Quantitative trait loci that determine lipoprotein cholesterol levels in an intercross of 129S1/SvImJ and CAST/Ei inbred mice

Into the Wild: A novel wild-derived inbred strain resource expands the genomic and phenotypic diversity of laboratory mouse models

The laboratory mouse has served as the premier animal model system for both basic and preclinical investigations for over a century. However, laboratory mice capture only a subset of the genetic variation found in wild mouse populations, ultimately limiting the potential of classical inbred strains to uncover phenotype-associated variants and pathways. Wild mouse populations are reservoirs of genetic diversity that could facilitate the discovery of new functional and disease-associated alleles, but the scarcity of commercially available, well-characterized wild mouse strains limits their broader adoption in biomedical research. To overcome this barrier, we have recently developed, sequenced, and phenotyped a set of 11 inbred strains derived from wild-caught Mus musculus domesticus. Each of these "Nachman strains" immortalizes a unique wild haplotype sampled from one of five environmentally distinct locations across North and South America. Whole genome sequence analysis reveals that each strain carries between 4.73-6.54 million single nucleotide differences relative to the GRCm39 mouse reference, with 42.5% of variants in the Nachman strain genomes absent from current classical inbred mouse strain panels. We phenotyped the Nachman strains on a customized pipeline to assess the scope of disease-relevant neurobehavioral, biochemical, physiological, metabolic, and morphological trait variation. The Nachman strains exhibit significant inter-strain variation in >90% of 1119 surveyed traits and expand the range of phenotypic diversity captured in classical inbred strain panels. These novel wild-derived inbred mouse strain resources are set to empower new discoveries in both basic and preclinical research.

Into the Wild: A novel wild-derived inbred strain resource expands the genomic and phenotypic diversity of laboratory mouse models

The laboratory mouse has served as the premier animal model system for both basic and preclinical investigations for a century. However, laboratory mice capture a narrow subset of the genetic variation found in wild mouse populations. This consideration inherently restricts the scope of potential discovery in laboratory models and narrows the pool of potentially identified phenotype-associated variants and pathways. Wild mouse populations are reservoirs of predicted functional and disease-associated alleles, but the sparsity of commercially available, well-characterized wild mouse strains limits their broader adoption in biomedical research. To overcome this barrier, we have recently imported, sequenced, and phenotyped a set of 11 wild-derived inbred strains developed from wild-caught Mus musculus domesticus. Each of these "Nachman strains" immortalizes a unique wild haplotype sampled from five environmentally diverse locations across North and South America: Saratoga Springs, New York, USA; Gainesville, Florida, USA; Manaus, Brazil; Tucson, Arizona, USA; and Edmonton, Alberta, Canada. Whole genome sequence analysis reveals that each strain carries between 4.73-6.54 million single nucleotide differences relative to the mouse reference assembly, with 42.5% of variants in the Nachman strain genomes absent from classical inbred mouse strains. We phenotyped the Nachman strains on a customized pipeline to assess the scope of disease-relevant neurobehavioral, biochemical, physiological, metabolic, and morphological trait variation. The Nachman strains exhibit significant inter-strain variation in >90% of 1119 surveyed traits and expand the range of phenotypic diversity captured in classical inbred strain panels alone. Taken together, our work introduces a novel wild-derived inbred mouse strain resource that will enable new discoveries in basic and preclinical research. These strains are currently available through The Jackson Laboratory Repository under laboratory code NachJ.

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.

Genetic linkage of hyperglycemia and dyslipidemia in an intercross between BALB/cJ and SM/J Apoe-deficient mouse strains

BACKGROUND

Individuals with dyslipidemia often develop type 2 diabetes, and diabetic patients often have dyslipidemia. It remains to be determined whether there are genetic connections between the 2 disorders.

METHODS

A female F2 cohort, generated from BALB/cJ (BALB) and SM/J (SM) Apoe-deficient (Apoe(-/-)) strains, was started on a Western diet at 6 weeks of age and maintained on the diet for 12 weeks. Fasting plasma glucose and lipid levels were measured before and after 12 weeks of Western diet. 144 genetic markers across the entire genome were used for quantitative trait locus (QTL) analysis.

RESULTS

One significant QTL on chromosome 9, named Bglu17 [26.4 cM, logarithm of odds ratio (LOD): 5.4], and 3 suggestive QTLs were identified for fasting glucose levels. The suggestive QTL near the proximal end of chromosome 9 (2.4 cM, LOD: 3.12) was replicated at both time points and named Bglu16. Bglu17 coincided with a significant QTL for HDL (high-density lipoprotein) and a suggestive QTL for non-HDL cholesterol levels. Plasma glucose levels were inversely correlated with HDL but positively correlated with non-HDL cholesterol levels in F2 mice on either chow or Western diet. A significant correlation between fasting glucose and triglyceride levels was also observed on the Western diet. Haplotype analysis revealed that "lipid genes" Sik3, Apoa1, and Apoc3 were probable candidates for Bglu17.

CONCLUSIONS

We have identified multiple QTLs for fasting glucose and lipid levels. The colocalization of QTLs for both phenotypes and the sharing of potential candidate genes demonstrate genetic connections between dyslipidemia and type 2 diabetes.

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.

Obesity-insulin targeted genes in the 3p26-25 region in human studies and LG/J and SM/J mice

Identifying metabolic syndrome (MetS) genes is important for novel drug development and health care. This study extends the findings on human chromosome 3p26-25 for an identified obesity-insulin factor QTL, with an LOD score above 3. A focused association analysis comprising up to 9578 African American and Caucasian subjects from the HyperGEN Network (908 African Americans and 1025 whites), the Family Heart Study (3035 whites in time 1 and 1943 in time 2), and the Framingham Heart Study (1317 in Offspring and 1320 in Generation 3) was performed. The homologous mouse region was explored in an F(16) generation of an advanced intercross between the LG/J and SM/J inbred strains, in an experiment where 1002 animals were fed low-fat (247 males; 254 females) or high-fat (253 males; 248 females) diets. Association results in humans indicate pleiotropic effects for SNPs within or surrounding CNTN4 on obesity, lipids and blood pressure traits and for SNPs near IL5RA, TRNT1, CRBN, and LRRN1 on central obesity and blood pressure. Linkage analyses of this region in LG/J×SM/J mice identify a highly significant pleiotropic QTL peak for insulin and glucose levels, as well as response to glucose challenge. The mouse results show that insulin and glucose levels interact with high and low fat diets and differential gene expression was identified for Crbn and Arl8b. In humans, ARL8B resides ~137kbps away from BHLHE40, expression of which shows up-regulation in response to insulin treatment. This focused human genetic analysis, incorporating mouse research evidenced that 3p26-25 has important genetic contributions to MetS components. Several of the candidate genes have functions in the brain. Their interaction with MetS and the brain warrants further investigation.

A pronounced evolutionary shift of the pseudoautosomal region boundary in house mice

The pseudoautosomal region (PAR) is essential for the accurate pairing and segregation of the X and Y chromosomes during meiosis. Despite its functional significance, the PAR shows substantial evolutionary divergence in structure and sequence between mammalian species. An instructive example of PAR evolution is the house mouse Mus musculus domesticus (represented by the C57BL/6J strain), which has the smallest PAR among those that have been mapped. In C57BL/6J, the PAR boundary is located just ~700 kb from the distal end of the X chromosome, whereas the boundary is found at a more proximal position in Mus spretus, a species that diverged from house mice 2-4 million years ago. In this study we used a combination of genetic and physical mapping to document a pronounced shift in the PAR boundary in a second house mouse subspecies, Mus musculus castaneus (represented by the CAST/EiJ strain), ~430 kb proximal of the M. m. domesticus boundary. We demonstrate molecular evolutionary consequences of this shift, including a marked lineage-specific increase in sequence divergence within Mid1, a gene that resides entirely within the M. m. castaneus PAR but straddles the boundary in other subspecies. Our results extend observations of structural divergence in the PAR to closely related subspecies, pointing to major evolutionary changes in this functionally important genomic region over a short time period.

Genetics and evolution of hybrid male sterility in house mice

Comparative genetic mapping provides insights into the evolution of the reproductive barriers that separate closely related species. This approach has been used to document the accumulation of reproductive incompatibilities over time, but has only been applied to a few taxa. House mice offer a powerful system to reconstruct the evolution of reproductive isolation between multiple subspecies pairs. However, studies of the primary reproductive barrier in house mice-hybrid male sterility-have been restricted to a single subspecies pair: Mus musculus musculus and Mus musculus domesticus. To provide a more complete characterization of reproductive isolation in house mice, we conducted an F(2) intercross between wild-derived inbred strains from Mus musculus castaneus and M. m. domesticus. We identified autosomal and X-linked QTL associated with a range of hybrid male sterility phenotypes, including testis weight, sperm density, and sperm morphology. The pseudoautosomal region (PAR) was strongly associated with hybrid sterility phenotypes when heterozygous. We compared QTL found in this cross with QTL identified in a previous F(2) intercross between M. m. musculus and M. m. domesticus and found three shared autosomal QTL. Most QTL were not shared, demonstrating that the genetic basis of hybrid male sterility largely differs between these closely related subspecies pairs. These results lay the groundwork for identifying genes responsible for the early stages of speciation in house mice.

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.

The mouse QTL map helps interpret human genome-wide association studies for HDL cholesterol

Genome-wide association (GWA) studies represent a powerful strategy for identifying susceptibility genes for complex diseases in human populations but results must be confirmed and replicated. Because of the close homology between mouse and human genomes, the mouse can be used to add evidence to genes suggested by human studies. We used the mouse quantitative trait loci (QTL) map to interpret results from a GWA study for genes associated with plasma HDL cholesterol levels. We first positioned single nucleotide polymorphisms (SNPs) from a human GWA study on the genomic map for mouse HDL QTL. We then used mouse bioinformatics, sequencing, and expression studies to add evidence for one well-known HDL gene (Abca1) and three newly identified genes (Galnt2, Wwox, and Cdh13), thus supporting the results of the human study. For GWA peaks that occur in human haplotype blocks with multiple genes, we examined the homologous regions in the mouse to prioritize the genes using expression, sequencing, and bioinformatics from the mouse model, showing that some genes were unlikely candidates and adding evidence for candidate genes Mvk and Mmab in one haplotype block and Fads1 and Fads2 in the second haplotype block. Our study highlights the value of mouse genetics for evaluating genes found in human GWA studies.

A model selection approach for expression quantitative trait loci (eQTL) mapping

Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to affect a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high-throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed specifically for expression QTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection-based approach that identifies multiple eQTL in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely coregulated, as demonstrated in a study of diabetes in mouse.

Quantitative trait locus analysis of neointimal formation in an intercross between C57BL/6 and C3H/HeJ apolipoprotein E-deficient mice

BACKGROUND

Inbred mouse strains C57BL/6J (B6) and C3H/HeJ (C3H) exhibit marked differences in neointimal formation after arterial injury when deficient in apolipoprotein E (apoE(-/-)) and fed a Western diet. Quantitative trait locus (QTL) analysis was performed on an intercross between B6.apoE(-/-) and C3H.apoE(-/-) mice to determine genetic factors contributing to the phenotype.

METHODS AND RESULTS

Female B6.apoE(-/-) mice were crossed with male C3H.apoE(-/-) mice to generate F(1)s, which were intercrossed to generate 204 male F(2) progeny. At 10 weeks of age, F(2)s underwent endothelium denudation injury to the left common carotid artery. Mice were fed a Western diet for 1 week before and 4 weeks after injury and analyzed for neointimal lesion size, plasma lipid and MCP-1 levels. One significant QTL, named Nih1 (61cM, LOD score: 5.02), on chromosome 12 and a suggestive locus on chromosome 13 (35cM, LOD: 2.67) were identified to influence lesion size. One significant QTL on distal chromosome 1 accounted for major variations in plasma non-HDL cholesterol and triglyceride levels. Four suggestive QTLs on chromosomes 1, 2, and 3 were detected for circulating MCP-1 levels. No correlations were observed between neointimal lesion size and plasma lipid levels or between lesion size and plasma MCP-1 levels.

CONCLUSIONS

Neointimal formation is controlled by genetic factors independent of those affecting plasma lipid levels and circulating MCP-1 levels in the B6 and C3H mouse model.

The Hyplip2 locus causes hypertriglyceridemia by decreased clearance of triglycerides

The Hyplip2 congenic mouse strain contains part of chromosome 15 from MRL/MpJ on the BALB/cJ background. Hyplip2 mice show increased plasma levels of cholesterol and predominantly triglycerides (TGs) and are susceptible to diet-induced atherosclerosis. This study aimed at elucidation of the mechanism(s) explaining the hypertriglyceridemia. Hypertriglyceridemia can result from increased intestinal or hepatic TG production and/or by decreased LPL-mediated TG clearance. The intestinal TG absorption and chylomicron formation were studied after intravenous injection of Triton WR1339 and an intragastric load of olive oil containing glycerol tri[(3)H]oleate. No difference was found in intestinal TG absorption. Moreover, the hepatic VLDL-TG production rate and VLDL particle production, after injection of Triton WR1339, were also not affected. To investigate the LPL-mediated TG clearance, mice were injected intravenously with glycerol tri[(3)H]oleate-labeled VLDL-like emulsion particles. In Hyplip2 mice, the particles were cleared at a decreased rate (half-life of 25 +/- 6 vs. 11 +/- 2 min; P < 0.05) concomitant with a decreased uptake of emulsion TG-derived (3)H-labeled fatty acids by the liver and white adipose tissue. The increased plasma TG levels in Hyplip2 mice do not result from an enhanced intestinal absorption or increased hepatic VLDL production but are caused by decreased LPL-mediated TG clearance.

A genome-wide set of congenic mouse strains derived from CAST/Ei on a C57BL/6 background

We previously reported the construction of two sets of heterozygous congenic strains spanning the mouse genome. For both sets, C57BL/6J was employed as the background strain while DNA from either DBA/2 or CAST/Ei was introgressed to form the congenic region. We have subsequently bred most of these strains to produce homozygous breeding stocks. Here, we report the characterization of the strain set based on CAST/Ei. CAST/Ei is the most genetically distant strain within the Mus mus species and many trait variations relevant to common diseases have been identified in CAST/Ei mice. Despite breeding difficulties for some congenic regions, presumably due to incompatible allelic variations between CAST/Ei and C57BL/6, the resulting congenic strains cover about 80% of the autosomal chromosomes and will be useful as a resource for the further analysis of quantitative trait loci between the strains.

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

The NZB/B1NJ (NZB) mouse strain exhibits high cholesterol and HDL levels in blood compared with several other strains of mice. To study the genetic regulation of blood lipid levels, we performed a genome-wide linkage analysis in 542 chow-fed F2 female mice from an NZBxRF/J (RF) intercross and in a combined data set that included NZBxRF and MRL/MpJxSJL/J intercrosses. In the NZBxRF F2 mice, the cholesterol and HDL concentrations were influenced by quantitative trait loci (QTL) on chromosome (Chr) 5 [logarithm of odds (LOD) 17-19; D5Mit10] that was in the region identified earlier in crosses involving NZB mice, but two QTLs on Chr 12 (LOD 4.7; D12Mit182) and Chr 19 (LOD 5.7; D19Mit1) were specific to the NZBxRF intercross. Triglyceride levels were affected by two novel QTLs at D12Mit182 (LOD 8.7) and D15Mit13 (LOD 3.5). The combined-cross linkage analysis (1,054 mice, 231 markers) 1) identified four shared QTLs (Chrs 5, 7, 14, and 17) that were not detected in one of the parental crosses and 2) improved the resolution of two shared QTLs. In summary, we report additional loci regulating lipid levels in NZB mice that had not been identified earlier in crosses involving the NZB strain of mice. The identification of shared loci from multiple crosses increases confidence toward finding the QTL gene.

QTL mapping for genetic determinants of lipoprotein cholesterol levels in combined crosses of inbred mouse strains

To identify additional loci that influence lipoprotein cholesterol levels, we performed quantitative trait locus (QTL) mapping in offspring of PERA/EiJxI/LnJ and PERA/EiJxDBA/2J intercrosses and in a combined data set from both crosses after 8 weeks of consumption of a high fat-diet. Most QTLs identified were concordant with homologous chromosomal regions that were associated with lipoprotein levels in human studies. We detected significant new loci for HDL cholesterol levels on chromosome (Chr) 5 (Hdlq34) and for non-HDL cholesterol levels on Chrs 15 (Nhdlq9) and 16 (Nhdlq10). In addition, the analysis of combined data sets identified a QTL for HDL cholesterol on Chr 17 that was shared between both crosses; lower HDL cholesterol levels were conferred by strain PERA. This QTL colocalized with a shared QTL for cholesterol gallstone formation detected in the same crosses. Haplotype analysis narrowed this QTL, and sequencing of the candidate genes Abcg5 and Abcg8 confirmed shared alleles in strains I/LnJ and DBA/2J that differed from the alleles in strain PERA/EiJ. In conclusion, our analysis furthers the knowledge of genetic determinants of lipoprotein cholesterol levels in inbred mice and substantiates the hypothesis that polymorphisms of Abcg5/Abcg8 contribute to individual variation in both plasma HDL cholesterol levels and susceptibility to cholesterol gallstone formation.

Quantitative trait loci for baseline white blood cell count, platelet count, and mean platelet volume

A substantial genetic contribution to baseline peripheral blood counts has been established. We performed quantitative trait locus/loci (QTL) analyses to identify chromosome (Chr) regions harboring genes influencing the baseline white blood cell (WBC) count, platelet (Plt) count, and mean platelet volume (MPV) in F(2) intercrosses between NZW/LacJ, SM/J, and C57BLKS/J inbred mice. We identified six significant WBC QTL: Wbcq1 (peak LOD score at 38 cM, Chr 1), Wbcq2 (42 cM, Chr 3), Wbcq3 (0 cM, Chr 15), Wbcq4 (58 cM, Chr 1), Wbcq5 (82 cM, Chr 1), and Wbcq6 (8 cM, Chr 14). Three significant Plt QTL were identified: Pltq1 (24 cM, Chr 2), Pltq2 (36 cM, Chr 7), and Pltq3 (10 cM, Chr 12). Two significant MPV QTL were identified, Mpvq1 (62 cM, Chr 15) and Mpvq2 (44 cM, Chr 8). In total, the WBC QTL accounted for up to 31% of the total variance in baseline WBC count, while the Plt and MPV QTL accounted for up to 30% and 49% of the total variance, respectively. These analyses underscore the genetic complexity underlying these traits in normal populations and provide the basis for future studies to identify novel genes involved in the regulation of mammalian hematopoiesis.

Identification of quantitative trait loci that regulate obesity and serum lipid levels in MRL/MpJ x SJL/J inbred mice

The total body fat mass and serum concentration of total cholesterol, HDL cholesterol, and triglyceride (TG) differ between standard diet-fed female inbred mouse strains MRL/MpJ (MRL) and SJL/J (SJL) by 38-120% (P < 0.01). To investigate genetic regulation of obesity and serum lipid levels, we performed a genome-wide linkage analysis in 621 MRLx SJL F2 female mice. Fat mass was affected by two significant loci, D11Mit36 [43.7 cM, logarithm of the odds ratio (LOD) 11.2] and D16Mit51 (50.3 cM, LOD 3.9), and one suggestive locus at D7Mit44 (50 cM, LOD 2.4). TG levels were affected by two novel loci at D1Mit43 (76 cM, LOD 3.8) and D12Mit201 (26 cM, LOD 4.1), and two suggestive loci on chromosomes 5 and 17. HDL and cholesterol concentrations were influenced by significant loci on chromosomes 1, 3, 5, 7, and 17 that were in the regions identified earlier for other strains of mice, except for a suggestive locus on chromosome 14 that was specific to the MRL x SJL cross. In summary, linkage analysis in MRL x SJL F2 mice disclosed novel loci affecting TG, HDL, and fat mass, a measure of obesity. Knowledge of the genes in these quantitative trait loci will enhance our understanding of obesity and lipid metabolism.

Apolipoprotein gene polymorphisms as cause of cholesterol QTLs in mice

Quantitative trait locus (QTL) analyses of plasma cholesterol levels were carried out in three sets of F(2) mice that were formed in a 'round-robin' manner from C57BL/6J, KK (-A(y)), and RR strains. Six QTLs were identified on chromosomes 1 (Cq1, Cq2, and Cq6), 3 (Cq3), and 9 (Cq4 and Cq5); of these, Cq2 colocalized with Cq6, and Cq4 colocalized with Cq5. The major candidate gene for Cq2 and Cq6 is Apoa2, and that for Cq4 and Cq5 is Apoa4. The adequacy of polymorphisms in candidate genes as cause of QTLs was investigated in this study. For Apoa2, three different alleles (Apoa2(a), Apoa2(b), and Apoa2(c)) are known. Since there was no significant physiologic difference between Apoa2(a) and Apoa2(c) alleles, previous hypothesis that Apoa2(b) was different from Apoa2(a) and Apoa2(c) in the ability to increase cholesterol levels was further supported. Presumably, G-to-A substitution at nucleotide 84 and/or C-to-T substitution at nucleotide 182 are crucial to make the Apoa2(b) unique. On the other hand, for Apoa4, the most striking polymorphism was the number of Glu-Gln-Ala/Val-Gln repeats in carboxyl end; however, this might not be responsible for QTLs. Instead, a silent mutation, C-to-T substitution at nucleotide 771, was shown to be completely correlated with the occurrence of QTLs in a total of six F(2) intercrosses. Provisionally, but reasonably, these base substitutions are qualified as primary causes that constitute QTL effect. The potential strategy for identifying genes and base substitutions underlying QTLs is discussed.

Single and interacting QTLs for cholesterol gallstones revealed in an intercross between mouse strains NZB and SM

Quantitative trait locus (QTL) mapping was employed to investigate the genetic determinants of cholesterol gallstone formation in a large intercross between mouse strains SM/J (resistant) and NZB/B1NJ (susceptible). Animals consumed a gallstone-promoting diet for 18 weeks. QTL analyses were performed using gallstone weight and gallstone absence/presence as phenotypes; various models were explored for genome scans. We detected seven single QTLs: three new, significant QTLs were named Lith17 [chromosome (Chr) 5, peak=60 cM, LOD=5.4], Lith18 (Chr 5, 76 cM, LOD=4.3), and Lith19 (Chr 8, 0 cM, LOD=5.3); two confirmed QTLs identified previously and were named Lith20 (Chr 9, 44 cM, LOD=2.7) and Lith21 (Chr 10, 24 cM, LOD=2.9); one new, suggestive QTL (Chr 17) remains unnamed. Upon searching for epistatic interactions that contributed to gallstone susceptibility, the final suggestive QTL on Chr 7 was determined to interact significantly with Lith18 and, therefore, was named Lith22 (65 cM). A second interaction was identified between Lith19 and a locus on Chr 11; this QTL was named Lith23 (13 cM). mRNA expression analyses and amino acid haplotype analyses likely eliminated Slc10a2 as a candidate gene for Lith19. The QTLs identified herein largely contributed to gallstone formation rather than gallstone severity. Cloning the genes underlying these murine QTLs should facilitate prediction and cloning of the orthologous human genes.

Association of a lithogenic Abcg5/Abcg8 allele on Chromosome 17 (Lith9) with cholesterol gallstone formation in PERA/EiJ mice

To examine further the genetic determinants of cholesterol gallstone susceptibility in inbred mice, we performed quantitative trait locus (QTL) analysis of an intercross of gallstone-susceptible PERA/EiJ and gallstone-resistant DBA/2J inbred mice. Three hundred twenty-four F2 offspring were phenotyped for cholelithiasis during consumption of a lithogenic diet and genotyped using microsatellite markers. Linkage analysis was performed by interval mapping. In addition, we analyzed the combined datasets from this cross and from an independent cross of strain PERA and gallstone-resistant I/Ln mice. QTL mapping detected one significant new gallstone susceptibility (Lith) locus on Chromosome 13 (Lith15). A second significant QTL on Chr 6 (Lith16) confirmed a previous QTL. Furthermore, suggestive QTLs confirmed Lith loci from previous crosses on Chromosomes 1, 2, 5, 16 and X. QTL analysis of the dataset derived from the combined crosses increased the detection power and narrowed confidence intervals of Lith loci on Chromosomes 2, 6, 13, and 16. Moreover, the analysis of combined datasets revealed a shared QTL between both crosses on Chromosome 17 (Lith9). Significantly higher mRNA expression of Abcg5 and Abcg8 in strain PERA compared with strains I/Ln and DBA/2 further substantiated that the PERA allele of Abcg5/Abcg8 was responsible for lithogenicity underlying Lith9.

Genome-wide search for new genes controlling plasma lipid concentrations in mice and humans

PURPOSE OF REVIEW

Quantitative trait locus analysis has been used in both humans and mice for the purpose of finding new genes regulating plasma lipid levels. We review these methods and discuss new approaches that can help find quantitative trait locus genes.

RECENT FINDINGS

Many quantitative trait loci have been found that regulate plasma levels for HDL cholesterol (37 in mice and 30 in humans), LDL cholesterol (25 in mice and 20 in humans) and triglycerides (19 in mice and 30 in humans). Most of the human quantitative trait loci have concordant mouse quantitative trait loci mapping to homologous regions (93% for HDL cholesterol, 100% for LDL cholesterol and 80% for triglycerides), suggesting that many genes identified in mice may also regulate the same traits in humans. New approaches based on recently developed genomic and bioinformatic technologies and resources should greatly facilitate finding these genes.

SUMMARY

New genes regulating plasma lipid levels can be found in mice and then tested in humans. Some of these genes could be potential therapeutic targets for human atherosclerosis.

Genetics of Variation in HDL Cholesterol in Humans and Mice

Plasma high-density lipoprotein cholesterol (HDL-C) concentrations are genetically determined to a great extent, and quantitative trait locus (QTL) analysis has been used to identify chromosomal regions containing genes regulating HDL-C levels. We discuss new genes found to participate in HDL metabolism. We also summarize 37 mouse and 30 human QTLs for plasma HDL-C levels, finding that all but three of the mouse QTLs have been confirmed by a second cross or a homologous human QTL, that the mouse QTL map is almost saturated because 92% of recently reported QTLs are repeats of those already found, and that 28 of the 30 human QTLs are located in regions homologous to mouse QTLs. This high degree of concordance between mouse and human QTLs suggests that the underlying genes may be the same. Strategies to more rapidly identify genes underlying mouse and human QTLs for HDL-C include focusing on the mouse and using mouse–human homologies, combining crosses, and haplotyping to narrow the region. Sequence analysis and expression studies can distinguish candidate genes consistent across multiple mouse crosses, and testing the candidate genes in human association studies can provide additional evidence for the candidacy of a gene. Together these strategies can accelerate the pace of finding genes that regulate HDL.

Haplotype analysis in multiple crosses to identify a QTL gene

Identifying quantitative trait locus (QTL) genes is a challenging task. Herein, we report using a two-step process to identify Apoa2 as the gene underlying Hdlq5, a QTL for plasma high-density lipoprotein cholesterol (HDL) levels on mouse chromosome 1. First, we performed a sequence analysis of the Apoa2 coding region in 46 genetically diverse mouse strains and found five different APOA2 protein variants, which we named APOA2a to APOA2e. Second, we conducted a haplotype analysis of the strains in 21 crosses that have so far detected HDL QTLs; we found that Hdlq5 was detected only in the nine crosses where one parent had the APOA2b protein variant characterized by an Ala61-to-Val61 substitution. We then found that strains with the APOA2b variant had significantly higher (P < or = 0.002) plasma HDL levels than those with either the APOA2a or the APOA2c variant. These findings support Apoa2 as the underlying Hdlq5 gene and suggest the Apoa2 polymorphisms responsible for the Hdlq5 phenotype. Therefore, haplotype analysis in multiple crosses can be used to support a candidate QTL gene.

Quantitative trait loci that determine plasma lipids and obesity in C57BL/6J and 129S1/SvImJ inbred mice

The plasma lipid concentrations and obesity of C57BL/6J (B6) and 129S1/SvImJ (129) inbred mouse strains fed a high-fat diet containing 15% dairy fat, 1% cholesterol, and 0.5% cholic acid differ markedly. To identify the loci controlling these traits, we conducted a quantitative trait loci (QTL) analysis of 294 (B6 x 129) F(2) females fed a high-fat diet for 14 weeks. Non-HDL cholesterol concentrations were affected by five significant loci: Nhdlq1 [chromosome 8, peak centimorgan (cM) 38, logarithm of odds [LOD] 4.4); Nhdlq4 (chromosome 10, cM 70, LOD 4.0); Nhdlq5 (chromosome 6, cM 0) interacting with Nhdlq4; Nhdlq6 (chromosome 7, cM 10) interacting with Nhdlq1; and Nhdlq7 (chromosome 15, cM 0) interacting with Nhdlq4. Triglyceride (TG) concentrations were affected by three significant loci: Tgq1 (chromosome 18, cM 42, LOD 3.2) and Tgq2 (chromosome 9, cM 66) interacting with Tgq3 (chromosome 4, cM 58). Obesity measured by percentage of body fat mass and body mass index was affected by two significant loci: Obq16 (chromosome 8, cM 48, LOD 10.0) interacting with Obq18 (chromosome 9, cM 65). Knowing the genes for these QTL will enhance our understanding of obesity and lipid metabolism.

Genetic contributors to lipoprotein cholesterol levels in an intercross of 129S1/SvImJ and RIIIS/J inbred mice

To determine the genetic contribution to variation among lipoprotein cholesterol levels, we performed quantitative trait locus (QTL) analyses on an intercross between mouse strains RIIIS/J and 129S1/SvImJ. Male mice of the parental strains and the reciprocal F1and F2populations were fed a high-cholesterol, cholic acid-containing diet for 8–12 wk. At the end of the feeding period, plasma total, high-density lipoprotein (HDL), and non-HDL cholesterol were determined. For HDL cholesterol, we identified three significant QTLs on chromosomes (Chrs) 1 ( D1Mit507, 88 cM, 72–105 cM, 4.8 LOD), 9 ( D11Mit149, 14 cM, 10–25 cM, 9.4 LOD), and 12 ( D12Mit60, 20 cM, 0–50 cM, 5.0 LOD). These QTLs were considered identical to QTLs previously named Hdlq5, Hdlq17, and Hdlq18, respectively, in crosses sharing strain 129. For total cholesterol, we identified two significant QTLs on Chrs 1 and 9, which were named Chol10 ( D1Mit507, 88 cM, 10–105 cM, 3.9 LOD) and Chol11 ( D11Mit149, 14 cM, 0–30 cM, 4.4 LOD), respectively. In addition, for total cholesterol, we identified two suggestive QTLs on Chrs 12 (distal) and 17, which remain unnamed. For non-HDL cholesterol, we identified and named one new QTL on Chr 17, Nhdlq3 ( D17Mit221, 58 cM, 45–60 cM, 3.4 LOD). Nhdlq3 colocalized with orthologous human QTLs for lipoprotein phenotypes, and with Abcg5 and Abcg8. Overall, we detected eight QTLs for lipoprotein cholesterol concentrations on Chrs 1, 9, 12, and 17 (each two per chromosome), including a new QTL for non-HDL cholesterol, Nhdlq3, on Chr 17.