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.

Cross -packing among retroviruses and characterization of the feline immunodeficiency virus (FIV) packaging signal: Implications for the development of FIV-based gene transfer systems

Feline immunodeficiency virus (FIV)-based gene transfer systems are being seriously considered for human gene therapy as an alternative to vectors based on primate lentiviruses, a genetically complex group of retroviruses capable of infecting non-dividing cells. The greater phylogenetic distance between the feline and primate lentiviruses is thought to reduce chances of the generation of recombinant viruses. However, safety of FIV-based vector systems has not been tested experimentally. Since primate lentiviruses such as human and simian immunodeficiency viruses (HIV/SIV) can cross-package each other's genomes, we tested this trait with respect to FIV. Unexpectedly, both feline and primate lentiviruses were reciprocally able to both cross-package and propagate each other's RNA genomes. This was largely due to the recognition of viral packaging signals by the heterologous proteins. However, a simple retrovirus such as Mason-Pfizer monkey virus (MPMV) was unable to package FIV RNA. Interestingly, FIV could package MPMV RNA, but not propagate it for further steps of replication. These findings suggest that upon co-infection of the same host, cross-packaging may allow distinct retroviruses to generate chimeric variants with unknown pathogenic potential. ^ In order to understand the packaging determinants in FIV, we conducted a detailed mutational analysis of the region thought to contain FIV packaging signal. We show that the first 90–120 nt of the 5′ untranslated region (UTR) and the first 90 nt of gag were simultaneously required for efficient FIV RNA packaging. These results suggest that the primary FIV packaging signal is multipartite and discontinuous, composed of two core elements separated by 150 nt of the 5 ′UTR. ^ The above studies are being used towards the development of safer FIV-based self-inactivating (SIN) vectors. These vectors are being designed to eliminate the ability of FIV transfer vector RNAs to be mobilized by primate lentiviral proteins that may be present in the target cells. Preliminary test of the first generation of these vectors has revealed that they are incapable of being propagated by feline proteins. The inability of FIV transfer vectors to express packageable vector RNA after integration should greatly increase the safety of FIV vectors for human gene therapy. ^