Dissecting the genetic and causal relationship between sleep-related traits and common brain disorders.

BACKGROUND: There is a profound connection between abnormal sleep patterns and brain disorders, suggesting a shared influential association. However, the shared genetic basis and potential causal relationships between sleep-related traits and brain disorders are yet to be fully elucidated. METHODS: Utilizing linkage disequilibrium score regression (LDSC) and bidirectional two-sample univariable Mendelian Randomization (UVMR) analyses with large-scale GWAS datasets, we investigated the genetic correlations and causal associations across six sleep traits and 24 prevalent brain disorders. Additionally, a multivariable Mendelian Randomization (MVMR) analysis evaluated the cumulative effects of various sleep traits on each brain disorder, complemented by genetic loci characterization to pinpoint pertinent genes and pathways. RESULTS: LDSC analysis identified significant genetic correlations in 66 out of 144 (45.8 %) pairs between sleep-related traits and brain disorders, with the most pronounced correlations observed in psychiatric disorders (66 %, 48/72). UVMR analysis identified 29 causal relationships (FDR<0.05) between sleep traits and brain disorders, with 19 associations newly discovered according to our knowledge. Notably, major depression, attention-deficit/hyperactivity disorder, bipolar disorder, cannabis use disorder, and anorexia nervosa showed bidirectional causal relations with sleep traits, especially insomnia's marked influence on major depression (IVW beta 0.468, FDR = 5.24E-09). MVMR analysis revealed a nuanced interplay among various sleep traits and their impact on brain disorders. Genetic loci characterization underscored potential genes, such as HOXB2, while further enrichment analyses illuminated the importance of synaptic processes in these relationships. CONCLUSIONS: This study provides compelling evidence for the causal relationships and shared genetic backgrounds between common sleep-related traits and brain disorders.

The Contribution of Mosaic Chromosomal Alterations to Schizophrenia.

BACKGROUND: Mosaic chromosomal alterations (mCAs) are implicated in neuropsychiatric disorders, yet the contribution to schizophrenia (SCZ) risk for somatic copy number variations (sCNVs) emerging in early developmental stages is not fully established. METHODS: We analyzed blood-derived genotype arrays from 9,715 SCZ patients and 28,822 controls of Chinese descent using a computational tool (MoChA) based on long-range chromosomal information to detect mCAs. We focused on probable early developmental sCNVs through stringent filtering. We assessed the sCNVs' burden across varying cell fraction (CF) cutoffs, as well as the frequency with which genes were involved in sCNVs. We integrated this data with the Psychiatric Genomics Consortium (PGC) dataset, which comprises 12,834 SCZ cases and 11,648 controls of European descent, and complemented it with genotyping data from postmortem brain tissue of 936 subjects (449 cases and 487 controls). RESULTS: Patients with SCZ had a significantly higher somatic losses detection rate than control subjects (1.00% vs 0.52%; odds ratio (OR) = 1.91; 95% CI, 1.47-2.49; two-sided Fisher's exact test, p=1.49×10-6). Further analysis indicated that the ORs escalated proportionately (from 1.91 to 2.78) with the increment in CF cutoffs. Recurrent sCNVs associated with SCZ (OR>8; Fisher's exact test, p<0.05) were identified, including notable regions at 10q21.1 (ZWINT), 3q26.1 (SLITRK3), 1q31.1 (BRINP3) and 12q21.31-21.32 (MGAT4C and NTS) in the Chinese cohort, some regions validated with PGC data. Cross-tissue validation pinpointed somatic losses at loci like 1p35.3-35.2 and 19p13.3-13.2. CONCLUSIONS: The study highlights mCAs' significant impact on SCZ, suggesting their pivotal role in the disorder's genetic etiology.