Inference of Cross-Level Interaction between Genes and Contextual Factors in a Matched Case-Control Metabolic Syndrome Study: A Bayesian Approach

<div><p>Genes, environment, and the interaction between them are each known to play an important role in the risk for developing complex diseases such as metabolic syndrome. For environmental factors, most studies focused on the measurements observed at the individual level, and therefore can only consider the gene-environment interaction at the same individual scale. Indeed the group-level (called contextual) environmental variables, such as community factors and the degree of local area development, may modify the genetic effect as well. To examine such <i>cross-level interaction</i> between genes and contextual factors, a flexible statistical model quantifying the variability of the genetic effects across different categories of the contextual variable is in need. With a Bayesian generalized linear mixed-effects model with an unconditional likelihood, we investigate whether the individual genetic effect is modified by the group-level residential environment factor in a matched case-control metabolic syndrome study. Such cross-level interaction is evaluated by examining the heterogeneity in allelic effects under various contextual categories, based on posterior samples from Markov chain Monte Carlo methods. The Bayesian analysis indicates that the effect of rs1801282 on metabolic syndrome development is modified by the contextual environmental factor. That is, even among individuals with the same genetic component of <i>PPARG</i>_Pro12Ala, living in a residential area with low availability of exercise facilities may result in higher risk. The modification of the group-level environment factors on the individual genetic attributes can be essential, and this Bayesian model is able to provide a quantitative assessment for such cross-level interaction. The Bayesian inference based on the full likelihood is flexible with any phenotype, and easy to implement computationally. This model has a wide applicability and may help unravel the complexity in development of complex diseases.</p> </div

Distribution of Physical Illnesses among Cases with Short-Term Mortality and Controls.

<p>Distribution of Physical Illnesses among Cases with Short-Term Mortality and Controls.</p

The posterior distributions of ( = 1,…,4) for four categories are displayed in (a)–(e) for SNP = 1, = 2,…, = 5, respectively, under the Bayesian unconditional likelihood model.

<p>The posterior distributions of ( = 1,…,4) for four categories are displayed in (a)–(e) for SNP  = 1,  = 2,…,  = 5, respectively, under the Bayesian unconditional likelihood model.</p

Overview for Study design.

<p>Overview for Study design.</p

The posterior distributions of for = 1, …, 5 SNP, respectively, under the Bayesian unconditional likelihood model.

<p>The posterior distributions of for  = 1, …, 5 SNP, respectively, under the Bayesian unconditional likelihood model.</p

Numbers are , the posterior probability of , for the -th SNP in the -th category (area) under the unconditional model.

<p>Numbers are , the posterior probability of , for the -th SNP in the -th category (area) under the unconditional model.</p

Distribution of Antipsychotics and Other Medications Used in Cases with Short-Term Mortality and Controls.

<p>Distribution of Antipsychotics and Other Medications Used in Cases with Short-Term Mortality and Controls.</p

In the upper half of the table, numbers in each row are the posterior means and standard deviations of the area-specific genetic effects () and variance (Var() = ) for each candidate SNP <i>g</i>.

<p>The bottom half of the table contains posterior means and standard deviations for parameters of the SNP-SNP interaction, and for variance component parameters.</p

Ordered-subset analyses for schizophrenia by age at onset, CPT, or WCST (only results with a significant change in LOD scores are shown here).

a<p>Families were randomly permuted for 1000 times with respect to the covariate ranking and a chromosome-wide p value for each chromosome was yielded.</p>b<p>Significance level derived from simulations; a genome-wide empirical <i>P</i>-value <0.0029 [i.e., 0.05/(17 covariates)] is denoted in boldface as reaching genome-wide significance.</p>c<p>The number of total families varied due to missing information on the covariate; subsets consisting of ≥ 15% of total families were reported here.</p><p>*<i>P</i><0.001, **<i>P</i><0.0001, for the mixed effect model comparing the families covariate values between the subset of families and the remaining one.</p

Cardiac Complications Associated with Short-Term Mortality in Schizophrenia Patients Hospitalized for Pneumonia: A Nationwide Case-Control Study

<div><p>Background</p><p>Pneumonia is one of most prevalent infectious diseases worldwide and is associated with considerable mortality. In comparison to general population, schizophrenia patients hospitalized for pneumonia have poorer outcomes. We explored the risk factors of short-term mortality in this population because the information is lacking in the literature.</p><p>Methods</p><p>In a nationwide schizophrenia cohort, derived from the National Health Insurance Research Database in Taiwan, that was hospitalized for pneumonia between 2000 and 2008 (n = 1,741), we identified 141 subjects who died during their hospitalizations or shortly after their discharges. Based on risk-set sampling in a 1∶4 ratio, 468 matched controls were selected from the study cohort (i.e., schizophrenia cohort with pneumonia). Physical illnesses were categorized as pre-existing and incident illnesses that developed after pneumonia respectively. Exposures to medications were categorized by type, duration, and defined daily dose. We used stepwise conditional logistic regression to explore the risk factors for short-term mortality.</p><p>Results</p><p>Pre-existing arrhythmia was associated with short-term mortality (adjusted risk ratio [RR] = 4.99, p<0.01). Several variables during hospitalization were associated with increased mortality risk, including incident arrhythmia (RR = 7.44, p<0.01), incident heart failure (RR = 5.49, p = 0.0183) and the use of hypoglycemic drugs (RR = 2.32, p<0.01). Furthermore, individual antipsychotic drugs (such as clozapine) known to induce pneumonia were not significantly associated with the risk.</p><p>Conclusions</p><p>Incident cardiac complications following pneumonia are associated with increased short-term mortality. These findings have broad implications for clinical intervention and future studies are needed to clarify the mechanisms of the risk factors.</p></div