Career case studies from BSU researchers

Jennifer Asimit

Jenn was awarded an MRC Career Development Award (in 2018) to develop methods to improve our genetic understanding of cardiometabolic traits through multiple traits and diverse population studies. This fellowship gives five years of support for both her research and to build her research team that combines ideas between statistics, cardiometabolics, and global health. She previously held an MRC Skills Development Award at the Wellcome Trust Sanger Institute.

Jenn explains: “There has been great success in identifying hundreds of genetic variants associated with many diseases and traits, but very few of these variants have an understood role in how they impact the trait. Recent technological advances have made it possible to obtain hundreds of measurements related to metabolism and understanding the genetic influences on human metabolism could improve our understanding of cardiometabolic diseases. The genetic analysis of many traits is often tackled by one-by-one analyses of individual traits without considering any correlations between them. Instead I am developing methods that identify associations between many traits with many genetic variants, as well as among multiple ethnicities. Data from both European and African ancestry populations will be analysed.”

(Photo credit: Sangar Institute, Wellcome Trust)

David Robertson

David’s work on correcting for bias in the selection and validation of informative diagnostic tests received the Young Biometrician Award in 2015 from the British and Irish Region of the International Biometric Society and the Fisher Memorial Trust. In 2018 he was awarded a three year Biometrika Trust Research fellowship to develop methods for error rate control in modern clinical trial designs that test multiple hypotheses simultaneously.

As David explains: “In the classical framework of drug development, the response to experimental therapies is evaluated one treatment at a time within a homogenous patient group. However, this paradigm is increasingly shifting towards testing multiple related hypotheses simultaneously. At the same time, there continues to be a strong focus (particularly from a regulatory perspective) on guaranteeing control of suitable error rates.

My research will develop the methodological tools for error control in trials where the multiple hypotheses tested have a natural grouping or ordering, as well as designs that allow a large number of new treatments to be added over the course of the trial. The new methodology will enable more robust inference to be made from such clinical trials, and help ensure that only truly beneficial treatments are recommended to future patients.”