Given that the magnitude of the association between ASD and the two autoimmune diseases, AS and MS, was either greater than or on par with the strength of association between what are considered now to be genetically similar autoimmune diseases, coupled with the lack of any other association of the same significance between ASD and the remaining autoimmune disorders examined, our results clearly demonstrated that there are true genomic links between ASD and the two autoimmune diseases, links that likely can inform our understanding of the genetics and treatments of ASD. However, further study and verification is required to characterize and explain these particular genomic associations. An interesting, albeit anecdotal, similarity between ASD and AS is that they both have an appreciable male bias. AS has a male-to-female ratio of approximately 2.5:142
that is of the same magnitude as the male bias of ASD's 4:143
while other autoimmune diseases, most importantly including MS,42, 43
generally show higher susceptibility in females. Also of interest to the observed genomic similarities between ASD and AS is anti-TNF (tumor necrosis factor) alpha treatment therapy. Anti-TNF agents are among the most efficacious options for treatment of AS,46, 47
and, although not well studied to date, ASD cases have been shown to have increased expression levels of TNF-alpha and IL-6,48
and separate cases have received anti-TNF therapy.49
Interestingly, anti-TNF agents tend to cause demyelination as an adverse side effect,50
a symptom that is typical in MS.
To rule out the possibility that the ratio of males to females in our original data sets biased our findings, we constructed sex-balanced and gender-specific data sets and recomputed the polygenic scores. The direction and relative strength of correlation with AS and MS remained unchanged, indicating that our results are not unduly influenced by the different numbers of male and female cases in the data sets. To rule out the possibility that differences in population heterogeneity and ethnic background between the ASD and the autoimmunity data collections biased the findings, we tested for association using only ASD individuals of European ancestry to match the ancestry of the individuals included within the autoimmune data sets. With a reduced number of 1019 affected trios from the ASD data set, we conducted TDT analysis and PS testing and found results consistent with those found using the complete ASD data set, a positive association with AS and negative association with MS and no other significant associations between ASD and the remaining autoimmune disorders studied. These results suggest that differences in mixed ancestry between the two data sets did not influence the associations discovered. However, this lack of bias from mixed ancestry does not preclude the possibility that geographical differences (our ASD collection was from the United States, while autoimmune collections were from the United Kingdom), and consequent differences in environmental exposure, could influence risk for disease differently as has been shown in the MS cases.51
However, differences in exposure related to geography are more likely to cause underestimates of association rather than overestimates and are thus unlikely to alter the results shown here. An additional potential bias could arise from the age of onset of autoimmune conditions. For example, MS tends to manifest in women during childbearing ages. Our ASD sample contains mothers who have, by definition, passed childbearing age, but because the mothers served as controls in the association testing, the observed negative association between ASD and MS suggests that mothers in the ASD set had a higher loading of MS-related alleles, not a lower loading. Under this assumption, it is unlikely that differences in ages of the sampled populations biased the significance or directionality of the association identified. Nevertheless, further studies with more equivalent samples across parameters, including age, ancestry and geography would be valuable to verify our findings.
In conclusion, we found significant, allele-specific genomic associations between ASD and two autoimmune diseases, AS and MS, which were supported using two complementary analytical strategies. The first, a PS approach, revealed that a collection of relatively weakly associated ASD susceptibility markers could still explain a significant percentage of the variation in both AS and MS cases. Coefficients from logistic regression analysis with the polygenic scores showed that the collective, allele-specific role of SNPs in ASD and AS was similar, whereas the roles of the alleles explaining the similarity between ASD and MS were of opposite effect, conferring risk in one and protection from onset in the other. The second, a genetic variation score approach, found the same results of allele-specific association between ASD and AS and ASD and MS with comparable or higher strength of association than that found between any autoimmune disease pair. Together these results suggest that common genetic mechanisms exist between ASD and AS and that opposing genetic mechanisms exist between ASD and MS. Both approaches pinpoint sets of SNPs that comprise the significant associations seen in our study and that may be of value as targets for further experiments aimed at understanding the genetic ties between ASD and autoimmune diseases.