Quantitative Genomics Training

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

Summer 2026: The Quantitative Genomics Training is a livestream, remote training that takes place over live, online video on June 1-2, 2026 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.  

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). 

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:

• Download R: R is the free software programming language we will use. Choose the correct version for your laptop: Mac/Windows
• Download R studio: RStudio is free software that will help us develop programs in R. Choose the correct version for your laptop: Mac/Windows
• How to Download R and R studio: A tutorial on how to download R and RStudio
• How to Install a Package in R studio: Steps to install a package in RStudio
• Best tutorial for Training Prep: R Programming Tutorial - Learn the Basics: A free datalab.cc class on R fundamentals

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

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.

Training scholarships are available for the Quantitative Genomics Training.

"I really enjoyed the extensive hands-on experience on running genomic variants analyses using different methodologies/techniques." - Statistician Lead at University of Alabama at Birmingham, 2025

"The course was delivered very well, well organized, and paced, with both genomics and statistics knowledge." - Faculty Scholar at University of Nairobi, 2025

"The Quantitative Genomics Training provides an overview of cutting-edge bioinformatic and statistical methods for analyzing whole-genome data in an easy-to-digest manner. The labs helped put the theoretical information learned to practical use." - Faculty Member at University of Iowa, 2025

"Thank you for a phenomenal hands on experience on running models across rare and common variant analyses using different methodologies." - Postdoc at Stanford School of Medicine, 2024

"The course is fast paced, but provided several examples of state of the art methods used to perform analysis. This is not a beginners course, and prior knowledge on the GWAS and genetic data analysis workflow is required." - Student at University of Texas at San Antonio, 2024

"It was a great experience. Although I had tried some of these methods before, I learned a lot of new ways to perform these analyses and learned about several phenotype driven prioritization of causal genes methods that I wouldn't have had an opportunity to learn otherwise." - Student at The Pennsylvania State University, 2024

"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

Registration Fee is based on your category and includes course material. Course material will be available to all attendees during and after the workshop. 

2026 Registration Category Rates: 

• ​​​Student/Postdoc/Trainee:
  • Early-bird rate: $995
  • Regular rate: $1,195
• Faculty/Academic Staff/Non-Profit Organizations/Government Agencies:
  • Early-bird rate: $1,195
  • Regular rate: $1,395
• Corporate/For-Profit Organizations:
  • Early-bird rate: $1,395
  • Regular rate: $1, 595

$200 early-bird discount is automatically applied if you register before the April 15 deadline.  

• $200 Early-bird Discount: This is automatically applied if you register before the April 15 early-bird deadline.  

• 10% Columbia Discount: This 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, see below.  

• 10% Mailman Alumni Discount: This is valid for any individual who graduated from the Columbia University Mailman School of Public Health. To access the Mailman Alumni discount and receive a registration code, please email sharp_program@cumc.columbia.edu your graduation year and degree.  

• Group discounts are available for organizations sending 5+ participants. Please contact us directly at sharp_program@cumc.columbia.edu for more information.  

If paying by internal transfer within Columbia, submit this Columbia Internal Transfer Request form (link to form coming soon) 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.

If you would prefer to pay by invoice/check, please submit this Invoice Request form (link to form coming soon) 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.

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 less than 14 days 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 so we can include them on attendee communications, gather registration details, and provide materials. Should the substitute fall within a different registration category (e.g., you are a faculty member and they are a postdoc), the credit card on file 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.