  Practical application of genomic-based risk stratification to clinical
diagnosis is appealing yet performance varies widely depending on the disease
and genomic risk score (GRS) method. Celiac disease (CD), a common
immune-mediated illness, is strongly genetically determined and requires
specific HLA haplotypes. HLA testing can exclude diagnosis but has low
specificity, providing little information suitable for clinical risk
stratification. Using six European CD cohorts, we provide a proof-of-concept
that statistical learning approaches which simultaneously model all SNPs can
generate robust and highly accurate predictive models based on genome-wide SNP
profiles. The high predictive capacity replicated both in cross-validation
within each cohort (AUC of 0.87-0.89) and in independent replication across
cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models
explained 30-35% of disease variance and up to $\sim43\%$ of heritability. The
GRS's utility was assessed in different clinically relevant settings.
Comparable to HLA typing, the GRS can be used to identify individuals without
CD with $\geq99.6\%$ negative predictive value however, unlike HLA typing,
patients can also be stratified into categories of higher-risk for CD who would
benefit from more invasive and costly definitive testing. The GRS is flexible
and its performance can be adapted to the clinical situation by adjusting the
threshold cut-off. Despite explaining a minority of disease heritability, our
findings indicate a predictive GRS provides clinically relevant information to
improve upon current diagnostic pathways for CD, and support further studies
evaluating the clinical utility of this approach in CD and other complex
diseases.
