Anav Babbar

National Human Genome Research Institute (NIH-NHGRI)

5 active projects

Hypothyroidism genomics v7

In previous work, certain genetic variants were found to be associated with hypothyroidism using techniques such as GWAS and PheWAS. Current work will leverage the diversity of the All of Us data to replicate and confirm the association between those…

Scientific Questions Being Studied

In previous work, certain genetic variants were found to be associated with hypothyroidism using techniques such as GWAS and PheWAS. Current work will leverage the diversity of the All of Us data to replicate and confirm the association between those variants and hypothyroidism, and possibly identify new variants that are associated with hypothyroidism.

Project Purpose(s)

  • Disease Focused Research (hypothyroidism)
  • Ancestry

Scientific Approaches

Will use All of Us genetic data and genomic techniques such as GWAS/PheWAS to identify genomic variants that are associated with hypothyroidism. Tools used include Python, R, Hail, SQL, etc.

Anticipated Findings

We seek to replicate and confirm previously identified variants associated with hypothyroidism, while possibly identifying new variants that could become targets of further study to understand the disease process of hypothyroidism. The diversity of the All of Us dataset may allow us to find these new variants.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Anav Babbar - Other, National Human Genome Research Institute (NIH-NHGRI)

Collaborators:

  • Ariel Williams - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)

CYP2C19 Clopidogrel Response - v7

Will use controlled tier data to identify association between CYP2C19 variants, clopidogrel usage, and rate of cardiac/clotting events post-MI/PCI.

Scientific Questions Being Studied

Will use controlled tier data to identify association between CYP2C19 variants, clopidogrel usage, and rate of cardiac/clotting events post-MI/PCI.

Project Purpose(s)

  • Disease Focused Research (Post-MI/PCI Clopidogrel interaction with CYP2C19 variants)

Scientific Approaches

WGS sequencing to identify participants with CYP2C19 variants;
EHR data to identify participants with hx MI/PCI/stenting;
Kaplan meier survival analysis
PheWAS

Anticipated Findings

Anticipate identifying how CYP2C19 variants interact with clopidogrel response post-MI/PCI with hopes of further clarifying role of CYP2C19 variants in drug processing and risk post-MI/stent

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Anav Babbar - Other, National Human Genome Research Institute (NIH-NHGRI)

Hypothyroidism genomics

In previous work, certain genetic variants were found to be associated with hypothyroidism using techniques such as GWAS and PheWAS. Current work will leverage the diversity of the All of Us data to replicate and confirm the association between those…

Scientific Questions Being Studied

In previous work, certain genetic variants were found to be associated with hypothyroidism using techniques such as GWAS and PheWAS. Current work will leverage the diversity of the All of Us data to replicate and confirm the association between those variants and hypothyroidism, and possibly identify new variants that are associated with hypothyroidism.

Project Purpose(s)

  • Disease Focused Research (hypothyroidism)
  • Ancestry

Scientific Approaches

Will use All of Us genetic data and genomic techniques such as GWAS/PheWAS to identify genomic variants that are associated with hypothyroidism. Tools used include Python, R, Hail, SQL, etc.

Anticipated Findings

We seek to replicate and confirm previously identified variants associated with hypothyroidism, while possibly identifying new variants that could become targets of further study to understand the disease process of hypothyroidism. The diversity of the All of Us dataset may allow us to find these new variants.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Ariel Williams - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
  • Anav Babbar - Other, National Human Genome Research Institute (NIH-NHGRI)

Collaborators:

  • Huan Mo - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
  • Tam Tran - Other, National Human Genome Research Institute (NIH-NHGRI)
  • Slavina Goleva - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
  • David Schlueter - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
  • Chenjie Zeng - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
  • Bennett Waxse - Research Fellow, National Institute of Allergy and Infectious Diseases (NIH - NIAID)
  • Anas Awan - Project Personnel, National Human Genome Research Institute (NIH-NHGRI)

CYP2C19 Clopidogrel Response

Will use controlled tier data to identify association between CYP2C19 variants, clopidogrel usage, and rate of cardiac/clotting events post-MI/PCI.

Scientific Questions Being Studied

Will use controlled tier data to identify association between CYP2C19 variants, clopidogrel usage, and rate of cardiac/clotting events post-MI/PCI.

Project Purpose(s)

  • Disease Focused Research (Post-MI/PCI Clopidogrel interaction with CYP2C19 variants)

Scientific Approaches

WGS sequencing to identify participants with CYP2C19 variants;
EHR data to identify participants with hx MI/PCI/stenting;
Kaplan meier survival analysis
PheWAS

Anticipated Findings

Anticipate identifying how CYP2C19 variants interact with clopidogrel response post-MI/PCI with hopes of further clarifying role of CYP2C19 variants in drug processing and risk post-MI/stent

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Anav Babbar - Other, National Human Genome Research Institute (NIH-NHGRI)

neural nets for gwas analysis

Can we leverage deep learning to interrogate variant-phenotype relationships to a deeper extent than logistic regression allows? Will deep learning allow enhanced prediction of disease compared to PRS?

Scientific Questions Being Studied

Can we leverage deep learning to interrogate variant-phenotype relationships to a deeper extent than logistic regression allows? Will deep learning allow enhanced prediction of disease compared to PRS?

Project Purpose(s)

  • Methods Development
  • Ancestry

Scientific Approaches

Bigquery - query EHR data to build phenotype
Hail - Access WGS data
Tensorflow/keras - build deep learning neural net to predict phenotype from variants

Anticipated Findings

We hope to identify a novel way of analyzing WGS and phenotype data in order to improve identification of complex variant-variant relationships as well as potentially improve understanding of how multiple pathologies interact.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Anav Babbar - Other, National Human Genome Research Institute (NIH-NHGRI)
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