Genetic resistance to diet-induced obesity in chromosome substitution strains of mice

Discovery of genes that confer resistance to diseases such as diet-induced obesity could have tremendous therapeutic impact. We previously demonstrated that the C57BL/6J-Chr^A/J/NaJ panel of chromosome substitution strains (CSSs) is a unique model for studying resistance to diet-induced obesity. In the present study, three replicate CSS surveys showed remarkable consistency, with 13 A/J-derived chromosomes reproducibly conferring resistance to high-fat-diet-induced obesity. Twenty CSS intercrosses, one derived from each of the 19 autosomes and chromosome X, were used to determine the number and location of quantitative trait loci (QTLs) on individual chromosomes and localized six QTLs. However, analyses of mean body weight in intercross progeny versus C57BL/6J provided strong evidence that many QTLs discovered in the CSS surveys eluded detection in these CSS intercrosses. Studies of the temporal effects of these QTLs suggest that obesity resistance was dynamic, with QTLs acting at different ages or after different durations of diet exposure. Thus, these studies provide insight into the genetic architecture of complex traits such as resistance to diet-induced obesity in the C57BL/6J-Chr^A/J/NaJ CSSs. Because some of the QTLs detected in the CSS intercrosses were not detected using a traditional C57BL/6J × A/J intercross, our results demonstrate that surveys of CSSs and congenic strains derived from them are useful complementary tools for analyzing complex traits.     

Genomic survey of prepulse inhibition in mouse chromosome substitution strains

Prepulse inhibition (PPI) is a measure of sensorimotor gating, a pre-attentional inhibitory brain mechanism that filters extraneous stimuli. Prepulse inhibition is correlated with measures of cognition and executive functioning, and is considered an endophenotype of schizophrenia and other psychiatric illnesses in which patients show PPI impairments. As a first step toward identifying genes that regulate PPI, we performed a quantitative trait locus (QTL) screen of PPI phenotypes in a panel of mouse chromosome substitution strains (CSSs). We identified five CSSs with altered PPI compared with the host C57BL/6J strain: CSS-4 exhibited decreased PPI, whereas CSS-10, -11, -16 and -Y exhibited higher PPI compared with C57BL/6J. These data indicate that A/J chromosomes 4, 10, 11, 16 and Y harbor at least one QTL region that modulates PPI in these CSSs. Quantitative trait loci for the acoustic startle response were identified on seven chromosomes. Like PPI, habituation of the startle response is also disrupted in schizophrenia, and in the present study CSS-7 and -8 exhibited deficits in startle habituation. Linkage analysis of an F₂ intercross identified a highly significant QTL for PPI on chromosome 11 between positions 101.5 and 114.4 Mb (peak LOD = 4.54). Future studies will map the specific genes contributing to these QTLs using congenic strains and other genomic approaches. Identification of genes that modulate PPI will provide insight into the neural mechanisms underlying sensorimotor gating, as well as the psychopathology of disorders characterized by gating deficits.     

A major QTL on chromosome 11 influences psychostimulant and opioid sensitivity in mice

The identification of genes influencing sensitivity to stimulants and opioids is important for determining their mechanism of action and may provide fundamental insights into the genetics of drug abuse. We used a panel of C57BL/6J (B6; recipient)× A/J (donor) chromosome substitution strains (CSSs) to identify quantitative trait loci (QTL) for both open field activity and sensitivity to the locomotor stimulant response to methamphetamine (MA). Mice were injected with saline (days 1 and 2) and MA (day 3; 2 mg/kg i.p.). We analyzed the total distance traveled in the open field for 30 min following each injection. CSS-8, -11 and -16 showed reduced MA-induced locomotor activity relative to B6, whereas CSS-10 and -12 showed increased MA-induced locomotor activity. Further analysis focused on CSS-11 because it was robustly different from B6 following MA injection, but did not differ in activity following saline injection and because it also showed reduced locomotor activity in response to the mu-opioid receptor agonist fentanyl (0.2 mg/kg i.p.). Thus, CSS-11 captures QTLs for the response to both psychostimulants and opioids. Using a B6 × CSS-11 F₂ intercross, we identified a dominant QTL for the MA response on chromosome 11. We used haplotype association mapping of cis expression QTLs and bioinformatic resources to parse among genes within the 95% confidence interval of the chromosome 11 QTL. Identification of the genes underlying QTLs for response to psychostimulants and opioids may provide insights about genetic factors that modulate sensitivity to drugs of abuse.     

Two quantitative trait loci for prepulse inhibition of startle identified on mouse chromosome 16 using chromosome substitution strains

Prepulse inhibition (PPI) of acoustic startle is a genetically complex quantitative phenotype of considerable medical interest due to its impairment in psychiatric disorders such as schizophrenia. To identify quantitative trait loci (QTL) involved in mouse PPI, we studied mouse chromosome substitution strains (CSS) that each carry a homologous chromosome pair from the A/J inbred strain on a host C57BL/6J inbred strain background. We determined that the chromosome 16 substitution strain has elevated PPI compared to C57BL/6J (P = 1.6 x 10(-11)), indicating that chromosome 16 carries one or more PPI genes. QTL mapping using 87 F(2) intercross progeny identified two significant chromosome 16 loci with LODs of 3.9 and 4.7 (significance threshold LOD is 2.3). The QTL were each highly significant independently and do not appear to interact. Sequence variation between B6 and A/J was used to identify strong candidate genes in the QTL regions, some of which have known neuronal functions. In conclusion, we used mouse CSS to rapidly and efficiently identify two significant QTL for PPI on mouse chromosome 16. The regions contain a limited number of strong biological candidate genes that are potential risk genes for psychiatric disorders in which patients have PPI impairments.     

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.     

Chromosome substitution strains: some quantitative considerations for genome scans and fine mapping

A chromosome substitution strain (CSS) is an inbred strain in which one chromosome has been substituted from a different inbred strain by repeated backcrossing. A complete CSS set has one strain representing each chromosome against a uniform background, thus allowing genome-wide scans to be carried out for quantitative trait loci (QTLs) influencing any trait of interest. A one-way ANOVA by strain is first carried out, followed by planned comparisons using Dunnetts method. A QTL is detected and mapped to a chromosome when a significant difference is observed in a background strain vs CSS comparison. The most efficient ratio of background to CSS mice in any one comparison is 4.5:1, and the threshold for p < .05 genome-wide significance is estimated to be p = .003 to .004, a much less stringent criterion than any other mammalian mapping population. The use of false discovery rates tends to further reduce threshold stringency. Comparisons are made to the widely used conventional F₂ intercross, and both advantages and disadvantages are noted. The proportion of the trait variance due to a QTL is often much larger than the same QTL in an F₂, and the number of generations to attain fine mapping is greatly reduced. To serve as guidelines for planning experiments, methods to estimate sample sizes for QTL detection are presented for the initial genome scan and for subsequent fine mapping.     

Quantitative trait loci analysis of egg and meat production traits in a red junglefowlxWhite Leghorn cross

Egg and production traits are of considerable economic importance in chickens. Using a White Leghorn x red junglefowl F(2) intercross, standard production measures of liver weight and colour, egg size, eggshell thickness, egg taste and meat quality were taken. A total of 160 markers covering 29 autosomes and the Z chromosome were genotyped on 175-243 individuals, depending on the trait under consideration. A total of nine significant quantitative trait loci (QTL) and three suggestive QTL were found on chicken chromosomes 1, 2, 4, 5, 7, 8, 10, 12, E47W24 and E22C19W28.     

A whole genome scanning for quantitative trait loci on traits related to sperm quality and ejaculation in pigs

To identify quantitative trait loci (QTL) for traits related to semen and ejaculation, phenotype data including semen volume, sperm concentration, total sperm per ejaculate, sperm motility, sperm abnormality rate, semen pH value, ejaculation times and ejaculation duration were measured on 206 F(2) boar at 240 days in a White Duroc x Erhualian intercross. A genome-wide scan was performed and the entire White Duroc x Erhualian intercross was genotyped for 183 microsatellite markers covering the whole pig genome. QTL analysis was performed using a composite regression interval mapping method via QTLExpress. A total of 18 QTL were detected, including 4 genome-wide significant QTL each for semen pH on pig chromosome (SSC) 2 and SSC12, for semen volume on SSC15, and for ejaculation times on SSC17. Fourteen suggestive QTL were found on SSC1, 2, 3, 4, 6, 9, 17 and 18. To our knowledge, this is the first report about the QTL for semen and ejaculation traits in pigs, providing a start point to decipher the genetic basis of these complex traits.     

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

To identify genes controlling plasma HDL and triglyceride levels, quantitative trait locus (QTL) analysis was performed in one backcross, (NZO/H1Lt × 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.     

Chromosome substitution strains: gene discovery, functional analysis, and systems studies

Laboratory mice are valuable in biomedical research in part because of the extraordinary diversity of genetic resources that are available for studies of complex genetic traits and as models for human biology and disease. Chromosome substitution strains (CSSs) are important in this resource portfolio because of their demonstrated use for gene discovery, genetic and epigenetic studies, functional characterizations, and systems analysis. CSSs are made by replacing a single chromosome in a host strain with the corresponding chromosome from a donor strain. A complete CSS panel involves a total of 22 engineered inbred strains, one for each of the 19 autosomes, one each for the X and Y chromosomes, and one for mitochondria. A genome survey simply involves comparing each phenotype for each of the CSSs with the phenotypes of the host strain. The CSS panels that are available for laboratory mice have been used to dissect a remarkable variety of phenotypes and to characterize an impressive array of disease models. These surveys have revealed considerable phenotypic diversity even among closely related progenitor strains, evidence for strong epistasis and for heritable epigenetic changes. Perhaps most importantly, and presumably because of their unique genetic constitution, CSSs, and congenic strains derived from them, the genetic variants underlying quantitative trait loci (QTLs) are readily identified and functionally characterized. Together these studies show that CSSs are important resource for laboratory mice.     

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.     

Genetic architecture of fear conditioning in chromosome substitution strains: relationship to measures of innate (unlearned) anxiety-like behavior

We measured fear conditioning (FC) in a panel of chromosome substitution strains (CSS) created using the C57BL/6J (B6) and A/J (AJ) inbred strains. Mice were trained to associate a specific context and tone with a foot shock. FC was measured by observing freezing behavior during re-exposure to the context and tone. Freezing to context was more than twofold greater in the AJ strain relative to the B6 strain. Among the CSS we identified four strains with higher (CSS-6, -10, -11, and -18) and two strains with lower (CSS-7 and -14) freezing to context. CSS-10 and -18 also showed higher freezing to tone, while CSS-12 showed less freezing to tone. CSS-1 has been implicated in open-field (OF) and light-dark box (LDB); we observed significant activity differences prior to training but no differences in FC. Chromosomes 6 and 10 have been associated with differences in anxiety-like behaviors, suggesting the existence of pleiotropic alleles that influence both learned and innate fear. By utilizing a genetic reference population, we have identified chromosomes that pleiotropically influence multiple phenotypes hypothesized to reflect a common ethologic construct that has been termed emotionality. The CSS provide a straightforward means of isolating the underlying genetic factors.     

Mapping the epistatic network underlying murine reproductive fatpad variation

Genome-wide mapping analyses are now commonplace in many species and several networks of interacting loci have been reported. However, relatively few details regarding epistatic interactions and their contribution to complex trait variation in multicellular organisms are available and the identification of positional candidate loci for epistatic QTL (epiQTL) is hampered, especially in mammals, by the limited genetic resolution inherent in most study designs. Here we further investigate the genetic architecture of reproductive fatpad weight in mice using the F(10) generation of the LG,SM advanced intercross (AI) line. We apply multiple mapping techniques including a single-locus model, locus-specific composite interval mapping (CIM), and tests for multiple QTL per chromosome to the 12 chromosomes known to harbor single-locus QTL (slQTL) affecting obesity in this cross. We also perform a genome-wide scan for pairwise epistasis. Using this combination of approaches we detect 199 peaks spread over all 19 autosomes, which potentially contribute to trait variation including all eight original F(2) loci (Adip1-8), novel slQTL peaks on chromosomes 7 and 9, and several novel epistatic loci. Extensive epistasis is confirmed involving both slQTL confidence intervals (C.I.) as well as regions that show no significant additive or dominance effects. These results provide important new insights into mapping complex genetic architectures and the role of epistasis in complex trait variation.     

Mapping quantitative trait loci for principal components of bone measurements and osteochondrosis scores in a wild boar x large white intercross

Data on osteochondrosis and femur dimensions from 195 F2 pigs from a wild boar x Large White intercross were analysed with the aim of detecting quantitative trait loci (QTLs) for normal and disturbed bone formation. The information from numerous recorded traits was summarized by principal component analysis and analysed by least-squares interval mapping. An increase in the proportion of wild boar alleles across the genome increased length versus width of femur and reduced the prevalence of osteochondrosis. The presence of QTLs with an impact on femur dimensions was indicated on chromosomes 2, 4, 16 and 17 and on osteochondrosis on chromosomes 5, 13 and 15. A substantial effect of the chromosome 5 QTL calls for further studies within commercial populations to evaluate whether marker-assisted selection could be used to reduce the prevalence of osteochondrosis.     

New quantitative trait loci for the genetic variance in circadian period of locomotor activity between inbred strains of mice

Provisional quantitative trait loci (QTL) for circadian locomotor period and wheel-running period have been identified in recombinant inbred (RI) mouse strains. To confirm those QTL and identify new ones, the genetic component of variance of the circadian period was partitioned among an F2 intercross of RI mouse strains (BXD19 and CXB07). First, a genomic survey using 108 SSLP markers with an average spacing of 15 cM was carried out in a population of 259 (BXD19 x CXB07)F2 animals. The genome-wide survey identified two significant QTL for period of locomotor activity measured by infrared photobeam crossings on mouse chromosomes 1 (lod score 5.66) and 14 (lod score 4.33). The QTL on distal chromosome 1 confirmed a previous report based on congenic B6.D2-Mtv7a/Ty mice. Lod scores greater than 2.0 were found on chromosomes 1, 2, 6, 12, 13, and 14. In a targeted extension study, additional genotyping was performed on these chromosomes in the full sample of 341 F2 progeny. The 6 chromosome-wide surveys identified 3 additional QTL on mouse chromosomes 6, 12, and 13. The QTL on chromosome 12 overlaps with circadian period QTL identified in several prior studies. For wheel-running period, the chromosome-wide surveys identified QTL on chromosomes 2 and 13 and one highly suggestive QTL on proximal chromosome 1. The results are compared to other published studies of QTL of circadian period.     

QTL mapping of grain length in rice (Oryza sativa L.) using chromosome segment substitution lines

Chromosome segment substitution (CSS) lines have the potential for use in QTL fine mapping and map-based cloning. The standard t-test used in the idealized case that each CSS line has a single segment from the donor parent is not suitable for non-idealized CSS lines carrying several substituted segments from the donor parent. In this study, we present a likelihood ratio test based on stepwise regression (RSTEP-LRT) that can be used for QTL mapping in a population consisting of non-idealized CSS lines. Stepwise regression is used to select the most important segments for the trait of interest, and the likelihood ratio test is used to calculate the LOD score of each chromosome segment. This method is statistically equivalent to the standard t-test with idealized CSS lines. To further improve the power of QTL mapping, a method is proposed to decrease multicollinearity among markers (or chromosome segments). QTL mapping with an example CSS population in rice consisting of 65 non-idealized CSS lines and 82 chromosome segments indicated that a total of 18 segments on eight of the 12 rice chromosomes harboured QTLs affecting grain length under the LOD threshold of 2.5. Three major stable QTLs were detected in all eight environments. Some minor QTLs were not detected in all environments, but they could increase or decrease the grain length constantly. These minor genes are also useful in marker-assisted gene pyramiding.     

Quantitative trait locus and haplotype mapping in closely related inbred strains identifies a locus for open field behavior

Quantitative trait locus (QTL) mapping in the mouse typically utilizes inbred strains that exhibit significant genetic and phenotypic diversity. The development of dense SNP panels in a large number of inbred strains has eliminated the need to maximize genetic diversity in QTL studies as plenty of SNP markers are now available for almost any combination of strains. We conducted a QTL mapping experiment using both a backcross (N₂) and an intercross (F₂) between two genetically similar inbred mouse strains: C57BL/6J (B6) and C57L/J (C57). A set of additive QTLs for activity behaviors was identified on Chrs 1, 9, 13, and 15. We also identified additive QTLs for anxiety-related behaviors on Chrs 7, 9, and 16. A QTL on Chr 11 is sex-specific, and we revealed pairwise interactions between QTLs on Chrs 1 and 13 and Chrs 10 and 18. The Chr 9 activity QTL accounts for the largest amount of phenotypic variance and was not present in our recent analysis of a B6 × C58/J (C58) intercross (Bailey et al. in Genes Brain Behav 7:761–769, 2008). To narrow this QTL interval, we used a dense SNP haplotype map with over 7 million real and imputed SNP markers across 74 inbred mouse strains (Szatkiewicz et al. in Mamm Genome 19(3):199–208, 2008). Evaluation of shared and divergent haplotype blocks among B6, C57, and C58 strains narrowed the Chr 9 QTL interval considerably and highlights the utility of QTL mapping in closely related inbred strains.     

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.     

Mapping Quantitative Trait Loci onto a Phylogenetic Tree

Despite advances in genetic mapping of quantitative traits and in phylogenetic comparative approaches, these two perspectives are rarely combined. The joint consideration of multiple crosses among related taxa (whether species or strains) not only allows more precise mapping of the genetic loci (called quantitative trait loci, QTL) that contribute to important quantitative traits, but also offers the opportunity to identify the origin of a QTL allele on the phylogenetic tree that relates the taxa. We describe a formal method for combining multiple crosses to infer the location of a QTL on a tree. We further discuss experimental design issues for such endeavors, such as how many crosses are required and which sets of crosses are best. Finally, we explore the method's performance in computer simulations, and we illustrate its use through application to a set of four mouse intercrosses among five inbred strains, with data on HDL cholesterol.