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


Quantitative Genomics Training WorkshopRegistration is open! Join us for the next live-stream Quantitative Genomics Training on June 14-15, 2022.

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 2022 dates: Live-stream, online training June 14-15, 2022; 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

Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.


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.


There are three prerequisites to attend this workshop:

  1. Each participant must have an introductory background in statistics and genetics, and/or in bioinformatics or the statistical analysis of genetic data.

  2. Experience using R/Linux is recommended to get the most out of lab sessions.

  3. Each participant is required to have a personal laptop/computer and a free, basic RStudio Cloud account. All lab sessions will be done using RStudio Cloud. 


Knowing the basic R platform and commands is recommended for the Boot Camp 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 R studio in the context of the Genomics Training, please email us.


Training scholarships are available for the Quantitative Genomics Training.


The Quantitative Genomics Training will no longer take place in person due to the COVID-19 pandemic. The Training will instead be a live-stream, remote training that takes place over live, online video on June 14-15, 2022 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.  


"This was a great introduction to many concepts that were broken down for us in background, rationale and methods. The combination of lecture and hands-on lab was highly integrative and the overall streamlining of the analytical approach was highly useful!" - Faculty member at University of Southern California, 2021

"The training provided an excellent characterization of both big principles behind GWAS analyses as well as in-depth coaching on how to perform these analyses at home." - Postdoc at Memorial Sloan Kettering Cancer Center, 2021

"This was a great comprehensive session on quantitative genomics, and introduced useful methods for analyzing genomic data.  The professors were very involved and ran well-explained hands-on labs that can be applicable to one's own research." - Alexandra I., Student at University of Pennsylvania, 2021

"This was a well-taught course by instructors who were very knowledgeable about their subject." - Academic Staff member at the Icahn School of Medicine at Mount Sinai, 2020.

Check out more testimonials here.


COVID-19 Update: With the training being offered virtually, we are passing along any and all costs saved to attendees.

  Early-Bird Rate (through 4/15/22) Regular Rate (4/16/22 - 6/18/22) Columbia Discount*
Student/Postdoc/Trainee  $925 $1,025 10% off
Faculty/Academic Staff/Non-Profit Organizations/Government Agencies $1,025 $1,225 10% off
Corporate/For-Profit Organizations $1,225 $1,425 NA


*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. To accss the Columbia discount, email Columbia.Genomics@gmail.com for instructions and specify the following: 1) your registration category from the table above, 2) if you are paying by credit card, or internal transfer within Columbia, and 3) if an internal transfer, indicate the registration category from the table above, and if grant funds are being used.

Invoice Payment and Group Registrations: If you would prefer to pay by invoice/check, or would like to pay for a group of registrants, please email Columbia.Genomics@gmail.com with the following details: 1) full attendee name(s) and applicable registration category from the table above, and 2) payment method (credit card, invoice, wire).

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. 




Want updates on new Genomics training details or registration deadlines? Subscribe here.

Questions? Email the Genomics team here.

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

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Additional Testimonials

"I was impressed with the amount of knowledge that was taught in the span of two days! Although meeting over Zoom was not ideal due to our current [Covid-19] situation, the transitions from speaker to speaker, lab to lab was very smooth. I enjoyed my time during this module and am excited to use what I've learned in my current position!" - Postdoc at the University of Southern California, 2020.

"The workshop was well planned and organized. The instructors and TAs were extremely helpful during lab sessions and with student's questions." - Non-profit Staff member, 2020.

"You will gain hands on experience learning quantitative / statistical genetics methods by experts. You will also get access to scripts and tools required to apply these methods to your own research." - Postdoc at the University of California, San Francisco, 2020.

"This was a good overview of methods to conduct GWAS and to interpret results. I appreciated the overview of different methods and discussing their benefits and drawbacks." - Student at the Columbia University Irving Medical Center, 2020.