Lines project; any confirmed participating laboratory group(s), which could have also both the ability and proficiency to use a sequencing and genotyping method (via either WGS/WES- or TAS-based approach with respective validated bioinformatic tools) to characterize other MHC regions for these 19th IHIW Cell Lines DNA Panels (such as non-classical HLA genes or MICA/MICB genes or C4A/C4B or any other MHC subregion of interest), are also definitely encouraged and very welcome to generate, review and upload this NGS genotyping data into the 19th IHIWS database that could be also included for this project. 

Project leader(s):

• Alicia Sanchez-Mazas (Department of Genetics and Evolution, University of Geneva, Switzerland)
• Natasja de Groot (Department of Comparative Genetics and Refinement, BPRC, The Netherlands)

Detailed Project Description: 

The PGAE project’s objective is to provide an up-to-date overview of MHC molecular diversity and evolution in global human populations by combining new HLA data with information submitted during previous workshops, and by including non-human primate MHC data to gain a better understanding of how evolution has shaped the immune genes. 

The upcoming19th IHIW, organized in Numazu, Japan, in 2026, provides a unique opportunity to summarize the progress achieved over the past thirty-five years since the very first Anthropology Component was initiated within the frame of the 11th IHIW held in Yokohama, indeed also in Japan, in 1991.  

Since then, our knowledge on HLA molecular variation improved considerably thanks to the substantial increase of HLA-tested populations at the worldwide scale, to the adoption of powerful typing methodologies, notably NGS, enabling the highest resolution levels to be reached, and to extensive developments of biostatistical tools adapted to the analysis of complex molecular data, with similar improvements made for the study of MHC in our closest relatives (chimpanzees and other great apes). 

At this stage, we thus encourage laboratories to submit new MHC-typed population data, focusing in priority to full-gene DNA sequences, but considering also other genotyping results. Useful datasets may include, for humans, both anthropologically-defined populations and donor registry samples and, for non-human primates, wild or captive great ape cohorts. 

Milestones in years: 

2024: 

• Starting collection of data typed by interested laboratories 
• Component meeting at EFI Conference 2024 

2024-2025:  

• Continuing collection of data 
• Starting data analyses 
• Component meeting at EFI Conference 2025 

2025-2026:  

• Finalizing data analyses 
• Summarizing results 
• Preparing presentations at 19th IHIW 

2026: 

• Presenting results at 19th IHIW 
• Preparing publications 

Sample Description : 

For HLA data: 

Human population samples from any country of the world, composed of: 

• unrelated individuals representative of an anthropologically-defined population, or 

• unrelated individuals from a blood or bone marrow donor registry 

Population Definitions: each population sample will have to be defined according to hla-net.eu recommendations (a questionnaire will be available online). 

Sample Sizes: we encourage a minimum of 100 individuals per population. No maximum (very large samples are encouraged). 

For non-human primates (NHP) MHC data: 

NHP population samples, in particular of great-ape species, composed of: 

• unrelated individuals from which (sub)species definition is required. 

Data Required : 

1. For HLA data: 

• • Electronic files including HLA high-resolution multi-locus genotypes or DNA sequences for HLA-A, -B, -C, -DRB1, DQB1, DQA1, DPB1 and DPA1. Full-gene DNA sequences are encouraged. Fewer or more loci may be considered.

2. For non-human primate (NHP) MHC data: 

• Electronic files including full-length gDNA or cDNA MHC typing for MHC-A, -B, -C, -DRB, DQB1, DQA1, DPB1 and DPA1. Any data from one of these loci is more than welcome from NHP. 

3. If no molecular typing is already available: 

• gDNA (or cDNA) samples for HLA or NHP-MHC typings to be performed (conditions to be decide

Project name: High-resolution KIR sequencing

Project leader(s): Danillo Augusto, Paul Norman, Jill HollenbachDetailed project description:

The goal of this ongoing project is to characterize the nature and extent of KIR allelic diversity across human populations using Next Generation Sequencing (NGS). Participating labs will perform high-resolution KIR genotyping in unrelated individuals from diverse populations. When available studies in families will also be conducted in order to define phased KIR haplotypes through segregation analysis.

Milestones in years:

2024: Participating lab registration

2024-2025: Completion of QC

2026: Final data collection

Data required (number, type of data, inclusion/exclusion criteria):

High resolution KIR allelic genotyping

Samples required (if applicable, number, type of samples, inclusion/exclusion criteria):

• Samples from individuals representing diverse worldwide populations
• Families, in order to characterize KIR haplotypes at high resolution

Reagents/additional assays required:

All participants performing genotyping locally will be asked to validate methods using a small control panel

Data infrastructure required:

TBD

Project Name: NGS HLA-IR10K (HLA and Immune Repertoire 10 000 cases) 

Project Leader: Dr Sami Djoulah and ….. (in discussion) 

Detailed Project Description:  

HLA, T & B repertoire (TCR, BCR) and the environment together, are strongly involved in our Immune system and defense against pathogens. They can generate millions of parameters potentially being meta-analyzed with AI. These data generated by these studies will provide opportunities machine learning, deep learning training in cohorts’ studies, to identify the relevant parameters and understand how the immune system deals with the pathogens and then implements action. These studies will significantly impact precision medicine, early diagnostic of diseases, monitor of diseases treatment with the selection of the best personalized medication, personalized bio surveillance of persons at risk of disease, check of Immune wellness/performance, and identification of good versus bad responders to drug treatments. 

The immune repertoire is the total sum of functionally diverse B and T cells (clonotyping with potential diversity of 1025   Clones ‘Adaptive Immunity’) in one’s body at a given moment. Each person has an individualized immune repertoire, shaped by three key factors: (1) The HLA genetic polymorphism, (2) The antigen exposure history; and (3) The constant regulation and modulation of the immune system according to environment. 

We are inviting researchers to participate to this project by submitting proposals. 

This project is an international collaboration effort to sequence Immune repertoire (T and B cells) and HLA from 10,000 samples that cover 100 diseases, it is led by 19th IHIWS.  

This project will combine analysis of HLA Class I and/or II with Immune Repertoire in the context of :  

disease,  vaccination/immune alterations and populations.   

We would expect samples : 

full HLA class I and class II typing by NGS. CD4 and  TCR/BCR clonetype by NGS 

Milestones in Years:  

Year 1:  

Building expert scientific advisory board. 

Promoting the project 

Establishing standard protocols 

Year 2: Coordination With participants 

Year 3-4: Data collections and standard meta analyses and IA Development  

Data required (number, type of data, inclusion/exclusion criteria): 

Process and criteria 

Proposal will be evaluated by members of the scientific advisory board based on the following criteria: 

1. Clinical Significance: Disease with higher incidence and/or higher mortality rate will be considered first. Currently, we are accepting proposals to study diseases in the following categories: Cancer, autoimmune diseases, inflammatory diseases, infectious diseases (including vaccine studies), psychiatric disorders. Diseases meeting criteria in other categories might be considered in the future. 
2. Clinical Urgency: Diseases (or conditions) currently do not have a good diagnostic or evaluation method will be considered first.
3. Relevance with Immune system: Diseases must have established relevance with the immune system. 
4. Clinical impact: Disease that NGS HLA-IR10K program can be immediately translated into actionable leads and to bring benefit to patient will be selected first. 

Reagents/Additional Assays Required: 

Data Infrastructure Required:  

Participants should discuss the following within five pages: 

1. Please provide a short background on the diseases or condition you wish to study. 
2. Describe scientific rationale to study TCR/BCR/HLA by NGS. 
3. Describe how the results will be used in your clinical defined condition, be applicable to development of diagnostic? prognostic marker? Will they be useful in stratification or classification of patients, patient treatment monitoring? Or other? 
4. Please provide information about the sample coordinator, etc.

Project Name:  Immunogenetics of type 1 diabetes in world populations 

Project Leader(s):  Janelle A. Noble, Steven J. Mack 

Detailed project description:  

This is a continuation of our project from the 18th IHIW to compare HLA associations with type 1 diabetes among populations that are ethnically, geographically, and socioeconomically diverse. Fifty years have elapsed since the first reports of HLA association with autoimmune diabetes, and the mechanisms that trigger beta-cell destruction and autoimmune destruction of pancreatic beta cells are still not understood. We aim to continue our analysis and comparisons of type 1 diabetes in youth in world populations, especially underserved populations, to address the mechanism(s) of the role of HLA in triggering and in the progression of disease.  

Milestones in Years  

Between now and May of 2026, we aim to:  

• • Arrange collaboration with diabetes researchers in clinics in additional under-resourced settings. (by mid 2024). 
  • Arrange for sample collection from patients and controls. (by mid 2025) 
  • Prepare DNA and arrange for whole-gene HLA sequencing of patient and control samples (by end of 2025). 
  • Analyze association data with existing methods and develop novel analysis methods.(mid 2025 through early 2026). 
  • Compare data with existing data (early 2026). 

Data Required (number, type of data, inclusion/exclusion criteria): 

• (Primary aim) Generate HLA genotyping data from ≥100 children and youth, aged 2 -22, diagnosed with type 1 diabetes. 

  HLA genotyping data from ≥200 non-diabetic control individuals from the same population, unrelated to the patients or to each other. 

  (Secondary) if, feasible, generate KIR data on the collected samples as well as GRS scores. 

  Target enrolment is 3-4 additional populations. 

Samples Required (if applicable, number, type of samples, inclusion/exclusion criteria): 

• We will ask for a minimum of 100 patients and 200 controls from each population.
• We will target populations from understudied populations that differ from those we already have, e.g., South American, some Asian, and Middle Eastern populations, and will use existing infrastructure and collaborators to secure collaborations. 
• Patients will be children and youth between the ages of 2 and 22, diagnosed with type 1 diabetes using WHO criteria; Controls will be individuals over the age of 22 with no diabetes or family history of diabetes, unrelated to any patient or any other control, having the same ethnic ancestry and living in the same environment as the patients.  

Reagents/Additional Assays Required: 

The main requirement will be HLA genotyping, either performed at the collection site, or performed at a central laboratory. We will solicit donations of genotyping from existing collaborators.  

Statistical analyses will be performed with available packages, and new assays will be developed. 

Data Infrastructure Required:  

(Primary aim) Generate HLA genotyping data from ³100 children and youth, aged 2 -22, diagnosed with type 1 diabetes 

HLA genotyping data from ³ 200 non-diabetic control individuals from the same population, unrelated to the patients or to each other 

(secondary) if, feasible, generate KIR data on the collected samples as well as GRS scores. 

Target enrolment is 3-4 additional populations. 

Samples required (if applicable, number, type of samples, inclusion/exclusion criteria): 

We will ask for a minimum of 100 patients and 200 controls from each population 

We will target populations from understudied populations that differ from those we already have, e.g., South American, some Asian, and Middle Eastern populations, and will use existing infrastructure and collaborators to secure collaborations. 

Patients will be children and youth between the ages of 2 and 22, diagnosed with type 1 diabetes using WHO criteria; Controls will be individuals over the age of 22 with no diabetes or family history of diabetes, unrelated to any patient or any other control, having the same ethnic ancestry and living in the same environment as the patients.  

Reagents/additional assays required: 

The main requirement will be HLA genotyping, either performed at the collection site, or performed at a central laboratory. We will solicit donations of genotyping from existing collaborators.  

Statistical analyses will be performed with available packages, and new assays will be developed. 

Data Infrastructure Required:  

Data will be maintained and backed up on local servers.  The permission they will be shared after publication, with permission of in-country investigators. 

Project Leaders:

• Dr. Jill A. Hollenbach
• Dr. Steven J. Mack
• Dr. Martin Maiers 

Detailed Project Description:

The goals of this project are to expand data collection and data-analytic capacity for HLA, KIR and other immune-related genes for the purpose of determining the immunogenetic basis of COVID-19 and other established and emerging viral diseases. 

Milestones in Years: 

2023: New participant enrolment, CHIC database update, public release of published data, collection of new immunogenetic data, collection of data for other viral diseases. 

2024: New participant enrolment, human-subjects certification collection, initiation of central meta-analyses of published data, user-analyses of new data, new tools development. 

2025: Continued enrolment, data collection and central and user data analyses. 

2026: Presentation of results at IHIW-19 

Data Required (number, type of data, inclusion/exclusion criteria): High-resolution or better genotype data for the HLA and KIR loci and other immune loci for COVID and other viral diseases, along with deidentified patient data regarding demographics, disease status and symptoms 

Samples required (if applicable, number, type of samples, inclusion/exclusion criteria): NONE, we will be collecting data only; participants will be responsible for sample collection, genotyping, IRB oversight, etc.  

Reagents/additional assays required: NONE 

Data Infrastructure Required: If feasible, we would like to create a portal or link from the 19th IHIW database to the CHIC database. It would also be helpful to coordinate CHIC user registration with 19th IHIW registration. 

Project Name: Creating Fully Representative MHC Reference Haplotypes 

Project Leader: Paul Norman (paul.norman@ucdenver.edu) 

Detailed Project Description: 

The human genome reference is a valuable resource for multiple branches of science, yet does not adequately represent human diversity, a shortcoming that is especially evident in the highly polymorphic MHC region. A fundamental endeavour of the IHIW was assembling a collection of DNA and cells from individuals identified as homozygous through large tracts of the MHC region. This collection proved invaluable for assay development in the H&I field and furthered our understanding of the MHC region; recently, we sequenced the entire collection of cell lines to full resolution and an additional 500 samples collected for the 18th Workshop. However, the samples do not well represent HLA haplotypes common in African, Oceanic and some Asian countries. Thus, the majority of MHC haplotypes, both common and rare throughout the world, and including many associated with disease, are not yet covered. In this workshop component, we are collecting the next generation of IHIW MHC-homozygous samples in order to expand, diversify and modernize this critical community resource that has been foundational to the field. For this effort, we are seeking samples from individuals who have been identified by typing labs as homozygous through all of their HLA class I and/or HLA class II genes. We are also seeking those individuals from whole-genome SNP or sequencing studies having extensive homozygosity tracts covering at least 2Mbp of the MHC region. We are particularly interested in HLA homozygous individuals of African, South, or Southeast Asian, or Oceanic ancestry. We will perform targeted sequencing of the MHC region from DNA samples. This project will be performed in collaboration with the NIH-sponsored HLA Region Genetics Consortium. The data will serve as a resource for investigators seeking to characterize variation across the MHC region for disease and population studies.  

Milestones in years: 

• 2023-25: Contributors to identify MHC region homozygous individuals 
• 2025: Collect and Sequence MHC-homozygous DNA samples 
• 2026: Create fully-annotated MHC-region reference  

Patient/sample description (if applicable, details, inclusion/exclusion criteria):

Samples identified as homozygous for HLA haplotypes. No exclusion, but African, Asian, or Oceanic ancestry preferred. 

Data required (number, type of data, inclusion/exclusion criteria): 

HLA genotype, or SNP data from the MHC-region, or MHC region from whole-genome/whole exome sequencing. 

Samples required (if applicable, number, type of samples, inclusion/exclusion criteria): 

We aim to collect 200 DNA samples, and expect labs to each donate a small number of samples. 

Reagents/additional assays required: 

Data infrastructure required: 

Project Leaders:

• Seik-Soon Khor (Charles)
• Kouyuki Hirayasu
• Masao Nagasaki
• Kazuyoshi Hosomichi
• James Robinson
• Martin Maiers

Detailed Project Description:

• Identify and quantify the different forms of killer cell immunoglobulin-like receptors (KIR) and Leukocyte immunoglobulin-like receptors (LILR) (copy number variants, structural variants, and allelic variants)
• Identification of KIR/LILR haplotypes
• Functional significance of unique KIR/LILR variants
• Develop a new database of LILR allelic variation in IPD-LILR
• Construction of LILR copy number/allele imputation system

Milestones by Year:

2023: Registration of participants
2024: Experimental design and validation

2025: Raw data generation & completion of QC

2026: Analysis & construction of IPD-LILR

Data Required (number, type of data, inclusion/exclusion criteria):

• Genomic DNA of families and/or populations
• Experimental KIR/LILR data
• SNP genotyping array data / Whole genome sequencing (WGS) data

Samples Required (if applicable, number, type of samples, inclusion/exclusion criteria):

• Samples from individuals representing diverse populations
• Family samples

Reagents/additional assays required:

• KIR/LILR genotyping will be primarily conducted by the project leaders
• All participants performing genotyping locally will be asked to validate methods using a small control panel

Data Infrastructure Required:

KIR/LILR genotyping results (txt/xlxs)

KIR/LILR sequencing fastq files

SNP genotyping array data and/or whole genome sequencing (WGS) data

Project name:

(1) Trans-ethnic meta-analysis for association of HLA with drug-induced skin rashes

Project leaders:

• Clara Gorodezky
• Maria Bettinotti
• Taisei Mushiroda

• Koya Fukunaga

Detailed project description:

In the last two decades, numerous HLA markers have been discovered for various adverse drug reactions, particularly severe drug-induced skin rashes. A recent Japanese report has demonstrated that the HLA-B*15:11 allele is associated with carbamazepine-induced SJS/TEN, but the HLA-A*31:01 allele exhibited the most substantial association with carbamazepine-induced DIHS/DRESS, MPE, and EM, while showing a weaker association with SJS/TEN (Fukunaga et al., 2023). These findings indicate that distinct types of skin rashes triggered by the same causative drug may be associated with different HLA alleles. However, accumulating a sufficient number of cases within a single country for such an analysis is exceedingly challenging. Therefore, this project aims to conduct a trans-ethnic meta-analysis to identify HLA markers significantly associated with each type of skin rash induced by the same drug.

Milestones by year:

2023: Starting collection of existing data

2024: Continuing collection of data, starting statistical analyses

2025: Summarizing results for 19th IHIWS

2026: Presentation at 19^th IHIWS, preparing publications

Data Required (number, type of data, inclusion/exclusion criteria):

Genotyping data for HLA class I and class II genes obtained from cases with drug-induced skin rash and the general population controls.

Samples Required (if applicable, number, type of samples, inclusion/exclusion criteria):

No/Yes (if genomic information is not available)

Reagents/additional assays required:

No

Data Infrastructure Required:

We would like to discuss this project with the 19^th IHIWS organizers and data team.

Project name:

Clinical Relevance of Donor-Derived DNA Measurement Assays in Post-transplant Surveillance

Project leader(s):

Amanda Blouin – Memorial Sloan Kettering Cancer Center
Omar Moussa – Medical University of South Carolina
Medhat Askar – Qatar University

Detailed project description:

The use of advanced cfDNA assays such as qPCR, NGS, and ddPCR has made it possible to assess the percentage of donor derived DNA in mixed donor-recipient biological samples at previously undetectable levels. In stem cell transplants, residual or re-emerging recipient DNA can be detected at very small quantities early after stem cell transplantation therapy. In solid organ transplantation, donor cfDNA can be identified with significantly greater sensitivity. However, the clinical and biological relevance (significance) of minor concentrations and smaller variations in donor derived DNA detected by those sensitive assays is not well understood.

A multi-center study has been initiated to assess the biological and clinical relevance of changes in donor derived DNA in post-transplant settings and correlation with clinical outcomes. For the stem cell transplant group, the research will focus on changes in donor chimerism, including recipient microchimerism, and their correlation with engraftment patterns across different cell subsets in various transplant contexts, including different donor types, diseases, GVHD prophylaxis, and preparative regimens. Changes in donor derived DNA will be correlated with post-transplant outcome, Disease Free Survival, and relapse.  In solid organ transplantation population, the study will track post-transplant changes in donor cfDNA and its relationship with allograft function, allograft survival and results of other antibody mediated rejection diagnostics such as donor specific HLA antibodies (DSA), biopsies, transcription profiles, etc.

Milestones in years:

Sep – Dec 2024: Submission of chimerism (limited to STR, qPCR, NGS and ddPCR) and cfDNA (limited to NGS and ddPCR) results and corresponding data

Jan – Dec 2025: Optional additional testing by higher sensitivity methods including qPCR, NGS and ddPCR of samples with STR results (stem cell transplant population only).

Jan – May 2026: Analysis, preparation of report

Patient/sample description (if applicable, details, inclusion/exclusion criteria):

• Stem cells transplant group: Chimerism results (as measured by NGS, ddPCR, qPCR, or STR) of unfractionated whole blood, bone marrow and/or different subsets such as T cells, B cells, myeloid, NK cells and CD34+ cells samples from post-allo HCT patients at different timepoints in first 2 years of transplantation.
• Solid organs transplant group: cfDNA data measured by NGS and ddPCR as part of the routine post-transplant allograft surveillance for cause will be collected

Data required (number, type of data, inclusion/exclusion criteria):

Stem cells transplant group:

1. Methodology (Limited to testing by NGS, ddPCR, qPCR and/or STR): Performance characteristics of the testing methods as established during initial validation such as sensitivity, LOD, accuracy (CV), etc
2. Chimerism results: % donor (including multiple donors), % recipient, unfractionated and cell subsets

• Transplant event information: date/donor type/HLA matching/Presence of DSA

1. Relevant clinical information: Diagnosis/reason for transplant, GVHD prophylaxis, preparative regimen

Samples required (if applicable, number, type of samples, inclusion/exclusion criteria):

• For samples tested by STR, 2 micrograms DNA extracted from post-Tx samples (for supplemental high sensitivity chimerism testing). In addition to 2 micrograms pre-transplant Recipient and Donor DNA (from blood or buccal swab) for identity testing (for supplemental chimerism testing).

Solid organs transplant group:

• Methodology (Limited to testing by NGS and ddPCR): Performance characteristics of the testing methods as established during initial validation such as sensitivity, LOD, accuracy (CV), etc
• cfDNA results.
• Transplant event information: date/donor type/HLA matching/Presence of DSA
• Relevant clinical information:

1. Diagnosis/underlying disease, pretransplant immunological risk assessment data such as physical and virtual crossmatch.
2. Clinical data collected at the immediate posttransplant period that could impact the cfDNA results such as surgical trauma, Ischemia-Reperfusion Injury, DGF.

• Routine allograft surveillance and/or for cause data such as DSA data, biochemical markers, biopsy data, and allograft rejection molecular markers.

1. Other post-transplant clinical data that might skew the cfDNA results: example, infections, inflammation, autoimmunity, allergic reaction, or drug toxicity.

Reagents/additional assays required:

• Chimerism testing methodology with STR, qPCR, NGS and ddPCR.

Data infrastructure required:

• Web-based data entry-sheet for participants to collect data listed above
• A database housing the collected data listed above

Project name: HLA Haplotypes in Families: Haplotype Segregation, HLA Typing Prediction From WGS Data & Artificial Intelligence Generation of Synthetic Haplotype Data

Project leader(s):

Medhat Askar – Qatar University College of Medicine, Doha, Qatar
Hamdi Mbarek – Qatar Genome Project, Doha, Qatar
Muhammad E. H. Chowdhury – Qatar University College of Engineering, Doha, Qatar

Detailed project description:

The project incorporates 3 components:

1. An anthropological study of families by NGS of full-length HLA genes to determine haplotype segregation in multiple populations. Samples from family quartets consisting of two parents and at least two non-HLA identical children or family trios consisting of one parent and at least two non-HLA identical children, two parents and one child or 2 parents and multiple HLA identical children are required. The families should be previously HLA typed at any level (serology, DNA) to establish relationship.
2. Validation of HLA typing algorithms on paired data for samples tested for both HLA typing & whole genome sequencing (WGS) developed by Qatar Genome Project
3. Participants with paired datasets that meet the above criteria can be provided algorithms to test on additional samples beyond those provided by the Qatar Genome Project, ensuring comprehensive validation and broader applicability. A web-based Google Cloud Platform will be used to deploy the models developed using QGP data and other participating institutions. A secure access will be provided to external institutions to test their data on the deployed algorithm.
4. Generation of synthetic HLA haplotype data using machine learning/artificial intelligence (ML/AI) algorithms developed by Qatar University Machine Learning Group (QU-MLG) based on data collected through segregated HLA haplotypes from families tested in the first component. There should be definition of performance metrics for evaluating the quality of synthetic data. The metrics may include accuracy, realism, diversity, and preservation of haplotype structure. Participants can also share their data processing code and trained model with weights we can test their model on the project dataset.
5. Participants who developed different prediction algorithms will be given the opportunity to have their algorithms validated using the Qatar Genome Project dataset as well as other datasets of willing participants. Cross algorithm prediction performance comparison among algorithm developed using Qatar Genome Project and other available algorithms will also be performed.

Investigators may submit samples, data, WGS prediction algorithms, ML/AI algorithims or any combination of the above.

Milestones in years:

2024 (June – Dec): Submission of participation plan and family pedigrees and any combination of the following: samples for HLA typing, samples for WGS, NGS results fastq files, WGS data files, participants developed prediction algorithms of HLA typing from WGS data and other pertinent data and preliminary analyses.

2025 (Jan – July): Preliminary analyses, HLA typing for selected families with WGS data available (testing can be performed by participants or by the histocompatibility lab in Qatar), WGS of selected samples with available HLA haplotype data (testing can be performed by participants, collaborators from the local institute(s) or by the Qatar Genome Project lab in Qatar)

2025 (Aug – Dec): Validation of HLA typing algorithms (by Qatar Genome Project team and interested participants), applying ML/AI algorithms to data collected from the other components to generate synthetic HLA haplotype data across different ethnic groups and with participation of labs from all over the world.

2026 (Jan – May): Final analysis and preparation of final report for presenting during the workshop

Patient/sample description (if applicable, details, inclusion/exclusion criteria):

HLA typing results of samples from family quartets consisting of two parents and at least two non-HLA identical children or family trios consisting of one parent and at least two non-HLA identical children are required. Families must have been previously HLA typed at any level (serology or DNA) to confirm relationships and HLA status.

Data required (number, type of data, inclusion/exclusion criteria):

Pedigree analysis including relations of all subjects tested

NGS testing results of all family members

Fastq files of samples tested by NGS

Any pertinent information to the subject carrying the allele of interest such as ethnic background

Target a minimum sequencing depth of 30X. Note that ideally the depth should be increased to 50X or 100X to capture the high variability of the HLA region and ensure accurate HLA typing. Each FASTQ’s sample should be accompanied by full metadata detailing the sequencing process, reliable HLA typing results and detailed demographic, ethnic and health information. In addition, detailed pedigree information is essential.

Samples must represent multiple ethnic groups to train models that can generate synthetic data reflective of global genetic diversity. Patients must have comprehensive HLA typing data available, ideally from both serology and DNA levels, to validate the synthetic data against real-world samples. Preference is given to samples from family quartets (two parents and two non-HLA identical children) or trios (various combinations of parents and children), which are critical for studying haplotype segregation. Availability of paired datasets, including both HLA typing and whole genome sequencing (WGS) data, to facilitate accurate synthetic data generation and validation. Inclusion of extended family members (grandparents, cousins) where available, to enhance the understanding of genetic inheritance patterns. Samples must be of high quality and completeness, with minimal missing data to ensure robust training and validation of ML/AI models.

No max or min number of samples required

Samples required (if applicable, number, type of samples, inclusion/exclusion criteria):

For samples not tested by NGS, 2 ug DNA of high quality suitable for. Long range PCR amplification.

A substantial number of samples (ideally thousands) is required to train robust machine learning models. Larger sample sizes help in capturing the genetic diversity and improving the algorithm’s predictive power. Ensure a balanced representation of different HLA alleles within the sample set to avoid skewed training that could bias the algorithm. Samples should represent a wide range of ethnic backgrounds to develop algorithms that are effective across different populations. This diversity ensures that the algorithms can generalize well and are not biased towards any specific population group. Samples must have high-coverage WGS data (typically ≥30x coverage) to ensure the accuracy of the sequencing and the reliability of subsequent analysis. Samples should have existing HLA typing data available at a high resolution (e.g., using serology or DNA methods). Validation of synthetic HLA haplotype data against real datasets to ensure accuracy and biological relevance. Cross-validation using datasets from diverse populations to assess generalizability. The type of samples would typically be DNA extracted from blood or possibly saliva, chosen for its reliability in both HLA typing and WGS. Inclusion criteria should target a diverse population to cover a wide array of HLA types and genetic backgrounds, including samples with well-documented HLA typing from previous reliable assays to serve as a benchmark. Exclusion criteria would likely include samples with low DNA quality or quantity that could impede accurate sequencing, as well as any samples without prior HLA typing data.

Reagents/additional assays required:

Not applicable

Data infrastructure required:

A database that house the collected typing results and fastq files similar to the database provided by the Stanford group during the 17^th IHIW. In addition, the consensus sequences of all alleles identified in members of families will be confirmed by comparing seqiuences of the same allele identified in multiple subjects carrying the same allele (established by segregation) in combination with different alleles.

To efficiently run the HLA typing prediction algorithm, a high-performance computing (HPC) infrastructure is crucial. This system should include multi-core processors capable of high-speed parallel processing to handle the computationally intensive tasks of gene sequencing and analysis. Incorporating GPUs with large VRAM (>=48 GB) capacities would significantly boost the performance of machine learning algorithms, particularly for training and executing models. Additionally, each node within the HPC should be equipped with substantial RAM (128 GB to 256 GB) to facilitate the handling of large datasets and complex computations in memory.

For storage, SSDs are recommended for their speed in read/write operations which is essential for tasks that require frequent data access such as alignment and genotyping. Long-term storage solutions should involve high-capacity HDDs or cloud storage, configured in RAID arrays to ensure data redundancy and protect against data loss. The infrastructure should also include a fast internal network (10 Gigabit Ethernet or faster) to efficiently manage the transfer of large genomic datasets between the storage and computational units. Regular backups and a robust disaster recovery strategy are essential to safeguard data, with off-site or cloud-based backups to secure against physical damage to on-site resources. This comprehensive setup ensures not only the capability to manage large volumes of data but also enhances the overall efficiency and reliability of genomic data processing and analysis.

To test on additional samples beyond those provided by the Qatar Genome Project, ensuring comprehensive validation and broader applicability, the developed model will be deployed in GCP and secured testing channel will be provided to the partner institutions for testing their datatset for an external validation. This will need to hire a Google Cloud Platform for the required duration (2-3 years) with 1 TB storage, 128 GB RAM and 48+ GB GPU capacity.