Difference between revisions of "Advgenemap2021"

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Advanced Gene Mapping Course

The Rockefeller University, New York 
Online
Monday through Friday, January 25-29, 2021

General Information

An Advanced Gene Mapping course will be held in New York from Monday through Friday January 27-31, 2020. The cost of the 5-day course is $100 for student, academic and government researchers and $2,800 for researchers working in industry. This fee covers tuition and course related expenses (handouts, etc.) but not room and board.

The course emphasis is on analyzing sequence and other omics data to elucidate the genetic etiology of complex human disease traits. Topics will include: data quality control of sequence and other types of data; single variant and aggregate rare variant association analysis of whole-genome data (genotype, sequence, and imputed) for qualitative and quantitative traits (population and family-based data); controlling for population admixture and substructure; generalized linear mix models and linear mixed models; meta-analysis; sample size estimation and power calculations; detecting gene x gene and gene x environmental interactions; analysis of epigenomic data, e.g methylation and chromatin; heritability estimation using variant and RNA-Seq data; analysis of RNA-Seq data; eQTL mapping; elucidating pleiotropy; functional prediction and variant annotation; estimation of polygenic risk scores; Mendelian randomization; mediation analysis; and fine mapping. As mandated by the NIH there will also be special session on responsible conduct of research that will include sessions on conflict of interest, research ethics, data management (security) and ethical use of human research subjects.

A variety of freely available software will be used to perform the practical exercises, due to differences in their functionality. PSEQ, SEQSpark, and VAT will be used to analyze sequence data to perform annotation, quality control, rare variant association analysis and meta-analysis. FaST-LMM, EPACTs, and GCTA-MLMA will be implemented to analyze population- and family-based data by applying generalized linear mixed models (qualitative traits) and linear mixed models (quantitative traits). For rare variant association analysis of trio data RV-TDT will be applied. MultiPhen (multivariate) and PLINK (univariate) will be contrasted in their ability to detect pleiotropy; Mediation analysis will be performed using R to aid in distinguishing between biological, mediated, and spurious pleiotropy. To make inferences on causality, Mendelian randomization will be performed using MR-base. Estimation of polygenic risk scores will be performed using LDpred. PAINTOR and FOCUS will be used for fine mapping to aid in the detection of causal susceptibility variants. Heritability estimates will be performed using GCTA. For analysis of eQTLs, Matrix eQTL will be used. Analysis of imputed expression data will be performed by applying PrediXCan; To perform analytical and empirical power analysis for single and rare variant aggregate tests, the Armitage Power Tool and the SKAT R library will be used. Additionally, variant annotation will be performed with ANNOVAR as well as directly using a variety of functional prediction and conservation tools, e.g. CADD, GERP, MutationTaster, MutPred, Polyphen-2, and SIFT.

Course Instructors

The instructors for the course are: Heather Cordell (University of Newcastle), Nancy Cox (Vanderbilt University), Andrew DeWan (Yale University), Suzanne Leal (The Rockefeller University & Columbia University),  Shamil Sunyaev (Harvard University) & Gao Wang (Columbia University)

More Information

The maximum number of participants for this online course is 25.  

Knowledge genetic association analysis, genetic epidemiology and/or statistical genetics are screening criteria for the selection of participants.  Please describe your experience in detail in your application. It is helpful if you also enclose a copy of your CV. We may contact you personally to discuss your application. Although experience of using LINUX is not necessary it is highly beneficial to have basic knowledge of this operating system before the start of the course.

For additional information, please contact Advanced Gene Mapping Course 2021 Katherine Montague: 
email: montagk@rockefeller.edu 

 

For additional information on the scientific program contact the course organizer Advanced Gene Mapping Course 2021 Suzanne Leal: 
email: suzannemleal@gmail.com or sml3@cumc.columbia.edu 

Course Photo

Handouts

All Lectures

All Exercises

Heather Cordell

Lectures

• Genome-wide association studies (GWAS) - Part 1
• Genome-wide association studies (GWAS) - Part 2

Exercises

• Family-based Association using FaST-LMM, PLINK and R | dockefile and docker image
• GCTA | dockefile and docker image
• Interaction analysis using PLINK and CASSI | dockerfile and docker image

Nancy Cox

Lectures

• Integrative Approaches in Biobanks: Getting to Biological Mechanisms of Disease

Andrew DeWan

Lectures

• From cross-phenotype associations to pleiotropy in human genetic studies

Exercises

• Pleiotropy | dockefile and docker image

Suzanne Leal

Lectures

• Data Quality Control
• NGS Data Quality Control
• Complex Trait Association Analysis of Rare Variants
• Power Analysis for Single and Rare Variant Aggregate Association Analyses
• National Heart Lung and Blood Institute Exome Sequencing Project

Exercises

• PLINK/SEQ (PSEQ) | dockefile and docker image
• Association Analysis of Sequence Data using Variant Association Tools for Complex Traits | dockerfile and docker image
• Cochran Armitage Trend Test for GWAS power analysis
• ANNOVAR Annotation | dockefile and docker image

Bogdan Pasanuic

Lectures

• Statistical fine-­mapping of GWAS signals

Shamil Sunyaev

Lectures

• Population Genetics
• Evolution, maintenance and allelic architecture of complex traits
• Polygenic Risk Score
• Functional Annotation

Exercises

• Polygenic risk prediction using non-parametric shrinkage | dockefile and docker image

This course is supported by a grant from the National Institute of Health (NIH) - National Human Genome Research Institute (NHGRI).