Background Recent advances in the identification of loci associated with susceptibility to complex disease have led to methods being developed that incorporate this information into genetic screening models to identify individuals at high risk of disease.
Objectives Here we present the first risk prediction model including all 46 known genetic loci associated with rheumatoid arthritis (RA) in Caucasian populations.
Methods Weighted genetic risk scores (wGRS) were created using odds ratios from 45 RA susceptibility SNPs  and a HLA-DRB1 tag SNP or imputed HLA-DRB1 alleles. The wGRS were tested in 11,370 RA cases and 15,536 healthy controls of known genotype. The risk of developing RA was assessed using logistic regression by dividing the wGRS into 5 quintiles. The ability of the wGRS to discriminate between cases and controls was assessed by receiver operator curve (ROC) analysis in all patients and patients with anti-CCP positive disease.
Results HLA-DRB1 imputation accuracy was assessed by checking concordance with ~1800 HLA-DRB1 typed samples: 2 digit (97%), 4 digit (91%). Using a model containing all 45 loci and imputed 4 digit HLA-DRB1 genotypes the odds of developing RA for those in the highest risk quintile compared to all others was 10.01 (95% CI 9.16-10.94) and this was further increased in anti-CCP positive individuals (OR 26.23 95% CI 22.88-30.08). The area under the ROC (AUC) showed that the ability of the model to discriminate correctly between individuals at risk and those not at risk was 0.72, improving in anti CCP positive individuals (AUC 0.78). Net reclassification tests showed that including 4 digit HLA-DRB1 alleles produced an overall reclassification improvement of 49% over a HLA-DRB1 tag SNP between models.
The wGRS model had a Sensitivity of 44% and specificity of 90% in CCP positive individuals, which resulted in an accuracy of 76%.
Conclusions Our study has shown the increased accuracy when using 4-digit HLA-DRB1 alleles in risk prediction models. We have shown that in RA the prediction performance remains modest when including all known genetic variants and it is too soon for these tests to be implemented in clinic or to have great utility in population screening.
Eyre S, Bowes J, Diogo D, Lee A, Barton A, Martin P, Zhernakova A, Stahl E, Viatte S, McAllister K et al.: High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat Genet 2012, 44:1336-1340.
Acknowledgements This work was supported by the IMI JU funded project BeTheCure, contract no 115142-2.
Disclosure of Interest None Declared
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