Correlation of tuberculosis and human immunodeficiency virus in Harris County, Texas from 2009 through 2010 using Geographic Information Systems

A population based ecological study was conducted to identify areas with a high number of TB and HIV new diagnoses in Harris County, Texas from 2009 through 2010 by applying Geographic Information Systems to determine whether distinguished spatial patterns exist at the census tract level through the use of exploratory mapping. As of 2010, Texas has the fourth highest occurrence of new diagnoses of HIV/AIDS and TB.[31] The Texas Department of State Health Services (DSHS) has identified HIV infected persons as a high risk population for TB in Harris County.[29] In order to explore this relationship further, GIS was utilized to identify spatial trends. ^ The specific aims were to map TB and HIV new diagnoses rates and spatially identify hotspots and high value clusters at the census tract level. The potential association between HIV and TB was analyzed using spatial autocorrelation and linear regression analysis. The spatial statistics used were ArcGIS 9.3 Hotspot Analysis and Cluster and Outlier Analysis. Spatial autocorrelation was determined through Global Moran's I and linear regression analysis. ^ Hotspots and clusters of TB and HIV are located within the same spatial areas of Harris County. The areas with high value clusters and hotspots for each infection are located within the central downtown area of the city of Houston. There is an additional hotspot area of TB located directly north of I-10 and a hotspot area of HIV northeast of Interstate 610. ^ The Moran's I Index of 0.17 (Z score = 3.6 standard deviations, p-value = 0.01) suggests that TB is statistically clustered with a less than 1% chance that this pattern is due to random chance. However, there were a high number of features with no neighbors which may invalidate the statistical properties of the test. Linear regression analysis indicated that HIV new diagnoses rates (β=−0.006, SE=0.147, p=0.970) and census tracts (β=0.000, SE=0.000, p=0.866) were not significant predictors of TB new diagnoses rates. ^ Mapping products indicate that census tracts with overlapping hotspots and high value clusters of TB and HIV should be a targeted focus for prevention efforts, most particularly within central Harris County. While the statistical association was not confirmed, evidence suggests that there is a relationship between HIV and TB within this two year period.^

Mechanisms of multidrug resistance: Differences between natural and acquired adriamycin -resistant human colon carcinoma cells

Mechanisms of multidrug resistance (MDR) were studied in two independent MDR sublines (AdR1.2 and SRA1.2) derived from the established human colon carcinoma cell line LoVo. AdR1.2 was developed by long-term continuous exposure of the cells to adriamycin (AdR) in stepwise increments of concentration, while SRA1.2 was selected by repetitive pulse treatments with AdR at a single concentration. In this dissertation, the hypothesis that the mechanism of drug resistance in SRA1.2 is different than that in AdR1.2 is tested. While SRA1.2 demonstrated similar biological characteristics when compared to the parental LoVo, AdR1.2 exhibited remarkable alterations in biological properties. The resistance phenotype of AdR1.2 was reversible when the cells were grown in the drug-free medium whereas SRA1.2 maintained its resistance for at least 10 months under similar conditions. Km and Vmax of carrier-mediated facilitated diffusion AdR transport were similar among the three lines. However, both resistant sublines exhibited an energy-dependent drug efflux. AdR1.2 appeared to possess an activated efflux pump, and a decreased nucleus-binding of AdR, whereas SRA1.2 showed merely a lower affinity in binding of AdR to the nuclei. Southern blot analysis showed no amplification of the MDR1 gene in either of the two resistant subclones. However, Western blot analysis using the C219 monoclonal antibody against P170 glycoprotein detected a Mr 150-kDa plasma protein (P150) in AdR1.2 but not in SRA1.2 or in the parental LoVo. In vitro phosphorylation studies revealed that P150 was a phosphoprotein; its phosphorylation was Mg$\sp{2+}$-dependent and could be enhanced by verapamil, an agent capable of increasing intracellular AdR accumulation in AdR1.2 cells. The phosphorylation studies also revealed elevated phosphorylation of a Mr 66-kDa plasma membrane protein that was detectable in the AdR1.2 revertant and in AdR1.2 when verapamil was present, suggesting that hyperphosphorylation of the Mr 66-kDa protein may be related to the reversal of MDR. SDS-PAGE of the plasma membrane protein demonstrated overproduction of a Mr 130-kDa, MDR-related protein in both the resistant sublines. The Mr 130-kDa, MDR-related protein in both the resistant sublines. The Mr 130-kDa protein was not immunoreactive with C219, but its absence in the AdR1.2 revertant and the parental LoVo suggests that it is an MDR-related plasma membrane protein. In conclusion, the results from this study support the author's hypothesis that the mechanisms responsible for "Acquired" and "Natural" MDR are not identical. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.