ArticleScientific reports2022
QTL-mapping in the obese Berlin Fat Mouse identifies additional candidate genes for obesity and fatty liver disease.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 5 citations in OpenAlex.
- MicroRNAs and rs1803274 SNP-based BuChe downregulation are associated with metabolic syndrome through ghrelin hydrolysis and expression quantitative trait loci regulation in PD patients.Frontiers in molecular neuroscience · 2025Article
- Identification of additional body weight QTLs in the Berlin Fat Mouse BFMI861 lines using time series data.Scientific reports · 2024Article
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5 authors at 1 institution in 1 country.
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Abstract
The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with liver weight, liver triglycerides, and body weight using the obese BFMI sub-line BFMI861-S1. BFMI861-S1 mice are insulin resistant and store ectopic fat in the liver. In generation 10, 58 males and 65 females of the advanced intercross line (AIL) BFMI861-S1xB6N were phenotyped under a standard diet over 20 weeks. QTL analysis was performed after genotyping with the MiniMUGA Genotyping Array. Whole-genome sequencing and gene expression data of the parental lines was used for the prioritization of positional candidate genes. Three QTLs associated with liver weight, body weight, and subcutaneous adipose tissue (scAT) weight were identified. A highly significant QTL on chromosome (Chr) 1 (157-168 Mb) showed an association with liver weight. A QTL for body weight at 20 weeks was found on Chr 3 (34.1-40 Mb) overlapping with a QTL for scAT weight. In a multiple QTL mapping approach, an additional QTL affecting body weight at 16 weeks was identified on Chr 6 (9.5-26.1 Mb). Considering sequence variants and expression differences, Sec16b and Astn1 were prioritized as top positional candidate genes for the liver weight QTL on Chr 1; Met and Ica1 for the body weight QTL on Chr 6. Interestingly, all top candidate genes have previously been linked with metabolic traits. This study shows once more the power of an advanced intercross line for fine mapping. QTL mapping combined with a detailed prioritization approach allowed us to identify additional and plausible candidate genes linked to metabolic traits in the BFMI861-S1xB6N AIL. By reidentifying known candidate genes in a different crossing population the causal link with specific traits is underlined and additional evidence is given for further investigations.
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