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Prof. Dr. Marcella Rietschel

Genetic Epidemiology in Psychiatry

The Department of Genetic Epidemiology in Psychiatry investigates the biological and environmental basis of psychiatric illness, as well as gene-environment interactions and the genetic basis of treatment response (pharmacogenetics). Since psychiatric-genetic research touches upon a range of ethically sensitive issues, the Department is also engaged in intensive scientific dissection of the inherent ethical dilemmas.

The main focuses of interest are affective and schizophrenic disorders. Key aspects of our research are

  • Phenotype characterization and investigation of endophenotypes
  • Biobanking
  • Genetic and biostatistical analysis
  • Environment and Genes
  • Ethical aspects of psychiatric-genetic research
Cumulative OR in target and replication data set. (a) Target data set and (b) replication data set. Top 100 scores are not plotted because of the small number of observations. Coloring scheme: Dark green, inSCZ—at least 4 inpatient admissions due to a main diagnosis of SCZ; Red, inANY—at least 4 inpatient admissions for any mental disorder; Green, ioSCZ—at least 4 in- or outpatient admissions because of a main diagnosis of SCZ; Orange, ioANY—at least 4 in- or outpatient admissions for any mental disorder. GRPS, genomic risk profile score.
From: Meier, S. M., Agerbo, E., Maier, R., Pedersen, C. B., Lang, M., Grove, J., . . . Mattheisen, M. (2016). High loading of polygenic risk in cases with chronic schizophrenia. Mol Psychiatry, 21(7), 969-974. doi:10.1038/mp.2015.130
Comparison of polygenetic risk scores between: (A) population-based controls vs all schizophrenia patients (SCZ); (B) patients responding to standard medication vs patients with treatment-resistant schizophrenia (TRS) requiring clozapine treatment; (C) patients responding to clozapine vs patients with extreme treatment-resistant schizophrenia (ETRS) not even responding to clozapine; (D) patients with ETRS only vs patients with ETRS and additional poor premorbid social adjustment and early and insidious disease onset (ETRS+; P-values derived from right-tailed logistic regression models).
From: Frank, J., Lang, M., Witt, S. H., Strohmaier, J., Rujescu, D., Cichon, S., . . . Rietschel, M. (2015). Identification of increased genetic risk scores for schizophrenia in treatment-resistant patients. Mol Psychiatry, 20(2), 150-151. doi:10.1038/mp.2014.56
Fig. 1 a Graph displays working memory performance in dependence of the positioning of COMT Val158Met genotypes along the hypothetical inverted U-shaped curve for adults and adolescents based on previous reports. b Graph displays resting state functional connectivity for COMT Val158Met genotypes (black bars mean and 95 % CI) in adolescents and adults between amPFC and peak regions (left vlPFC, left PHG, left dlPFC, right PHG), controlled for main effects. Please note the similarity between assumptions on behavioral level (a) and resting state functional connectivity data (b). c Interaction effect of COMT Val158Met 9 developmental stage (age) controlled for gender. Positive effects indicate a stronger coupling with the seed region for adult Val homozygotes and a weaker coupling for adolescent Val homozygotes compared to Met homozygotes. Results of the seed in the anterior medial prefrontal cortex (amPFC) are shown on the lateral view. Results shown on the medial view are the vertex-wise smallest interaction effect of four lateral seeds to illustrate the extend of the ‘‘dorsal nexus’’ within the DMN. d Centered peak coordinates of significant clusters in the left vlPFC, the left PHG, the left dlPFC and the right PHG (p\0.05 corrected). Results were mapped on an averaged anatomical template with a threshold of p\0.001 in line with the family-wise multiple comparison correction (volumetric view, z-values). amPFC anterior medial prefrontal cortex, vlPFC ventrolateral prefrontal cortex, dlPFC dorsolateral prefrontal cortex, PHG parahippocampal gyrus, DMN default mode network.
From: Meyer, B. M., Huemer, J., Rabl, U., Boubela, R. N., Kalcher, K., Berger, A., . . . Pezawas, L. (2016). Oppositional COMT Val158Met effects on resting state functional connectivity in adolescents and adults. Brain Struct Funct, 221(1), 103-114. doi:10.1007/s00429-014-0895-5

Selection of recent publications

  1. Cnv, Schizophrenia Working Groups of the Psychiatric Genomics, C., & Psychosis Endophenotypes International, C. (2016). Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat Genet. doi:10.1038/ng.3725
  2. Consortium on Lithium, G., Hou, L., Heilbronner, U., Rietschel, M., Kato, T., Kuo, P. H., . . . Schulze, T. G. (2014). Variant GADL1 and response to lithium in bipolar I disorder. N Engl J Med, 370(19), 1857-1859. doi:10.1056/NEJMc1401817#SA4
  3. Frank, J., Lang, M., Witt, S. H., Strohmaier, J., Rujescu, D., Cichon, S., . . . Rietschel, M. (2015). Identification of increased genetic risk scores for schizophrenia in treatment-resistant patients. Mol Psychiatry, 20(2), 150-151. doi:10.1038/mp.2014.56
  4. Hibar, D. P., Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivieres, S., Jahanshad, N., . . . Medland, S. E. (2015). Common genetic variants influence human subcortical brain structures. Nature, 520(7546), 224-229. doi:10.1038/nature14101
  5. Hou, L., Heilbronner, U., Degenhardt, F., Adli, M., Akiyama, K., Akula, N., . . . Schulze, T. G. (2016). Genetic variants associated with response to lithium treatment in bipolar disorder: a genome-wide association study. Lancet, 387(10023), 1085-1093. doi:10.1016/S0140-6736(16)00143-4
  6. Juraeva, D., Treutlein, J., Scholz, H., Frank, J., Degenhardt, F., Cichon, S., . . . Rietschel, M. (2015). XRCC5 as a risk gene for alcohol dependence: evidence from a genome-wide gene-set-based analysis and follow-up studies in Drosophila and humans. Neuropsychopharmacology, 40(2), 361-371. doi:10.1038/npp.2014.178
  7. Rietschel, L., Streit, F., Zhu, G., McAloney, K., Kirschbaum, C., Frank, J., . . . Martin, N. G. (2016). Hair Cortisol and Its Association With Psychological Risk Factors for Psychiatric Disorders: A Pilot Study in Adolescent Twins. Twin Res Hum Genet, 19(5), 438-446. doi:10.1017/thg.2016.50
  8. Schizophrenia Working Group of the Psychiatric Genomics, C. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511(7510), 421-427. doi:10.1038/nature13595
  9. Streit, F., Bekrater-Bodmann, R., Diers, M., Reinhard, I., Frank, J., Wust, S., . . . Rietschel, M. (2015). Concordance of Phantom and Residual Limb Pain Phenotypes in Double Amputees: Evidence for the Contribution of Distinct and Common Individual Factors. J Pain, 16(12), 1377-1385. doi:10.1016/j.jpain.2015.08.013
  10. Witt, S. H., Juraeva, D., Sticht, C., Strohmaier, J., Meier, S., Treutlein, J., . . . Rietschel, M. (2014). Investigation of manic and euthymic episodes identifies state- and trait-specific gene expression and STAB1 as a new candidate gene for bipolar disorder. Transl Psychiatry, 4, e426. doi:10.1038/tp.2014.71

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