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: June 11-12, 2020; 9:00am-4:30pm
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 preferred.
- Each participant must bring a laptop.
Training scholarships are available for the Quantitative Genomics Training.
The Quantitative Genomics Training will take place on the Columbia University Irving Medical Campus (CUIMC) in New York City, specifically at Columbia Mailman School of Public Health, 722 W. 168th Street, Allan Rosenfield Building 8th Floor Auditorium. Please note that the entrance to the building is on the 10th floor (training is located two floors below entrance).
General transportation and lodging information can be found in the Getting Around section. A PDF map of the Workshop location on the CUIMC campus will be available closer to training dates.
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.
|Early-Bird Rate (through 4/15/20)||Regular Rate (4/16/20 - 5/20/20)||Columbia Discount*|
|Faculty/Academic Staff/Non-Profit Organizations||$1,350||$1,550||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.
Registration Fee: This fee includes course material, breakfast, lunch, and refreshment breaks. Course material will be provided to all participants after the workshop. Lodging and transportation are not included.
Cancellations: Cancellation notices must be received via email at least 30 days prior to the workshop 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 workshop will receive a 50% refund, minus a $75 administrative fee. Please email your cancellation notice to Columbia.Genomics@gmail.com. Due to workshop capacity, we regret that we are unable to refund registration fees for cancellations after these dates.
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.