Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits
<p>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 potentia...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2022
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| _version_ | 1826310120471003136 |
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| author | Zhang, BC |
| author2 | Palamara, P |
| author_facet | Palamara, P Zhang, BC |
| author_sort | Zhang, BC |
| collection | OXFORD |
| description | <p>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.</p>
<p>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.</p> |
| first_indexed | 2024-03-07T07:45:56Z |
| format | Thesis |
| id | oxford-uuid:c4e8fb87-618b-4ca1-ad04-16f415ce5021 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:45:56Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | oxford-uuid:c4e8fb87-618b-4ca1-ad04-16f415ce50212023-06-05T14:15:27ZBiobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traitsThesishttp://purl.org/coar/resource_type/c_db06uuid:c4e8fb87-618b-4ca1-ad04-16f415ce5021Genetics--Statistical methodsEnglishHyrax Deposit2022Zhang, BCPalamara, P<p>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.</p> <p>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.</p> |
| spellingShingle | Genetics--Statistical methods Zhang, BC Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits |
| title | Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits |
| title_full | Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits |
| title_fullStr | Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits |
| title_full_unstemmed | Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits |
| title_short | Biobank-scale ancestral recombination graphs: inference and applications to the analysis of complex traits |
| title_sort | biobank scale ancestral recombination graphs inference and applications to the analysis of complex traits |
| topic | Genetics--Statistical methods |
| work_keys_str_mv | AT zhangbc biobankscaleancestralrecombinationgraphsinferenceandapplicationstotheanalysisofcomplextraits |