Difference between revisions of "Advgenemap2021"

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==Handouts==
 
==Handouts==
====[//www.statgen.us/files/2020/01/docs/lectures/Lecture_booklet.pdf All Lectures]====
 
====[//www.statgen.us/files/2020/01/docs/exercises/Booklet_exercises.pdf All Exercises]====
 
 
===Heather Cordell===
 
===Heather Cordell===
====Lectures====
 
* [//www.statgen.us/files/2020/01/docs/lectures/NY2020-lecture1-nopause_4.pdf Genome-wide association studies (GWAS) - Part 1]
 
* [//www.statgen.us/files/2020/01/docs/lectures/NY2020-lecture2-nopause_4.pdf Genome-wide association studies (GWAS) - Part 2]
 
 
 
====Exercises====
 
====Exercises====
 +
* [http://statgen.us/files/2021/02/exercises/FASTLMM-NY2020.pdf Family-based Association using FaST-LMM, PLINK and R] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]
 +
* [http://statgen.us/files/2021/02/exercises/GCTA-NY2021.pdf GCTA] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]
 +
* [http://statgen.us/files/2021/02/exercises/Epistasis-NY2020.pdf Interaction analysis using PLINK and CASSI] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/epistasis.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/epistasis/tags docker image]
 +
<!--
 
* [[Tutorials#FastLMM | Family-based Association using FaST-LMM, PLINK and R]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]
 
* [[Tutorials#FastLMM | Family-based Association using FaST-LMM, PLINK and R]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]
 
* [[Tutorials#GCTA|GCTA]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]
 
* [[Tutorials#GCTA|GCTA]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/fastlmm-gcta.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/fastlmm-gcta/tags docker image]
 
* [[Tutorials#Epistasis_.28PLINK_and_CASSI.29 | Interaction analysis using PLINK and CASSI]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/epistasis.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/epistasis/tags docker image]
 
* [[Tutorials#Epistasis_.28PLINK_and_CASSI.29 | Interaction analysis using PLINK and CASSI]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/epistasis.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/epistasis/tags docker image]
 +
-->
  
 
===Nancy Cox===
 
===Nancy Cox===
====Lectures====
 
* [//www.statgen.us/files/2020/01/docs/lectures/LealCourse_Cox_Jan2020.pdf Integrative Approaches in Biobanks: Getting to Biological Mechanisms of Disease]
 
  
 
===Andrew DeWan===
 
===Andrew DeWan===
====Lectures====
 
* [//www.statgen.us/files/2020/01/docs/lectures/Rockefeller_Adv_Course_DeWan_pleiotropy_4.pdf From cross-phenotype associations to pleiotropy in human genetic studies]
 
  
 
====Exercises====
 
====Exercises====
 +
* [http://statgen.us/files/2021/02/exercises/pleio_exercise.pdf Pleiotropy] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/pleiotropy.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/pleiotropy/tags docker image]
 +
* [http://statgen.us/files/2021/02/exercises/pleio_exercise_answers.pdf. Answers to Pleitropy Exercise]
 +
<!--
 
* [[Tutorials#Pleiotropy|Pleiotropy]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/pleiotropy.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/pleiotropy/tags docker image]
 
* [[Tutorials#Pleiotropy|Pleiotropy]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/pleiotropy.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/pleiotropy/tags docker image]
 +
-->
  
 
===Suzanne Leal===
 
===Suzanne Leal===
====Lectures====
 
* [//www.statgen.us/files/2020/01/docs/lectures/data_quality_control_1.pdf Data Quality Control]
 
* [//www.statgen.us/files/2020/01/docs/lectures/NGS_data_qc.pdf NGS Data Quality Control]
 
* [//www.statgen.us/files/2020/01/docs/lectures/analysis_rare_variants_part_1.pdf Complex Trait Association Analysis of Rare Variants]
 
* [//www.statgen.us/files/2020/01/docs/lectures/power_sample_sizes.pdf Power Analysis for Single and Rare Variant Aggregate Association Analyses]
 
* [//www.statgen.us/files/2020/01/docs/lectures/Analysis_rare_variants_part_2.pdf National Heart Lung and Blood Institute Exome Sequencing Project]
 
  
 
====Exercises====
 
====Exercises====
 +
* [[Tutorials#PSEQ|PLINK/SEQ (PSEQ)]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/pseq.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/pseq/tags docker image]
 +
* [[Tutorials#Variant_Association_Tools|Association Analysis of Sequence Data using Variant Association Tools for Complex Traits]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/vat.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/vat/tags docker image]
 +
* [http://statgen.us/files/2021/02/exercises/Armitage_sample_size.pdf Cochran Armitage Trend Test for GWAS power analysis]
 +
* [http://statgen.us/files/2021/02/exercises/FunctionalAnnotation.2021.pdf ANNOVAR Annotation] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/annovar.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/annovar/tags docker image]
 +
<!--
 
* [[Tutorials#PSEQ|PLINK/SEQ (PSEQ)]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/pseq.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/pseq/tags docker image]
 
* [[Tutorials#PSEQ|PLINK/SEQ (PSEQ)]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/pseq.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/pseq/tags docker image]
 
* [[Tutorials#Variant_Association_Tools|Association Analysis of Sequence Data using Variant Association Tools for Complex Traits]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/vat.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/vat/tags docker image]
 
* [[Tutorials#Variant_Association_Tools|Association Analysis of Sequence Data using Variant Association Tools for Complex Traits]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/vat.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/vat/tags docker image]
 
* [[Tutorials#Cochran_Armitage_Trend_Test|Cochran Armitage Trend Test for GWAS power analysis]]
 
* [[Tutorials#Cochran_Armitage_Trend_Test|Cochran Armitage Trend Test for GWAS power analysis]]
* [[Tutorials#Annotation|ANNOVAR Annotation]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/annovar.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/annovar/tags docker image]
+
* [http://statgen.us/files/2021/02/exercises/FunctionalAnnotation.2021.pdf ANNOVAR Annotation] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/annovar.dockerfile dockefile] and [https://hub.docker.com/r/statisticalgenetics/annovar/tags docker image]
 
+
-->
 
===Shamil Sunyaev===
 
===Shamil Sunyaev===
 
====Exercises====
 
====Exercises====
 
* [http://statgen.us/files/2021/02/exercises/NPS.pdf Polygenic risk prediction using non-parametric shrinkage] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/nps.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/nps/tags docker image]
 
* [http://statgen.us/files/2021/02/exercises/NPS.pdf Polygenic risk prediction using non-parametric shrinkage] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/nps.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/nps/tags docker image]
 +
 
<!--
 
<!--
 
* [[Tutorials#Polygenic_risk_prediction_.28NPS_method.29|Polygenic risk prediction using non-parametric shrinkage]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/nps.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/nps/tags docker image]
 
* [[Tutorials#Polygenic_risk_prediction_.28NPS_method.29|Polygenic risk prediction using non-parametric shrinkage]] | [https://github.com/statgenetics/statgen-courses/blob/master/docker/nps.dockerfile dockerfile] and [https://hub.docker.com/r/statisticalgenetics/nps/tags docker image]
 
-->
 
-->
 
 
This course is supported by a grant from the National Institute of Health (NIH) - National Human Genome Research Institute (NHGRI).
 
This course is supported by a grant from the National Institute of Health (NIH) - National Human Genome Research Institute (NHGRI).
  
 
 
===Heather Cordell===
 
===Nancy Cox===
 
===Andrew DeWan===
 
===Suzanne Leal===
 
===Shamil Sunyaev===
 
 
===Gao Wang===
 
===Gao Wang===

Revision as of 06:47, 20 January 2021

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 online from Monday through Friday, January 25-29, 2021. The cost of the 5-day course is $100 for student, academic, and government researchers and $1,500 for researchers working in industry. This fee covers tuition and course-related expenses (cloud computing, etc.).

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 a 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 and VAT will be used to analyze sequence data to perform annotation, quality control, rare variant association analysis, and meta-analysis. FaST-LMM, GCTA-MLMA, REGENIE 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 and non-parametric shrinkage. SuSie 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, a variety of tools will be used that includes 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). A special guest lecture will be given by Jurg Ott (Rockefeller University).

Additional Information

The maximum number of participants for this online course is 34.  

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


Applications are no longer being accepted.


Click here for course schedule

Click here for the application form

Click here for course flyer (please post and distribute)

Handouts

Heather Cordell

Exercises

Nancy Cox

Andrew DeWan

Exercises

Suzanne Leal

Exercises

Shamil Sunyaev

Exercises

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

Gao Wang