Advanced Gene Mapping Course
The Rockefeller University, New York
Monday through Friday, January 22-26, 2018
The course emphasis is analysis of complex human disease traits; concentrating on methods to detect rare and common variant associations. The course includes theory as well as practical exercises. The exercises will be carried out using a variety of computer programs including BEAM3, GERP, GotCloud, GenAbel, Matrix-eQTL, PLINK, PrediXCan, Polyphen-2, R, SEQPower, SEQSpark, EPACTs, etc. Topics include: Analysis of whole genome association studies; analysis of rare variants using next generation sequence data; analysis of qualitative and quantitative traits (population and family-based data); Linear mixed models; eQTL mapping; prediction models using RNAseq and array data; inferences for heritability estimation and prediction; functional predication of variant sites, variant annotation; variant calling, controlling for population substructure\admixture (principal components analysis\multidimensionality scaling); data quality control of genotype and sequence data; meta-analysis; gene x gene interaction; sample size estimation and evaluating power for common and rare variants.
The instructors for the course are: Goncalo Abecasis (University of Michigan), Heather Cordell (University of Newcastle), Nancy Cox (Vanderbilt University), Suzanne Leal (The Rockefeller University & Baylor College of Medicine), and Shamil Sunyaev (Harvard University).
The cost of the 5-day course is $100 for student, academic and government researchers and $2,800 for researchers working in industry. This fee covers tuition and course related expenses (handouts, etc.) but not room and board. Four travel stipends of up to $1,000 each are available. Knowledge genetic association analysis, genetic epidemiology and/or statistical genetics are screening criteria for selection of course participants.
Application deadline is Monday, November 6, 2017
For additional information, course schedule and application form please visit the course website:
https://statgen.research.bcm.