A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

Background: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs.

Testing associations between human anxiety and genes previously implicated by mouse anxiety models.

Anxiety disorders are common and can be debilitating, with effective treatments remaining hampered by an incomplete understanding of the underlying genetic etiology. Improvements have been made in understanding the genetic influences on mouse behavioral models of anxiety, yet it is unclear the extent to which genes identified in these experimental systems contribute to genetic variation in human anxiety phenotypes. Leveraging new and existing large-scale human genome-wide association studies, we tested whether sets of genes previously identified in mouse anxiety-like behavior studies contribute to a range of human anxiety disorders.

Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits.

It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power.

Examination of a novel expression-based gene-SNP annotation strategy to identify tissue-specific contributions to heritability in multiple traits.

Complex traits show clear patterns of tissue-specific expression influenced by single nucleotide polymorphisms (SNPs), yet current strategies aggregate SNP effects to genes by employing simple physical proximity-based windows. Here, we examined whether incorporating SNPs with effects on tissue-specific cis-expression would improve our ability to detect trait-relevant tissues across 31 complex traits using stratified linkage disequilibrium score regression (S-LDSC). We found that a physical proximity annotation produced more significant tissue enrichments and larger S-LDSC regression coefficients, as compared to an expression-based annotation.

Testing Association of Previously Implicated Gene Sets and Gene-Networks in Nicotine Exposed Mouse Models with Human Smoking Phenotypes.

Introduction: Smoking behaviors are partly heritable, yet the genetic and environmental mechanisms underlying smoking phenotypes are not fully understood. Developmental nicotine exposure (DNE) is a significant risk factor for smoking and leads to gene expression changes in mouse models; however, it is unknown whether the same genes whose expression is impacted by DNE are also those underlying smoking genetic liability. We examined whether genes whose expression in D1-type striatal medium spiny neurons due to DNE in the mouse are also associated with human smoking behaviors.

Validation of Induced Microglia-Like Cells (iMG Cells) for Future Studies of Brain Diseases.

Microglia are the primary resident immune cells of the central nervous system that maintain physiological homeostasis in the brain and contribute to the pathogenesis of many psychiatric disorders and neurodegenerative diseases. Due to the lack of appropriate human cellular models, it is difficult to study the basic pathophysiological processes linking microglia to brain diseases. In this study, we adopted a microglia-like cellular model derived from peripheral blood monocytes with granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin-34 (IL-34).

Retraction Note: Identification of de novo mutations in prenatal neurodevelopment-associated genes in schizophrenia in two Han Chinese patient-sibling family-based cohorts.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.

Identification of de novo mutations in prenatal neurodevelopment-associated genes in schizophrenia in two Han Chinese patient-sibling family-based cohorts.

Schizophrenia (SCZ) is a severe psychiatric disorder with a strong genetic component. High heritability of SCZ suggests a major role for transmitted genetic variants. Furthermore, SCZ is also associated with a marked reduction in fecundity, leading to the hypothesis that alleles with large effects on risk might often occur de novo.

Polygenic Risk Scores for Subtyping of Schizophrenia.

Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS).

A Frameshift Variant in the CHST9 Gene Identified by Family-Based Whole Genome Sequencing Is Associated with Schizophrenia in Chinese Population.

Recent studies imply that rare variants contribute to the risk of schizophrenia, however, the exact variants or genes responsible for this condition are largely unknown. In this study, we conducted whole genome sequencing (WGS) of 20 Chinese families. Each family consisted of at least two affected siblings diagnosed with schizophrenia and at least one unaffected sibling.

Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits.

Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS + SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation.