David Geffen School of Medicine at UCLA
Department of Human Genetics

Speaker Series - Winter Quarter 2009

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

Wed, Jan 07
Talk is scheduled 1-2pm in the Neuroscience Research Building Auditorium
METSIM Study: Insulin sensitivity, and CVD risk factors in prediabetic individuals
Markku Laakso, MD, Academy Professor, Department of Medicine, University of Kuopio, Finland
Contact & Intro: Paivi Pajukanta, ext 72011
Mon, Jan 12
Neuroscience Research Building Auditorium
Information Integration Approaches to Biological Discovery
John Quackenbush, PhD, Professor of Computational Biology and Bioinformatics, Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts
Contact & Intro: Chiara Sabatti, ext 49567
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  1. Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples. Mar JC, Rubio R, Quackenbush, J. Genome Biology Open Access 7:R119 (2006).
  2. Seeded Bayesian Networks:Constructing genetic networks from microarray data. Djebbari A, Quackenbush, J. BMC Systems Biology 57:1-13 (2008).
  3. Extracting biology from high-dimensional biological data. Quackenbush, J. The Journal of Experimental Biology 210:1507-1517 (2007).
Mon, Jan 26
Exosome Biogenesis and Retrovirus Budding
Stephen Gould, PhD, Professor of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland
Contact & Intro: Esteban Dell'Angelica, ext 63749
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ABSTRACT: Animal cells secrete exosomes, small spherical vesicles with the same topology as the cell and a diameter of ~50-250 nm. Here we show that higher-order oligomerization and PM binding are sufficient to target proteins to sites of exosome budding (endosome-like domains of the plasma membrane, or ELDs) and into exosomes. These same signals target proteins to sites of HIV budding and into HIV particles. Retroviral Gag proteins are trafficked to ELDs and exosomes, and their budding is mediated by higher-order oligomerization and PM binding. Outward vesicle budding, which includes exosome biogenesis, is thought to depend on the ESCRT machinery. However, we find that a dominant-negative form of VPS4B does not impair exosome biogenesis in human cells. Moreover, depletion of ESCRT proteins does not block exosome biogenesis in fly S2 cells. These data appear highly relevant to the budding of HIV. The dependence of HIV budding on its late domains is only evident in certain cell types, and even then can be suppressed by inactivation of the viral protease. Furthermore, the ESCRT-dependence of HIV budding is relieved when the p6 domain is removed from the virus. Taken together, these results indicate that the p6 domain and the ESCRT machinery play indirect and unexpectedly complex roles in the budding of HIV and other retroviruses.

  1. Exosomes and HIV Gag bud from endosome-like domains of the T cell plasma membrane. Booth AM, Fang Y, Fallon JK, Yang, J-M, Hildreth JEK, Gould SJ. The Journal of Cell Biology 172: 923–935 (2006).
  2. Higher-Order Oligomerization Targets Plasma Membrane Proteins and HIV Gag to Exosomes. Fang Y, Wu N, Gan X, Yan W, Morrell JC, Gould SJ. PLoS Biology 56: 1267-1283 (2007). E-published: www.plosbiology.org
Mon, Feb 02
Genes, Gene Networks, and Type 2 Diabetes
Alan Attie, PhD, Professor, Department of Biochemistry, University of Wisconsin, Madison, Wisconsin
Contact & Intro: Paivi Pajukanta, ext 72011
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ABSTRACT: Our research program studies two mouse strains that differ in obesity-induced type 2 diabetes. Our objective is to identify genes that are causal for diabetes or are part of a pathway leading to type 2 diabetes. We carry out positional cloning projects wherein QTLs are mapped in an F2 and genes are identified through the phenotyping of interval-specific congenic mouse strains. We also measure mRNA and micro-RNA abundance as a trait in our F2 panels. This enables us to construct causal network models that link QTLs to particular mRNAs and then finally to clinical traits.

  1. Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling. Ferrara CT, Wang P, Neto EC, Stevens RD, Bain JR, Wenner BR, Ilkayeva OR, Keller MP, Blasiole DA, Kendziorski C, Yandell BS, Newgard CB, Attie AD. PLoS Genetics. Volume 4 Issue 3 (2008). www.plosgenetics.org
  2. A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Keller MP, Choi YJ, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS, Argmann C, Schueler KL, Edwards S, Steinberg HA, Neto EC, Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kendziorski C, Attie AD. Genome Research 18:706-716 (2008). Originally published online Mar 17, 2008; www.genome.org
Mon, Feb 09
Interrogating Multiple 8q24 regions for Prostate Cancer Risk Loci
Rick Kittles, PhD, Associate Professor, Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, Illinois
Contact & Intro: Paivi Pajukanta, ext 72011
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ABSTRACT: The traditional model of human disease genetics, suggest that mutations in coding regions of the genome are assumed to underlie disease phenotypes. However functional non-coding regions have been implicated in diseases like cancer, and little is known about the role of DNA sequence variation leading to chromosomal breakage (fragile sites) and regulatory elements. Recent studies have shown that numerous single nucleotide polymorphisms (SNPs) within a region of 8q24 which independently predict risk for prostate cancer. These variants all map to a region <1Mb on 8q24. Although there are few known genes within this interval, the proto-oncogene MYC lies just downstream, which suggests that this associated region of risk may contain yet unknown prostate cancer susceptibility gene(s), and/or chromosomal breakpoint(s) or functional element(s) implicated in MYC regulation. The goals of the project are to formally evaluate genetic variation along 8q24 and susceptibility to prostate cancer among African American men. Our underlying objective is to determine genetic mechanisms responsible for the increased genetic risk along 8q24.

  1. Confirmation study of prostate cancer risk variants at 8q24 in African Americans identifies a novel risk locus. Robbins C, Benn Torres J, Hooker S, Bonilla C, Hernandez W, Candreva A, Ahaghotu C, Kittles RA, Carpten J. Genome Research. 17:1717-22 (2007).
Mon, Feb 23
Neuroscience Research Building Auditorium
Forensic DNA analysis and multi-locus match probabilities in finite populations
Yun S. Song, PhD, Assistant Professor, Electrical Engineering & Computer Sciences & Department of Statistics, University of California, Berkeley, Berkeley, California
Contact & Intro: Chiara Sabatti, ext 49567
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ABSTRACT: A fundamental problem in population genetics, which being also of importance to forensic science, is to compute the match probability (MP) that two individuals randomly chosen from a population have identical alleles at a collection of loci. At present, 11 to 13 unlinked autosomal microsatellite loci are typed for forensic use. In a finite population, the genealogical relationships of individuals can create statistical non-independence of alleles at unlinked loci. However, the so-called product rule, which is used in courts in the US, computes the MP for multiple unlinked loci by assuming statistical independence, multiplying the one-locus MPs at those loci. Analytically testing the accuracy of the product rule for more than 5 loci has hitherto remained an open problem.

In this talk, I will describe how a flexible graphical framework can be employed to compute multi-locus MPs analytically. I will consider two standard models of random mating, namely the Wright-Fisher and Moran models, and describe the computation of MPs for up to 10 loci in the Wright-Fisher model and up to 13 loci in the Moran model. For a finite population, I will show that the MPs for a large number of loci predicted by the product rule are highly sensitive to mutation rates in the range of interest, while the true multi-locus MPs are not. Furthermore, I will show that the Wright-Fisher and Moran models may produce drastically different MPs for a finite population, and that this difference grows with the number of loci and mutation rates. Although the two models converge to the same coalescent or diffusion limit, in which the population size approaches infinity, I will demonstrate that, when multiple loci are considered, the rate of convergence in the Moran model is significantly slower than that in the Wright-Fisher model. Hence, our work reveals a striking fundamental difference between the two standard models of random mating. If time permits, I will also discuss the effects of monogamy and population structure on the multi-locus match probability.

  1. A graphical approach to multi-locus match probability computation: revisiting the product rule. Song YS, Slatkin M. Theoretical Population Biology 72:96-110 (2007).
  2. Average probability that a "cold hit" in a DNA database search results in an erroneous attribution. Song YS, Patil A, Murphy EE, Slatkin M. Journal of Forensic Sciences, in press. (available online in advance of print:http://www3.interscience.wiley.com/cgi-bin/fulltext/121540161/HTMLSTART)
Mon, Mar 02
Human Genetics & Biostatistics Seminar
Tests of gene-environment interactions with family based designs
Nan Laird, PhD, Professor, Department of Biostatistics, Harvard University, Boston, Massachusetts
Contact & Intro: Chiara Sabatti, ext 49567 and Rob Weiss, ext 69626
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ABSTRACT: The widespread availability of genetic markers for samples of reasonable size has intensified interest in testing for gene-environment interactions with complex diseases. Both traditional case-control and family-based designs are used in genetic association studies, the latter having the advantage of eliminating problems due to population substructure, as well as sensitivity to modeling the genetic effect when testing for genetic effects alone. Here we address the issue of extending the family design to test gene-environment interactions. Robustness to population substructure can be maintained, but robustness to model specification is not. We also discuss joint-tests of gene-environment interaction which are generally more powerful, as well as completely robust to the genetic model and population substructure.



Mon, Mar 09
Neuroscience Research Building Auditorium
Bridging the human genome to complex traits of late adult life: The role of autonomic "intermediate phenotypes" in twin pairs
Daniel O'Connor, MD, Professor, Medicine and Pharmacology, Co-Director, Center for Human Genetics and Genomics (CHGG), University of California, San Diego
Contact & Intro: Eleazar Eskin, ext 51322
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ABSTRACT: Each of us has a genome of increasingly well-defined sequence of ~3.3 Mbp. And each of us differs from one another in a host of traits (phenotypes), ranging from physical appearance to disease. But to what extent does common genetic variation in the population determine our traits, and especially our predisposition to disease? And what are the relative roles of heredity and environment ("nature versus nurture") in trait determination?

Our lab works on genetic determination of common, complex human cardiovascular/renal traits, such as hypertension, renal function/disease, heritable responses to environmental stress, and antihypertensive drug responses. Our work employs several complementary disciplines, amounting to a "toolbox": Phenotyping of human twin pairs and pedigrees [which help us to tease out the relative roles of heredity and environment on trait determination]; Population genetics; Genome technology (DNA sequencing for polymorphism discovery and typing); informatics (relational databasing of phenotypic and genomic information); Statistical genetics (mapping genetic variation onto phenotypic variance); Bioinformatics (understanding the predicted significance of genetic variation); and Molecular/cell biology (experimentally determining whether genetic variants alter function of either transcriptional units or open reading frames, usually in chromaffin cells). This may sound like quite a mouthful, but these are essential and complementary elements in the quest to determine how genetic variation influences such complex traits as hypertension.

  1. Tyrosine hydroxylase, the rate-limiting enzyme in catecholamine biosynthesis discovery of common human genetic variants governing transcription, autonomic activity, and blood pressure in vivo. Rao F, Zhang L, Wessel J, Zhang K, Wen G, Kennedy BP, Rana BK, Das M, Rodriguez-Flores JL, Smith DW, Cadman PE, Salem RM, Mahata SK, Schork NJ, Taupenot L, Ziegler MG, O’Connor DT. Circulation: Journal of the American Heart Association 116:993-1006 (2007)
  2. Functional allelic heterogeneity and pleiotropy of a repeat polymorphism in tyrosine hydroxylase: prediction of catecholamines and response to stress in twins. Zhang L, Rao F, Wessel J, Kennedy BP, Rana BK, Taupenot L, Lillie EO, Cockburn M, Schork NJ, Ziegler MG, O’Connor DT. Physiological Genomics 9: 277–291 (2004)

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