Modeling Complex Patterns of Differential DNA Methylation That Associate with Expression Change

Gene expression is driven by specific combinations of transcription factors binding to regulatory sequences to define cell type expression profiles. Changes in DNA sequence alter transcription factor binding affinities and gene expression, and DNA methylation is an additional source of variation that is maintained throughout cellular division. Numerous genomic studies are underway to determine which genes are abnormally regulated by DNA methylation in disease. However, we have a poor understanding of how disease-specific methylation variation affects expression. Global DNA demethylation agents have been clinically approved for use in cancer, which has spurred interest in identifying genes which would be most susceptible for targeted demethylation therapies. In this work, I developed multiple tools to increase our knowledge about the relationship between methylation and gene expression in both tissue specificity and disease. I first developed a computational strategy to identify amplifications and deletions from restriction enzyme-based methylation datasets. In a model of endocrine therapy resistant breast cancer, I identify ESR1 as the most amplified genomic region in response to estrogen deprivation. I develop a qPCR-based assay to probe the amplification in cell lines, formalin-fixed paraffin embedded samples, patient tumors, and xenograft samples. This data is consistent with the hypothesis that in a subset of patients, the ESR1 amplification results in increased levels of ER. These are produced in response to estrogen deprivation to sensitize breast cancer to low available quantities of estrogen for cellular growth. Next, to explain specific variation in methylation that associates with expression change in both disease and tissue-specificity, I developed an integrative analysis tool, Methylation-based Gene Expression Classification (ME-Class). This model captures the complexity of methylation changes around a gene promoter. Using whole-genome bisulfite sequencing and RNA-seq datasets from different tissue samples, ME-Class significantly outperforms published methods using methylation to predict differential gene expression change. To demonstrate its utility, I used ME-Class to analyze different hematopoietic cell types, and identified that expressionassociated methylation changes were predominantly found when comparing cells from distantly related lineages, implying that changes in the cell’s transcriptional program precede associated methylation changes. Training ME-Class on normal-tumor pairs indicated that cancer-specific expression-associated methylation changes differ from tissue-specific changes. I further show that ME-Class can detect functionally relevant cancer-specific, expression-associated methylation changes that are reversed upon the removal of methylation in a model of colon cancer. Lastly, I extended ME-Class to incorporate 5-hydroxymethylcytosine and uncovered gene regulatory logic involving 5hmC and 5mC in mammalian development and disease. As more large-scale, genome-wide, differential DNA methylation studies become available, tools such as ME-class will prove invaluable to understand how specific methylation changes affect transcription. Our results show this toolset can identify genes that are dysregulated by methylation in disease, and could be used to facilitate the identification of patients who may benefit from clinically-approved demethylating therapeutics

Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs

Environmental movements and campaigns against waste infrastructure in the United States

Campaigns against waste infrastructure in the US emerged in the 1970s against a background of increasing public anxiety about the impacts of high-tech industrialism upon the environment and human health. Independently of major environmental NGOs, and unlike earlier anti-nuclear campaigns, which also involved grassroots protests, waste campaigners quickly became networked and raised new issues of environmental justice. Initially focused upon landfills and hazardous waste, the environmental justice movement took up and amplified local protests against waste incineration. Independently of popular protest, changes in public policy and the economics of the waste industry also contributed to the unpopularity of waste incineration, and recycling regained appeal. Campaigns against waste infrastructure have contributed to the broadening of the US environmental movement as well as to ecological modernisation

Racial Inequality in the Distribution of Hazardous Waste: A National-Level Reassessment

National-level studies examining racial disparities around hazardous waste treatment, storage, and disposal facilities have been very influential in defining the academic and political debates about the existence and impor-tance of “environmental injustice. ” However, these studies tend to employ methods that fail to adequately control for proximity between environmentally hazardous sites and nearby residential populations. By using GIS and applying methods increasingly used in environmental inequality research that better control for proximity, we con-duct a comprehensive reassessment of racial inequality in the distribution of the nation’s hazardous waste facilities. We compare the magnitude of racial disparities found with those of prior studies and test competing racial, eco-nomic, and sociopolitical explanations for why such disparities exist. We find that the magnitude of racial dispari-ties around hazardous waste facilities is much greater than what previous national studies have reported. We also find these disparities persist even when controlling for economic and sociopolitical variables, suggesting that factors uniquely associated with race, such as racial targeting, housing discrimination, or other race-related factors are associated with the location of the nation’s hazardous waste facilities. We further conclude that the more recent methods for controlling for proximity yield more consistent and definitive results than those used previously, and therefore argue for their wider utilization in environmental inequality research. Keywords: environmental justice, environmental inequality, environmental racism, racial inequality, hazardous waste, GIS