Published online before print January 22, 2004, doi: 10.1161/01.RES.0000117770.03168.E7
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© 2004 American Heart Association, Inc.
Molecular Medicine |
Genome-Wide Scan Identifies Novel QTLs for Cholesterol and LDL Levels in F2[Dahl RxS]-Intercross Rats
From the Department of Medicine (V.L.M.H., T.D., L.V.L., N.R.-O.), Section of Molecular Medicine, Boston University School of Medicine; Department of Neurology (R.H.M.), Boston University School of Medicine, Boston, Mass.
Correspondence to Victoria L.M. Herrera, MD, Whitaker Cardiovascular Institute, W609, Boston University School of Medicine, 700 Albany St, Boston MA 02118. E-mail vherrera{at}bu.edu
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Abstract |
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Hypercholesterolemia is a significant risk factor for coronary artery disease development. Genes influencing nonmonogenic hypercholesterolemia susceptibility in humans remain to be identified. Animal models are key investigative systems because major confounding variables such as diet, activity, and genetic background can be controlled. We performed a 121-marker, total genome-analysis of an F2[Dahl RxS]-intercross selected for contrasting parental strain susceptibilities for hyperlipidemia on regular rat diets at 6 months of age. Quantitative traits studied were plasma total cholesterol, triglyceride, HDL, and LDL levels adjusted for obesity. Genome-wide analysis of 200 F2-intercross male rats detects two QTLs with highly significant linkage for total cholesterol (TC) on chromosome (chr) 5-133.3 Mbp (LOD 5.8), and chr5-54.2 Mbp (LOD 4.8), and two QTLs with significant linkage for TC: on chromosome 8, chr8-60.4 Mbp (LOD 3.8), and chromosome 2, chr2-243.5 Mbp (LOD 3.4). A QTL for LDL with significant linkage is detected on chromosome 5, chr5-104 Mbp (LOD 3.7). These QTLs contribute from 7% to 12% of total trait variance, respectively, with Dahl-S allele effects resulting in increased TC and LDL levels consistent with hyperlipidemia susceptibility in the parental Dahl-S rat strain. Predicted QTL-peaks do not coincide with previous genome scans. Human homologues of two TC-QTLs span genes listed in a LocusLink profile for cholesterol. Only suggestive loci were detected for HDL and total triglyceride levels. Altogether, the data demonstrates the contribution of multiple QTLs to hypercholesterolemia making a multipathway pathogenic framework imperative. QTL-peak candidate genes delineated are syntenic between rat and human genomes, increasing clinical relevance and mandating further study.
Key Words: genetics • total cholesterol • low-density lipoprotein • quantitative trait locus
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Atherosclerosis is the primary cause of coronary heart disease and stroke accounting for about 50% of deaths in Western countries.1 Research has identified numerous risk factors that are either largely environmental, or with significant genetic component.1 Of these, the relative abundance of plasma lipoproteins (hypercholesterolemia, hypertriglyceridemia, or both) has primary importance, because raised levels of atherogenic lipoproteins are a prerequisite for most forms of atherosclerosis.1 Cumulative information directly links hyperlipidemia not just to pathways involved in lesion initiation but also to lesion progression and destabilization such as inflammation, thrombogenicity, and endothelial dysfunction.2–5 Although interrelated, different cholesterol-containing lipoproteins play different roles and each has been associated with atherosclerosis as independent or interdependent risk factors. LDL-cholesterol (LDL) has been shown to be the most predictive of atherogenic susceptibility6 and has been demonstrated to exhibit oxidative susceptibility, which then relates to pathogenic oxidative processes implicated in coronary atherosclerotic plaque progression and destabilization.7 Yet, not all questions are answered by LDL levels or LDL-targeted interventions, hence the continued investigation into different parameters of hyperlipidemia. These observations place renewed importance on the investigation of the genetic determinants of hyperlipidemia because their identification would elucidate mechanisms, help clarify risk factors, as well as provide insight into mechanism-based pathways for intervention and/or prevention.
It is clear that nonmonogenic or "common" hyperlipidemia is a multifaceted and multifactorial trait with many contributing quantitative trait loci (QTLs) and interacting environmental factors. Although elucidation of QTLs for total cholesterol,8–11 HDL,12–14 LDL,13,15–20 and triglycerides8,11,16,17 have been reported in multiple studies in humans and animal models, a much-needed unifying picture of genetic determinants and mechanisms has been elusive. Critical to these analyses is the need for inter- and cross-species corroboration which will bring focus to key QTLs and candidate hyperlipidemia susceptibility genes. Just as critical is the need to dissect genetic determinants of hyperlipidemia in different human populations, animal species and animal models for a much-needed comprehensive grasp of the complexities and interrelationships.
We report the results of a genome-wide scan for QTLs affecting plasma total cholesterol (TC) and triglycerides (TG), HDL and LDL levels in an F2 intercross derived from inbred hyperlipidemia-susceptible and salt-sensitive Dahl (S) rats and inbred hyperlipidemia-resistant and salt-resistant Dahl (R) rats. We report highly significant and significant QTLs for TC and LDL levels. Although further analysis is necessary, compelling candidate genes are identified based on closest proximity to QTL position and concordance with known role(s) in cholesterol metabolism or transport.
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Materials and Methods |
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Genetic Crosses
Inbred Dahl S/hsd and Dahl R/hsd rat strains (Harlan, Indianapolis, Indiana) were used in this study according to institutional animal care and use guidelines. Parental strains (Dahl R femalexDahl S male) were crossed to produce F1[Dahl RxS] progeny. The F2 cohort was derived from brother-to-sister mating of F1 hybrids to produce an F2 male segregating population (n=200), F2[Dahl RxS]-intercross. F2 female rats were assigned to a breast cancer susceptibility study.
Phenotypic Characterization
All animal procedures were performed in accordance with institutional guidelines. Animals were maintained on a Pico rodent chow containing 0.3% NaCl. The food pellets and water were made available ad libitum. The animals were fasted for 16 hours and then euthanized at 6 months of age. Plasma total cholesterol, HDL-cholesterol, and total triglyceride concentrations were determined as described21 using standardized kits (WAKO chemicals USA Inc., Richmond, VA; Sigma) as per manufacturer’s specifications. Analysis was limited to fresh plasma for optimal results (unpublished observations, 2004). HDL levels were determined in all F2 intercross rats, in parental (n=6) and F1 (n=4) groups due to limited HDL kit availability while complying with stringent experimental design: analysis of fresh plasma levels at a fixed time point and specific rat groups used to produce the F2 cohort. LDL concentrations were calculated using the formula [LDL=TC-HDL-TG/5] as described.22 Obesity index was calculated by dividing the cubic root the body weight (g) by the nasoanal length (mm)x104 as described.24 Statistical analyses performed were as follows: one-way ANOVA, all pairwise multiple comparison using Tukey test, and regression analysis (SigmaStat).
Intercross Linkage Analysis
Genotyping was done by the NHLBI Mammalian Genotyping Service at the Center for Medical Genetics, Marshfield Medical Research Foundation (Wisconsin). We used 121 microsatellite markers informative for our F2[Dahl RxS]-intercross with an average density of 12.4 cM. Distributions were analyzed for normality; data transformations were done and transformed datasets that passed Kolmogorov-Smirnov normality testing (SigmaStat) were used for linkage analysis. QTL analysis was performed using log[plasma total cholesterol], HDL-cholesterol, ranked LDL-cholesterol and ranked total triglyceride concentrations as quantitative traits. For marker regression and interval mapping analyses, each quantitative trait was adjusted for obesity using the obesity index.23 Linkage maps, marker regression, and composite interval mapping were done with the Map Manager QTXb17 (MMQTXb17) program for Windows,24 which generates a likelihood ratio statistic (LRS) as a measure of the significance of a possible QTL. Genetic distances were calculated using Kosambi mapping function (genetic distances are expressed in centimorgans, cM). Critical significance values (LRS values) for interval mapping were determined by a permutation test (2000 permutations at 10-cM interval) on our 200 informative progeny using Kosambi mapping function and a free regression model. This permutation analysis, specific to this data set, revealed the minimum values for suggestive linkage LRS=8.8 (LOD 1.91); for significant linkage, LRS=15 (LOD 3.26); for highly significant linkage, LRS=21.4 (LOD 4.65). LRS 4.6 delineates LOD 1-support interval. Regression analysis using a free model fit, as well as constrained additive, dominant, and recessive models were applied. Data are presented for the free model fit since this analysis fits separate regression coefficients for both additive and dominance components (QTX Map Manager, MMQTXb17).
Confidence interval for a QTL location was estimated by bootstrap resampling method wherein histogram single peak delineates the QTL and peak widths define confidence interval for the QTL. Histograms that show more than one peak warn that the position for the QTL is not well defined or that there may be multiple linked QTLs (QTX Map Manager).
Interaction Analysis
Interaction analysis was done using the Map Manager QTXb17 program applying a two-stage test paradigm for determination of interaction in which the pair of loci must pass two tests in order to be reported as having a significant interaction effect. As recommended, first, we use P=0.00001 as the probability value chosen for the setting of Search and Linkage Criterion for the total effect of the two loci (the significance of the total effect of the two loci must be <0.00001). Second, the pairs of loci must exhibit a value of P<0.01 for the interaction effect.
QTL-Region Analysis for Human Homologues and Candidate Genes
Predicted QTL-peaks were analyzed for chromosomal location, syntenic human homologues and candidate genes using MapViewer database (http://www.ncbi./nlm.nih.gov/mapview/map_search.cgi?chr=rat.inf) with the rat QTL-peak marker as reference. This database also allows identification of a panel of QTL-peak flanking rat markers with increased density. Candidate genes were also analyzed in corresponding human homologues using the LocusLink profile for cholesterol (LocusLink database at http://www.ncbi.nlm.nih.gov/LocusLink). For all other analyses, Rat Genome Resource was used (http://www.ncbi.nlm.nih.gov/genome/guide/rat).
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Results |
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Parental Strains
The Dahl S strain is better known as a hypertensive strain.25 Its robust atherosclerosis-susceptibility trait—characterized by severe hyperlipidemia (increase in total cholesterol and triglycerides levels) associated with significant coronary heart disease, which simulates many features of human coronary heart disease—was first observed by us in Dahl S rats transgenic for the human cholesteryl transfer protein (hCETP).21,26,27 The hyperlipidemia-profile component of the atherosclerosis-susceptibility trait of the Dahl S rat strain can be seen in the 2- to 3-fold higher plasma total cholesterol, LDL, HDL, and total triglyceride levels detected in Dahl S rats when compared with the Dahl R rat strain (ANOVA P<10-5 to 10-9, pairwise multiple comparison P<0.05, Table 1).
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F2 Intercross Linkage Analysis
To identify putative loci influencing TC, LDL, HDL, and TG levels in Dahl rats, we performed QTL analysis on an F2[Dahl RxS]-intercross male cohort phenotyped for TC, LDL, HDL, and TG concentrations. Except for HDL, which exhibited normal distribution, lipid profile data were transformed to pass normality testing.
Two hundred F2 male hybrids were genotyped at 121 markers informative for Dahl S and Dahl R strains. Using a free regression model, four QTLs influencing TC were detected with significant to highly significant linkage (Figure, panels A through C; Table 2): two QTLs on chromosome 5 with highly significant linkage, TC-1 centered at D5Mit13-5 cM (LOD=5.8, genome-wide significance P=0.0004) and TC-2 centered at D5Mit10+3 cM (LOD=4.8, genome-wide significance P=0.001); and two QTLs with significant linkage on chromosome 8, TC-3 centered at D8Rat153+4 cM (LOD=3.8, genome-wide significance P=0.0160) and on chromosome 2, TC-4 centered at D2Rat67 (LOD=3.4, genome-wide significance P=0.0360). The genome-wide scan revealed one QTL affecting LDL levels with significant linkage (Figure, panel D; Table 2), LDL-1 on chromosome 5 centered at D5Rat23-2 cM (LOD=3.7, genome-wide significance P=0.0195). These QTLs contribute from 7% to 12% of the total phenotype variance (Table 2). Additional QTLs with suggestive linkage were detected for TC and LDL (Table 3).
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indicates orientation of chromosome from 0 Mbp; horizontal green lines mark LOD values for significance of linkage, from top to bottom: highly significant LOD 
4.65; significant LOD 
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Analysis of phenotype trait specific to genotype for the QTL-peak marker detects significant differences between S-allele and R-allele effects consistent with genome-wide level of significance (Table 4). The S-allele increases total cholesterol and LDL levels consistent with the observed hyperlipidemia susceptibility of the parental Dahl S rat strain. The genome search did not detect QTLs with significant linkage to HDL and to TG levels in this cohort. Only QTLs with suggestive linkage were detected for HDL and TG (Table 3). We did not detect any QTL for obesity index in this cohort. Analysis using constrained regression models (additive, dominant, and recessive) did not elucidate any more QTLs with significant or highly significant linkage.
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Interaction Analysis
Putative interactive loci were investigated by using a two-stage test, which determined that only pairs of loci that show a value of P<0.00001 for the total effect and a value of P<0.01 for the interaction effect are considered positive for interaction.24 Our analysis did not reveal interacting loci for TC, LDL, HDL, and TG quantitative traits that surpassed the threshold criteria.
Determination of Candidate Genes
Candidate gene analysis of QTL-peaks delineates the following genes in close proximity to respective QTL-peaks, with functional concordance to cholesterol metabolism and/or listed in a LocusLink profile for cholesterol. Candidate genes are well within the 1-LOD support interval (Figure; Table 2). The sterol carrier protein 2 (Scp2) and cytochrome P450 (CYP4)-4A11 genes were delineated for TC-1 QTL; acetyl-coenzyme A acetyltransferase 1 (ACAT1) for TC-3; and the adipose differentiation-related protein (ADRP) gene for LDL-1. Interestingly, these candidate genes are syntenic in the human homologues for said TC-QTL regions, respectively, thus increasing relevance to human studies. Candidate genes for TC-2 and TC-4 were not self-evident based on current database analyses.
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Discussion |
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We report the identification of four QTLs with significant to highly significant linkage to plasma total cholesterol levels in a male F2[Dahl RxS]-intercross cohort on regular rat chow. To date, these QTL-peaks do not coincide with QTL-peaks in previous rat total genome scan studies.9,10 Likewise, genome-scan QTL-peaks influencing total cholesterol detected in humans28,29 and in mouse intercross studies30–32 are not in synteny with QTL-peaks reported here. However, based on LocusLink profile for cholesterol, human homologues for TC-1 and TC-3 (Table 2) span genes identified to be linked to cholesterol metabolism through gene knockout studies or candidate gene studies. These data add to a growing list of genetic determinants, hence pathways, affecting total cholesterol levels. Given identical phenotype parameters, the observation that different cholesterol QTLs are detected in different study designs, which vary with age at phenotype analysis, gender, diet, and genetic background, indicate the impact of differential genetic susceptibilities and environmental factor interactions. If one considers strain-specific genetic differences as representative of subtypes, the identification of different QTLs for hyperlipidemia-susceptibility in different animal models would imply existence of subtypes of genetic susceptibility in humans, as well as subtype-specific environmental risk factors. This notion raises the level of complexity in the elucidation of hypercholesterolemia-susceptibility genes in humans.
Bypassing this complexity might be possible through the valid translation of QTLs identified in animal models to humans. This can be done through the identification and subsequent investigation of candidate genes that fulfill certain criteria. Parallel to the study of other quantitative traits, aside from corroboration of the QTL, making the case for animal model–derived candidate genes for cholesterol-QTLs would necessitate (1) functional concordance of candidate gene function with cholesterol homeostasis, transport or modulation, (2) identification of a functionally significant and physiologically relevant nucleotide polymorphism, and (3) genetic manipulation experiments to determine impact of said candidate gene on cholesterol metabolism in vivo.
A review of candidate genes complying with closest-proximity to QTL and functional relevance delineates a "first-list" of candidate genes that provide support for existing hypotheses or validate new ones. Candidate gene analysis of TC-1 QTL, which to date has the highest level of significance for total cholesterol QTL (LOD 5.8), brings focus to sterol carrier protein-2, which plays a role in intracellular cholesterol trafficking and hepatic cholesterol metabolism,33,34 or alternatively to CYP4-A11, which belongs to a cytochrome P450-A subfamily that is involved in metabolism of arachidonic acid and fatty acids, and transcriptionally induced by clofibrate through a peroxisome proliferator–activated receptor.35 Likewise, analysis of TC-3 QTL brings focus to ACAT1 gene, which is involved in macrophage foam cell formation in atherosclerotic plaques.36 The LDL-1 QTL with significant linkage (LOD 3.7) exhibits one of the higher levels of significance compared with other QTLs detected in primate and human studies for LDL levels or size,13,15–20,37 exclusive of monogenic diseases, and localizes to a unique chromosomal region not previously implicated in LDL levels. Although much study needs to be done, it is interesting to note that the adipose differentiation-related protein (ADRP) is the closest functionally concordant candidate gene to the predicted peak of LDL-1 QTL. The hypothesis implicating ADRP in LDL levels is consistent with the role of ADRP in binding fatty acids38 and/or cholesterol,39 as well as in caveolin-associated lipid droplet function.40 Interestingly, LDL receptors have recently been found to be mainly located in caveolae in the liver41 and ADRP is regulated by Scp2, a candidate gene for the highly significant TC-1 QTL (Table 2).
In summary, we report the identification of four significant to highly significant QTLs influencing plasma total cholesterol levels in an F2[Dahl RxS]-intercross and one QTL affecting LDL levels. Detection of novel QTLs collectively suggests additional genetic determinants of plasma cholesterol homeostasis. Although corroboration of these novel QTLs and determination of the underlying genes affecting cholesterol metabolism remain to be done, the data reiterate the complexity in the elucidation of genetic susceptibility to hypercholesterolemia and demonstrate that the current framework of cholesterol homeostasis is incomplete. Furthermore, along with previously identified QTLs, testing whether novel QTLs for total cholesterol and LDL translate into genetic risk markers of CHD and/or stroke with greater predictive value than plasma levels of total cholesterol and LDL provides additional near-term value.
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Acknowledgments |
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This work was supported by a grant from the National Heart, Lung, and Blood Institute (NHLBI) (HL62857). Genotyping was done through the NHLBI Mammalian Genotyping Service at the Center for Medical Genetics, Marshfield Medical Research Foundation (Wisconsin).
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Footnotes |
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Original received November 4, 2003; revision received January 9, 2004; accepted January 9, 2004.
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