The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum

Regulation of lymphoid-myeloid lineage bias through Regnase-1/3-mediated control of Nfkbiz

Regulation of lineage biases in hematopoietic stem and progenitor cells (HSPCs) is pivotal for balanced hematopoietic output. However, little is known about the mechanism behind lineage choice in HSPCs. Here, we show that mRNA decay factors Regnase-1 (Reg1; Zc3h12a) and Regnase-3 (Reg3; Zc3h12c) are essential for determining lymphoid fate and restricting myeloid differentiation in HSPCs. Loss of Reg1 and Reg3 resulted in severe impairment of lymphopoiesis and a mild increase in myelopoiesis in the BM. single cell RNA sequencing analysis (scRNA-seq) revealed that Reg1 and Reg3 regulate lineage directions in HSPCs via the control of a set of myeloid-related genes. Reg1- and Reg3-mediated control of mRNA encoding Nfkbiz, a transcriptional and epigenetic regulator, was essential for balancing lymphoid/myeloid lineage output in HSPCs in vivo. Furthermore, single cell-assay for transposase-accessible chromatin sequencing (scATAC-seq) analysis revealed that Reg1 and Reg3 control the epigenetic landscape on myeloid-related gene loci in early-stage HSPCs via Nfkbiz. Consistently, an antisense oligonucleotide designed to inhibit Reg1- and Reg3-mediated Nfkbiz mRNA degradation primed HSCs toward myeloid lineages by enhancing Nfkbiz expression. Collectively, the collaboration between post-transcriptional control and chromatin remodeling by the Reg1/Reg3-Nfkbiz axis governs HSPC lineage biases, ultimately dictating the fate of lymphoid versus myeloid differentiation

Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait

Bipolar multiplex families have an increased burden of common risk variants for psychiatric disorders

Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development.</p