77 research outputs found
Supplemental Material - Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine)
No full textSupplemental Material for Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine) by Desmond I. Bannon, Wenjun Bao, James F. Dillman, Russ Wolfinger, Christopher S. Phillips, and Edward J. Perkins in International Journal of Toxicology</p
Supplemental Material - Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine)
No full textSupplemental Material for Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine) by Desmond I. Bannon, Wenjun Bao, James F. Dillman, Russ Wolfinger, Christopher S. Phillips, and Edward J. Perkins in International Journal of Toxicology</p
Supplemental Material - Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine)
No full textSupplemental Material for Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine) by Desmond I. Bannon, Wenjun Bao, James F. Dillman, Russ Wolfinger, Christopher S. Phillips, and Edward J. Perkins in International Journal of Toxicology</p
Gene expression profiling using mixed models
No full textThis book is a very welcome addition to the toolbox of anyone using the powerful SAS System to analyze the genetic basis of complex traits. Both classic and Bayesian approaches are discussed, with a focus on genetic parameter estimation and gene mapping. An especially nice feature, and indeed worth the price of the book by themselves, are chapters discussing important, but underappreciated, approaches for AMMI modeling of genotype-environment interactions, the analysis of longitudinal traits, and empirical Bayes estimates
ANALYZING SPLIT-PLOT ANDREPEATED-MEASURES DESIGNSUSING MIXED MODELS
No full textWe first introduce the general linear mixed model and provide a justification for using REML to fit it. Then, for an irrigation example, we present several mixed models of increasing complexity. The initial model corresponds to a typical split-plot analysis. Next, we present covariance structures that directly describe the variability of repeated measures within whole plots. Finally, we combine the above types into more complicated mixed models, and compare them using likelihood-based criteria
Simultaneous Assessment of Transcriptomic Variability and Tissue Effects in the Normal Mouse
No full textAnalysis of Gene Expression Profiles with Linear Mixed Models
No full textWith the emergence of high throughput technology, proper interpretation of data has become critical for many aspects of biomedical research. My dissertation explores two major issues in gene expression profile microarray data analysis. One is quantification of variation across and among species and its effect on biological interpretation. The second part of my work is to develop better statistical estimates that can account for different sources of variation for significant gene detection.
A previously published dataset of oligonucleotide array data for three primate species was analyzed with linear mixed models. By decomposing the variation of expression into different explanatory factors, the differences among species as well as between tissues was revealed at the expression level. Issues of cross-species hybridization and expression divergence compared to mutation-drift equilibrium were addressed.
The power and flexibility of the linear mixed model framework for detection of differentially expressed genes was then explored with a dataset that includes spiked-in controls. The impact of probe-level sequence variation on cross-hybridization was detected through a Gibb's sampling method that highlights potential problems for short oligonucleotide microarray data analysis. A motif as short as fifteen bases can possibly cause significant cross-hybridization.
Finally, a bivariate model using information from both perfect match probes and mismatch probes was proposed as a means to increase the statistical power for detection of significant differences in gene expression. The improved performance of the method was demonstrated through Monte Carlo simulation. The detection power can increase as much as 20% with 5% false positive rate under certain circumstances
Addressing Sources of Bias in Genetic Association Studies
No full textGenome-wide association studies (GWAS) have become a popular method for the
discovery of genetic variants associated with complex diseases or traits. As the size and
scope of these studies increase in order to obtain higher power for determining significant
associations, careful consideration of population structure becomes paramount. If individ-
uals in a study come from different ethnic or ancestral backgrounds, variation in allele
frequencies and disproportionate ancestry representation in cases and controls can lead
to inflated Type I error rates. Over the years, several methods for controlling population
stratification have been introduced, many of which rely on the use of multivariate dimension
reduction methods. An important aspect of population stratification is to determine which
loci exhibit evidence of population allele frequency differences. We introduce a method
based on Hardy-Weinberg Disequilibrium to find substructure-informative markers coupled
with the use of nonmetric Multidimensional Scaling (NMDS) in order to visualize popula-
tion structure in a sample. We extend the use of NMDS in conjunction with nonparametric
clustering to develop a test for association that corrects for population stratification. We
show that NMDS is a preferable visualization technique for detecting multiple levels of
relatedness within a set of individuals and that the subsequent test correction model is a
more powerful test under realistic scenarios. Recent research has shown that technical bias
due to differential genotyping errors between cases and controls can also inflate the Type I
error rate, possibly an even more severe source of bias in GWAS. Current genotype calling
algorithms rely on processing samples in batches due to computational constraints as well
as concerns of differences in DNA collection, lab preparation and heterogeneous samples
that can skew results of genotype calls. This thesis also addresses possible bias caused
by differential genotyping due to batch size and composition effects for the widely used
BRLMM algorithm recommended for the Affymetrix GeneChip Human Mapping 500 K ar-
ray set. Samples obtained from the Wellcome Trust Case Control Consortium are utilized
to determine differential results due to genotype calling batch differences
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