Human Genotyping—Scientist Spotlight
Understanding complex genetic traits has become easier with the biobank work of Professor Mark McCarthy (University of Oxford, UK) and his team. The team has used complex trait genetics to identify at least 50 new regions involved in susceptibility to type 2 diabetes and a similar number impacting other traits.
To achieve this, they combined the samples and associated information in biobanks with new study designs and technological advances to greatly improve our understanding of complex genetic traits. This visionary initiative offers access to new phenotypes, outcomes data, and the opportunity for new study paradigms.
Early successes in genome-wide association studies (GWAS) identified the cause of a number of conditions, but the majority of GWAS loci are yet to be translated into the insights needed to combat disease. To address these challenges, translational researchers, like Professor McCarthy, need to identify as many genetic variations as possible. They need more alleles, more phenotypes, more samples, and more analyses. Acquiring more phenotypes means expanding into new case-control studies, especially within large biobanks. These biobanks have access to health records, a broader spectrum of phenotypes, and also the necessary information to identify and validate biomarkers. With technology available to analyze such vast numbers of alleles, Professor McCarthy's team focuses on the design of each array based on biobank information to produce the most valuable biological insights for the project.
Professor McCarthy's team taps into biobanks to discover how variants from one disease contribute to risk in other diseases. The scale and standardization of phenotype and genotype that biobanks provide boost the power of complex trait genetics above and beyond that which is possible from the sample size alone. Professor McCarthy anticipates that over the next four to five years significant sequence and genotyping data will be gathered, allowing the development of a genetic architecture of complex traits. These answers will also improve the use of genetic data to drive prediction and stratification of disease.
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