ZarLab goes to Catalina Island: 2017 UCLA Bioinformatics IDP Retreat

Several members of our lab recently traveled to Catalina Island for the 2017 UCLA Bioinformatics Interdepartmental Program retreat. Every year, graduate students in the Bioinformatics IDP organize a retreat in southern California for faculty and graduate students to develop research ideas and get to know each other.

This year, Bioinformatics IDP PhD students Kikuye Koyano and Artur Jaroszewicz organized the retreat at the University of Southern California’s Wrigley Marine Science Center in Two Harbors, 20 miles offshore from Los Angeles. The Wrigley Center maintains houses, dormitories, laboratories, waterfront, and conference facilities for environmental scientists and other ventures that support the center’s mission. Situated in Big Fisherman’s Cove, a nearby marine life refuge established in 1989 offers soft clean sediment, kelp forests and a wide diversity of marine life.

During the 3-day retreat, Bioinformatics IDP graduate students presented research papers and held forums on applying for fellowships, writing letters of intent, managing graduate school funding packages, and using campus mental health resources. In addition to science, faculty and students enjoyed hiking the chaparral hills, kayaking in the bay, snorkeling in the kelp forest, and enjoying dinner overlooking the beach in Two Harbors.

Eleazar Eskin joined the retreat as a faculty advisor, along with Jason Ernst, Peipei Ping, Sriram Sankararaman, and Roy Wollman. Rob Brown, a senior graduate student in our lab, helped faculty judge best abstracts and best presentations. Harry Yang, a first-year graduate student in our lab, presented work from one of his first year lab rotations in Kathrin Plath’s lab. Harry’s work addressed developing methods for identifying transcription factors that induce the maturation of neurons. Specifically, his work focused on refining single-cell RNA sequencing methods in a reproducible way and applying the method to neuron-differentiated stem cells and fetal brain samples.

Many thanks to Kikuye and Artur for organizing a very successful and enjoyable retreat!

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ZarLab goes to Hong Kong 2017 RECOMB Meetings

Last week many members of our group traveled to Hong Kong, China, for the 21st Annual International Conference on Research in Computational Molecular Biology (RECOMB 2017). This year’s meeting, which took place May 3-7, 2017, featured over 300 talks, workshops, and poster presentations on topics from all areas of computational molecular biology.

This year, our group contributed 7 poster presentations, 2 research talks, and 1 research highlight (see below for a complete list). In addition to science, ZarLab members enjoyed traveling about Hong Kong and taking in the food, sights, and bicycle paths.

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Our Papers:

Yue (Ariel) Wu, Farhad Hormozdiari, Jong Wha J Joo and Eleazar Eskin.
Improving imputation accuracy by inferring causal variants in genetic studies

Elior Rahmani, Regev Schweiger, Liat Shenhav, Eleazar Eskin and Eran Halperin.
A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference


Our Highlights:

Elior Rahmani, Noah Zaitlen, Yael Baran, Celeste Eng, Donglei Hu, Joshua Galanter, Sam Oh, Esteban Burchard, Eleazar Eskin, James Zou and Eran Halperin
Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies


Our Posters:

Yue (Ariel) Wu, Eleazar Eskin and Sriram Sankararaman
Improving imputation by maximizing power

Lisa Gai, Dat Duong and Eleazar Eskin
Finding associated variants in genome-wide associations studies on multiple traits

Harry Taegyun Yang, Serghei Mangul, Noah Zaitlen, Sagiv Shifman and Eleazar Eskin
Repeat Elements Profile Across Different Tissues in GTEx Samples

Dat Duong, Lisa Gai, Sagi Snir, Eun Yong Kang, Buhm Han, Jae Hoon Sul and Eleazar Eskin
Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

Jennifer Zou, Farhad Hormozdiari, Jason Ernst, Jae-Hoon Sul and Eleazar Eskin
Leveraging allele-specific expression to improve fine-mapping for eQTL studies

Serghei Mangul, Igor Mandric, Alex Zelikovsky and Eleazar Eskin
Profiling adaptive immune repertoires across multiple human tissues by RNA Sequencing

Robert Brown, Eleazar Eskin and Bogdan Pasaniuc
Haplotype-based eQTL Mapping Increases Power to Identify eGenes

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.