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:

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.

Widespread Allelic Heterogeneity in Complex Traits

This week, our group published a paper in the American Journal of Human Genetics that presents a new computational method for improving the accuracy of genome wide association studies. ZarLab alumni Farhad Hormozdiari (PhD, 2016) developed the method, CAVIAR (CAusal Variants Identification in Associated Regions), a statistical framework that quantifies the probability of each variant to be causal while allowing an arbitrary number of causal variants.

Genome-wide association studies (GWASs) identify genetic variants associated with diseases and traits. Recent successes in GWASs make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. A more comprehensive understanding of these aspects will guide the development of new methods for fine mapping and association mapping of complex traits—and the discovery of new biomarkers for disease diagnosis and treatment.

One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH). Allelic heterogeneity occurs when different mutations at the same locus affects the same phenotype. AH is very common in Mendelian traits, but we know little about the extent to which AH contributes to common, complex disease. Undetected AH could potentially bias results of an association study, leading to false positive results.

Levels of Allelic Heterogeneity in eQTL Studies. For more information, see our paper.

In order to take AH into account while conducting a GWAS, we developed a computational method to infer the probability of AH. Our method quantifies the number of independent causal variants at a locus that can be responsible for the observed association signals detected in a GWAS. Our method is incorporated into the CAVIAR approach, and it is based on the principle of jointly analyzing association signals (i.e., summary level Z-scores) and LD structure in order to estimate the number of causal variants.

Our results show that our method is more accurate than the standard conditional method (CM). We applied our novel method to three GWASs and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4,152 loci with strong evidence of the presence of AH. The proportion of all loci with identified AH is 4%–23% in eQTLs, 35% in GWASs of high-density lipoprotein (HDL), and 23% in GWASs of schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH, indicating that statistical power prevents identification of AH in other loci.

One of the main benefits of our method is that it requires only summary statistics. Summary statistics of a GWAS or eQTL study are widely available, so our method is applicable to most existing datasets. We have shown that AH is widespread and more common than previously estimated in complex traits, both in GWASs and eQTL studies.

Our results highlight the importance of accounting for the presence of multiple causal variants when characterizing the mechanism of genetic association in complex traits. Falling to account for AH can reduce the power to detect true causal variants and can explain the limited success of fine mapping of GWASs.

In a related study, researchers at University of California, Irvine, and University of Kansas, identified an analogous signal in eQTLs from genetic sequencing of flies. King et al. (2014) observe that the vast majority of genes with eQTL are more consistent with heterogeneity than bi-allelism. Read more about this related study, “Genetic Dissection of the Drosophila melanogaster Female Head Transcriptome Reveals Widespread Allelic Heterogeneity.”

CAVIAR was created by Farhad Hormozdiari, Emrah Kostem, Eun Yong Kang, Bogdan Pasaniuc and Eleazar Eskin. Software is freely available for download:

For more information, see our full paper, which can be accessed through AJHG

The full citation of our paper:
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.

Incorporating prior information into association studies

Genome-wide association studies (GWAS) seek to identify genetic variants involved in specific traits. GWAS are advantageous for linking variants with traits, because they interrogate the genome in a uniform way. In other words, they examine the whole genome without a preconceived notion of where the associations may lie.

However, we now know a lot about the putative function of genetic variants due to tremendous progress in functional genomics. In many cases, we even know which variants are more likely to be involved in disease when compared to others. Advancements in our understanding of functional genomics motivate the strategic incorporation of prior information in GWAS.

Our group has been interested in this problem for many years. One challenge to addressing this problem is that the widely utilized approach for GWAS involves evaluating an association statistic at each single nucleotide polymorphism (SNP), and these methods take into account only one SNP at a time. The results are then adjusted for multiple testing, and an association is identified if a statistic exceeds a certain threshold. This approach can be described as a frequentist approach. On the other hand, one can incorporate prior information on which SNPs are likely to be the causal variants affecting the trait. This approach is inherently a Bayesian concept. Reconciling these two approaches is not straightforward.

Average power under varying relative risks. For more information, see our paper.

In a 2008 paper published in Genome Research, our group proposed a modification of the multiple testing framework to address this problem. Instead of using the same specific threshold for all of the association statistics, we use a different threshold for each association statistic, where the thresholds are adjusted based on the prior information. Our method takes advantage of the correlation structure by considering multiple markers within a region. In our paper, we demonstrate how to set the thresholds in order to optimally utilize prior information and maximize statistical power.

Using prior information in genetic association studies increases power over traditional association studies while maintaining the same overall false-positive rate. Compared to standard methods, our approach is equally simple to apply to association studies, produces interpretable results as p-values, and is optimal in its use of prior information in regards to statistical power.

In 2012, we extended this work to use only tag SNPs for the putative causal variant. This project was developed by Gregory Darnell (then UCLA undergraduate, now PhD student at Princeton University), Dat Duong (then UCLA undergraduate, now UCLA PhD student), and Buhm Han.

More recently, we have applied this framework to incorporate functional information in analysis of eQTL data. In this case, incorporating genomic annotation of variants significantly increases the statistical power of existing eQTL methods and detects more eGenes in comparison to standard approaches. Read the blog post on this paper, and download the full article.

For more information on our general approach, see our paper, which is available for download through Bioinformatics:
In addition, the open source implementation of our 2012 paper, MASA, which was developed by Greg Darnell and Dat Duong, is freely available for download at

The full citations to our papers on this topic are:

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

Eleazar Eskin. “Increasing Power in Association Studies by using Linkage Disequilibrium
Structure and Molecular Function as Prior Information.” Genome Research.
18(4):653-60 Special Issue Proceedings of the 12th Annual Conference on Research
in Computational Biology (RECOMB-2008), 2008.