We teach a course called “Computational Genetics” each year at UCLA. This course is taken by both graduate and undergraduate students from both the Computer Science department and the many biology and medical school programs. In this course we cover both topics related to genome wide association studies (GWAS) and topics related to next generation sequencing studies. One lecture that is given each year is an introductory lecture to sequencing and read mapping. The video of this lecture is available here. Please excuse the poor cinematography. This lecture was recorded from the back of the classroom.
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:
In: PLoS Genet, 10 (1), pp. e1004022, 2014, ISSN: 1553-7404.
In: Bioinformatics, 2013, ISSN: 1367-4811.
In: J Comput Biol, 17 (3), pp. 533-46, 2010, ISSN: 1557-8666.
Our DNA can tell us a lot about who our relatives are. Recently, several companies including 23andMe and AncestryDNA now provide services where they collect DNA from individuals and then match the DNA to a database of the DNA of other people to identify relatives. Relatives are then informed by the company that their DNAs match. Our lab was interested if we can perform this same type of service but without involving a company and more generally without involving any third party. One way to do this would be to have individuals obtain their own DNA sequences and then share their DNA sequences directly with each other. Unfortunately, DNA sequences are considered medical information and it is inappropriate to share them in this way.
Through a collaboration between our lab and the UCLA cryptography group, we recently published a paper that combines cryptography and genetics which describes an approach for identifying relatives without compromising privacy. Our paper was published in the April 2014 issue of Genome Research. The key ideas is that individuals release an encrypted version of their DNA information. Another individual can download this encrypted version and then use their own DNA information to try to decrypt it. If the are related to each other, their DNA sequences will be close enough that the decryption will work telling the individual that they are related. While if they are unrelated, the decryption will fail. What is important in this approach is that individuals who are not related do not obtain any information about each other’s DNA sequences.
The intuitive idea behind the approach is the following. Individuals each release a copy of their own genomes encrypted with a key that is based on the genome itself. Other users then download this encrypted information and try to decrypt it using their own genomes as the key. The encryption scheme is designed to allow for decryption if the encrypting key and decrypting key are “close enough”. Since related individuals share a portion of their genomes, we set the threshold for “close enough” to be exactly the threshold of relatedness that we want to detect.
Our approach uses a relatively new type of cryptographic technique called Fuzzy Extractors which were pioneered by our co-authors on this study, Amit Sahai and Rafail Ostrovsky. This type of technique allows for encryption and decryption with keys that match inexactly. Students in our group who were involved are Dan He, Nick Furlotte, Farhad Hormozdiari, and Jong Wha (Joanne) Joo. This research was supported by National Science Foundation grant 1065276.
The full citation of our paper is here:
Identifying genetic relatives without compromising privacy. Journal Article
In: Genome Res, 2014, ISSN: 1549-5469.