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Columbia University: Quantitative Genomics Training: Methods and tools for whole-genome and transcriptome analyses

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