The mother of all genome-wide association meta-analyses for Parkinson’s disease made its grand appearance last night—both in the journal Nature Genetics and on the revamped PDGene website. Led by Andrew Singleton at the National Institute on Aging (NIA) in Bethesda, Maryland, the international effort corralled data from 15 genome-wide association studies (GWAS) to identify 28 genetic variants associated with PD risk, six of them brand-new. Formal publication follows presentation of the initial results at a Keystone meeting earlier this year (see Mar 2014 conference coverage) and coincides with the debut of PDGene’s new online interface. There, researchers will have the sizable dataset at their fingertips and can run customized meta-analyses of selected PDGene data and even their own results.
The Patterns of PD Chaos. The largest meta-analysis of PD GWAS conducted to date found 28 SNPs associated with the disease. Researchers can explore the data on the revamped PDGene website (Manhattan plot shown). [Image courtesy of Lars Bertram.]
“The study confirms that there are many genetic loci that impart a small genetic risk for PD,” Mark Cookson of NIA wrote in an email to Alzforum. “How many more there are is an open question.” As part of the International Parkinson’s Disease Genomics Consortium (IPDGC) that contributed a large portion of GWA data to the study, Cookson was a co-author among dozens of others. “The more we keep filling out those pieces the more we’ll grasp the underlying processes, which of course should help point to which targets are more likely to be important therapeutically.”
The new meta-analysis is the largest of several such studies conducted in recent years that seek to tease out the genetic factors contributing to PD risk. Only a small proportion of people with PD harbor causal mutations, such as those within the coding regions of LRRK2 and SNCA. More than 90 percent of PD patients suffer from sporadic forms of the disease, which researchers suspect are caused by a smattering of genetic and environmental factors. Despite reports that approximately one-third of sporadic disease is mediated by genetic factors, only about 10 percent of the genes involved are known (see Keller et al., 2012).
The only way to find more needles in this genetic haystack is to increase the size of the haystack and improve methods for digging through it. That’s what researchers have done by pooling GWAS into successively larger meta-analyses (see Jul 2011 news story, Pankratz et al., 2012). In 2012, curators of the PDGene database combined several GWAS and other PD association studies to run their own meta-analysis, which identified 11 loci linked to PD risk (see Lill et al., 2012).
To perform the latest meta-analysis, co-first authors Mike Nalls of NIA and Nathan Pankratz of the University of Minnesota in Minneapolis and colleagues pooled data from 15 discrete GWAS conducted in the United States and Europe, which together included nearly 14,000 people with PD and more than 95,000 controls. The researchers used the process of imputation, which relies on the observation that certain variants are co-inherited, to fill in missing genotype information between the studies. After identifying nearly 30 PD-associated SNPs within the nearly 8 million included in the study, the researchers confirmed those hits in another cohort of more than 5,000 PD cases and 5,000 controls. Ultimately, the authors of this paper identified and confirmed 28 PD-associated SNP variants spread across 24 loci. They include six that had not been discovered in previous GWAS: SIPA1L2, INPP5F, MIR4697, GCH1, VPS13C, and DDRGK1.
The researchers do not yet know whether or how the variants affect nearby genes, but some of the new candidates could potentially play disease-related roles. For example, the enzyme GTP cyclohydrolase 1 (GCH1) produces dopamine, and people with mutations in the gene were recently reported to develop parkinsonism (see Mencacci et al., 2014). The data also confirmed previously reported associations, including with LRRK2, SNCA, MAPT.
The researchers then totaled up the risk associated with each SNP to create genetic-risk profiles. They found that people with the highest genetic risk profiles were more than three times as likely to have PD than those with the lowest combined risk.
Most of the PD-associated SNPs fell into noncoding regions. This means they might influence expression levels of one or more nearby genes. To explore this possibility, the researchers measured mRNA expression as well as CpG DNA methylation in postmortem brain tissue samples from healthy donors. By cross-referencing these data with the genotype of each nearby PD-associated SNP, the researchers identified 30 significant associations between the variants and either mRNA levels or CpG methylation of nearby genes. In some cases, a single variant was associated with the expression and/or methylation of more than one gene, suggesting that disease risk may result from complicated relationships between genes, even from a single polymorphism. For example, one PD-risk associated SNP on chromosome 7—rs199347—was associated with both decreased methylation of the nearest gene, GPNMB, as well as increased expression of another nearby gene, NUPL2. Therefore, this SNP could heighten PD risk by modulating expression of either or both of these genes.
The massive dataset still holds many undiscovered genetic gems that could shed light on disease mechanisms. Researchers will have free access to the windfall in the revamped PDGene database. The new interface will house detailed information not only about the 28 PD-associated variants identified in the study, but also about the top 10,000 runners-up. With the MyMeta function, users now can run customized meta-analyses of PDGene data, as well as append their own unpublished data to gauge the significance of promising SNPs being studied in their lab. These calculations are done “on the fly,” meaning the system does not store them in order to maintain confidentiality of the user’s own data. The same scripts used for meta-analysis calculation throughout PDGene are applied in the new MyMeta function.
As with the recently updated ALSGene database (see Jun 2014 news story), the new PDGene will allow researchers to connect PDGene meta-analysis data with a custom track on the University of California, Santa Cruz Genome Browser, a popular tool that includes a wealth of genetic information.
The results of the large GWAS meta-analysis will help researchers better understand the pathological mechanisms that underlie PD, Lars Bertram and Christina Lill of the Max Planck Institute for Molecular Genetics in Berlin wrote in an email to Alzforum. Bertram and Lill led the painstaking effort of incorporating data from the new meta-analysis into PDGene, as well as updating the website. “Researchers will be able to use the database to search for ‘their’ polymorphism or gene of interest, and will be able to see the cumulative evidence for association in their candidates,” they wrote. “This goes far beyond the publication of the meta-analysis study itself, which focuses on the top results only.”
So far, the new database only houses results from the latest meta-analysis, which overlaps with a majority of previous studies and supersedes them. However, researchers can still access the old version of the database.—Jessica Shugart
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