scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis.

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes.

Long-term metabolic side effects of second-generation antipsychotics in Chinese patients with schizophrenia: A within-subject approach with modelling of dosage effects.

Background: Second-generation antipsychotics (SGAs) are commonly used to treat schizophrenia (SCZ), but SGAs may differ in the severity of side effects. Long-term studies are lacking, and previous observational studies have limitations, such as failure to account for confounding factors and short follow-up durations.

Aims: To compare the long-term anthropometric and metabolic side effects of seven SGAs in a Chinese population, using a within-subject approach to reduce the risk of confounding.

Association of COVID-19 vaccination with risks of hospitalization due to cardiovascular and other diseases: A study using data from the UK Biobank.

Objectives: To explore whether COVID-19 vaccination protects against hospital admission by preventing infections and severe disease.

Methods: We leveraged the UK Biobank and studied associations of COVID-19 vaccination (BioNTech-BNT162b2 or Oxford-AstraZeneca-ChAdOx1) with hospitalizations from cardiovascular and other selected diseases (N = 393,544; median follow-up = 54 days among vaccinated individuals). Multivariable Cox, Poisson regression, propensity score matching, and inverse probability treatment weighting analyses were performed.

INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.

RNA sequencing (RNA-Seq) is widely used to capture transcriptome dynamics across tissues, biological entities, and conditions. Currently, few or no methods can handle multiple biological variables (e.g.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g.

Dyslexia-related loci are significantly associated with language and literacy in Chinese-English bilingual Hong Kong Chinese twins.

A recent genome-wide association study on dyslexia in 51,800 affected European adults and 1,087,070 controls detected 42 genome-wide significant single nucleotide variants (SNPs). The association between rs2624839 in SEMA3F and reading fluency was replicated in a Chinese cohort. This study explores the genetic overlap between Chinese and English word reading, vocabulary knowledge and spelling, and aims at replicating the association in a unique cohort of bilingual (Chinese-English) Hong Kong Chinese twins.

The genetic basis of onset age in schizophrenia: evidence and models.

Schizophrenia is a heritable neurocognitive disorder affecting about 1% of the population, and usually has an onset age at around 21-25 in males and 25-30 in females. Recent advances in genetics have helped to identify many common and rare variants for the liability to schizophrenia. Earlier evidence appeared to suggest that younger onset age is associated with higher genetic liability to schizophrenia.

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach.

Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance.

Analysis of genetic differences between psychiatric disorders: exploring pathways and cell types/tissues involved and ability to differentiate the disorders by polygenic scores.

Although displaying genetic correlations, psychiatric disorders are clinically defined as categorical entities as they each have distinguishing clinical features and may involve different treatments. Identifying differential genetic variations between these disorders may reveal how the disorders differ biologically and help to guide more personalized treatment. Here we presented a statistical framework and comprehensive analysis to identify genetic markers differentially associated with various psychiatric disorders/traits based on GWAS summary statistics, covering 18 psychiatric traits/disorders and 26 comparisons.

Turning genome-wide association study findings into opportunities for drug repositioning.

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery.

Exploring Diseases/Traits and Blood Proteins Causally Related to Expression of ACE2, the Putative Receptor of SARS-CoV-2: A Mendelian Randomization Analysis Highlights Tentative Relevance of Diabetes-Related Traits.

Objective: COVID-19 has become a major public health problem. There is good evidence that ACE2 is a receptor for SARS-CoV-2, and high expression of may increase susceptibility to infection. We aimed to explore risk factors affecting susceptibility to infection and prioritize drug repositioning candidates, based on Mendelian randomization (MR) studies on lung expression.

Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology.

Classifying subjects into clinically and biologically homogeneous subgroups will facilitate the understanding of disease pathophysiology and development of targeted prevention and intervention strategies. Traditionally, disease subtyping is based on clinical characteristics alone, but subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (e.

Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning.

Background: Depression and anxiety disorders (AD) are the first and sixth leading causes of disability worldwide. Despite their high prevalence and significant disability resulted, there are limited advances in new drug development. Recently, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic basis underlying psychiatric disorders.

Differential associations of depression-related phenotypes with cardiometabolic risks: Polygenic analyses and exploring shared genetic variants and pathways.

Background: Numerous studies have suggested associations between depression and cardiometabolic (CM) diseases. However, little is known about the mechanism underlying this comorbidity, and whether the relationship differs by depression subtypes.

Methods: Using polygenic risk scores (PRS) and linkage disequilibrium (LD) score regression, we investigated the genetic overlap of various depression-related phenotypes with a comprehensive panel of 20 CM traits.

Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups.

Schizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data.

Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry.

Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders.