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.

Whole-exome sequencing in a Chinese sample provides preliminary evidence for the link between rare/low-frequency immune-related variants and early-onset schizophrenia.

Rare and low-frequency variants contribute to schizophrenia (SCZ), and may influence its age-at-onset (AAO). We examined the association of rare or low-frequency deleterious coding variants in Chinese patients with SCZ. We collected DNA samples in 197 patients with SCZ spectrum disorder and 82 healthy controls (HC), and performed exome sequencing.

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.

Early-onset schizophrenia is associated with immune-related rare variants in a Chinese sample.

Background: Rare variants are likely to contribute to schizophrenia (SCZ), given the large discrepancy between the heritability estimated from twin and GWAS studies. Furthermore, the nature of the rare-variant contribution to SCZ may vary with the "age-at-onset" (AAO), since early-onset has been suggested as being indicative of neurodevelopment deviance.

Objective: To examine the association of rare deleterious coding variants in early- and adult-onset SCZ in a Chinese sample.

Pathway-Specific Polygenic Scores Improve Cross-Ancestry Prediction of Psychosis and Clinical Outcomes.

Psychotic disorders are debilitating conditions with disproportionately high public health burden. Genetic studies indicate high heritability, but current polygenic scores (PGS) account for only a fraction of variance in psychosis risk. PGS often show poor portability across ancestries, performing significantly worse in non-European populations.

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.

The single-cell chromatin accessibility landscape in mouse perinatal testis development.

Spermatogenesis depends on an orchestrated series of developing events in germ cells and full maturation of the somatic microenvironment. To date, the majority of efforts to study cellular heterogeneity in testis has been focused on single-cell gene expression rather than the chromatin landscape shaping gene expression. To advance our understanding of the regulatory programs underlying testicular cell types, we analyzed single-cell chromatin accessibility profiles in more than 25,000 cells from mouse developing testis.

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach.

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models.

Contributions of common genetic variants to specific languages and to when a language is learned.

Research over the past two decades has identified a group of common genetic variants explaining a portion of variance in native language ability. The present study investigates whether the same group of genetic variants are associated with different languages and languages learned at different times in life. We recruited 940 young adults who spoke from childhood Chinese and English as their first (native) (L1) and second (L2) language, respectively, who were learners of a new, third (L3) language.

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.

Exploring Drugs and Vaccines Associated with Altered Risks and Severity of COVID-19: A UK Biobank Cohort Study of All ATC Level-4 Drug Categories Reveals Repositioning Opportunities.

Effective therapies for COVID-19 are still lacking, and drug repositioning is a promising approach to address this problem. Here, we adopted a medical informatics approach to repositioning. We leveraged a large prospective cohort, the UK-Biobank (UKBB, ~ 397,000), and studied associations of prior use of all level-4 ATC drug categories ( = 819, including vaccines) with COVID-19 diagnosis and severity.

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.

Association of Sleep Traits and Heel Bone Mineral Density: Observational and Mendelian Randomization Studies.

Observational studies have suggested that sleep and circadian disturbances are potentially modifiable risk factors for low bone mineral density (BMD), but the causal relationship is unclear. This study aimed to (i) replicate the findings by examining observational association of sleep traits with low estimated BMD); (ii) examine whether these associations were causal by using Mendelian randomization (MR) analyses; and (iii) investigate potential modulation effects of sex and menopause. A total of 398,137 White British subjects (aged 39 to 73 years) with valid BMD estimated by quantitative ultrasound of the heel (eBMD) at baseline were included.

Causal associations of short and long sleep durations with 12 cardiovascular diseases: linear and nonlinear Mendelian randomization analyses in UK Biobank.

Aims: Observational studies have suggested strong associations between sleep duration and many cardiovascular diseases (CVDs), but causal inferences have not been confirmed. We aimed to determine the causal associations between genetically predicted sleep duration and 12 CVDs using both linear and nonlinear Mendelian randomization (MR) designs.

Methods And Results: Genetic variants associated with continuous, short (≤6 h) and long (≥9 h) sleep durations were used to examine the causal associations with 12 CVDs among 404 044 UK Biobank participants of White British ancestry.