Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes

Dat Duong, a graduate student in our lab, developed a novel method that will help find more eQTLs and eGenes in gene expression data from many tissues. A paper presenting his method is published in an upcoming issue of Bioinformatics.

Genome-wide association studies (GWAS) seek links between single-nucleotide polymorphisms (SNPs) and traits or diseases. SNPs are the most commonly occurring sources of variation in the human genome. Many SNPs identified by GWAS are located in intergenic regions, stretches of DNA sequences located between genes. SNPs identified in these primarily noncoding regions often do not have an obvious relationship to the disease phenotype. Other lines of evidence, such as gene expression, are required to explore this relationship and learn about disease function.

Gene expression, an intermediate phenotype between a causal SNP and a disease, can be used to interpret positive results produced by a GWAS. Common data types include expression quantitative trait loci (eQTLs), genetic variants associated with gene expression in particular tissue types, and eGenes, genes whose expression levels are associated with genetic variants. Both eQTL studies and GWAS focus on SNPs, but eQTL studies may provide biological insights into the disease development mechanism. For this reason, we pay special attention to the variants that are eQTLs or eGenes and have strong association signals identified by GWAS.

Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. However, these datasets have small sample sizes in some tissues. Many meta-analysis methods have been designed to increase power for finding eQTLs and eGenes by combining gene expression data across many tissues However, these techniques cannot scale to datasets containing many tissue types, like the GTEx data. Such methods also ignore a biological principle that the same variant may be associated with the same gene across similar tissues.


Venn diagram of the numbers of eGenes found by existing methods and RECOV, along with correlation matrices comparing methods. For more information, read our full paper.

To leverage the analytical power of eQTLs and eGenes in association studies, Duong and his team developed a new meta-analysis method named RECOV. Based on the principle that a SNP may have similar effect on the same gene in related tissues, RECOV can be applied to large gene expression datasets and can analyze all 44 tissues present in the GTEx data.

In our Bioinformatics paper, we use simulated datasets to show that RECOV has a correct false positive rate. When applied to real multi-tissue expression data from the GTEx dataset, RECOV detects 3% more eGenes than previous methods. RECOV is a general framework for meta-analysis that can be used with any COV matrix. We hope this software will be used by other researchers in the scientific community!

RECOV was developed by Dat Duong. The source code for RECOV is freely available at:

Our paper can be downloaded at Bioinformatics:


The full reference for our paper is:
Duong, D., Gai, L., Snir, S., Kang, E.Y., Han, B., Sul, J.H. and Eskin, E., 2017. Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes. Bioinformatics, 33(14), pp.i67-i74.

Addressing the Digital Divide in Contemporary Biology: Lessons from Teaching UNIX

Serghei Mangul and Lana Martin, together with Alexander Hoffmann, Matteo Pellegrini, and Eleazar Eskin, recently published a paper describing a workshop model for training scientists, who have no computer science background, to use UNIX. Our paper is available online as a preprint and will appear in an upcoming “Scientific Life” section of Trends in Biotechnology.

Scientists who are not trained in computer science face an enormous challenge analyzing high-throughput data. Serghei developed a series of workshops in response to growing demand for life and medical science researchers to analyze their own data using the command line.

Administered by UCLA’s Institute for Quantitative and Computational Biosciences (QCBio), these workshops are designed to help life and medical science researchers use applications that lack a graphical interface. Our paper presents a training model for these workshops—a flexible approach that can be implemented at any institution to teach use of command-line tools when the learner has little to no prior knowledge of UNIX.

QCBio currently offers similar workshops to the UCLA community. In tandem with this publication, we created an online catalogue of resources and papers aimed to provide first-time learners with basic knowledge of command line:

We encourage fellow instructors of Bioinformatics, as well as scientists who are new learners of the command line, to read our paper and share their thoughts! Email us at: lana [dot] martin [at] ucla [dot] edu.


The full citation of our paper:
Mangul, Serghei, Martin, Lana S., Hoffmann, Alexander, Pellegrini, Matteo, and Eskin, Eleazar. Addressing the Digital Divide in Contemporary Biology: Lessons from Teaching UNIX. Trends in Biotechnology; doi: 10.1016/j.tibtech.2017.06.007.

Advance preprint copies of our paper may be downloaded here:

Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes

In a recent project, Farhad Hormozdiari and Eleazar Eskin contributed data analysis and interpretation to a project identifying new genes and genomic regions associated with metabolic function in mice. Our paper presents a comprehensive picture of the transcriptome of the mouse hypothalamus and its genetic variation and regulation. This project, which was published in eLife, was led by fellow UCLA researchers Yehudit Hasin-Brumshtein, Jake Lusis, and Desmond Smith.

Mice and humans share virtually the same set of genes; thus, mapping the mouse genome is an important step toward understanding genetic factors in common, complex human diseases such as obesity, heart disease, and diabetes. In metabolic tissues, the integration of genome-wide expression profiles with genetic and phenotypic variance can provide valuable insight into a disease’s underlying molecular mechanism. Measuring gene activity can reveal new molecules that clinical translation efforts may target to treat metabolic disorders.

Our project uses RNA-Seq to characterize transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. Mice were fed a high, high sugar diet, and all strains were comprehensively genotyped and phenotyped for 150 metabolic traits. Our study examines tissues relevant to the hypothalmus, the brain region that controls metabolism and regulates body weight and appetite.

We sequenced 285 samples from all 99 strains of the HMDP. Using methods described in our paper, we identified thousands of new isoforms and >400 new genes. The HMDP allowed us to map Quantitative Trait Loci (eQTLs) with high resolution and power, identifying both local and trans acting variants—or, variants that affect a molecule from within and from outside, respectively.

Groups of genes are associated with multiple related phenotypes in HMDP, although not necessarily enriched for GO ontology or specific pathways. For more information, see our paper.

We report numerous novel transcripts supported by proteomic analyses, as well as novel non-coding RNAs. High resolution genetic mapping of transcript levels in HMDP reveals both local and trans expression eQTLs, identifying two trans eQTL ’hotspots’ associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. We further showed that the genes associated with trans eQTL hotspots correlate to physiological phenotypes, such as HDL and triglyceride levels. This discovery provides insight into the mechanism behind correlation of these genotypes with complex traits.

Our data capture the various non-neuronal cell types, such as microglia or astrocytes, which are often overlooked in the mostly neuron focused studies of the hypothalamus. These cells are important mediators of hypothalamic inflammation and other processes induced by a high fat diet. Regulation of gene expression in these cell types impacts every aspect of metabolism, and our data provide a robust framework recapitulating transcriptional processes affecting multiple cell populations. Our approach is thus complementary to on-going cell type-specific transcriptomic efforts.

For more information, see our paper, which is available for download through eLife:

The full citation to our paper is: 

Hasin-Brumshtein, Yehudit; Hormozdiari, Farhad ; Martin, Lisa ; van Nas, Atila ; Eskin, Eleazar ; Lusis, Aldons J; Drake, Thomas A

Allele-specific expression and eQTL analysis in mouse adipose tissue. Journal Article

In: BMC Genomics, 15 (1), pp. 471, 2014, ISSN: 1471-2164.

Abstract | Links | BibTeX

See our blog post on a recent paper reviewing the HMDP data set: