Rui-Ru Ji1*, Tatiana Serebriyskaya2,3, Natalia Kuzkina2,3
1Alexion Pharmaceuticals, Inc., 121 Seaport Boulevard, Boston, MA 02210, USA
2EPAM Systems, 22/2 Zastavskaya Street, MegaPark, 196084, Saint-Petersburg, Russia
3Moscow Institute of Physics and Technology, School of Biological and Medical Physics, 9 Institutskiy per., Dolgoprudny, 141701, Moscow, Russia
Genetic information provides important guidance for long-term management of patients with atypical hemolytic uremic syndrome (aHUS), an extremely rare disease that primarily affects a patient’s kidney. To better understand the phenotypic impact of variants identified in aHUS patients, we systematically mined the National Library of Medicine database for case studies of aHUS patients with identifiable genetic variants. Allelic variants from 10 genes (C3, CFB, CFH, CFI, CFHR1, CFHR3, CFHR5, DGKE, CD46/MCP, and THBD) associated with aHUS were collected from 1652 patients. We analyze the enrichment of genetic variants in this “literature cohort” compared with a reference population, the Genome Aggregation Database (gnomAD). We also used a number of tools to predict the pathogenicity of the variants, attempting to reconcile all the results using the protein structure and conservation data. In total, we identified 447 unique genetic variants: 301 of these were not present in the gnomAD database and thus have “moderate” evidence of pathogenicity; 33 variants have “strong” evidence of pathogenicity by enrichment analysis. This study showcases an in silico framework that patient data aggregation and a large scale sequencing database provided a novel opportunity to understand genotype-phenotype associations in aHUS. This framework can be efficiently applied to other rare diseases where data are sparse to help improve the diagnosis and management of these patients.
aHUS: atypical hemolytic uremic syndrome; gnomAD: Genome Aggregation Database; CD46/MCP: cluster of differentiation 46/membrane cofactor protein; CFH: complement factor H; CFI: complement factor I; CFB: complement factor B; C3: complement component 3; ACMG: American College of Medical Genetics; AF: allele frequency; CFHR1: complement factor H-related protein 1; CFHR3: complement factor H-related protein 3; CFHR5: complement factor H-related protein 5; DGKE: diacylglycerol kinase epsilon; THBD: thrombomodulin; MEDLINE: Medical Literature Analysis and Retrieval System Online; VEP: variant effect predictor; SIFT: sorts intolerant from tolerant substitutions; PROVEAN: protein variation effect analyzer; FATHMM: functional analysis through Hidden Markov Models
DOI: 10.29245/2572-9411/2018/1.1168 Read More