The immunogenetic architecture of autoimmune disease

Abstract

The development of most autoimmune diseases includes a strong heritable component. This genetic contribution to disease ranges from simple Mendelian inheritance of causative alleles to the complex interactions of multiple weak loci influencing risk. The genetic variants responsible for disease are being discovered through a range of strategies from linkage studies to genome-wide association studies. Despite the rapid advances in genetic analysis, substantial components of the heritable risk remain unexplained, either owing to the contribution of an as-yet unidentified, "hidden," component of risk, or through the underappreciated effects of known risk loci. Surprisingly, despite the variation in genetic control, a great deal of conservation appears in the biological processes influenced by risk alleles, with several key immunological pathways being modified in autoimmune diseases covering a broad spectrum of clinical manifestations. The primary translational potential of this knowledge is in the rational design of new therapeutics to exploit the role of these key pathways in influencing disease. With significant further advances in understanding the genetic risk factors and their biological mechanisms, the possibility of genetically tailored (or "personalized") therapy may be realized.

Figure 1.

Genetic risk distribution in cases and controls. (A) The effectiveness of a hypothetical “diagnostic chip” measuring all genetic variants (including currently unknown variants) can be assessed by considering the distribution of genetic risk at birth among the general population (blue) and affected individuals (red) under a multiplicative model with many risk variants involved (Pharoah et al. 2002; Clayton 2009; Sawcer et al. 2010). The probability distribution of risk is calculated for a population with a prevalence (=mean risk in the general population) of 1/1000 and a λ_s of 10. The variance in risk observed (=2* ln λ_s) is the same for the general population and for individuals who go on to develop the disease. The distribution in cases is, however, shifted toward a higher risk, with a magnitude equal to the variance. (B) The number of false-negative and false-positive diagnostic calls is dictated by the absolute number of healthy (blue) and affected (red) individuals at any given level of risk. Under this scenario a diagnostic chip with a false-negative rate of 50% (i.e., capable of detecting 50% of affected patients) would have only a 3% true-positive rate, as it would produce a positive result for all individuals in the highest 1.6% risk bracket, of which 97% would still come from the healthy population (false positives).