Comparison of AIR repertoire generation models. (A) The process of recombining variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be described statistically as repertoire generation models (RGMs). So far, it remains unknown whether VDJ recombination rules differ across individuals. We set out to resolve this question by developing a distance measure that enables the quantification of RGM parameter (RGMP) similarity. (B) Accounting for several sources of noise in murine and human Ig sequencing data (by leveraging various types of replicates), as well as allelic diversity, (C) we were able to implement a noise-aware, sensitivity-tested statistical test for comparing RGM similarity. We call our method desYgnator for DEtection of SYstematic differences in GeneratioN of Adaptive immune recepTOr Repertoires (desYgnator). Using desYgnator, we found that replicate samples of the same subject are consistently more similar to each other than to samples from other unrelated individuals or even monozygotic twins (or inbred mice) indicating that not only genetic but also nongenetic factors contribute to the individualization of an RGM. We validated desYgnator by showing that RGM did not differ across synthetic and experimental replicates. We quantified the implication of individual RGMs on Ig repertoire architecture in a data set of approximately 100 human individuals by showing that the same (antigen-annotated) Ig sequence can have different generation probabilities across individuals. Thus, the available Ig sequence space is individually biased, predisposed by the individual RGM.
