Quantitative Genomics Training

Methods and tools for whole-genome and transcriptome analyses
10:00 am
5:00 pm
Add to Calendar:
Virtual, Live-stream
Hae Kyung Im, PhD; Iuliana Ionita-Laza, PhD; Kai Wang, PhD
Hae Kyung Im, PhD, Department of Genetic Medicine, University of Chicago; Iuliana Ionita-Laza, PhD, Department of Biostatistics; Kai Wang, PhD, CHOP and University of Pennsylvania.
Department of Environmental Health Sciences
SHARP Training Program
Open to the Public
This two-day intensive workshop will provide a rigorous introduction to several different techniques to analyze whole-genome sequencing and transcriptome data. Led by a team of experts in statistical genomics and bioinformatics, who have developed their own methods to analyze such data, the training will integrate seminar lectures with hands-on computer lab sessions to put concepts into practice. The training will focus on reviewing existing approaches based on predicted expression association with traits, colocalization of causal variants, and Mendelian Randomization, including discussion on how they relate to each other, and their advantages and limitations.

By the end of the live-stream, virtual workshop, participants will be familiar with the following topics:

-Sequence based association tests (Burden, SKAT and extensions)
-Functional genomic annotations
-Analysis of genomic variants in human diseases
-Transcriptome wide association tests (PrediXcan, MetaXcan, and extensions)
-Mendelian Randomization techniques
-Colocalization techniques

Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. Please note this training is live-stream, virtual training. It is not a self-paced, online course.


-Hae Kyung Im, PhD, Department of Genetic Medicine, University of Chicago.
-Iuliana Ionita-Laza, PhD, Department of Biostatistics, Columbia University.
-Kai Wang, PhD, CHOP and University of Pennsylvania.


There are three prerequisites to attend this workshop:
-Each participant must have an introductory background in statistics and genetics, and/or in the statistical analysis of genetic data.
-Experience using R/Linux is recommended to get the most out of lab sessions.
-Each participant must bring a laptop with R/RStudio downloaded and installed prior to the first day of training.

- Scholarships are available: https://www.publichealth.columbia.edu/research/precision-prevention/professional-development-scholarships
- Subscribe for updates: http://eepurl.com/gNkdQD
- Email our team: Columbia.Genomics@gmail.com

Capacity is limited. Paid Registration is required to attend.


Quantitative Genomics Training