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

June 24-25, 2024 | Livestream, virtual

quantiative genomics training

Registration is open! Join us for the next livestream Quantitative Genomics Boot Camp training on June 24-25, 2024. 

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.

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Quantitative Genomics Training Overview

Summer 2024 dates: Livestream, online training June 24-25, 2024; 10:00am - ~5:00pm EDT

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

Audience and Requirements

Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements 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 is recommended to get the most out of lab sessions.
  3. 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).   


Hae Kyung Im, PhD, Department of Genetic Medicine, University of Chicago. 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.

Iuliana Ionita-Laza, PhD, Department of Biostatistics, Columbia University. 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.

Kai Wang, PhD, CHOP and University of Pennsylvania. 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.

R Tutorials

Knowing the basic R platform and commands is recommended for the training as noted in the prerequisites above to get the most out of the training. If you are new to R or need a refresher, review the below tutorials to be well prepared for the labs:

If you have any specific questions about R and RStudio in the context of the Genomics Training, please email us.


Training scholarships are available for the Quantitative Genomics Training.


Summer 2024: The Quantitative Genomics Training is a livestream, remote training that takes place over live, online video on June 24-25, 2024 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.  


"This training guides participants through the basic steps of how to identify causal genetic variants in individual and population-level datasets. The training is heavily focused on statistic approaches and provides hands-on experience using R for data manipulation and generating basic plots for visualization." - Postdoctoral Associate, University of Pittsburgh Medical Center, 2023

"Really enjoyed learning more about this field, and I hope to integrate what I learned into my research in the future. Lectures were generally easy to follow and didn't feel rushed, would recommend to others looking for a general introduction into quantitative genomics." - Postdoctoral Trainee, Duke University, 2023

"This training was a comprehensive, hands-on experience that provided me with knowledge and skills in new quantitative genetic techniques. I would recommend that anyone thinking about attending be quite familiar with quant genetics and R." - Postdoctoral Trainee, VCU Health, 2023

"I thought it was well-executed. I appreciated the clarity of instruction and overall organization of the materials (e.g., instructions)." - Assistant Professor, Icahn School of Medicine at Mt. Sinai, 2023

Additional Testimonials

"Excellent summary overview of a complex topic directly from experts who developed landmark tools in the field." - Postdoctoral Trainee at the University of North Carolina Chapel Hill, 2022

"For anyone who is new but wants to start their GWAS analyses, this was the go-to introductory course that balanced well between principles, approaches and hands-on computing experience. The instructors are very knowledgeable about their fields of study. I was marveled by how skillful and well-paced they guided us through the R session." - Academic Staff, Duke University, 2022

"Overall, this training was well done and provided the expected level of exposure to these topics.  I walked away feeling that I had a solid grasp of the available tools for quantitative genomics analyses." - Faculty/Physician at Washington University School of Medicine in St. Louis, 2022

"This training gives you a solid grasp of potential tools to apply to genomic datasets in your own research and leaves you wanting to dig deeper into these tools. " -Student, Cincinnati Children's Hospital Medical Center, 2022

Registration Fees

  Early-Bird Rate (through 4/10/24) Regular Rate (4/11/24 - 6/17/24) Columbia Discount*
Student/Postdoc/Trainee  $995   $1,195 10%
Faculty/Academic Staff/Non-Profit Organizations $1,195 $1,395 10%
Corporate/For-Profit Organizations $1,395 $1,595 NA


*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. If paying by credit card, use your Columbia email address during the registration process to automatically have the discount applied. If paying by internal transfer within Columbia, submit this Columbia Internal Transfer Request form to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.  

Invoice Payment: If you would prefer to pay by invoice/check, please submit this Invoice Request form to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

Registration Fee: This fee includes course material, which will be made available to all participants both during and after the conclusion of the training.

Cancellations: Cancellation notices must be received via email at least 30 days prior to the training start date in order to receive a full refund, minus a $75 administrative fee. Cancellation notices received via email 14-29 days prior to the training will receive a 75% refund, minus a $75 administrative fee. Please email your cancellation notice to Columbia.Genomics@gmail.com. Due to workshop capacity and preparation, we regret that we are unable to refund registration fees for cancellations <14days prior to the training.

If you are unable to attend the training, we encourage you to send a substitute within the same registration category. Please inform us of the substitute via email at least one week prior to the training to include them on attendee communications, updated registration forms, and materials. Should the substitute fall within a different registration category your credit card will be credited/charged respectively. Please email substitute inquiries to Columbia.Genomics@gmail.com. In the event Columbia must cancel the event, your registration fee will be fully refunded.

Additional Information

The Quantitative Genomics Training is hosted by Columbia University's SHARP Program at the Mailman School of Public Health.

Jump to:  Overview  |  Audience & Requirements | InstructorsR Tutorials  |  Scholarships  |  Locations  |  Testimonials  |  Registration Fees  |  Additional Information