This application requests continued support for the long-term objectives of MH 51372, namely to develop and implement high throughput strategies for the detection and fine mapping of mental health-related quantitative trait loci (QTL), for the integration of QTL mapping with functional genomics and for the detection and confirmation of the relevant quantitative trait gene(s) (QTGs). This integrated approach to QTL analysis requires the development and utilization of detailed brain gene expression and sequence datasets. The phenotypes of interest are open-field activity (OFA) and prepulse inhibition (PPI) of the startle response. This renewal application also emphasizes the detection of gene networks associated with OFA and PPI. This competing renewal has 5 specific aims. 1) To fine map in heterogeneous stock (HS) animals (to a resolution of 1 cM or less) high quality QTLs for OFA (2 on Chr 1 at ~ 110 and 175 Mbp) and PPI (Chr 11 at ~ 70 Mbp and Chr 16 at ~ 45 Mbp). 2) To integrate the QTL data with interval relevant gene expression and sequence data. 3) To determine in HS4 animals the gene coexpression networks associated with OFA and PPI. 4) To determine if the modules identified in aim 3 are the same modules that differentiate short-term selective breeding (STSB) lines, selected for High and Low OFA and PPI. 5) To determine if the OFA and PPI modules identified in aims 3 and 4 are also detected in a more genetically diverse mouse population of mice, namely an outbred population derived from the Collaborative Cross. The primary mapping population is a HS formed from the C57BL/6, DBA.2j, BALB/c and LP strains. Mapping in these HS4 will provide QTL resolution of approximately 1-2 cM. As each interval will be completely sequenced, all causative polymorphisms will be detected. In addition to 3'biased oligonucleotide gene expression analysis, we will test the idea that some QTLs may be generated by alternative exon usage. Our ability to sort through the large amount of data generated is made possible by precisely knowing the haplotype of each QTL. The proposed work builds upon data collected during the current grant period and importantly incorporates new genomic technologies which make possible our fully integrated approach.
The proposed work attempts to understand the role(s) of genetic factors in behaviors that are of mental health significance. The eventual goal is to determine which genes and gene networks make some individuals more prone (increased risk) for developing abnormal behaviors such as schizophrenia and anxiety disorders. With this information in hand, it should be possible to develop new therapeutic strategies and interventions. .
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| Iancu, Ovidiu D; Kawane, Sunita; Bottomly, Daniel et al. (2012) Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics 28:1592-7 |
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