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.

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.

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.