The overall aim of this project is to facilitate the integration of measured genotypes in etiological models of behavioral traits and psychiatric disorders. This integration is critically important for two reasons. First, the etiology of these phenotypes is multifactorial and complex. The use of techniques that can model these complexities may increase statistical power and enable researchers to detect genes that do not work in a simple """"""""main effects"""""""" fashion. Second, the inclusion of measured genotypes will improve our understanding of the phenotypes. That is, the unique properties of measured genes will be helpful to study traditional research questions from a new perspective and examine a whole new domain of risk mechanisms that describe how genes and environmental variables act in concert. To guide our research we will focus on three traditional research themes: a) comorbidity, b) pathology and development, c) causal mechanisms as well as three themes that pertain to the interplay of genotypes and environment: a) vulnerability, resilience, and protective factors, b) correlations between environmental factors and health problems, c) sensitivity to environmental fluctuations. For these themes we will further develop and specify models that give more insight into the nature of the genetic effects on behavioral traits and psychiatric disorder, and demonstrate these models using four existing samples in which genotypephenotype associations have been found. Successful completion of this project means that we have created a complete statistical tool kit for modeling the role of genes in the etiology of complex traits where many tools have been made available to other researchers via the Mx software in a user-friendly way. Furthermore, the empirical applications will not only have been of scientific importance but, e.g. by refining diagnostic classification, also have implications for prevention and clinical intervention.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Study Section
Social Sciences, Nursing, Epidemiology and Methods 4 (SNEM)
Program Officer
Farmer, Mary E
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Virginia Commonwealth University
Schools of Medicine
United States
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van den Oord, Edwin J C G (2008) Controlling false discoveries in genetic studies. Am J Med Genet B Neuropsychiatr Genet 147B:637-44
van den Oord, Edwin; McClay, Joseph; York, Timothy et al. (2007) Genetics and diagnostic refinement. Behav Genet 37:535-45
Bukszar, Jozsef; van den Oord, Edwin J C G (2006) Accurate and efficient power calculations for 2 x m tables in unmatched case-control designs. Stat Med 25:2632-46
Vargas-Irwin, Cristina; van den Oord, Edwin J C G; Beardsley, Patrick M et al. (2006) A method for analyzing strain differences in acquisition of IV cocaine self-administration in mice. Behav Genet 36:525-35
McClay, Joseph L; van den Oord, Edwin J C G (2006) Variance component analysis of polymorphic metabolic systems. J Theor Biol 240:149-59
Robles, Jaime R; van den Oord, Edwin J C G (2006) A cautionary note on the use of simulation procedures for analyzing contingency tables containing small expected cell frequencies. Am J Med Genet B Neuropsychiatr Genet 141B:414-7
York, Timothy P; Eaves, Lindon J; van den Oord, Edwin J C G (2006) Multivariate adaptive regression splines: a powerful method for detecting disease-risk relationship differences among subgroups. Stat Med 25:1355-67
van den Oord, E J C G (2005) Controlling false discoveries in candidate gene studies. Mol Psychiatry 10:230-1
Kooij, J J Sandra; Buitelaar, Jan K; van den Oord, Edwin J et al. (2005) Internal and external validity of attention-deficit hyperactivity disorder in a population-based sample of adults. Psychol Med 35:817-27
van den Oord, Edwin J C G; MacGregor, Alex J; Snieder, Harold et al. (2004) Modeling with measured genotypes: effects of the vitamin D receptor gene, age, and latent genetic and environmental factors on bone mineral density. Behav Genet 34:197-206

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