David Geffen School of Medicine at UCLA
Department of Human Genetics

Speaker Series - Fall Quarter 2008

Mondays, 11am - 12pm, Gonda Building First Floor Conference Room, 1357

Fri, Sep 26
Neuroscience Research Building Auditorium, 9am - 5pm
Progress in Understanding Genetic Diseases
Special Symposium in Honor of the 10th Anniversary of the Gonda (Goldschmied) Neuroscience & Genetics Research Center, sponsored by the UCLA Brain Research Institute, Department of Human Genetics, and Department of Pediatrics
Contact & Intro: Please register at http://www.genetics.ucla.edu/speakers/symposium08/
Mon, Oct 06
Computational Challenges in Discovering the Genetic Basis of Complex Traits in Inbred Mouse Strains
Eleazar Eskin, PhD, Assistant Professor, Departments of Computer Science and Human Genetics, UCLA
Contact & Intro: Chiara Sabatti, ext 49567
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ABSTRACT: Inbred mouse strains are a very powerful and well studied human disease and complex trait model. A tremendous amount of information is available for various inbred strains including phenotypic information stored in the Mouse Phenome Database (MPD) and high-throughput genomic data such as expression microarray data. Recently, several high density SNP maps have also been developed for inbred mouse stains. These resources, combined with what is already known about mouse genetics in terms of quantitative trait loci (QTLs) and known pathways, make inbred mouse strains an ideal model system. However, several key computational hurdles complicate the design and analysis of inbred strain association mapping studies.

In this talk, I will present some recent progress in addressing these challenges which enabled us to perform whole genome association analysis in inbred mouse strains. I will describe how we can augment our association analysis results with information from expression data, known pathways and QTLs. I will demonstrate how our approach is able to discover many regions in the mouse genome associated with phenotypes and how many of our predictions are consistent with genes known to influence specific traits.

  1. Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots. Kang HM, Ye C, Eskin E. Genetics, in press (2008).
  2. Efficient Control of Population Structure in Model Organism Association Mapping. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E. Genetics 178:1709-23 (2008).
  3. A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Frazer KA, Eskin E, Kang HM, Bogue MA, Hinds DA, Beilharz EJ, Gupta RV, Montgomery J, Morenzoni MM, Nilsen GB, Pethiyagoda CL, Stuve LL, Johnson FM, Daly MJ, Wade CM, Cox DR. Nature 448:1050-3 (2007).
Mon, Oct 13
Model-Based Meta-Analysis of CHIP
Debashis Ghosh, PhD, Associate Professor, Departments of Statistics and Public Health Sciences, Center for Comparative Genomics and Bioinformatics, Penn State University, College Park, Pennsylvania
Contact & Intro: Chiara Sabatti, ext 49567
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ABSTRACT: My major research interest is in the analysis of data from "-omics" platforms. In considering such data, there are several levels of analysis that are typically needed. I am very interested in thinking about how to combine and integrate information generated across multiple platforms. Such application drives my methodological interests, which fall generally into the areas of multiple testing and data mining. This also leads to development and consideration of statistical properties of such algorithms. One of the goals of generating such data, especially in human disease settings, is to potentially lead to better medical decision-making procedures. I am also interested in the development of statistical methodology to address this problem.

  1. Genome-wide mapping of in vivo protein-DNA interactions. Johnson DS, Mortazavi A, Myers RM, Wold B. Science 316:1497-502 (2007). Epub 2007 May 31. Comment in Science 316:1441-2 (2007).
  2. Mapping of transcription factor binding regions in mammalian cells by ChIP: comparison of array- and sequencing-based technologies. Euskirchen GM, Rozowsky JS, Wei CL, Lee WH, Zhang ZD, Hartman S, Emanuelsson O, Stolc V, Weissman S, Gerstein MB, Ruan Y, Snyder M. Genome Research 17:898-909 (2007).
Mon, Oct 20
The Role of BANK1 in Susceptibility for SLE
Marta E. Alarcón-Riquelme, MD, PhD, Associate Professor, Medical Genetics, Researcher for the Royal Academy of Sciences, Department of Genetics & Pathology, Uppsala University, Uppsala, Sweden
Contact & Intro: Paivi Pajukanta, ext 72011
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ABSTRACT: Functional Variants in the B-Cell Gene BANK1 are Associated with SLE
The main hallmark of SLE is the production of autoantibodies and the hyperreactivity of B cells. A 100k genome-wide scan identified an association detected by a non-synonymous substitution (rs10516487, R61H) in the B- cell scaffold protein with ankyrin repeats gene, BANK1. Analysis of BANK1 cDNA revealed two isoforms, a full- length and an alternative spliced lacking exon 2 (2) encoding a protein without putative IP3R-binding domain. The transcripts were differentially expressed depending on a branch-point site SNP rs17266594 in strong LD with rs10516487. A third associated variant was found in the ankyrin domain (rs3733197, A383T). Our findings implicate BANK1 as a susceptibility gene for lupus, with variants affecting regulatory sites and key functional domains. The effect of the polymorphisms might result in sustained BCR- signaling and B-cell hyperactivity, characteristic of this disease.

Kozyrev SV, Abelson AK, Wojcik J, Zaghlool A, Reddy MVPL, Sanchez E, Gunnarsson I, Svenungsson E, Sturfelt G, Jönsen A, Truedsson L, Pons-Estel B, Witte T, D’Alfonso S, Barrizzone N, Danieli MG, Gutierrez C, Suarez A, Junker P, Laustrup H, González-Escribano MF, Martin J, Abderrahim H, Alarcón-Riquelme ME.

  1. A regulatory polymorphism within the PDCD1gene is associated with susceptibility to systemic lupus erythematosus in humans. Prokunina L, Castillejo-López C, Öberg F, Gunnarsson I, Berg L, Magnusson V, Brookes AJ, Tentler D, Kristjansdottir H, Gröndal G, Bolstad AI, Svenungsson E, Lundberg I, Sturfelt G, Jönssen A, Truedsson L, Lima G, Alcocer-Varela J, Jonsson R, Gyllensten U, Harley JB, Alarcón-Segovia D, Steinsson K, and Alarcón-Riquelme ME. Nature Genetics 32:666-669 (2002).
  2. A Common Haplotype of Interferon Regulatory Factor 5 (IRF5) Regulates Splicing and Expression and is Associated with Increased Risk of Systemic Lupus Erythematosus. Graham RR, Kozyrev SV, Baechler EC, Linga Reddy PMV, Plenge RM, Bauer JW, Ortmann WA, Koeuth T, Gonzalez-Escribano MF, Pons-Estel B, Petri M, Daly M, Gregersen PK, Martin J, Altshuler D, Behrens TW, Alarcón-Riquelme ME. Nature Genetics 38:550-555 (2006). (italics indicates shared authors)
  3. Structural insertion/deletion Variation in IRF5 is Associated with a Risk Haplotype and Defines the Precise Isoforms Expressed in SLE. Kozyrev SV, Lewén S, Linga Reddy MVP, Pons-Estel BA, The Argentine Collaborative Group, Witte T, The German Collaborative Group, Junker P, Laustrup H, Gutiérrez C, Suárez A, González-Escribano MF, Martín J. Arthritis & Rheumatism 56:1234-41 (2007).
Mon, Oct 27
Type 2 Diabetes Genetics and the Legacy of James Neel
Richard M. Watanabe, PhD, Associate Professor, Preventive Medicine and Physiology & Biophysics, Associate Program Director, General Clinical Research Center, University of Southern California, Los Angeles, California
Contact & Intro: Paivi Pajukanta, ext 72011
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ABSTRACT: The late James Neel made many contributions to the field of genetics during his prolific career. In terms of the genetics of diabetes, Neel made two seminal contributions. First, he noted the challenges that would be encountered in attempting to understand the genetic basis for diabetes, labeling the disease “the geneticist’s nightmare”. Second, he introduced the concept of the “thrifty gene hypothesis”, which hypothesized that humans were essentially designed to store energy, which was advantageous in early human history. However, in the modern world this architecture has become a liability and results in susceptibility to diabetes. Genome-wide association studies have identified several susceptibility loci for type 2 diabetes and additional loci are likely to be revealed in the near future. Additional loci underlying variation in diabetes-related quantitative traits have also been identified using the same genome-wide approach. These discoveries have given us a small window to better understand the biology underlying the pathogenesis of type 2 diabetes. We will review the discovery of these loci, but also discuss the relevance of some of these loci to the pathogenesis of type 2 diabetes. Our journey will be guided by examples from the Finland-United States Investigation of Non-insulin-dependent Diabetes Genetics (FUSION) and BetaGene studies.

  1. Transcription factor 7-like 2 (TCF7L2) is associated with gestational diabetes and interacts with adiposity to alter insulin secretion in Mexican Americans. Watanabe RM, et al. Diabetes 56:1481-1485 (2007).
  2. Genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Scott LJ, et al. Science 316:1341-1345 (2007).
  3. Meta-analysis of genome-wide association data and large-scale replication identifies several additional susceptibility loci for type 2 diabetes. Zeggini E, et al. Nature Genetics 40:638-645 (2008).
  4. Genomewide association studies in Caucasians identify variants in the G6PC2/ABCB11 region regulating fasting glucose levels. Chen W-M, et al. The Journal of Clinical Investigation 118:2620-2628 (2008).
Mon, Nov 03
The Ins and Outs of Transporting Transporters
Elizabeth Conibear, PhD, Assistant Professor, Department of Medical Genetics, Centre for Molecular Medicine & Therapeutics, University of British Columbia, Vancouver, Canada
Contact & Intro: Esteban Dell'Angelica, ext 63749
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ABSTRACT: We are investigating how membrane proteins are recruited from intracellular compartments and transported to the cell surface in response to extracellular cues. This process is crucial for the redistribution of neurotransmitter receptors during synaptic remodeling and the translocation of the glucose transporter GLUT4 in response to insulin. In yeast, the trafficking of the chitin synthase Chs3 provides a good model for regulated translocation in higher cells. Like GLUT4 and many neurotransmitter receptors, Chs3 is maintained in intracellular storage compartments by endosomal recycling until a signal causes its redistribution to the plasma membrane. We have developed a novel genomics approach for the discovery of factors that regulate the stimulus- induced transport of Chs3. This led to the discovery of a novel palmitolytransferase Pfa4 that modifies Chs3 and regulates its ER export. We are now investigating the quality control mechanisms that maintain Chs3 in the ER when it is not palmitoylated. We are also using this approach to identify the regulatory machinery that couples stress signaling to Chs3 transport, and characterize factors that direct Chs3 into intracellular storage.

  1. Palmitoylation by the DHHC protein Pfa4 regulates the ER exit of Chs3. Lam K, Davey M, Sun B, Roth AF, Davis NG, Conibear E. Journal of Cell Biology 174:19-25 (2006).
  2. Global Analysis of Yeast Endosomal Transport Identifies the Vps55/68 Sorting Complex. Schluter C, Lam KK, Brumm J, Wu BW, Saunders M, Stevens TH, Bryan J, Conibear E. Molecular Biology of the Cell. 19:1282-1294 (2008).
Mon, Nov 10
This Dog Just Don't Hunt - Is it finally time to euthanize the Human Genome Project?
Joseph Terwilliger, PhD, Associate Professor of Neuroscience and Genetics and Development, Department of Genetics and Development, Columbia University Medical Center, New York
Contact & Intro: Chiara Sabatti, ext 49567
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ABSTRACT: See literature

  1. Natural experiments in human gene mapping: the intersection of anthropological genetics and genetic epidemiology. Terwilliger JD, Lee JH. Anthropological Genetics: Theory, Methods and Applications. Crawford MH (ed). University of Cambridge Press. 1-38 (2006).
  2. Confounding, ascertainment bias, and the blind quest for the fountain of youth. Terwilliger JD, Weiss KM. Trends in Molecular Medicine, Annals of Medicine, Taylor and Francis (2003).
  3. How many diseases does it take to map a gene with SNPs? Terwilliger JD, Weiss KM. Nature America Inc. 26:151-157 (2000).
  4. Linkage analysis in the presence of errors IV: joint pseudomarker analysis of linkage and/or linkage disequilibrium on a mixture of pedigrees and singletons when the mode of inheritance cannot be accurately specified. HHH Goring, JD Terwilliger. American Journal of Human Genetics 66:1310–1327 (2000)
Mon, Nov 17
Challenges in the Analysis of Clinical Genomics Studies
John Storey, PhD, Associate Professor, Department of Molecular Biology, Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey
Contact & Intro: Chiara Sabatti, ext 49567
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ABSTRACT: It has been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. We introduce "surrogate variable analysis" (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We analyze data from the Inflammation and the Host Response to Injury Program (IHRIP). We show that widespread heterogeneity obscures the relationship between genomic signatures and clinical outcomes. We apply the SVA approach to carefully model and account for heterogeneity in the IHRIP data, resulting in substantial improvements in reproducibility of both genome-wide signal and specific functional pathways associated with the clinical outcome. We show that the IHRIP study is reproducible and yields consistent biologically meaningful results when heterogeneity is taken into account.

  1. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. Leek JT, Storey JD. PLoS Genetics 3(9): e161. doi:10.1371/journal.pgen.0030161 (2007)
Mon, Nov 24
Neuroscience Research Building Auditorium
Common DNA Sequence Variants, Blood Lipids, and Risk for Myocardial Infarction
Sekar Kathiresan, MD, Medical Director, Cardiovascular Disease Prevention Center, Massachusetts General Hospital, Boston, Massachusetts
Contact & Intro: Paivi Pajukanta, ext 72011
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ABSTRACT: Myocardial infarction (MI) is the leading cause of death in the United States and blood levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides are risk factors for MI. Both MI and blood lipids are heritable traits. To map genomic regions related to MI and blood lipids, we have used the genome-wide association approach and identified 8 gene regions related to MI and 30 gene regions related to LDL cholesterol, HDL cholesterol, and/or triglycerides. I will describe these findings, discuss the implications for understanding the biology of these traits and consider potential applications to clinical medicine.

  1. Polymorphisms Associated with Cholesterol and Risk of Cardiovascular Events. Kathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, Hirschhorn JN, Berglund G, Hedblad B, Groop L, Altshuler DM, Newton-Cheh C, Orho-Melander M. The New England Journal of Medicine 358:1240-9 (2008).
  2. A PCSK9 Missense Variant Associated with a Reduced Risk of Early-Onset Myocardial Infarction. Kathiresan S. The New England Journal of Medicine 358:21 (2008).
  3. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, Wahlstrand B, Hedner T, Corella D, Shyong Tai E, Ordovas JM, Berglund G, Vartiainen E, Jousilahti P, Hedblad B, Taskinen MJ, Newton-Cheh C, Salomaa V, Peltonen L, Groop L, Altshuler DM, Orho-Melander, M. Nature Genetics 40:189-197 (2008).
Mon, Dec 01
Embryonic Stem Cells: Building Alternative Pluripotency Networks
Thomas Zwaka, MD, PhD, Center for Cell and Gene Therapy, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas
Contact & Intro: Guoping Fan, ext 70439
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ABSTRACT: Pluripotency is a unique biological state that allows cells to differentiate into any tissue in the body. Here we describe a novel candidate pluripotency factor, Ronin, that acts independently of canonical transcription factors and possesses a THAP domain, which is associated with sequence-specific DNA binding and epigenetic silencing of gene expression. Ronin is expressed primarily during the earliest stages of murine embryonic development, and its deficiency in mice produces periimplantational lethality and defects in the inner cell mass. Ronin ablation by a conditional knockout strategy prevents the growth of ES cells by inducing rapid cell death. Most critically, forced expression of Ronin allows ES cells to proliferate without differentiation under conditions that normally do not promote self-renewal, and it partly compensates for the effects of Oct4 knockdown. In ES cells ectopically expressing Ronin, both Oct4 and Nanog are downregulated with no effects on pluripotency. We demonstrate that Ronin binds directly to the HCF-1 protein, a key regulator of transcriptional control. Our findings identify Ronin as an essential factor underlying embryogenesis and ES cell pluripotency. Its direct binding to HCF-1 supports an epigenetic mechanism of gene repression in pluripotent cells.

  1. Ronin is essential for embryogenesis and the pluripotency of mouse embryonic stem cells. Dejosez M, Krumenacker JS, Zitur LJ, Passeri M, Chu LF, Songyang Z, Thomson JA, Zwaka TP. Cell 133:1162-74 (2008).
  2. Caspase activity mediates the differentiation of embryonic stem cells. Fujita J, Crane AM, Souza MK, Dejosez M, Kyba M, Flavell RA, Thomson JA, Zwaka TP. Cell Stem Cell 2:595-601 (2008).

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