Genetic associations between bipolar disorder and brain structural phenotypes.

Patients with bipolar disorder (BD) and their first-degree relatives exhibit alterations in brain volume and cortical structure, whereas the underlying genetic mechanisms remain unclear. In this study, based on the published genome-wide association studies (GWAS), the extent of polygenic overlap between BD and 15 brain structural phenotypes was investigated using linkage disequilibrium score regression and MiXeR tool, and the shared genomic loci were discovered by conjunctional false discovery rate (conjFDR) and expression quantitative trait loci (eQTL) analyses. MiXeR estimated the overall measure of polygenic overlap between BD and brain structural phenotypes as 4-53% on a 0-100% scale (as quantified by the Dice coefficient). Subsequent conjFDR analyses identified 54 independent loci (71 risk single-nucleotide polymorphisms) jointly associated with BD and brain structural phenotypes with a conjFDR < 0.05, among which 33 were novel that had not been reported in the previous BD GWAS. Follow-up eQTL analyses in respective brain regions both confirmed well-known risk genes (e.g. CACNA1C, NEK4, GNL3, MAPK3) and discovered novel risk genes (e.g. LIMK2 and CAMK2N2). This study indicates a substantial shared genetic basis between BD and brain structural phenotypes, and provides novel insights into the developmental origin of BD and related biological mechanisms.

Bidirectional genetic overlap between bipolar disorder and intelligence.

BACKGROUND: Bipolar disorder (BD) is a highly heritable psychiatric illness exhibiting substantial correlation with intelligence. METHODS: To investigate the shared genetic signatures between BD and intelligence, we utilized the summary statistics from genome-wide association studies (GWAS) to conduct the bivariate causal mixture model (MiXeR) and conjunctional false discovery rate (conjFDR) analyses. Subsequent expression quantitative trait loci (eQTL) mapping in human brain and enrichment analyses were also performed. RESULTS: Analysis with MiXeR suggested that approximately 10.3K variants could influence intelligence, among which 7.6K variants were correlated with the risk of BD (Dice: 0.80), and 47% of these variants predicted BD risk and intelligence in consistent allelic directions. The conjFDR analysis identified 37 distinct genomic loci that were jointly associated with BD and intelligence with a conjFDR < 0.01, and 16 loci (43%) had the same directions of allelic effects in both phenotypes. Brain eQTL analyses found that genes affected by the "concordant loci" were distinct from those modulated by the "discordant loci". Enrichment analyses suggested that genes related to the "concordant loci" were significantly enriched in pathways/phenotypes related with synapses and sleep quality, whereas genes associated with the "discordant loci" were enriched in pathways related to cell adhesion, calcium ion binding, and abnormal emotional phenotypes. CONCLUSIONS: We confirmed the polygenic overlap with mixed directions of allelic effects between BD and intelligence and identified multiple genomic loci and risk genes. This study provides hints for the mesoscopic phenotypes of BD and relevant biological mechanisms, promoting the knowledge of the genetic and phenotypic heterogeneity of BD. The essential value of leveraging intelligence in BD investigations is also highlighted.

A multivariable model of BRAFV600E and ultrasonographic features for predicting the risk of central lymph node metastasis in cN0 papillary thyroid microcarcinoma.

Background: Prophylactic central lymph node dissection (CLND) in papillary thyroid microcarcinoma (PTMC) patients without clinical evidence of central lymph node metastasis (CLNM) remains controversial. The purpose of our study is to identify preoperative predictive factors for finding CLNM in Chinese PTMC patients, which may allow tailored CLND. Methods: We retrospectively reviewed 182 consecutive Chinese PMTC patients with negative central lymph nodes who underwent total thyroidectomy plus central neck dissection from October 2015 to December 2017. Chi-squared and multivariate analysis were performed to evaluate the association of CLNM with ultrasonographic and clinicopathologic characteristics. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the utility of markers in predicting CLNM. Results: The CLNM was found in 39.0% (71 of 182) of cN0 PTMC patients. In multivariate analysis, tumor size>7 mm (OR: 3.636, 95% CI: 1.671-7.914), marked hypoechogenicity (OR: 2.686, 95% CI: 1.080-6.678), multifocality (OR: 4.184, 95% CI: 1.707-10.258) and BRAFV600E mutation (OR: 5.339, 95% CI: 2.529-11.272) were independent predictors of CLNM. In ROC analysis integrating these predictors, the sensitivity was 63.4% and specificity was 80.2%, and the area under the ROC (AUC) was 0.755. Conclusion: In conclusion, we found tumor size>7 mm, marked hypoechogenicity, multifocality, and BRAFV600E mutation were risk factors for CLNM. In term of these preoperative risk factors for CLNM, prophylactic CLND should be cautiously performed in cN0 PTMC patients.