Population matched (PM) germline allelic variants of immunoglobulin (IG) loci: New pmIG database to better understandIGrepertoire and selection processes in disease and vaccination
Authors: Khatri Indu , Berkowska Magdalena A. , van den Akker Erik B. , Teodosio Cristina , Reinders Marcel J.T. , van Dongen Jacques J.M. 
Published: April 2020

At the population level, immunoglobulin (IG) loci harbor inter-individual allelic variants in the many different germlineIGvariable (V), Diversity (D) and Joining (J) genes of theIGheavy (IGH),IGkappa (IGK) andIGlambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These inter-individual allelic variants can be shared between or be specific to human populations. The currentIGdatabases IMGT, VBASE2 and IgPdb hold information about germline alleles, most of which are partial sequences, obtained from a mixture of human (B-cell) samples, many with sequence errors and/or acquired (non-germline)IGvariations, induced by somatic hypermutation (SHM) during antigen-specific B-cell responses. We systematically identified true germline alleles (without SHM) from 26 different human populations around the world, profiled by the “1000 Genomes data”. Our resource is uniquely enriched with completeIGallele sequences and their frequencies across human populations. We identified 409IGHV, 179IGKV, and 199IGLVgermline allelessupported by at least seven haplotypes(= minimum of four individuals), after removal of potential false-positives, based on using other genomic databases, i.e. ENSEMBL, TopMed, ExAC, ProjectMine. Remarkably, the positions of the identified variant nucleotides of the different alleles are not at random (as observed in case of SHM), but show striking patterns, restricted to limited nucleotide positions, the same as found in otherIGdata bases, suggesting over-time evolutionary selection processes. The identification of these specific patterns provides extra evidence that the identified variant nucleotides are not sequencing errors, but genuine allelic variants. The diversity of germline allelic variants inIGHandIGLloci is the highest in Africans, while theIGKlocus is most diverse in Europeans. We also report on the presence of recombination signal sequences (RSS) inVpseudogenes, explaining their usage inV(D)Jrearrangements. We propose that this new set of genuine germlineIGsequences can serve as a new population-matchedIG(pmIG) database for better understanding B-cell repertoire and B-cell receptor selection processes in disease and vaccination within and between different human populations. The database in format of fasta is available via GitHub (https://github.com/InduKhatri/pmIG).

Contribution to the Field Statement

We present a catalogue of immunoglobulin (IG) germline-alleles of unprecedented completeness and accuracy from 26 different human populations belonging to five different large ethnicities (Source: 1000 Genomes). We identified the population distribution of several known germline alleles and identified multiple new alleles, especially in African populations, indicative of high allelic diversity ofIGgenes in Africa. Strikingly, the identified variant nucleotides of the different alleles are not at random, but show striking patterns, restricted to limited nucleotide positions, the same as found in otherIGdatabases, suggesting over-time evolutionary selection processes. Furthermore, we identified recombination signal sequences in pseudogenes (previously not known). We provide an overview ofIGgermline alleles shared with and between known databases and also point to potential sources of non-germline variation and incompleteness of the existingIGdatabases. More importantly, we believe that this information can serve as a novel population-matchedIG(pmIG) database, highly valuable for the research community in supporting the dissection and understanding of differences in effectiveness of antibody-based immune responses in infectious diseases, other (immune) diseases and vaccination within and between human populations. Such knowledge might be used in developing population-specific vaccination strategies e.g. for currently ongoing SARS-CoV2 pandemic.