Alexander Berry

Research Associate, Geisinger Clinic

6 active projects

FH & ASCVD Risk

Familial hypercholesterolemia (FH) is an inherited disorder characterized by lifelong elevated low density lipoprotein cholesterol (LDL-C) and dramatically increased risk for premature atherosclerotic cardiovascular disease (ASCVD). Genetic studies now suggest that familial hypercholesterolemia (FH) encompasses five discrete subtypes based on…

Scientific Questions Being Studied

Familial hypercholesterolemia (FH) is an inherited disorder characterized by lifelong elevated low density lipoprotein cholesterol (LDL-C) and dramatically increased risk for premature atherosclerotic cardiovascular disease (ASCVD). Genetic studies now suggest that familial hypercholesterolemia (FH) encompasses five discrete subtypes based on LDL-c levels, 1) a monogenic FH variant, 2) a high low density lipoprotein cholesterol (LDL-c) polygenic score, 3) elevated lipoprotein(a), 4) elevated LDL-c polygenic score with elevated lipoprotein(a), and 5) a positive family history without an identifiable genetic cause, or true “phenotypic FH.” The primary question of this project is: Are there differences in treatment, comorbidities, and ASCVD outcomes between FH subtypes?

Project Purpose(s)

  • Disease Focused Research (familial hypercholesterolemia)
  • Ancestry

Scientific Approaches

We plan to screen all individuals with whole genome sequences available for monogenic variants in an FH gene. We will calculate an LDL cholesterol polygenic risk score from each participant’s whole genome sequence. Using labs and measurements, we will identify individuals with an FH subtype. Additionally, we will stratify the cohort into statin treated and untreated individuals using medication data. Finally, we will use EH data to determine comorbidities and ASCVD outcomes. We will primarily use regression analyses to compare ASCVD risk in those with each FH subtype to individuals without FH.

Anticipated Findings

We expect ASCVD risk to vary among FH subtypes and to be more pronounced in each of the subtypes, relative to those without FH. We also anticipate ASCVD risk and comorbidities to vary between individuals using lipid-lowering medication and those not using medication. Our findings may demonstrate the importance of considering subtypes in ASCVD risk assessment for patients with the FH phenotype.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Alex Winters - Research Associate, Geisinger Clinic

X and Y Chromosome Intensity

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Scientific Questions Being Studied

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Project Purpose(s)

  • Other Purpose (Demonstrate to the All of Us Researcher Workbench users how to get started with the All of Us genomic data and tools. It includes an overview of all the All of Us genomic data and shows some simple examples on how to use these data.)

Scientific Approaches

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Anticipated Findings

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sex Chromosome Aneuploidies and Neuropsychiatric Disorders

Some individuals are born with sex chromosome aneuploidies, an extra or loss of an X or Y chromosome. Sex chromosome aneuploidies are known to associate with cognitive impairment, but the full spectrum of brain disorders is not well described. The…

Scientific Questions Being Studied

Some individuals are born with sex chromosome aneuploidies, an extra or loss of an X or Y chromosome. Sex chromosome aneuploidies are known to associate with cognitive impairment, but the full spectrum of brain disorders is not well described. The primary question of this project is “What is the relationship between sex chromosome aneuploidies and neuropsychiatric disorders?”

Project Purpose(s)

  • Disease Focused Research (Sex chromosome aneuploidies)
  • Ancestry

Scientific Approaches

Sex-chromosome aneuploidies will be identified from the array of genotype data. We will screen for sex-chromosome imbalances by identifying outliers in the array intensity values. We will use whole-genome data to orthogonally confirm the presence of a sex-chromosome aneuploidy called from the genotype array data. We plan to use comprehensive EHR data, conditions, drug exposures, lab/measurements, and procedures to create NPD outcomes and exposure variables. We will primarily use regression analyses to compare the rates of NPD in those with a sex chromosome aneuploidy compared to those without a sex chromosome aneuploidy.

Anticipated Findings

We will derive population-based estimates for NPD risk caused by the presence of a sex chromosome aneuploidy. Our findings may demonstrate the importance of considering sex chromosome aneuploidies as a common cause of NPD.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of How to Work with All of Us Genomic Data (Hail - Plink)(v6)

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Scientific Questions Being Studied

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Project Purpose(s)

  • Other Purpose (Demonstrate to the All of Us Researcher Workbench users how to get started with the All of Us genomic data and tools. It includes an overview of all the All of Us genomic data and shows some simple examples on how to use these data.)

Scientific Approaches

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Anticipated Findings

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of How to Get Started with Registered Tier Data (tier 5)

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. What should you expect? This notebook will give you an overview of what data is available in the current…

Scientific Questions Being Studied

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.

What should you expect? This notebook will give you an overview of what data is available in the current Curated Data Repository (CDR). It will also teach you how to retrieve information about Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Tutorial Workspace. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation.)

Scientific Approaches

This Tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). Each notebook is divided into the following sections:

1. Setup: How to set up this notebook, install and import software packages, and select the correct version of the CDR.
2. Data Availability Part 1: How to summarize the number of unique participants with major data types: Physical Measurements, Survey, and EHR;
3. Data Availability Part 2: How to delve a little deeper into data availability within each major data type;
4. Data Organization: An explanation of how data is organized according to our common data model.
5. Example Queries: How to directly query the CDR, using two examples of SQL queries to extract demographic data.
6. Expert Tip: How to access the base version of the CDR, for users that want to do their own cleaning.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following:

All of Us data are made available in a Curated Data Repository. Participants may contribute any combination of survey, physical measurement, and electronic health record data. Not all participants contribute all possible data types. Each unique piece of health information is given a unique identifier called a concept_id and organized into specific tables according to our common data model. You can use these concept_ids to query the CDR and pull data on specific health information relevant to your analysis. See our support article Learning the Basics of the All of Us Dataset for more info.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Duplicate of How to Get Started with Controlled Tier Data

1. Socio-Economic Metrics: How to retrieve participants' socio-economic data from the CDR. 2. Observation Date: How to query and plot an observation date using survey completion date as example. 3. Demographics: Examples of how to query and plot participant demographic…

Scientific Questions Being Studied

1. Socio-Economic Metrics: How to retrieve participants' socio-economic data from the CDR.
2. Observation Date: How to query and plot an observation date using survey completion date as example.
3. Demographics: Examples of how to query and plot participant demographic data.
4. Death Cause: How to retrieve and plot deceased participants' death causes.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Featured Workspace: - teaches the users how to set up this notebook, install and import software packages, and select the correct version of the CDR. - gives an overview of the data types available in the current Controlled Tier Curated Data Repository (CDR) that are not available in the Registered Tier - shows how to retrieve and summarize this data.)

Scientific Approaches

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. The tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). It contains helper functions for repeatedly, code readability and efficiency and repeatedly.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following: All of Us data are made available in two Curated Data Repository: the Registered Tier and Controlled Tier. The latter was subject to more relaxed privacy rules relative to the Registered Tier. As a result, you can expect to find more concept ids in certain data types such as EHR and Survey.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

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Request a Review of this Research Project

You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.