In Silico Analysis of NF2 Gene Missense Mutations in Neurofibromatosis Type 2: From Genotype to Phenotype.
OBJECTIVE
Computer-based (in silico) protein modeling to examine genotype-phenotype relationships for a given mutation has been applied to many genes but never to NF2.


BACKGROUND
Missense mutations in the merlin protein occur in approximately 9% of patients with neurofibromatosis type 2 (NF2). Within this subset of patients, no genotype-phenotype correlations have been established. The aim of this study was to determine if genotype correlates with phenotype in the cohort of NF2 patients with missense mutations as a first step to defining a method to predict clinical phenotype from genotype for these patients.


METHODS
We analyzed 45 patients with NF2 as a result of missense mutations drawn from the United Kingdom NF2 registry. Our analysis included 17 different NF2 mutations from NF2 patients and six single-nucleotide polymorphisms (SNP)--presumed benign because they are observed in the dbSNP National Center for Biotechnology Information database and 1000 Genomes. We analyzed the mutations using three mutation tolerance prediction approaches: Align GVGD, SIFT, and PolyPhen-2. The mutation sites were also modeled on the three-dimensional crystal structure of merlin to investigate the spatial relationship of NF2-causing mutations.


RESULTS
Two mutation tolerance predictors (SIFT and PolyPhen-2) were able to distinguish NF2-causing mutations from non-NF2-causing SNPs (p < 0.05). Mapping mutations on the molecular structure of merlin suggest that mutations resulting in greater structural conflicts within the protein are more likely to correlate with severe phenotypes.


CONCLUSIONS
This work is a step toward a better understanding of genotype-phenotype relationships in NF2 caused by missense mutations using a computer-based methodology.
*Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College/New York Presbyterian Hospital, New York, New York, U.S.A.; †University Department of Genomic Medicine, University of Manchester, MAHSC, St Mary's Hospital, Manchester, England, U.K.; and ‡Department of Physiology and Biophysics and HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, U.S.A.
Thomas E
TE
Heineman
D Gareth R
DG
Evans
Fabien
F
Campagne
Samuel H
SH
Selesnick
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D003198:Computer Simulation; D005260:Female; D016515:Genes, Neurofibromatosis 2; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D020125:Mutation, Missense; D016518:Neurofibromatosis 2 / Q000235:genetics*; D006113:United Kingdom; D055815:Young Adult