- Guintivano J, Arad M, Gould TD, Payne JL, Kaminsky ZA. Antenatal prediction of postpartum depression with blood DNA methylation biomarkers. Molecular Psychiatry 2013; 2013/05/22 edn2013. PMID: 23689534
- Guintivano J, Aryee M, Kaminsky ZA. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics 2013; 8(3). PMID: 23426267
- Guintivano J, Arad M, Tamashiro KL, Gould TD, Kaminsky ZA. BioTile, A Perl based tool for the identification of differentially enriched regions in tiling microarray data. BMC Bioinformatics 2013; 14: 76. PMID: 23452827
- Zachary Kaminsky, Mamoru Tochigi., Peixin Jia, Jonathan Mill, Andrew Kwan, Francine Benes, Ilya Ioshikhes, John Vincent, James Kennedy, John Strauss, Shraddha Pai, Sun-Chong Wang, Arturas Petronis. A multi-tissue analysis identifies HCG9 methylation differences in bipolar disorder. Molecular Psychiatry. 2012; Jul;17(7):728-40. PMID: 21647149
- Zachary A. Kaminsky, Thomas Tang., Sun-Chong Wang, Carolyn Ptak, Gabriel H.T. Oh, Albert H.C.Wong, Laura A. Feldcamp, Carl Virtanen, Jonas Halfvarson, Curt Tysk, Allan F. McRae, Peter M.Visscher, Grant W. Montgomery, Irving I. Gottesman, Nicholas G. Martin, Art Petronis. DNA Methylation Profiles in Monozygotic and Dizygotic Twins. Nature Genetics.2009; 41(2):240-5.
- PMID: 19151718
The focus of the laboratory is to identify epigenetic factors underlying psychiatric disease with a particular focus on mood disorders. We employ genome wide exploratory analyses using microarrays to identify disease associated epigenetic variation. Our current focus is on major depression, postpartum depression, and suicide. We also study animal models of psychiatric conditions side by side with human studies in attempts to better understand the molecular epigenetic underpinnings of psychiatric phenotypes and to enable the generation of true “bench to bedside” translational findings. The laboratory activities are divided between the development of novel methods and bioinformatic tools to aid in the study of epigenetics and basic disease and translational research.
A recent focus in the laboratory has been to generate a method to address cellular heterogeneity confounds in brain derived epigenomic datasets. We have recently generated a bioinformatic method in an R package called CETS that is capable of quantifying and normalizing for cellular heterogeneity based on DNA methylation proxies of neuronal and glial densities. We have also generated an analysis program called BioTile designed for the identification of differentially methylated regions in tiling microarray data, which is well suited to identifying small and relatively short DNA methylation changes such as those that might be expected to be found in epigenomic studies of the brain. It is our hope that these tools will better enable the identification of robust epigenetic changes important for the etiology of psychiatric phenotypes.
An additional focus of the lab is the development of disease risk predictive biomarkers utilizing DNA methylation marks in peripheral tissues. We are currently in the process of developing biomarkers predictive of postpartum depression and suicide risk.