Genomic Prediction of Celiac Disease

Accurate and Robust Genomic Prediction of Celiac Disease Using Statistical Learning
Gad Abraham, Jason A. Tye-Din, Oneil G. Bhalala, Adam Kowalczyk, Justin Zobel, Michael Inouye, PLoS Genetics 2014. 10(2): e1004137. doi:10.1371/journal.pgen.1004137

Genomic prediction of celiac disease targeting HLA-positive individuals
Gad Abraham, Alexia Rohmer, Jason A. Tye-Din*, Michael Inouye*, Genome Medicine 2015. 7:72. doi:10.1186/s13073-015-0196-5

General review of genomic prediction:
Genomic risk prediction of complex human disease and its clinical application
Gad Abraham, Michael Inouye, Current Opinion in Genetics and Development 2015. 33:10-16. doi:10.1016/j.gde.2015.06.005

The clinical utility of genomic data has yet to be fully realized. The first paper from Gad Abraham and the Inouye group was one of the first studies to successfully employ genomic prediction for a complex human disease. As a proof-of-concept, the team constructed and externally validated a genomic risk score for celiac disease which attained sensitivity and specificity to a clinically relevant degree. The second paper showed that genomic prediction targeting the high-risk subgroup of HLA-DQ2.5+ individuals can also be an efficient clinical strategy to avoid unnecessary follow-up tests. Overall, our research shows that genomic prediction utilising existing microarray technologies has the potential to remodel diagnostic pathways for celiac disease and is likely extendable to other common human diseases.

Kernel density estimates of the risk scores predicted using models on UK2 and tested in the combined dataset Finn+NL+IT, for cases and controls.
Distribution of a genomic risk score for celiac disease on external British case-control genetic data.