Principal Investigator: Dr Olivier Lichtarge
Department: Baylor College of Medicine, Houston, Texas, USATags: 55532, aging, cancer, diabetes, drug response, heart disease, neurological diseases
My computational biology group seeks to unravel the molecular and genetic basis of complex human diseases in order to bring about more effective personalized strategies for disease prevention, screening, stratification and therapy. Through the UK biobank data, we hope specifically to develop and test novel analytic methods to identify genetic variations that contribute to genetic health risks and that may be targeted to decrease morbidity and mortality. We thus plan to develop and test general methods:
(i) To find genes associated with complex disease,
(ii) To identify networks or pathways that can be targeted for precision therapy, and
(iii) To develop models of patient risk. Target areas include cancer, heart disease, diabetes, and neurological diseases.
We will control these studies against known disease genes as well as against other state-of-the-art statistical and machine learning approaches. On the one hand these studies will benefit from the UK biobank’s vast number of patients, which enhance statistical power; detailed clinical annotations, which are critical to distinguish disease subgroups; and high-quality genome sequencing, which is critical to tie these observations to specific genome perturbations often unique to each individual. On the other hand, we hope to bring to those studies a unique set of additional, complementary data from molecular evolution. This is a field in which my Lab has been deeply invested, leading to state-of-the art techniques for weighing the relative impact of mutations in proteins. This capability is derived from the vast trove of mutation experiments with over 2 to 3 billion years of evolution led to divergence and speciation of all life forms, and which now give us a free handbook for evaluating how mutations affect fitness over the long term.
Our hypothesis is that this mutation impact information provided by evolution will also inform the interpretation of human mutations, here and now, in many clinical settings . Preliminary evidence support this possibility but the data from the UK Biobank will be critical to evaluate and replicate the predictive accuracy and power of our computational analyses against separate, independent patient cohorts. The impact on public health will be two-fold, we hope, first, improved assignment of each individual patient to the most effective treatment protocol and, second, even before a disease starts to develop, through early identification of risk in healthy individuals so they may benefit from effective screening based on their own individual genetic profile.