analyses 2 . This limits the discovery of genetic diversity contributing to Mendelian diseases and to explore associations between genetic variants and trait variation across populations.
In recent years, awareness has been increasing about the limited generalizability of findings across populations, motivating the inclusion of diverse, multiethnic populations in large-scale genomic studies 3, 4 . For example, novel single nucleotide polymorphisms (SNPs) that are clinically associated with warfarin dosing were discovered in large scale genomics studies in individuals of African descent that had not been discovered in Europeans 5,6 . Whole-genome sequencing in individuals of African descent [7] [8] [9] and whole-exome sequencing in a southern African population 10 Additional efforts have been made through international collaborations to establish reference genome datasets and recommendations for research in diverse populations; including the GenomeAsia 100K Project, the Human Heredity and Health in Africa (H3Africa) initiative, and the Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group (ADWG) [13] [14] [15] [16] .
Khatri and colleagues discovered the 3-gene signature for diagnosis of tuberculosis based on transcriptome profiles of participants from 11 countries 17 , a finding that was generalized to patient populations from every inhabited continent in a span of 3 years 18, 19 . Most importantly, the 3-gene signature has been clinically translated to the point-of-care test by Cepheid 20 .
The inclusion of diverse populations in genomic studies has demonstrated benefits in the discovery and interpretation of gene-trait associations. Similarly, greater diversity in immunogenomics research will enable the discovery of novel genetic traits associated with immune system phenotypes that are common across populations. Broader inclusion of diverse populations may also enable researchers to address genetic heterogeneity in the context of translational research and clinical drug development, possibly revealing clinically relevant genomic signatures that are more prevalent in some populations than others.
Immunogenomics is a field in which genetic information at different levels of biological organization (epigenetics, transcriptomics, metabolomics, cells, tissues, and clinical data) has been characterized and utilized to understand the immune system and immune responses. Here Table 1 ).
RNA-seq has traditionally been mapped to study entire cellular populations instead of amplifying at the specific regions. Given the complexity of the TCR and BCR genomic loci, the accurate determination of germline immune receptor genes, from bulk RNA-seq or whole-genome sequencing, has proved challenging 45 . Several computational methods show promise [46] [47] [48] , but the mapping rate and accuracy remain to be improved. Additionally, a wide-scale comparison is needed between results obtained from methods for deriving germline receptor genes from RNAseq studies, those obtained from established methods such as, targeted PCR and sequencing of genomic DNA, the sequencing and assembly of bacterial artificial chromosome (BAC) and fosmid clones 49 , and those from more recent methods such as inference from AIRR-seq repertoires 50 .
Many population genetic differences have been observed in genomics studies and immunogenomics is no exception [51] [52] [53] . The current public databases of adaptive immune receptor germline genes are essential for AIRR-seq analysis and immunogenomics studies. However, the most widely used reference database for immunogenetics data, the international ImMunoGeneTics information system (IMGT) 63 , lacks a comprehensive set of human TCR and BCR alleles representing diverse populations worldwide. The same issue exists in HLA databases: over 70% of rare HLA variants from Oceania and west Asia populations were found to be absent in the 1000 Genomes Project panel 64 . There is still more uncertainty due to the fact that descriptions of sample populations in databases are often self-identified based on geography or ethnicity, rather than genetic ancestry.
However, progress has been made to address this issue in immunogenomics studies. For example, the AIRR Community, an international community formed to promote high standard research in adaptive immune repertoire research, introduced the Open Germline Receptor Database (OGRDB) in September 2019 as a resource platform for germline gene discovery and validation from AIRR-seq data, to enrich the IMGT database 65 . Collaborators in our team also created VDJbase, a platform for the inferred genotypes and haplotypes from AIRR-seq data 66 .
These efforts provide the opportunities to infer genetic ancestry. Nevertheless, the majority of available germline sequences either lack population-level annotations or are biased toward samples of European descent. We argue that this shortcoming must be addressed through focused efforts that seek to include more diverse populations in immunogenomics research.
As an interdisciplinary group, with expertise in biomedical and translational research, population biology, computational biology, and immunogenomics, we wish to raise awareness about the value of including diverse populations in AIRR-seq and immunogenetics research. In the areas of genetic disease research and cancer genomics, enhanced genetic diversity has led to demonstrable insights 67, 68 . However, the field of immunogenomics has yet to benefit from a similar growth in diversity. At the current stage of the global COVID-19 pandemic, numerous vaccine trials are underway in many countries worldwide, offering opportunities to investigate genetic factors in vaccine responses. Yet, this will require careful clinical study designs that can effectively address confounding factors such as environmental and socio-economic differences. HIV-1 71 , Zika 72 , and SARS CoV-2 73 . We expect that vaccine and infection outcomes can also be shaped by genetic variability, including specific effects driven by immune-related genes 58 . Here, we make several recommendations for increasing diversity in immunogenomics research.
First, we propose that the community should make a greater effort to include underrepresented populations in AIRR-seq and immunogenomics studies. Already, those that have conducted AIRR-seq in populations of non-European descent have uncovered evidence for extensive germline diversity. For example, in a study of South African HIV patients, Scheepers and colleagues discovered 123 IGHV alleles that were not represented in IMGT 54 . This promoted the HIV vaccine design by understanding the immunoglobulin heavy chain variable region (IGHV) profile in the South African population. In a study in the Papua New Guinea population, one novel IGHV gene and 16 IGHV allelic variants were identified from AIRR-seq data 55 . These discoveries of novel alleles indicate the need to generate population-based AIRR-seq datasets.
We do not recommend generalizing AIRR-seq findings to populations that are underrepresented in research, due to missing variation and lack of validation, which limits our ability to leverage AIRR-seq datasets in biomedical applications.Therefore, increasing population diversity in immunogenomics studies can lead to improvements in a wide range of applications, including drug discovery and development, vaccine design and development. Promoting precision medicine for underrepresented populations and improving predictions for treatment outcomes will become more feasible in the future with broader participation and inclusion.
Second, we argue that there are existing genomic datasets that could potentially be leveraged to augment IG/TCR germline databases, and inform the interpretation of AIRR-seq and immunogenomics studies across populations. Extraction of population immunogenomics information from existing genomic datasets could be an effective strategy, as well as carefully embracing non-targeted sequencing data (eg. RNA-seq) to focus on genetic diversity of samples.
Ancestry-associated genetic markers in short-read genome sequencing may help overcome the difficulties of relying on sample metadata in AIRR-seq datasets. This may also be time-efficient relative to waiting for the availability of sufficiently diverse AIRR-seq datasets. Researchers have attempted to utilize paired-end RNA-seq data in the Cancer Genome Atlas (TCGA) to infer the complementarity determining region 3 (CDR3) of tumor-infiltrating T-cells 74 , and to apply a computational method to RNA-seq data in the Genotype-Tissue Expression Consortium (GTEx) to profile immunoglobulin repertoires 48 . Similar ideas could be adapted to the direct prediction of allelic variants from short-read genomic sequence data 75, 76 . However, challenges need to be overcome, including the high levels of copy number variation and segmental duplication in the BCR and TCR loci, and the need for protocols to validate novel allelic variants gleaned from short-read sequencing data 45, 77 Finally, we suggest the need for additional infrastructure and expertise in regions and countries with populations underrepresented in research, and to enhance collaborations between countries, which are critical in minimizing global health disparities. Online training sessions that are customized for conducting immunogenomics research in diverse populations would be beneficial to the biomedical community, perhaps especially in those regions. The content of these trainings might include participant recruitment strategies with a commitment to outreach and education to increase participation, sample collection methods, steps to running sequence experiments onsite or in collaboration with other academic institutions or commercial companies, uploading sequencing data to appropriate repositories, and performing bioinformatics analyses. Virtual learning platforms for bioinformaticians have been established by members in our group, and these could be leveraged to provide such trainings 79 . Our interdisciplinary group consists of leading researchers from 13 countries, including the US, Canada, Norway, France, Sweden, Russia, the UK, Israel, China, South Africa, Chile, Peru, and French Polynesia. We share concerns about the lack of diversity in immunogenomics and embrace the need for engaging our combined efforts to tackle this challenge. To spearhead the enterprise of fostering diversity in the field, we have formed this task force with the aim of developing a global consortium on diversity in immunogenomics. This consortium will seek to promote inclusive, international, and interdisciplinary research, supported by transparent and open-source learning materials and datasets, with the goal of enhancing representation of diverse populations in immunogenomics.