Thesis Defense: Dr. Zhanyong (Jerry) Wang

Jerry Wang defended his thesis on September 8, 2014 in 4760 Boelter Hall.

His thesis topic was Efficient Statistical Models For Detection And Analysis Of Human Genetic Variations. The video of his full defense can be viewed on the ZarlabUCLA YouTube page here.


In recent years, the advent of genotyping and sequencing technologies has enabled human genetics to discover numerous genetic variants. Genetic variations between individuals can range from Single Nucleotide Polymorphisms (SNPs) to differences in large segments of DNA, which are referred to as Structural Variations (SVs), including insertions, deletions, and copy number variations (CNVs).

First proposed was a probabilistic model, CNVeM, to detect CNVs from High-Throughput Sequencing (HTS) data. The experiment showed that CNVeM can estimate the copy numbers and boundaries of copied regions more precisely than previous methods.

Genome-wide association studies (GWAS) have discovered numerous individual SNPs involved in genetic traits. However, it is likely that complex traits are influenced by interaction of multiple SNPs. In his thesis, Jerry proposed a two-stage statistical model, TEPAA, to reduce the computational time greatly while maintaining almost identical power to the brute force approach which considers all combinations of SNP interactions. The experiment on the Northern Finland Birth Cohort data showed that TEPAA achieved 63 times speedup.

Another drawback of GWAS is that rare causal variants will not be identified. Rare causal variants are likely to be introduced in a population recently and are likely to be in shared Identity-By-Descent (IBD) segments. Jerry proposed a new test statistic to detect IBD segments associated with quantitative traits and made a connection between the proposed statistic and linear models so that it does not require permutations to assess the significance of an association. In addition, the method can control population structure by utilizing linear mixed models.


The full paper on topics covered in Jerry’s thesis defense can be found below:

Wang, Zhanyong; Sul, Jae Hoon ; Snir, Sagi ; Lozano, Jose A; Eskin, Eleazar

Gene-Gene Interactions Detection Using a Two-Stage Model Book Chapter

In: Research in Computational Molecular Biology, pp. 340-355, Springer International Publishing, 2014.

Abstract | Links | BibTeX

Thesis Defense: Dr. Eun Yong Kang

Eun Yong Kang in our group defended his thesis on Monday Nov 25th, 2013. 2:30pm – 4:30pm in 4760 Boelter Hall.

The title of his defense was “Computational Genetic Approaches for Understanding the Genetic Architecture of Complex Traits”. The video of this defense is now available here. Fortunately for the lab, Eun is now a post-doc in the group.

The abstract of his thesis defense was:
Recent advances in genotyping and sequencing technology have enabled researchers to collect an enormous amount of high-dimensional genotype data. These large scale genomic data provide unprecedented opportunity for researchers to study and analyze the genetic factors of human complex traits. One of the major challenges in analyzing these high-dimensional genomic data is requiring effective and efficient computational methodologies. In this talk, I will focus on three problems that I have worked on. First, I will introduce a method for inferring biological networks from high-throughput data containing both genetic variation and gene expression profiles from genetically distinct strains of an organism. For this problem, I use causal inference techniques to infer the presence or absence of causal relationships between yeast gene expressions in the framework of graphical causal models. Second, I introduce efficient pairwise identity by descent (IBD) association mapping method, which utilizes importance sampling to improve efficiency and enable approximation of extremely small p-values. Using the WTCCC type 1 diabetes data, I show that Fast-Pairwise cansuccessfully pinpoint a gene known to be associated to the disease within the MHC region. Finally, I introduce a novel meta analytic approach (Meta-GxE) to identify gene-by-environment interactions by aggregating the multiple studies with varying environmental conditions. Meta-GxE approach jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. This approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. Application of this approach to 17 mouse studies identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which show significant evidence of involvement in gene-by-environment interactions.

Eun’s talk covered the following papers:

Kang, Eun Yong; Han, Buhm; Furlotte, Nicholas; Joo, Jong Wha J; Shih, Diana; Davis, Richard C; Lusis, Aldons J; Eskin, Eleazar

Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice Journal Article

In: PLoS Genet, 10 (1), pp. e1004022, 2014, ISSN: 1553-7404.

Abstract | Links | BibTeX

Han, Buhm; Kang, Eun Yong ; Raychaudhuri, Soumya ; de Bakker, Paul I W; Eskin, Eleazar

Fast Pairwise IBD Association Testing in Genome-wide Association Studies. Journal Article

In: Bioinformatics, 2013, ISSN: 1367-4811.

Abstract | Links | BibTeX

Kang, Eun Yong; Ye, Chun ; Shpitser, Ilya ; Eskin, Eleazar

Detecting the presence and absence of causal relationships between expression of yeast genes with very few samples. Journal Article

In: J Comput Biol, 17 (3), pp. 533-46, 2010, ISSN: 1557-8666.

Abstract | Links | BibTeX

Thesis Defense: Dr. Jae Hoon Sul

Dr. Jae Hoon Sul with his committee.

Dr. Jae Hoon Sul with his committee.

Jae Hoon Sul successfully defended his thesis on Wednesday September 19th.  His talk is posted on our YouTube Channel ZarlabUCLA.  Jae Hoon’s talk discusses several projects including using mixed model to correct for population structure, rare variant association studies and a meta-analysis approach for detecting multi-tissue eQTLs.  Fortunately for the lab, Jae Hoon is staying at UCLA for another year as a post-doc.

More details about what he talks about in his talk are available in the papers he discusses:

Sul, Jae Hoon; Han, Buhm ; Ye, Chun ; Choi, Ted ; Eskin, Eleazar

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches Journal Article

In: PLoS Genet, 9 (6), pp. e1003491, 2013, ISSN: 1553-7404.

Abstract | Links | BibTeX

Sul, Jae Hoon; Han, Buhm ; He, Dan ; Eskin, Eleazar

An Optimal Weighted Aggregated Association Test for Identification of Rare Variants Involved in Common Diseases. Journal Article

In: Genetics, 188 (1), pp. 181-188, 2011, ISSN: 1943-2631.

Abstract | Links | BibTeX

Kang, Hyun Min; Sul, Jae Hoon ; Service, Susan K; Zaitlen, Noah A; Kong, Sit-Yee Y; Freimer, Nelson B; Sabatti, Chiara ; Eskin, Eleazar

Variance component model to account for sample structure in genome-wide association studies. Journal Article

In: Nat Genet, 42 (4), pp. 348-54, 2010, ISSN: 1546-1718.

Abstract | Links | BibTeX