The relationship between rheumatoid arthritis and reproductive function

There is growing support for the theory that an interaction between the immune and reproductive/endocrine systems underlies the pathogenesis of autoimmune rheumatic diseases. Most of the recent evidence derives from studies of sex hormones and pregnancy in women with systemic lupus. Other than an ameliorative effect of pregnancy, little is known about reproductive factors in relation to rheumatoid arthritis. To elucidate the relationship, a population-based retrospective study was undertaken. Included were 378 female residents of Olmsted County, Minnesota diagnosed with rheumatoid arthritis between 1950 and 1982 (cases) and 325 arthritis-free, married female controls matched to the 324 married cases on birth-year, age at first marriage, and duration of Olmsted County residency. Information of reproductive factors was extracted from the medical records system maintained by the Mayo Clinic.^ Cases had lower fertility rates compared with the female population of Minnesota (rate ratio = 0.86, 95% confidence interval (CI)= 0.80-0.92). Fertility was significantly reduced even prior to the onset of rheumatoid factor positive arthritis. Restricting the comparison to married Olmsted County residents did not alter the results. Further adjustments for time not at risk of conception using survival analysis and proportional hazards modeling only intensified the fertility reduction in the married cases compared with controls. Nulligravidity was more common among cases than controls (odds ratio = 3.16, CI = 1.61-6.20). Independent of fertility, pregnancy had a protective effect against rheumatoid arthritis (odds ratio = 0.31, CI = 0.11-0.89), which was dramatically reversed in the 12 months postpartum (odds ratio = 4.67, CI = 1.50-14.47). Cases were younger at menopause than controls (p $<$ 0.01).^ Small but statistically insignificant associations were observed between rheumatoid arthritis and the following factors: increased frequency of complaints to a physician of infertility; increased frequency of spontaneous abortion, premature birth, and congenital malformations following arthritis onset; and increased prevalence of menopause at arthritis onset. Cases did not differ from controls on age at menarche, duration of pregnancy, or birth weight.^ The findings provide further support for the involvement of the reproductive/endocrine systems in the pathogenesis of autoimmune rheumatic disease. The search for biological mechanisms should be intensified. ^

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.