Addiction places a tremendous burden on individuals and society, and an improved understanding of the underlying genetic and environmental factors that contribute to this disease will aid prevention and treatment efforts. This project will use three large, genetically informative datasets that include comprehensive assessments of substance dependence, comorbid disorders and environmental factors: the Collaborative Study on the Genetics of Alcoholism, the Family Study of Cocaine Dependence, and the Collaborative Genetic Study of Nicotine Dependence. This project has four aims:
Aim 1 : To study genetic findings in three databases in the search for common and specific factors involved in the development of addiction. Genetic findings will be examined within and across datasets so that a better understanding of common and specific genetic factors in the development of addiction can be developed.
Aim 2 : To examine refined phenotypes, such as quantitative phenotypes of smoking, alcoholism, and polysubstance dependence in genetic analyses. The refinement of quantitative phenotypes such as a polysubstance symptom count will be undertaken in order to further the study of genetic polymorphisms and addiction. Because these three studies share a common assessment, these refined phenotypes can be applied to all databases.
Aim 3 : To examine the influence of ethnicity, gender and psychiatric comorbidity on the """"""""at risk"""""""" genotypes. Phenotype and genotype correlations will be examined to determine whether distinct dependence subtypes are associated with specific """"""""at risk"""""""" genotypes. This will include an examination by ethnicity, gender, additional substance dependence, and psychiatric comorbidity.
Aim 4 : To examine complex genetics of substance dependence and quantitative phenotypes using additional genetic analyses of pleiotropy, gene-gene, and gene-environment interactions. The candidate is an independent investigator who has already made important contributions to the study of the genetic epidemiology of addiction. This Independent Scientist Award will allow her to continue to pursue her research on the human genetics of addiction. She will continue to collaborate with investigators at Washington University and nationally in order to apply state of the art approaches to genetic analysis of this complex disorder. By comparing and contrasting findings in three datasets, a more thorough understanding of common and specific factors underlying human addiction will be developed.
| Krauss, Melissa J; Grucza, Richard A; Bierut, Laura J et al. (2017) ""Get drunk. Smoke weed. Have fun."": A Content Analysis of Tweets About Marijuana and Alcohol. Am J Health Promot 31:200-208 |
| Hou, Liping; Heilbronner, Urs; Degenhardt, Franziska et al. (2016) Genetic variants associated with response to lithium treatment in bipolar disorder: a genome-wide association study. Lancet 387:1085-1093 |
| Hou, Liping; Bergen, Sarah E; Akula, Nirmala et al. (2016) Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum Mol Genet 25:3383-3394 |
| Cabrera-Nguyen, E Peter; Cavazos-Rehg, Patricia; Krauss, Melissa et al. (2016) Young Adults' Exposure to Alcohol- and Marijuana-Related Content on Twitter. J Stud Alcohol Drugs 77:349-53 |
| Cavazos-Rehg, Patricia A; Krauss, Melissa J; Spitznagel, Edward L et al. (2015) Monitoring of non-cigarette tobacco use using Google Trends. Tob Control 24:249-55 |
| Krauss, Melissa J; Sowles, Shaina J; Moreno, Megan et al. (2015) Hookah-Related Twitter Chatter: A Content Analysis. Prev Chronic Dis 12:E121 |
| Cavazos-Rehg, Patricia A; Krauss, Melissa J; Spitznagel, Edward L et al. (2015) Maternal age and risk of labor and delivery complications. Matern Child Health J 19:1202-11 |
| Ament, Seth A; Szelinger, Szabolcs; Glusman, Gustavo et al. (2015) Rare variants in neuronal excitability genes influence risk for bipolar disorder. Proc Natl Acad Sci U S A 112:3576-81 |
| Chen, Li-Shiun; Hung, Rayjean J; Baker, Timothy et al. (2015) CHRNA5 risk variant predicts delayed smoking cessation and earlier lung cancer diagnosis--a meta-analysis. J Natl Cancer Inst 107: |
| Chen, Li-Shiun; Baker, Timothy B; Bierut, Laura J (2015) The value of control conditions for evaluating pharmacogenetic effects. Pharmacogenomics 16:2005-6 |
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