Predicting Severity of Disease-Causing Variants

Most diseases, including those of genetic origin, express a continuum of severity. Clinical interventions for numerous diseases are based on the severity of the phenotype. Predicting severity due to genetic variants could facilitate diagnosis and choice of therapy. Although computational predictions have been used as evidence for classifying the disease relevance of genetic variants, special tools for predicting disease severity in large scale are missing. Here, we manually curated a dataset containing variants leading to severe and less severe phenotypes and studied the abilities of variation impact predictors to distinguish between them. We found that these tools cannot separate the two groups of variants. Then, we developed a novel machine-learning-based method, PON-PS (http://structure.bmc.lu.se/PON-PS), for the classification of amino acid substitutions associated with benign, severe, and less severe phenotypes. We tested the method using an independent test dataset and variants in four additional proteins. For distinguishing severe and nonsevere variants, PON-PS showed an accuracy of 61% in the test dataset, which is higher than for existing tolerance prediction methods. PON-PS is the first generic tool developed for this task. The tool can be used together with other evidence for improving diagnosis and prognosis and for prioritization of preventive interventions, clinical monitoring, and molecular tests.

Analysis of candidate modifier loci for the severity of colonic familial adenomatous polyposis, with evidence for the importance of the N-acetyl transferases

BACKGROUND

We have recently shown that the severity of human colonic familial adenomatous polyposis (FAP) varies in a manner consistent with the action of modifier genes. These modifier genes may harbour common alleles which increase the risk of colorectal cancer (CRC) in the general population. Analyses have suggested several common polymorphisms as risk alleles for CRC.

METHODS

We determined the association between the severity of colonic FAP (151 patients) and polymorphisms in MTHFR, NAT1, NAT2, GSTM, GSTT, cyclin D1, E-cadherin, and APC. All of these loci have been suggested as influencing the risk of CRC. Colonic FAP severity was quantitated as the number of polyps per colectomy specimen, standardised for colon size. We analysed the relationship between disease severity and genotype at the polymorphic site, making allowance for the position of the germline APC mutation.

RESULTS

We identified significant associations between more severe disease and the absence of the NAT1*10 genotype in the whole group of patients. In a subset of patients with germline mutations in the so-called "mutation cluster region", there was an association between more severe disease and the presence of NAT2*fast alleles. In the whole patient set, a relatively strong association existed between more severe disease and possession of both the NAT1*non-10 and NAT2*fast genotypes. There was weak evidence for an association between the APCT1493C allele and more severe disease in the whole patient group. No consistent association with disease severity was found for the other polymorphisms.

CONCLUSION

The severity of colonic FAP may be modified by alleles at the NAT1 and/or NAT2 loci. The identity of any functional variation remains unknown as NAT1*10 appears to be non-functional and there is linkage disequilibrium between alleles at multiple sites within these loci which are adjacent on chromosome 8p22. While evidence from this study cannot be conclusive, our data suggest that NAT1 and NAT2 variants may explain an approximately twofold increase in polyp number in the FAP colon.