HSM:Development of methodology and computationally efficient software for analysis of PGx exome sequencing studies of complex "time-to-event" outcomes

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Personalised medicine is an approach to treating patients where individual information like demographics, clinical and genetic information is used in deciding how to treat them - for example which drug or how much of a drug to give. The ultimate goal is to maximise benefit and minimise harm from treatment. To identify genetic information to be used in guiding treatment, pharmacogenetics (PGx) studies are used, which involve analysing DNA of patients and looking for correlation between their genetic information and drug response. The most common study design is the genome-wide association study (GWAS), which involves testing patients at hundreds of thousands of genetic variants known as single-nucleotide polymorphisms (SNPs) and measuring their drug response. However, the statistical methods used to analyse GWAS are suitable only for investigating correlation with common SNPs, where the minor allele frequency (MAF)-the frequency of the least common version of SNP-is greater than 5%. When common SNPs have been associated with outcomes of treatment response, their effect has typically been small, so GWAS have only had very limited success in identifying genetic variants to guide treatment. An example of this is PGx research into anti-epileptic drug response, for which GWAS have been conducted, yet little is still known about its genetic predictors.Whole-exome sequencing is an alternative approach to GWAS with many advantages and is cheaper than sequencing the whole genome. In this approach, genetic information is collected from areas of the genome (exomic regions) with a significant proportion of genetic variants which are highly likely to have an effect on molecular function, so are biologically feasible as predictors of treatment response. The approach also allows for rare variants (MAF<5%) to be investigated and they can be grouped together into sets believed to have similar molecular and biological effect, then analysed together using what is known as 'gene-based' analyses. These analyses are generally better at identifying correlations with outcome than single-variant approaches.Analysing whole-exome sequencing data requires different statistical methods to analysing GWAS data, and whilst appropriate methods have been developed, these are only for studies with binary or continuous outcomes. PGx studies are often interested in 'time-to-event' outcomes, for example time to disease remission or time to drug withdrawal, so we are experiencing an analytical bottleneck for identifying genetic variants associated with PGx outcomes. In this project we aim to address this bottleneck by developing novel statistical methods and appropriate software for time to event outcomes that can cope with the scale and complexity of exome sequence data. To achieve these aims, we have set the following objectives:1. Develop new statistical methods for PGx studies with complex time to event outcomes;2. Develop user-friendly, computationally efficient, free software implementing the methods, to deal with the scale and complexity of exome sequence datasets. 3. Demonstrate efficiency of the new methods compared to existing analysis approaches by simulating exome-sequence data under a variety of different assumptions. 4. Apply the new methods and software to an exome-sequence dataset to identify biomarkers of anti-epileptic (AED) response.5. Offer training on the new methods and software through practical workshops.We will also apply our methods to whole exome sequence data from UK Biobank, to identify genetic variants associated with time to onset of cardiovascular disease and type 2 diabetes. Our proposed methodology and software will allow for more powerful analysis of age of onset of these diseases, pointing to genes that lead to disease occurring earlier in life, where the effect of environmental risk factors are less important, and the impact of treatment is greater. These genes could then be used in drug development.