ALSGene has been overhauled. The publicly available database, which houses findings from genetic-association studies in amyotrophic lateral sclerosis, showcases meta-analyses of GWAS data and allows researchers to compare their own unpublished results to those already integrated within the database. Released originally in December 2013, the new version of ALSGene now contains interactive data on thousands of genetic polymorphisms garnered from large genome-wide association studies.
A few rare mutations have been linked to familial ALS, but the genetic factors behind the sporadic form of the disease, which accounts for more than 90 percent of cases, remain largely unknown. Researchers have generated hefty genomic data sets in recent years in search of genetic associations, but results are spread out across a fragmented literature. “As the data landscape in ALS—as well as in many other diseases—becomes increasingly complex, it can become very hard to keep track of the results,” Lars Bertram of the Max Planck Institute for Molecular Genetics in Berlin wrote in an email to Alzforum. “This is something we would like to facilitate.” Bertram co-led the creation of the AlzGene database in 2005 and led the first version of ALSGene in 2010 (see Dec 2010 news story, as well as PDGene, SZGene, and MSGene). He still leads the maintenance of Alz-, ALS-, and PDGene, which is nearing completion of a substantial upgrade as well.
Collectively, these gene databases have supported geneticists across those fields. They have also broadened interest in the genes implicated in these diseases beyond the confines of the human neurogenetics specialty to researchers interested in all aspects of these neurodegenerative disorders. Bertram believes the revamped ALSGene database will influence the direction investigators take in their studies. “Our hope is that this will also facilitate the translation of basic science into the clinic,” he said. For example, genetic data frequently inspires molecular biologists to tackle the underlying mechanism of genes highly ranked on ALSGene and other databases.
Bertram and colleagues curated published genetic-association studies to generate the ALSGene database and built in meta-analysis capability. So far, ALSGene contains data from 297 genetic studies spanning nearly 240,000 polymorphisms, most of which have been woven into meta-analyses. The updated ALSGene has a separate section dedicated to GWAS, where users can automatically visualize the data on Manhattan plots (see the granddaddy of these plots here). Users can also now connect their ALS genetic results with the University of California, Santa Cruz, genome browser. This popular tool allows users to expand their analysis to nearby genes, see whether the single-nucleotide polymorphism (SNP) that interests them has been linked to other diseases, or probe the genetic architecture of the region. The UCSC browser also houses data from studies examining copy numbers and expression levels of genes in different tissues. A custom track now displays ALSGene results alongside this wealth of related information.
ALSGene curators have already uploaded several GWAS to the database, the largest of which is a meta-analysis of GWAS data from the United States and seven European countries (see Shatunov et al., 2010, and the study’s page on ALSGene). When put together, the data sets include genetic information from more than 12,000 people—4,133 ALS patients and 8,130 controls. Researchers hope this data set will grow further if results from an Italian cohort, along with a fresh analysis, can be added to the database in the future (see Fogh et al., 2014).
“The new data is beautifully presented and the output is easily understood,” Ammar Al-Chalabi of King’s College London told Alzforum. “I encourage everyone to use ALSGene.” Al-Chalabi supplied the data from largest GWAS in the database so far, and said he plans to make available data from more studies in the future. Al-Chalabi led the development of ALSoD, another ALS genetics database that predates ALSGene by a decade. ALSoD includes rare mutations that cause familial ALS and focuses more on the phenotypic outcomes of genetic variations than does ALSGene. “We are interested in all variants that modify disease, including rare variants,” Al-Chalabi said. “So the two databases form a complementary system.”
The “My Meta” tool is another part of the upgrade to ALSGene. It allows researchers to combine their own unpublished genetic data with those in the database. This gives researchers the opportunity to calculate their own meta-analyses “on the fly,” Bertram wrote.
The meta-analysis tools provide a broader sense of what findings are significant versus what remains unclear or not replicated in the ALS research field, said Neta Zach of Prize4Life, the Israel-based nonprofit foundation that provided the bulk of the funding to build and maintain the database. “Given the complexity of the field, this is quite important,” Zach told Alzforum.
Bryan Traynor of the National Institutes of Health in Bethesda, Maryland, considers the meta-analysis tool ALSGene’s greatest strength. “That’s really the power of the database,” he told Alzforum. Traynor is an ALS geneticist who played an advisory role in creating ALSGene. He said he will take advantage of the meta-analysis tool to screen promising SNPs that pop up in his own research. “This will be a nice utility to check when there has been hint of a signal from the same SNP in GWAS from other laboratories,” Traynor said.
ALSGene could become even more powerful by adding raw genetic data of association studies, not just results, Traynor said. As of now, researchers must perform meta-analyses SNP by SNP; with the raw data available, a researcher could screen thousands of polymorphisms at once. Christina Lill, co-PI on the ALSGene project, said she and Bertram looked into this, but obtaining and uploading such data is challenging because it could violate privacy regulations.
Still, Traynor praised the database for gathering massive amounts of genetic results in one place, thus increasing the strength of the data overall. “The people who pay for this research, and the patients who donate their samples, would want us to put our data together to increase our power, and I think this database goes a long way toward that.”—Jessica Shugart
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