Tanushree Haldar
Project Personnel, University of California, San Francisco
7 active projects
Genotype Imputation (v7)
Scientific Questions Being Studied
We want to impute available microarray genotype data (Array) using available whole genome sequence (WGS) data as reference. The imputed genotype data would be used for GWAS analysis, to calculate polygenic risk score etc.
Project Purpose(s)
- Ancestry
Scientific Approaches
Available whole genome sequence (WGS) data and microarray genotype data (Array) would be used for analysis. We are going to use Eagle2 software to phase both dataset. Minimac4 would be used to impute array data using sequence data as reference.
Anticipated Findings
We would be able to impute genotype. Imputed genotype data would help us to do meta analysis across multiple populations. We anticipate imputation quality >0.99 for more than 90% variants.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierResearch Team
Owner:
- Tanushree Haldar - Project Personnel, University of California, San Francisco
Collaborators:
- Scott Huntsman - Project Personnel, University of California, San Francisco
- Donglei Hu - Project Personnel, University of California, San Francisco
Factors influencing pharmacogical response in underrepresented populations (v7)
Scientific Questions Being Studied
Pharmacogenomics has the potential to dramatically improve health care outcomes but is currently failing on diversity among its research participants. Our aim is to understand all the factors influencing pharmacogical response in underrepresented populations, including those that contribute to racial/ethnic differences in drug efficacy and safety as reported by Food and Drug Administration (FDA) drug labels.
Project Purpose(s)
- Drug Development
- Ancestry
Scientific Approaches
Health record data, lab results, prescription dispensing record would be used to define phenotype. Different statistical methods (like logistic regression, survival analysis etc.) would be used to analyze the data.
We shall do GWAS using genotype data.
We are mainly going to use R and Python for the analysis.
Anticipated Findings
We anticipate that our analysis will identify novel social determinants of drug response depending on the specific drug response phenotype.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierCHD PRS & statin effectiveness on ASCVD (control tierV7)
Scientific Questions Being Studied
The primary objective is to determine how statin effectiveness is modified by CHD polygenic risk score in a real-world cohort of primary prevention participants.
We will investigate coronary heart disease polygenic risk scores for statin effectiveness of atherosclerotic cardiovascular disease in stratified race/ethnicity and age groups so that we can (1) study this relationship in subsets of the population that are traditionally excluded from statin randomized controlled trials and (2) determine the impact of social determinants of health which vary across these stratified groups.
Project Purpose(s)
- Disease Focused Research (arteriosclerotic cardiovascular disease)
- Drug Development
- Ancestry
Scientific Approaches
Health record data, lab results, prescription dispensing record would be used to define phenotype. Different statistical methods (like logistic regression, survival analysis etc.) would be used to analyze the data.
We shall calculate CHD polygenic risk scores of participants in the cohort using genotype data. Covariate-adjusted Cox regression models will be used to compare the risk of cardiovascular outcomes between statin users and matched nonusers.
We are mainly going to use R for the analysis.
Anticipated Findings
We anticipate that for primary prevention patients undergoing routine care, CHD polygenic risk modifies statin relative risk reduction of incident myocardial infarction independent of statin LDL-C lowering. Our findings will replicate our prior work which identified a subset of patients with attenuated clinical benefit from statins.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierFactors influencing pharmacogical response in underrepresented populations
Scientific Questions Being Studied
Pharmacogenomics has the potential to dramatically improve health care outcomes but is currently failing on diversity among its research participants. Our aim is to understand all the factors influencing pharmacogical response in underrepresented populations, including those that contribute to racial/ethnic differences in drug efficacy and safety as reported by Food and Drug Administration (FDA) drug labels.
Project Purpose(s)
- Drug Development
- Ancestry
Scientific Approaches
Health record data, lab results, prescription dispensing record would be used to define phenotype. Different statistical methods (like logistic regression, survival analysis etc.) would be used to analyze the data.
We shall do GWAS using genotype data.
We are mainly going to use R and Python for the analysis.
Anticipated Findings
We anticipate that our analysis will identify novel social determinants of drug response depending on the specific drug response phenotype.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierCHD Polygenic Risk Score & statin effectiveness on ASCVD (control tierV6)
Scientific Questions Being Studied
The primary objective is to determine how statin effectiveness is modified by CHD polygenic risk score in a real-world cohort of primary prevention participants.
We will investigate coronary heart disease polygenic risk scores for statin effectiveness of atherosclerotic cardiovascular disease in stratified race/ethnicity and age groups so that we can (1) study this relationship in subsets of the population that are traditionally excluded from statin randomized controlled trials and (2) determine the impact of social determinants of health which vary across these stratified groups.
Project Purpose(s)
- Disease Focused Research (arteriosclerotic cardiovascular disease)
- Drug Development
- Ancestry
Scientific Approaches
Health record data, lab results, prescription dispensing record would be used to define phenotype. Different statistical methods (like logistic regression, survival analysis etc.) would be used to analyze the data.
We shall calculate CHD polygenic risk scores of participants in the cohort using genotype data. Covariate-adjusted Cox regression models will be used to compare the risk of cardiovascular outcomes between statin users and matched nonusers.
We are mainly going to use R for the analysis.
Anticipated Findings
We anticipate that for primary prevention patients undergoing routine care, CHD polygenic risk modifies statin relative risk reduction of incident myocardial infarction independent of statin LDL-C lowering. Our findings will replicate our prior work which identified a subset of patients with attenuated clinical benefit from statins.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierGenotype Imputation
Scientific Questions Being Studied
We want to impute available microarray genotype data (Array) using available whole genome sequence (WGS) data as reference. The imputed genotype data would be used for GWAS analysis, to calculate polygenic risk score etc.
Project Purpose(s)
- Ancestry
Scientific Approaches
Available whole genome sequence (WGS) data and microarray genotype data (Array) would be used for analysis. We are going to use Eagle2 software to phase both dataset. Minimac4 would be used to impute array data using sequence data as reference.
Anticipated Findings
We would be able to impute genotype. Imputed genotype data would help us to do meta analysis across multiple populations. We anticipate imputation quality >0.99 for more than 90% variants.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierResearch Team
Owner:
- Tanushree Haldar - Project Personnel, University of California, San Francisco
Collaborators:
- Scott Huntsman - Project Personnel, University of California, San Francisco
- Donglei Hu - Project Personnel, University of California, San Francisco
CHD Polygenic Risk Score & statin effectiveness on ASCVD (control tier)
Scientific Questions Being Studied
The primary objective is to determine how statin effectiveness is modified by CHD polygenic risk score in a real-world cohort of primary prevention participants.
We will investigate coronary heart disease polygenic risk scores for statin effectiveness of atherosclerotic cardiovascular disease in stratified race/ethnicity and age groups so that we can (1) study this relationship in subsets of the population that are traditionally excluded from statin randomized controlled trials and (2) determine the impact of social determinants of health which vary across these stratified groups.
Project Purpose(s)
- Disease Focused Research (arteriosclerotic cardiovascular disease)
- Drug Development
- Ancestry
Scientific Approaches
Health record data, lab results, prescription dispensing record would be used to define phenotype. Different statistical methods (like logistic regression, survival analysis etc.) would be used to analyze the data.
We shall calculate CHD polygenic risk scores of participants in the cohort using genotype data. Covariate-adjusted Cox regression models will be used to compare the risk of cardiovascular outcomes between statin users and matched nonusers.
We are mainly going to use R for the analysis.
Anticipated Findings
We anticipate that for primary prevention patients undergoing routine care, CHD polygenic risk modifies statin relative risk reduction of incident myocardial infarction independent of statin LDL-C lowering. Our findings will replicate our prior work which identified a subset of patients with attenuated clinical benefit from statins.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierRequest a Review of this Research Project
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