Lithium response in lymphoblastoid cell line samples.

<p>ANCOVA analysis was performed to compare the three groups (Controls, Responders, and Non-responders to lithium treatment) separately for Syn2a and Syn2b expression. The variables “Age at sampling” and “LCL frozen storage” were used as covariates.</p

Cell lines expression for SYN2b.

<p>Expression in (A) HEK293 embryonic kidney cells, (B) SK-N-AS neuroblastoma cells, and (C) and U-118 MG glioblastoma/astrocytoma cells for the Synapsin IIb variant compared to GAPDH. P-values depicting the mean differences between 3 independent experiments for each cell line at each of the 3 treatment concentration of either lithium or vehicle (0.5 mM, 1.0 mM, and 2.0 mM).</p

Cell lines expression for SYN2a.

<p>Expression in (A) HEK293 embryonic kidney cells, (B) SK-N-AS neuroblastoma cells, and (C) and U-118 MG glioblastoma/astrocytoma cells for the Synapsin IIa variant compared to GAPDH. P-values depicting the mean differences between 3 independent experiments for each cell line at each of the 3 treatment concentration of either lithium or vehicle (0.5 mM, 1.0 mM, and 2.0 mM).</p

The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases

<div><p>Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of “non-cases”) reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.</p></div

Genome-wide analysis of the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes type 1 (T1D) and type 2 (T2D) under heterogeneity: twenty most significant associations [−log(p-values)].

<p>SNP = single nucleotide polymorphism; β = admixture.</p

The impact of heterogeneity on the sample size (cases and controls) required for 90% of statistical power.

<p>The minimum sample size to achieve to detect association was calculated in simulated case-control data with increasing proportion of “non-cases” considering a disease prevalence of 0.01. Data are reported for minor allele frequencies (MAF) of 0.01 (black), 0.05 (grey), 0.2 (red) and 0.5 (blue). The results are reported for dominant (panels A, C, and E) and multiplicative (panels B, D, and F) genetic models. RR = relative risk.</p

The impact of heterogeneity on the estimation of the genetic effect size.

<p>Odds ratios from simulated case-control data were calculated for each step of admixture. Data are reported for minor allele frequencies (MAF) of 0.01 (black), 0.05 (grey), 0.2 (red) and 0.5 (blue). The results are reported for dominant (panels A, C, and E) and multiplicative (panels B, D, and F) genetic models. RR = relative risk; OR = odds ratio.</p

Additional file 1: Table S1. of DNA hypomethylation of Synapsin II CpG islands associates with increased gene expression in bipolar disorder and major depression

Diagnostic group statistics and comparisons. No difference between potential confounders. Normality of each variable was computed per each diagnostic group. When the data were normally distributed, unpaired Student’s T-tests were performed, and when at least one group was not normally distributed (Shapiro-Wilk p-value ≤0.05), non-parametric tests were performed. Differences in Gender were tested with Fisher’s Exact tests. (XLSX 12 kb

Additional file 2: Table S2. of DNA hypomethylation of Synapsin II CpG islands associates with increased gene expression in bipolar disorder and major depression

Mixed Model ANOVA results for SYN1, SYN2, and SYN3 promoter CpG methylation. A. SYN1: Type III Tests of Fixed Effects. B. SYN2: Type III Tests of Fixed Effects. C. SYN3: Type III Tests of Fixed Effects. (XLSX 11 kb

Results of the INRICH pathway analysis.

<p>Results of the INRICH pathway analysis are shown in bar plot format. The x-axis shows negative logarithmic enrichment p-values for all nominally associated pathways containing two and more genes prior to- (gray) and after- (blue) correction for multiple testing. The red horizontal line indicates a p-value of 0.05.</p