SAAMP 2.0: An algorithm to predict genotype‐phenotype correlation of lysosomal storage diseases

SAAMP 2.0: An algorithm to predict genotype‐phenotype correlation of lysosomal storage diseases

Lysosomal storage diseases (LSDs) are a group of genetic disorders, resulting from deficiencies of lysosomal enzyme. Genotype‐phenotype correlation is essential for timely and proper treatment allocation. Recently, by integrating prediction outcomes of 7 bioinformatics tools, we developed a SAAMP al...

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Bibliographic Details
Published in:Clinical genetics Vol. 93; no. 5; pp. 1008 - 1014
Main Authors: Ou, L., Przybilla, M.J., Whitley, C.B.
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01-05-2018
Subjects:
Bioinformatics
Disease
Gangliosidosis
Genetic disorders
Genotype & phenotype
Genotypes
genotype‐phenotype correlation
GM1 gangliosidosis
Homology
homology modeling
in silico
lysosomal storage disease
Lysosomal storage diseases
Morquio syndrome
Mutation
Phenotypes
Single-nucleotide polymorphism
Morquio syndrome
homology modeling
in silico
lysosomal storage disease
GM1 gangliosidosis
genotype-phenotype correlation
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Summary:Lysosomal storage diseases (LSDs) are a group of genetic disorders, resulting from deficiencies of lysosomal enzyme. Genotype‐phenotype correlation is essential for timely and proper treatment allocation. Recently, by integrating prediction outcomes of 7 bioinformatics tools, we developed a SAAMP algorithm to predict the impact of individual amino‐acid substitution. To optimize this approach, we evaluated the performance of these bioinformatics tools in a broad array of genes. PolyPhen and PROVEAN had the best performances, while SNP&GOs, PANTHER and I‐Mutant had the worst performances. Therefore, SAAMP 2.0 was developed by excluding 3 tools with worst performance, yielding a sensitivity of 94% and a specificity of 90%. To generalize the guideline to proteins without known structures, we built the three‐dimensional model of iduronate‐2‐sulfatase by homology modeling. Further, we investigated the phenotype severity of known disease‐causing mutations of the GLB1 gene, which lead to 2 LSDs (GM1 gangliosidosis and Morquio disease type B). Based on the previous literature and structural analysis, we associated these mutations with disease subtypes and proposed a theory to explain the complicated genotype‐phenotype correlation. Collectively, an updated guideline for phenotype prediction with SAAMP 2.0 was proposed, which will provide essential information for early diagnosis and proper treatment allocation, and they may be generalized to many monogenic diseases.
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ISSN:0009-9163
1399-0004
DOI:10.1111/cge.13226