Longitudinal modelling of genetic risk for common diseases

Thesis

Recent large cohort studies follow participants for tens of years to collect their medical histories, allowing longitudinal analysis of many complex diseases. These datasets are typically accompanied by genotype data, whose effects on diseases are typically estimated as static values without reference to potential age varying effects. In this work we developed methods to study the relationship between genetic variations and complex diseases as a function of age. In the first step, we developed methods that focus on a single disease at a time, which we applied to infer the age-varying genetic risk profiles for common complex diseases. When applying the methods to 24 common diseases in the UK Biobank, we found a common decreasing pattern of genetic risk effect for several complex diseases, after using a frailty model to adjust for unobserved risk factors such as environmental factors. In the second step, we developed methods that look at many common diseases simultaneously, which we applied to infer age-dependent multi-morbidity profiles under a low-rank representation framework. We analysed the shared genetic variations underlying clusters of diseases as well as the genetic heterogeneity among subtypes of diseases implied by the comorbidity profiles. When jointly modelling 349 Phecodes that have more than 1000 occurrences in the UK Biobank, we identified several disease topics that contain genetically- correlated diseases. When assigning incident diseases in the UK Biobank to these topics, several complex diseases are stratified into subtypes that have different genetic profiles. Our results suggested that genetic risks are modulated by contexts including age and comorbid diseases. To better predict disease incidences and identify biological pathways, genetic information should be modelled jointly with other information available in biobank data sets.

Authors: Jiang, X

• DOI: 10.5287/ora-zbgvxddk8
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: genetics; Frailty model; Bayesian statistics; Low-rank representation; epidemiology; longitudinal data; Survival analysis; EHR; Multi-morbidity; Genetic subtypes
• Subjects: Genetics; Statistics; Epidemiology