The goals of this project are to build resources that make it easier to identify the genetic component to observed health …

Using a Biobank and Electronic Health Records to Characterize Genome Variation Affecting Health Disparities

The goals of this project are to build resources that make it easier to identify the genetic component to observed health disparities in disease risk and/or outcomes and, investigate some specific health disparities in which genetic risk factors clearly contribute to differences between continental population in lifetime risk of the phenotype of interest.

Specific Aims

  1. Systematically identify phenotypes with marked differences in lifetime risks between Americans of recent European and Americans of recent African ancestry
  2. Systematically identify and characterize the genetic factors contributing to phenotypes with significantly different lifetime risks among individuals from populations with different historical geographic ancestry.
  3. Comprehensively characterize the relative contribution of genetic risk factors to health disparities in lifetime risk and outcomes for a) asthma and pre-term birth (year 1), phenotypes with existing evidence for genetic contributions to health disparities, b) cervical cancer and BMI (year 2), phenotypes investigated in our other projects, and c) phenotypes discovered through research conducted in Aims 1 and 2, chosen from other grants funded in this initiative, or are disparities in the LGBT community (years 3-5).

Research Team


  • Investigating the heritability of pharmacogene expression
    • In this project, we aim to utilize expression data from the Genotype-Tissue Expression (GTEx) Consortium to understand how genes that play a role in response to pharmacological treatment are regulated across tissues. Postdoc Sabrina Mitchell is working on this project in collaboration with the Davis Lab and the Altman Lab in Stanford University.
  • Inverse axis of risk of polygenic and rare variant burdens
    • Research has shown that both polygenic risk and risk from rare but highly penetrant variants contribute to many complex traits. If an individual may develop a particular phenotype by crossing either a polygenic risk liability threshold or a rare variant liability threshold, we would expect to detect an inverse correlation between these two orthogonal sources of genetic risk among cases. Our studies involving simulated and real data from multiple complex phenotypes, including Tourette Syndrome (TS), obsessive-compulsive disorder (OCD), autism spectrum disorder (ASD), and type 1 diabetes (T1D) have shown that, indeed, both sources of genomic risk are critically important, may be inversely related, and should be considered jointly.
  • Identifying and characterizing genetic factors of high-risk phenotypes associated with ancestry
    • This project examines the risk factors contributing to health disparities in disease and outcomes by systematically identifying phenotypes with marked differences in lifetime risk across ancestral backgrounds. We are using BioVU to identify phenotypes for which prevalence, outcomes, and response to treatments vary by ancestry after controlling for environmental risk factors. The goal of this analysis is to discover health risks that may disproportionately affect minority populations so we can improved personalized medicine in all populations.
  • Systematic assessment of signatures of selection across brain-related phenotypes
    • Inter-individual variation in neuropsychiatric traits is present across diverse human populations, has persisted through recorded history, and has been shown to have a genetic basis primarily accounted for by common (minor allele frequency > 5%) SNPs. Many of the characteristics of psychiatric disorders, including their early age of onset, moderate to high prevalence (1% for schizophrenia – 20% for major depression), reduced fecundity (Power et al., 2013), and high heritability (Polderman et al., 2015) have led researchers to question how risk alleles have persisted throughout evolutionary history (Huxley et al., 1964; Jablensky et al., 1992; Bigdeli et al., 2013). While several potential mechanisms for maintaining high allele frequency of risk variants have been hypothesized (e.g., weak positive selection, balancing selection), few have been empirically tested (Pearlson and Folley, 2008; Keller and Miller, 2006). Together with the Capra Lab we are investigating the influence of numerous selective pressures across different epochs in shaping the landscape of brain-related genetic variation.
  • Psychiatric genetics in the context of the medical phenome
    • The large-scale collection of DNA combined with expansive phenotype information from electronic medical records provides a broader context (i.e., “ the phenome”) in which to study the genetics of neuropsychiatric phenotypes.  The Davis lab is working with faculty and trainees at Vanderbilt and across the country to develop gold standard phenotyping algorithms for a number of neuropsychiatric and developmental diagnoses including OCD, Tourette Syndrome, anorexia nervosa and autism spectrum disorders. Additionally, we are implementing polygenic approaches to identify potential novel biomarkers from our collection of > 500 routinely-collected clinically-validated lab tests. Identifying correlated phenotypes and laboratory measurements, and assessing the broader role of implicated variants/genes are only a few of the many ways in which these studies could provide a unique lens through which to interpret genetic findings.