As the discovery of the genetic substrates of neuropsychiatric disorders is hoped to lead to improved pathophysiologic understanding, enhanced treatments and relief from stigma for both the affected individuals and the field, the lack of strong, reliable and translatable genetic findings is a critical problem. My long-term career goal is to become a fully-funded, independent investigator at the intersection of neuropsychiatry and computational genetics. My long-term research goal is to identify, and characterize the complex relationships among, susceptibility genes for several major neuropsychiatric disorders, at both the population and individual levels, to the end of advancing knowledge, diagnosis and treatment of these devastating disorders. The objective of the present application is to develop these career and research trajectories by providing the multidisciplinary training and mentorship needed in the fields of molecular biology, bioinformatics and computational biology. The specific goals of the research project are: 1) to use pathways-based analyses to identify gene sets, defined in terms of shared biological attributes-including ontologies, biological pathways, protein domain structure, orthology and brain expression signatures-that are most likely to contain general and disease-specific susceptibility genes across affected bipolar, schizophrenia and substance abuse populations;and 2) to use advanced computational methods to determine the underlying genetic architecture of bipolar disease susceptibility in affected individuals, including the relative contributions of genetic heterogeneity and interactions. Uncovering the biological attributes defining susceptibility genes is likely to be of much greater significance to our understanding of disease and to future discovery efforts than any current single marker finding. And deciphering the complex genetic architecture will inform valid models of genetic transmission upon which the design of future studies can be based. This application addresses one of the greatest challenges facing psychiatry today, which is not how it will collect new and better data, but how it will translate the massive repositories of genetic, biological and bioinformatics data into better understanding of disease and rational clinical care.
This grant will support the training of a physician-scientist to develop an independent research career at the intersection of psychiatry and computational genetics. More immediately, it will support an analysis of existing genetic data to discover the types of genes relevant to psychiatric diseases and the nature of their roles in disease susceptibility. Such interdisciplinary work is both timely and critical to progress in the field.
| Askland, Kathleen D; Garnaat, Sarah; Sibrava, Nicholas J et al. (2015) Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy. Int J Methods Psychiatr Res 24:156-69 |
| Askland, Kathleen; Read, Cynthia; O'Connell, Chloe et al. (2012) Ion channels and schizophrenia: a gene set-based analytic approach to GWAS data for biological hypothesis testing. Hum Genet 131:373-91 |
| Tang, Brady; Thornton-Wells, Tricia; Askland, Kathleen D (2011) Comparative linkage meta-analysis reveals regionally-distinct, disparate genetic architectures: application to bipolar disorder and schizophrenia. PLoS One 6:e19073 |
