Autoimmune disease classification by inverse association with SNP alleles

Abstract

With multiple genome-wide association studies (GWAS) performed across autoimmune diseases, there is a great opportunity to study the homogeneity of genetic architectures across autoimmune disease. Previous approaches have been limited in the scope of their analysis and have failed to properly incorporate the direction of allele-specific disease associations for SNPs. In this work, we refine the notion of a genetic variation profile for a given disease to capture strength of association with multiple SNPs in an allele-specific fashion. We apply this method to compare genetic variation profiles of six autoimmune diseases: multiple sclerosis (MS), ankylosing spondylitis (AS), autoimmune thyroid disease (ATD), rheumatoid arthritis (RA), Crohn's disease (CD), and type 1 diabetes (T1D), as well as five non-autoimmune diseases. We quantify pair-wise relationships between these diseases and find two broad clusters of autoimmune disease where SNPs that make an individual susceptible to one class of autoimmune disease also protect from diseases in the other autoimmune class. We find that RA and AS form one such class, and MS and ATD another. We identify specific SNPs and genes with opposite risk profiles for these two classes. We furthermore explore individual SNPs that play an important role in defining similarities and differences between disease pairs. We present a novel, systematic, cross-platform approach to identify allele-specific relationships between disease pairs based on genetic variation as well as the individual SNPs which drive the relationships. While recognizing similarities between diseases might lead to identifying novel treatment options, detecting differences between diseases previously thought to be similar may point to key novel disease-specific genes and pathways.

Figure 1. Disease heatmap based on genetic variation profiles.

This diagram shows correlations between disease genetic variation profiles. Positive relationships between a pair of diseases are shown in brown, negative relationships are shown in purple. The diseases highlighted in blue have an autoimmune component. Hierarchical clustering using these correlations as a distance metric is shown on the left. Approximately Unbiased (AU) probability values (%) for each cluster indicating how strongly the cluster is supported by data are shown in red. Clusters with AU larger than 95% are strongly supported by data.

Figure 2. Genetic Variation Scores for RA (WTCCC) and ATD.

Genetic Variation Scores (GVS) for SNPs that are significantly associated with both diseases (p<0.05) are shown in black. The non-significant GVS are shown in gray. The best fit linear model of the data is shown in red.

Figure 3. Genetic Variation Scores for RA (WTCCC) and MS (WTCCC).

Genetic Variation Scores (GVS) for SNPs that are significantly associated with both diseases (p<0.05) are shown in black. The non-significant GVS are shown in gray. The best fit linear model of the data is shown in red.

Figure 4. Genetic Variation Scores for AS and MS (WTCCC).

Genetic Variation Scores (GVS) for SNPs that are significantly associated with both diseases (p<0.05) are shown in black. The non-significant GVS are shown in gray. The best fit linear model of the data is shown in red.

Figure 5. Genetic Variation Scores for ATD and T1D.

Genetic Variation Scores (GVS) for SNPs that are significantly associated with both diseases (p<0.05) are shown in black. The non-significant GVS are shown in gray. The best fit linear model of the data is shown in red.