{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/395179"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/395179","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"A multi-ancestry genetic analysis of type 2 diabetes","abstract":"Diabetes affects more than 589 million people worldwide. It is one of the leading causes of death and costs over $1 trillion in annual global health expenditures. Type 2 diabetes (T2D) comprises ~90% of diabetes cases, is characterised by pancreatic islet beta cell dysfunction and reduced insulin response in insulin-sensitive tissues, and is associated with a myriad of risk factors, including diet, obesity, activity measures, and genetics. Over the past century, there have been extensive research efforts to describe the regulation of insulin secretion and downstream mechanisms of action, leading to the development of several efficacious therapies. Yet, despite these successes, the underlying molecular and cellular mechanisms leading to T2D are not yet fully understood. Human genetics offers a promising avenue to guide future research efforts in common diseases, such as T2D, by identifying disease-associated loci. From those, we can nominate causal genes and molecular/cellular processes, inform therapeutic development, and identify individuals with high genetic predisposition for disease for early interventional strategies. To date, genetic analyses of T2D have observed heritability between 25-70% and have identified over 500 associated loci. However, understanding the complex genetics governing T2D heritability still faces challenges, limiting the translational value of these studies. Among the most difficult is the challenge to identify the causal mechanisms as >90% of T2D-associated signals lie in non-coding regions of the genome. Moreover, often-modest effect sizes and varying frequency of alleles across populations mean there are loci that we have yet to identify since the vast majority of current studies have been limited to western European and East Asian genetic ancestries. In this thesis, I describe my work to mitigate some of these shortcomings by expanding diversity in T2D genetic studies to improve both locus discovery and risk screening and by integrating microRNA (miRNAs) expression in human pancreatic islets with genetic data to nominate potentially causal mechanisms at T2D-associated loci. Focusing on discovery and risk screening, I perform genetic analyses in the BELIEVE cohort and the All of Us Research Program (AoURP). Based in Bangladesh, BELIEVE is a ~74,000 person cohort of predominantly South Asian genetic ancestry, a particularly underrepresented population in T2D genetic studies. Using both imputed and sequenced genetic data from BELIEVE, I perform the largest genetic analysis of T2D in South Asian individuals to date, characterising the genetic architecture of T2D across common and rare variants. Notably, I identify several genetic associations with implications for clinical diagnoses or links to well-known T2D-related biological processes, underscoring the importance of including diverse populations for genetic discovery. Building on these ancestry-specific insights, I next expand the scope to a global scale by leading the largest multi-ancestry genetic analysis of T2D to date. Here, I apply polygenic scores derived from variant clusters reflecting distinct cardiometabolic processes to predict T2D-related clinical outcomes in the AoURP. In addition to reporting novel locus discoveries from this substantially larger dataset, I highlight the potential of these variant clusters to distinguish individuals at risk for clinical complications that share underlying pathophysiology with T2D. These results provide an important step towards the development of equitable and individualised prevention techniques that can be effective across global populations. I also conduct a study to link T2D-associated loci to potential causal molecular mechanisms. Previous efforts have focused largely on mRNA expression and chromatin accessibility. Yet, there are many other molecular modalities that may play an important role in T2D pathophysiology, including miRNAs. To investigate the role of miRNAs in T2D heritability, I present the largest sequencing-based analysis of miRNA expression in human pancreatic islets to date and evaluate islet miRNA expression in the context of T2D. I characterise the genetic regulation of miRNA expression by decomposing the heritability of miRNAs into cis- and trans-acting genetic components and mapping cis-acting miRNA-expression quantitative trait loci (miRNA-eQTLs). I use several strategies to link these findings to T2D, including colocalising miRNA-eQTLs with loci associated with T2D and related glycaemic traits, identifying T2D-related variants that may disrupt miRNA binding potential, and performing differential expression analysis across T2D status and polygenic scores for T2D-related traits. Overall, I provide key insight into the genetic regulation of miRNA in human pancreatic islets and nominate several miRNAs that may be important in T2D pathophysiology. To conclude, human genetics presents the opportunity to improve therapeutic development and tailor personalised clinical care for T2D, but a better understanding is needed of the loci that contribute to T2D risk and the biological pathways they influence. By substantially expanding diversity amongst underrepresented populations and incorporating a large-scale study of miRNA expression, I contribute important analyses that advance our understanding of the molecular drivers of T2D and the development of polygenic scores that can more accurately inform diabetes care globally.","abstract_html":"Diabetes affects more than 589 million people worldwide. It is one of the leading causes of death and costs over $1 trillion in annual global health expenditures. Type 2 diabetes (T2D) comprises ~90% of diabetes cases, is characterised by pancreatic islet beta cell dysfunction and reduced insulin response in insulin-sensitive tissues, and is associated with a myriad of risk factors, including diet, obesity, activity measures, and genetics. Over the past century, there have been extensive research efforts to describe the regulation of insulin secretion and downstream mechanisms of action, leading to the development of several efficacious therapies. Yet, despite these successes, the underlying molecular and cellular mechanisms leading to T2D are not yet fully understood. Human genetics offers a promising avenue to guide future research efforts in common diseases, such as T2D, by identifying disease-associated loci. From those, we can nominate causal genes and molecular/cellular processes, inform therapeutic development, and identify individuals with high genetic predisposition for disease for early interventional strategies. To date, genetic analyses of T2D have observed heritability between 25-70% and have identified over 500 associated loci. However, understanding the complex genetics governing T2D heritability still faces challenges, limiting the translational value of these studies. Among the most difficult is the challenge to identify the causal mechanisms as >90% of T2D-associated signals lie in non-coding regions of the genome. Moreover, often-modest effect sizes and varying frequency of alleles across populations mean there are loci that we have yet to identify since the vast majority of current studies have been limited to western European and East Asian genetic ancestries. In this thesis, I describe my work to mitigate some of these shortcomings by expanding diversity in T2D genetic studies to improve both locus discovery and risk screening and by integrating microRNA (miRNAs) expression in human pancreatic islets with genetic data to nominate potentially causal mechanisms at T2D-associated loci. Focusing on discovery and risk screening, I perform genetic analyses in the BELIEVE cohort and the All of Us Research Program (AoURP). Based in Bangladesh, BELIEVE is a ~74,000 person cohort of predominantly South Asian genetic ancestry, a particularly underrepresented population in T2D genetic studies. Using both imputed and sequenced genetic data from BELIEVE, I perform the largest genetic analysis of T2D in South Asian individuals to date, characterising the genetic architecture of T2D across common and rare variants. Notably, I identify several genetic associations with implications for clinical diagnoses or links to well-known T2D-related biological processes, underscoring the importance of including diverse populations for genetic discovery. Building on these ancestry-specific insights, I next expand the scope to a global scale by leading the largest multi-ancestry genetic analysis of T2D to date. Here, I apply polygenic scores derived from variant clusters reflecting distinct cardiometabolic processes to predict T2D-related clinical outcomes in the AoURP. In addition to reporting novel locus discoveries from this substantially larger dataset, I highlight the potential of these variant clusters to distinguish individuals at risk for clinical complications that share underlying pathophysiology with T2D. These results provide an important step towards the development of equitable and individualised prevention techniques that can be effective across global populations. I also conduct a study to link T2D-associated loci to potential causal molecular mechanisms. Previous efforts have focused largely on mRNA expression and chromatin accessibility. Yet, there are many other molecular modalities that may play an important role in T2D pathophysiology, including miRNAs. To investigate the role of miRNAs in T2D heritability, I present the largest sequencing-based analysis of miRNA expression in human pancreatic islets to date and evaluate islet miRNA expression in the context of T2D. I characterise the genetic regulation of miRNA expression by decomposing the heritability of miRNAs into cis- and trans-acting genetic components and mapping cis-acting miRNA-expression quantitative trait loci (miRNA-eQTLs). I use several strategies to link these findings to T2D, including colocalising miRNA-eQTLs with loci associated with T2D and related glycaemic traits, identifying T2D-related variants that may disrupt miRNA binding potential, and performing differential expression analysis across T2D status and polygenic scores for T2D-related traits. Overall, I provide key insight into the genetic regulation of miRNA in human pancreatic islets and nominate several miRNAs that may be important in T2D pathophysiology. To conclude, human genetics presents the opportunity to improve therapeutic development and tailor personalised clinical care for T2D, but a better understanding is needed of the loci that contribute to T2D risk and the biological pathways they influence. By substantially expanding diversity amongst underrepresented populations and incorporating a large-scale study of miRNA expression, I contribute important analyses that advance our understanding of the molecular drivers of T2D and the development of polygenic scores that can more accurately inform diabetes care globally.","abstract_has_math":false,"creators":["Taylor, Henry"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Danesh, John","Butterworth, Adam","Collins, Francis","Denny, Joshua"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-12","date_published":"2025-08-12","updated_at":"2026-07-24T01:33:21Z","subjects":["genetic epidemiology","genomics","GWAS","human genetics","type 2 diabetes"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/691a736a-167a-477d-a5a2-050f8f927ebd/download","https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.124763","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Danesh, John","Butterworth, Adam","Collins, Francis","Denny, Joshua"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["NIH Oxford-Cambridge Scholars Program, Gates Cambridge Scholarship"]},{"key":"dc:creator","label":"Author","values":["Taylor, Henry"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-08-12"]},{"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/1810/395179"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["genetic epidemiology","genomics","GWAS","human genetics","type 2 diabetes"]}]},{"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.repository.cam.ac.uk/bitstreams/691a736a-167a-477d-a5a2-050f8f927ebd/download","https://creativecommons.org/licenses/by/4.0/"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2027-01-12"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.124763"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/3fde2de8-8204-417c-89a0-185282bb9d46/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Diabetes affects more than 589 million people worldwide. It is one of the leading causes of death and costs over $1 trillion in annual global health expenditures. Type 2 diabetes (T2D) comprises ~90% of diabetes cases, is characterised by pancreatic islet beta cell dysfunction and reduced insulin response in insulin-sensitive tissues, and is associated with a myriad of risk factors, including diet, obesity, activity measures, and genetics. Over the past century, there have been extensive research efforts to describe the regulation of insulin secretion and downstream mechanisms of action, leading to the development of several efficacious therapies. Yet, despite these successes, the underlying molecular and cellular mechanisms leading to T2D are not yet fully understood. Human genetics offers a promising avenue to guide future research efforts in common diseases, such as T2D, by identifying disease-associated loci. From those, we can nominate causal genes and molecular/cellular processes, inform therapeutic development, and identify individuals with high genetic predisposition for disease for early interventional strategies. To date, genetic analyses of T2D have observed heritability between 25-70% and have identified over 500 associated loci. However, understanding the complex genetics governing T2D heritability still faces challenges, limiting the translational value of these studies. Among the most difficult is the challenge to identify the causal mechanisms as >90% of T2D-associated signals lie in non-coding regions of the genome. Moreover, often-modest effect sizes and varying frequency of alleles across populations mean there are loci that we have yet to identify since the vast majority of current studies have been limited to western European and East Asian genetic ancestries. In this thesis, I describe my work to mitigate some of these shortcomings by expanding diversity in T2D genetic studies to improve both locus discovery and risk screening and by integrating microRNA (miRNAs) expression in human pancreatic islets with genetic data to nominate potentially causal mechanisms at T2D-associated loci. Focusing on discovery and risk screening, I perform genetic analyses in the BELIEVE cohort and the All of Us Research Program (AoURP). Based in Bangladesh, BELIEVE is a ~74,000 person cohort of predominantly South Asian genetic ancestry, a particularly underrepresented population in T2D genetic studies. Using both imputed and sequenced genetic data from BELIEVE, I perform the largest genetic analysis of T2D in South Asian individuals to date, characterising the genetic architecture of T2D across common and rare variants. Notably, I identify several genetic associations with implications for clinical diagnoses or links to well-known T2D-related biological processes, underscoring the importance of including diverse populations for genetic discovery. Building on these ancestry-specific insights, I next expand the scope to a global scale by leading the largest multi-ancestry genetic analysis of T2D to date. Here, I apply polygenic scores derived from variant clusters reflecting distinct cardiometabolic processes to predict T2D-related clinical outcomes in the AoURP. In addition to reporting novel locus discoveries from this substantially larger dataset, I highlight the potential of these variant clusters to distinguish individuals at risk for clinical complications that share underlying pathophysiology with T2D. These results provide an important step towards the development of equitable and individualised prevention techniques that can be effective across global populations. I also conduct a study to link T2D-associated loci to potential causal molecular mechanisms. Previous efforts have focused largely on mRNA expression and chromatin accessibility. Yet, there are many other molecular modalities that may play an important role in T2D pathophysiology, including miRNAs. To investigate the role of miRNAs in T2D heritability, I present the largest sequencing-based analysis of miRNA expression in human pancreatic islets to date and evaluate islet miRNA expression in the context of T2D. I characterise the genetic regulation of miRNA expression by decomposing the heritability of miRNAs into cis- and trans-acting genetic components and mapping cis-acting miRNA-expression quantitative trait loci (miRNA-eQTLs). I use several strategies to link these findings to T2D, including colocalising miRNA-eQTLs with loci associated with T2D and related glycaemic traits, identifying T2D-related variants that may disrupt miRNA binding potential, and performing differential expression analysis across T2D status and polygenic scores for T2D-related traits. Overall, I provide key insight into the genetic regulation of miRNA in human pancreatic islets and nominate several miRNAs that may be important in T2D pathophysiology. To conclude, human genetics presents the opportunity to improve therapeutic development and tailor personalised clinical care for T2D, but a better understanding is needed of the loci that contribute to T2D risk and the biological pathways they influence. By substantially expanding diversity amongst underrepresented populations and incorporating a large-scale study of miRNA expression, I contribute important analyses that advance our understanding of the molecular drivers of T2D and the development of polygenic scores that can more accurately inform diabetes care globally."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["5bfe6fe9a6a68729163f7b7f834108da","87eda9de84448d1f82354d60eee3eb5f"]},{"key":"dc:title","label":"Title","values":["A multi-ancestry genetic analysis of type 2 diabetes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Danesh, John","Butterworth, Adam","Collins, Francis","Denny, Joshua"],"dc:contributor.sponsor":["NIH Oxford-Cambridge Scholars Program, Gates Cambridge Scholarship"],"dc:creator":["Taylor, Henry"],"dc:date.issued":["2025-08-12"],"dc:description.abstract":["Diabetes affects more than 589 million people worldwide. It is one of the leading causes of death and costs over $1 trillion in annual global health expenditures. Type 2 diabetes (T2D) comprises ~90% of diabetes cases, is characterised by pancreatic islet beta cell dysfunction and reduced insulin response in insulin-sensitive tissues, and is associated with a myriad of risk factors, including diet, obesity, activity measures, and genetics. Over the past century, there have been extensive research efforts to describe the regulation of insulin secretion and downstream mechanisms of action, leading to the development of several efficacious therapies. Yet, despite these successes, the underlying molecular and cellular mechanisms leading to T2D are not yet fully understood. Human genetics offers a promising avenue to guide future research efforts in common diseases, such as T2D, by identifying disease-associated loci. From those, we can nominate causal genes and molecular/cellular processes, inform therapeutic development, and identify individuals with high genetic predisposition for disease for early interventional strategies. To date, genetic analyses of T2D have observed heritability between 25-70% and have identified over 500 associated loci. However, understanding the complex genetics governing T2D heritability still faces challenges, limiting the translational value of these studies. Among the most difficult is the challenge to identify the causal mechanisms as >90% of T2D-associated signals lie in non-coding regions of the genome. Moreover, often-modest effect sizes and varying frequency of alleles across populations mean there are loci that we have yet to identify since the vast majority of current studies have been limited to western European and East Asian genetic ancestries. In this thesis, I describe my work to mitigate some of these shortcomings by expanding diversity in T2D genetic studies to improve both locus discovery and risk screening and by integrating microRNA (miRNAs) expression in human pancreatic islets with genetic data to nominate potentially causal mechanisms at T2D-associated loci. Focusing on discovery and risk screening, I perform genetic analyses in the BELIEVE cohort and the All of Us Research Program (AoURP). Based in Bangladesh, BELIEVE is a ~74,000 person cohort of predominantly South Asian genetic ancestry, a particularly underrepresented population in T2D genetic studies. Using both imputed and sequenced genetic data from BELIEVE, I perform the largest genetic analysis of T2D in South Asian individuals to date, characterising the genetic architecture of T2D across common and rare variants. Notably, I identify several genetic associations with implications for clinical diagnoses or links to well-known T2D-related biological processes, underscoring the importance of including diverse populations for genetic discovery. Building on these ancestry-specific insights, I next expand the scope to a global scale by leading the largest multi-ancestry genetic analysis of T2D to date. Here, I apply polygenic scores derived from variant clusters reflecting distinct cardiometabolic processes to predict T2D-related clinical outcomes in the AoURP. In addition to reporting novel locus discoveries from this substantially larger dataset, I highlight the potential of these variant clusters to distinguish individuals at risk for clinical complications that share underlying pathophysiology with T2D. These results provide an important step towards the development of equitable and individualised prevention techniques that can be effective across global populations. I also conduct a study to link T2D-associated loci to potential causal molecular mechanisms. Previous efforts have focused largely on mRNA expression and chromatin accessibility. Yet, there are many other molecular modalities that may play an important role in T2D pathophysiology, including miRNAs. To investigate the role of miRNAs in T2D heritability, I present the largest sequencing-based analysis of miRNA expression in human pancreatic islets to date and evaluate islet miRNA expression in the context of T2D. I characterise the genetic regulation of miRNA expression by decomposing the heritability of miRNAs into cis- and trans-acting genetic components and mapping cis-acting miRNA-expression quantitative trait loci (miRNA-eQTLs). I use several strategies to link these findings to T2D, including colocalising miRNA-eQTLs with loci associated with T2D and related glycaemic traits, identifying T2D-related variants that may disrupt miRNA binding potential, and performing differential expression analysis across T2D status and polygenic scores for T2D-related traits. Overall, I provide key insight into the genetic regulation of miRNA in human pancreatic islets and nominate several miRNAs that may be important in T2D pathophysiology. To conclude, human genetics presents the opportunity to improve therapeutic development and tailor personalised clinical care for T2D, but a better understanding is needed of the loci that contribute to T2D risk and the biological pathways they influence. By substantially expanding diversity amongst underrepresented populations and incorporating a large-scale study of miRNA expression, I contribute important analyses that advance our understanding of the molecular drivers of T2D and the development of polygenic scores that can more accurately inform diabetes care globally."],"dc:format.checksum.md5":["5bfe6fe9a6a68729163f7b7f834108da","87eda9de84448d1f82354d60eee3eb5f"],"dc:identifier.doi":["https://doi.org/10.17863/CAM.124763"],"dc:identifier.uri":["https://www.repository.cam.ac.uk/bitstreams/3fde2de8-8204-417c-89a0-185282bb9d46/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/395179"],"dc:rights":["https://www.repository.cam.ac.uk/bitstreams/691a736a-167a-477d-a5a2-050f8f927ebd/download","https://creativecommons.org/licenses/by/4.0/"],"dc:rights.embargodate":["2027-01-12"],"dc:rights.embargotype":["embargo"],"dc:subject":["genetic epidemiology","genomics","GWAS","human genetics","type 2 diabetes"],"dc:title":["A multi-ancestry genetic analysis of type 2 diabetes"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:33:21Z"}