CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data

Whole-genome sequencing analyses suggest novel genetic factors associated with Alzheimer's disease and a cumulative effects model for risk liability

Genome-wide association studies (GWAS) on Alzheimer's disease (AD) have predominantly focused on identifying common variants in Europeans. Here, we performed whole-genome sequencing (WGS) of 1,559 individuals from a Korean AD cohort to identify various genetic variants and biomarkers associated with AD. Our GWAS analysis identified a previously unreported locus for common variants (APCDD1) associated with AD. Our WGS analysis was extended to explore the less-characterized genetic factors contributing to AD risk. We identified rare noncoding variants located in cis-regulatory elements specific to excitatory neurons associated with cognitive impairment. Moreover, structural variation analysis showed that short tandem repeat expansion was associated with an increased risk of AD, and copy number variant at the HPSE2 locus showed borderline statistical significance. APOE ε4 carriers with high polygenic burden or structural variants exhibited severe cognitive impairment and increased amyloid beta levels, suggesting a cumulative effects model of AD risk.

Lessons from national biobank projects utilizing whole-genome sequencing for population-scale genomics

Large-scale national biobank projects utilizing whole-genome sequencing have emerged as transformative resources for understanding human genetic variation and its relationship to health and disease. These initiatives, which include the UK Biobank, All of Us Research Program, Singapore's PRECISE, Biobank Japan, and the National Project of Bio-Big Data of Korea, are generating unprecedented volumes of high-resolution genomic data integrated with comprehensive phenotypic, environmental, and clinical information. This review examines the methodologies, contributions, and challenges of major WGS-based national genome projects worldwide. We first discuss the landscape of national biobank initiatives, highlighting their distinct approaches to data collection, participant recruitment, and phenotype characterization. We then introduce recent technological advances that enable efficient processing and analysis of large-scale WGS data, including improvements in variant calling algorithms, innovative methods for creating multi-sample VCFs, optimized data storage formats, and cloud-based computing solutions. The review synthesizes key discoveries from these projects, particularly in identifying expression quantitative trait loci and rare variants associated with complex diseases. Our review introduces the latest findings from the National Project of Bio-Big Data of Korea, which has advanced our understanding of population-specific genetic variation and rare diseases in Korean and East Asian populations. Finally, we discuss future directions and challenges in maximizing the impact of these resources on precision medicine and global health equity. This comprehensive examination demonstrates how large-scale national genome projects are revolutionizing genetic research and healthcare delivery while highlighting the importance of continued investment in diverse, population-specific genomic resources.

MIRACN: a residual convolutional neural network for predicting cell line specific functional regulatory variants

In post-genome-wide association study era, interpretation of noncoding variants remains a significant challenge due to their complexity and the limited understanding of their functions. Here, we developed MIRACN, a novel residual convolutional neural network designed to predict cell line-specific functional regulatory variants. By utilizing a substantial dataset from massively parallel reporter assays (MPRAs) and employing a multitask learning strategy, MIRACN was trained across seven distinct cell lines, attaining superior performance compared to existing methods, especially in predicting cell type specificity. Comparative evaluations on an independent MPRA test dataset demonstrated that MIRACN not only outperformed in identifying regulatory variants but also provided valuable insights into their cellular context-specific regulatory mechanisms. MIRACN is capable of not only providing scores for functional variants but also pinpointing the specific cell line in which these variants display their function. This enhancement has improved the resolution of current research on the functionality of noncoding variants and has paved the way for more precise diagnostic and therapeutic strategies.

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart

Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, rare variants exert more cell-type-shared regulatory effects, with selective pressures particularly targeting variants affecting fetal brain neurons. To prioritize de novo mutations with extreme regulatory effects, we developed FLARE, a context-specific functional genomic model of constraint. FLARE outperformed other methods in prioritizing case mutations from autism-affected families near syndromic autism-associated genes; for example, identifying mutation outliers near CNTNAP2 that would be missed by alternative approaches. Overall, our findings demonstrate the potential of integrating single-cell maps with population genetics and deep learning-based variant effect prediction to elucidate mechanisms of development and disease-ultimately, supporting the notion that genetic contributions to neurodevelopmental disorders are predominantly rare.

An integrative single-cell atlas for exploring the cellular and temporal specificity of genes related to neurological disorders during human brain development

Single-cell technologies have enhanced comprehensive knowledge regarding the human brain by facilitating an extensive transcriptomic census across diverse brain regions. Nevertheless, understanding the cellular and temporal specificity of neurological disorders remains ambiguous due to developmental variations. To address this gap, we illustrated the dynamics of disorder risk gene expression under development by integrating multiple single-cell RNA sequencing datasets. We constructed a comprehensive single-cell atlas of the developing human brain, encompassing 393,060 single cells across diverse developmental stages. Temporal analysis revealed the distinct expression patterns of disorder risk genes, including those associated with autism, highlighting their temporal regulation in different neuronal and glial lineages. We identified distinct neuronal lineages that diverged across developmental stages, each exhibiting temporal-specific expression patterns of disorder-related genes. Lineages of nonneuronal cells determined by molecular profiles also showed temporal-specific expression, indicating a link between cellular maturation and the risk of disorder. Furthermore, we explored the regulatory mechanisms involved in early brain development, revealing enriched patterns of fetal cell types associated with neuronal disorders indicative of the prenatal stage's influence on disease determination. Our findings facilitate unbiased comparisons of cell type‒disorder associations and provide insight into dynamic alterations in risk genes during development, paving the way for a deeper understanding of neurological disorders.