The Multivariate Normal Distribution Framework for Analyzing Association Studies: Overview

The use of the multivariate normal (MVN) model has been a powerful tool in our groups research and it has been utilized in many of our papers. Jose Lozano (University of the Basque Country, San Sebastian, Spain), along with Eleazar Eskin and three ZarLab alumni—Farhad Hormozdiari (postdoc at Harvard), Jong Wha (Joanne) Joo (faculty at Dongguk University in Seoul), and Buhm Han (faculty at University of Ulsan College of Medicine in Seoul)—recently published a review of the multivariate normal (MVN) distribution framework in genome-wide association studies (GWAS) studies.

Genome-wide association studies (GWAS) have discovered thousands of variants involved in common human diseases. In these studies, frequencies of genetic variants are compared between a population of individuals with a disease (cases) and a population of healthy individual controls). Any variant that has a significantly different frequency between the two populations is considered an associated variant.

A major challenge in the analysis of GWAS studies is the fact that human population history causes nearby genetic variants in the genome to be correlated with each other. In this review, we demonstrate how to utilize the MVN distribution to explicitly take into account the correlation between genetic variants and provide a comprehensive framework for analysis of GWAS.

In this paper, we show how the MVN framework can be applied to perform association testing, correct for multiple hypothesis, testing, estimate statistical power, and perform fine mapping and imputation. In future blog posts, we will highlight different ways the MVN framework can be used in association studies.

An illustration of the multivariate normal model (a) Type I Error (b) Power.

Many of the authors are the alumni of the group who pioneered the use of the MVN in various problems in association studies. Here is a list of papers that our group published using the MVN framework:

Joo, Jong Wha J; Hormozdiari, Farhad; Han, Buhm; Eskin, Eleazar

Multiple testing correction in linear mixed models. Journal Article

In: Genome Biol, 17 (1), pp. 62, 2016, ISSN: 1474-760X.

Abstract | Links | BibTeX

Hormozdiari, Farhad ; Kang, Eun Yong ; Bilow, Michael ; Ben-David, Eyal ; Vulpe, Chris ; McLachlan, Stela ; Lusis, Aldons J; Han, Buhm ; Eskin, Eleazar

Imputing Phenotypes for Genome-wide Association Studies. Journal Article

In: Am J Hum Genet, 99 (1), pp. 89-103, 2016, ISSN: 1537-6605.

Abstract | Links | BibTeX

Duong, Dat ; Zou, Jennifer ; Hormozdiari, Farhad ; Sul, Jae Hoon ; Ernst, Jason ; Han, Buhm ; Eskin, Eleazar

Using genomic annotations increases statistical power to detect eGenes. Journal Article

In: Bioinformatics, 32 (12), pp. i156-i163, 2016, ISSN: 1367-4811.

Abstract | Links | BibTeX

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

Joo, Jong Wha J; Kang, Eun Yong; Org, Elin; Furlotte, Nick; Parks, Brian; Hormozdiari, Farhad; Lusis, Aldons J; Eskin, Eleazar

Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Journal Article

In: Genetics, 204 (4), pp. 1379-1390, 2016, ISSN: 1943-2631.

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; 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

Kichaev, Gleb; Yang, Wen-Yun Y; Lindstrom, Sara ; Hormozdiari, Farhad ; Eskin, Eleazar ; Price, Alkes L; Kraft, Peter ; Pasaniuc, Bogdan

Integrating functional data to prioritize causal variants in statistical fine-mapping studies. Journal Article

In: PLoS Genet, 10 (10), pp. e1004722, 2014, ISSN: 1553-7404.

Abstract | Links | BibTeX

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

Flint, Jonathan; Eskin, Eleazar

Genome-wide association studies in mice Journal Article

In: Nature Reviews Genetics, 13 (11), pp. 807-17, 2012, ISSN: 1471-0064.

Abstract | Links | BibTeX

Han, Buhm; Kang, Hyun Min ; Eskin, Eleazar

Rapid and accurate multiple testing correction and power estimation for millions of correlated markers. Journal Article

In: PLoS Genet, 5 (4), pp. e1000456, 2009, ISSN: 1553-7404.

Abstract | Links | BibTeX

Eskin, Eleazar

Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior information. Journal Article

In: Genome Res, 18 (4), pp. 653-60, 2008, ISSN: 1088-9051.

Abstract | Links | BibTeX

Eskin, Eleazar

Increasing Power in Association Studies by Using Linkage Disequilibrium Structure and Molecular Function as Prior Information Conference

Lecture Notes in Computer Science, 4955/2008 , Lecture Notes in Computer Science Springer Berlin / Heidelberg, 2008, ISSN: 0302-9743 (Print) 1611-3349 (Online).

Abstract | Links | BibTeX

  • Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Chelsea J.-T. Ju, Ayellet V. Segre, Jong Wha J. Joo, Hyejung Won, Sriram Sankararaman, Bogdan Pasaniuc, Sagiv Shifman, and Eleazar Eskin. Widespread allelic heterogeneity in complex traits. The American Journal of Human Genetics, 100(5):789{802, may 2017.
  • Yue Wu, Farhad Hormozdiari, Jong Wha J. Joo, and Eleazar Eskin. Improving imputation accuracy by inferring causal variants in genetic studies. In Lecture Notes in Computer Science, pages 303{317. Springer International Publishing, 2017.

The paper was written by Jose A. Lozano, Farhad Hormozdiari, Jong Wha (Joanne) Joo, Buhm Han, and Eleazar Eskin, and it is available at: https://www.biorxiv.org/content/early/2017/10/28/208199.

The full citation to our paper is:

Jose A. Lozano, Farhad Hormozdiari, Jong Wha (Joanne) Joo, Buhm Han, Eleazar Eskin. 2017. The Multivariate Normal Distribution Framework for Analyzing Association Studies. bioRxiv doi: https://doi.org/10.1101/208199.

Involving undergraduates in genomics research to narrow the education-research gap

Serghei Mangul and Lana Martin, together with Eleazar Eskin, recently wrote a paper describing a model for training undergraduates in Bioinformatics. Our paper is available online as a preprint and is under review at a peer-reviewed journal.

The Education-Research Gap in Universities.

While the benefits of undergraduate research experiences (UREs) are recognized for undergraduates, the advantages of UREs for graduate students, post-doctoral scholars, and faculty are not clearly outlined.

Based on our experience mentoring undergraduates in ZarLab, we believe that the analysis of genomic data is particularly well-suited for successful involvement of undergraduates. In computational genomics research, undergraduate trainees who master a particular skill can contribute sufficient work to gain authorship on a peer-reviewed paper.

In our paper, we offer a framework for engaging undergraduates in genomics research while simultaneously improving lab productivity: first, identify particular “low-level” tasks that may take up to a week for an undergraduate to complete. Second, encourage students to “outsource” foundational education needs with workshops, online resources, and review articles. Third, genomics research labs can take advantage of department- and campus-wide undergraduate research and training initiatives.

The proposed strategy can be easily reproduced at other institutions, is pedagogically flexible, and is scalable from smaller to larger laboratory sizes. We hope that genomics researchers will involve undergraduates in more computational tasks that benefit both students and senior laboratory members.

Preprint copies of our manuscript are available for download here: https://peerj.com/preprints/3149/

In tandem with this paper, we created an online catalogue of resources and papers aimed at bridging the research-teaching divide in computational genomics: https://smangul1.github.io/undergraduates-in-genomics/

The full citation of our paper:
Mangul, S., Martin, L. and Eskin, E., 2017. Involving undergraduates in genomics research to narrow the education-research gap. PeerJ Preprints, 5, p.e3149v1.

 

Benefits of UREs to Research Lab and Undergraduates.

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: https://github.com/datduong/RECOV.

Our paper can be downloaded at Bioinformatics: https://academic.oup.com/bioinformatics/article/33/14/i67/3953939/Applying-meta-analysis-to-genotype-tissue

 

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.