About this Research Topic
While type 2 diabetes and obesity are identified as major risk factors for liver lipid disorders, evidence suggests that MASLD is partially regulated by genetic components, having reported cases in 10-20% of non-obese populations. Advancements in genetics and genomics approaches have yielded a better understanding of metabolic diseases. GWAS have identified many new loci and genes associated with traits such as BMI, blood glucose levels, and blood lipid levels, which are instrumental in aggravating liver diseases, plus identified variants and genes directly associated with fatty liver diseases.
Genomic approaches, such as gene expression quantitative trait loci (eQTL) and accessible chromatin quantitative trait loci (caQTL), have aided in refining many genes and identifying causal variants at GWAS loci. Moreover, the application of single-nuclei transcriptomics and proteomics studies have enhanced our understanding of liver cellular heterogeneity, with a focus on more active genes involved in liver lipid and glucose metabolism.
This Research Topic aims to delve into this subject, looking to provide recent breakthrough research on genetics, genomics, and multi-omics approaches concerning metabolic liver disorders. Contributions are sought across several study areas, including:
- GWAS and other genetics approaches to identify genes and variants associated with MASLD and any other metabolic traits that are risk factors for MASLD.
- eQTL and caQTL studies to map and identify causal liver regulatory variants and genes at metabolic trait GWAS loci.
- Multi-omics approaches, inclusive of single-nuclei transcriptomics, proteomics, or metabolomics in the identification of novel genes/pathways involved in the pathophysiology of liver metabolic diseases.
- Functional studies predicting the roles of variants and genes associated with MASLD and its spectrum of diseases.
The collection welcomes original research articles and/or review articles that offer a translational application for this specialized issue.
Keywords: Genetics, Genomics, Proteomics, lipidomics, Liver disease, NAFLD, Metabolic disease, RNA sequencing
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