IBC-2010 : Programme Changes

11:00 Reitoria Auditorium
Invited Session 2: Genomic data integration using sparse matrix decomposition methods
Chair: Aeilko H Zwinderman (Netherlands)

11:00 A general framework for sparse matrix decomposition techniques
Daniela Witten (University of Washington, US)

11:30 Genetic control of genome wide expression in brain cancer tissue: nonlinear sparse CCA
Sandra Waaijenborg (University Medical Center Utrecht, The Netherlands)

12:00 Unravelling omics datasets with sparse PLS
Kim-Anh Le Cao (University of Queensland, Australia)

12:30 Discussant
Paul Eilers (Erasmus University Rotterdam, The Netherlands)

10:20 Garapuvu Auditorium
Invited Session 10: Missing data in clinical trials: The way forward
Chair: Herbert Thijs (Belgium)

10:20 Conceptual Considerations Regarding Choice of endpoints, Hypotheses, and Analyses In Longitudinal Clinical Trials
Craig Mallinckrodt (Eli Lilly and Company, US)

10:50 Missing and Nonexistent Data
Thomas Permutt (FDA, US)

11:20 Methods for handling dropout in clinical trials
Michael G. Kenward (London School of Hygiene and Tropical Medicine, UK)

11:50 Discussant
Geert Molenberghs (Universiteit Hasselt and Katholieke Universiteit Leuven, Belgium)

15:40 Garapuvu Auditorium
Invited Session 12: Statistical challenges and advancements in eQTL mapping studies
Chair: Yuehua Cui (MI)

15:40 Design of microarray experiments for genetical genomics studies with outbred populations
Guilherme J.M. Rosa (University of Wisconsin, US) 

16:05 Nonparametric modeling of RNA-seq (see abstract below)
Ping Ma (University of Illinois at Urbana-Champaign, US)

16:30 Linear mixed model analysis to identify cis-acting eQTL and candidate genes in crosses between breeds of livestock
Juan P. Steibel (Michigan State University, US)

16:55 Single Feature Polymorphism Detection in Mapping Population and their Application in eQTL Analysis
Xinping Cui (University of California Riverside, US)

The following contributions will no longer be presented (by author request)

11:00 CONTRIBUTED ORAL 1: Bayesian Methods 1
Computational implementation of an reversible jump mcmc algorithm to garch models aplied to climatological time series
Gabriel Sarmanho, Afrânio Vieira, UnB; Paulo Lucio, UFRN

11:00 CONTRIBUTED ORAL 1: Capture/Recapture Estimation Methods
Stochastic animal movement models generating circular distributions
William Reed, Univ of Victoria

10:20 CONTRIBUTED ORAL 9: Times Series Analysis
Garch models for short-term climate prediction via Bayesian approach
Gabriel Sarmanho, Afrânio Vieira, UnB; Paulo Lucio, UFRN

10:20 CONTRIBUTED ORAL 9: Disease Mapping
Chilean cardiovascular disease mortality atlases, 2000-2007
M Gloria Icaza, Loreto Núñez, Univ de Talca; Francisco Torres Avilés, Univ de Santiago de Chile; Nora Díaz Sanzana, Univ de Talca; José Emilio Villarroel de la Sota
Dept de Epidemiología, Ministerio de Salud de Chile

10:45 CONTRIBUTED ORAL 13: Multiple Testing 
Evaluation of testing methods for multiple correlated endpoints 
Ting-Li Su, John Whitehead, Lancaster Univ, UK; Michael Branson, Ekkehard Glimm, Novartis Pharma AG, Basel, Switzerland

08:15 CONTRIBUTED ORAL 4: Mixed Models
Approximate inference in generalized linear mixed models with flexible random effects densities
Georgios Papageorgiou, John Hinde, Natl Univ of Ireland, Galway
PRESENTING AUTHOR: JOHN HINDE

10:30 Contributed oral 5: High Dimensional Data II 
Predicting Multitrait Phenotyes from Genomic and Genetic data via Gaussian Markov Random Fields and L1 Penalties 
Patricia Menendez, Martin Boer, Cajo ter Braak, Fred van Eeuwijk, Biometris, Paul Eilers, Biometris. 
Wageningen University.

10:45 CONTRIBUTED ORAL 13: Genetics
Applying nonlinear mixed regression models in the design of new probiotic products
Birgitt Wiese, Inst for Biometrics; Elena Bru, María Silvina Juarez Tomás, Carolina Espeche, Ctr de Referencia para Lactobacilos; Natalia Cecilia Maldonado, Ctr de Referencia; Esteban Vera Pingitore, María Elena Fatima Nader-Macías, Ctr de Referencia para Lactobacilos
TO BE PRESENTED BY ONE OF THE CO-AUTHORS

Nonparametric modeling of RNA-seq
Ping Ma
Department of Statistics and Institute for Genomic Biology
University of Illinois at Urbana-Champaign
pingma@illinois.edu

Studies of expression quantitative trait loci (eQTLs) have become an
important tool for  understanding the genetic mechanisms underlying natural
variation in gene expression which is a central goal of both medical and
evolutionary genetics.  Although all eQTL studies so far have measured mRNA
levels using microarrays, recent advances in RNA sequencing (RNA-seq) enable
the analysis of transcript variation at unprecedented resolution.   The
reads produced by RNA-Seq are first mapped to the genome and/or to the
reference transcripts. Then, the output of RNA-Seq can be summarized by a
sequence of 'counts'. That is, for each position in the genome or on a
putative transcript, it gives a count standing for the number of reads whose
mapping starts at that position. Quantitative inference of RNA-Seq data,
such as calculating gene expression levels and isoform expression levels, is
based on these counts. To utilize the data efficiently, it is crucial to
have an appropriate statistical model for these counts. In this talk, we
present some nonparametric models in analyzing the RNA-seq data of nine cell
lines. We will also discuss its potential utility in eQTL mapping studies.