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Identification of a novel Parkinson's disease locus via stratified genome-wide association study.

ABSTRACT: Parkinson's disease (PD) is complex and heterogeneous. The numerous susceptibility loci that have been identified reaffirm the complexity of PD but do not fully explain it; e.g., it is not known if any given PD susceptibility gene is associated with all PD or a disease subtype. We also suspect that important disease genes may have escaped detection because of this heterogeneity. We used presence/absence of family history to subdivide the cases and performed genome-wide association studies (GWAS) in Sporadic-PD and Familial-PD separately. The aim was to uncover new genes and gain insight into the genetic architecture of PD.Employing GWAS on the NeuroGenetics Research Consortium (NGRC) dataset stratified by family history (1565 Sporadic-PD, 435 Familial-PD, 1986 controls), we identified a novel locus on chromosome 1p21 in Sporadic-PD (PNGRC?=?4 × 10(-8)) and replicated the finding (P(Replication) = 6 × 10(-3); P(Pooled) = 4 × 10(-10)) in 1528 Sporadic-PD and 796 controls from the National Institutes of Neurologic Disease and Stroke (NINDS) Repository. This is the fifth PD locus to be mapped to the short arm of chromosome 1. It is flanked by S1PR1 and OLFM3 genes, and is 200 kb from a multiple sclerosis susceptibility gene. The second aim of the study was to extend the stratified GWAS to the well-established PD genes. SNCA_ rs356220 was associated with both Sporadic-PD (OR = 1.37, P = 1 × 10(-9)) and Familial-PD (OR = 1.40, P = 2 × 10(-5)). HLA_rs3129882 was more strongly associated with Sporadic-PD (OR = 1.38, P = 5 × 10(-10)) than Familial-PD (OR = 1.12, P = 0.15). In the MAPT region, virtually every single nucleotide polymorphism (SNP) had a stronger effect-size and lower P-value in Familial-PD (peak P = 8 × 10(-7)) than in Sporadic-PD (peak P = 2 × 10(-5)).We discovered and replicated a new locus for Sporadic-PD which had escaped detection in un-stratified GWAS. This demonstrates that by stratifying on a key variable the power gained due to diminished heterogeneity can sometimes outweigh the power lost to reduced sample size. We also detected distinct patterns of disease associations for previously established PD susceptibility genes, which gives an insight to the genetic architecture of the disease and could aid in the selection of appropriate study population for future studies.

SUBMITTER: Hill-Burns EM

PROVIDER: S-EPMC3925254 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Identification of a novel Parkinson's disease locus via stratified genome-wide association study.

Hill-Burns Erin M EM Wissemann William T WT Hamza Taye H TH Factor Stewart A SA Zabetian Cyrus P CP Payami Haydeh H

BMC genomics 20140210

<h4>Background</h4>Parkinson's disease (PD) is complex and heterogeneous. The numerous susceptibility loci that have been identified reaffirm the complexity of PD but do not fully explain it; e.g., it is not known if any given PD susceptibility gene is associated with all PD or a disease subtype. We also suspect that important disease genes may have escaped detection because of this heterogeneity. We used presence/absence of family history to subdivide the cases and performed genome-wide associa ...[more]

PMID: 24511991

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Project description:BackgroundParkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome-wide association studies. The most recent large-scale PD genome-wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome-wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus.MethodsWe included all significant genome-wide signals from multiple recent PD genome-wide association studies including themost recent PD risk genome-wide association study, age-at-onset genome-wide association study, progression genome-wide association study, and Asian population PD risk genome-wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self-ranked criteria. Multiple databases were queried for each gene to collect additional causal data.ResultsWe created a PD genome-wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow-up functional studies to identify potential therapeutic targets.ConclusionsOur PD genome-wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large-scale PD genome-wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Project description:BackgroundGenome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease.MethodsWe did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation.FindingsBetween Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10-7).InterpretationThese data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data.FundingThe National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).

Project description:BACKGROUND: Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation. RESULTS: In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS. CONCLUSIONS: These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation. AVAILABILITY: The software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip.

Dataset Information

Identification of a novel Parkinson's disease locus via stratified genome-wide association study.

Publications

Identification of a novel Parkinson's disease locus via stratified genome-wide association study.

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