Beyond Single SNPs: Revealing the True Genetic Impact of IL2RA in Multiple Sclerosis
Genome-wide association studies (GWAS) have become a cornerstone of complex disease genetics, enabling the identification of numerous susceptibility loci across the genome. However, GWAS are fundamentally designed to detect statistical associations rather than to quantify the true biological effect of genes. The article by Babron et al. critically examines this limitation using IL2RA as a case study in multiple sclerosis (MS), demonstrating that the strength of a single-SNP association signal can substantially underestimate the actual genetic contribution of a locus.
Biological Relevance of IL2RA in Autoimmune Disease
The IL2RA gene, encoding the α-chain of the interleukin-2 receptor (CD25), plays a central role in immune tolerance and T-cell regulation. Its involvement in autoimmunity was initially supported by rare monogenic immune disorders and later reinforced by associations with several complex diseases, including type 1 diabetes and MS. Although previous GWAS consistently identified intronic variants such as rs2104286 as markers of MS susceptibility, the causal variation and its mode of action remained unresolved.
Rationale for Multi-SNP Genetic Modeling
A key conceptual contribution of this study is the recognition that IL2RA likely exerts its effect through multiple correlated variants rather than a single polymorphism. When an observed SNP is only in linkage disequilibrium with the causal variant(s), genotype relative risks derived from association studies may be distorted. The authors therefore hypothesized that a combination of SNPs could better capture the underlying genetic architecture and more accurately reflect disease risk.
Study Design and Analytical Strategy
To test this hypothesis, the authors analyzed a family-based dataset comprising over 500 trio families and nearly 250 affected sib-pairs of European ancestry, genotyped for 26 tag SNPs spanning IL2RA. Their analytical framework integrated association testing with linkage information by: (i) identifying SNP sets that best discriminate between cases and controls, (ii) estimating genotype relative risks for these SNP sets, and (iii) validating the resulting genetic models against identity-by-descent (IBD) allele sharing in affected siblings.
Identification of a Superior Two-SNP Model
After correction for multiple testing, the study identified a two-SNP combination—rs2256774 and rs3118470—as providing the strongest discrimination between cases and controls. This model yielded a relative risk of 3.54 between the least and most at-risk genotypes, markedly higher than the approximately 1.5-fold risk attributed to the previously reported single SNP rs2104286. Importantly, these two SNPs are in low linkage disequilibrium, suggesting complementary contributions to disease susceptibility.
Consistency with Linkage Evidence from Affected Sib-Pairs
A decisive strength of the study lies in its use of affected sib-pair data to validate genetic models. The allele-sharing patterns predicted by the rs2256774–rs3118470 model were fully consistent with observed IBD distributions, whereas the model based on rs2104286 alone was statistically rejected. This finding demonstrates that the most strongly associated SNP in GWAS does not necessarily represent the true genetic effect of a locus.
Implications for Missing Heritability and Pathway Analysis
The results have broad implications for complex disease genetics. They suggest that part of the so-called “missing heritability” may arise from inadequate modeling of known susceptibility genes rather than from undiscovered loci. Accurate gene modeling is essential not only for estimating genetic risk but also for detecting gene–gene interactions and reconstructing biological pathways. The study therefore argues for renewed emphasis on candidate-gene strategies that integrate association and linkage data, particularly when family-based resources are available.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Babron, MC., Perdry, H., Handel, A. et al. Determination of the real effect of genes identified in GWAS: the example of IL2RA in multiple sclerosis. Eur J Hum Genet 20, 321–325 (2012). https://doi.org/10.1038/ejhg.2011.197
