Inferring causal cell types of human diseases and risk variants from candidate regulatory elements.

The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.

Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.

Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.

Genome-Wide Identification of MYC Transcription Factors and Their Potential Functions in the Growth and Development Regulation of Tree Peony (Paeonia suffruticosa).

Tree peony (Paeonia suffruticosa Andr.) is a traditional Chinese flower with significant ornamental and medicinal value. Its growth and development process is regulated by some internal and external factors, and the related regulatory mechanism is largely unknown. Myelocytomatosis transcription factors (MYCs) play significant roles in various processes such as plant growth and development, the phytohormone response, and the stress response. As the identification and understanding of the MYC family in tree peony remains limited, this study aimed to address this gap by identifying a total of 15 PsMYCs in tree peony and categorizing them into six subgroups based on bioinformatics methods. Furthermore, the gene structure, conservative domains, cis-elements, and expression patterns of the PsMYCs were thoroughly analyzed to provide a comprehensive overview of their characteristics. An analysis in terms of gene structure and conserved motif composition suggested that each subtribe had similarities in function. An analysis of the promoter sequence revealed the presence of numerous cis-elements associated with plant growth and development, the hormone response, and the stress response. qRT-PCR results and the protein interaction network further demonstrated the potential functions of PsMYCs in the growth and development process. While in comparison to the control, only PsMYC2 exhibited a statistically significant variation in expression levels in response to exogenous hormone treatments and abiotic stress. A promoter activity analysis of PsMYC2 revealed its sensitivity to Flu and high temperatures, but exhibited no discernible difference under exogenous GA treatment. These findings help establish a basis for comprehending the molecular mechanism by which PsMYCs regulate the growth and development of tree peony.

Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association study.

Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N = 1 296 908) for IS together with molecular QTLs data, including mRNA, splicing, enhancer RNA (eRNA), and protein expression data from up to 50 tissues (total N = 11 588). We identify 136 genes/eRNA/proteins associated with IS risk across 60 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.

Novel insight into the etiology of ischemic stroke gained by integrative transcriptome-wide association study.

Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N=1,296,908) for IS together with mRNA, splicing, enhancer RNA (eRNA) and protein expression data (N=11,588) from 50 tissues. We identify 136 genes/eRNA/proteins associated with IS risk across 54 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.

twas_sim, a Python-based tool for simulation and power analysis of transcriptome-wide association analysis.

SUMMARY: Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex disease risk; however, most of these associations are non-coding, complicating identifying their proximal target gene. Transcriptome-wide association studies (TWASs) have been proposed to mitigate this gap by integrating expression quantitative trait loci (eQTL) data with GWAS data. Numerous methodological advancements have been made for TWAS, yet each approach requires ad hoc simulations to demonstrate feasibility. Here, we present twas_sim, a computationally scalable and easily extendable tool for simplified performance evaluation and power analysis for TWAS methods. AVAILABILITY AND IMPLEMENTATION: Software and documentation are available at https://github.com/mancusolab/twas_sim.

Callus induction and transcriptomic analysis of in vitro embryos at different developmental stages of peony.

The efficient induction of peony embryogenic callus is of great significance to the improvement and establishment of its regeneration technology system. In this study, the in vitro embryos of 'Fengdanbai' at different developmental stages were selected as explants, the effects of different concentrations and types of plant growth regulator combinations on the induction and proliferation of embryonic callus at different developmental stages were investigated, and comparative transcriptome analysis of callus with different differentiation potentials were performed to explore the molecular mechanisms affecting callus differentiation. The results showed that the germination rate of 90d seed embryo was the best, which was 94.17%; the 70d and 80d cotyledon callus induction effect was the best, both reaching 100%, but the 80d callus proliferation rate was higher, the proliferation rate reached 5.31, and the optimal induction medium was MS+0.1 mg·L-1NAA+0.3 mg·L-1TDZ+3 mg·L-12,4-D, the callus proliferation multiple was 4.77. Based on the comparative transcriptomic analysis, we identified 3470 differentially expressed genes (DEGs) in the callus with high differentiation rate and low differentiation rate, including 1767 up-regulated genes and 1703 down-regulated genes. Pathway enrichment analysis showed that the "Phenylpropanoid biosynthesis" metabolic pathway was significantly enriched, which is associated with promoting further development of callus shoots and roots. This study can provide reference for genetic improvement and the improvement of regeneration technology system of peony.

Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies.

Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average nEA = 511 k, nAA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10-15). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.

Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization.

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.

Integrative analyses identify susceptibility genes underlying COVID-19 hospitalization.

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrated a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n=18,502). We identified 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterized the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (BioVU; n=85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicated these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in BioVU, pan-UK Biobank, and Biobank Japan. Our study highlights putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.