Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria.
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 patie...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Nature Publishing Group
2014
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| _version_ | 1797092878430765056 |
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| author | Wettstein, S Underhaug, J Perez, B Marsden, B Yue, W Martinez, A Blau, N |
| author_facet | Wettstein, S Underhaug, J Perez, B Marsden, B Yue, W Martinez, A Blau, N |
| author_sort | Wettstein, S |
| collection | OXFORD |
| description | 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 (rs=0.479), between protein stability and allelic phenotype (rs=-0.458), as well as between enzyme activity and allelic phenotype (rs=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.European Journal of Human Genetics advance online publication, 18 June 2014; doi:10.1038/ejhg.2014.114. |
| first_indexed | 2024-03-07T03:52:18Z |
| format | Journal article |
| id | oxford-uuid:c1af12a2-e401-427f-b8fa-45281c869c0b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:52:18Z |
| publishDate | 2014 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | oxford-uuid:c1af12a2-e401-427f-b8fa-45281c869c0b2022-03-27T06:03:25ZLinking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c1af12a2-e401-427f-b8fa-45281c869c0bEnglishSymplectic Elements at OxfordNature Publishing Group2014Wettstein, SUnderhaug, JPerez, BMarsden, BYue, WMartinez, ABlau, NThe 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 (rs=0.479), between protein stability and allelic phenotype (rs=-0.458), as well as between enzyme activity and allelic phenotype (rs=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.European Journal of Human Genetics advance online publication, 18 June 2014; doi:10.1038/ejhg.2014.114. |
| spellingShingle | Wettstein, S Underhaug, J Perez, B Marsden, B Yue, W Martinez, A Blau, N Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria. |
| title | Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria. |
| title_full | Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria. |
| title_fullStr | Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria. |
| title_full_unstemmed | Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria. |
| title_short | Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria. |
| title_sort | linking genotypes database with locus specific database and genotype phenotype correlation in phenylketonuria |
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