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1)    “How genetic risk for common disease changes with age” - Xilin Jiang - DPhil student, working with Professor Gil McVean and Professor Chris Holmes.

Genetics risk scores have great potential for disease prediction. However, most methods for estimating genetic risk assume that the effect is constant over age. Here, we present a framework for estimating how genetic risk changes with age and demonstrate that for many common diseases there is both age-dependent heterogeneity in how genetic risk affects future disease and, for a subset of diseases, multiple components of risk with distinct longitudinal profiles.

To analyse longitudinal patterns of genetic risk, we use the proportional hazards model to estimate genetics effect sizes within age groups, conditioning on survival (i.e. no mortality, censoring or disease).  We show that this approach is needed to avoid biases that arise in naive GWAS approaches, which are affected by the depletion of risk alleles in unaffected individuals over time and changes in baseline risk.  We apply this model to the UK Biobank dataset, analysing 30 ICD-10 disease codes with prevalence > 1% and at least 20 independent associated variants.  We use a Bayesian clustering approach on summary statistics to estimate latent curves and their posterior distributions, using spline functions to encourage smoothness in risk profiles over age and permutation tests to assess the evidence for distinct groups of variants with different age-related profiles.  We identify 7 diseases with evidence for age-specific heterogeneity, including heart disease, skin cancer and gall-bladder diseases, several of which show evidence for more than one curve.  We discuss biological processes that can result in such age-specific risk, notably gene-environment interactions, and the implications of these results for genetic prediction of risk.

2) " Genotype-phenotype maps, fitness landscapes and higher-order epistasis in microorganisms "- Luca Ferretti- Fraser group

In recent years, advances in experimental evolution of microorganisms has enabled exploration of the fine structure of local genotype-phenotype maps and especially of fitness landscapes, i.e. genotype-fitness maps. These landscapes have been experimentally probed to an unprecedented level, obtaining information on fitness effects and epistasis at a local scale. 

Unfortunately, the high-dimensional nature of gene- or genome-level maps hinders all efforts towards a systematic exploration of their global properties, which are key to understand evolution on longer time scales. Hence, it is key to understand how local measures can be used to assess global features of these maps. The theory of fitness landscapes suggests that the structure of epistatic interactions plays a crucial role in determining the global topography of the landscape. 

In this talk, we will give an overview of recent developments in relation to local measures and global properties of genotype-phenotype maps.  For experimentally resolved landscapes, several global properties can be predicted from local measures of epistasis. However, it is still unclear which features of long-term evolutionary dynamics could be predicted by experimental measures on small or sparse landscapes.