Computational Genomics Summer Institute 2019

The 2019 UCLA Computational Genomics Summer Institute.CGSI brings together mathematical and computational scientists, sequencing technology developers in both industry and academia, and the biologists who use the instruments for particular research applications. Research talks, workshops, journal clubs, and social events provide a unique opportunity to foster interactions between these three communities over an extended period of time and advance the mathematical foundations of this exciting field.

DATES
SHORT PROGRAM #1: July 15 – 19, 2019
SHORT PROGRAM #2: July 29 – August 2, 2019
LONG PROGRAM: July 10 – August 2, 2019

@ UCLA Campus, Los Angeles
Visit our website to learn more: CGSI 2019
Check out the schedule from last year: CGSI 2018

The application is open now!
Apply now for this upcoming summer’s Short and Long Courses:
STEP #1 APPLY
STEP #2 SEND YOUR CV to uclacgsi@gmail.com

The regular registration deadline to apply for the 2019 programs is March 1st, 2019.
Overview
In 2015, Profs. Eleazar Eskin (UCLA), Eran Halperin (UCLA), John Novembre (The University of Chicago), and Ben Raphael (Brown University) created the Computational Genomics Summer Institute (CGSI). A collaboration with the Institute for Pure and Applied Mathematics (IPAM), CGSI aims to build a community focused on methods development in Bioinformatics.

The 2019 CGSI Organizers

CGSI Program Co-directors:
Eleazar Eskin, UCLA
Eran Halperin, UCLA
Dima Shlyakhtenko, UCLA IPAM

CGSI Organizing Committee:
Jessica (Jingyi) Li, UCLA
Sriram Sankararaman, UCLA
David Koslicki, Oregon State University
Eran Halperin, UCLA
Eleazar Eskin, UCLA

The 2016, 2017 and 2018 programs were a huge success – we hope you will be a part of the program this summer!
You can see our 2018 schedule with the videos here.

Social Programs from previous years includes:
Concert at the Hollywood Bowl
Bike rides along the Pacific Coast
Retreat in the Mountains

2019 Faculty
(Additional faculty will be added in the next few months)
Can Alkan, Bilkent University
David Anastasiu, San Jose State University
Valerie Arboleda, UCLA
Sharon Aviran, UC Davis
Vineet Bafna, UCSD
Lisa Bastarache, Vanderbilt University
Dan Benjamin, USC
Gillian Belbin, Icahn School of Medicine at Mount Sinai
Ran Blekhman, University of Minnesota
Paul Boutros, UCLA
Na Cai, EBI
Shai Carmi, The Hebrew University of Jerusalem
Rayan Chikhi, CNRS
Karen Conneely, Emory University
Nancy Cox, Vanderbilt University
Jason Ernst, UCLA
Eleazar Eskin, UCLA
Jonathan Flint, UCLA
Nandita Garud, UCLA
Simon Gravel, McGill University
Eran Halperin, UCLA
Jo Hardin, Pomona College
Fereydoun Hormozdiari, UC Davis
Haky (Hae Kyung) Im, University of Chicago
Iuliana Ionita, Columbia University
Hyun Min Kang, University of Michigan
Eimear Kenny, Icahn School of Medicine at Mount Sinai
David Koslicki, Oregon State University
Smita Krishnaswamy, Yale University
Jessica (Jingyi) Li, UCLA
Jian Ma, CMU
Anna Spafo Malaspina, SIB
Jonathan Marchini, Regeneron Genetics Center
Siavash Mir Arabbaygi, UCSD
Quaid Morris, University of Toronto
Magnus Nordborg, GMI, Austria
John Novembre, University of Chicago
Layla Oesper, Carleton College
Bogdan Pasaniuc, UCLA
Ben Raphael, Princeton University
Saharon Rosset, Tel Aviv University
Sushmita Roy, WID
Cenk Sahinalp, Indiana University Bloomington
Sriram Sankararaman, UCLA
Michael Schatz, Johns Hopkins University
Alexander Schönhuth, CWI, Amsterdam
Sagiv Shifman, The Hebrew University of Jerusalem
Sagi Snir, University of Haifa
Jae-Hoon Sul, UCLA
Fabio Vandin, University of Padova
Peter Vischer, University of Queensland, Australia
Naomi Wray, University of Queensland, Australia
Yi Xing, UCLA
Alex Zelikovsky, Georgia State University
James Zou, Stanford University
Or Zuk, Hebrew University

Computational Genomics Summer Institute – Apply Today

 

CGSI – extended application deadline

Rolling admissions are starting March 15th.
Registration Fee:

$550 apply by April 1st
$650 apply by April 15th
$750 apply after April 15th
Subsidized housing for CGSI is guaranteed for anyone who applied by April 15th.  Housing for applicants who apply after April 15th will be given on a first come first serve basis subject to availability.

We have filled most of the slots in the 2018 CGSI Long Course, however, there are still a few available slots.
The long program has been a huge success last year and many people were not able to be admitted as they did not apply on time – make sure that this year you are not left behind!
There are also still available spaces in the 2018 CGSI Short Course.
Register now to get the lower rate and subsidized housing.

DATES
SHORT PROGRAM #1: July 16 – 20, 2018
SHORT PROGRAM #2: July 30 – August 3, 2018
LONG PROGRAM: July 11 – August 3, 2018

@ UCLA Campus, Los Angeles
Visit our website to learn more.

The application is open!

Apply now for this upcoming summer’s Short and Long Courses:

STEP #1 APPLY
STEP #2 SEND YOUR CV

 

Watch the best talks in 2018 CGWI

Our first offering of the Computational Genomics Winter Institute was a success. In our feedback survey, we asked the participants to pick three talks they wanted to highlight on our website. We would first like to emphasize that the feedback we got was that all the talks in CGWI were excellent. But we are happy to announce that the ones that received the most votes are the talks of Brian Browning, Casey Greene, Su-In Lee, and John Novembre. We now have links to these videos highlighted on the front page of CGWI for easy access, and links to all of the talks are also available at the CGSI website.

Su-In Lee: “Interpretable Machine Learning for Precision Medicine.”


Casey Greene: “Deep learning: privacy preserving data sharing along with some hints and tips.”


John Novembre: “Computational tools for understanding geographic structure in genetic variation data.”

The 2018 CGSI Organizers

CGSI Co-organizers:
Fereydoun Hormozdiari, UC Davis
David Koslicki, Oregon State University
Kirk Lohmueller, UCLA
Ran Blekhman, University of Minnesota

CGSI Program Co-directors:
Eleazar Eskin, UCLA
Eran Halperin, UCLA
Dima Shlyakhtenko, UCLA IPAM

CGSI Steering Committee
Eleazar Eskin, UCLA
Eran Halperin, UCLA
John Novembre, University of Chicago
Ben Raphael, Princeton University

The Multivariate Normal Distribution Framework for Analyzing Association Studies: Overview

The use of the multivariate normal (MVN) model has been a powerful tool in our groups research and it has been utilized in many of our papers. Jose Lozano (University of the Basque Country, San Sebastian, Spain), along with Eleazar Eskin and three ZarLab alumni—Farhad Hormozdiari (postdoc at Harvard), Jong Wha (Joanne) Joo (faculty at Dongguk University in Seoul), and Buhm Han (faculty at University of Ulsan College of Medicine in Seoul)—recently published a review of the multivariate normal (MVN) distribution framework in genome-wide association studies (GWAS) studies.

Genome-wide association studies (GWAS) have discovered thousands of variants involved in common human diseases. In these studies, frequencies of genetic variants are compared between a population of individuals with a disease (cases) and a population of healthy individual controls). Any variant that has a significantly different frequency between the two populations is considered an associated variant.

A major challenge in the analysis of GWAS studies is the fact that human population history causes nearby genetic variants in the genome to be correlated with each other. In this review, we demonstrate how to utilize the MVN distribution to explicitly take into account the correlation between genetic variants and provide a comprehensive framework for analysis of GWAS.

In this paper, we show how the MVN framework can be applied to perform association testing, correct for multiple hypothesis, testing, estimate statistical power, and perform fine mapping and imputation. In future blog posts, we will highlight different ways the MVN framework can be used in association studies.

An illustration of the multivariate normal model (a) Type I Error (b) Power.

Many of the authors are the alumni of the group who pioneered the use of the MVN in various problems in association studies. Here is a list of papers that our group published using the MVN framework:

Joo, Jong Wha J; Hormozdiari, Farhad; Han, Buhm; Eskin, Eleazar

Multiple testing correction in linear mixed models. Journal Article

In: Genome Biol, 17 (1), pp. 62, 2016, ISSN: 1474-760X.

Abstract | Links | BibTeX

Hormozdiari, Farhad ; Kang, Eun Yong ; Bilow, Michael ; Ben-David, Eyal ; Vulpe, Chris ; McLachlan, Stela ; Lusis, Aldons J; Han, Buhm ; Eskin, Eleazar

Imputing Phenotypes for Genome-wide Association Studies. Journal Article

In: Am J Hum Genet, 99 (1), pp. 89-103, 2016, ISSN: 1537-6605.

Abstract | Links | BibTeX

Duong, Dat ; Zou, Jennifer ; Hormozdiari, Farhad ; Sul, Jae Hoon ; Ernst, Jason ; Han, Buhm ; Eskin, Eleazar

Using genomic annotations increases statistical power to detect eGenes. Journal Article

In: Bioinformatics, 32 (12), pp. i156-i163, 2016, ISSN: 1367-4811.

Abstract | Links | BibTeX

Hormozdiari, Farhad; van de Bunt, Martijn; Segrè, Ayellet V; Li, Xiao; Joo, Jong Wha J; Bilow, Michael; Sul, Jae Hoon; Sankararaman, Sriram; Pasaniuc, Bogdan; Eskin, Eleazar

Colocalization of GWAS and eQTL Signals Detects Target Genes. Journal Article

In: Am J Hum Genet, 2016, ISSN: 1537-6605.

Abstract | Links | BibTeX

Joo, Jong Wha J; Kang, Eun Yong; Org, Elin; Furlotte, Nick; Parks, Brian; Hormozdiari, Farhad; Lusis, Aldons J; Eskin, Eleazar

Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Journal Article

In: Genetics, 204 (4), pp. 1379-1390, 2016, ISSN: 1943-2631.

Abstract | Links | BibTeX

Hormozdiari, Farhad; Kichaev, Gleb; Yang, Wen-Yun Y; Pasaniuc, Bogdan; Eskin, Eleazar

Identification of causal genes for complex traits. Journal Article

In: Bioinformatics, 31 (12), pp. i206-i213, 2015, ISSN: 1367-4811.

Abstract | Links | BibTeX

Hormozdiari, Farhad; Kichaev, Gleb; Yang, Wen-Yun Y; Pasaniuc, Bogdan; Eskin, Eleazar

Identification of causal genes for complex traits. Journal Article

In: Bioinformatics, 31 (12), pp. i206-i213, 2015, ISSN: 1367-4811.

Abstract | Links | BibTeX

Hormozdiari, Farhad; Kostem, Emrah ; Kang, Eun Yong ; Pasaniuc, Bogdan ; Eskin, Eleazar

Identifying causal variants at Loci with multiple signals of association. Journal Article

In: Genetics, 198 (2), pp. 497-508, 2014, ISSN: 1943-2631.

Abstract | Links | BibTeX

Kichaev, Gleb; Yang, Wen-Yun Y; Lindstrom, Sara ; Hormozdiari, Farhad ; Eskin, Eleazar ; Price, Alkes L; Kraft, Peter ; Pasaniuc, Bogdan

Integrating functional data to prioritize causal variants in statistical fine-mapping studies. Journal Article

In: PLoS Genet, 10 (10), pp. e1004722, 2014, ISSN: 1553-7404.

Abstract | Links | BibTeX

Darnell, Gregory; Duong, Dat ; Han, Buhm ; Eskin, Eleazar

Incorporating prior information into association studies. Journal Article

In: Bioinformatics, 28 (12), pp. i147-i153, 2012, ISSN: 1367-4811.

Abstract | Links | BibTeX

Flint, Jonathan; Eskin, Eleazar

Genome-wide association studies in mice Journal Article

In: Nature Reviews Genetics, 13 (11), pp. 807-17, 2012, ISSN: 1471-0064.

Abstract | Links | BibTeX

Han, Buhm; Kang, Hyun Min ; Eskin, Eleazar

Rapid and accurate multiple testing correction and power estimation for millions of correlated markers. Journal Article

In: PLoS Genet, 5 (4), pp. e1000456, 2009, ISSN: 1553-7404.

Abstract | Links | BibTeX

Eskin, Eleazar

Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior information. Journal Article

In: Genome Res, 18 (4), pp. 653-60, 2008, ISSN: 1088-9051.

Abstract | Links | BibTeX

Eskin, Eleazar

Increasing Power in Association Studies by Using Linkage Disequilibrium Structure and Molecular Function as Prior Information Conference

Lecture Notes in Computer Science, 4955/2008 , Lecture Notes in Computer Science Springer Berlin / Heidelberg, 2008, ISSN: 0302-9743 (Print) 1611-3349 (Online).

Abstract | Links | BibTeX

  • Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Chelsea J.-T. Ju, Ayellet V. Segre, Jong Wha J. Joo, Hyejung Won, Sriram Sankararaman, Bogdan Pasaniuc, Sagiv Shifman, and Eleazar Eskin. Widespread allelic heterogeneity in complex traits. The American Journal of Human Genetics, 100(5):789{802, may 2017.
  • Yue Wu, Farhad Hormozdiari, Jong Wha J. Joo, and Eleazar Eskin. Improving imputation accuracy by inferring causal variants in genetic studies. In Lecture Notes in Computer Science, pages 303{317. Springer International Publishing, 2017.

The paper was written by Jose A. Lozano, Farhad Hormozdiari, Jong Wha (Joanne) Joo, Buhm Han, and Eleazar Eskin, and it is available at: https://www.biorxiv.org/content/early/2017/10/28/208199.

The full citation to our paper is:

Jose A. Lozano, Farhad Hormozdiari, Jong Wha (Joanne) Joo, Buhm Han, Eleazar Eskin. 2017. The Multivariate Normal Distribution Framework for Analyzing Association Studies. bioRxiv doi: https://doi.org/10.1101/208199.