Statistical Validation of Rare Complement Variants Provides Insights into the Molecular Basis of Atypical Hemolytic Uremic Syndrome and C3 Glomerulopathy.
Amy J OsborneMatteo BrenoNicolo Ghiringhelli BorsaFengxiao BuVéronique Frémeaux- BacchiDaniel P GaleLambertus P van den HeuvelDavid KavanaghMarina NorisSheila PintoPavithra M RallapalliGuiseppe RemuzziSantiago Rodríguez de CórdobaAngela RuizRichard J H SmithPaula Vieira-MartinsElena VolokhinaValerie WilsonTimothy H J GoodshipStephen J PerkinsPublished in: Journal of immunology (Baltimore, Md. : 1950) (2018)
Atypical hemolytic uremic syndrome (aHUS) and C3 glomerulopathy (C3G) are associated with dysregulation and overactivation of the complement alternative pathway. Typically, gene analysis for aHUS and C3G is undertaken in small patient numbers, yet it is unclear which genes most frequently predispose to aHUS or C3G. Accordingly, we performed a six-center analysis of 610 rare genetic variants in 13 mostly complement genes (CFH, CFI, CD46, C3, CFB, CFHR1, CFHR3, CFHR4, CFHR5, CFP, PLG, DGKE, and THBD) from >3500 patients with aHUS and C3G. We report 371 novel rare variants (RVs) for aHUS and 82 for C3G. Our new interactive Database of Complement Gene Variants was used to extract allele frequency data for these 13 genes using the Exome Aggregation Consortium server as the reference genome. For aHUS, significantly more protein-altering rare variation was found in five genes CFH, CFI, CD46, C3, and DGKE than in the Exome Aggregation Consortium (allele frequency < 0.01%), thus correlating these with aHUS. For C3G, an association was only found for RVs in C3 and the N-terminal C3b-binding or C-terminal nonsurface-associated regions of CFH In conclusion, the RV analyses showed nonrandom distributions over the affected proteins, and different distributions were observed between aHUS and C3G that clarify their phenotypes.
Keyphrases
- monte carlo
- copy number
- genome wide
- genome wide identification
- dna methylation
- genome wide analysis
- case report
- bioinformatics analysis
- mycobacterium tuberculosis
- transcription factor
- oxidative stress
- electronic health record
- machine learning
- binding protein
- protein protein
- small molecule
- amino acid
- deep learning
- adverse drug
- nk cells