Clinical OMICS

JAN-FEB 2018

Healthcare magazine for research scientists, labs, pathologists, hospitals, cancer centers, physicians and biopharma companies providing news articles, expert interviews and videos about molecular diagnostics in precision medicine

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Page 16 of 51 January/February 2018 Clinical OMICs 15 molecular medicine at the Perelman School of Medicine, University of Pennsylvania, "It is not a cheap endeavor to actually create this kind of infrastructure." "It's hard to put concrete numbers on it," said Rader, "but I can tell you that to assemble a biobank of 50,000 people, all of whom are genotyped and all of whom have electronic health record data in a way that one could easily use for a sophis- ticated PheWAS analysis, is well north of 15 to 20 million dollars." While large pharmaceutical companies, including Regeneron, Merck, and GSK, have already incorporated PheWAS into their drug development programs by partnering with institutions that had the foresight to develop these biobanks, small drug compa- nies may have to wait until it becomes more of a commodity before they can add PheWAS to their toolboxes. However, it's not just about accessing the trove of information deposited in these biobanks. Investigators also need to take the size and composi- tion of the cohort into account when evaluating what questions they can ask. Small cohorts may not have the statistical power necessary to give researchers confidence in the results— especially for diseases that occur in the population at a relatively low frequency. However, while large cohorts increase statistical power, they may still suffer from a lack of diversity. "The vast majority of genetic information we have linked to phenotype data is in people of European ancestry," admitted Rader. "Mov- ing the field to more actively study people of African ancestry, of South Indian ancestry, of Asian ancestry can only enhance our ability to make new discoveries that are going to ultimately impact on new drug targets and on drug development." Another insight Rader shared on how the field will move as the future of genomics in drug development becomes less of a wonder- land and more of a reality revolves around the types of variants studied: common versus rare, and harmful versus protective. While researchers agree that analyzing common and rare genetic vari- ants together provides a more complete picture of health and disease, the majority of both GWASs and PheWASs focus on common variants. To analyze rare variants, the field will need to move beyond SNPs to whole-genome and whole-exome sequencing. Sim- ilarly, while studies focus predominantly on variants that increase disease-risk, the concept that genetics could also unearth variants that protect people from developing a disease is another powerful paradigm shift that could point drug developers toward new targets. As scientists search to answer the riddles of human pathology by staring into the looking-glass of genetics, one theme will run throughout their adventures— that sometimes it takes a new perspective to find the answers. Digging into the Data of Complex Traits Marylyn Ritchie's research lab tries to un- derstand the genetic architecture of com- plex traits by developing new methods to study genetic variation. "Calculating the statistics for a GWAS or a PheWAS is not the challenging part anymore," comment- ed Ritchie. "Now the major challenge is figuring out which results are interest- ing and should be followed up in future studies." In November 2016, Ritchie's lab pub- lished in Nature Communications the release of a new software analysis tool. Platform for the Analysis, Translation, and Organization of large-scale data (PLATO) streamlines the pipeline for complex as- sociation analyses by integrating features previously only available through multi- ple data analysis packages that required additional installations, data reformat- ting, and user proficiencies. Its authors described PLATO as a Swiss Army knife, "while an entire toolbox of methods have been developed across a wide scope of domains, PLATO is a ver- satile and user-friendly tool for exploring several essential types of complex associ- ations on a single platform." They further demonstrated the utility of their all-in- one package by applying it to data from the Marshfield Personalized Medicine Re- search Project and exploring the effects of genetic and environmental contributions to type 2 diabetes using main effect, rare variant, gene-environment interaction, and PheWAS analyses. By equipping the community with a Swiss Army knife for complex association analysis, Ritchie's lab hopes to solve the case of missing heritability. n PASIEKA / Getty Images S i - G a l / G e t t y I m a g e s

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