This application both requests continued support for the research objectives of MH 51372 and simultaneously proposes to develop and implement a high throughput strategy for the detection of quantitative trait genes (QTGs).
The aims of the application may be summarized: 1. To fine map in heterogeneous stock (HS) animals (to a resolution of 1 cM or less) known QTLs for basal locomotor activity, the acoustic startle response (ASR) and prepulse inhibition (PPI) of the ASR ; 2. To integrate short-term selective breeding and gene expression analysis. The goal of this aim will be to both further confirm the QTLs detected in Specific Aim 1 and to determine which genes within the QTL interval, exhibit expression patterns that segregate with phenotype. 3. To determine which genes within the QTL interval (aim 1) show the genotype x phenotype appropriate expression pattern. 4. To determine which genes within the QTL interval are cis-regulated. For the candidate eQTGs detected in aims 2 and 3 to generate QTLs, they must exhibit cis-regulation (Brem et al. 2002; Schadt et al. 2003; Darvasi, 2003). Cis-regulation will be measured in BXD recombinant inbred (RI) animals with sufficient power (N=32 strains) to detect eQTL effect sizes of h2QTL = 0.33. Previous studies have shown that the effect size of cis-regulated eQTLS generally exceed this threshold (Brem et al. 2002; Schadt et al. 2003a). 5. To use all available strategies to provide additional proof of QTL -> QTG (see Belknap et al. 2001). When appropriate, the preferred method of proof will be the formation of bacterial artificial chromosome (BAC) transgenic mice. We first suggested (our view) of an integrated strategy for the detection of QTGs in Belknap et al. (2001) (but also see Phillips et al. 2001). Over the past three years the strategy has been refined and updated, reflecting new developments in mouse genetics. Finally, the strategy outlined in aims 1-5, provides interlocking levels of proof on several levels, in keeping with the recommendations of a recent """"""""white"""""""" paper on QTL analysis (Flaherty et al. 2003).
Chesler, Elissa J; Gatti, Daniel M; Morgan, Andrew P et al. (2016) Diversity Outbred Mice at 21: Maintaining Allelic Variation in the Face of Selection. G3 (Bethesda) 6:3893-3902 |
Zheng, Christina L; Wilmot, Beth; Walter, Nicole Ar et al. (2015) Splicing landscape of the eight collaborative cross founder strains. BMC Genomics 16:52 |
Iancu, Ovidiu D; Colville, Alexandre; Oberbeck, Denesa et al. (2015) Cosplicing network analysis of mammalian brain RNA-Seq data utilizing WGCNA and Mantel correlations. Front Genet 6:174 |
Hitzemann, Robert; Darakjian, Priscila; Walter, Nikki et al. (2014) Introduction to sequencing the brain transcriptome. Int Rev Neurobiol 116:1-19 |
Hitzemann, Robert; Bottomly, Daniel; Iancu, Ovidiu et al. (2014) The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits. Mamm Genome 25:12-22 |
Iancu, Ovidiu D; Oberbeck, Denesa; Darakjian, Priscila et al. (2013) Differential network analysis reveals genetic effects on catalepsy modules. PLoS One 8:e58951 |
Hitzemann, R; Bottomly, D; Darakjian, P et al. (2013) Genes, behavior and next-generation RNA sequencing. Genes Brain Behav 12:1-12 |
Iancu, O D; Darakjian, P; Malmanger, B et al. (2012) Gene networks and haloperidol-induced catalepsy. Genes Brain Behav 11:29-37 |
Iancu, Ovidiu D; Darakjian, Priscila; Kawane, Sunita et al. (2012) Detection of expression quantitative trait Loci in complex mouse crosses: impact and alleviation of data quality and complex population substructure. Front Genet 3:157 |
Iancu, Ovidiu D; Kawane, Sunita; Bottomly, Daniel et al. (2012) Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics 28:1592-7 |
Showing the most recent 10 out of 33 publications