@article{SchulzRuppertHermsetal.2017,
  author    = {Schulz, Herbert and Ruppert, Ann-Kathrin and Herms, Stefan and Wolf, Christiane and Mirza-Schreiber, Nazanin and Stegle, Oliver and Czamara, Darina and Forstner, Andreas J. and Sivalingam, Sugirthan and Schoch, Susanne and Moebus, Susanne and P{\"u}tz, Benno and Hillmer, Axel and Fricker, Nadine and Vatter, Hartmut and M{\"u}ller-Myhsok, Bertram and N{\"o}then, Markus M. and Becker, Albert J. and Hoffmann, Per and Sander, Thomas and Cichon, Sven},
  title     = {Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus},
  series = {Nature Communications},
  volume    = {8},
  journal   = {Nature Communications},
  doi       = {10.1038/s41467-017-01818-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-173168},
  year      = {2017},
  abstract  = {Emerging evidence emphasizes the strong impact of regulatory genomic elements in neurodevelopmental processes and the complex pathways of brain disorders. The present genome-wide quantitative trait loci analyses explore the \(cis\)-regulatory effects of single-nucleotide polymorphisms (SNPs) on DNA methylation (meQTL) and gene expression (eQTL) in 110 human hippocampal biopsies. We identify \(cis\)-meQTLs at 14,118 CpG methylation sites and \(cis\)-eQTLs for 302 3′-mRNA transcripts of 288 genes. Hippocampal \(cis\)-meQTL-CpGs are enriched in flanking regions of active promoters, CpG island shores, binding sites of the transcription factor CTCF and brain eQTLs. \(Cis\)-acting SNPs of hippocampal meQTLs and eQTLs significantly overlap schizophrenia-associated SNPs. Correlations of CpG methylation and RNA expression are found for 34 genes. Our comprehensive maps of \(cis\)-acting hippocampal meQTLs and eQTLs provide a link between disease-associated SNPs and the regulatory genome that will improve the functional interpretation of non-coding genetic variants in the molecular genetic dissection of brain disorders.},
  language  = {en}
}
@article{ZieglerRadtkeVitaleetal.2021,
  author    = {Ziegler, Georg C. and Radtke, Franziska and Vitale, Maria Rosaria and Preuße, Andr{\´e} and Klopocki, Eva and Herms, Stefan and Lesch, Klaus-Peter},
  title     = {Generation of multiple human iPSC lines from peripheral blood mononuclear cells of two SLC2A3 deletion and two SLC2A3 duplication carriers},
  series = {Stem Cell Research},
  volume    = {56},
  journal   = {Stem Cell Research},
  doi       = {10.1016/j.scr.2021.102526},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-264696},
  year      = {2021},
  abstract  = {Copy number variants of SLC2A3, which encodes the glucose transporter GLUT3, are associated with several neuropsychiatric and cardiac diseases. Here, we report the successful reprogramming of peripheral blood mononuclear cells from two SLC2A3 duplication and two SLC2A3 deletion carriers and subsequent generation of two transgene-free iPSC clones per donor by Sendai viral transduction. All eight clones represent bona fide hiPSCs with high expression of pluripotency genes, ability to differentiate into cells of all three germ layers and normal karyotype. The generated cell lines will be helpful to enlighten the role of glucometabolic alterations in pathophysiological processes shared across organ boundaries.},
  language  = {en}
}
@article{BreuerMattheisenFranketal.2018,
  author    = {Breuer, Ren{\´e} and Mattheisen, Manuel and Frank, Josef and Krumm, Bertram and Treutlein, Jens and Kassem, Layla and Strohmaier, Jana and Herms, Stefan and M{\"u}hleisen, Thomas W. and Degenhardt, Franziska and Cichon, Sven and N{\"o}then, Markus M. and Karypis, George and Kelsoe, John and Greenwood, Tiffany and Nievergelt, Caroline and Shilling, Paul and Shekhtman, Tatyana and Edenberg, Howard and Craig, David and Szelinger, Szabolcs and Nurnberger, John and Gershon, Elliot and Alliey-Rodriguez, Ney and Zandi, Peter and Goes, Fernando and Schork, Nicholas and Smith, Erin and Koller, Daniel and Zhang, Peng and Badner, Judith and Berrettini, Wade and Bloss, Cinnamon and Byerley, William and Coryell, William and Foroud, Tatiana and Guo, Yirin and Hipolito, Maria and Keating, Brendan and Lawson, William and Liu, Chunyu and Mahon, Pamela and McInnis, Melvin and Murray, Sarah and Nwulia, Evaristus and Potash, James and Rice, John and Scheftner, William and Z{\"o}llner, Sebastian and McMahon, Francis J. and Rietschel, Marcella and Schulze, Thomas G.},
  title     = {Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics},
  series = {International Journal of Bipolar Disorders},
  volume    = {6},
  journal   = {International Journal of Bipolar Disorders},
  doi       = {10.1186/s40345-018-0132-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220509},
  year      = {2018},
  abstract  = {Background Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Results Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. Conclusion Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.},
  language  = {en}
}