Dataset Information

Advantages and pitfalls of an extended gene panel for investigating complex neurometabolic phenotypes.

ABSTRACT: Neurometabolic disorders are markedly heterogeneous, both clinically and genetically, and are characterized by variable neurological dysfunction accompanied by suggestive neuroimaging or biochemical abnormalities. Despite early specialist input, delays in diagnosis and appropriate treatment initiation are common. Next-generation sequencing approaches still have limitations but are already enabling earlier and more efficient diagnoses in these patients. We designed a gene panel targeting 614 genes causing inborn errors of metabolism and tested its diagnostic efficacy in a paediatric cohort of 30 undiagnosed patients presenting with variable neurometabolic phenotypes. Genetic defects that could, at least partially, explain observed phenotypes were identified in 53% of cases. Where biochemical abnormalities pointing towards a particular gene defect were present, our panel identified diagnoses in 89% of patients. Phenotypes attributable to defects in more than one gene were seen in 13% of cases. The ability of in silico tools, including structure-guided prediction programmes to characterize novel missense variants were also interrogated. Our study expands the genetic, clinical and biochemical phenotypes of well-characterized (POMGNT1, TPP1) and recently identified disorders (PGAP2, ACSF3, SERAC1, AFG3L2, DPYS). Overall, our panel was accurate and efficient, demonstrating good potential for applying similar approaches to clinically and biochemically diverse neurometabolic disease cohorts.

SUBMITTER: Reid ES

PROVIDER: S-EPMC5091046 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Advantages and pitfalls of an extended gene panel for investigating complex neurometabolic phenotypes.

Reid Emma S ES Papandreou Apostolos A Drury Suzanne S Boustred Christopher C Yue Wyatt W WW Wedatilake Yehani Y Beesley Clare C Jacques Thomas S TS Anderson Glenn G Abulhoul Lara L Broomfield Alex A Cleary Maureen M Grunewald Stephanie S Varadkar Sophia M SM Lench Nick N Rahman Shamima S Gissen Paul P Clayton Peter T PT Mills Philippa B PB

Brain : a journal of neurology 20161101 11

Neurometabolic disorders are markedly heterogeneous, both clinically and genetically, and are characterized by variable neurological dysfunction accompanied by suggestive neuroimaging or biochemical abnormalities. Despite early specialist input, delays in diagnosis and appropriate treatment initiation are common. Next-generation sequencing approaches still have limitations but are already enabling earlier and more efficient diagnoses in these patients. We designed a gene panel targeting 614 gene ...[more]

PMID: 27604308

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Project description:BackgroundThe risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.ResultsWe developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306-315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs.ConclusionPIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.

Dataset Information

Advantages and pitfalls of an extended gene panel for investigating complex neurometabolic phenotypes.

Publications

Advantages and pitfalls of an extended gene panel for investigating complex neurometabolic phenotypes.

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