Xavier Bledsoe

Graduate Trainee, Vanderbilt University

1 active project

Development and Application of Multi-omics Methods for Complex Trait Analyses

We are interested in generating novel methods that leverage multi-omics data to inform the molecular and biologic mechanisms underlying complex disease broadly. Complex traits differ from mendelian conditions in which a disease causing variant can be localized both genetically (to…

Scientific Questions Being Studied

We are interested in generating novel methods that leverage multi-omics data to inform the molecular and biologic mechanisms underlying complex disease broadly. Complex traits differ from mendelian conditions in which a disease causing variant can be localized both genetically (to a single locus) and biologically (to DNA). Complex traits are associated with a wide range of genetic loci and the relevant biological factors span components including DNA, RNA, Proteins, Lipids, Cell types, Signaling molecules, etc. Broadly, our hypothesis is that heritable drivers of health and disease rely on the effective regulation and cooperation of these components with each other. In this project we aim to characterize the relationships between molecular phenotypes as they relate to states of health and disease. We have a particular interest in generating resources that are calibrated to individuals across a range of genetic ancestries to reduce disparities in the benefit of scientific discoveries.

Project Purpose(s)

  • Methods Development
  • Ancestry

Scientific Approaches

We will accomplish our aims through the application of statistical methods to diverse datasets including molecular, environmental, phenotypic, and demographic data. We will use standard regression approaches including linear, logistic, mixed, and machine learning models to identify context specific relationships between potentially causal components of human biology. Instrumental variable analyses such as Mendelian Randomization will be applied in the interest of imputing causality.

Anticipated Findings

The analyses proposed under this project will contribute to the forward progression and lateral application of multi-omics research as relates to human health and disease. Through methods development approaches, we will provide the scientific community with new tools for quantifying and assessing the impact of biologic variation in a broad array of biological measurements. Through the validation and application of these resources , we will contribute to thein questions of health-related significance, we will advance the state of scientific knowledge with regard to causal mechanisms, disease associations, predictive biomarkers, and systems biology. In performing this work in populations of multiple genetic ancestries, we will improve the accessibility, utility, and the equity of modern scientific advancements for a broader swath of the human diaspora.

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:

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