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Eur J Hum Genet. 2015 Mar;23(3):302-9. doi: 10.1038/ejhg.2014.114. Epub 2014 Jun 18.

Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria.

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Division of Metabolism, University Children's Hospital, Zürich, Switzerland.
Department of Biomedicine, University of Bergen, Bergen, Norway.
Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biologia Molecular UAM-CSIC, CIBERER, IDIPAZ Universidad Autonoma de Madrid, Madrid, Spain.
Structural Genomics Consortium, University of Oxford, Oxford, UK.
1] Division of Metabolism, University Children's Hospital, Zürich, Switzerland [2] Division of Inborn Metabolic Diseases, University Children's Hospital, Heidelberg, Germany.


The wide range of metabolic phenotypes in phenylketonuria is due to a large number of variants causing variable impairment in phenylalanine hydroxylase function. A total of 834 phenylalanine hydroxylase gene variants from the locus-specific database PAHvdb and genotypes of 4181 phenylketonuria patients from the BIOPKU database were characterized using FoldX, SIFT Blink, Polyphen-2 and SNPs3D algorithms. Obtained data was correlated with residual enzyme activity, patients' phenotype and tetrahydrobiopterin responsiveness. A descriptive analysis of both databases was compiled and an interactive viewer in PAHvdb database was implemented for structure visualization of missense variants. We found a quantitative relationship between phenylalanine hydroxylase protein stability and enzyme activity (r(s) = 0.479), between protein stability and allelic phenotype (r(s) = -0.458), as well as between enzyme activity and allelic phenotype (r(s) = 0.799). Enzyme stability algorithms (FoldX and SNPs3D), allelic phenotype and enzyme activity were most powerful to predict patients' phenotype and tetrahydrobiopterin response. Phenotype prediction was most accurate in deleterious genotypes (≈ 100%), followed by homozygous (92.9%), hemizygous (94.8%), and compound heterozygous genotypes (77.9%), while tetrahydrobiopterin response was correctly predicted in 71.0% of all cases. To our knowledge this is the largest study using algorithms for the prediction of patients' phenotype and tetrahydrobiopterin responsiveness in phenylketonuria patients, using data from the locus-specific and genotypes database.

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