Excess of singleton loss-of-function variants in Parkinson’s disease contributes to genetic risk (original) (raw)
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Journal of Biomedical Science, 2014
Background: Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson's disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects. Results: We propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson's disease case-control dataset as a model to demonstrate the application of our method. Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinson's disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson's disease. Conclusions: Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson's disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.
Genome-Wide Polygenic Risk Score Identifies Individuals at Elevated Parkinson’s Disease Risk
2020
SUMMARYParkinson’s Disease (PD) is the second most common and fastest-growing neurological disorder. Polygenic Risk Scores (PRS) using hundreds to thousands of PD-associated variants support polygenic heritability. Here, for the first time, we apply a genome-wide polygenic risk score approach using 6.2 million variants to compute a PD genome-wide polygenic risk score (PD-GPRS) via the LDPred algorithm. PD-GPRS validation and testing used Accelerating Medicines Partnership – Parkinson’s Disease (AMP-PD) and FinnGen Consortia genomic data from 1,654 PD Cases and 79,123 Controls. PD odds for the top 8%, 2.5%, and 1% of PD-GPRS were three-, four-, and seven times greater compared with lower percentiles, respectively (p<1e-10). PD age of onset and MDS-UPDRS motor scores also differed by PD-GPRS decile. Enrichment for phagosome related, dopamine signaling, immune related, and neuronal signaling pathways was found for genes nearest high PD-GPRS variants identified by MAF analysis. PD-GP...
Genome‐Wide Analysis of Structural Variants in Parkinson Disease
Annals of Neurology
ObjectiveIdentification of genetic risk factors for Parkinson disease (PD) has to date been primarily limited to the study of single nucleotide variants, which only represent a small fraction of the genetic variation in the human genome. Consequently, causal variants for most PD risk are not known. Here we focused on structural variants (SVs), which represent a major source of genetic variation in the human genome. We aimed to discover SVs associated with PD risk by performing the first large‐scale characterization of SVs in PD.MethodsWe leveraged a recently developed computational pipeline to detect and genotype SVs from 7,772 Illumina short‐read whole genome sequencing samples. Using this set of SV variants, we performed a genome‐wide association study using 2,585 cases and 2,779 controls and identified SVs associated with PD risk. Furthermore, to validate the presence of these variants, we generated a subset of matched whole‐genome long‐read sequencing data.ResultsWe genotyped an...
Frontiers in aging neuroscience, 2018
Background: Parkinson's disease (PD) is a complex disease with its monogenic forms accounting for less than 10% of all cases. Whole-exome sequencing (WES) technology has been used successfully to find mutations in large families. However, because of the late onset of the disease, only small families and unrelated patients are usually available. WES conducted in such cases yields in a large number of candidate variants. There are currently a number of imperfect software tools that allow the pathogenicity of variants to be evaluated. Objectives: We analyzed 48 unrelated patients with an alleged autosomal dominant familial form of PD using WES and developed a strategy for selecting potential pathogenetically significant variants using almost all available bioinformatics resources for the analysis of exonic areas. Methods: DNA sequencing of 48 patients with excluded frequent mutations was performed using an Illumina HiSeq 2500 platform. The possible pathogenetic significance of identified variants and their involvement in the pathogenesis of PD was assessed using SNP and Variation Suite (SVS), Combined Annotation Dependent Depletion (CADD) and Rare Exome Variant Ensemble Learner (REVEL) software. Functional evaluation was performed using the Pathway Studio database. Results: A significant reduction in the search range from 7082 to 25 variants in 23 genes associated with PD or neuronal function was achieved. Eight (FXN, MFN2, MYOC, NPC1, PSEN1, RET, SCN3A and SPG7) were the most significant. Conclusions: The multistep approach developed made it possible to conduct an effective search for potential pathogenetically significant variants, presumably involved in the pathogenesis of PD. The data obtained need to be further verified experimentally.
The Lancet, 2011
Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies International Parkinson Disease Genomics Consortium Summary Background-Genome-wide association studies (GWAS) for Parkinson's disease have linked two loci (MAPT and SNCA) to risk of Parkinson's disease. We aimed to identify novel risk loci for Parkinson's disease. Methods-We did a meta-analysis of datasets from five Parkinson's disease GWAS from the USA and Europe to identify loci associated with Parkinson's disease (discovery phase). We then did replication analyses of significantly associated loci in an independent sample series. Estimates of population-attributable risk were calculated from estimates from the discovery and replication phases combined, and risk-profile estimates for loci identified in the discovery phase were calculated. Findings-The discovery phase consisted of 5333 case and 12-019 control samples, with genotyped and imputed data at 7-689-524 SNPs. The replication phase consisted of 7053 case and 9007 control samples. We identified 11 loci that surpassed the threshold for genome-wide significance (p<5×10 −8). Six were previously identified loci (MAPT, SNCA, HLA-DRB5, BST1, GAK and LRRK2) and five were newly identified loci (ACMSD, STK39, MCCC1/LAMP3, SYT11, and CCDC62/HIP1R). The combined population-attributable risk was 60•3% (95% CI 43•7-69•3). In the risk-profile analysis, the odds ratio in the highest quintile of disease risk was 2•51 (95% CI 2•23-2•83) compared with 1•00 in the lowest quintile of disease risk. Interpretation-These data provide an insight into the genetics of Parkinson's disease and the molecular cause of the disease and could provide future targets for therapies. Funding-Wellcome Trust, National Institute on Aging, and US Department of Defense.
Interpreting Genetic Variants: Hints from a Family Cluster of Parkinson’s Disease
Journal of Parkinson's Disease, 2018
Technological innovation related to the advent and development of the Next-Generation Sequencing (NGS) has provided significant advances in the diagnosis of disorders with genetic and phenotypic variability, such as neurodegenerative diseases. However, the interpretation of NGS data often remains challenging, although advanced prediction tools have contributed to primarily assess the impact of some missense variants. Here, we report a patient with Parkinson's disease (PD) and a family history of disease, in which a panel of 29 disease-causing or risk genes for PD were analyzed. We identified a new missense variant in the SNCA gene. Although this variant might be associated with PD in this family, it has been currently classified as a "Variant of Unknown Significance" because of the lack of segregation with disease. Indeed, we subsequently found the same mutation in an unaffected sister. Nevertheless, this finding may help clinicians and researchers in questioning the causative role of genetic variants within the daily clinical and diagnostic settings.
Frontiers in Neurology
Genetic risk factors for Parkinson's disease (PD) risk and progression have been identified from genome-wide association studies (GWAS), as well as studies of familial forms of PD, implicating common variants at more than 90 loci and pathogenic or likely pathogenic variants at 16 loci. With the goal of understanding whether genetic variants at these PD-risk loci/genes differentially contribute to individual clinical phenotypic characteristics of PD, we used structured clinical documentation tools within the electronic medical record in an effort to provide a standardized and detailed clinical phenotypic characterization at the point of care in a cohort of 856 PD patients. We analyzed common SNPs identified in previous GWAS studies, as well as low-frequency and rare variants at parkinsonism-associated genes in the MDSgene database for their association with individual clinical characteristics and test scores at baseline assessment in our community-based PD patient cohort: age at ...
Genome-wide analysis of Structural Variants in Parkinson’s Disease using Short-Read Sequencing data
Parkinson’s disease is a complex neurodegenerative disorder, affecting approximately one million individuals in the USA alone. A significant proportion of risk for Parkinson’s disease is driven by genetics. Despite this, the majority of the common genetic variation that contributes to disease risk is unknown, in-part because previous genetic studies have focussed solely on the contribution of single nucleotide variants. Structural variants represent a significant source of genetic variation in the human genome. However, because assay of this variability is challenging, structural variants have not been cataloged on a genome-wide scale, and their contribution to the risk of Parkinson’s disease remains unknown. In this study, we 1) leveraged the GATK-SV pipeline to detect and genotype structural variants in 7,772 short-read sequencing data and 2) generated a subset of matched whole-genome Oxford Nanopore Technologies long-read sequencing data from the PPMI cohort to allow for comprehe...
Genomics, 2020
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