Discovering SNPs Regulating Human Gene Expression Using Allele Specific Expression from RNA-Seq data

Analyses of expression quantitative trait loci (eQTL), genomic loci that contribute to variation in genetic expression levels, are essential to understanding the mechanisms of human disease. These studies identify regulators of gene expression as either cis-acting factors that regulate nearby genes, or trans-acting factors that affect unlinked genes through various functions.  Traditional eQTL studies treat expression as a quantitative trait and associate it with genetic variation. This approach has identified many loci involved in the genetic regulation of common, complex diseases.

Standard eQTL methods are limited in power and accuracy by several phenomena common to genomic datasets. First, the correlation structure of genetic variation in the genome, known as linkage disequilibrium (LD), limits the ability of these methods to differentiate between the regulatory variant and neighboring variants that are in LD. Second, like other quantitative traits, the total expression of a gene is influenced by multiple genetic and environmental factors. The effect size for any given variant is therefore small, and standard methods require a large sample size to identify the effect.


ASE example and corresponding mathematical representation of three individuals (1, 2, 3). We assume that the third SNP is the causal SNP site affecting the differential gene expression level (Allele A/ Allele T).

Our forthcoming paper in Genetics presents a new method that improves the accuracy and computational power of eQTL mapping with incorporation of allele specific expression (ASE) analysis. Our novel method uses genome sequencing, alongside measurements of ASE from RNA-seq data, to identify cis-acting regulatory variants.

In standard eQTLs studies, the analysis of ASE is influenced by LD structure and the amount of allelic heterogeneity present in the genome. Individual effects appear weak since the effect of a variant is modest when compared to the variance of total expression. In our approach, the genotypes of each single individual with ASE provides information useful to determining variants causal for the observed ASE. Our approach actually leverages the relationship between LD and variant identification to map the variants affecting expression. Thus, analysis of ASE is advantageous over analysis of total expression levels, the standard approach to eQTL mapping.

We demonstrate the utility of our method by analyzing RNA-seq data from 77 unrelated northern and western European individuals (CEU). To map each gene, we simultaneously compare ASE measurements across a set of sequenced individuals. We then identify genetic variants that are in proximity to those genes and capable of explaining observed patterns of ASE. Here, we characterize the efficacy of this method as the ratio termed “reduction rate” and denoted as the ratio between the number of candidate regulatory SNPs to the total number of SNPs in the proximal region of the gene.

When applied to the CEU dataset, our method reduced the set of candidate SNPs from ten to two (a reduction rate of 80%). Allowing for one error increases the number of candidate SNPs to five and decreases the reduction rate to 50%. We also observe that the relationship between LD and variant identification has a different quality in ASE mapping when compared to eQTL studies, and produces different types of information useful to eQTL mapping studies.

ASE studies are a powerful approach to identifying associations between genetic variation and gene expression. Accurate measurement of ASE can identify cis-acting regulatory variants associated with common diseases. Our novel method for ASE mapping is based on a robust and computationally efficient non-parametric approach, and we hope it advances our understanding of functional risk alleles and facilitates development of new hypotheses for the causes and treatment of common diseases.

This project used software developed by Jennifer Zou, which is available for download at:

This project was led by Eun Yong Kang and involved Serghei Mangul, Buhm Han, and Sagiv Shifman. The article is available at:

The full citation to our paper is:

Kang, Eun Yong; Martin, Lisa; Mangul, Serghei; Isvilanonda, Warin; Zou, Jennifer; Ben-David, Eyal; Han, Buhm; Lusis, Aldons J; Shifman, Sagiv; Eskin, Eleazar

Discovering SNPs Regulating Human Gene Expression Using Allele Specific Expression from RNA-Seq Data. Journal Article

In: Genetics, 2016, ISSN: 1943-2631.

Abstract | Links | BibTeX

A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping

Meta-analyses of genome-wide association studies (GWASs) have become essential to identifying new loci associated with human diseases. We recently developed a novel framework that improves the accuracy and power of meta-analyses, which we describe in our recent Human Molecular Genetics paper. This framework can be applied to the fixed effects (FE) model, which assumes that effect sizes of genetic variants are constant across studies, and the random effects (RE) model, which assumes that effect sizes can be different among studies.

Almost all GWAS publications today employ meta-analysis methodologies, the majority of which assume that component studies are independent and that individuals among studies are unrelated. Yet many studies today use shared controls to reduce genotyping or sequencing cost. These “shared control” individuals can inadvertently overlap between multiple studies and, if not accounted for in the methodology, induce false associations in GWAS results. Most meta-analysis tools, including the RE model, cannot account for these overlapping subjects.

In our paper, we propose a general framework for adjusting association statistics to account for overlapping subjects within a meta-analysis. The key idea of our method is to transform the covariance structure of the data so it can be used in methods that strictly assume independence between studies. Specifically, our method decouples dependent studies into independent studies and adjusts association statistics to account for uncertainties in dependent studies. As a result, our approach enables general meta-analysis methods, including the FE and RE models, to account for overlapping subjects. Existing pipelines implementing these models can be reused for dependent studies if our framework is applied at the front end of the analysis procedure.


A simple example of our decoupling approach. Ω and ΩDecoupled are the covariance matrices of the statistics of three studies A, B and C before and after decoupling, respectively. The thickness of the edges denotes the amount of correlation between the studies. After decoupling, the size of the nodes reflects the information that the studies contain in terms of the inverse variance.

We tested our framework for accuracy and power with five simulated datasets, each containing 1000 to 5000 individuals and 10,000 shared controls. A standard approach produced an inflated number of false positive. Our decoupling method, which systemically accounts for overlapping individuals in meta-analysis, and a standard splitting method, which splits controls into individual studies, both correctly controlled for type 1 errors. The advantage of our framework is apparent when assessing power; in one scenario, we gained 25% power in accounting for overlapping subjects with the decoupling when compared to the splitting method.

Next, we assessed the potential of our framework in identifying casual loci shared by multiple diseases and leveraging information from multiple tissues to increase power for eQTL identification. The decoupling and splitting methods controlled false-positive rates and produced significant p-values at several previously identified candidate shared loci among the three autoimmune conditions present in the Wellcome Trust Case Control Consortium (WTCCC) data. In comparison to the splitting method, our decoupling framework increased the significance of p-values in the shared loci test and increased the number of discovered eQTLs by 19%.

Our approach is flexible and allows many meta-analysis methods, such as the RE model, to account for dependency between studies and overlapping subjects. We developed this approach to complement standard software packages in the meta-analysis of GWAS. This project was led by Buhm Han and involved Dat Duong and Jae Hoon Sul. The article is available at:

The full citation to our paper is:

Han, Buhm; Duong, Dat; Sul, Jae Hoon; de Bakker, Paul I W; Eskin, Eleazar; Raychaudhuri, Soumya

A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping. Journal Article

In: Hum Mol Genet, 2016, ISSN: 1460-2083.

Abstract | Links | BibTeX

ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis

Our group recently published a paper in G3 that presents a new method for interpreting meta-analysis of genomic studies. Our software, called ForestPMPlot, is a free, open-source, python-interfaced R package tool available for download from ZarLab Software. In our article, we demonstrate how ForestPMPlot facilitates interpretation of meta-analysis results by producing a plot that visualizes the heterogeneous genetic effects on the phenotype in different study conditions. We show an example analysis where our visualization framework leads to plausible interpretations of gene-by-environment interaction and multiple tissue eQTL, which would not have been straightforward with the traditional framework.

Meta-analysis has become a popular tool for increasing power in genetic association studies, yet it remains a methodological challenge. Genetic association studies can differ from each other in terms of environmental conditions, study design, population types and sizes, statistical noise, and analytical use of covariates. These factors produce different effect sizes between studies, a phenomenon called between-study heterogeneity. Correctly interpreting and accounting for heterogeneity in genetic association studies would give us a more accurate model of the true effects genetic variants have on traits under specific conditions.

Compared to traditional forest plotting techniques, ForestPMPlot visualizes a broader depth of information useful to interpretation of meta-analysis results. Specifically, our tool helps visualize differences in the effect sizes of genetic association studies and clarify why such studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. To distinguish studies with an effect from studies without an effect, we use the m-value framework. The m-value (Han and Eskin 2012; Kang et al. 2014) is the posterior probability that the effect exists in each study. In our paper, we explain how to compute an m-value and propose using the PM-plot framework (Han and Eskin 2012) to plot the P-values and m-values of each study together. The PM-Plot visualizes the relationship between m-values and P-values in a two-dimensional space, allowing a researcher to easily distinguish which study is predicted to have an effect, and which study is predicted not to have an effect.

We applied ForestPMPlot to a GWAS meta-analysis of 17 HDL mouse studies that have different environmental conditions, such as diet (e.g., high fat/low fat), and genetic knockouts, including homozygous deficiency in leptin receptor (db/db), LDL receptor knockouts, and Apoe gene knockouts. Here, we observe that two confidence intervals of effect estimates overlap each other when only considering the effect size estimates in forest plot format. This result is ambiguous if the observed heterogeneity is a result of stochastic errors. However, in the PM-Plot, we observe that the posterior probabilities are well segregated for these two studies (m-value: 0.93 vs. 0.03), allowing us to hypothesize that the SNP effects on HDL in these strains under the Western diet condition can be interacting with sex.


Seventeen mouse HDL studies with various environmental/genetic conditions are combined in this meta-analysis. (A) Forest plot and (B) PM-plot for rs32595861 locus (Fabp3 gene) analyzing data from the Kang et al. (2014) study.


We continue to develop new applications for ForestPMPlot, and we hope that our tool will facilitate more accurate interpretations of meta-analysis in future genetic association research.

ForestPMPlot was developed by Eun Yong Kang and Yurang Park. The article is available at:

Visit the following page to download ForestPMPlot:

The full citation to our paper is: 

Kang, Eun Yong; Park, Yurang; Li, Xiao; Segrè, Ayellet V; Han, Buhm; Eskin, Eleazar

ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity between Studies in Meta-analysis. Journal Article

In: G3 (Bethesda), 6 (7), pp. 1793-8, 2016, ISSN: 2160-1836.

Abstract | Links | BibTeX

This paper describes methods implemented based on research originally published by this group: 

Han, Buhm; Eskin, Eleazar

Interpreting meta-analyses of genome-wide association studies. Journal Article

In: PLoS Genet, 8 (3), pp. e1002555, 2012, ISSN: 1553-7404.

Abstract | Links | BibTeX

Han, Buhm; Eskin, Eleazar

Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies. Journal Article

In: Am J Hum Genet, 88 (5), pp. 586-98, 2011, ISSN: 1537-6605.

Abstract | Links | BibTeX

We discussed these methods and papers in a 2013 blog post: