Exploiting large-scale exome sequence data to determine the genetic control of healthy aging

Genome-wide association studies (GWAS) have been effective at beginning to unravel the genetics of traits underlying healthy aging, such as bone strength and cognition by locating common variants of small effect. However, there has been limited success at rare variant detection affecting these and other phenotypes. Detection of rare variants of large effect would improve our ability to dissect causal pathways underlying these traits as well as to predict individual risk, potentially contributing to lengthening 'healthspan' (the proportion of lifespan that we are in good health).
We previously developed regional heritability mapping approaches that have been effective at detecting clusters of rare and common variants that have escaped detection in standard GWAS. By taking advantage of the particular characteristics of rare variants and newly available sequence information in large numbers of individuals, both from local cohorts and the UK Biobank, this project will explore the effectiveness of Regional Heritability Mapping in simulated data and apply this promising approach to real population high-density exome sequence data to uncover novel associations and follow-up these results by integrating our findings with publicly available resources including gene expression data as well as local resources including methylation and proteomic data, to better understand variation related to healthy aging. We will explore causality in Mendelian randomisation frameworks and understand the correlation between phenotypes using multitrait analyses when appropriate.
With 4000 participants, the local data we have access to are very rich both in terms of relevant phenotypes (including DEXA scans, metabolomics data and cognitive tests) and high density sequence data. We will complement our local data with UK Biobank data for a selection of overlapping relevant traits (for example DEXA scan phenotypes and cognition). The scale of the data utilised will require high-performance computing resources.
Our project fits within Strategic research priority 3 - bioscience for health as it will contribute to increase understanding of the genetic basis of characters that are key to the healthy aging process, such as bone strength, body composition and cognition. Body composition traits are also relevant to animal breeding, hence understanding of their genetic architecture will contribute to Strategic research priority 1 - agriculture and food security. More broadly, we will contribute to the BBSRC's remit by producing high quality multidisciplinary research, bringing together statistical genomics, computational biology and population health sciences and providing high quality training to produce a skilled computational biology researcher with transferable skills also sought after in industry.