{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/332884"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/332884","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Statistical methods to improve understanding of the genetic basis of complex diseases","abstract":"Robust statistical methods, utilising the vast amounts of genetic data that is now available, are required to resolve the genetic aetiology of complex human diseases including immune-mediated diseases. Essential to this process is firstly the use of genome-wide association studies (GWAS) to identify regions of the genome that determine the susceptibility to a given complex disease. Following this, identified regions can be fine-mapped with the aim of deducing the specific sequence variants that are causal for the disease of interest. Functional genomic data is now routinely generated from high-throughput experiments. This data can reveal clues relating to disease biology, for example elucidating the functional genomic annotations that are enriched for disease-associated variants. In this thesis I describe a novel methodology based on the conditional false discovery rate (cFDR) that leverages functional genomic data with genetic association data to increase statistical power for GWAS discovery whilst controlling the FDR. I demonstrate the practical potential of my method through applications to asthma and type 1 diabetes (T1D) and validate my results using the larger, independent, UK Biobank data resource. Fine-mapping is used to derive credible sets of putative causal variants in associated regions from GWAS. I show that these sets are generally over-conservative due to the fact that fine-mapping data sets are not randomly sampled, but are instead sampled from a subset of those with the largest effect sizes. I develop a method to derive credible sets that contain fewer variants whilst still containing the true causal variant with high probability. I use my method to improve the resolution of fine-mapping studies for T1D and ankylosing spondylitis. This enables a more efficient allocation of resources in the expensive functional follow-up studies that are used to elucidate the true causal variants from the prioritised sets of variants. Whilst GWAS investigate genome-wide patterns of association, it is likely that studying a specific biological factor using a variety of data sources will give a more detailed perspective on disease pathogenesis. Taking a more holistic approach, I utilise a variety of genetic and functional genomic data in a range of statistical genetics techniques to try and decipher the role of the Ikaros family of transcription factors in T1D pathogenesis. I find that T1D-associated variants are enriched in Ikaros binding sites in immune-relevant cell types, but that there is no evidence of epistatic effects between causal variants residing in the Ikaros gene region and variants residing in genome-wide binding sites of Ikaros, thus suggesting that these sets of variants are not acting synergistically to influence T1D risk. Together, in this thesis I develop and examine a range of statistical methods to aid understanding of the genetic basis of complex human diseases, with application specifically to immune-mediated diseases.","abstract_html":"Robust statistical methods, utilising the vast amounts of genetic data that is now available, are required to resolve the genetic aetiology of complex human diseases including immune-mediated diseases. Essential to this process is firstly the use of genome-wide association studies (GWAS) to identify regions of the genome that determine the susceptibility to a given complex disease. Following this, identified regions can be fine-mapped with the aim of deducing the specific sequence variants that are causal for the disease of interest. Functional genomic data is now routinely generated from high-throughput experiments. This data can reveal clues relating to disease biology, for example elucidating the functional genomic annotations that are enriched for disease-associated variants. In this thesis I describe a novel methodology based on the conditional false discovery rate (cFDR) that leverages functional genomic data with genetic association data to increase statistical power for GWAS discovery whilst controlling the FDR. I demonstrate the practical potential of my method through applications to asthma and type 1 diabetes (T1D) and validate my results using the larger, independent, UK Biobank data resource. Fine-mapping is used to derive credible sets of putative causal variants in associated regions from GWAS. I show that these sets are generally over-conservative due to the fact that fine-mapping data sets are not randomly sampled, but are instead sampled from a subset of those with the largest effect sizes. I develop a method to derive credible sets that contain fewer variants whilst still containing the true causal variant with high probability. I use my method to improve the resolution of fine-mapping studies for T1D and ankylosing spondylitis. This enables a more efficient allocation of resources in the expensive functional follow-up studies that are used to elucidate the true causal variants from the prioritised sets of variants. Whilst GWAS investigate genome-wide patterns of association, it is likely that studying a specific biological factor using a variety of data sources will give a more detailed perspective on disease pathogenesis. Taking a more holistic approach, I utilise a variety of genetic and functional genomic data in a range of statistical genetics techniques to try and decipher the role of the Ikaros family of transcription factors in T1D pathogenesis. I find that T1D-associated variants are enriched in Ikaros binding sites in immune-relevant cell types, but that there is no evidence of epistatic effects between causal variants residing in the Ikaros gene region and variants residing in genome-wide binding sites of Ikaros, thus suggesting that these sets of variants are not acting synergistically to influence T1D risk. Together, in this thesis I develop and examine a range of statistical methods to aid understanding of the genetic basis of complex human diseases, with application specifically to immune-mediated diseases.","abstract_has_math":false,"creators":["Hutchinson, Anna"],"institution":"University of Cambridge","degree_name":null,"degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Wallace, Chris"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-10-01","date_published":"2021-10-01","updated_at":"2026-07-22T22:24:01Z","subjects":["gwas","fine-mapping","T1D"],"languages":["eng"],"rights":[],"rights_urls":["https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000292244410"],"render_values":[{"text":"0000-0002-9224-4410","href":"https://orcid.org/0000-0002-9224-4410","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.80315","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wallace, Chris"]},{"key":"dc:creator","label":"Author","values":["Hutchinson, Anna"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000292244410"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2021-10-01"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/332884"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["gwas","fine-mapping","T1D"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.17863/CAM.80315"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/7a7828e5-6fa4-4622-9aa4-3c139b7e4161/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Robust statistical methods, utilising the vast amounts of genetic data that is now available, are required to resolve the genetic aetiology of complex human diseases including immune-mediated diseases. 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