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J Lipid Res. Oct 2009; 50(10): 2083–2094.
PMCID: PMC2739753

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


To identify genes controlling plasma HDL and triglyceride levels, quantitative trait locus (QTL) analysis was performed in one backcross, (NZO/H1Lt × NON/LtJ) × NON/LtJ, and three intercrosses, C57BL/6J × DBA/2J, C57BL/6J × C3H/HeJ, and NZB/B1NJ × 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.

Keywords: high density lipoprotein, triglyceride, quantitative trait locus

Epidemiological studies show that high plasma triglyceride (TG) levels and low HDL cholesterol levels increase the risk of cardiovascular disease (1, 2). Raising HDL and lowering TG are therefore critical goals for coronary heart disease prevention. Two drug classes that raise HDL are available, fibrates and niacin; each has been shown to reduce the number of coronary events (39) even though they only modestly raise HDL, typically 5–20%. Furthermore, clinical use of niacin is limited due to a side effect of cutaneous flushing (10), while fibrates are associated with an increase in noncardiovascular mortality (11, 12). TG levels can be lowered through fibrates or omega-3 fatty acid treatment. Treatment with omega-3 fatty acids appears to reduce cardiovascular events (13, 14); however, other effects of treatment may contribute to the reduction of cardiovascular events since the decrease of TG is modest.

HDL levels vary considerably in humans, and this variation is at least 50% genetically determined (15). Several genes have been shown to contribute to the genetic variation of HDL in humans (16). With the advances in single nucleotide polymorphism (SNP) genotyping technology, genome-wide association studies have identified common variants in a small number of genes as being associated with human plasma lipid levels (1726). For HDL, many of the significant SNPs have been reported in known genes, such as cholesterol ester transfer protein, hepatic lipase, lipoprotein lipase, ATP-binding cassette subfamily A member 1, lecithin cholesterol acyltransferase, and endothelial lipase. However, the effect sizes of the reported associations remain small, 5–10% cumulatively, suggesting that other genes and loci remain to be found.

Although quantitative trait loci (QTL) for lipids in animal models and humans have been extensively investigated, mouse QTL remain a required part of the genetic mapping toolbox, as modeling environmental effects in humans is challenging. Plasma HDL and TG levels vary among common strains of inbred mice, and numerous QTL for HDL have been mapped by crossing different strains (27, 28). While it is believed the HDL QTL map is approaching saturation with few new loci being reported (27), the additional crosses provide important information that can be used for combining data sets and haplotype analysis to further narrow loci and identify the genes (29). In comparison, few TG QTL have been reported, and the seven novel TG QTL reported here will likely aid the identification of TG QTL genes in the future as more crosses are performed.

This study reports plasma HDL and TG QTL from four new mouse crosses involving seven different strains. Furthermore, six additional HDL QTL crosses have been published since the last review (27) (Table 1). These separate crosses will aid haplotype analysis, and where possible, raw data from multiple crosses can be combined to narrow the QTL region and identify the underlying genes. In this study, some of the new QTL were used for combined-cross analysis with haplotype analysis, mRNA expression analysis, and quantitative complementation testing to identify and prove that endothelial lipase (Lipg) is an HDL quantitative trait gene on distal mouse chromosome (Chr) 18.

New and recent HDL and TG QTL mouse crosses



C57BL/6J (B6), C3H/HeJ (C3H), DBA/2J (D2), NZO/HlLtJ (NZO), NON/LtJ (NON), NZB/B1NJ (NZB), and NZW/LacJ (NZW) inbred mouse strains and B6.129S-Lipgtm1Tq/J (B6.Lipg−/−, stock no. 005681) were obtained from The Jackson Laboratory. Mice were housed in a climate-controlled facility with a 12 h light/12 h dark cycle and allowed ad libitum access to water and a chow diet containing 6% fat (LabDiets 5K52; St. Louis, MO). Animal protocols were reviewed and approved by the Animal Care and Use Committee of The Jackson Laboratory.

Each cross, carried out for different reasons, is summarized here. B6 × D2 is a cross originally studied for albuminuria (30) phenotyped 335 chow-fed male F2 mice at 8 weeks of age. B6 × C3H, a cross with 277 female F2 mice generated from reciprocal mating, fed mice an atherogenic diet (Ath) as described previously from 8 to 14 weeks of age when they were phenotyped. (NZO × NON) × NON, a cross previously described for diabesity phenotypes (31), measured HDL and TG levels in 146 chow-fed males at 24 weeks of age. NZB × NZW, a cross with 264 F2 male and female mice, measured HDL and TG in mice fed the atherogenic diet from 8–16 weeks of age. TGs for this cross have been published previously (32).

Quantitative complementation testing was performed for Lipg. Male B6.Lipg−/− and B6 Lipg+/+ mice were crossed with female B6, C3H, D2, NZB, or SM mice. Both sexes of the chow-fed F1 progeny from each mating were measured for HDL at 8 weeks of age.

Plasma lipid analysis

Crosses B6 × C3H and NZB × NZW were fasted for 4 h in the morning, while crosses for B6 × D2 and (NZO × NON) × NON were not fasted. Fasting does not usually affect HDL but may significantly alter TG levels, which could reduce the ability to identify TG QTL (false negatives) but should not create false positives. Thus, TG QTL are also reported for the unfasted crosses because they may be informative for future positional mapping of these QTL genes.

Blood from the retro-orbital sinus was collected in tubes containing EDTA and centrifuged at 9,000 rpm for 5 min. Plasma was frozen at −20°C until assay. Plasma HDL, total cholesterol (TC), and TG concentrations were measured using enzymatic reagent kits (Beckman Coulter, Fullerton, CA) according to the manufacturer's recommendations on the Synchron CX Delta System (Beckman Coulter).


DNA was extracted from tail tips using phenol-chloroform. For the crosses B6 × D2 and NZB × NZW, SNPs were genotyped by the Allele-Typing Service at The Jackson Laboratory in conjunction with KBiosciences (Hoddesdon, UK). For the crosses B6 × C3H and (NZO × NON) × NON, polymorphic MIT microsatellite markers were genotyped using agarose gel electrophoresis (NuSieve 3:1; FMC BioProducts, Rockland, ME). Markers were chosen at evenly spaced intervals where possible, and further details of the markers used are available in supplementary Tables I–IV. All data from these crosses are available in the Mouse Phenome Database in QTL archive under Su1 (jax.org/phenome;qtlarchive).

QTL analysis

QTL mapping was performed using R/QTL (version 1.07-12, available at http://www.rqtl.org) as described previously (32, 33). HDL, TC, and TG values were transformed using log base 10 to obtain a normal distribution (supplementary ). Main-effect QTL were computed at 2 centimorgan (cM) increments over the entire genome. QTL were deemed significant if they either met or exceeded the 95% genome-wide adjusted threshold, which was assessed by 1,000 permutations; they were deemed suggestive if they either met or exceeded the 37% genome-wide adjusted threshold but were not significant (34). Approximate cM coordinates for markers were obtained by dividing base pair positions (mouse genome build 36) by a factor of 2 except for Chr 19, where Mb/1.04 was used. The validity of this approximation was confirmed by comparison to estimated map positions in R/QTL and also from previous cM-to-Mb comparisons in mouse (35). Simultaneous pair-wise genome scans were performed to detect gene interactions; however, no significant interactions were identified.

Combining crosses was performed as previously described (36). Briefly, genotypes from Chr 18 in the B6 × C3H and NZB × SM crosses were recoded, so that the B6 and SM genotypes became L for low HDL alleles and the C3H and NZB genotypes became H for high HDL alleles. A logarithm (base 10 of odds (LOD) score was computed at 2 cM intervals across the QTL interval for each cross separately and then for both crosses combined. The combined data were analyzed with “sex” and “cross” as additive covariates.

The raw data sets for each of these crosses, as well as multiple previously published HDL QTL studies, are available online in the Mouse Phenome Database (http://jax.org/phenome/qtlarchive).

SNP and haplotype analysis

Recently, >8 million SNPs were released for 16 mouse inbred strains (37). These data and several other sources were used to infer genotypes for a 50 strain set using a Hidden Markov Model; this imputed SNP resource is available at http://cgd.jax.org/ImputedSNPData/v1.1/ (38) and also in the Mouse Phenome Database on the center for genome dynamics (CGD) set of SNPs. SNPs for Chr 18 (n = 291,266) were downloaded, and locations that met the haplotype conditions were identified using a script implemented in the R statistical package. To carry out haplotype analysis, genomic regions within the QTL were excluded if the pair of strains that gave rise to a QTL had an identical haplotype pattern (B6 = C3H, B6 = DBA/2, and NZB = SM). Such regions are deemed identical by descent and are very unlikely to contain the causal genetic polymorphism underlying a common QTL (39). The remaining QTL regions were selected if the strains carrying the allele that increased HDL (C3H = D2 = NZB) were identical, if the strains carrying the allele that decreased HDL (B6 = SM) were identical, and if the haplotype pattern differed between the high and low allele strains (C3H, D2, NZB ≠ B6, SM). Gene lists for these regions were extracted from Ensembl (www.ensembl.org). These genes were examined in the Mouse Phenome Database (http://www.jax.org/phenome) for coding differences.


Additional sequencing was performed to confirm sequence variants between the different strains for Mro and Lipg. The genomic sequences of Mro (ENSMUST00000120033) and Lipg (ENSMUST00000066532) were obtained from the Ensembl (http://www.ensembl.org) mouse genome assembly, and primers were designed to amplify each exon, including at least 50 nucleotides of the adjacent introns. Sequences around noncoding SNPs were obtained from http://www.ncbi.nlm.nih.gov/SNP/. Standard PCR was performed using primers listed in supplementary Table V. Purified PCR products were subjected to thermocycle sequencing on capillary-based machines by the Jackson Laboratory DNA Sequence Laboratory. The sequence was analyzed using Sequencher software (version 4.1.4; GeneCodes Technology).

Chr 18/Lipg locus expression analysis

Liver mRNA expression levels were interrogated for the genes within the Chr 18 QTL region, narrowed after haplotype analysis to determine if parental strains for the three crosses (B6 × D2, B6 × C3H, and NZB × SM) differed in expression. The livers were obtained from five male and five female 12 week old mice of each strain fed chow or atherogenic diet. Total RNA was extracted from five mice of each strain-sex-diet group; three were chosen using random numbers from each group of five for microarrays, and all five were used for real-time PCR. Expression analysis was performed using Mouse Genome 430 2.0 Gene-Chip arrays (Affymetrix). The array data has been deposited at the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/; series number: GSE10493). RNA extraction, quantification, cDNA synthesis, and data analysis were carried out as described previously (40).

For real-time PCR, cDNA samples were mixed with SYBR Green Master Mix (Applied Biosystems, Foster City, CA) and gene-specific primers in a total volume of 25 µl. The primer pairs are as follows: Lipg forward 5′- TGGCTGCAGGAGAAGGAAGA-3′ and reverse 5′-CAGCGTGTAGGTATGCAGGA-3′ and β-Actin forward 5′-CTTCTTGGGTATGGAATCC-3′ and reverse 5′-GCTCAGGAGGAGCGGTGAT-3′. PCR was performed in 96-well optical reaction plates with an ABI PRISM 7500 sequence detection system (Applied Biosystems). Cycling parameters were 2 min at 50°C, 10 min at 95°C, and 40 cycles of 15 s at 95°C, and 1 min at 60°C. After PCR, a dissociation curve was constructed by increasing temperature from 65°C to 95°C for detection of PCR product specificity. PCR reactions were set up in triplicate for each strain-sex-diet group, and the expression of Lipg was normalized to the expression of β-actin.

Statistical analysis for complementation test

A least square means analysis was used to examine the interaction in F1 mice between Lipg (knockout or wild-type) by “allele” (high or low HDL in the crosses) for the quantitative complementation analysis. The null hypothesis for this test is that the difference in HDL between F1 mice carrying the wild-type alleles of Lipg and crossed to either the low HDL allele or high HDL allele strain at this QTL locus (Δ2) should not differ from the difference in HDL between F1 mice carrying Lipg−/+ alleles (Δ1). If there was a significant difference (P < 0.01), then the candidate gene is considered to successfully complement. Data were analyzed using JMP version 7.0 (SAS Institute, Cary, NC).


HDL and TC QTL mapping

The four crosses (B6 × C3H, B6 × DBA/2, NZO × NON, and NZW × NZB) revealed 14 significant (P < 0.05) and 11 suggestive (P < 0.63) HDL QTL on Chrs 1–6, 8, 11, 12, 15, 16, 18, and 19 (Fig. 1; Table 2). All HDL QTL have been identified in other mouse crosses (27), except those on Chrs 1, 8, 12, and 16 observed in the NZB × NZW cross. Distal Chr 1 was the most frequently identified QTL, observed in three crosses, including B6 × D2, B6 × C3H, and (NZO × NON) × NON, and its causal gene is most likely Apoa2 (41). A second locus with a peak at 49 cM on Chr 1 was identified in the cross NZB × NZW. Recently, Farp2 and/or Stk25 were reported as the candidate genes for this QTL using the NZB × NZW cross (42); both genes have some evidence, but further evidence is needed to choose between the two. Three significant QTL were identified on Chr 5 at 26 cM (LOD = 9.0) and 57 cM (LOD = 12.7) in the NZB × NZW cross and at 41 cM (LOD = 3.4) in the (NZO × NON) × NON cross. Interestingly, HDL levels are raised by heterozygous alleles for the Chr 5 QTL in the (NZO × NON) × NON cross. It has been previously shown that heterotic effects in an NZO × NON outcross exacerbated both diabesity and perturbations in lipid metabolism (43). Two significant QTL were identified on Chr 3 at 26 cM (LOD = 4.5, NZB × NZW cross) and at 64 cM (LOD = 3.0, B6 × D2 cross). All these QTL have been observed multiple times in other crosses (27).

Significant and suggestive lipid QTL from four new mouse crosses
Fig. 1.
Genome-wide scans for HDL. A: B6 × D2. B: B6 × C3H. C: (NZO × NON) × NZO. D: NZB × NZW. The horizontal dashed lines represent suggestive (P = 0.63) and significant (P = 0.05) levels as determined by 1,000 permutation ...

For TC, in the chow-fed crosses B6 × D2 and (NZO × NON) × NON, three suggestive QTL distributed on Chrs 2, 7, and 8 were identified specifically for TC; the other seven QTL were identified for both TC and HDL. This indicates that the majority of the cholesterol QTL observed in mice maintained on a chow diet are represented by HDL loci. In the atherogenic diet-fed crosses, B6 × C3H and NZB × NZW, 11 QTL were identified for TC, six of them (54.5%) were independent of HDL QTL (Table 2; see supplementary ). This indicates that nonHDL cholesterol levels differ between strains after consuming atherogenic diet and yield QTL different from HDL loci.

TG QTL mapping

Five significant TG QTL were identified (Fig. 2; Table 2). Two were mapped at approximately the same location on Chr 2 at 73 cM (LOD = 2.4) in (NZO × NON) × NON and 80 cM (LOD = 4.7) in B6 × C3H, and these shared approximately the same confidence interval. Three additional significant QTL were mapped located on Chr 12 in the B6 × D2 cross (14 cM, LOD = 3.1), Chr 18 in the B6 × C3H cross (39 cM, LOD = 3.2), and distal Chr 1 near the HDL/Apoa2 locus (89 cM, LOD = 3.5) in the (NZO × NON) × NON cross. Previously reported QTL for the NZB × NZW cross mapped to Chr 7 (50 cM, LOD = 3.1) and Chr 8 (11 cM, LOD = 3.2) (32).

Fig. 2.
Genome-wide scans for TG. A: B6 × D2. B: B6 × C3H. C: (NZO × NON) × NZO. The horizontal dashed lines represent suggestive (P = 0.63) and significant (P = 0.05) levels as determined by 1,000 permutation tests.

TG levels were positively correlated with HDL levels in three crosses; however, for all three crosses, the proportion of TG explained by the relationship of TG to HDL was small; B6 × C3H (r2 = 0.14, P < 0.0001), B6 × D2 (r2 = 0.05, P < 0.0001), and NZB × NZW (r2 = 0.03, P = 0.0068). TG levels were not correlated with HDL in the (NZO × NON) × NON cross. A stronger correlation between TG and HDL was not predictive of more overlapping HDL and TG QTL. Nonetheless, five significant TG QTL (Chrs 1, 2, 8, 12, and 18) had overlapping 95% confidence intervals with HDL QTL (Table 2).

Combining crosses and haplotype analysis reduced the Chr 18 HDL QTL to six genes

Several crosses detected HDL QTLs on Chr 18 as listed in Table 3. The LOD score plots for the crosses, where available, are reproduced in Fig. 3A. These LOD score plots indicate as many as four Chr 18 QTL at 23, 55, 77, and 85 Mb. Table 3 lists which crosses contain each QTL (if the LOD score plots are not available, the reported peak and confidence intervals were used to estimate the presence or absence of the QTL with surrounding regions denoted by “–?”). Further analysis presented here focuses on the Chr 18 QTL at 77 Mb, which is found in at least four crosses: B6 × C3H, B6 × D2, NZB × SM, and B6 × CAST. The allele causing high HDL in these crosses was C3H, D2, NZB, or CAST. From the LOD score curves, it can be seen that the B6 × 129 and the NZB × NZW crosses detect a QTL at 55 Mb but not at 77 Mb (Fig. 3A).

Fig. 3.
LOD score plots for the QTLs on distal chromosome 18. A: The LOD score plots are shown for B6 × C3H (dotted line), NZB × SM (thin dashed line), NZB × NZW (solid line), and B6 × 129 (thick dashed line). Raw data for B6 × ...
Crosses that detected a QTL on Chr 18

The B6 × D2 cross reported in this article did not detect the Chr 18 QTL, but another B6 × D2 cross in the literature carried out in female mice did detect a Chr 18 QTL (44). It is not clear why the B6 × D2 cross reported here failed to detect a Chr 18 QTL; however, the complementation test discussed below shows some consistency with this observation. The mode of inheritance differs for some of these crosses, but the mode of inheritance may be influenced or obscured by a closely linked QTL, especially if it is stronger, or by the genetic background. Thus, the different modes of inheritance should not hinder combined cross analysis (36, 45).

Combining crosses can be a powerful method for narrowing QTL. If the QTL in separate crosses are caused by the same gene, then combining crosses will increase the LOD score and narrow the confidence interval; if the hypothesis that the QTL are caused by the same gene is incorrect, the LOD score will not increase and the confidence interval remains the same or increases. Combining crosses requires access to the raw data, and for the Chr 18 QTL at 77 Mb, data are available only for the crosses B6 × C3H and NZB × SM. To combine the data, the genotype information was recoded from a strain-specific code to a phenotype-specific code; B6 and SM genotypes became L for the low HDL allele, while C3H and NZB genotypes became H for the high HDL allele. This analysis reduced the QTL to a 9 Mb region spanning from 72–81 Mb (Figs. 3B and and4A).4A). The QTL was further narrowed by reducing the region to that overlapping the human QTL at 18q12.1-22.2 (46), thus reducing the region to 6.9 Mb and 42 genes (Fig. 4A, step c).

Fig. 4.
HDL QTL on chromosome 18 narrowed to six genes by haplotype analysis. A: The 95% confidential intervals for each individual cross with a peak at 77 Mb (a) and the combined cross interval (b). Comparative genomics comparing human HDL QTL that overlap this ...

The QTL was further narrowed by haplotype analysis using the same two crosses as well as adding the B6 × D2 cross (44). The B6 × CAST cross could also be included; however, strains recently derived from the wild such as CAST may not share the identical by descent regions of the classic inbred strains so haplotyping is often uninformative, as in this case (data not shown). Haplotyping is based on the assumptions that the gene causing the QTL is the same in all three crosses and that the mutation is an ancestral mutation, which is reasonable since it was found in multiple crosses. Of the approximately 300,000 SNPs available for Chr 18, approximately 30,000 mapped within the QTL confidence interval. These SNPs were interrogated to identify regions that fit the following criteria: the strains that carry the allele that increased HDL had identical haplotypes (C3H, D2, and NZB), the strains that carry the allele that decreased HDL had identical haplotypes (B6 and SM), and these two haplotypes differed from each other. This haplotype analysis reduced the QTL region to six genes as potential candidates: Smad4, Mro, Ccdc11, Lipg, Dym, and Gm672 (Fig. 4).

Further analysis of the six genes by sequence and expression differences

It is expected that the QTL gene should have a difference in function caused by an amino acid change in the coding region or a noncoding sequence difference that alters expression levels, mRNA stability, splice sites, or other regulatory regions. Analysis of the coding SNPs in these six genes (Mouse Phenome Database; www.jax.org/phenome/SNP) revealed two genes with SNPs that caused nonsynonymous amino acid changes: Asp218Glu in maestro (Mro) and Tyr262Cys in endothelial lipase (Lipg). These two SNPs were resequenced in B6, C3H, and D2 to confirm the public data and also in NZB and SM, strains that had not previously been genotyped for these SNPs. However, sequencing showed that strains NZB and SM did not differ at the SNP in Mro, making Mro an unlikely candidate gene. Following resequencing of Lipg, the reported SNP in D2 causing the Tyr262Cys change, originally identified by Mural et al. (47), was shown to be an error; all five strains have Tyr-262. Sequencing Mro and Lipg exons for all five strains showed that except for the Asp218Glu Mro SNP (B6 = Asp; C3H, D2, NZB, and SM = Glu), there was no other coding variants among these strains for Mro and Lipg coding regions. Consequently, a coding region sequence variant causing a functional change is not likely to be the cause of this common QTL.

Since these six genes do not appear to have a coding region polymorphism that alters protein function, the QTL may be mediated by a sequence variant affecting differences in the mRNA. To further characterize the Chr 18 QTL, microarrays were used to examine gene expression of the six genes (Table 4). Parental expression of these genes from mouse livers from each parental strain-sex-diet group (n = 3) giving rise to the original QTL were examined. The three original Chr 18 QTL yield six strain-sex-diet comparisons: NZB × SM (males and females on chow and high-fat atherogenic diet), D2 × B6 (females on chow), and C3H × B6 (females on high-fat atherogenic diet). Of the six genes identified through haplotyping, only Lipg is consistently significantly differentially expressed (five of six possible comparisons, false discovery rate < 0.05). Two other probe sets have two of six possible comparisons that are significant (Table 4). Lipg expression was confirmed by real-time PCR in mouse livers (n = 5/strain-sex-diet group) among all the parental strain combinations that detected the Chr 18 QTL. In this case, Lipg expression was observed to be significantly different in all six comparisons with lower expression in the NZB, D2, and C3H strains carrying the allele that increased HDL compared with the B6 and SM strains carrying the allele that decreased HDL levels (Fig. 5). Thus, Lipg is the most likely candidate gene, and there must be a sequence polymorphism that affects its expression.

Fig. 5.
Lipg mRNA analysis in parental strains involved in the HDL QTL crosses. Real-time gene expression to confirm microarray observations; total RNA was extracted from mouse livers (n = 5 per sex-strain-diet group leading to a total of n = 30 for low-allele ...
Liver expression comparisons for probe sets representing genes narrowed by haplotype analysis

To find the polymorphism that regulates Lipg expression, transcription factor binding sites in the upstream region of Lipg were examined using public SNPs with TRANSFAC software (http://www.biobase.de/pages/index.php?id=271). Two SNPs between the low-allele strain B6 and high-allele strains C3H and D2 are inside or right next to putative transcription factor binding sites for c-ETS-1 and v-MYB (Table 5), and an additional 10 SNPs are near transcription factor binding sites. These SNPs were also sequenced for NZB and SM if the public databases were missing data for these strains. These SNPs are arranged in genomic order for all the strains that were parents of QTL crosses that detected a QTL on Chr 18, including the two crosses B6 × 129 and NZB × NZW that do not detect the QTL at 77Mb, but rather detecting an independent QTL at the 55 Mb locus (Table 5). Strains B6, SM, and 129 share the same haplotype block, while strains NZB, NZW, C3H, and D2 fall into a second haplotype block (gray region of Table 5). This shows that B6 × 129 and NZW × NZB crosses could not have detected the QTL caused by Lipg because the parental strains are identical to each other. CAST has its own unique haplotype typical of a wild-derived strain (data not shown).

Analysis of SNPs in or near transcription factor binding sites upstream of Lipg

Deficiency complementation of Lipg

Lipg has a known role in HDL metabolism, and its knockout and transgene alter HDL levels. However, as a further test of its role as the causal gene for this QTL, we carried out a deficiency complementation test (48, 49). In such a test, the B6.129-Lipg−/− strain is mated to both parents of a QTL cross. As a control, the B6 strain is mated to the same parents. The four different F1 populations are tested for HDL levels. The null hypothesis for a deficiency complementation test is that the difference (Δ2 in Fig. 6) between F1 mice that carry the Lipg wild-type gene is the same as the difference (Δ1 in Fig. 6) between F1 mice that are heterozygous for the Lipg knockout. If Δ1 is equal to Δ2, then reduced expression of Lipg in the Lipg−/+ F1 has no effect on HDL and there is no complementation between Lipg expression levels and the high or low HDL allele. However, if Δ1 differs significantly from Δ2, then the Lipg allele from the knockout complements the Lipg allele in one parental strain, indicating that the knockout gene and the QTL gene are the same. We crossed B6.129-Lipg−/− and strain B6 (B6.Lipg+/+) to each of the parents of the three crosses that detected the QTL, to strains B6 and C3H (Fig. 6A), to strains B6 and D2 (Fig. 6B), and to strains NZB and SM (Fig. 6C). As shown in Fig. 6, in each case, Δ2 was significantly larger than Δ1 (P < 0.001), indicating that Lipg did complement and is therefore the QTL gene. For the B6 × D2 complementation test (Fig. 6B), the change between Δ2 and Δ1 in females is considerably greater than it is between males, which may explain why this QTL was detected in a female cross and not in the male cross reported here.

Fig. 6.
Complementation test of parental strains with Lipg−/−. Data for HDL presented as means (mg/dl) ± SD. The number of F1 mice used for each group is n = 10−15. Open circles are the high allele strains (C3H, D2, and NZB); closed ...

The complementation test works best when both the QTL and the knockout are fully recessive (50). However, heterozygous Lipg−/+ mice are additive for the HDL phenotype (51). Although in the B6 × C3H QTL, the C3H allele is recessive, the QTL shows additive inheritance in the B6 × D2 and the NZB × SM cross. It is interesting that the complementation test worked just as well for these crosses, showing additive inheritance as for the cross showing the recessive inheritance. This indicates that while it is not optimal to have both the knockout gene and the QTL gene show additive inheritance, it does not rule out the possibility of observing complementation as we did for all three crosses.

It is also important to note that the Lipg−/− mice are not on a pure B6 background. This raises the possibility that passenger genes from the 129 strain could have caused the results we observe in the complementation test. However, this is unlikely because no QTL for HDL levels is present in the Lipg region, as demonstrated recently in a large (n = 528) F2 cross between B6 and 129/S (52) and by the haplotype pattern in Table 5 showing that B6 and 129 are identical in the Lipg region.


To identify QTL genes, traditional techniques have relied upon genetic methods such as further mapping through the creation of congenic strains (53, 54). However, with the availability of dense SNPs for most of the common inbred strains (37, 38), fine mapping QTL should be easier since many of the polymorphic regions in any cross will be known. Furthermore, the ability to combine crosses based on the assumption that the causal alleles are ancestral demonstrates a need for further QTL studies. Generating an F2 or backcross population may be more efficient than constructing a set of congenic strains and can help map multiple common QTL (55).

In this study, four additional mouse crosses are reported for HDL, bringing the total number of HDL QTL studies to 35. HDL is one of the most characterized quantitative phenotypes in mouse models, second only to obesity and body weight phenotypes (56). However, as most crosses were originally performed with varying purposes, focusing on specific diets or sexes or optimizing the study for atherosclerosis phenotypes (57), identifying causal genes through haplotyping and combined crosses remains a challenge. The four crosses described here identify HDL QTL, most of which have been replicated in previous studies, indicating that the QTL are controlled by ancestral mutations common among inbred strains.

The additional crosses allow for one more HDL QTL gene, Lipg, to be identified. For the Chr 18 QTL, combining crosses and haplotype analysis based primarily on the dense resequencing of B6, D2, and C3H (http://mouse.perlegen.com/mouse/) identified six likely candidates. Sequence analysis revealed that these genes did not contain coding sequence variants that could explain the QTL. However, an examination of expression analysis reveals that of the six genes narrowed by haplotype, only Lipg is consistently differentially expressed between relevant strains. Indeed, when examining all 165 probe sets in the 95% combined-cross confidence interval (data not shown), Lipg remains the most consistently differentially regulated gene with five of six possible significant differences by microarray analysis (Table 4). Furthermore, for Lipg, there is a consistent pattern whereby the strains with the high HDL allele expressed lower Lipg mRNA compared with the strains with the low HDL allele (Fig. 5). Finally, a deficiency complementation test implicates Lipg as the gene involved in three of the QTL on distal Chr 18. In this test, the interaction of Lipg genotype with either high- or low-allele strains was significant, demonstrating complementation of Lipg with the Chr 18 HDL QTL. Likely it is this consistent difference in expression levels, caused by a noncoding polymorphism that causes the distal Chr 18 HDL QTL. The relevant haplotype region contains at least two transcription factor binding sites with such a polymorphism.

The results presented here for Lipg are consistent with the gene function and the phenotypes observed both in Lipg knockout mice and transgenic mice. Lipg encodes endothelial lipase, and endothelial lipase hydrolyzes HDL phospholipids in vivo, thus generating smaller phospholipid-depleted HDL particles that are more rapidly catabolized by both the kidney and the liver (58). Lipg knockout mice have increased HDL levels along with increased plasma cholesterol, phospholipids, and associated apolipoproteins (51, 59). Overexpression of human Lipg in mice results in a significant decrease in HDL cholesterol and apolipoprotein A-I levels (59).

In humans, Lipg maps to 18q21.32 and is included in a human QTL for HDL (46) and apolipoprotein A-I levels (60). It has been shown that apolipoprotein A-I is involved in the pathway modulating endothelial lipase function and HDL regulation (61). In addition, associations between LIPG SNPs or haplotypes and HDL have been found in several human studies (51, 62, 63) with indications that SNPs in regulatory regions are important (64). Exactly which and how distant the regulatory regions operate to regulate Lipg remains an important question that further mouse studies could help address in the future.

The number of QTL crosses available for meta-analysis is a limiting factor; however, as more data sets become available, the ability to fine map the causal genes should also increase. This strategy relies heavily on the quality of databases, in particular the accuracy of SNPs. Currently, it is estimated that there is a ~57% false-negative rate of discovery in the mouse inbred strain data set (37). Even a single SNP between two strains is sufficient for a QTL, and this can be overlooked by such an approach, requiring a broader application of tools such as complementation testing to prove candidate genes. However, as SNP databases increase in both size and accuracy, the false-negative rate should decline, and the success rate of identifying QTL genes should improve.


The authors thank Dr. Keith DiPetrillo who performed the B6 × D2 cross, Peter Reifsnyder and Pam Stanley who performed the (NZO × NON) × NON cross, and Harry Whitmore and Susan Sheehan for excellent technical assistance.



quantitative trait locus
single nucleotide polymorphism
total cholesterol

This work was funded by US National Institutes of Health grants to B.P. (HL-81162, HL-74086, and HL-77796) and GM-070683 to G.A.C., by American Heart Association Grant 0725905T to Z.S., and by the Novartis Institutes for Biomedical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

[S]The online version of this article (available at http://www.jlr.org) contains supplementary data in the form of two figures and five tables.


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