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Customer case study Field of study: Computational biology Rockefeller University Scientist Builds Mutation Analysis Tools with HGMD In New York, Yuval Itan is developing new tools to help scientists sort through exome and genome data and find disease-causing mutations more rapidly. HGMD® provided the foundation for tools designed to improve gene- and mutation-level data analysis Sample to Insight At Rockefeller University in New York, research associate Yuval Itan has built two computational tools that may fundamentally change how scientists associate genetic mutations with human disease. As a member of the Casanova lab, Itan puts exome or genome and efficiently home in on his computational biology background to use the mutation of interest. in the effort to understand the genetic underpinnings of Mendelian disease. The lab focuses To accomplish this goal, Itan turned to a primarily on children, tracking genetic muta- resource he trusts: Human Gene Mutation tions that confer heightened susceptibility to Database (HGMD), a carefully annotated col- diseases such as flu or tuberculosis. lection of data associated with observed mutations. HGMD contains all known human inher- Itan and his colleagues use whole exome and ited disease mutations, allowing scientists to whole genome sequencing to analyze patients, determine quickly whether a variant is novel or mostly with severe cases of infectious disease has been reported previously and to discover that are suspected of having a genetic compo- the genetic basis of a disease. nent, often comparing those patients to healthy parents or to other patients with similar pheno- HGMD proved to be just the repository Itan types to find relevant mutations. “I’m interested needed to build the new tools that will be freely in correlating the specific genotypes of the available to the genomics community: the gene patients to the specific phenotypes they are damage index and the mutation significance displaying,” he says. “We deal with huge data cutoff. Both tools provide scientists a stream- — it’s really a needle-in-the-haystack problem.” lined workflow to find causal mutations as well as greater confidence in their results. In Itan’s view, the scientific problem is just as much about finding the right needle as it is about not getting distracted by the rest of the GDI haystack. In the quest for a causative mutation, 2 chasing down unrelated mutations is costly, The gene damage index (GDI) was born from both in time and resources, he says. His goal the observation that highly mutated genes in two recent projects was to develop tools rarely harbor the kinds of disease-causing that would significantly improve the accuracy changes that Itan and his fellow scientists are of mutation prediction and effectively remove looking for when they dive into the variant lists as many false leads as possible. These tools generated from exome or genome sequencing. will enable other scientists to sift through the “Genes that are highly mutated in healthy indi- massive numbers of mutations revealed in any viduals are very unlikely to cause the severe Rockefeller University Scientist Builds Mutation Analysis Tools with HGMD diseases we’re investigating,” Itan says. “A by exome or genome sequencing. Using this big proportion of the mutations we find belong method “massively increases the chance that to genes that are naturally very highly mutated investigators will see the true mutation,” Itan in the general population. These genes can says. “In many cases mutations that seem be removed from the analysis, and that would very promising are not disease-causing and remove a lot of the noise.” can be a waste of time.” Of course, pulling so many mutations out of the investigation requires an objective, high- “ We improved the prediction rate from about 40 percent with current methods to about 95 percent when the mutations’ population frequency is taken into consideration. MSC confidence, gene-level approach focused on detecting genes irrelevant to disease (rather For the other tool, Itan zoomed down from than methods for detecting relevant genes such the gene level to the mutation level. Aware as RVIS and de novo excess) — an approach that many scientists use tools such as CADD, that didn’t exist when Itan began looking. PolyPhen-2, or SIFT to determine whether He reasoned that a careful analysis of how a mutation will be deleterious or not, Itan mutated each gene was would provide the looked into these widely available tools and foundation for this approach. found a disturbing trend: many mutations that were known to cause disease and that are Itan used HGMD to help with this gene-level not extremely rare in the general population analysis. He began by detailing the accumu- were predicted to be benign. “It’s very risky lated mutational damage in each human gene, to remove mutations from the analysis based assigning a GDI score. The higher the score, on these predictions,” he says. “If you remove the more heavily mutated a gene is in healthy the true mutation, then your project is dead.” individuals — and therefore less likely to be associated with disease causation. Once each Itan believes the problem lies in a uni- gene was indexed, Itan had to determine an form benign/damaging cutoff for all human appropriate threshold between the potentially genes, based on a single instance per known disease-causing and the overly mutated genes. disease-causing mutation. “When frequency “I took all the disease-causing genes from of the variant in the population is taken into HGMD and looked at the profile of the GDI for account, then about 60 percent of true dis- genes that are known to be disease-causing,” ease-causing mutations would be removed he says. “Then I set the cutoff above which GDI from the analysis,” he says. The tools he would not be disease causing.” assessed seemed to have a strong correlation to the rarity of a variant. Evaluations of the GDI approach show that “these genes can be very safely removed from That set Itan on the path to creating the the analysis of patients,” Itan says, noting that mutation significance cutoff (MSC), a tool the method allows for stripping out up to 60 designed to provide similar information to percent of the thousands of mutations detected scientists as existing tools but with a more Rockefeller University Scientist Builds Mutation Analysis Tools with HGMD 3 nuanced understanding of the DNA in quesThat work paid off. By setting gene-specific who have been on diagnostic RGI Ingenuity Variant Analysis one tool tion. “Thereodysseys. is a big difference in evolutionary cutoff isvalues andthey’ve incorporating those into enables patients to obtain genome or exome used for RGI patients, and Haraksingh says pressure acting upon different human genes, prediction scores for mutations, “we improved sequencing, often supported by crowdfundthat its of use makes the application but currently available methods useease a fixed the prediction rate from aabout 40 percent with ing. But there’s still a cutoff lot of to data interpretation natural fithe forsays. volunteers who methods come fromto other predict damaging or benign,” current about 95 percent when required to get useful backthat to cutoff backgrounds. “It allows people who aren’t frequency is taken Withinformation his approach, is far more taithe mutations’ population patients. Often, the lored, standard analysis experts in cull genomics to quickly ask questions allowing him topermore accurately the into consideration,” Itan says. “Also, you can formed by sequencingdamaging centers ismutations not sufficient about the data without having to worry about from all others. now be relatively confident that if you remove for understanding these rare and particularly putting all the components together make the a variant in atospecific gene, there is only a 5 challenging cases. best says.that “Variant Again, Itan relied on HGMD to getinformatics started. Hepipelines,” percentshe chance you have removed the true Analysis is so intuitive and easy to use that looked up each human gene associated with disease-causing mutation.” “As the director of RGI’s Science 2.0 checked initia- existing they’re tools’ able toprepick it up quickly.” disease and then tive, I lead a team ofdiction 12 researchers the scores for with the disease-causing mutations For both tools, Itan focused on user-friendliness, mission of analyzing inthe genomic Ultimately, Haraksingh hopes that deep every gene.and The mediscores varied significantly, designing them the for use by biologists and bioincal data from our patients to try figure out analysis to patients will “Iyield meaning thatto an appropriate cutoff she for can one provide formaticians alike. really hope the GDI will what’s going on,” Haraksingh says. Ultimately, progress, if not the exact answer, for each case gene would be completely useless for another help people to have very clean data and to the volunteers write angene. in-depth research report — be it a new genetic lead orthe connecting For every gene with at least two known increase discoverytherate of genes that are for each patient that disease-causing is used to refer the case patient to a specialist who can provide and morewith MSC I hope it will mutations, Itan calculated a relevant to disease, to outside specialists confidence or garner knowledge for insight. “It’s been extremely gratifying to apply interval based on the expected help to remove variants that are truly benign future research from range RGI’s global network of my expertise in the nonprofit says.mutations that are truly of predictions and established a safe and helpsector,” peopleshe to infer scientists. cutoff value. “I would not have been able to disease-causing,” he says. do it without HGMD,” he says. “It was crucial. There was no other way to do this with such high quality and large scale.” QIAGEN Silicon Valley V 1700 Seaport Blvd. Third Floor Tel. +1 650 381 5100 Redwood City ty, CA 94063 Fax. +1 650 381 5190 info@ing genuityy.com www.ing genuityy.com © QIAGEN 2015. All Rights Reserved. Impact of Human Variation on Disease 4 Rockefeller University Scientist Builds Mutation Analysis Tools with HGMD