Atherosclerosis quantitative trait loci are sex- and lineage-dependent in an intercross of C57BL/6 and FVB/N low-density lipoprotein receptor(–/–) mice

Atherosclerosis is a complex disease that is affected by environmental as well as genetic factors. The aim of the present study was to identify loci of atherosclerosis susceptibility in a cross of atherosclerosis-susceptible C57BL/6 and atherosclerosis-resistant FVB/N mice on the low-density lipoprotein (LDL) receptor (LDLR)-deficient background (LDLR(–/–)) and to test whether these loci are affected by lineage. A total of 459 F(2)s were generated in two ways: In cross “mB6xfFVB,” male B6.LDLR(–/–) mice were crossed to female FVB.LDLR(–/–) mice to generate 107 female and 112 male F(2)s. In cross “mFVBxfB6,” male FVB.LDLR(–/–) mice were crossed to female B6.LDLR(–/–) mice to generate 120 female and 120 male F(2)s. Animals were phenotyped for cross-sectional atherosclerotic lesion area at the aortic root, and a genome scan was carried out with 192 microsatellite markers. Quantitative trait locus mapping revealed significant loci of atherosclerosis susceptibility on chromosomes 3, 10, and 12. On chromosome 10 maximal logarithm of odds (LOD) scores of 13.1 (D10Mit16, 16 cM) and 5.7 (D10Mit168, 9 cM) were found in female and male mice, respectively. On chromosome 3, a maximal LOD score of 5.1 (D3Mit45, 79 cM) was detected only in females. On proximal chromosome 12 significant LOD scores were lineage-dependent, with maximal LOD scores of 3.9 (D12Mit82, 3 cM) and 4.8 (D12Mit189, 24 cM) present only in female mice of cross mB6xfFVB and male mice of cross mFVBxfB6, respectively. We conclude that, in this intercross, loci of atherosclerosis susceptibility are in part sex- and lineage-dependent. Awareness of these complexities may have major consequences for the identification of atherosclerosis susceptibility genes by quantitative trait locus mapping

Identification of Abcc6 as the major causal gene for dystrophic cardiac calcification in mice through integrative genomics

The genetic factors contributing to the complex disorder of myocardial calcification are largely unknown. Using a mouse model, we fine-mapped the major locus (Dyscalc1) contributing to the dystrophic cardiac calcification (DCC) to an 840-kb interval containing 38 genes. We then identified the causal gene by using an approach integrating genetic segregation and expression array analyses to identify, on a global scale, cis-acting DNA variations that perturb gene expression. By studying two intercrosses, in which the DCC trait segregates, a single candidate gene (encoding the ATP-binding cassette transporter ABCC6) was identified. Transgenic complementation confirmed Abcc6 as the underlying causal gene for Dyscalc1. We demonstrate that in the cross, the expression of Abcc6 is highly correlated with the local mineralization regulatory system and the BMP2-Wnt signaling pathway known to be involved in the systemic regulation of calcification, suggesting potential pathways for the action of Abcc6 in DCC. Our results demonstrate the power of the integrative genomics in discovering causal genes and pathways underlying complex traits

Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis

In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates

Differences in DBA/1J and DBA/2J reveal lipid QTL genes*s⃞

Recent advances in mouse genomics have revealed considerable variation in the form of single-nucleotide polymorphisms (SNPs) among common inbred strains. This has made it possible to characterize closely related strains and to identify genes that differ; such genes may be causal for quantitative phenotypes. The mouse strains DBA/1J and DBA/2J differ by just 5.6% at the SNP level. These strains exhibit differences in a number of metabolic and lipid phenotypes, such as plasma levels of triglycerides (TGs) and HDL. A cross between these strains revealed multiple quantitative trait loci (QTLs) in 294 progeny. We identified significant TG QTLs on chromosomes (Chrs) 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 16, and 19, and significant HDL QTLs on Chrs 3, 9, and 16. Some QTLs mapped to chromosomes with limited variability between the two strains, thus facilitating the identification of candidate genes. We suggest that Tshr is the QTL gene for Chr 12 TG and HDL levels and that Ihh may account for the TG QTL on Chr 1. This cross highlights the advantage of crossing closely related strains for subsequent identification of QTL genes