Henry Taylor

Graduate Trainee, National Human Genome Research Institute (NIH - NHGRI)

4 active projects

T2D metaGRS

Previous genetic studies have successfully identified >240 loci associated with type 2 diabetes (T2D). However, many of the identified loci lie in non-coding regions of the genome, masking the underlying “effector gene”. Additionally, these studies were primarily performed in individuals…

Scientific Questions Being Studied

Previous genetic studies have successfully identified >240 loci associated with type 2 diabetes (T2D). However, many of the identified loci lie in non-coding regions of the genome, masking the underlying “effector gene”. Additionally, these studies were primarily performed in individuals of European and East Asian ancestry. As genetic findings are integrated into the clinic, this limited genetic understanding threatens to exacerbate existing health disparities among non-European communities disproportionately suffering from T2D and T2D-related health complications. Expanding our genetic understanding into non-European ancestries is essential as these studies will mitigate such health disparities and improve our ability to identify causal loci and mechanisms. In this study, I will leverage the diversity found within All of Us to perform a trans-ancestry genetic analysis of T2D, identifying likely causal variants and evaluating their potential for clinical utility.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Drug Development
  • Ancestry

Scientific Approaches

Using well-established techniques in statistical genetics, I will (1) conduct a genome wide association study of common variants, as well as other forms of genetic variation (e.g., rare variants, copy number variations), to identify trans-ancestry and ancestry-specific disease-associated loci, (2) use statistical and functional fine-mapping techniques to identify the likely causal variants at disease-associated loci, validating my findings with experimental approaches, and (3) evaluate the clinical utility of my findings through polygenic scores (PGSs) and screening for potential targets of drug repurposing. For these analyses, I will use all participants with genetic data (whole genome and SNP-array). I will develop a phenotypic algorithm to classify participants into T2D cases and controls based on previously published work in electronic health records.

Anticipated Findings

Genetic studies of T2D have already provided valuable insight into disease pathophysiology. However, the vast majority of these studies have been performed in European and East Asian ancestries, threatening to exacerbate existing health disparities in populations carrying a disproportionate disease burden as genetics enters the clinic through preventative and therapeutic interventions. In the present study, I aim to address these shortcomings by leveraging the diversity found within All of Us to expand our genetic understanding of T2D. Based on the prior success of similar studies, I expect to identify many rare and common T2D-associated loci that are shared across ancestries as well as ancestry-specific. Combined with experimental and computational approaches, I will use these results to identify variants that are likely causal for T2D, propose the mechanism by which these variants contribute to T2D risk, and propose ways to translate my findings into clinical intervention strategies.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Henry Taylor - Graduate Trainee, National Human Genome Research Institute (NIH - NHGRI)

Trans-ancestry analysis of Type 2 Diabetes (T2DGGI)

Previous genetic studies have successfully identified >240 loci associated with type 2 diabetes (T2D). However, many of the identified loci lie in non-coding regions of the genome, masking the underlying “effector gene”. Additionally, these studies were primarily performed in individuals…

Scientific Questions Being Studied

Previous genetic studies have successfully identified >240 loci associated with type 2 diabetes (T2D). However, many of the identified loci lie in non-coding regions of the genome, masking the underlying “effector gene”. Additionally, these studies were primarily performed in individuals of European and East Asian ancestry. As genetic findings are integrated into the clinic, this limited genetic understanding threatens to exacerbate existing health disparities among non-European communities disproportionately suffering from T2D and T2D-related health complications. Expanding our genetic understanding into non-European ancestries is essential as these studies will mitigate such health disparities and improve our ability to identify causal loci and mechanisms. In this study, I will leverage the diversity found within All of Us to perform a trans-ancestry genetic analysis of T2D, identifying likely causal variants and evaluating their potential for clinical utility.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Drug Development
  • Ancestry

Scientific Approaches

Using well-established techniques in statistical genetics, I will (1) conduct a genome wide association study of common variants, as well as other forms of genetic variation (e.g., rare variants, copy number variations), to identify trans-ancestry and ancestry-specific disease-associated loci, (2) use statistical and functional fine-mapping techniques to identify the likely causal variants at disease-associated loci, validating my findings with experimental approaches, and (3) evaluate the clinical utility of my findings through polygenic scores (PGSs) and screening for potential targets of drug repurposing. For these analyses, I will use all participants with genetic data (whole genome and SNP-array). I will develop a phenotypic algorithm to classify participants into T2D cases and controls based on previously published work in electronic health records.

Anticipated Findings

Genetic studies of T2D have already provided valuable insight into disease pathophysiology. However, the vast majority of these studies have been performed in European and East Asian ancestries, threatening to exacerbate existing health disparities in populations carrying a disproportionate disease burden as genetics enters the clinic through preventative and therapeutic interventions. In the present study, I aim to address these shortcomings by leveraging the diversity found within All of Us to expand our genetic understanding of T2D. Based on the prior success of similar studies, I expect to identify many rare and common T2D-associated loci that are shared across ancestries as well as ancestry-specific. Combined with experimental and computational approaches, I will use these results to identify variants that are likely causal for T2D, propose the mechanism by which these variants contribute to T2D risk, and propose ways to translate my findings into clinical intervention strategies.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Henry Taylor - Graduate Trainee, National Human Genome Research Institute (NIH - NHGRI)

OmicsPred

Multi-omic analysis has become a powerful approach to predict disease and analyze its underlying biology. However, collecting transcriptomic, proteomic, metabolomic and other modalities is an extremely expensive and time-consuming process. Because of these barriers, large-scale population cohorts typically generate multi-omic…

Scientific Questions Being Studied

Multi-omic analysis has become a powerful approach to predict disease and analyze its underlying biology. However, collecting transcriptomic, proteomic, metabolomic and other modalities is an extremely expensive and time-consuming process. Because of these barriers, large-scale population cohorts typically generate multi-omic data for only a subset of participants (or not at all), which reduces statistical power and creates inequities for studies without ample resources, particularly in underrepresented demographics. Genetic scores can provide an avenue to remediate this gap in data. Recently, several studies have generated genetic scores for multi-omic data. Here, we aim to evaluate the effectiveness of these scores in exploring the biology underlying disease.

Project Purpose(s)

  • Drug Development
  • Ancestry

Scientific Approaches

Using well-established techniques in statistical genetics, we will (1) impute genetic scores for multi-omic data using genetic weights of multi-omic data (e.g., gene expression or protein expression data) from previously-published studies, (2) perform a phenome-wide association study for each imputed multi-ome score, (3) compare associated phenotypes found within All of Us with other cohorts (e.g., UKBB). For these analyses, we will use all participants with whole genome sequencing data. To classify participants as cases or controls for the phenome-wide association study, we will use algorithms implemented by Phecode and the electronic health record data.

Anticipated Findings

Genetic studies of T2D have already provided valuable insight into disease pathophysiology. However, the underlying molecular processes that go awry and lead to disease remain largely unknown. In the present study, we aim to address these shortcomings by imputing multi-omic profiles and identifying associations with phenotypes. Based on prior success of previous studies (particularly in the transcriptome-wide association space), we expect to find many effects and will use these effects to define distinct multi-omic profiles enriched in disease subtypes. Additionally, we will evaluate how effective these scores are at imputing multi-omic profiles in participants of diverse ancestry by comparing scores in All of Us with scores from the original training/test data, highlighting the need for more diverse multi-omic datasets.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Henry Taylor - Graduate Trainee, National Human Genome Research Institute (NIH - NHGRI)

Collaborators:

  • Tam Tran - Other, National Human Genome Research Institute (NIH - NHGRI)

Trans-ancestry analysis of Type 2 Diabetes

Previous genetic studies have successfully identified >240 loci associated with type 2 diabetes (T2D). However, many of the identified loci lie in non-coding regions of the genome, masking the underlying “effector gene”. Additionally, these studies were primarily performed in individuals…

Scientific Questions Being Studied

Previous genetic studies have successfully identified >240 loci associated with type 2 diabetes (T2D). However, many of the identified loci lie in non-coding regions of the genome, masking the underlying “effector gene”. Additionally, these studies were primarily performed in individuals of European and East Asian ancestry. As genetic findings are integrated into the clinic, this limited genetic understanding threatens to exacerbate existing health disparities among non-European communities disproportionately suffering from T2D and T2D-related health complications. Expanding our genetic understanding into non-European ancestries is essential as these studies will mitigate such health disparities and improve our ability to identify causal loci and mechanisms. In this study, I will leverage the diversity found within All of Us to perform a trans-ancestry genetic analysis of T2D, identifying likely causal variants and evaluating their potential for clinical utility.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Drug Development
  • Ancestry

Scientific Approaches

Using well-established techniques in statistical genetics, I will (1) conduct a genome wide association study of common variants, as well as other forms of genetic variation (e.g., rare variants, copy number variations), to identify trans-ancestry and ancestry-specific disease-associated loci, (2) use statistical and functional fine-mapping techniques to identify the likely causal variants at disease-associated loci, validating my findings with experimental approaches, and (3) evaluate the clinical utility of my findings through polygenic scores (PGSs) and screening for potential targets of drug repurposing. For these analyses, I will use all participants with genetic data (whole genome and SNP-array). I will develop a phenotypic algorithm to classify participants into T2D cases and controls based on previously published work in electronic health records.

Anticipated Findings

Genetic studies of T2D have already provided valuable insight into disease pathophysiology. However, the vast majority of these studies have been performed in European and East Asian ancestries, threatening to exacerbate existing health disparities in populations carrying a disproportionate disease burden as genetics enters the clinic through preventative and therapeutic interventions. In the present study, I aim to address these shortcomings by leveraging the diversity found within All of Us to expand our genetic understanding of T2D. Based on the prior success of similar studies, I expect to identify many rare and common T2D-associated loci that are shared across ancestries as well as ancestry-specific. Combined with experimental and computational approaches, I will use these results to identify variants that are likely causal for T2D, propose the mechanism by which these variants contribute to T2D risk, and propose ways to translate my findings into clinical intervention strategies.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Henry Taylor - Graduate Trainee, National Human Genome Research Institute (NIH - NHGRI)

Collaborators:

  • Huan Mo - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
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