David Geffen School of Medicine at UCLA
Department of Human Genetics

Speaker Series - Fall Quarter 2010

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

Thu, Sep 16
Neuroscience Research Building Auditorium
F-Statistics: A Methodology For Learning About History of Many Populations
Nick Patterson, Broad Institute of MIT & Harvard
Contact & Intro: Marc Suchard, ext. 57442 & msuchard@ucla.edu
Mon, Oct 04
The Nucleosome - Integrator and Executor of Epigenomic Information
Peter A. Jones, Ph.D., Professor, Keck School of Medicine, USC
Contact & Intro: Julian Martinez, ext. 42405
Mon, Oct 11
A Prospective Approach to the Identification of Human Teratogens: The OTIS Research Center at UCSD
Kenneth Jones, Professor, Department of Pediatrics, University of California, San Diego
Contact & Intro: Rita Cantor, ext. 72440
Mon, Oct 18
The Genomic Signature of Admixture Between Modern Humans and Archaics
Jeffrey Long, Ph.D., Professor, Department of Anthropology, University of New Mexico
Contact & Intro: Janet Sinsheimer, ext. 58002
Mon, Oct 25
Genetics of Sex Determination: Insights from the B6-YPOS Sex Reversal Mouse Model
Valerie Arboleda, Department of Human Genetics, UCLA
Contact & Intro: Paivi Pajukanta, ppajukanta@mednet.ucla.edu
Mon, Nov 01
Methods for Detecting Interactions in High-throughput Genetic Data
Alison Motsinger, Ph.D., Assistant Professor, Department of Statistics, North Carolina State University
Contact & Intro: Marc Suchard, ext. 57442 & msuchard@ucla.edu
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ABSTRACT: The explosion of genetic information over the last decade presents an analytical challenge for genetic association studies. As the number of genetic variables examined per individual increases, both variable selection and statistical modeling tasks must be performed during analysis. While these tasks could be performed separately, coupling them is necessary to select meaningful variables that effectively model the data. This challenge is heightened due to the complex nature of the phenotypes under study and the complex underlying genetic etiologies. To address this problem, a number of novel methods have been developed. In the current study, we compare the performance of six analytical approaches to detect both main effects and gene-gene interactions in a range of genetic models. Multifactor dimensionality reduction, grammatical evolution neural networks, random forests, focused interaction testing framework, step-wise logistic regression, and explicit logistic regression were compared. As one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of each method and illustrates the importance of continued methods development.

LITERATURE:
  1. A comparison of analytical methods for genetic association studies.Motsinger-Reif AA, Reif DM, Fanelli TJ, Ritchie MD.Genet Epidemiol. 2008 Dec;32(8):767-78
Mon, Nov 08
Genomic Sequencing for Variant Discovery
Stan Nelson, M.D., Professor in Residence, Department of Human Genetics, UCLA
Contact & Intro: Marc Suchard, x57442 & msuchard@ucla.edu
Mon, Nov 15
Understanding Nature's Complexity: Why Do Mammals Have Three Lipins?
Lauren Csaki, Graduate Student, Department of Human Genetics, UCLA
Contact & Intro: Paivi Pajukanta, ppajunkata@mednet.ucla.edu
Mon, Nov 22
Leveraging Age Information to Increase Power in Association Studies
Alkes Price, Ph.D., Assistant Professor, Department of Biostatistics & Epidemiology, Harvard School of Public Health
Contact & Intro: Marc Suchard, ext. 57442 & msuchard@ucla.edu

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