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Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene.

ABSTRACT: To identify genes controlling plasma HDL and triglyceride levels, quantitative trait locus (QTL) analysis was performed in one backcross, (NZO/H1Lt x NON/LtJ) x NON/LtJ, and three intercrosses, C57BL/6J x DBA/2J, C57BL/6J x C3H/HeJ, and NZB/B1NJ x NZW/LacJ. HDL concentrations were affected by 25 QTL distributed on most chromosomes (Chrs); those on Chrs 1, 8, 12, and 16 were newly identified, and the remainder were replications of previously identified QTL. Triglyceride concentrations were controlled by nine loci; those on Chrs 1, 2, 3, 7, 16, and 18 were newly identified QTL, and the remainder were replications. Combining mouse crosses with haplotype analysis for the HDL QTL on Chr 18 reduced the list of candidates to six genes. Further expression analysis, sequencing, and quantitative complementation testing of these six genes identified Lipg as the HDL QTL gene on distal Chr 18. The data from these crosses further increase the ability to perform haplotype analyses that can lead to the identification of causal lipid genes.

SUBMITTER: Su Z

PROVIDER: S-EPMC2739753 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene.

Su Zhiguang Z Ishimori Naoki N Chen Yaoyu Y Leiter Edward H EH Churchill Gary A GA Paigen Beverly B Stylianou Ioannis M IM

Journal of lipid research 20090512 10

To identify genes controlling plasma HDL and triglyceride levels, quantitative trait locus (QTL) analysis was performed in one backcross, (NZO/H1Lt x NON/LtJ) x NON/LtJ, and three intercrosses, C57BL/6J x DBA/2J, C57BL/6J x C3H/HeJ, and NZB/B1NJ x NZW/LacJ. HDL concentrations were affected by 25 QTL distributed on most chromosomes (Chrs); those on Chrs 1, 8, 12, and 16 were newly identified, and the remainder were replications of previously identified QTL. Triglyceride concentrations were contro ...[more]

PMID: 19436067

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Project description:BackgroundThe Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with impaired glucose metabolism using the obese lines BFMI861-S1 and BFMI861-S2, which are genetically closely related, but differ in several traits. BFMI861-S1 is insulin resistant and stores ectopic fat in the liver, whereas BFMI861-S2 is insulin sensitive.MethodsIn generation 10, 397 males of an advanced intercross line (AIL) BFMI861-S1 × BFMI861-S2 were challenged with a high-fat, high-carbohydrate diet and phenotyped over 25 weeks. QTL-analysis was performed after selective genotyping of 200 mice using the GigaMUGA Genotyping Array. Additional 197 males were genotyped for 7 top SNPs in QTL regions. For the prioritization of positional candidate genes whole genome sequencing and gene expression data of the parental lines were used.ResultsOverlapping QTL for gonadal adipose tissue weight and blood glucose concentration were detected on chromosome (Chr) 3 (95.8-100.1 Mb), and for gonadal adipose tissue weight, liver weight, and blood glucose concentration on Chr 17 (9.5-26.1 Mb). Causal modeling suggested for Chr 3-QTL direct effects on adipose tissue weight, but indirect effects on blood glucose concentration. Direct effects on adipose tissue weight, liver weight, and blood glucose concentration were suggested for Chr 17-QTL. Prioritized positional candidate genes for the identified QTL were Notch2 and Fmo5 (Chr 3) and Plg and Acat2 (Chr 17). Two additional QTL were detected for gonadal adipose tissue weight on Chr 15 (67.9-74.6 Mb) and for body weight on Chr 16 (3.9-21.4 Mb).ConclusionsQTL mapping together with a detailed prioritization approach allowed us to identify candidate genes associated with traits of the metabolic syndrome. In addition, we provided evidence for direct and indirect genetic effects on blood glucose concentration in the insulin-resistant mouse line BFMI861-S1.

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Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene.

Publications

Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene.

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