Cell type-specific epigenetic links to schizophrenia risk in the brain

Background The importance of cell type-specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generate cell type-specific whole-genome methylomes (N = 95) and transcriptomes (N = 89) from neurons and oligodendrocytes obtained from brain tissue of patients with schizophrenia and matched controls. Results The methylomes of the two cell types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation differences between cases and controls are subtle compared to cell type differences, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in cell type differentially methylated sites, highlighting the significance of cell type-specific epigenetic dysregulation in a complex neuropsychiatric disorder. Conclusions Our results provide novel and comprehensive methylome and transcriptome data from distinct cell populations within patient-derived brain tissues. This data clearly demonstrate that cell type epigenetic-differentiated sites are preferentially targeted by disease-associated epigenetic dysregulation. We further show reduced cell type epigenetic distinction in schizophrenia.GK is a Jon Heighten Scholar in Autism Research at UT Southwestern. This work was supported by the Uehara Memorial Foundation to NU; JSPS Grant-in-Aid for Early-Career Scientists (18 K14814) to NU; Scientific Research (C) (18K06977) to KT; Takeda Science Foundation to NU; the JSPS Program for Advancing Strategic International Networks to Accelerate the Circulation of Talented Researchers (S2603) to SB, NU, KT, and GK; the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition – Scholar Award to GK; National Science Foundation (SBE-131719) to SVY; the National Chimpanzee Brain Resource, NIH R24NS092988, the NIH National Center for Research Resources P51RR165 (superseded by the Office of Research Infrastructure Programs/OD P51OD11132) to TMP; and the NIMH (MH103517) to TMP, GK, and SVY. Human tissue samples were obtained from the NIH NeuroBioBank (The Harvard Brain Tissue Resource Center, funded through HHSN-271-2013-00030C; the Human Brain and Spinal Fluid Mendizabal et al. Genome Biology (2019) 20:135 Page 18 of 21 Resource Center, VA West Los Angeles Healthcare Center; and the University of Miami Brain Endowment Bank) and the UT Psychiatry Psychosis Research Program (Dallas Brain Collection)

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Associations between weekend catch-up sleep and health-related quality of life with focusing on gender differences

Abstract This study investigated associations between weekend catch-up sleep (WCUS) and health-related quality of life (HRQoL) in 15,837 participants from the 7th (2016–2018) Korea National Health and Nutrition Examination Survey. We categorized WCUS durations into four groups: none (≤ 0 h [h]), short (> 0 h, ≤ 1 h), medium (> 1 h, ≤ 2 h), and long (> 2 h), and performed complex samples logistic regression and likelihood ratio χ2 test. The study found significant associations in women for the European Quality of Life-5 Dimensions (EQ-5D) index and three EQ-5D subdomains (self-care, usual activities, and anxiety/depression) with the WCUS durations, but no significant association in men. Compared to the non-WCUS, the short or medium WCUS was positively associated with the EQ-5D index and EQ-5D subdomains (usual activities and anxiety/depression) in women, while the long WCUS significantly reduced the quality of life in the self-care domain. In an additional subgroup analysis by age, middle-aged and elderly women had a more noticeable effect of WCUS on HRQoL than young women, and the short or medium WCUS improved HRQoL in middle-aged and elderly women in general. Therefore, we recommend appropriate WCUS durations to improve HRQoL, considering both gender and age

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits.

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket) for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively

Sans-gêne

26 octobre 19351935/10/26 (A16,N840)