Mapping genetic loci that regulate lipid levels in a NZB/B1NJxRF/J intercross and a combined intercross involving NZB/B1NJ, RF/J, MRL/MpJ, and SJL/J mouse strains

Genetic Evidence for a Causal Relationship between Hyperlipidemia and Type 2 Diabetes in Mice

Dyslipidemia is considered a risk factor for type 2 diabetes (T2D), yet studies with statins and candidate genes suggest that circulating lipids may protect against T2D development. Apoe-null (Apoe^-/-) mouse strains develop spontaneous dyslipidemia and exhibit a wide variation in susceptibility to diet-induced T2D. We thus used Apoe^-/- mice to elucidate phenotypic and genetic relationships of circulating lipids with T2D. A male F2 cohort was generated from an intercross between LP/J and BALB/cJ Apoe^-/- mice and fed 12 weeks of a Western diet. Fasting, non-fasting plasma glucose, and lipid levels were measured and genotyping was performed using miniMUGA arrays. We uncovered a major QTL near 60 Mb on chromosome 15, Nhdlq18, which affected non-HDL cholesterol and triglyceride levels under both fasting and non-fasting states. This QTL was coincident with Bglu20, a QTL that modulates fasting and non-fasting glucose levels. The plasma levels of non-HDL cholesterol and triglycerides were closely correlated with the plasma glucose levels in F2 mice. Bglu20 disappeared after adjustment for non-HDL cholesterol or triglycerides. These results demonstrate a causative role for dyslipidemia in T2D development in mice.

Polygenic Control of Carotid Atherosclerosis in a BALB/cJ × SM/J Intercross and a Combined Cross Involving Multiple Mouse Strains

Atherosclerosis in the carotid arteries is a major cause of ischemic stroke, which accounts for 85% of all stroke cases. Genetic factors contributing to carotid atherosclerosis remain poorly understood. The aim of this study was to identify chromosomal regions harboring genes contributing to carotid atherosclerosis in mice. From an intercross between BALB/cJ (BALB) and SM/J (SM) apolipoprotein E-deficient (Apoe^-/-) mice, 228 female F2 mice were generated and fed a "Western" diet for 12 wk. Atherosclerotic lesion sizes in the left carotid artery were quantified. Across the entire genome, 149 genetic markers were genotyped. Quantitative trait locus (QTL) analysis revealed eight loci for carotid lesion sizes, located on chromosomes 1, 5, 12, 13, 15, 16, and 18. Combined cross-linkage analysis using data from this cross, and two previous F2 crosses derived from BALB, C57BL/6J and C3H/HeJ strains, identified five significant QTL on chromosomes 5, 9, 12, and 13, and nine suggestive QTL for carotid atherosclerosis. Of them, the QTL on chromosome 12 had a high LOD score of 9.95. Bioinformatic analysis prioritized Arhgap5, Akap6, Mipol1, Clec14a, Fancm, Nin, Dact1, Rtn1, and Slc38a6 as probable candidate genes for this QTL. Atherosclerotic lesion sizes were significantly correlated with non-HDL cholesterol levels (r = 0.254; p = 0.00016) but inversely correlated with HDL cholesterol levels (r = -0.134; p = 0.049) in the current cross. Thus, we demonstrated the polygenic control of carotid atherosclerosis in mice. The correlations of carotid lesion sizes with non-HDL and HDL suggest that genetic factors exert effects on carotid atherosclerosis partially through modulation of lipoprotein homeostasis.

Exercise Capacity and Response to Training Quantitative Trait Loci in a NZW X 129S1 Intercross and Combined Cross Analysis of Inbred Mouse Strains

Genetic factors determining exercise capacity and the magnitude of the response to exercise training are poorly understood. The aim of this study was to identify quantitative trait loci (QTL) associated with exercise training in mice. Based on marked differences in training responses in inbred NZW (-0.65 ± 1.73 min) and 129S1 (6.18 ± 3.81 min) mice, a reciprocal intercross breeding scheme was used to generate 285 F₂ mice. All F₂ mice completed an exercise performance test before and after a 4-week treadmill running program, resulting in an increase in exercise capacity of 1.54 ± 3.69 min (range = -10 to +12 min). Genome-wide linkage scans were performed for pre-training, post-training, and change in run time. For pre-training exercise time, suggestive QTL were identified on Chromosomes 5 (57.4 cM, 2.5 LOD) and 6 (47.8 cM, 2.9 LOD). A significant QTL for post-training exercise capacity was identified on Chromosome 5 (43.4 cM, 4.1 LOD) and a suggestive QTL on Chromosomes 1 (55.7 cM, 2.3 LOD) and 8 (66.1 cM, 2.2 LOD). A suggestive QTL for the change in run time was identified on Chromosome 6 (37.8 cM, 2.7 LOD). To identify shared QTL, this data set was combined with data from a previous F₂ cross between B6 and FVB strains. In the combined cross analysis, significant novel QTL for pre-training exercise time and change in exercise time were identified on Chromosome 12 (54.0 cM, 3.6 LOD) and Chromosome 6 (28.0 cM, 3.7 LOD), respectively. Collectively, these data suggest that combined cross analysis can be used to identify novel QTL and narrow the confidence interval of QTL for exercise capacity and responses to training. Furthermore, these data support the use of larger and more diverse mapping populations to identify the genetic basis for exercise capacity and responses to training.

A forest-based feature screening approach for large-scale genome data with complex structures

Background

Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations. Feature screening has proved to be an effective and powerful approach to handle ultrahigh dimensional data statistically, yet it has not received much attention in GWAS. Feature screening reduces the feature space from millions to hundreds by removing non-informative noise. However, the univariate measures used to rank features are mainly based on individual effect without considering the mutual interactions with other features. In this article, we explore the performance of a random forest (RF) based feature screening procedure to emphasize the SNPs that have complex effects for a continuous phenotype.

Results

Both simulation and real data analysis are conducted to examine the power of the forest-based feature screening. We compare it with five other popular feature screening approaches via simulation and conclude that RF can serve as a decent feature screening tool to accommodate complex genetic effects such as nonlinear, interactive, correlative, and joint effects. Unlike the traditional p-value based Manhattan plot, we use the Permutation Variable Importance Measure (PVIM) to display the relative significance and believe that it will provide as much useful information as the traditional plot.

Conclusion

Most complex traits are found to be regulated by epistatic and polygenic variants. The forest-based feature screening is proven to be an efficient, easily implemented, and accurate approach to cope whole genome data with complex structures. Our explorations should add to a growing body of enlargement of feature screening better serving the demands of contemporary genome data.

Genetic Architecture of Atherosclerosis in Mice: A Systems Genetics Analysis of Common Inbred Strains

Common forms of atherosclerosis involve multiple genetic and environmental factors. While human genome-wide association studies have identified numerous loci contributing to coronary artery disease and its risk factors, these studies are unable to control environmental factors or examine detailed molecular traits in relevant tissues. We now report a study of natural variations contributing to atherosclerosis and related traits in over 100 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP). The mice were made hyperlipidemic by transgenic expression of human apolipoprotein E-Leiden (APOE-Leiden) and human cholesteryl ester transfer protein (CETP). The mice were examined for lesion size and morphology as well as plasma lipid, insulin and glucose levels, and blood cell profiles. A subset of mice was studied for plasma levels of metabolites and cytokines. We also measured global transcript levels in aorta and liver. Finally, the uptake of acetylated LDL by macrophages from HMDP mice was quantitatively examined. Loci contributing to the traits were mapped using association analysis, and relationships among traits were examined using correlation and statistical modeling. A number of conclusions emerged. First, relationships among atherosclerosis and the risk factors in mice resemble those found in humans. Second, a number of trait-loci were identified, including some overlapping with previous human and mouse studies. Third, gene expression data enabled enrichment analysis of pathways contributing to atherosclerosis and prioritization of candidate genes at associated loci in both mice and humans. Fourth, the data provided a number of mechanistic inferences; for example, we detected no association between macrophage uptake of acetylated LDL and atherosclerosis. Fifth, broad sense heritability for atherosclerosis was much larger than narrow sense heritability, indicating an important role for gene-by-gene interactions. Sixth, stepwise linear regression showed that the combined variations in plasma metabolites, including LDL/VLDL-cholesterol, trimethylamine N-oxide (TMAO), arginine, glucose and insulin, account for approximately 30 to 40% of the variation in atherosclerotic lesion area. Overall, our data provide a rich resource for studies of complex interactions underlying atherosclerosis.

While recent genetic association studies in human populations have succeeded in identifying genetic loci that contribute to coronary artery disease (CAD) and related phenotypes, these loci explain only a small fraction of the genetic variation in CAD and associated traits. Here, we present a complementary approach using association analysis of atherosclerotic traits among inbred strains of mice. A strength of this approach is that it enables in-depth phenotypic characterization including gene expression and metabolic profiling across a variety of tissues, and integration of these molecular phenotypes with coronary artery disease itself. A striking finding was the large fraction of atherosclerosis that was explained by genetic interactions. Association analysis allowed us to identify genetic loci for atherosclerotic lesion area as well as transcript, cytokine and metabolite levels, and relationships among the traits were examined by correlation and network modeling. The plasma metabolites associated with atherosclerosis in mice, namely, LDL/VLDL-cholesterol, TMAO, arginine, glucose and insulin, overlapped with those observed in humans and accounted for approximately 30 to 40% of the observed variation in atherosclerotic lesion area. In summary, our data provide a detailed overview of the genetic architecture of atherosclerosis in mice and a rich resource for studies of the complex genetic and metabolic interactions that underlie the disease.

A graphical weighted power improving multiplicity correction approach for SNP selections

Controlling for the multiplicity effect is an essential part of determining statistical significance in large-scale single-locus association genome scans on Single Nucleotide Polymorphisms (SNPs). Bonferroni adjustment is a commonly used approach due to its simplicity, but is conservative and has low power for large-scale tests. The permutation test, which is a powerful and popular tool, is computationally expensive and may mislead in the presence of family structure. We propose a computationally efficient and powerful multiple testing correction approach for Linkage Disequilibrium (LD) based Quantitative Trait Loci (QTL) mapping on the basis of graphical weighted-Bonferroni methods. The proposed multiplicity adjustment method synthesizes weighted Bonferroni-based closed testing procedures into a powerful and versatile graphical approach. By tailoring different priorities for the two hypothesis tests involved in LD based QTL mapping, we are able to increase power and maintain computational efficiency and conceptual simplicity. The proposed approach enables strong control of the familywise error rate (FWER). The performance of the proposed approach as compared to the standard Bonferroni correction is illustrated by simulation and real data. We observe a consistent and moderate increase in power under all simulated circumstances, among different sample sizes, heritabilities, and number of SNPs. We also applied the proposed method to a real outbred mouse HDL cholesterol QTL mapping project where we detected the significant QTLs that were highlighted in the literature, while still ensuring strong control of the FWER.

Identification of a novel polymorphism in X-linked sterol-4-alpha-carboxylate 3-dehydrogenase (Nsdhl) associated with reduced high-density lipoprotein cholesterol levels in I/LnJ mice

Loci controlling plasma lipid concentrations were identified by performing a quantitative trait locus analysis on genotypes from 233 mice from a F2 cross between KK/HlJ and I/LnJ, two strains known to differ in their high-density lipoprotein (HDL) cholesterol levels. When fed a standard diet, HDL cholesterol concentration was affected by two significant loci, the Apoa2 locus on Chromosome (Chr) 1 and a novel locus on Chr X, along with one suggestive locus on Chr 6. Non-HDL concentration also was affected by loci on Chr 1 and X along with a suggestive locus on Chr 3. Additional loci that may be sex-specific were identified for HDL cholesterol on Chr 2, 3, and 4 and for non-HDL cholesterol on Chr 5, 7, and 14. Further investigation into the potential causative gene on Chr X for reduced HDL cholesterol levels revealed a novel, I/LnJ-specific nonsynonymous polymorphism in Nsdhl, which codes for sterol-4-alpha-carboxylate 3-dehydrogenase in the cholesterol synthesis pathway. Although many lipid quantitative trait locus have been reported previously, these data suggest there are additional genes left to be identified that control lipid levels and that can provide new pharmaceutical targets.

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.

High-resolution genetic mapping using the Mouse Diversity outbred population

The JAX Diversity Outbred population is a new mouse resource derived from partially inbred Collaborative Cross strains and maintained by randomized outcrossing. As such, it segregates the same allelic variants as the Collaborative Cross but embeds these in a distinct population architecture in which each animal has a high degree of heterozygosity and carries a unique combination of alleles. Phenotypic diversity is striking and often divergent from phenotypes seen in the founder strains of the Collaborative Cross. Allele frequencies and recombination density in early generations of Diversity Outbred mice are consistent with expectations based on simulations of the mating design. We describe analytical methods for genetic mapping using this resource and demonstrate the power and high mapping resolution achieved with this population by mapping a serum cholesterol trait to a 2-Mb region on chromosome 3 containing only 11 genes. Analysis of the estimated allele effects in conjunction with complete genome sequence data of the founder strains reduced the pool of candidate polymorphisms to seven SNPs, five of which are located in an intergenic region upstream of the Foxo1 gene.

HDL cholesterol and bone mineral density: is there a genetic link?

Overwhelming evidence has linked cardiovascular disease and osteoporosis, but the shared root cause of these two diseases of the elderly remains unknown. Low levels of high density lipoprotein cholesterol (HDL) and bone mineral density (BMD) are risk factors for cardiovascular disease and osteoporosis respectively. A number of correlation studies have attempted to determine if there is a relationship between serum HDL and BMD but these studies are confounded by a number of variables including age, diet, genetic background, gender and hormonal status. Collectively, these data suggest that there is a relationship between these two phenotypes, but that the nature of this relationship is context specific. Studies in mice plainly demonstrate that genetic loci for BMD and HDL co-map and transgenic mouse models have been used to show that a single gene can affect both serum HDL and BMD. Work completed to date has demonstrated that HDL can interact directly with both osteoblasts and osteoclasts, but no direct evidence links bone back to the regulation of HDL levels. Understanding the genetic relationship between BMD and HDL has huge implications for understanding the clinical relationship between CVD and osteoporosis and for the development of safe treatment options for both diseases.

A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models

We report a systems genetic analysis of high density lipoprotein (HDL) levels in an F2 intercross between inbred strains CAST/EiJ and C57BL/6J. We previously showed that there are dramatic differences in HDL metabolism in a cross between these strains, and we now report co-expression network analysis of HDL that integrates global expression data from liver and adipose with relevant metabolic traits. Using data from a total of 293 F2 intercross mice, we constructed weighted gene co-expression networks and identified modules (subnetworks) associated with HDL and clinical traits. These were examined for genes implicated in HDL levels based on large human genome-wide associations studies (GWAS) and examined with respect to conservation between tissue and sexes in a total of 9 data sets. We identify genes that are consistently ranked high by association with HDL across the 9 data sets. We focus in particular on two genes, Wfdc2 and Hdac3, that are located in close proximity to HDL QTL peaks where causal testing indicates that they may affect HDL. Our results provide a rich resource for studies of complex metabolic interactions involving HDL. This article is part of a Special Issue entitled Advances in High Density Lipoprotein Formation and Metabolism: A Tribute to John F. Oram (1945-2010).

Untangling HDL quantitative trait loci on mouse chromosome 5 and identifying Scarb1 and Acads as the underlying genes

Two high-density lipoprotein cholesterol quantitative trait loci (QTL), Hdlq1 at 125 Mb and Hdlq8 at 113 Mb, were previously identified on mouse distal chromosome 5. Our objective was to identify the underlying genes. We first used bioinformatics to narrow the Hdlq1 locus to 56 genes. The most likely candidate, Scarb1 (scavenger receptor B1), was supported by gene expression data consistent with knockout and transgenic mouse models. Then we confirmed Hdlq8 as an independent QTL by detecting it in an intercross between NZB and NZW (LOD = 12.7), two mouse strains that have identical genotypes for Scarb1. Haplotyping narrowed this QTL to 9 genes; the most likely candidate was Acads (acyl-coenzymeA dehydrogenase, short chain). Sequencing showed that Acads had an amino acid polymorphism, Gly94Asp, in a conserved region; Western blotting showed that protein levels were significantly different between parental strains. A previously known spontaneous deletion causes loss of ACADS activity in BALB/cBy mice. We showed that HDL levels were significantly elevated in BALB/cBy compared with BALB/c mice and that this HDL difference cosegregated with the Acads mutation. We confirmed that Hdlq1 and Hdlq8 are independent QTL on mouse chromosome 5 and demonstrated that Scarb1 and Acads are the underlying genes.

Genotype-phenotype associations in understanding the role of corticosteroid-binding globulin in health and disease animal models

Corticosteroid-binding globulin (CBG) is a plasma glycoprotein discovered more than 60 years ago for its high-affinity for glucocorticoids. Although its molecular structure and its biochemical properties have been described, its various biological roles and its importance are not yet fully understood. This review focuses first on studies that have used no-hypothesis-driven genetic approaches in animal models to reveal the higher than expected importance of CBG in particular in glucocorticoid stress responses. Then the dissection of some CBG physiological roles in an animal model of genetic CBG deficiency is reported. Finally, studies on the role of CBG genetic variability in human obesity traits are reviewed and discussed.

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

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.

Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci

Dissecting the genes involved in complex traits can be confounded by multiple factors, including extensive epistatic interactions among genes, the involvement of epigenetic regulators, and the variable expressivity of traits. Although quantitative trait locus (QTL) analysis has been a powerful tool for localizing the chromosomal regions underlying complex traits, systematically identifying the causal genes remains challenging. Here, through its application to plasma levels of high-density lipoprotein cholesterol (HDL) in mice, we demonstrate a strategy for narrowing QTL that utilizes comparative genomics and bioinformatics techniques. We show how QTL detected in multiple crosses are subjected to both combined cross analysis and haplotype block analysis; how QTL from one species are mapped to the concordant regions in another species; and how genomewide scans associating haplotype groups with their phenotypes can be used to prioritize the narrowed regions. Then we illustrate how these individual methods for narrowing QTL can be systematically integrated for mouse chromosomes 12 and 15, resulting in a significantly reduced number of candidate genes, often from hundreds to <10. Finally, we give an example of how additional bioinformatics resources can be combined with experiments to determine the most likely quantitative trait genes.

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

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.

Chemical mutagenesis induced two high bone density mouse mutants map to a concordant distal chromosome 4 locus

Phenotype-driven mutagenesis approach in the mouse holds much promise as a method for revealing gene function. Earlier, we have described an N-ethyl-N-nitrosourea (ENU) mutagenesis screen to create genome-wide dominant mutations in the mouse model. Using this approach, we describe identification of two high bone density mutants in C57BL/6J (B6) background. The mutants, named as 12184 and 12137, have been bred more than five generations with wild-type B6 mice, each producing >200 backcross progeny. The average total body areal bone mineral density (aBMD) was 13-17% higher in backcrossed progeny from both mutant lines between 6 and 10 weeks of age, as compared to wild-type (WT) B6 mice (n=60-107). At 3 weeks of age the aBMD of mutant progeny was not significantly affected as compared to WT B6 mice. Data from 10- and 16-week old progeny show that increased aBMD was mainly related to a 14-20% higher bone mineral content, whereas bone size was marginally increased. In addition, the average volumetric BMD (vBMD) was 5-15% higher at the midshaft tibia or femur, as compared to WT mice. Histomorphometric analysis revealed that bone resorption was 23-34% reduced in both mutant mice. Consistent with histomorphometry data, the mRNA expression of genes that regulate osteoclast differentiation and survival were altered in the 12137 mutant mice. To determine the chromosomal location of the ENU mutation, we intercrossed both mutant lines with C3H/HeJ (C3H) mice to generate B6C3H F2 mice (n=164 for line 12137 and n=137 F2 for line 12184). Interval mapping using 60 microsatellite markers and aBMD phenotype revealed only one significant or suggestive linkage on chromosome 4. Since body weight was significantly higher in mutant lines, we also used body weight as additive and interactive covariate for interval mapping; both analyses showed higher LOD scores for both 12137 and 12184 mutants without affecting the chromosomal location. The large phenotype in the mutant mice compared to generally observed QTL effects (<5%) would increase the probability of identifying the mutant gene.