Quantitative Genomics Training (Live-stream, virtual)

Methods and tools for whole-genome and transcriptome analyses
10:00 am
4:30 pm
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Hae Kyung Im, PhD, University of Chicago; Iuliana Ionita-Laza, PhD, Columbia University; Kai Wang, PhD, CHOP and University of Pennsylvania
Department of Environmental Health Sciences
Columbia SHARP Training Program
Open to the Public
**Due to the uncertainty in the coming months around COVID-19, we are transitioning the Quantitative Genomics Training to a live-stream, virtual training for this summer. With so many conferences and events being cancelled, we hope that offering this training to be “in-person” via a live-stream, remote format will allow our scientific community to continue learning and developing professionally together. We are working hard on integrating these hands-on skills to an interactive online format.**

The Quantitative Genomics Training is a two-day intensive training of seminars and hands-on analytical sessions to provide an overview of concepts, methods, and tools for whole-genome and transcriptome analyses in human health studies.

Genome-wide association studies have discovered tens of thousands of loci significantly associated with complex traits. However, the majority of these loci are located outside of protein-coding regions making it difficult to determine the causal gene or the mechanism through which the phenotype is affected. With whole-genome and RNA sequencing becoming increasingly accessible and feasible to conduct large-scale analyses, we can use different quantitative genomics methods to address these challenges in human health studies.

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. Emphasis will also be given to reviewing integrative sequence based association studies for whole-genome sequencing data, and functional annotation of variants in noncoding regions of the genome.

By the end of the 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.

There are three prerequisites to attend this workshop:

1. Each participant must have an introductory background in statistics and genetics, and/or in the statistical analysis of genetic data.
2. Experience using R/Linux is preferred.
3. Each participant must bring a laptop.

Capacity is limited. Registration is required to attend.


JoAnn Schneider, MPA