Tabitha Harrison
Graduate Trainee, University of Washington
2 active projects
Duplicate of Genetic epidemiology of common cancers in US populations
Scientific Questions Being Studied
The overarching goal is to study the genetic architecture of cancer risk/mortality, with an emphasis on cross-cancer analyses. We will build on previous observations of genetic correlations across cancers and jointly study multiple cancer types. We will leverage genetic, health/behavioral/environmental, biomarker data from All of Us. Our Aims are to:
1. Identify germline genetic risk factors for cancer risk and mortality, leveraging both GWAS and sequencing data.
2. Further characterize the germline genetic contribution to cancer, using approaches such as genetic correlation, fine-mapping and functional enrichment analyses across cancers.
3. Quantify the contribution of dominance effects to cancer heritability.
4. Study the joint effects of biomarkers, environmental and genetic factors on cancer risk and mortality.
5. Generate and validate cancer risk and mortality prediction models that incorporate genetic, biomarker and environmental data.
Project Purpose(s)
- Disease Focused Research (Common cancers (breast, lung, prostate, colorectal, endometrial, ovarian, etc.))
- Ancestry
Scientific Approaches
We request the data from the full cohort. We will generate cancer risk/mortality prediction models, using cohort approaches. We will:
1) Apply state-of-the-art methods to study genetic architecture of cancer, including heritability approaches (GCTA, LD score regression, etc), gene-environment (GE) interaction tests (Empirical Bayes estimator, two-step approaches, etc) and cross-trait meta-analysis (ASSET).
2) Study genetic variation and cancer risk/mortality with regression analyses adjusting for sex, age, array/batch, PCs and other relevant covariates.
3) Extend additive GWAS models, adding dominance effects terms and applying LD score regression to estimate heritability.
4) Conduct genome-wide GE analyses using 2-step approaches to screen SNPs, and conduct 1df interaction tests on a multiplicative scale
5) Study biomarker and environmental risk factors across genetic strata by generating genome-wide polygenic models that take linkage disequilibrium into account (e.g. LDPred).
Anticipated Findings
We anticipate that the proposed research will lead to a deeper understanding of the genetic architecture of cancer risk and mortality. In particular, we aim to study multiple cancer types simultaneously to gain insights into general mechanisms contributing to cancer risk and mortality. Further, the proposed research will aim to improve current individual prediction models for cancer risk and mortality that incorporate genetic, biomarker, demographic and environmental data.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Sara Lindstroem - Mid-career Tenured Researcher, University of Washington
- Tabitha Harrison - Graduate Trainee, University of Washington
Collaborators:
- Madeline Louie - Undergraduate Student, University of Washington
- Austin Hammermeister Suger - Graduate Trainee, University of Washington
Genetic epidemiology of common cancers in US populations
Scientific Questions Being Studied
The overarching goal is to study the genetic architecture of cancer risk/mortality, with an emphasis on cross-cancer analyses. We will build on previous observations of genetic correlations across cancers and jointly study multiple cancer types. We will leverage genetic, health/behavioral/environmental, biomarker data from UK Biobank. Our Aims are to:
1. Identify germline genetic risk factors for cancer risk and mortality, leveraging both GWAS and sequencing data.
2. Further characterize the germline genetic contribution to cancer, using approaches such as genetic correlation, fine-mapping and functional enrichment analyses across cancers.
3. Quantify the contribution of dominance effects to cancer heritability.
4. Study the joint effects of biomarkers, environmental and genetic factors on cancer risk and mortality.
5. Generate and validate cancer risk and mortality prediction models that incorporate genetic, biomarker and environmental data.
Project Purpose(s)
- Disease Focused Research (Common cancers (breast, lung, prostate, colorectal, endometrial, ovarian, etc.))
- Ancestry
Scientific Approaches
We request the data from the full cohort. We will generate cancer risk/mortality prediction models, using cohort approaches. We will:
1) Apply state-of-the-art methods to study genetic architecture of cancer, including heritability approaches (GCTA, LD score regression, etc), gene-environment (GE) interaction tests (Empirical Bayes estimator, two-step approaches, etc) and cross-trait meta-analysis (ASSET).
2) Study genetic variation and cancer risk/mortality with regression analyses adjusting for sex, age, array/batch, PCs and other relevant covariates.
3) Extend additive GWAS models, adding dominance effects terms and applying LD score regression to estimate heritability.
4) Conduct genome-wide GE analyses using 2-step approaches to screen SNPs, and conduct 1df interaction tests on a multiplicative scale
5) Study biomarker and environmental risk factors across genetic strata by generating genome-wide polygenic models that take linkage disequilibrium into account (e.g. LDPred).
Anticipated Findings
We anticipate that the proposed research will lead to a deeper understanding of the genetic architecture of cancer risk and mortality. In particular, we aim to study multiple cancer types simultaneously to gain insights into general mechanisms contributing to cancer risk and mortality. Further, the proposed research will aim to improve current individual prediction models for cancer risk and mortality that incorporate genetic, biomarker, demographic and environmental data.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Sara Lindstroem - Mid-career Tenured Researcher, University of Washington
- Tabitha Harrison - Graduate Trainee, University of Washington
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