Autoimmune diseases - connecting risk alleles with molecular traits of the immune system

Abstract

Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.

Figure 1. Genetic variation, intermediate immunological phenotypes and disease

Genetic variation (top left) may influence molecular phenotypes, including gene transcription, DNA–DNA interactions, transcription factor binding, histone modifications, DNA methylation, mRNA stability and translation, protein levels, and protein–protein interactions (top right). These cellular processes may affect or be affected by immunophenotypes, such as signalling response, cell-type abundances and cytokine production (bottom right). Immunophenotypes in turn can influence or be influenced by the manifestation of autoimmune diseases and affect different parts of the body (bottom left). DC, dendritic cell; MHC, major histocompatibility complex; TCR, T cell receptor; T_H cell, T helper cell; T_Reg, regulatory T cell.

Figure 2. Candidate variant fine-mapping based on functional annotations

Different types of functional annotations, such as missense variants (a,b) or regulatory marks (c), can lead to prioritization of candidate risk variants. a | Human leukocyte antigen (HLA) locus in chromosome 6, where genes pertaining to major histocompatibility complex (MHC) class I, II and III are found. b | By testing for associations between amino acid residues and rheumatoid arthritis (RA), investigators were able to fine-map independent risk variants that cause changes in amino acids found in the binding pocket of the MHC class II molecule DRβ1. Specifically, ~90% of the MHC risk in RA is attributable to a specific amino acid residue in position 13 at the bottom of the DRβ1 antigen-binding groove, and amino acids 71 and 74 (whose side chains point into the antigen-binding groove) independently modulate RA susceptibility c | Other functional annotations, such as histone modifications, can be used to prioritize non-coding candidate risk variants^,. In the hypothetical example shown, four non-coding variants in linkage disequilibrium in a disease susceptibility locus have equal posterior probability of being causative for the disease. However, if one uses information on cell-type-specific regulatory annotation (in this case histone H3 trimethylation on Lys4 (H3K4me3)), and knowledge of the most relevant cell type as the genetic mediator of the disease in question (in this case, B cells), one can assign a higher posterior probability to a variant overlapping a B cell H3K4me3 peak. Part a adapted from REF. 199, Nature Publishing Group. Part b adapted from REF. 48, Nature Publishing Group.

Figure 3. Cell state-dependent eQTLs

a | An immune cell type can be treated with different types of stimuli (such as different cytokines, antigens or non-antigen T cell receptor (TCR) stimulation). b | If this is done in many genotyped individuals from a certain population, genetic variants influencing gene expression levels can be found. In this hypothetical example, a single-nucleotide polymorphism (SNP) affects the expression of a gene in the second stimulation condition from part a (middle) and not the others (for similar studies, see REFS 117,,,–145). c | In heterozygous individuals, allele-specific expression for the affected gene can be observed (see REFS 119,179). A mechanism by which the state-dependent regulatory effect may be acting is by the presence of a transcription factor (red symbol) in the second condition whose regulatory element has a variant that prevents its binding. eQTLs, expression quantitative trait loci.

Figure 4. Immunophenotypes

By drawing blood from a single individual, many different immunophenotypes can be measured. These can be measured directly from blood plasma or from cells that can be cultured and subject to different states: resting or under cytokine, non-antigenic or antigenic stimulations (top panel). The petri dish represents stimulation of cells in culture to measure additional response phenotypes. Investigators use different techniques depending on the phenotypes to be measured (middle panel), including flow cytometry, carboxyfluorescein succinimidyl ester (CFSE), mass cytometry, Luminex or mass spectrometry (not shown) (BOX 2). Measurements using these techniques (bottom panel) include signalling response, cell proliferation, cell frequencies and serum protein levels (see REFS 128,– for applications). CXCL5, CXC chemokine ligand 5; IFNγ, interferon-γ; IL, interleukin; TNF, tumour necrosis factor.