Quantitative Genomics
Master quantitative genomics in two days. Dive into seminars and hands-on sessions covering whole-genome and transcriptome analyses for human health studies.
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Course Description
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.
- Learn to apply Burden, SKAT, and their extensions to identify genetic variants associated with complex traits using whole-genome sequencing data.
- Understand how to annotate genomic variants in noncoding regions, enhancing comprehension of their functional implications in human diseases.
- Gain proficiency in PrediXcan, MetaXcan, and their extensions to elucidate the association between gene expression and traits using transcriptome data.
- Acquire skills to utilize Mendelian Randomization and colocalization techniques for causal inference and understanding the relationship between genetic variants and traits.
Course Prerequisites
- Each participant must have an introductory background in statistics and genetics, and/or in the statistical analysis of genetic data.
- Experience using R is recommended to get the most out of lab sessions.
- Each participant should have a laptop with R/RStudio downloaded and installed prior to the first day of training. All lab sessions will be done using Posit Cloud (formerly RStudio Cloud).
What You Will Learn
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
Instructors
Dr. Im is a statistician who is passionate about using quantitative and computational methods to uncover hidden patterns in data. Her research is at the intersection of statistics, genomics, medicine, and big data analytics. She has been the lead developer of widely used tools such as PrediXcan and related methods on genetic prediction models of transcriptome levels based on GTEx data.
Dr. Ionita-Laza’s research interests lie at the interface between statistics and genomics. She is particularly interested in developing statistical and computational methods for the analysis of high-dimensional genetic and functional genomics data, and has proposed several well-known tools in this area. She is also involved in applications of such methods to understand the genetic basis of complex diseases and traits, including autism spectrum disorders and schizophrenia.
Dr. Wang’s research focuses on the development of bioinformatics methods to improve our understanding of the genetic basis of human diseases, and the integration of electronic health records and genomic information to facilitate genomic medicine on scale. Current projects involve the development of bioinformatics methods to understand personal genomes, computational algorithms for long-read sequencing data, and deep phenotyping of electronic health records. He is the author of widely used tools such as ANNOVAR and PennCNV.