Dataset Information

Beyond Parkinson disease: amyotrophic lateral sclerosis and the axon guidance pathway.

ABSTRACT:

Background

We recently described a genomic pathway approach to study complex diseases. We demonstrated that models constructed using single nucleotide polymorphisms (SNPs) within axon guidance pathway genes were highly predictive of Parkinson disease (PD) susceptibility, survival free of PD, and age at onset of PD within two independent whole-genome association datasets. We also demonstrated that several axon guidance pathway genes represented by SNPs within our final models were differentially expressed in PD.

Methodology/principal findings

Here we employed our genomic pathway approach to analyze data from a whole-genome association dataset of amyotrophic lateral sclerosis (ALS); and demonstrated that models constructed using SNPs within axon guidance pathway genes were highly predictive of ALS susceptibility (odds ratio = 1739.73, p = 2.92x10(-60)), survival free of ALS (hazards ratio = 149.80, p = 1.25x10(-74)), and age at onset of ALS (R(2) = 0.86, p = 5.96x10(-66)). We also extended our analyses of a whole-genome association dataset of PD, which shared 320,202 genomic SNPs in common with the whole-genome association dataset of ALS. We compared for ALS and PD the genes represented by SNPs in the final models for susceptibility, survival free of disease, and age at onset of disease and noted that 52.2%, 37.8%, and 34.9% of the genes were shared respectively.

Conclusions/significance

Our findings for the axon guidance pathway and ALS have prior biological plausibility, overlap partially with PD, and may provide important insight into the causes of these and related neurodegenerative disorders.

SUBMITTER: Lesnick TG

PROVIDER: S-EPMC2175528 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Beyond Parkinson disease: amyotrophic lateral sclerosis and the axon guidance pathway.

Lesnick Timothy G TG Sorenson Eric J EJ Ahlskog J Eric JE Henley John R JR Shehadeh Lina L Papapetropoulos Spiridon S Maraganore Demetrius M DM

PloS one 20080116 1

<h4>Background</h4>We recently described a genomic pathway approach to study complex diseases. We demonstrated that models constructed using single nucleotide polymorphisms (SNPs) within axon guidance pathway genes were highly predictive of Parkinson disease (PD) susceptibility, survival free of PD, and age at onset of PD within two independent whole-genome association datasets. We also demonstrated that several axon guidance pathway genes represented by SNPs within our final models were differe ...[more]

PMID: 18197259

Similar Datasets

Project description:BACKGROUND:18F-FDG brain PET measures metabolic changes in neurodegenerative disorders and may discriminate between different diseases even at an early stage. The objective of this study was to classify patients with amyotrophic lateral sclerosis (ALS) and Parkinson plus syndromes (PP). To this end, different approaches were evaluated using generalized linear models and corresponding glucose metabolic brain patterns. Besides direct classification, healthy controls were also included to generate disease-specific metabolic brain patterns and to perform a classification using disease expression scores. METHODS:ALS patients (n?=?70) and PP patients (n?=?33: 20 PSP, 3 CBD, and 10 MSA) were available from an existing database of patients with neuromuscular and movement disorders while age-matched healthy controls (n?=?29) were selected from a prospective study. To generate both disease-discriminative (direct classification) and disease-specific (classification versus controls) metabolic brain patterns, data were spatially normalized and a principal component analysis (PCA) was performed prior to classification using either logistic regression (PCA-LR) or a support vector machine (PCA-SVM). Furthermore, a direct SVM approach was considered. To compare the three different approaches, Pearson correlations (r) between pattern expression scores and metabolic brain patterns were evaluated, while pairs of ALS- and PP-specific pattern expression scores were compared using the RV coefficient. RESULTS:Classification between ALS and PP resulted in a sensitivity and specificity ??0.82 for both direct classification and classification according to disease-specific pattern expression scores. PCA-LR, PCA-SVM, and SVM generated very similar metabolic brain patterns with voxelwise correlations ??0.66, while all patterns allowed straightforward identification of ALS- and PP-specific brain regions of hyper- and hypometabolism. Moreover, pattern expression scores were highly correlated among different classifiers with a mean r of 0.94 while a RV coefficient ??0.91 was found between pairs of ALS- and PP-specific pattern expression scores. CONCLUSION:We demonstrated that a classification between ALS and PP using expression scores of an ALS and PP metabolic brain pattern leads to a similar and high prediction accuracy as direct classification between ALS and PP. Classification performance and disease-specific metabolic patterns, which could support visual reading and improve insight in brain pathology, were very related for different classifiers.

Dataset Information

Beyond Parkinson disease: amyotrophic lateral sclerosis and the axon guidance pathway.

Background

Methodology/principal findings

Conclusions/significance

Publications

Beyond Parkinson disease: amyotrophic lateral sclerosis and the axon guidance pathway.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets