Alicia Huerta

Research Fellow, Broad Institute

2 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

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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