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.

About Lana Martin

i write and teach.