Integrating leiomyoma genetics, epigenomics, and single-cell transcriptomics reveals causal genetic variants, genes, and cell types.
Kadir BuyukcelebiAlexander J DuvalFatih AbdulaHoda ElkafasFidan Seker-PolatMazhar AdliPublished in: Nature communications (2024)
Uterine fibroids (UF), that can disrupt normal uterine function and cause significant physical and psychological health problems, are observed in nearly 70% of women of reproductive age. Although heritable genetics is a significant risk factor, specific genetic variations and gene targets causally associated with UF are poorly understood. Here, we performed a meta-analysis on existing fibroid genome-wide association studies (GWAS) and integrated the identified risk loci and potentially causal single nucleotide polymorphisms (SNPs) with epigenomics, transcriptomics, 3D chromatin organization from diverse cell types as well as primary UF patient's samples. This integrative analysis identifies 24 UF-associated risk loci that potentially target 394 genes, of which 168 are differentially expressed in UF tumors. Critically, integrating this data with single-cell gene expression data from UF patients reveales the causal cell types with aberrant expression of these target genes. Lastly, CRISPR-based epigenetic repression (dCas9-KRAB) or activation (dCas9-p300) in a UF disease-relevant cell type further refines and narrows down the potential gene targets. Our findings and the methodological approach indicate the effectiveness of integrating multi-omics data with locus-specific epigenetic editing approaches for identifying gene- and celt type-targets of disease-relevant risk loci.
Keyphrases
- genome wide
- single cell
- dna methylation
- rna seq
- gene expression
- copy number
- high throughput
- genome wide association
- mental health
- end stage renal disease
- systematic review
- healthcare
- randomized controlled trial
- big data
- genome wide identification
- ejection fraction
- case report
- cell therapy
- poor prognosis
- public health
- newly diagnosed
- physical activity
- prognostic factors
- human health
- risk factors
- genome wide association study
- data analysis
- polycystic ovary syndrome
- depressive symptoms
- pregnant women
- patient reported outcomes
- mesenchymal stem cells
- health promotion
- bioinformatics analysis