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

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4 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1008-1014
Number of pages7
JournalClinical Genetics
Volume93
Issue number5
DOIs
StatePublished - May 1 2018

Fingerprint

Lysosomal Storage Diseases
Genetic Association Studies
Computational Biology
Iduronate Sulfatase
Mucopolysaccharidosis IV
GM1 Gangliosidosis
Guidelines
Phenotype
Mutation
Inborn Genetic Diseases
Amino Acid Substitution
Genes
Single Nucleotide Polymorphism
Early Diagnosis
Enzymes
Proteins

Keywords

  • GM1 gangliosidosis
  • Morquio syndrome
  • genotype-phenotype correlation
  • homology modeling
  • in silico
  • lysosomal storage disease

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

Cite this

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title = "SAAMP 2.0: An algorithm to predict genotype-phenotype correlation of lysosomal storage diseases",
abstract = "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.",
keywords = "GM1 gangliosidosis, Morquio syndrome, genotype-phenotype correlation, homology modeling, in silico, lysosomal storage disease",
author = "Li Ou and Przybilla, {M. J.} and Whitley, {Chester B}",
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T2 - An algorithm to predict genotype-phenotype correlation of lysosomal storage diseases

AU - Ou, Li

AU - Przybilla, M. J.

AU - Whitley, Chester B

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N2 - 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.

AB - 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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