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

Additional file 3: Figure S3. of Text mining for identifying topics in the literatures about adolescent substance use and depression

The 10 most probable words in the topics of LDA with 50 topics. (PDF 263 kb

Additional file 1: Figure S1. of Text mining for identifying topics in the literatures about adolescent substance use and depression

The 10 most probable words in the topics of LDA with 5 topics. (PDF 126 kb

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

The variance parameters represent the variability among areas for each SNP.

<p>Numbers are posterior means and standard deviations of variance components under the Bayesian conditional logistic regression 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

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

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

The observed genotype counts of metabolic cases (cs) and controls (cn) under each category of exercise facility availability.

<p>The observed genotype counts of metabolic cases (cs) and controls (cn) under each category of exercise facility availability.</p

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies