Joint genetic susceptibility to type 1 diabetes and autoimmune thyroiditis: from epidemiology to mechanisms

Abstract

Type 1 diabetes (T1D) and autoimmune thyroid diseases (AITD) frequently occur together within families and in the same individual. The co-occurrence of T1D and AITD in the same patient is one of the variants of the autoimmune polyglandular syndrome type 3 [APS3 variant (APS3v)]. Epidemiological data point to a strong genetic influence on the shared susceptibility to T1D and AITD. Recently, significant progress has been made in our understanding of the genetic association between T1D and AITD. At least three genes have been confirmed as major joint susceptibility genes for T1D and AITD: human leukocyte antigen class II, cytotoxic T-lymphocyte antigen 4 (CTLA-4), and protein tyrosine phosphatase non-receptor type 22. Moreover, the first whole genome linkage study has been recently completed, and additional genes will soon be identified. Not unexpectedly, all the joint genes for T1D and AITD identified so far are involved in immune regulation, specifically in the presentation of antigenic peptides to T cells. One of the lessons learned from the analysis of the joint susceptibility genes for T1D and AITD is that subset analysis is a key to dissecting the etiology of complex diseases. One of the best demonstrations of the power of subset analysis is the CTLA-4 gene in T1D. Although CTLA-4 showed very weak association with T1D, when analyzed in the subset of patients with both T1D and AITD, the genetic effect of CTLA-4 was significantly stronger. Gene-gene and genetic-epigenetic interactions most likely play a role in the shared genetic susceptibility to T1D and AITD. Dissecting these mechanisms will lead to a better understanding of the etiology of T1D and AITD, as well as autoimmunity in general.

Figure 1

The principle of LD. A, Assuming two SNPs, A and B, each with two alleles (A1/A2 and B1/B2, respectively) with allele population frequencies of p1/p2 and q1/q2, respectively, the table shows the expected frequencies of each combination of alleles of the two SNPs. For example, the expected frequency of the A1B2 combination is p1xq2. Any deviation from these expected frequencies is due to LD. For example, if the observed frequency of the A1B2 combination is significantly higher than the expected frequency (p1q2), this means that these two alleles are in LD. B, Numerical example of the principle of LD. Alleles A1/A2 have population frequencies of 0.3/0.7, respectively, and alleles B1/B2 have population frequencies of 0.2/0.8. The expected frequencies of each combination of alleles is shown in gray (for example, the combination A1B1 is expected to have a frequency of 0.3 × 0.2 = 0.06. The actual observed frequencies are shown in black within parentheses. As can be seen, these deviate significantly from the expected frequencies. For example, the observed frequency for the A1B1 combination is 0.14, and that is significantly higher than the expected frequency of this combination (0.06). Therefore, the A1 and B1 alleles tend to occur together more frequently than expected by random chance, or are in LD.

Figure 2

LD blocks and tag-SNPs. The HapMap project demonstrated that most of the human genome is composed of LD blocks that contain SNPs in tight LD. Shown here are two LD blocks separated by hot spots of recombination (large filled circles). Each block in the figure contains 10 SNPs (small empty circles). One can then select one (or more) SNPs from each block (arrows) to represent all the SNPs in the block (tag-SNPs). The tag-SNPs can be used to test for association of each block with disease.

Figure 3

CTLA-4 function. APCs present peptide antigens to T cells within peptide pockets of HLA class II molecules. However, to activate T cells, costimulation is required. One costimulatory molecule is CD28, which is activated by B7–1 and B7–2 molecules on the surface of APCs. CTLA-4 suppresses T cell activation either by competing with CD28 for binding to B7–1 and B7–2 or by direct suppression of T cell receptor (TCR) signaling pathway.

Figure 4

A graph demonstrating the effect of subset size on the odds ratio of an association with a certain gene variant. The x-axis shows the percentage of patients among the entire dataset that belong to the subset that is associated with the gene variant. The y-axis shows the odds ratio. Simulations were made assuming a dataset of 200 patients and 200 controls. We also assumed that the frequency of the disease-associated variant is 60% in the subset of patients that is associated with this gene variant. We then simulated the frequency of the disease-associated variant in the controls and in the patients not belonging to the subset to be 20% (circles), 30% (triangles), 40% (squares), and 50% (diamonds). Asterisks indicate the conditions in which the association is still statistically significant (P < 0.05). These simulations show that as the percentage of patients among the entire dataset that belong to the subset decreases, the odds ratio of the association with the gene variant decreases exponentially. For example, assuming a frequency of the disease associate allele of 40% among controls and patients that do not belong to the subset (squares), the odds ratio drops from 2.3 to 1.2, and once the subset consists of no more than 40% of the entire dataset, the association becomes not significant.

Figure 5

Two potential mechanisms by which HLA class II molecules can predispose to both T1D and AITD. A, Two distinct HLA class II molecules (e.g., DQB1*0201 and DR3) with distinct pocket structures are frequently inherited together and expressed on APCs because they are in tight LD. B, Two distinct HLA class II molecules with pocket structures fitting different peptides (e.g., insulin and Tg) share an amino acid (marked A) that serves to anchor the HLA class II molecule to the T cell receptor.