Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits

Thesis

Across living species, DNA is transmitted from generation to generation via the processes of inheritance, mutation, and recombination. The history of these processes can be recorded using genome-wide gene genealogies. Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range of analyses, but is computationally challenging. In this thesis, we introduce a scalable method, called ARG-Needle, that uses genotype hashing and a coalescent hidden Markov model to infer genome-wide genealogies from sequencing or genotyping array data in modern biobanks. We develop strategies that utilise the inferred genome-wide genealogies within linear mixed models to perform association and other analyses of biomedical traits.

We validate the accuracy and scalability of ARG-Needle through extensive coalescent simulations, and use ARG-Needle to build genome-wide genealogies from genotypes of 337,464 UK Biobank individuals. We perform genealogy-based association analysis of 7 complex traits, detecting more rare and ultra-rare signals (N = 133, frequency range 0.0004% − 0.1%) than genotype imputation from ∼65,000 sequenced haplotypes (N = 65). We validate these signals using exome sequencing data from 138,039 individuals. ARG-Needle associations strongly tag (average r = 0.72) underlying sequencing variants that are enriched for missense (2.3×) and loss-of-function (4.5×) variation. Compared to imputation, inferred genealogies also capture additional signals for higher frequency variants. These results demonstrate that biobank-scale inference of gene genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

Authors: Zhang, BC

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: statistical genetics; ancestral recombination graph; population genetics
• Subjects: Genetics--Statistical methods