Multi-trait and cross-population genome-wide association studies across autoimmune and allergic diseases identify shared and distinct genetic component

Abstract

ObjectivesAutoimmune and allergic diseases are outcomes of the dysregulation of the immune system. Our study aimed to elucidate differences or shared components in genetic backgrounds between autoimmune and allergic diseases.MethodsWe estimated genetic correlation and performed multi-trait and cross-population genome-wide association study (GWAS) meta-analysis of six immune-related diseases: rheumatoid arthritis, Graves’ disease, type 1 diabetes for autoimmune diseases and asthma, atopic dermatitis and pollinosis for allergic diseases. By integrating large-scale biobank resources (Biobank Japan and UK biobank), our study included 105 721 cases and 433 663 controls. Newly identified variants were evaluated in 21 778 cases and 712 767 controls for two additional autoimmune diseases: psoriasis and systemic lupus erythematosus. We performed enrichment analyses of cell types and biological pathways to highlight shared and distinct perspectives.ResultsAutoimmune and allergic diseases were not only mutually classified based on genetic backgrounds but also they had multiple positive genetic correlations beyond the classifications. Multi-trait GWAS meta-analysis newly identified six allergic disease-associated loci. We identified four loci shared between the six autoimmune and allergic diseases (rs10803431 at PRDM2, OR=1.07, p=2.3×10−8, rs2053062 at G3BP1, OR=0.90, p=2.9×10−8, rs2210366 at HBS1L, OR=1.07, p=2.5×10−8 in Japanese and rs4529910 at POU2AF1, OR=0.96, p=1.9×10−10 across ancestries). Associations of rs10803431 and rs4529910 were confirmed at the two additional autoimmune diseases. Enrichment analysis demonstrated link to T cells, natural killer cells and various cytokine signals, including innate immune pathways.ConclusionOur multi-trait and cross-population study should elucidate complex pathogenesis shared components across autoimmune and allergic diseases.

PheWAS and cross-disorder analyses reveal genetic architecture, pleiotropic loci and phenotypic correlations across 11 autoimmune disorders

“…We used the four-factor model identified in EFA and included the disorders with loadings greater than |0.3| in each factor. The CFA analysis in GenomicSEM showed a good fit of the model to the data (χ 2 (12) =16.1; AIC =64.1; CFI = 0.98; SRMR = 0.07). The first factor included VITE, VITL and MG. MG, RA and SLE were included in the second factor, while the third factor consisted of T1D, PSC and MG (with a negative loading).…”

“…The genetic correlation across multiple ADs has not been fully explored (11,12). So far, crossdisorder GWAS meta-analyses have only focused on a few ADs, usually three to seven at a time (12)(13)(14)(15), while others have only focused on pairwise meta-analyses (16,17).…”

“…The genetic correlation across multiple ADs has not been fully explored (11,12). So far, crossdisorder GWAS meta-analyses have only focused on a few ADs, usually three to seven at a time (12)(13)(14)(15), while others have only focused on pairwise meta-analyses (16,17). Given the wide comorbidities observed in epidemiological studies and the evidence for sharing of common genetic background across multiple ADs, a systematic large scale analysis is warranted.…”

“…However, many additional non-HLA genes are also found to be associated with different ADs, and many times they are implicated in more than one disorder (10). The genetic correlation across multiple ADs has not been fully explored (11,12). So far, cross-disorder GWAS meta-analyses have only focused on a few ADs, usually three to seven at a time (12–15), while others have only focused on pairwise meta-analyses (16,17).…”

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

“…The rheumatoid arthritis (RA) and type 2 diabetes (T2D) GWAS from UKBB have moderate and significant correlations with asthma, which are partially recapitulated in the BBJ results that showed a moderate but not significant correlation between the BBJ GWAS of RA and of asthma, and a small but significant correlation between the BBJ GWAS of T2D and the GBMI leave-BBJ-out meta-analysis of asthma. Several studies in the literature have reported a relationship between risk for RA and asthma [73][74][75][76][77][78] , as well as T2D and asthma [79][80][81] , but more genetic studies in different populations and biobanks are needed to investigate the potential shared genetic architecture of these diseases. Importantly, causal relationships between asthma and genetically correlated phenotypes are not yet well-understood, and other methods such as Mendelian randomization could be applied to identify potential causal associations 82 .…”