Fine Mapping Causal Variants and Allelic Heterogeneity

On Friday, April 28, 2017, in the CNSI Auditorium, Eleazar Eskin presented ZarLab’s research on fine mapping causal variants and allelic heterogeneity at the 2nd Annual Institute for Quantitative and Computational Biosciences (QCBio) Symposium.

Geneticists use a technique called Genome Wide Association Studies (GWAS) to identify genetic variants that cause an individual to exhibit a particular trait or disease. Typically, GWAS identifies an association signal which suggests that genetic variants within a region of the genome — known as a locus —  are associated with the condition. The process of identifying the actual variant in the region which has an affect on the disease is referred to as “fine mapping.”

In addition to finding the actual variants affecting a disease, fine mapping also seeks to address questions that are related to the genetic basis of disease. First, how many causal variants does a locus contain? A disease could be caused by one, single variant or multiple variants that independently affect disease status. We refer to the latter phenomenon as allelic heterogeneity (AH).

Second, when analyzing results from multiple GWASes, can the same causal variant identified in one study be assumed causal in other studies? A GWAS can identify many variants that are associated with two or more traits; however, this correlation can be induced by a confounding factor known as linkage disequilibrium. Colocalization methods seek to identify shared and distinct causal variants.

Farhad Hormozdiari, a recent alumnus of our group and a post-doc at Harvard University, developed several novel approaches for improving the accuracy and efficiency of fine mapping despite presence of AH in the study population. Hormozdiari’s software, CAVIAR, CAVIAR-Genes, and eCAVIAR, are capable of quantifying the probability of a variant to be causal in GWAS and eQTL studies, while allowing for an arbitrary number of causal variants.

In a video of his presentation, Eskin summarizes the progress on these problems.  A video of Eskin’s presentation may be found on the QCBio website: https://qcb.ucla.edu/events-seminars/symposium/#toggle-id-2

More details about our research in fine mapping are available in the following papers:

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

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

Hormozdiari F, Zhu A, Kichaev G, Ju CJ, Segrè AV, Joo JW, Won H, Sankararaman S, Pasaniuc B, Shifman S, Eskin E. Widespread allelic heterogeneity in complex traits. The American Journal of Human Genetics. 2017 May 4;100(5):789-802.

Video Tutorial: Serghei Mangul’s Introduction to UNIX Workshops

We present three video recordings of workshops that ZarLab postdoctoral scholar Serghei Mangul developed under the UCLA Institute for Quantitative and Computational Biosciences Collaboratory and delivered to Bruins-In-Genomics (B.I.G.) SUMMER participants. B.I.G. SUMMER is an intensive, practical experience in genomics and bioinformatics for undergraduate students who are interested in integrating quantitative and biological knowledge and considering pursuing graduate degrees in the biological, biomedical, or health sciences.

An important question for undergraduates considering careers in the biosciences is whether or not biologists need to develop robust programming skills. Biology students without backgrounds in computer science are often intimidated by applications that require inputting code or negotiating systems that lack a graphical interface, such as Unix, R, SASS, and Python.

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“Becoming a programmer” may seem daunting to many students in biology, but an ability to analyze sequencing data represents a competitive advantage in today’s age of big data and next generation sequencing. By gaining familiarity with Unix, these students may find it easier to engage with other applications and programming languages commonly used in computational biology. In order to use Unix effectively, students must learn how to directly enter functional commands line-by-line into a workbench that manages multiple platforms and a unified filesystem—without the familiar aid of a graphical interface.

In this three-part series of workshops, Dr. Mangul provides just enough information for students with no computational background to get started using Unix for analytical tasks. These workshops aim to help participants learn key commands and develop fundamental skills, such as connecting, writing, and submitting basic shell scripts to a cluster.

Slides and more information about the workshop are available at the following webpage:
qcb.ucla.edu/collaboratory/workshops/collaboratory-workshop-1/

Introduction to UNIX 1/3
https://www.youtube.com/watch?v=liC5uM8czyo

Introduction to UNIX 2/3
https://www.youtube.com/watch?v=ArbOG6YpakU

Introduction to UNIX 3/3
https://www.youtube.com/watch?v=PHmfgIuOMFQ

 

Thesis Defense: Dr. Farhad Hormozdiari

Farhad Hormozdiari successfully defended his thesis,”Statistical Methods to Understand the Genetic Architecture of Complex Traits,” on Tuesday, May 17, 2016 in Boelter 4760. His talk, which is posted on our YouTube channel ZarlabUCLA, discusses methods for applying CAVIAR to understand the underlying mechanism of GWAS risk loci, introduces eCAVIAR, a statistical method capable of computing the probability that the same variant is responsible for both the GWAS and eQTL signal, while accounting for complex LD structure, and proposes an approach called phenotype imputation that allows GWAS computation on a phenotype that is difficult to collect.

 

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More details about Farhad’s research are available in the following papers:

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; Joo, Jong Wha J; Wadia, Akshay ; Guan, Feng ; Ostrosky, Rafail ; Sahai, Amit ; Eskin, Eleazar

Privacy preserving protocol for detecting genetic relatives using rare variants. Journal Article

In: Bioinformatics, 30 (12), pp. i204-i211, 2014, 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

Eskin, Itamar; Hormozdiari, Farhad; Conde, Lucia; Riby, Jacques; Skibola, Chris; Eskin, Eleazar; Halperin, Eran

eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data. Journal Article

In: J Comput Biol, 2013, ISSN: 1557-8666.

Abstract | Links | BibTeX