Infectious diseases, immune status, and health insurance among child care providers in Houston, Texas

The objectives of this study were to compare female child-care providers with female university workers and with mothers of children in child-care centers for: (1) frequency of illness and work loss days due to infectious diseases, (2) prevalence of antibodies against measles, rubella, mumps, hepatitis B, hepatitis A, chickenpox and cytomegalovirus (CMV), and (3) status regarding health insurance and job benefits.^ Subjects from twenty child-care centers and twenty randomly selected departments of a university in Houston, Texas were studied in a cross-sectional fashion.^ A cluster sample of 281 female child-care providers from randomly selected child-care centers, a cluster sample of 286 university workers from randomly selected departments and a systematic sample of 198 mothers of children from randomly selected child-care centers.^ Main outcome measures were: (1) self-reported frequency of infectious diseases and number of work-days lost due to infectious diseases; (2) presence of antibodies in blood; and (3) self-reported health insurance and job benefits.^ In comparison to university workers, child-care providers reported a higher prevalence of infectious diseases in the past 30 days; lost three times more work-days due to infectious diseases; and were more likely to have anti-core antibodies against hepatitis B (odds ratio = 3.16 95% CI 1.27-7.85) and rubella (OR 1.88, 95% CI 1.02-3.45). Child-care providers had less health insurance and job-related benefits than mothers of children attending child-care centers.^ Regulations designed to reduce transmission of vaccine and non-vaccine preventable diseases in child-care centers should be strictly enforced. In addition policies to improve health insurance and job benefits of child-care providers are urgently needed. ^

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.