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Shaunna Clark vita

Assistant Professor

BS, Statistics, University of California, Berkeley, 2003
MA, Advanced Quantitative Methods, University of California, Los Angeles, 2004
PhD, Advanced Quantitative Methods, University of California, Los Angeles, 2010

Research Statement

**Please note: Dr. Clark will be accepting at least one graduate student for the fall 2019 admission class. If you are a potential applicant, please consider applying!**

 

Dr. Clark’s research seeks to understand how biological and environmental factors shape substance use and addiction. Specifically, the lab focuses on the role of genetics and epigenetics in the etiology of substance use and addiction and identifying (epi)genetic biomarkers for substance use. This line of research will eventually lead to the improvement of diagnosis, prognosis and treatment of substance addiction and its related health effects. We approach these research questions using a translational framework that incorporates both human and animal studies, big data, and advanced statistical modeling techniques.

 

 Current Projects:

  • Examining the role of DNA methylation in alcohol dependence using human post-mortem tissue samples from multiple brain regions
  • Identifying methylation biomarkers of alcohol and related health effects in blood and saliva
  • Exploring the overlap in alcohol-related methylation in blood and brain in rodent models of addiction
  • Investigating the environmental factors that mediate the relationship between methylation and alcohol use and addiction
  • Developing latent variables models to better understand mental health outcomes
  • Applying machine learning techniques to epigenomic data

Related Programs

Professional Societies

Related Links

Selected Publications

  • Han, K.M., Aghajani, M., Clark, S.L., Hattab, M.W., Shabalin, A.A., Zhao, M., Kumar, G., Chan, R.F., Xie, L.Y., Jansen, R., Aberg, K.A., van den Oord, J.C.G., Penninx, B.W.J.H.
    Epigenetic Aging in Major Depressive Disorder
    inPress, American Journal of Psychiatry
  • Clark, S.L., McClay, J.L., Adkins, D.E., Aberg, K.A., Nerella, S., Xie, L., Collins, A., Crowley, J.J., Quakenbush, C., Hillard, C., Gao, G., Shabalin, A.A., Vrieze, S.I., Peterson, R.E., Copeland, W., Silberg, J., McGue, M., Maes, H., Iacono, W.G., Sullivan, P.F., Costello, E.J., van den Oord, E.J.
    Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence
    2017, Alcoholism: Clinical and Experimental Research, 41:711-718
  • 26. Clark, S.L., Aberg, K.A., Nerella, S., Kumar, G., McClay, J.L., Chen, W., Xie, L.Y., Hudson, A., Harada, A., Gao, G., Hultman, C.M., Magnusson, P.K.E., Sullivan, P.F., van den Oord, E.J.C.G.
    Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use
    2015, Alcoholism: Clinical and Experimental Research, 39:1396-405
  • Hattab, M.W., Clark, S.L., van den Oord, E.J.C.G
    Overestimation of the classification accuracy of a biomarker for assessing heavy alcohol use
    inPress, Molecular Psychiatry
  • Shabalin, A.A., Hattab, M.W., Clark, S.L., Chan, R.F., Kumar, G., Xie, L.Y., Zhao, M., Aberg, K.A., van den Oord, E.J.C.G.
    RaMWAS: fast methylome-wide association study pipeline for enrichment platforms
    inPress, Bioinformatics
  • Neale, M.C., Clark, S.L., Dolan, C.V., Hunter, M.
    Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots
    2016, Structural Equation Modeling, 23:221-233
  • Clark, S.L., Gillespie, N., Adkins, D.A., Kendler, K.S., Neale, M.C
    Psychometric modeling of abuse and dependence symptoms across six illicit substances indicates novel dimensions of misuse
    2016, Addictive Behaviors, 53:132-140
  • more publications