Common variants contribute to intrinsic human brain functional networks.

The human brain forms functional networks of correlated activity, which have been linked with both cognitive and clinical outcomes. However, the genetic variants affecting brain function are largely unknown. Here, we used resting-state functional magnetic resonance images from 47,276 individuals to discover and validate common genetic variants influencing intrinsic brain activity.

Acute COVID-19 gene-expression profiles show multiple etiologies of long-term sequelae.

Two years into the SARS-CoV-2 pandemic, the post-acute sequelae of infection are compounding the global health crisis. Often debilitating, these sequelae are clinically heterogeneous and of unknown molecular etiology. Here, a transcriptome-wide investigation of this new condition was performed in a large cohort of acutely infected patients followed clinically into the post-acute period.

Downregulation of exhausted cytotoxic T cells in gene expression networks of multisystem inflammatory syndrome in children.

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56CD57 natural killer (NK) cells and exhausted CD8 T cells.

Common genetic variation influencing human white matter microstructure.

Brain regions communicate with each other through tracts of myelinated axons, commonly referred to as white matter. We identified common genetic variants influencing white matter microstructure using diffusion magnetic resonance imaging of 43,802 individuals. Genome-wide association analysis identified 109 associated loci, 30 of which were detected by tract-specific functional principal components analysis.

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks.

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer's disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes.

Big Data Analysis and Genetic Liability to Neuropsychiatric Disease.

The majority of risk genetic variants for common and complex neuropsychiatric traits lie within noncoding regions. Previous efforts have linked risk variants to specific genes by leveraging transcriptome data and expression quantitative trait loci. Most recently, the generation of large-scale epigenome data and the availability of epigenome quantitative trait loci provide a powerful discovery tool for assigning a functional role to the genetic variation in neuropsychiatric traits.

A chromosomal connectome for psychiatric and metabolic risk variants in adult dopaminergic neurons.

Background: Midbrain dopaminergic neurons (MDN) represent 0.0005% of the brain's neuronal population and mediate cognition, food intake, and metabolism. MDN are also posited to underlay the neurobiological dysfunction of schizophrenia (SCZ), a severe neuropsychiatric disorder that is characterized by psychosis as well as multifactorial medical co-morbidities, including metabolic disease, contributing to markedly increased morbidity and mortality.