title: Location of mutations within the PKD2 gene influences clinical outcome.
abstract: BACKGROUND
  Since the cloning of the gene for autosomal dominant polycystic kidney disease type 2 (PKD2), approximately 40 different mutations of that gene have been reported to be associated with the disease. The relationship between the PKD2 genotype and phenotype, however, remains unclear.
  
  
  METHODS
  Detailed clinical information was collected for PKD2 families in which the underlying mutation had been identified. Logistic regression analysis was employed to assess the influence of age and sex on hypertension, hematuria, renal calculi, and urinary tract infections, and a clinical phenotype score was computed. Patients were then grouped according to the relative location of their mutation within the cDNA sequence, and differences in the mean phenotypic score between groups were tested for statistical significance by means of a multiple pairwise t-test.
  
  
  RESULTS
  While phenotypic scores for each mutational group revealed a considerable degree of intragroup variability, the variability in phenotypic scores was significantly higher between mutational groups than within groups. A group-wise comparison of the mean phenotypic scores confirmed the observation of significant nonlinear variation in disease severity, with high- and low-scoring mutational groups interspersed along the gene sequence.
  
  
  CONCLUSIONS
  The identification of groups of mutations in the PKD2 gene, which differ significantly with respect to clinical outcome, is to our knowledge the first description of a genotype/phenotype correlation in autosomal dominant polycystic kidney disease. It also provides evidence against complete loss of function of the mutant PKD2 gene product.
authors.affiliation: Institute of Medical Genetics, University Hospital of Wales, Cardiff, UK. Hateboer@Cardiff.AC.UK
authors.forename: N
authors.initials: N
authors.lastname: Hateboer
authors.forename: B
authors.initials: B
authors.lastname: Veldhuisen
authors.forename: D
authors.initials: D
authors.lastname: Peters
authors.forename: M H
authors.initials: MH
authors.lastname: Breuning
authors.forename: J L
authors.initials: JL
authors.lastname: San-Millán
authors.forename: N
authors.initials: N
authors.lastname: Bogdanova
authors.forename: E
authors.initials: E
authors.lastname: Coto
authors.forename: M A
authors.initials: MA
authors.lastname: van Dijk
authors.forename: A R
authors.initials: AR
authors.lastname: Afzal
authors.forename: S
authors.initials: S
authors.lastname: Jeffery
authors.forename: A K
authors.initials: AK
authors.lastname: Saggar-Malik
authors.forename: R
authors.initials: R
authors.lastname: Torra
authors.forename: D
authors.initials: D
authors.lastname: Dimitrakov
authors.forename: I
authors.initials: I
authors.lastname: Martinez
authors.forename: S S
authors.initials: SS
authors.lastname: de Castro
authors.forename: M
authors.initials: M
authors.lastname: Krawczak
authors.forename: D
authors.initials: D
authors.lastname: Ravine
mesh_terms: D000704:Analysis of Variance; D002874:Chromosome Mapping; D004252:DNA Mutational Analysis; D005260:Female; D005838:Genotype; D006801:Humans; D008297:Male; D008565:Membrane Proteins / Q000235:genetics*; D009154:Mutation / Q000235:genetics*; D017711:Nonlinear Dynamics; D010641:Phenotype; D016891:Polycystic Kidney, Autosomal Dominant / Q000235:genetics* / Q000503:physiopathology*; D012044:Regression Analysis; D012720:Severity of Illness Index; D050396:TRPP Cation Channels
chemical_list: D008565:Membrane Proteins; D050396:TRPP Cation Channels