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In our lab group, we study behavior and its environmental and genetic influences.

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The Behavior, Environment, and Genetics lab (BEGL)

Research interests

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Genetic & environmental risk (and protective) factors

for substance use disorders

I analyze genome-wide and epigenome-wide data to better understand the genetic architecture and underlying biology of substance use disorders. Many of these projects require large-scale collaborations via international consortia to achieve adequate statistical power. I also leverage self-report survey data in datasets like the All of Us Research Program to characterize the individual and systemic environmental factors that contribute to substance use disorders, and how these factors may interact with genomic risk.

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Unique and shared genetic factors underlying co-occurring health conditions and behaviors

 

Using methods such as genomic structural equation modeling, I examine the genetic overlap (and points of divergence) among different behaviors and health conditions. For example, in a recent study, we identified relatively large genetic correlations between suicide-related behaviors and substance use disorders; furthermore, these relationships persist even when accounting for the genetics of depression, a risk factor often evaluated in clinical settings. These findings underscore the transdiagnostic nature of suicide-related behaviors.

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Autozygosity and complex traits

I use genome-wide data to ask evolutionarily-informed questions about human behaviors and psychiatric disorders - for example, do we see evidence that genetic risk variants for schizophrenia have been selected against over evolutionary time? Studying genome-wide autozygosity can reveal the extent to which different types of genetic variants may contribute to risk of a certain disorder (e.g., rare, recessive variants), but requires large sample sizes and may be prone to confounding by assortative mating and other factors. Future directions include looking at associations in within-sibling designs, as within-sibling comparisons should avoid potential confounding that can plague population-level estimates. 

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