Ravi Mandla

Project Personnel, Broad Institute

3 active projects

ML Genetic Diabetes v6

Large-scale genome wide association studies (GWAS) have identified many genetic variants associated with complex diseases. Most GWAS have been developed within European ancestries and have shown to perform poorly in other race/ethnic groups, exaggerating health disparities across ancestries. Scientific aim…

Scientific Questions Being Studied

Large-scale genome wide association studies (GWAS) have identified many genetic variants associated with complex diseases. Most GWAS have been developed within European ancestries and have shown to perform poorly in other race/ethnic groups, exaggerating health disparities across ancestries.

Scientific aim 1: Collection, harmonization and integration of large-scale, multi-ancestry cohorts with diabetes traits across the life-span and genomics for the discovery of genetic variants associated with several forms of diabetes.
Scientific aim 2: Development of methods to improve PRS prediction in non-European populations by using Bayesian approaches that allow integration of linkage disequilibrium and summary statistics from several ancestries.
Scientific aim 3: Development, testing, and comparing performance of PRS for each trait, development of risk prediction tools that integrate clinical and genetic risk factors, and assessment of scenarios where PRS improve the prediction.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus, type 1 diabetes mellitus)
  • Educational
  • Drug Development
  • Methods Development
  • Control Set
  • Ancestry

Scientific Approaches

We will integrate GWAS data from other large-scale genetic studies with the ALLofUs data in order to:

1- Maximize the discovery of ancestry-specific genetic variants associated with a variety of forms of diabetes and complications. We will use a variety of genetic association tools, including Regenie, for GWAS analysis, METAL, for meta-analysis of results across ancestries, Finemap and Susie, and COJO to identify distinct signals.

2- Develop PRSs for diverse ancestries. We will use PRS-CS, PRS-CSx and other methods to develop and test PRSs for all ancestries.

3- We will functionally annotate genetic variants using publicly available data like the Roadmap Epigenomics browser, FANTOM, VEP, GNOMAD, etc …

Anticipated Findings

We anticipate that that results of this study will, in part, address health disparities in several ways:

1- We will improve PRSs for all ancestries so that they can be useful for all individuals if applied clinically.

2- We will identify potential drug targets, including some drug targets that can only be identified when analyzing specific ancestries.

3- We will discover novel variants or genes that will help better understand the biology of diabetes and related traits.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Josephine Li - Research Fellow, Mass General Brigham

VV Lab Analyses

We are interested in studying how genetic variants impact function of the sinoatrial node (SAN), a region of the heart where the electrical impulses which generate heart beats are initiated. Pacemaker Cells are primarily responsible for the SAN's ability to…

Scientific Questions Being Studied

We are interested in studying how genetic variants impact function of the sinoatrial node (SAN), a region of the heart where the electrical impulses which generate heart beats are initiated. Pacemaker Cells are primarily responsible for the SAN's ability to autonomously intiate action potentials, but these cells have historically been understudied because of their rarity and difficulties to work with these cells in-vitro. Studying the genetic architecture of these cells, and learning more about specific diseases which might specifically be caused by malfunction of pacemaker cells can highlight new potential therapeutic targets for chronic disease treatment and facilitate the creation of long-term biological pacemakers to replace artificial pacemakers.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We plan to extract specific variants from WGS data from AllofUs, which have previously been described as potentially impacting SAN function, and evaluating their relationship with a variety of SAN-related phenotypes using regression models.

Anticipated Findings

We hope to functionally annotate variants confirmed as having an impact on SAN data within the AllofUs dataset to validate previous results. These findings will highlight how important these variants are for susceptibility for various chronic heart conditions, rare heritable genetic cardiac diseases, and partially explain the heart rate variability within different populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

ML Genetic Diabetes

Large-scale genome wide association studies (GWAS) have identified many genetic variants associated with complex diseases. Most GWAS have been developed within European ancestries and have shown to perform poorly in other race/ethnic groups, exaggerating health disparities across ancestries. Scientific aim…

Scientific Questions Being Studied

Large-scale genome wide association studies (GWAS) have identified many genetic variants associated with complex diseases. Most GWAS have been developed within European ancestries and have shown to perform poorly in other race/ethnic groups, exaggerating health disparities across ancestries.

Scientific aim 1: Collection, harmonization and integration of large-scale, multi-ancestry cohorts with diabetes traits across the life-span and genomics for the discovery of genetic variants associated with several forms of diabetes.
Scientific aim 2: Development of methods to improve PRS prediction in non-European populations by using Bayesian approaches that allow integration of linkage disequilibrium and summary statistics from several ancestries.
Scientific aim 3: Development, testing, and comparing performance of PRS for each trait, development of risk prediction tools that integrate clinical and genetic risk factors, and assessment of scenarios where PRS improve the prediction.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus, type 1 diabetes mellitus)
  • Educational
  • Drug Development
  • Methods Development
  • Control Set
  • Ancestry

Scientific Approaches

We will integrate GWAS data from other large-scale genetic studies with the ALLofUs data in order to:

1- Maximize the discovery of ancestry-specific genetic variants associated with a variety of forms of diabetes and complications. We will use a variety of genetic association tools, including Regenie, for GWAS analysis, METAL, for meta-analysis of results across ancestries, Finemap and Susie, and COJO to identify distinct signals.

2- Develop PRSs for diverse ancestries. We will use PRS-CS, PRS-CSx and other methods to develop and test PRSs for all ancestries.

3- We will functionally annotate genetic variants using publicly available data like the Roadmap Epigenomics browser, FANTOM, VEP, GNOMAD, etc …

Anticipated Findings

We anticipate that that results of this study will, in part, address health disparities in several ways:

1- We will improve PRSs for all ancestries so that they can be useful for all individuals if applied clinically.

2- We will identify potential drug targets, including some drug targets that can only be identified when analyzing specific ancestries.

3- We will discover novel variants or genes that will help better understand the biology of diabetes and related traits.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Josephine Li - Research Fellow, Mass General Brigham
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