Difference between revisions of "Advgenemap2021"
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===[https://github.com/statgenetics/statgen-courses/wiki/How-to-launch-course-tutorials How to run the exercises]=== | ===[https://github.com/statgenetics/statgen-courses/wiki/How-to-launch-course-tutorials How to run the exercises]=== | ||
| − | |||
==Handouts== | ==Handouts== | ||
===Heather Cordell=== | ===Heather Cordell=== | ||
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===Nancy Cox=== | ===Nancy Cox=== | ||
| + | [http://statgen.us/files/2021/02/bios/about_njc.pdf about Nancy Cox] | ||
| + | |||
| + | ====Lectures==== | ||
| + | [http://statgen.us/files/2021/02/lectures/LealCourse_Cox_Jan2021.pdf Integrative Approaches in Biobanks: Getting to Biological Mechanisms of Disease] | ||
| + | |||
===Andrew DeWan=== | ===Andrew DeWan=== | ||
[http://www.statgen.us/files/2021/02/bios/about_atd.pdf About Andy DeWan] | [http://www.statgen.us/files/2021/02/bios/about_atd.pdf About Andy DeWan] | ||
| Line 59: | Line 63: | ||
====Exercises==== | ====Exercises==== | ||
| − | * [http://statgen.us/files/2021/02/exercises/pleio_exercise.pdf Pleiotropy | + | * [http://statgen.us/files/2021/02/exercises/pleio_exercise.pdf Pleiotropy] |
| − | * [http://statgen.us/files/2021/02/exercises/pleio_exercise_answers.pdf | + | * [http://statgen.us/files/2021/02/exercises/pleio_exercise_answers.pdf Answers to Pleitropy Exercise] |
* [http://statgen.us/files/2021/02/exercises/MR_exercise.pdf Mendelian Randomization] | * [http://statgen.us/files/2021/02/exercises/MR_exercise.pdf Mendelian Randomization] | ||
* [http://www.statgen.us/files/2021/02/exercises/MR_exercise_answers.pdf Mendelian Randomization Answers] | * [http://www.statgen.us/files/2021/02/exercises/MR_exercise_answers.pdf Mendelian Randomization Answers] | ||
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| − | |||
| − | |||
===Suzanne Leal=== | ===Suzanne Leal=== | ||
[http://www.statgen.us/files/2021/02/bios/about_sml.pdf About Suzanne Leal] | [http://www.statgen.us/files/2021/02/bios/about_sml.pdf About Suzanne Leal] | ||
| Line 92: | Line 93: | ||
===Jurg Ott=== | ===Jurg Ott=== | ||
[http://www.statgen.us/files/2021/02/bios/about_jo.pdf About Jurg Ott] | [http://www.statgen.us/files/2021/02/bios/about_jo.pdf About Jurg Ott] | ||
| + | |||
====Lectures==== | ====Lectures==== | ||
[http://www.statgen.us/files/2021/02/lectures/FPMforInteractions2021-01.pptx Frequent Pattern Mining Methods for Finding SNP-SNP Interactions] | [http://www.statgen.us/files/2021/02/lectures/FPMforInteractions2021-01.pptx Frequent Pattern Mining Methods for Finding SNP-SNP Interactions] | ||
===Shamil Sunyaev=== | ===Shamil Sunyaev=== | ||
| + | [http://www.statgen.us/files/2021/02/bios/about_ss.pdf About 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] | ||
| − | + | ====Lectures==== | |
| − | * [ | + | * [http://statgen.us/files/2021/02/lectures/Rockefeller_complex_traits_2021.pdf Complex Traits] |
| − | + | * [http://statgen.us/files/2021/02/lectures/Rockefeller_functional_annotation_2021.pdf Functional Annotation] | |
| + | * [http://statgen.us/files/2021/02/lectures/Rockefeller_polygenic_risk_score_2021.pdf Polygenic Risk Score] | ||
| + | * [http://statgen.us/files/2021/02/lectures/Rockefeller_population_genetics_2021.pdf Population Genetics] | ||
| + | |||
===Gao Wang=== | ===Gao Wang=== | ||
[http://www.statgen.us/files/2021/02/bios/about_gw.pdf About Gao Wang] | [http://www.statgen.us/files/2021/02/bios/about_gw.pdf About Gao Wang] | ||
| + | |||
====Lectures==== | ====Lectures==== | ||
| + | * [http://statgen.us/files/2021/02/lectures/Lecture_Univariate_Fine_Mapping.pdf Statistical fine-mapping] | ||
| + | * [http://statgen.us/files/2021/02/lectures/Lecture_Multivariate_Analysis.pdf Multivariate analysis] | ||
====Exercises==== | ====Exercises==== | ||
| Line 111: | Line 121: | ||
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). | ||
| + | |||
| + | ===Dan Zhou=== | ||
| + | [http://www.statgen.us/files/2021/02/bios/about_dz.pdf About Dan Zhou] | ||
| + | ====Computer Demonstration==== | ||
| + | * [http://statgen.us/files/2021/02/lectures/MR-JTI.pdf MR-JTI: a framework for Joint Tissue Imputation-based TWAS and Mendelian Randomization] | ||
Latest revision as of 18:03, 29 January 2021
Contents
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). Judy Matuk (HRP Consulting Group) will lecture on ethics and the regulation of human subject research. 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)
How to run the exercises
Handouts
Heather Cordell
Lectures
Exercises
- Family-based Association using FaST-LMM, PLINK and R
- GCTA
- Interaction analysis using PLINK and CASSI
Nancy Cox
Lectures
Integrative Approaches in Biobanks: Getting to Biological Mechanisms of Disease
Andrew DeWan
Lecture
Exercises
Suzanne Leal
Lectures
- Data Quality Control
- NGS Data Quality Control
- Complex Trait Association Analysis of Rare Variants Obtained from Sequence Data: Population-Based Data
- Power Analysis for Single and Rare Variant Aggregate Association Analyses
Exercises
- PLINK/SEQ (PSEQ)
- Association Analysis of Sequence Data using Variant Association Tools for Complex Traits
- Cochran Armitage Trend Test for GWAS power analysis
- ANNOVAR Annotation
- Genome-Wide Association - Association Analysis Controlling for Population Substructure
- Genome-Wide Association Analysis - Data Quality Control
Judy Matuk
Lectures
Jurg Ott
Lectures
Frequent Pattern Mining Methods for Finding SNP-SNP Interactions
Shamil Sunyaev
Exercises
Lectures
Gao Wang
Lectures
Exercises
- Statistical fine-mapping in association studies | dockerfile and docker image
- Answers to fine-mapping exercise questions
This course is supported by a grant from the National Institute of Health (NIH) - National Human Genome Research Institute (NHGRI).
