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.     

High‐precision genetic mapping of behavioral traits in the diversity outbred mouse population

Historically our ability to identify genetic variants underlying complex behavioral traits in mice has been limited by low mapping resolution of conventional mouse crosses. The newly developed Diversity Outbred (DO) population promises to deliver improved resolution that will circumvent costly fine‐mapping studies. The DO is derived from the same founder strains as the Collaborative Cross (CC), including three wild‐derived strains. Thus the DO provides more allelic diversity and greater potential for discovery compared to crosses involving standard mouse strains. We have characterized 283 male and female DO mice using open‐field, light–dark box, tail‐suspension and visual‐cliff avoidance tests to generate 38 behavioral measures. We identified several quantitative trait loci (QTL) for these traits with support intervals ranging from 1 to 3 Mb in size. These intervals contain relatively few genes (ranging from 5 to 96). For a majority of QTL, using the founder allelic effects together with whole genome sequence data, we could further narrow the positional candidates. Several QTL replicate previously published loci. Novel loci were also identified for anxiety‐ and activity‐related traits. Half of the QTLs are associated with wild‐derived alleles, confirming the value to behavioral genetics of added genetic diversity in the DO. In the presence of wild‐alleles we sometimes observe behaviors that are qualitatively different from the expected response. Our results demonstrate that high‐precision mapping of behavioral traits can be achieved with moderate numbers of DO animals, representing a significant advance in our ability to leverage the mouse as a tool for behavioral genetics .     

A large QTL for fear and anxiety mapped using an F2 cross can be dissected into multiple smaller QTLs

Using chromosome substitution strains (CSS), we previously identified a large quantitative trait locus (QTL) for conditioned fear (CF) on mouse chromosome 10. Here, we used an F₂ cross between CSS‐10 and C57BL/6J (B6) to localize that QTL to distal chromosome 10. That QTL accounted for all the difference between CSS‐10 and B6. We then produced congenic strains to fine‐map that interval. We identified two congenic strains that captured some or all the QTL. The larger congenic strain (Line 1: 122.387121–129.068 Mb; build 37) appeared to account for all the difference between CSS‐10 and B6. The smaller congenic strain (Line 2: 127.277–129.068 Mb) was intermediate between CSS‐10 and B6. We used haplotype mapping followed by quantitative polymerase chain reaction to identify one gene that was differentially expressed in both lines relative to B6 (Rnf41) and one that was differentially expressed between only Line 1 and B6 (Shmt2). These cis‐eQTLs may cause the behavioral QTLs; however, further studies are required to validate these candidate genes. More generally, our observation that a large QTL mapped using CSS and F₂ crosses can be dissected into multiple smaller QTLs shows a weaknesses of two‐stage approaches that seek to use coarse mapping to identify large regions followed by fine‐mapping. Indeed, additional dissection of these congenic strains might result in further subdivision of these QTL regions. Despite these limitations, we have successfully fine‐mapped two QTLs to small regions and identified putative candidate genes, showing that the congenic approach can be effective for fine‐mapping QTLs .     

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.     

Overlapping mouse subcongenic strains successfully separate two linked body fat QTL on distal MMU 2

Background

Mouse chromosome 2 is linked to growth and body fat phenotypes in many mouse crosses. With the goal to identify the underlying genes regulating growth and body fat on mouse chromosome 2, we developed five overlapping subcongenic strains that contained CAST/EiJ donor regions in a C57BL/6J^hg/hg background (hg is a spontaneous deletion of 500 Kb on mouse chromosome 10). To fine map QTL on distal mouse chromosome 2 a total of 1,712 F2 mice from the five subcongenic strains, plus 278 F2 mice from the HG2D founder congenic strain were phenotyped and analyzed. Interval mapping (IM) and composite IM (CIM) were performed on body weight and body fat traits on a combination of SNP and microsatellite markers, which generated a high-density genotyping panel.

Results

Phenotypic analysis and interval mapping of total fat mass identified two QTL on distal mouse chromosome 2. One QTL between 150 and 161 Mb, Fatq2a, and the second between 173.3 and 175.6 Mb, Fatq2b. The two QTL reside in different congenic strains with significant total fat differences between homozygous cast/cast and b6/b6 littermates. Both of these QTL were previously identified only as a single QTL affecting body fat, Fatq2. Furthermore, through a novel approach referred here as replicated CIM, Fatq2b was mapped to the Gnas imprinted locus.

Conclusions

The integration of subcongenic strains, high-density genotyping, and CIM succesfully partitioned two previously linked QTL 20 Mb apart, and the strongest QTL, Fatq2b, was fine mapped to a ~2.3 Mb region interval encompassing the Gnas imprinted locus.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1191-8) contains supplementary material, which is available to authorized users.     

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.     

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.     

Identifying human disease genes through cross-species gene mapping of evolutionary conserved processes

Background

Understanding complex networks that modulate development in humans is hampered by genetic and phenotypic heterogeneity within and between populations. Here we present a method that exploits natural variation in highly diverse mouse genetic reference panels in which genetic and environmental factors can be tightly controlled. The aim of our study is to test a cross-species genetic mapping strategy, which compares data of gene mapping in human patients with functional data obtained by QTL mapping in recombinant inbred mouse strains in order to prioritize human disease candidate genes.

Methodology

We exploit evolutionary conservation of developmental phenotypes to discover gene variants that influence brain development in humans. We studied corpus callosum volume in a recombinant inbred mouse panel (C57BL/6J×DBA/2J, BXD strains) using high-field strength MRI technology. We aligned mouse mapping results for this neuro-anatomical phenotype with genetic data from patients with abnormal corpus callosum (ACC) development.

Principal Findings

From the 61 syndromes which involve an ACC, 51 human candidate genes have been identified. Through interval mapping, we identified a single significant QTL on mouse chromosome 7 for corpus callosum volume with a QTL peak located between 25.5 and 26.7 Mb. Comparing the genes in this mouse QTL region with those associated with human syndromes (involving ACC) and those covered by copy number variations (CNV) yielded a single overlap, namely HNRPU in humans and Hnrpul1 in mice. Further analysis of corpus callosum volume in BXD strains revealed that the corpus callosum was significantly larger in BXD mice with a B genotype at the Hnrpul1 locus than in BXD mice with a D genotype at Hnrpul1 (F = 22.48, p<9.87*10⁻⁵).

Conclusion

This approach that exploits highly diverse mouse strains provides an efficient and effective translational bridge to study the etiology of human developmental disorders, such as autism and schizophrenia.     

Fine mapping of a seizure susceptibility locus on mouse Chromosome 1: nomination of Kcnj10 as a causative gene

Previous quantitative trait loci (QTL) mapping studies document that the distal region of mouse Chromosome (Chr) 1 contains a gene(s) that is in large part responsible for the difference in seizure susceptibility between C57BL/6 (B6) (relatively seizure-resistant) and DBA/2 (D2) (relatively seizure-sensitive) mice. We now confirm this seizure-related QTL (Szs1) using reciprocal, interval-specific congenic strains and map it to a 6.6-Mb segment between Pbx1 and D1Mit150. Haplotype conservation between strains within this segment suggests that Szs1 may be localized more precisely to a 4.1-Mb critical interval between Fcgr3 and D1Mit150. We compared the coding region sequences of candidate genes between B6 and D2 mice using RT-PCR, amplification from genomic DNA, and database searching and discovered 12 brain-expressed genes with SNPs that predict a protein amino acid variation. Of these, the most compelling seizure susceptibility candidate is Kcnj10. A survey of the Kcnj10 SNP among other inbred mouse strains revealed a significant effect on seizure sensitivity such that most strains possessing a haplotype containing the B6 variant of Kcnj10 have higher seizure thresholds than those strains possessing the D2 variant. The unique role of inward-rectifying potassium ion channels in membrane physiology coupled with previous strong association between ion channel gene mutations and seizure phenotypes puts even greater focus on Kcnj10 in the present model. In summary, we confirmed a seizure-related QTL of large effect on mouse Chr 1 and mapped it to a finely delimited region. The critical interval contains several candidate genes, one of which, Kcnj10, exhibits a potentially important polymorphism with regard to fundamental aspects of seizure susceptibility.     

High-resolution genetic mapping of rice bacterial blight resistance gene Xa23

Bacterial blight (BB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is the most devastating bacterial disease of rice (Oryza sativa L.), a staple food crop that feeds half of the world’s population. In management of this disease, the most economical and effective approach is cultivating resistant varieties. Due to rapid change of pathogenicity in the pathogen, it is necessary to identify and characterize more host resistance genes for breeding new resistant varieties. We have previously identified the BB resistance (R) gene Xa23 that confers the broadest resistance to Xoo strains isolated from different rice-growing regions and preliminarily mapped the gene within a 1.7 cm region on the long arm of rice chromosome 11. Here, we report fine genetic mapping and in silico analysis of putative candidate genes of Xa23. Based on F₂ mapping populations derived from crosses between Xa23-containing rice line CBB23 and susceptible varieties JG30 or IR24, six new STS markers Lj36, Lj46, Lj138, Lj74, A83B4, and Lj13 were developed. Linkage analysis revealed that the new markers were co-segregated with or closely linked to the Xa23 locus. Consequently, the Xa23 gene was mapped within a 0.4 cm region between markers Lj138 and A83B4, in which the co-segregating marker Lj74 was identified. The corresponding physical distance between Lj138 and A83B4 on Nipponbare genome is 49.8 kb. Six Xa23 candidate genes have been annotated, including four candidate genes encoding hypothetical proteins and the other two encoding a putative ADP-ribosylation factor protein and a putative PPR protein. These results will facilitate marker-assisted selection of Xa23 in rice breeding and molecular cloning of this valuable R gene.     

QTL Analysis of Dietary Obesity in C57BL/6byj X 129P3/J F2 Mice: Diet- and Sex-Dependent Effects

Obesity is a heritable trait caused by complex interactions between genes and environment, including diet. Gene-by-diet interactions are difficult to study in humans because the human diet is hard to control. Here, we used mice to study dietary obesity genes, by four methods. First, we bred 213 F₂ mice from strains that are susceptible [C57BL/6ByJ (B6)] or resistant [129P3/J (129)] to dietary obesity. Percent body fat was assessed after mice ate low-energy diet and again after the same mice ate high-energy diet for 8 weeks. Linkage analyses identified QTLs associated with dietary obesity. Three methods were used to filter candidate genes within the QTL regions: (a) association mapping was conducted using >40 strains; (b) differential gene expression and (c) comparison of genomic DNA sequence, using two strains closely related to the progenitor strains from Experiment 1. The QTL effects depended on whether the mice were male or female or which diet they were recently fed. After feeding a low-energy diet, percent body fat was linked to chr 7 (LOD = 3.42). After feeding a high-energy diet, percent body fat was linked to chr 9 (Obq5; LOD = 3.88), chr 12 (Obq34; LOD = 3.88), and chr 17 (LOD = 4.56). The Chr 7 and 12 QTLs were sex dependent and all QTL were diet-dependent. The combination of filtering methods highlighted seven candidate genes within the QTL locus boundaries: Crx, Dmpk, Ahr, Mrpl28, Glo1, Tubb5, and Mut. However, these filtering methods have limitations so gene identification will require alternative strategies, such as the construction of congenics with very small donor regions.     

An integrative approach for the identification of quantitative trait loci

The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence-based analysis, bioinformatics and gene expression.     

Molecular Dissection of a Genomic Region Governing Root Traits Associated with Drought Tolerance Employing a Combinatorial Approach of QTL Mapping and RNA-seq in Rice

Drought is considered as one of the major obstacles for progressive yield enhancement and stability in rice, especially in rain-fed conditions. Being a complex trait, drought is regulated by numerous quantitative trait loci (QTL), of which, however, very few underlying genes have been cloned. In the present investigation, we made an attempt to uncover the candidate gene(s) behind a major QTL, rdw8.1 governing drought tolerance traits viz., root dry weight and root length. The targeted QTL has been delimited to 366.75 kb from 10.17 Mb by QTL mapping in BC₁F₂ population. Further, the targeted region was delineated employing next-generation sequencing based RNA-seq. Based on the QTL mapping and RNA-seq approaches, the plausible candidate gene underlying the QTL region was identified as a wound inducible protein (LOC_Os08g08090). This gene can be of potential value to enhance the drought tolerance of the elite rice varieties through molecular breeding.     

Quantitative trait analysis of the development of pulmonary tolerance to inhaled zinc oxide in mice

Background

Individuals may develop tolerance to the induction of adverse pulmonary effects following repeated exposures to inhaled toxicants. Previously, we demonstrated that genetic background plays an important role in the development of pulmonary tolerance to inhaled zinc oxide (ZnO) in inbred mouse strains, as assessed by polymorphonuclear leukocytes (PMNs), macrophages, and total protein in bronchoalveolar lavage (BAL) phenotypes. The BALB/cByJ (CBy) and DBA/2J (D2) strains were identified as tolerant and non-tolerant, respectively. The present study was designed to identify candidate genes that control the development of pulmonary tolerance to inhaled ZnO.

Methods

Genome-wide linkage analyses were performed on a CByD2F2 mouse cohort phenotyped for BAL protein, PMNs, and macrophages following 5 consecutive days of exposure to 1.0 mg/m³ inhaled ZnO for 3 hours/day. A haplotype analysis was carried out to determine the contribution of each quantitative trait locus (QTL) and QTL combination to the overall BAL protein phenotype. Candidate genes were identified within each QTL interval using the positional candidate gene approach.

Results

A significant quantitative trait locus (QTL) on chromosome 1, as well as suggestive QTLs on chromosomes 4 and 5, for the BAL protein phenotype, was established. Suggestive QTLs for the BAL PMN and macrophage phenotypes were also identified on chromosomes 1 and 5, respectively. Analysis of specific haplotypes supports the combined effect of three QTLs in the overall protein phenotype. Toll-like receptor 5 (Tlr5) was identified as an interesting candidate gene within the significant QTL for BAL protein on chromosome 1. Wild-derived Tlr5-mutant MOLF/Ei mice were tolerant to BAL protein following repeated ZnO exposure.

Conclusion

Genetic background is an important influence in the acquisition of pulmonary tolerance to BAL protein, PMNs, and macrophages following ZnO exposure. Promising candidate genes exist within the identified QTL intervals that would be good targets for additional studies, including Tlr5. The implications of tolerance to health risks in humans are numerous, and this study furthers the understanding of gene-environment interactions that are likely to be important factors from person-to-person in regulating the development of pulmonary tolerance to inhaled toxicants.     

Composite interval mapping and mixed models reveal QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4

Interval mapping (IM) implemented in QTL Express or GridQTL is widely used, but presents some limitations, such as restriction to a fixed model, risk of mapping two QTL when there may be only one and no discrimination of two or more QTL using both cofactors located on the same and other chromosomes. These limitations were overcome with composite interval mapping (CIM). We reported QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4 through implementation of CIM and analysis of phenotypic data using mixed models. Thirty-four microsatellite markers were used to genotype 360 F₂ chickens from crosses between males from a layer line and females from a broiler line. Sixteen QTL were mapped using CIM and 14 QTL with IM. Furthermore, of those 30 QTL, six were mapped only when CIM was used: for body weight at 35 days (first and third peaks on GGA4), body weight at 41 days (GGA1B and second peak on GGA4), and weights of back and legs (both on GGA4). Three new regions had evidence for QTL presence: one on GGA1B associated with feed intake 35–41 d at 404 cM (LEI0107-ADL0183) and two on GGA4 associated with weight of back at 163 cM (LEI0076-MCW0240) and weight gain 35–41 d, feed efficiency 35–41 d and weight of legs at 241 cM (LEI0085-MCW0174). We dissected one more linked QTL on GGA4, where three QTL for BW35 and two QTL for BW41 were mapped. Therefore, these new regions mapped here need further investigations using high-density SNP to confirm these QTL and identify candidate genes associated with those traits.     

Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.     

Gene expression profiles of a mouse congenic strain carrying an obesity susceptibility QTL under obesigenic diets

Genetic factors are strongly involved in the development of obesity, likely through the interactions of susceptibility genes with obesigenic environments, such as high-fat, high-sucrose (HFS) diets. Previously, we have established a mouse congenic strain on C57BL/6 J background, carrying an obesity quantitative trait locus (QTL), tabw2, derived from obese diabetic TALLYHO/JngJ mice. The tabw2 congenic mice exhibit increased adiposity and hyperleptinemia, which becomes exacerbated upon feeding HFS diets. In this study, we conducted genome-wide gene expression profiling to evaluate differentially expressed genes between tabw2 and control mice fed HFS diets, which may lead to identification of candidate genes as well as insights into the mechanisms underlying obesity mediated by tabw2. Both tabw2 congenic mice and control mice were fed HFS diets for 10 weeks beginning at 4 weeks of age, and total RNA was isolated from liver and adipose tissue. Whole-genome microarray analysis was performed and verified by real-time quantitative RT–PCR. At False Discovery Rate adjusted P < 0.05, 1026 genes were up-regulated and 308 down-regulated in liver, whereas 393 were up-regulated and 187 down-regulated in adipose tissue in tabw2 congenic mice compared to controls. Within the tabw2 QTL interval, 70 genes exhibited differential expression in either liver or adipose tissue. A comprehensive pathway analysis revealed a number of biological pathways that may be perturbed in the diet-induced obesity mediated by tabw2.     

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.