Whole genome survey of coding SNPs reveals a reproducible pathway determinant of Parkinson disease

Department of Statistics, Stanford University, Stanford, California, USA.
Human Mutation (Impact Factor: 5.14). 02/2009; 30(2):228-38. DOI: 10.1002/humu.20840
Source: PubMed


It is quickly becoming apparent that situating human variation in a pathway context is crucial to understanding its phenotypic significance. Toward this end, we have developed a general method for finding pathways associated with traits that control for pathway size. We have applied this method to a new whole genome survey of coding SNP variation in 187 patients afflicted with Parkinson disease (PD) and 187 controls. We show that our dataset provides an independent replication of the axon guidance association recently reported by Lesnick et al. [PLoS Genet 2007;3:e98], and also indicates that variation in the ubiquitin-mediated proteolysis and T-cell receptor signaling pathways may predict PD susceptibility. Given this result, it is reasonable to hypothesize that pathway associations are more replicable than individual SNP associations in whole genome association studies. However, this hypothesis is complicated by a detailed comparison of our dataset to the second recent PD association study by Fung et al. [Lancet Neurol 2006;5:911-916]. Surprisingly, we find that the axon guidance pathway does not rank at the very top of the Fung dataset after controlling for pathway size. More generally, in comparing the studies, we find that SNP frequencies replicate well despite technologically different assays, but that both SNP and pathway associations are globally uncorrelated across studies. We thus have a situation in which an association between axon guidance pathway variation and PD has been found in 2 out of 3 studies. We conclude by relating this seeming inconsistency to the molecular heterogeneity of PD, and suggest future analyses that may resolve such discrepancies.

Download full-text


Available from: Caroline G L Lee,
32 Reads
  • Source
    • "In the rat SN, using a complementary approach, we found that expression of wild-type but not inactive Nedd4 decreased degeneration of dopaminergic neurons that is induced by chronic overexpression of A53T mutant human α-synuclein. A role for Nedd4 in the human condition is supported by the identification of a coding SNP as a risk factor for idiopathic PD (Srinivasan et al., 2009), the finding that Nedd4 mRNA expression is increased in brain regions with Lewy body pathology (Dumitriu et al., 2012) and our neuropathological observations that Nedd4 is up-regulated in a subpopulation of pigmented neurons containing Lewy bodies (Tofaris et al., 2011). Since Nedd4 is a ubiquitin ligase that functions in the endosomal–lysosomal pathway, our data are also consistent with other studies which have implicated this pathway in PD pathogenesis (Dodson et al., 2012) and genetic screens showing that genetic Fig. 2. Increased neuronal Nedd4 reduces α-synuclein levels in the Drosophila brain. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Parkinson's disease is a neurodegenerative disorder, characterised by accumulation and misfolding of α-synuclein. Although the level of α-synuclein in neurons is fundamentally linked to the onset of neurodegeneration, multiple pathways have been implicated in its degradation, and it remains unclear which are the critical ubiquitination enzymes that protect against α-synuclein accumulation in vivo. The ubiquitin ligase Nedd4 targets α-synuclein to the endosomal-lysosomal pathway in cultured cells. Here we asked whether Nedd4-mediated degradation protects against α-synuclein-induced toxicity in the Drosophila and rodent models of Parkinson's disease. We show that overexpression of Nedd4 can rescue the degenerative phenotype from ectopic expression of α-synuclein in the Drosophila eye. Overexpressed Nedd4 in the Drosophila brain, prevented the α-synuclein-induced locomotor defect whereas reduction in endogenous Nedd4 by RNAi led to worsening motor function and increased loss of dopaminergic neurons. Accordingly, AAV-mediated expression of wild-type but not the catalytically inactive Nedd4 decreased the α-synuclein-induced dopaminergic cell loss in the rat substantia nigra and reduced α-synuclein accumulation. Collectively, our data in two evolutionarily distant model organisms strongly suggest that Nedd4 is a modifier of α-synuclein pathobiology and thus a potential target for small molecule activators.
    Neurobiology of Disease 12/2013; 64(100). DOI:10.1016/j.nbd.2013.12.011 · 5.08 Impact Factor
  • Source
    • "Building on approaches originally developed in the context of microarray gene expression experiments, the common theme in the pathway analysis approaches is that they examine whether a group of related loci in the same biological pathway are jointly associated with a trait of interest. In line with the observations in microarray gene expression studies, it has been shown that in those cases where there is only a modest overlap in the variant or gene-level findings between different studies, due to factors such as differences in the genetic structure, the pathway-level associations may be much more reproducible even between different study populations [57-60]. These findings support the concept that individuals with the same disease phenotype may have marked inter-individual genetic heterogeneity in the sense that their disease predisposing variants may lie in distinct loci within the same or related pathways [14]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future.
    BioData Mining 03/2013; 6(1):5. DOI:10.1186/1756-0381-6-5 · 2.02 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Early genome-wide association (GWA) studies on Parkinson's disease (PD) have not been able to yield conclusive, replicable signals of association, perhaps due to limited sample size. We aimed to investigate whether association signals derived from the meta-analysis of the first two GWA investigations might be replicable in different populations. We examined six single-nucleotide polymorphisms (SNPs) (rs1000291, rs1865997, rs2241743, rs2282048, rs2313982, and rs3018626) that had reached nominal significance with at least two of three different strategies proposed in a previous analysis of the original GWA studies. Investigators from the "Genetic Epidemiology of Parkinson's Disease" (GEOPD) consortium were invited to join in this study. Ten teams contributed replication data from 3,458 PD cases and 3,719 controls. The data from the two previously published GWAs (599 PD cases, 592 controls and 443 sibling pairs) were considered as well. All data were synthesized using both fixed and random effects models. The summary allelic odds ratios were ranging from 0.97 to 1.09 by random effects, when all data were included. The summary estimates of the replication data sets (excluding the original GWA data) were very close to 1.00 (range 0.98-1.09) and none of the effects were nominally statistically significant. The replication data sets had significantly different results than the GWA data. Our data do not support evidence that any of these six SNPs reflect susceptibility markers for PD. Much stronger signals of statistical significance in GWA platforms are needed to have substantial chances of replication. Specifically in PD genetics, this would require much larger GWA studies and perhaps novel analytical techniques.
    American Journal of Medical Genetics Part B Neuropsychiatric Genetics 01/2009; 153B(1):220-8. DOI:10.1002/ajmg.b.30980 · 3.42 Impact Factor
Show more