This application seeks five years of support to develop, evaluate and fine-tune a new research education program (WU-STAGEM: Washington University Statistical Training in Addiction Genetics Methodology), to train postdoctoral level scientists (9 over the 5-year funding period). Trainees ("program participants") will include both US and foreign nationals, drawn from backgrounds such as biostatistics, mathematics, quantitative psychology and statistical genetics. We will provide trainees with both (i) the expertise to develop, test and apply new statistical and computational models to address genetics-based research problems in addiction, and (ii) sufficient training to allow participants to work collaboratively in multidisciplinary teams alongside basic and clinical researchers engaged in addiction research. Program postdoctoral scientists will train under the guidance of mentoring teams composed of (a) clinical researchers in addiction, (b) basic scientists and, most critically, (c) researchers with pertinent statistical or computational modeling expertise, with the support of individually tailored formal coursework. They will work with addiction geneticists at the host institution and elsewhere (e.g., through the NIDA Genetics Consortium) to identify and conduct research in areas where unique and urgently needed contributions to the field of addiction genetics can be made, producing products such as novel computer software, novel algorithms or approaches to statistical genetic analysis, to advance the field of addiction genetics. These priority areas will include (varying according to trainee backgrounds and interests and expert input): 1) incorporation of bioinformatics data in the statistical analysis and interpretation of GWAS data, 2) challenges in the combination of GWAS data across data-sets characterized by differing sampling schemes and somewhat different phenotypic assessments, and 3) modeling of genotype x environment interaction effects, and incorporation of consideration of GxE effects in efforts at gene-discovery. We will take advantage of existing data sets such as those at the NIDA Center for Genetic studies;zork.wustl.edu/nida/ and at dbGap, as well as more extensive data-sets associated with local investigators, in this training effort. Through infrastructure development and continuing evaluation efforts, building upon outstanding existing institutional strengths, we will seek to develop a self-sustaining program, drawing upon the wide-ranging expertise at Washington University that can supply highly trained experts in statistical and computational modeling to the field of addiction genetics.
Licit and illicit drug dependence represents a considerable personal and public health burden. The important role of individual genetic vulnerability in contributing to addiction risk is well established. However, there is a critical shortage of investigators with expertise in statistical genetics and computational genomics working in addiction research, a need that this application seeks to address through its postdoctoral research education program. NARRATIVE Licit and illicit drug dependence represents a considerable personal and public health burden. The important role of individual genetic vulnerability in contributing to addiction risk is well established. However, there is a critical shortage of investigators with expertise in statistical genetics and computational genomics working in addiction research, a need that this application seeks to address through its postdoctoral research education program.
|Li, Daofeng; Zhang, Bo; Xing, Xiaoyun et al. (2015) Combining MeDIP-seq and MRE-seq to investigate genome-wide CpG methylation. Methods 72:29-40|
|Zhou, Xin; Li, Daofeng; Lowdon, Rebecca F et al. (2014) methylC Track: visual integration of single-base resolution DNA methylation data on the WashU EpiGenome Browser. Bioinformatics 30:2206-7|
|Yang, Wei; Charles Gu, C (2014) Random forest fishing: a novel approach to identifying organic group of risk factors in genome-wide association studies. Eur J Hum Genet 22:254-9|
|Cruchaga, Carlos; Karch, Celeste M; Jin, Sheng Chih et al. (2014) Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease. Nature 505:550-4|
|Garfield, Lauren D; Dixon, David; Nowotny, Petra et al. (2014) Common selective serotonin reuptake inhibitor side effects in older adults associated with genetic polymorphisms in the serotonin transporter and receptors: data from a randomized controlled trial. Am J Geriatr Psychiatry 22:971-9|
|Nagarajan, Raman P; Zhang, Bo; Bell, Robert J A et al. (2014) Recurrent epimutations activate gene body promoters in primary glioblastoma. Genome Res 24:761-74|
|Mulligan, Richard C; Kristjansson, Sean D; Reiersen, Angela M et al. (2014) Neural correlates of inhibitory control and functional genetic variation in the dopamine D4 receptor gene. Neuropsychologia 62:306-18|
|Kapoor, Manav; Wang, Jen-Chyong; Wetherill, Leah et al. (2013) A meta-analysis of two genome-wide association studies to identify novel loci for maximum number of alcoholic drinks. Hum Genet 132:1141-51|
|Xie, Mingchao; Hong, Chibo; Zhang, Bo et al. (2013) DNA hypomethylation within specific transposable element families associates with tissue-specific enhancer landscape. Nat Genet 45:836-41|
|Zhang, Bo; Zhou, Yan; Lin, Nan et al. (2013) Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm. Genome Res 23:1522-40|
Showing the most recent 10 out of 21 publications