COVID-19 UPDATE: THE 2020 Quantitative GEnomics Training WILL BE HELD REMOTELY VIA LIVE-STREAM, June 11-12 BEGINNING AT 10AM EDT.
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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Summer 2020 dates: Live-stream, online training June 11-12, 2020; 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.
There are three prerequisites to attend this workshop:
- Each participant must have an introductory background in statistics and genetics, and/or in the statistical analysis of genetic data.
- Experience using R/Linux is recommended to get the most out of lab sessions.
- Each participant must bring a laptop with R/RStudio downloaded and installed prior to the first day of training.
Training scholarships are available for the Quantitative Genomics Training.
COVID-19 Update: 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 11-12, 2020 from 10am EDT - 5pm EDT. Please note this training is not a self-paced, pre-recorded online training.
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, deep phenotyping of electronic health records. He is the author of widely used tools such as ANNOVAR and PennCNV.
COVID-19 Update: With the training being offered virtually, we are passing along any and all costs saved to attendees.
|Early-Bird Rate (through
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|Faculty/Academic Staff/Non-Profit Organizations||10% off|
*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 if you are paying by credit card, or internal transfer within Columbia.
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 details.
Registration Fee: This fee includes course material, which will be provided to all participants after the workshop.
Cancellations: For summer 2020, no administrative fees will be assessed due to the evolving COVID-19 situation. Cancellation notices must be received via email at least 14 days prior to the workshop start date in order to receive a full refund. 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 after these dates, unless a new COVID-19 restriction is implemented that impedes virtual attendance, in which case any registration cancellation <14 days prior to a training related to COVID-19 restriction beyond your control (institutional policy, shift in work responsibilities, etc.) will be fully refunded and no administrative fee will be assessed. Because of the significant resources required to develop these trainings, you will be asked to submit supporting documentation (e.g. employer email notice, local regulations, etc.) for any COVID-19 related cancellation <14 days before a given 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.