Format

Send to

Choose Destination
Genome Med. 2015 Jul 16;7(1):72. doi: 10.1186/s13073-015-0196-5. eCollection 2015.

Genomic prediction of celiac disease targeting HLA-positive individuals.

Author information

1
Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, 3010 Victoria Australia ; Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, The University of Melbourne, Parkville, 3010 Victoria Australia.
2
Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, The University of Melbourne, Parkville, 3010 Victoria Australia ; Faculty of Life Science, University of Strasbourg, Strasbourg, 67084 CEDEX France.
3
The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052 Victoria Australia ; Department of Medical Biology, The University of Melbourne, Parkville, 3010 Victoria Australia ; Department of Gastroenterology, The Royal Melbourne Hospital, Grattan St., Parkville, 3050 Victoria Australia ; Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria 3050 Australia.

Abstract

BACKGROUND:

Genomic prediction aims to leverage genome-wide genetic data towards better disease diagnostics and risk scores. We have previously published a genomic risk score (GRS) for celiac disease (CD), a common and highly heritable autoimmune disease, which differentiates between CD cases and population-based controls at a clinically-relevant predictive level, improving upon other gene-based approaches. HLA risk haplotypes, particularly HLA-DQ2.5, are necessary but not sufficient for CD, with at least one HLA risk haplotype present in up to half of most Caucasian populations. Here, we assess a genomic prediction strategy that specifically targets this common genetic susceptibility subtype, utilizing a supervised learning procedure for CD that leverages known HLA-DQ2.5 risk.

METHODS:

Using L1/L2-regularized support-vector machines trained on large European case-control datasets, we constructed novel CD GRSs specific to individuals with HLA-DQ2.5 risk haplotypes (GRS-DQ2.5) and compared them with the predictive power of the existing CD GRS (GRS14) as well as two haplotype-based approaches, externally validating the results in a North American case-control study.

RESULTS:

Consistent with previous observations, both the existing GRS14 and the GRS-DQ2.5 had better predictive performance than the HLA haplotype approaches. GRS-DQ2.5 models, based on directly genotyped or imputed markers, achieved similar levels of predictive performance (AUC = 0.718-0.73), which were substantially higher than those obtained from the DQ2.5 zygosity alone (AUC = 0.558), the HLA risk haplotype method (AUC = 0.634), or the generic GRS14 (AUC = 0.679). In a screening model of at-risk individuals, the GRS-DQ2.5 lowered the number of unnecessary follow-up tests for CD across most sensitivity levels. Relative to a baseline implicating all DQ2.5-positive individuals for follow-up, the GRS-DQ2.5 resulted in a net saving of 2.2 unnecessary follow-up tests for each justified test while still capturing 90 % of DQ2.5-positive CD cases.

CONCLUSIONS:

Genomic risk scores for CD that target genetically at-risk sub-groups improve predictive performance beyond traditional approaches and may represent a useful strategy for prioritizing individuals at increased risk of disease, thus potentially reducing unnecessary follow-up diagnostic tests.

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center