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Practical application of genomic-based risk stratification to clinicaldiagnosis is appealing yet performance varies widely depending on the diseaseand genomic risk score (GRS) method. Celiac disease (CD), a commonimmune-mediated illness, is strongly genetically determined and requiresspecific HLA haplotypes. HLA testing can exclude diagnosis but has lowspecificity, providing little information suitable for clinical riskstratification. Using six European CD cohorts, we provide a proof-of-conceptthat statistical learning approaches which simultaneously model all SNPs cangenerate robust and highly accurate predictive models based on genome-wide SNPprofiles. The high predictive capacity replicated both in cross-validationwithin each cohort (AUC of 0.87-0.89) and in independent replication acrosscohorts (AUC of 0.86-0.9), despite differences in ethnicity. The modelsexplained 30-35% of disease variance and up to $\sim43\%$ of heritability. TheGRS's utility was assessed in different clinically relevant settings.Comparable to HLA typing, the GRS can be used to identify individuals withoutCD with $\geq99.6\%$ negative predictive value however, unlike HLA typing,patients can also be stratified into categories of higher-risk for CD who wouldbenefit from more invasive and costly definitive testing. The GRS is flexibleand its performance can be adapted to the clinical situation by adjusting thethreshold cut-off. Despite explaining a minority of disease heritability, ourfindings indicate a predictive GRS provides clinically relevant information toimprove upon current diagnostic pathways for CD, and support further studiesevaluating the clinical utility of this approach in CD and other complexdiseases.

Accurate and robust genomic prediction of celiac disease using statistical learning