MiDAS-Meaningful Immunogenetic Data at Scale

Abstract

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. However, association studies involving immunogenetic loci most commonly involve simple analyses of classical HLA allelic diversity, resulting in limitations regarding the interpretability and reproducibility of results. We here present MiDAS, a comprehensive R package for immunogenetic data transformation and statistical analysis. MiDAS recodes input data in the form of HLA alleles and KIR types into biologically meaningful variables, allowing HLA amino acid fine mapping, analyses of HLA evolutionary divergence as well as experimentally validated HLA-KIR interactions. Further, MiDAS enables comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS thus closes the gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to immune and disease biology. It is freely available under a MIT license.

Fig 1. MiDAS data transformation functions.

MiDAS can transform HLA and KIR input data to test association hypotheses beyond single allele or KIR gene approaches. HLA alleles can be grouped according to their interactions with KIR, and sequence information is used to infer variable amino acid positions for statistical fine-mapping. Amino acid level information is also used to calculate evolutionary divergence of HLA allele pairs for a given gene. If both HLA and KIR data is available, biologically validated receptor-ligand interactions can be coded according to the definitions summarized by Pende et al.[2]

Fig 2. Example of amino acid fine-mapping analysis.

Example analysis flow for HLA amino acid analysis. In the first step, HLA and clinical data were combined in a MiDAS object using the ‘prepareMiDAS’ function, which also performed HLA data transformation to amino acid level (specified as ‘experiment’). Before the association analysis, a statistical model was defined. ‘term’ is a placeholder that is replaced by each tested amino acid, covariates (‘covar’) can be categorical or numeric. It is also possible to define interaction terms (e.g. ‘term:covar’, not shown). ‘runMiDAS’ was then run twice, first to perform an omnibus test on all variable amino acid positions, and then to calculate effect estimates for all residues (F,Y,L) at the top-associated position (DQB1_9). ‘getAllelesforAA’ was then used to map all HLA-DQB1 alleles in the dataset to the three DQB1_9 residues.