GUT paper on coeliac disease selected for F1000Prime

A recent paper by the Wijmenga group has been selected for F1000Prime. It was recommended as being of special significance in its field by Faculty Member Piero Portincasa: "In this paper, the authors highlight the importance of a more comprehensive genetic test for the diagnosis of celiac disease (CD). As CD occurs with a large variability in age at onset and clinical presentation {1}, the improvement of genetic diagnosis is mandatory. Genetic testing of CD is represented by HLA-DQ2 and HLA-DQ8 screening, but HLA-DQ2 and HLA-DQ8 alleles have been found only in about 40% of the population {2}. In this work, risk prediction of CD was evaluated by using 10, 26 and 57 single nucleotide polymorphisms (SNPs) in 2675 cases and 2815 controls. The new model based on 57 non-HLA variants combined with HLA testing was superior to those based on HLA only, HLA plus 10 SNPs and HLA plus 26 SNPs. The new model by Romanos et al. increases the sensitivity of genetic risk testing and appears to be of great interest, as it might stratify individuals into more accurate risk categories."

Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants

by Romanos J et al. and published in Gut, 2014 Mar;63(3):415-22. doi: 10.1136/gutjnl-2012-304110. Epub 2013 May 23. Open access, Full text

Abstract

Background The majority of coeliac disease (CD) patients are not being properly diagnosed and therefore remain untreated, leading to a greater risk of developing CD-associated complications. The major genetic risk heterodimer, HLA-DQ2 and DQ8, is already used clinically to help exclude disease. However, approximately 40% of the population carry these alleles and the majority never develop CD.

Objective We explored whether CD risk prediction can be improved by adding non-HLA-susceptible variants to common HLA testing.

Design We developed an average weighted genetic risk score with 10, 26 and 57 single nucleotide polymorphisms (SNP) in 2675 cases and 2815 controls and assessed the improvement in risk prediction provided by the non-HLA SNP. Moreover, we assessed the transferability of the genetic risk model with 26 non-HLA variants to a nested case–control population (n=1709) and a prospective cohort (n=1245) and then tested how well this model predicted CD outcome for 985 independent individuals.

Results Adding 57 non-HLA variants to HLA testing showed a statistically significant improvement compared to scores from models based on HLA only, HLA plus 10 SNP and HLA plus 26 SNP. With 57 non-HLA variants, the area under the receiver operator characteristic curve reached 0.854 compared to 0.823 for HLA only, and 11.1% of individuals were reclassified to a more accurate risk group. We show that the risk model with HLA plus 26 SNP is useful in independent populations.

Conclusions Predicting risk with 57 additional non-HLA variants improved the identification of potential CD patients. This demonstrates a possible role for combined HLA and non-HLA genetic testing in diagnostic work for CD.

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