Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels

The use of inbred strains of mice to dissect the genetic complexity of common diseases offers a viable alternative to human studies, given the control over experimental parameters that can be exercised. Central to efforts to map susceptibility loci for common diseases in mice is a comprehensive map of DNA variation among the common inbred strains of mice. Here we present one of the most comprehensive high-density, single nucleotide polymorphism (SNP) maps of mice constructed to date. This map consists of 10,350 SNPs genotyped in 62 strains of inbred mice. We demonstrate the utility of these data via a novel integrative genomics approach to mapping susceptibility loci for complex traits. By integrating in silico quantitative trait locus (QTL) mapping with progressive QTL mapping strategies in segregating mouse populations that leverage large-scale mapping of the genetic determinants of gene expression traits, we not only facilitate identification of candidate quantitative trait genes, but also protect against spurious associations that can arise in genetic association studies due to allelic association among unlinked markers. Application of this approach to our high-density SNP map and two previously described F2 crosses between strains C57BL/6J (B6) and DBA/2J and between B6 ApoE(-/-) and C3H/HeJ ApoE(-/-) results in the identification of Insig2 as a strong candidate susceptibility gene for total plasma cholesterol levels.     

Quantitative trait loci affecting amylose,amylopectin and starch content in maize recombinant inbred lines

The loci explaining the variability of quantitative traits related to starch content and composition (amylose, amylopectin and water soluble fraction) were searched for in maize kernels. Multifactorial genetic methods were used to detect and locate QTLs (quantitative trait loci) on a genetic map consisting mainly of RFLP markers for genes with known function. The genetic material was recombinant inbred lines originating from parents differing in starch structure (dent vs. flint). Kernels were harvested from field grown plants for two successive years and under two pollination systems. Main effect and epistasis QTLs were detected using two methods, composite interval mapping (MQTL) and ANOVA. Despite large year-to-year differences, physiologically meaningful co-locations were observed between trait QTLs. Moreover, the number of expressed sequences on our map allowed the search for co-locations between QTLs and genes involved in carbohydrate metabolism. The main co-location was between an amylose QTL and Shrunken 2 (SH2) locus, on chromosome 3 (SH2 encoding for the large subunit of ADPglucose pyrophosphorylase). The importance of this locus as a candidate gene for a starch QTL is in agreement with previous studies based either on QTL co-locations or on revertant analysis. Other co-locations were observed between amylose and amylopectin QTLs and the two loci of IVR1 invertase genes on chromosomes 2 and 10. Further comparison with previously detected QTLs for carbohydrate metabolism in maize leaves showed consistent co-location in map regions devoid of candidate genes, such as near chromosome 1S telomere. The possible contribution of regulatory genes in this region is discussed.     

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.     

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.     

QTL mapping in a mouse model of cardiomyopathy reveals an ancestral modifier allele affecting heart function and survival

The progression from myocardial hypertrophy to heart failure is a complex process, involving genetic and environmental factors. Elucidating the genetic components contributing to heart failure has been difficult, largely because of the heterogeneity of human populations. We have employed a strategy to map genetic loci that modify the heart failure phenotype in a transgenic mouse model of cardiomyopathy caused by cardiac-specific overexpression of calsequestrin. Strain-specific differences in both cardiac function and survival are observed when the transgene is moved into different inbred mouse strains. We have previously reported linkage results from mapping in reciprocal backcrosses between C57/BL6 (BL6) and DBA/2J (DBA) and a backcross between DBA/AKR and AKR. Here we report the results of a genome-wide linkage scan in the reciprocal backcross between DBA/AKR and DBA. We identified one novel locus on Chromosome (Chr) 18 that affects heart function and a second on Chr 3 that shows significant linkage to both survival and heart function. Intriguingly, the Chr 3 allele of AKR shows a susceptibility effect on phenotype, whereas the overall effect of the AKR genetic background is protective. The Chr 3 locus also completely overlaps the Hrtfm2 locus, which was previously mapped in crosses between DBA and BL6. Mapping the same QTL in two different crosses allowed us to use ancestral haplotypes to narrow the candidate gene interval from 9 to 2 Mb. Identification of the genes at these QTLs in the mouse will provide novel candidate genes that can be evaluated for their role in human heart failure.     

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.     

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.     

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

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

Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat

Plant breeding data comprise unbalanced phenotypic data for inbreds with complex pedigrees. As traditional methods to map quantitative trait loci (QTL) cannot exploit plant breeding data, an alternative approach is QTL mapping via a mixed-model procedure. Our objective was to validate mixed-model QTL mapping for self-pollinated crops by detecting QTL for kernel hardness and dough strength from data in a bread wheat (Triticum aestivum L.) breeding program. We studied 80 parental and 373 experimental inbreds genotyped for 65 simple sequence repeat (SSR) markers and three candidate loci. The methodology involved three steps: variance component estimation, single-marker analyses, and a final multiple-marker analysis with marker effects treated as fixed effects. Two QTLs for kernel hardness were detected on chromosomes 1A (close to candidate locus GluA3) and 5D (close to candidate locus Ha). Four QTLs were detected for dough strength on chromosomes 1A, 1B, 1D, and 5B. Candidate gene GluA1, which was associated with dough strength, was the only candidate locus found significant. Results were consistent with previously reported markers and QTLs associated with kernel hardness and dough strength. Unlike previous studies that have assumed QTL effects as random, the assumption of fixed marker effects identified the favorable marker alleles to select for. We conclude that the detection of previously mapped QTL validates the usefulness of mixed-model QTL mapping in the context of a plant-breeding program.     

High-density genetic map construction and identification of a locus controlling weeping trait in an ornamental woody plant (Prunus mume Sieb. et Zucc)

High-density genetic map is a valuable tool for fine mapping locus controlling a specific trait especially for perennial woody plants. In this study, we firstly constructed a high-density genetic map of mei (Prunus mume) using SLAF markers, developed by specific locus amplified fragment sequencing (SLAF-seq). The linkage map contains 8,007 markers, with a mean marker distance of 0.195 cM, making it the densest genetic map for the genus Prunus. Though weeping trees are used worldwide as landscape plants, little is known about weeping controlling gene(s) (Pl). To test the utility of the high-density genetic map, we did fine-scale mapping of this important ornamental trait. In total, three statistic methods were performed progressively based on the result of inheritance analysis. Quantitative trait loci (QTL) analysis initially revealed that a locus on linkage group 7 was strongly responsible for weeping trait. Mutmap-like strategy and extreme linkage analysis were then applied to fine map this locus within 1.14 cM. Bioinformatics analysis of the locus identified some candidate genes. The successful localization of weeping trait strongly indicates that the high-density map constructed using SLAF markers is a worthy reference for mapping important traits for woody plants.     

Genetic control of X chromosome inactivation in mice: definition of the Xce candidate interval

In early mammalian development, one of the two X chromosomes is silenced in each female cell as a result of X chromosome inactivation, the mammalian dosage compensation mechanism. In the mouse epiblast, the choice of which chromosome is inactivated is essentially random, but can be biased by alleles at the X-linked X controlling element (Xce). Although this locus was first described nearly four decades ago, the identity and precise genomic localization of Xce remains elusive. Within the X inactivation center region of the X chromosome, previous linkage disequilibrium studies comparing strains of known Xce genotypes have suggested that Xce is physically distinct from Xist, although this has not yet been established by genetic mapping or progeny testing. In this report, we used quantitative trait locus (QTL) mapping strategies to define the minimal Xce candidate interval. Subsequent analysis of recombinant chromosomes allowed for the establishment of a maximum 1.85-Mb candidate region for the Xce locus. Finally, we use QTL approaches in an effort to identify additional modifiers of the X chromosome choice, as we have previously demonstrated that choice in Xce heterozygous females is significantly influenced by genetic variation present on autosomes (Chadwick and Willard 2005). We did not identify any autosomal loci with significant associations and thus show conclusively that Xce is the only major locus to influence X inactivation patterns in the crosses analyzed. This study provides a foundation for future analyses into the genetic control of X chromosome inactivation and defines a 1.85-Mb interval encompassing all the major elements of the Xce locus.     

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.     

Candidate genes for plasma triglyceride, FFA, and glucose revealed from an intercross between inbred mouse strains NZB/B1NJ and NZW/LacJ

To identify the genes controlling plasma concentrations of triglycerides (TGs), FFAs, and glucose, we carried out a quantitative trait loci (QTL) analysis of the closely related mouse strains New Zealand Black (NZB/B1NJ) and New Zealand White (NZW/LacJ), which share 63% of their genomes. The NZB x NZW F(2) progeny were genotyped and phenotyped to detect QTL, and then comparative genomics, bioinformatics, and sequencing were used to narrow the QTL and reduce the number of candidate genes. Triglyceride concentrations were linked to loci on chromosomes (Chr) 4, 7, 8, 10, and 18. FFA concentrations were affected by a significant locus on Chr 4, a suggestive locus on Chr 16, and two interacting loci on Chr 2 and 15. Plasma glucose concentrations were affected by QTL on Chr 2, 4, 7, 8, 10, 15, 17, and 18. Comparative genomics narrowed the QTL by 31% to 86%; haplotype analysis was usually able to further narrow it by 80%. We suggest several candidate genes: Gba2 on Chr 4, Irs2 on Chr 8, and Ppargc1b on Chr 18 for TG; A2bp1 on Chr 16 for FFA; and G6pc2 on Chr 2 and Timp3 on Chr 10 for glucose.     

A novel strategy for genetic dissection of complex traits: the population of specific chromosome substitution strains from laboratory and wild mice

The mouse is an irreplaceable model for understanding the precise genetic mechanisms of mammalian physiological pathways. Thousands of quantitative trait loci (QTLs) have been mapped onto the mouse genome during the last two decades. However, only a few genes’ underlying complex traits have been successfully identified, and rapid fine mapping of QTL genes still remains a challenge for mouse geneticists. Currently, the Collaborative Cross (CC) has proceeded to the goal of establishing more than 1,000 recombinant inbred strains for the sub-centimorgan mapping resolution of QTL loci. In this article, a novel complementary strategy, designated as population of specific chromosome substitution strains or PSCSS, is proposed for rapid fine mapping of QTLs on the substituted chromosome. One specific chromosome (Chr 1) of recipient mouse strain C57BL/6 J has been substituted by homologous counterparts from five different inbred strains (C3H/He, FVB/N, AKR, NOD/LtJ, NZW/LacJ), an outbred line Kunmin mouse in China, and 23 wild mice captured in different localities. The primary genetic studies on the structure of these wild donor chromosomes (Chr 1) show that these donor chromosomes harbor extensive genetic polymorphisms, with a high density of SNP distribution, abundant variations of STR alleles, and a high level of historical recombination accumulation. These specific chromosome substitution strains eventually form a special population that has the identical genetic background of the recipient strain and differs only in the donor chromosomes. With simple association studies, known QTLs on the donor chromosome can be rapidly mapped in high resolution without requirement of further crosses. This approach, taking advantage of the extensive genetic polymorphisms of wild resources and chromosome substitution strategy, brings a new outlook for genetic dissection of complex traits.     

Identification of genetic determinants of IGF‐1 levels and longevity among mouse inbred strains

The IGF‐1 signaling pathway plays an important role in regulating longevity. To identify the genetic loci and genes that regulate plasma IGF‐1 levels, we intercrossed MRL/MpJ and SM/J, inbred mouse strains that differ in IGF‐1 levels. Quantitative trait loci (QTL) analysis of IGF‐1 levels of these F2 mice detected four QTL on chromosomes (Chrs) 9 (48 Mb), 10 (86 Mb), 15 (18 Mb), and 17 (85 Mb). Haplotype association mapping of IGF‐1 levels in 28 domesticated inbred strains identified three suggestive loci in females on Chrs 2 (13 Mb), 10 (88 Mb), and 17 (28 Mb) and in four males on Chrs 1 (159 Mb), 3 (52 and 58 Mb), and 16 (74 Mb). Except for the QTL on Chr 9 and 16, all loci co‐localized with IGF‐1 QTL previously identified in other mouse crosses. The most significant locus was the QTL on Chr 10, which contains the Igf1 gene and which had a LOD score of 31.8. Haplotype analysis among 28 domesticated inbred strains revealed a major QTL on Chr 10 overlapping with the QTL identified in the F2 mice. This locus showed three major haplotypes; strains with haplotype 1 had significantly lower plasma IGF‐1 and extended longevity (P < 0.05) than strains with haplotype 2 or 3. Bioinformatic analysis, combined with sequencing and expression studies, showed that Igf1 is the most likely QTL gene, but that other genes may also play a role in this strong QTL.     

Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross

Most common diseases are attributed to multiple genetic variants, and the feasibility of identifying inherited risk factors is often restricted to the identification of alleles with high or intermediate effect sizes. In our previous studies, we identified single loci associated with hepatic fibrosis (Hfib1–Hfib4). Recent advances in analysis tools allowed us to model loci interactions for liver fibrosis. We analysed 322 F2 progeny from an intercross of the fibrosis-susceptible strain BALB/cJ and the resistant strain FVB/NJ. The mice were challenged with carbon tetrachloride (CCl₄) for 6 weeks to induce chronic hepatic injury and fibrosis. Fibrosis progression was quantified by determining histological fibrosis stages and hepatic collagen contents. Phenotypic data were correlated to genome-wide markers to identify quantitative trait loci (QTL). Thirteen susceptibility loci were identified by single and composite interval mapping, and were included in the subsequent multiple QTL model (MQM) testing. Models provided evidence for susceptibility loci with strongest association to collagen contents (chromosomes 1, 2, 8 and 13) or fibrosis stages (chromosomes 1, 2, 12 and 14). These loci contained the known fibrosis risk genes Hc, Fasl and Foxa2 and were incorporated in a fibrosis network. Interestingly the hepatic fibrosis locus on chromosome 1 (Hfib5) connects both phenotype networks, strengthening its role as a potential modifier locus. Including multiple QTL mapping to association studies adds valuable information on gene–gene interactions in experimental crosses and human cohorts. This study presents an initial step towards a refined understanding of profibrogenic gene networks.     

A composite method for mapping quantitative trait loci without interference of female achiasmatic and gender effects in silkworm,Bombyx mori

The silkworm, Bombyx mori, is an economically important insect that was domesticated more than 5000 years ago. Its major economic traits focused on by breeders are quantitative traits, and an accurate and efficient QTL mapping method is necessary to explore their genetic architecture. However, current widely used QTL mapping models are not well suited for silkworm because they ignore female achiasmate and gender effects. In this study, we propose a composite method combining rational population selection and special mapping methods to map QTL in silkworm. By determining QTL for cocoon shell weight (CSW), we demonstrated the effectiveness of this method. In the CSW mapping process, only 56 markers were used and five loci or chromosomes were detected, more than in previous studies. Additionally, loci on chromosomes 1 and 11 dominated and accounted for 35.10% and 15.03% of the phenotypic variance respectively. Unlike previous studies, epistasis was detected between loci on chromosomes 11 and 22. These mapping results demonstrate the power and convenience of this method for QTL mapping in silkworm, and this method may inspire the development of similar approaches for other species with special genetic characteristics.     

Mapping of 19032 mouse cDNAs on mouse chromosomes

Finding genes by the positional candidate approach requires abundant cDNAs mapped to chromosomes. To provide such important information, we computationally mapped 19032 of our mouse cDNAs to mouse chromosomes by using data from public databases. We used 2 approaches. In the first, we integrated the mapping data of cDNAs on the human genome, known gene-related data, and comparative mapping data. From this, we calculated map positions on the mouse chromosomes. For this first approach, we developed a simple and powerful criterion to choose the correct map position from candidate positions in sequence homology searches. In the second approach, we related cDNAs to expressed sequence tags (EST) previously mapped in radiation hybrid experiments. We discuss improving the mapping by combining the 2 methods.