We conducted a cross-trait meta-analysis of genome-wide association study on schizophrenia (SCZ) (n = 65,967), bipolar disorder (BD) (n = 41,653), autism spectrum disorder (ASD) (n = 46,350), attention deficit hyperactivity disorder (ADHD) (n = 55,374), and depression (DEP) (n = 688,809). After the meta-analysis, the number of genomic loci increased from 14 to 19 in ADHD, from 3 to 10 in ASD, from 45 to 57 in DEP, from 8 to 54 in BD, and from 64 to 87 in SCZ. We observed significant enrichment of overlapping genes among different disorders and identified a panel of cross-disorder genes. A total of seven genes were found being commonly associated with four out of five psychiatric conditions, namely GABBR1, GLT8D1, HIST1H1B, HIST1H2BN, HIST1H4L, KCNB1, and DCC. The SORCS3 gene was highlighted due to the fact that it was involved in all the five conditions of study. Analysis of correlations unveiled the existence of two clusters of related psychiatric conditions, SCZ and BD that were separate from the other three traits, and formed another group. Our results may provide a new insight for genetic basis of the five psychiatric disorders.

Context Iron overload is a known risk factor for type 2 diabetes (T2D); however, iron overload and iron deficiency have both been associated with metabolic disorders in observational studies. Objective Using mendelian randomization (MR), we assessed how genetically predicted systemic iron status affected T2D risk. Methods A 2-sample MR analysis was used to obtain a causal estimate. We selected genetic variants strongly associated (P < 5 × 10⁻⁸) with 4 biomarkers of systemic iron status from a study involving 48 972 individuals performed by the Genetics of Iron Status consortium and applied these biomarkers to the T2D case-control study (74 124 cases and 824 006 controls) performed by the Diabetes Genetics Replication and Meta-analysis consortium. The simple median, weighted median, MR-Egger, MR analysis using mixture-model, weighted allele scores, and MR based on a Bayesian model averaging approaches were used for the sensitivity analysis. Results Genetically instrumented serum iron (odds ratio [OR]: 1.07; 95% CI, 1.02-1.12), ferritin (OR: 1.19; 95% CI, 1.08-1.32), and transferrin saturation (OR: 1.06; 95% CI, 1.02-1.09) were positively associated with T2D. In contrast, genetically instrumented transferrin, a marker of reduced iron status, was inversely associated with T2D (OR: 0.91; 95% CI, 0.87-0.96). Conclusion Genetic evidence supports a causal link between increased systemic iron status and increased T2D risk. Further studies involving various ethnic backgrounds based on individual-level data and studies regarding the underlying mechanism are warranted for reducing the risk of T2D.

BACKGROUND. Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and 3 kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression). METHODS. Genetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis. RESULTS. Genetic correlations of PTSD with the depressive phenotypes were in the range of 0.71–0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicated that the genetically determined depressive phenotypes confer a causal effect on PTSD (b = 0.21–0.31). Notably, genetically determined PTSD confers a causal effect on DEP (b = 0.14) and DAF (b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identified 47 genomic loci, including 29 loci shared between PTSD and MDD. CONCLUSION. Evidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology. FUNDING. The National Key Research and Development Program of China and the National Natural Science Foundation of China.

Preeclampsia (PE) is a leading cause of maternal mortality worldwide. Several studies have detected some differentially expressed microRNAs in the preeclamptic placenta, but few of the identified microRNAs demonstrated consistent findings among different research studies. In this study, high-throughput microRNA sequencing (HTS) of 9 preeclamptic and 9 normal placentas was performed. Seventeen microRNAs were identified to be up-regulated and 8 down-regulated in preeclamptic placentas. Eight differentially expressed microRNAs except one identified in our study were determined to be consistent with at least one previous study, while sixteen were newly found. We performed qRT-PCR with independent 22 preeclamptic placentas and 20 control placentas to verify the differentially expressed microRNAs and ten microRNAs were validated. The predicted target genes of the aberrantly expressed miR-193b-3p were enriched in the following gene ontology categories: cell motility and migration, cell proliferation and angiogenesis. We also found that miR-193b-3p significantly decreased the migration and invasion of trophoblast (HTR-8/SVneo) cells and that miR-193b-3p could regulate trophoblasts migration and invasion through binding onto the 3′UTR target site of TGF-β2. In conclusion, we identified a list of differentially expressed microRNAs in PE placentas by HTS and provided preliminary evidence for the role of miR-193b-3p in the pathogenesis of preeclampsia.

Many population studies have shown that maternal prenatal nutrition deficiency may increase the risk of neurodevelopmental disorders in their offspring, but its potential transcriptomic effects on brain development are not clear. We aimed to investigate the transcriptional regulatory interactions between genes in particular pathways responding to the prenatal nutritional deficiency and to explore their effects on neurodevelopment and related disorders. We identified three modules in rat hippocampus responding to maternal prenatal nutritional deficiency and found 15 key genes (Hmgn1, Ssbp1, LOC684988, Rpl23, Gga1, Rhobtb2, Dhcr24, Atg9a, Dlgap3, Grm5, Scn2b, Furin, Sh3kbp1, Ubqln1, and Unc13a) related to the rat hippocampus developmental dysregulation, of which Hmgn1, Rhobtb2 and Unc13a related to autism, and Dlgap3, Grm5, Furin and Ubqln1 are related to Alzheimer's disease, and schizophrenia. Transcriptional alterations of the hub genes were confirmed except for Atg9a. Additionally, through modeling miRNA-mRNA-transcription factor interactions for the hub genes, we confirmed a transcription factor, Cebpa, is essential to regulate the expression of Rhobtb2. We did not find singificent singals in the prefrontal cortex responding to maternal prenatal nutritional deficiency. These findings demonstrated that these genes with the three modules in rat hippocampus involved in synaptic development, neuronal projection, cognitive function, and learning function are significantly enriched hippocampal CA1 pyramidal neurons and suggest that three genetic regulatory subnetworks and thirteen key regulating genes in rat hippocampus perturbed by a prenatal nutrition deficiency. These genes and related subnetworks may be prenatally involved in the etiologies of major brain disorders, including Alzheimer's disease, autism, and schizophrenia. We compared the transcriptomic differences in the hippocampus and prefrontal cortex between 10 rats with prenatal nutritional deficiency and 10 rats with prenatal normal chow feeding by differential analysis and co-expression network analysis. A network-driven integrative analysis with microRNAs and transcription factors was performed to define significant modules and hub genes responding to prenatal nutritional deficiency. Meanwhile, the module preservation test was conducted between the hippocampus and prefrontal cortex. Expression levels of the hub genes were further validated with a quantitative real-time polymerase chain reaction based on additional 40 pairs of rats.