Multiple phenotype modeling in pleiotropic effect studies of quantitative trait loci

Pleiotropy refers to the shared effects of a gene or genes on multiple phenotypes, a major reason for genetic correlation between phenotypes. For example, for osteoporosis, bone mineral densities at different skeletal sites may share common genetic factors; thus, examining the shared effects of genes may enable more effective fracture treatments. To date, methods are not available for estimating and testing the pleiotropic effects of single nucleotide polymorphisms (SNPs) in genetic association studies. In this dissertation, we explore two types of methods to evaluate the SNP-specific pleiotropic effect based on multivariate techniques. First, we propose two approaches based on variance components (VC) analysis for family-based studies, which quantify and test the pleiotropic effect by examining the contribution of specific genetic marker(s) to polygenic correlation or covariance of traits. Second, we propose a multivariate linear regression approach for population-based studies with samples of families or unrelated subjects. This method partitions the specific effect of the marker(s) from phenotypic covariance. We evaluate the performance of our proposed methods in simulation studies, compare them to existing multivariate analysis methods and illustrate their application using real data to assess candidate SNPs for osteoporosis-related phenotypes in the Framingham Osteoporosis Study. In contrast to existing methods, our newly proposed approaches allow the quantification of pleiotropic effects. The bootstrap resampling percentile method is used to construct confidence intervals for statistical hypothesis testing. Simulation results suggest that the VC-based approaches are affected by the polygenic correlation level. The covariance analysis approach outperforms the VC-based approaches, with unbiased estimates and better power, which remain consistent regardless of the polygenic correlation. In addition, the covariance analysis approach is simple to implement and can be applied to both family data and genetically unrelated data. Using simulation, we also show that existing methods, such as MANOVA, can have high rejection rates when a SNP has a large effect on a single trait, which prevent us from using them for pleiotropic effect analysis. In summary, this dissertation introduces promising new approaches in multiple phenotypic models for SNP-specific pleiotropic effect

The Smoking Paradox in the Development of Psoriatic Arthritis among Psoriasis Patients – A Population-Based Study

Objectives: Smoking is strongly associated with an increased risk of psoriatic arthritis (PsA) in the general population, but not among psoriasis patients. We sought to clarify the possible methodologic mechanisms behind this paradox. Methods: Using 1995-2015 data from The Health Improvement Network, we performed survival analysis to examine the association between smoking and incident PsA in the general population and among psoriasis patients. We clarified the paradox using mediation analysis and conducted bias sensitivity analyses to evaluate the potential impact of index event bias and quantify its magnitude from uncontrolled/unmeasured confounders. Results: Of 6.65 million subjects without PsA at baseline, 225,213 participants had psoriasis and 7,057 developed incident PsA. Smoking was associated with an increased risk of PsA in the general population (RR, 1.27; 95% CI, 1.19-1.36), but with a decreased risk among psoriasis patients (RR 0.91; 95% CI, 0.85-0.99). Mediation analysis showed that the effect of smoking on the risk of PsA was mediated almost entirely through its effect on psoriasis. Bias sensitivity analyses indicated that even when the relation of uncontrolled confounders to either smoking or PsA was modest (both RRs = ~1.50), it could reverse the biased estimate of effect of smoking among psoriasis patients (RR=0.9). Conclusions: In this large cohort representative of the UK general population, smoking was positively associated with PsA risk in the general population, but negatively associated among psoriasis patients. Conditioning on a causal intermediate variable (psoriasis) can reverse the association between smoking and PsA, explaining the smoking paradox for the risk of PsA among psoriasis patients

The PLIN4 Variant rs8887 Modulates Obesity Related Phenotypes in Humans through Creation of a Novel miR-522 Seed Site

PLIN4 is a member of the PAT family of lipid storage droplet (LSD) proteins. Associations between seven single nucleotide polymorphisms (SNPs) at human PLIN4 with obesity related phenotypes were investigated using meta-analysis followed by a determination if these phenotypes are modulated by interactions between PLIN4 SNPs and dietary PUFA. Samples consisted of subjects from two populations of European ancestry. We demonstrated association of rs8887 with anthropometrics. Meta-analysis demonstrated significant interactions between the rs8887 minor allele with PUFA n3 modulating anthropometrics. rs884164 showed interaction with both n3 and n6 PUFA modulating anthropometric and lipid phenotypes. In silico analysis of the PLIN4 3′UTR sequence surrounding the rs8887 minor A allele predicted a seed site for the human microRNA-522 (miR-522), suggesting a functional mechanism. Our data showed that a PLIN4 3′UTR luciferase reporter carrying the A allele of rs8887 was reduced in response to miR-522 mimics compared to the G allele. These results suggest variation at the PLIN4 locus, and its interaction with PUFA as a modulator of obesity related phenotypes, acts in part through creation of a miR-522 regulatory site

The Obesity Paradox in Recurrent Attacks of Gout in Observational Studies: Clarification and Remedy

OBJECTIVE: Obesity is strongly associated with incident gout risk; its association with risk of recurrent gout attacks has been null or weak, constituting an obesity paradox. We sought to demonstrate and overcome the methodologic issues associated with the obesity paradox for risk of recurrent gout attacks. METHODS: Using the MRFIT database, we decomposed the total effect of obesity into its direct and indirect (i.e., mediated) effects using marginal structural models. We also estimated the total effect of BMI change from baseline among incident gout patients. RESULTS: Of 11,816 gout-free subjects at baseline, we documented 408 incident gout cases, with 132 developing recurrent gout attacks over a 7-year follow-up. The adjusted odds ratio (OR) for incident gout among obese individuals was 2.6, while that for recurrent gout attacks among gout patients was 0.98 (i.e., the obesity paradox). These ORs correlated well with the ORs for the indirect and direct effects of obesity on risk of recurrent gout attacks (i.e., 2.83 and 0.98, respectively). Compared with no BMI change, the OR of losing vs. gaining >5% of baseline BMI was 0.61 and 1.60 for recurrent gout attacks, respectively (P for trend <0.01), suggesting a dose-response association. CONCLUSION: The obesity paradox for risk of recurrent gout attacks is explained by the absence of the direct effect, which is often measured in conventional analyses and misinterpreted as the intended total effect of interest. In contrast, the BMI change analysis correctly estimated the intended total effect of BMI, and revealed a dose-response relationship