In silico mapping of mouse quantitative trait loci

Quantitative trait mapping in Diversity Outbred mice identifies novel genomic regions associated with the hepatic glutathione redox system

The tripeptide glutathione (GSH) is instrumental to antioxidant protection and xenobiotic metabolism, and the ratio of its reduced and oxidized forms (GSH/GSSG) indicates the cellular redox environment and maintains key aspects of cellular signaling. Disruptions in GSH levels and GSH/GSSG have long been tied to various chronic diseases, and many studies have examined whether variant alleles in genes responsible for GSH synthesis and metabolism are associated with increased disease risk. However, past studies have been limited to established, canonical GSH genes, though emerging evidence suggests that novel loci and genes influence the GSH redox system in specific tissues. The present study marks the most comprehensive effort to date to directly identify genetic loci associated with the GSH redox system. We employed the Diversity Outbred (DO) mouse population, a model of human genetics, and measured GSH and the essential redox cofactor NADPH in liver, the organ with the highest levels of GSH in the body. Under normal physiological conditions, we observed substantial variation in hepatic GSH and NADPH levels and their redox balances, and discovered a novel, significant quantitative trait locus (QTL) on murine chromosome 16 underlying GSH/GSSG; bioinformatics analyses revealed Socs1 to be the most likely candidate gene. We also discovered novel QTL associated with hepatic NADP⁺ levels and NADP⁺/NADPH, as well as unique candidate genes behind each trait. Overall, these findings transform our understanding of the GSH redox system, revealing genetic loci that govern it and proposing new candidate genes to investigate in future mechanistic endeavors.

Integration of genome-wide association and extant brain expression QTL identifies candidate genes influencing prepulse inhibition in inbred F1 mice

Genetic association mapping in structured populations of model organisms can offer a fruitful complement to human genetic studies by generating new biological hypotheses about complex traits. Here we investigated prepulse inhibition (PPI), a measure of sensorimotor gating that is disrupted in a number of psychiatric disorders. To identify genes that influence PPI, we constructed a panel of half-sibs by crossing 30 females from common inbred mouse strains with inbred C57BL/6J males to create male and female F1 offspring. We used publicly available single nucleotide polymorphism (SNP) genotype data from these inbred strains to perform a genome-wide association scan using a dense panel of over 150,000 SNPs in a combined sample of 604 mice representing 30 distinct F1 genotypes. We identified two independent PPI-associated loci on Chromosomes 2 and 7, each of which explained 12-14% of the variance in PPI. Searches of available databases did not identify any plausible causative coding polymorphisms within these loci. However, previously collected expression quantitative trait locus (eQTL) data from hippocampus and striatum indicated that the SNPs on Chromosomes 2 and 7 that showed the strongest association with PPI were also strongly associated with expression of several transcripts, some of which have been implicated in human psychiatric disorders. This integrative approach successfully identified a focused set of genes which can be prioritized for follow-up studies. More broadly, our results show that F1 crosses among common inbred strains can be used in combination with other informatics and expression datasets to identify candidate genes for complex behavioral traits.

Limitation of number of strains and persistence of false positive loci in QTL mapping using recombinant inbred strains

While the identification of causal genes of quantitative trait loci (QTL) remains a difficult problem in the post-genome era, the number of QTL continues to accumulate, mainly identified using the recombinant inbred (RI) strains. Over the last decade, hundreds of publications have reported nearly a thousand QTL identified from RI strains. We hypothesized that the inaccuracy of most of these QTL makes it difficult to identify causal genes. Using data from RI strains derived from C57BL/6J (B6) X DBA/2J (D2), we tested the possibility of detection of reliable QTL with different numbers of strains in the same trait in five different traits. Our results indicated that studies using RI strains of less than 30 in general have a higher probability of failing to detect reliable QTL. Errors in many studies could include false positive loci, switches between QTL with small and major effects, and missing the real major loci. The similar data was obtained from a RI strain population derived from a different pair of parents and a RI strain population of rat. Thus, thousands of reported QTL from studies of RI strains may need to be double-checked for accuracy before proceeding to causal gene identification.

In silico QTL mapping of maternal nurturing ability with the mouse diversity panel

Significant variation exists for maternal nurturing ability in inbred mice. Although classical mapping approaches have identified quantitative trait loci (QTL) that may account for this variation, the underlying genes are unknown. In this study, lactation performance data among the mouse diversity panel were used to map genomic regions associated with this variation. Females from each of 32 inbred strains (n = 8-19 dams/strain) were studied during the first 8 days of lactation by allowing them to raise weight- and size-normalized cross-foster litters (10 pups/litter). Average daily weight gain (ADG) of litters served as the primary indicator of milk production. The number of pups successfully reared to 8 days (PNUM8) also served as a related indicator of maternal performance. Initial haplotype association analysis using a Bonferroni-corrected, genome-wide threshold revealed 10 and 15 associations encompassing 11 and 13 genes for ADG and PNUM8, respectively. The most significant of these associated haplotype blocks were found on MMU 8, 11, and 19 and contained the genes Nr3c2, Egfr, Sec61g, and Gnaq. Lastly, two haplotype blocks on MMU9 were detected in association with PNUM8. These overlapped with the previously described maternal performance QTL, Neogq1. These results suggest that the application of in silico QTL mapping is a useful tool in discovering the presence of novel candidate genes involved in determining lactation capacity in mice.

Forward genetic approaches to understanding complex behaviors

Assigning function to genes has long been a focus of biomedical research.Even with complete knowledge of the genomic sequences of humans, mice and other experimental organisms, there is still much to be learned about gene function and control. Ablation or overexpression of single genes using knockout or transgenic technologies has provided functional annotation for many genes, but these technologies do not capture the extensive genetic variation present in existing experimental mouse populations. Researchers have only recently begun to truly appreciate naturally occurring genetic variation resulting from single nucleotide substitutions,insertions, deletions, copy number variation, epigenetic changes (DNA methylation,histone modifications, etc.) and gene expression differences and how this variation contributes to complex phenotypes. In this chapter, we will discuss the benefits and limitations of different forward genetic approaches that capture the genetic variation present in inbred mouse strains and present the utility of these approaches for mapping QTL that influence complex behavioral phenotypes.

Quantitative trait Loci association mapping by imputation of strain origins in multifounder crosses

Although mapping quantitative traits in inbred strains is simpler than mapping the analogous traits in humans, classical inbred crosses suffer from reduced genetic diversity compared to experimental designs involving outbred animal populations. Multiple crosses, for example the Complex Trait Consortium's eight-way cross, circumvent these difficulties. However, complex mating schemes and systematic inbreeding raise substantial computational difficulties. Here we present a method for locally imputing the strain origins of each genotyped animal along its genome. Imputed origins then serve as mean effects in a multivariate Gaussian model for testing association between trait levels and local genomic variation. Imputation is a combinatorial process that assigns the maternal and paternal strain origin of each animal on the basis of observed genotypes and prior pedigree information. Without smoothing, imputation is likely to be ill-defined or jump erratically from one strain to another as an animal's genome is traversed. In practice, one expects to see long stretches where strain origins are invariant. Smoothing can be achieved by penalizing strain changes from one marker to the next. A dynamic programming algorithm then solves the strain imputation process in one quick pass through the genome of an animal. Imputation accuracy exceeds 99% in practical examples and leads to high-resolution mapping in simulated and real data. The previous fastest quantitative trait loci (QTL) mapping software for dense genome scans reduced compute times to hours. Our implementation further reduces compute times from hours to minutes with no loss in statistical power. Indeed, power is enhanced for full pedigree data.

In silico QTL mapping of basal liver iron levels in inbred mouse strains

Both iron deficiency and iron excess are detrimental in many organisms, and previous studies in both mice and humans suggest that genetic variation may influence iron status in mammals. However, these genetic factors are not well defined. To address this issue, we measured basal liver iron levels in 18 inbred strains of mice of both sexes on a defined iron diet and found ∼4-fold variation in liver iron in males (lowest 153 μg/g, highest 661 μg/g) and ∼3-fold variation in females (lowest 222 μg/g, highest 658 μg/g). We carried out a genome-wide association mapping to identify haplotypes underlying differences in liver iron and three other related traits (copper and zinc liver levels, and plasma diferric transferrin levels) in a subset of 14 inbred strains for which genotype information was available. We identified two putative quantitative trait loci (QTL) that contain genes with a known role in iron metabolism: Eif2ak1 and Igf2r. We also identified four putative QTL that reside in previously identified iron-related QTL and 22 novel putative QTL. The most promising putative QTL include a 0.22 Mb region on Chromosome 7 and a 0.32 Mb region on Chromosome 11 that both contain only one candidate gene, Adam12 and Gria1, respectively. Identified putative QTL are good candidates for further refinement and subsequent functional studies.

Strain screen and haplotype association mapping of wheel running in inbred mouse strains

Previous genetic association studies of physical activity, in both animal and human models, have been limited in number of subjects and genetically homozygous strains used as well as number of genomic markers available for analysis. Expansion of the available mouse physical activity strain screens and the recently published dense single-nucleotide polymorphism (SNP) map of the mouse genome (approximately 8.3 million SNPs) and associated statistical methods allowed us to construct a more generalizable map of the quantitative trait loci (QTL) associated with physical activity. Specifically, we measured wheel running activity in male and female mice (average age 9 wk) in 41 inbred strains and used activity data from 38 of these strains in a haplotype association mapping analysis to determine QTL associated with activity. As seen previously, there was a large range of activity patterns among the strains, with the highest and lowest strains differing significantly in daily distance run (27.4-fold), duration of activity (23.6-fold), and speed (2.9-fold). On a daily basis, female mice ran further (24%), longer (13%), and faster (11%). Twelve QTL were identified, with three (on Chr. 12, 18, and 19) in both male and female mice, five specific to males, and four specific to females. Eight of the 12 QTL, including the 3 general QTL found for both sexes, fell into intergenic areas. The results of this study further support the findings of a moderate to high heritability of physical activity and add general genomic areas applicable to a large number of mouse strains that can be further mined for candidate genes associated with regulation of physical activity. Additionally, results suggest that potential genetic mechanisms arising from traditional noncoding regions of the genome may be involved in regulation of physical activity.

A survey of airway responsiveness in 36 inbred mouse strains facilitates gene mapping studies and identification of quantitative trait loci

Airway hyper-responsiveness (AHR) is a critical phenotype of human asthma and animal models of asthma. Other studies have measured AHR in nine mouse strains, but only six strains have been used to identify genetic loci underlying AHR. Our goals were to increase the genetic diversity of available strains by surveying 27 additional strains, to apply haplotype association mapping to the 36-strain survey, and to identify new genetic determinants for AHR. We derived AHR from the increase in airway resistance in females subjected to increasing levels of methacholine concentrations. We used haplotype association mapping to identify associations between AHR and haplotypes on chromosomes 3, 5, 8, 12, 13, and 14. And we used bioinformatics techniques to narrow the identified region on chromosome 13, reducing the region to 29 candidate genes, with 11 of considerable interest. Our combined use of haplotype association mapping with bioinformatics tools is the first study of its kind for AHR on these 36 strains of mice. Our analyses have narrowed the possible QTL genes and will facilitate the discovery of novel genes that regulate AHR in mice.

Assessing the prospects of genome-wide association studies performed in inbred mice

The remarkable success in mapping genes linked to a number of disease traits using genome-wide association studies (GWAS) in human cohorts has renewed interest in applying this same technique in model organisms such as inbred laboratory mice. Unlike humans, however, the limited genetic diversity in the ancestry of laboratory mice combined with selection pressure over the past decades have yielded an intricate population genetic structure that can complicate the results obtained from association studies. This problem is further exacerbated by the small number of strains typically used in such studies where multiple spurious associations arise as a result of random chance. We sought to empirically assess the viability of GWAS in inbred mice using hundreds of expression traits for which the true location of the expression quantitative trait locus was known a priori. We then measured transcript abundance levels for these expression traits in 16 classical and 3 wild-derived inbred strains and carried out a genome-wide association scan, demonstrating the low statistical power of such studies and empirically estimating the large extent to which allelic association of transcripts gives rise to spurious associations. We provide evidence illustrating that in a large fraction of cases, the marker with the most significant p values fails to map to the location of the true eQTL. Finally, we provide experimental support for hundreds of traits, and that combining linkage analysis with association mapping provides significant increases in statistical power over a stand-alone GWAS as well as significantly higher mapping resolution than either study alone.

An experimental assessment of in silico haplotype association mapping in laboratory mice

Background

To assess the utility of haplotype association mapping (HAM) as a quantitative trait locus (QTL) discovery tool, we conducted HAM analyses for red blood cell count (RBC) and high density lipoprotein cholesterol (HDL) in mice. We then experimentally tested each HAM QTL using published crosses or new F2 intercrosses guided by the haplotype at the HAM peaks.

Results

The HAM for RBC, using 33 classic inbred lines, revealed 8 QTLs; 2 of these were true positives as shown by published crosses. A HAM-guided (C57BL/6J × CBA/J)F2 intercross we carried out verified 2 more as true positives and 4 as false positives. The HAM for HDL, using 81 strains including recombinant inbred lines and chromosome substitution strains, detected 46 QTLs. Of these, 36 were true positives as shown by published crosses. A HAM-guided (C57BL/6J × A/J)F2 intercross that we carried out verified 2 more as true positives and 8 as false positives. By testing each HAM QTL for RBC and HDL, we demonstrated that 78% of the 54 HAM peaks were true positives and 22% were false positives. Interestingly, all false positives were in significant allelic association with one or more real QTL.

Conclusion

Because type I errors (false positives) can be detected experimentally, we conclude that HAM is useful for QTL detection and narrowing. We advocate the powerful and economical combined approach demonstrated here: the use of HAM for QTL discovery, followed by mitigation of the false positive problem by testing the HAM-predicted QTLs with small HAM-guided experimental crosses.

Replication and narrowing of gene expression quantitative trait loci using inbred mice

Gene expression quantitative trait locus (eQTL) mapping has become a powerful tool in systems biology. While many authors have made important discoveries using this approach, one persistent challenge in eQTL studies is the selection of loci and genes that should receive further biological investigation. In this study we compared eQTL generated from gene expression profiling in the livers of two panels of mouse strains: 41 BXD recombinant inbred and 36 Mouse Diversity Panel (MDP) strains. Cis-eQTL, loci in which the transcript and its maximum QTL are colocated, have been shown to be more reproducible than trans-eQTL, which are not colocated with the transcript. We observed that between 9.9 and 12.1% of cis-eQTL and between 2.0 and 12.6% of trans-eQTL replicated between the two panels depending on the degree of statistical stringency. Notably, a significant eQTL hotspot on distal chromosome 12 observed in the BXD panel was reproduced in the MDP. Furthermore, the shorter linkage disequilibrium in the MDP strains allowed us to considerably narrow the locus and limit the number of candidate genes to a cluster of Serpin genes, which code for extracellular proteases. We conclude that this strategy has some utility in increasing confidence and resolution in eQTL mapping studies; however, due to the high false-positive rate in the MDP, eQTL mapping in inbred strains is best carried out in combination with an eQTL linkage study.

Multiple loci in silico mapping in inbred lines

The in silico mapping (ISM) technique and its extension represent major advances for novel gene discovery in germplasm resources of inbred lines. However, the techniques suffer from a relatively high false-positive rate (FPR) and they do not consider the effect of linkage disequilibrium (LD) markers around the identified quantitative trait locus (QTL). In addition, it has not yet been established whether it is optimal to use absolute trait differences as the response variable. To address these problems, this article presents the multiple loci ISM (MLISM) approach, which uses all markers on the entire genome, along with a penalized maximum likelihood. The method proposed here was verified by a series of simulation experiments with a maize pedigree population of inbred lines of known ancestry. Results from the simulated studies show that the best response variable is the trait product. The MLISM FPR is substantially decreased and the proportion of the number of false QTL to the number of LD markers around the identified QTL is adequately reduced. The MLISM method, with the trait product as the response variable, is an improvement on the existing methods for novel QTL mapping in germplasm resources of inbred lines.

Progress in genetic studies of pain and analgesia

Interindividual variability in pain sensitivity and the response to analgesic manipulations remains a considerable clinical challenge as well as an area of intense scientific investigation. Techniques in this field have matured rapidly so that much relevant data have emerged only in the past few years. Our increasing understanding of the genetic mediation of these biological phenomena have nonetheless revealed their surprising complexity. This review provides a comprehensive picture and critical analysis of the field and its prospects.

In silico whole genome association scan for murine prepulse inhibition

BACKGROUND

The complex trait of prepulse inhibition (PPI) is a sensory gating measure related to schizophrenia and can be measured in mice. Large-scale public repositories of inbred mouse strain genotypes and phenotypes such as PPI can be used to detect Quantitative Trait Loci (QTLs) in silico. However, the method has been criticized for issues including insufficient number of strains, not controlling for false discoveries, the complex haplotype structure of inbred mice, and failing to account for genotypic and phenotypic subgroups.

METHODOLOGY/PRINCIPAL FINDINGS

We have implemented a method that addresses these issues by incorporating phylogenetic analyses, multilevel regression with mixed effects, and false discovery rate (FDR) control. A genome-wide scan for PPI was conducted using over 17,000 single nucleotide polymorphisms (SNPs) in 37 strains phenotyped. Eighty-nine SNPs were significant at a false discovery rate (FDR) of 5%. After accounting for long-range linkage disequilibrium, we found 3 independent QTLs located on murine chromosomes 1 and 13. One of the PPI positives corresponds to a region of human chromosome 6p which includes DTNBP1, a gene implicated in schizophrenia. Another region includes the gene Tsn which alters PPI when knocked out. These genes also appear to have correlated expression with PPI.

CONCLUSIONS/SIGNIFICANCE

These results support the usefulness of using an improved in silico mapping method to identify QTLs for complex traits such as PPI which can be then be used for to help identify loci influencing schizophrenia in humans.

Mouse genome-wide association mapping needs linkage analysis to avoid false-positive Loci

We carried out genome-wide association (GWA) studies in inbred mouse strains characterized for their lung tumor susceptibility phenotypes (spontaneous or urethane-induced) with panels of 12,959 (13K) or 138,793 (140K) single-nucleotide polymorphisms (SNPs). Above the statistical thresholds, we detected only SNP rs3681853 on Chromosome 5, two SNPs in the pulmonary adenoma susceptibility 1 (Pas1) locus, and SNP rs4174648 on Chromosome 16 for spontaneous tumor incidence, urethane-induced tumor incidence, and urethane-induced tumor multiplicity, respectively, with the 13K SNP panel, but only the Pas1 locus with the 140K SNP panel. Haplotype analysis carried out in the latter panel detected four additional loci. Loci reported in previous GWA studies failed to replicate. Genome-wide genetic linkage analysis in urethane-treated (BALB/cxC3H/He)F2, (BALB/cxSWR/J)F2, and (A/JxC3H/He)F2 mice showed that Pas1, but none of the other loci detected previously or herein by GWA, had a significant effect. The Lasc1 gene, identified by GWA as a functional element (Nat. Genet., 38:888-95, 2006), showed no genetic effects in the two independent intercross mouse populations containing both alleles, nor was it expressed in mouse normal lung or lung tumors. Our results indicate that GWA studies in mouse inbred strains can suffer a high rate of false-positive results and that such an approach should be used in conjunction with classical linkage mapping in genetic crosses.

Haplotype association mapping in mice

Haplotype Association Mapping (HAM) is a novel phenotype-driven approach to identify genetic loci and was originally developed for mice. This method, which is similar to Genome-Wide Association (GWA) studies in humans, looks for associations between the phenotype and the haplotypes of mouse inbred strains, treating inbred strains as individuals. Although this approach is still in development, we review the current literature, present the different methods and applications that are in use, and provide a glimpse of what is to come in the near future.

Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci

Dissecting the genes involved in complex traits can be confounded by multiple factors, including extensive epistatic interactions among genes, the involvement of epigenetic regulators, and the variable expressivity of traits. Although quantitative trait locus (QTL) analysis has been a powerful tool for localizing the chromosomal regions underlying complex traits, systematically identifying the causal genes remains challenging. Here, through its application to plasma levels of high-density lipoprotein cholesterol (HDL) in mice, we demonstrate a strategy for narrowing QTL that utilizes comparative genomics and bioinformatics techniques. We show how QTL detected in multiple crosses are subjected to both combined cross analysis and haplotype block analysis; how QTL from one species are mapped to the concordant regions in another species; and how genomewide scans associating haplotype groups with their phenotypes can be used to prioritize the narrowed regions. Then we illustrate how these individual methods for narrowing QTL can be systematically integrated for mouse chromosomes 12 and 15, resulting in a significantly reduced number of candidate genes, often from hundreds to <10. Finally, we give an example of how additional bioinformatics resources can be combined with experiments to determine the most likely quantitative trait genes.

Methodologies for segregation analysis and QTL mapping in plants

Most characters of biological interest and economic importance are quantitative traits. To uncover the genetic architecture of quantitative traits, two approaches have become popular in China. One is the establishment of an analytical model for mixed major-gene plus polygenes inheritance and the other the discovery of quantitative trait locus (QTL). Here we review our progress employing these two approaches. First, we proposed joint segregation analysis of multiple generations for mixed major-gene plus polygenes inheritance. Second, we extended the multilocus method of Lander and Green (1987), Jiang and Zeng (1997) to a more generalized approach. Our methodology handles distorted, dominant and missing markers, including the effect of linked segregation distortion loci on the estimation of map distance. Finally, we developed several QTL mapping methods. In the Bayesian shrinkage estimation (BSE) method, we suggested a method to test the significance of QTL effects and studied the effect of the prior distribution of the variance of QTL effect on QTL mapping. To reduce running time, a penalized maximum likelihood method was adopted. To mine novel genes in crop inbred lines generated in the course of normal crop breeding work, three methods were introduced. If a well-documented genealogical history of the lines is available, two-stage variance component analysis and multi-QTL Haseman-Elston regression were suggested; if unavailable, multiple loci in silico mapping was proposed.

Genomewide association analysis in diverse inbred mice: power and population structure

The discovery of quantitative trait loci (QTL) in model organisms has relied heavily on the ability to perform controlled breeding to generate genotypic and phenotypic diversity. Recently, we and others have demonstrated the use of an existing set of diverse inbred mice (referred to here as the mouse diversity panel, MDP) as a QTL mapping population. The use of the MDP population has many advantages relative to traditional F(2) mapping populations, including increased phenotypic diversity, a higher recombination frequency, and the ability to collect genotype and phenotype data in community databases. However, these methods are complicated by population structure inherent in the MDP and the lack of an analytical framework to assess statistical power. To address these issues, we measured gene expression levels in hypothalamus across the MDP. We then mapped these phenotypes as quantitative traits with our association algorithm, resulting in a large set of expression QTL (eQTL). We utilized these eQTL, and specifically cis-eQTL, to develop a novel nonparametric method for association analysis in structured populations like the MDP. These eQTL data confirmed that the MDP is a suitable mapping population for QTL discovery and that eQTL results can serve as a gold standard for relative measures of statistical power.

Prospects for association mapping in classical inbred mouse strains

The collection of classical inbred mouse strains displays heritable variation in a large number of complex traits. Many generations of historical recombination have contributed to the panel of classical strain genomes, raising the possibility that quantitative trait loci could be located with high resolution by correlating strain genotypes and phenotypes. Although this association mapping framework has been successful in several empirical applications, its expected performance remains unclear. We used computer simulations based on a publicly available, dense single-nucleotide polymorphism (SNP) map to measure the power and false-positive rate of association mapping on a genomic scale across 30 commonly used classical inbred strains. Expected power is (i) often low for phenotypic effect sizes that are realistic for complex traits, (ii) highly variable across the genome, and (iii) correlated with linkage disequilibrium, aspects of the allele frequency distribution, and haplotype characteristics, as predicted by theory. Simulations also demonstrate clear potential for spurious associations to be generated by unequal relatedness among the strains. These findings suggest that association mapping in the classical strains is best applied in combination with other procedures, such as QTL mapping.

An integrated in silico gene mapping strategy in inbred mice

In recent years in silico analysis of common laboratory mice has been introduced and subsequently applied, in slightly different ways, as a methodology for gene mapping. Previously we have demonstrated some limitation of the methodology due to sporadic genetic correlations across the genome. Here, we revisit the three main aspects that affect in silico analysis. First, we report on the use of marker maps: we compared our existing 20,000 SNP map to the newly released 140,000 SNP map. Second, we investigated the effect of varying strain numbers on power to map QTL. Third, we introduced a novel statistical approach: a cladistic analysis, which is well suited for mouse genetics and has increased flexibility over existing in silico approaches. We have found that in our examples of complex traits, in silico analysis by itself does fail to uniquely identify quantitative trait gene (QTG)-containing regions. However, when combined with additional information, it may significantly help to prioritize candidate genes. We therefore recommend using an integrated work flow that uses other genomic information such as linkage regions, regions of shared ancestry, and gene expression information to obtain a list of candidate genes from the genome.

Quantitative trait loci analysis of mice administered the methionine-choline deficient dietary model of experimental steatohepatitis

BACKGROUND

Nonalcoholic steatohepatitis (NASH) is a common disease with a poorly understood etiology, and the methionine-choline-deficient (MCD) diet is a nutritional model of NASH. Quantitative trait loci (QTL) analysis is a standard method for chromosomal mapping of polygenic disease traits. The purpose of this study is to administer mice an MCD diet in order to determine the strain-specific susceptibility for developing steatohepatitis, and to apply a computational methodology of QTL analysis to identify associated chromosomal susceptibility loci.

METHODS

Inbred mice were fed an MCD diet and alanine aminotransferase (ALT), hepatic triglycerides, liver weight, and weight loss were measured as phenotypic markers of steatohepatitis.

RESULTS

A/J mice developed the highest ALT and hepatic triglyceride levels. Using linear regression analysis, gene loci affecting serum ALT levels were identified on four chromosomes, and four loci that affect liver weight were also identified. In contrast, no QTLs for hepatic triglycerides or body weight were identified. Of note, loci for ALT and liver weight co-localized to proximal segments of chromosomes 2 and 15, in regions previously identified as QTLs for liver fibrosis.

CONCLUSIONS

These data indicate that experimental steatohepatitis is a polygenic disease with genes determining ALT, liver weight, and liver fibrosis.

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.

Identification of candidate alkylator-induced cancer susceptibility genes by whole genome scanning in mice

Secondary malignancies are a serious adverse consequence of alkylator chemotherapy. The risk of developing an alkylator-associated malignancy is influenced by genetic background, although the relevant genetic factors are poorly understood. To screen for novel susceptibility factors, we established a mouse model of alkylator-induced malignancy. We exposed mice from 20 inbred strains to the prototypical alkylating agent, N-nitroso-N-ethylurea (ENU). ENU was a potent carcinogen in many of the strains tested, inducing 140 tumors in 240 ENU-treated mice (66% incidence of at least one tumor in evaluable mice), compared with a background incidence of 8% spontaneous tumors in 240 strain-, age-, and sex-matched control mice (relative risk, 8.4; P < 0.0001). A wide variety of tumor histologies were noted, including epithelial carcinomas, soft tissue sarcomas, and hematopoietic tumors. Cancer susceptibility was a heritable trait for the most common tumor types, lung adenocarcinoma (H(2) = 0.25), T cell lymphoma (H(2) = 0.19), and myeloid malignancies (H(2) = 0.10). Quantitative trait locus mapping identified regions on chromosomes 3, 6, 9, and 15 containing candidate genes associated with lung adenoma, lung carcinoma, and lymphoma susceptibility. This novel mouse model recapitulates many features of human alkylator-associated cancer and supports the hypothesis that susceptibility to this syndrome is influenced by inherited polymorphisms that could be used to make informed clinical treatment decisions.

An interaction between genetic factors and gender determines the magnitude of the inflammatory response in the mouse air pouch model of acute inflammation

The widely used mouse air pouch model of acute inflammation is inducible in a variety of inbred strains, but the potential influence of genetic background and gender on inflammation severity has never been examined. We directly compared the degree of inflammation induced in the air pouch model across four commonly utilized inbred strains in both male and female mice. We then applied an in silico mapping method to identify loci potentially associated with determining inflammation severity for each gender. Air pouches were induced by subcutaneous injection 3 (3 cc) and 5 (1.5 cc) days prior to the experiment. 4h after carrageenan injection, exudates were retrieved and leukocyte concentration quantified using a hemocytometer. The in silico mapping method was applied as described below. The strain order for mean leukocyte count/mL in inflamed exudates differed between genders. In males, the order was C57BL/6J > BALB/cByJ > DBA/2J > DBA/1J, while in females the order was BALB/cByJ > DBA/2J > C57BL/6J > DBA/1J. The difference in inflammation severity between genders reached significance only in C57BL/6J mice. Independent in silico analysis based on phenotypic data from male versus female mice identified distinct sets of loci as potentially associated with the exudate count reached. We conclude that the degree of inflammation induced in the mouse air pouch model of inflammation is strain-specific and, therefore, genetically based, and the pattern of interstrain differences is altered in male relative to female mice. The loci identified by in silico mapping likely contain genes with differential roles in determining this phenotype between genders.

Finding the molecular basis of complex genetic variation in humans and mice

I survey the state of the art in complex trait analysis, including the use of new experimental and computational technologies and resources becoming available, and the challenges facing us. I also discuss how the prospects of rodent model systems compare with association mapping in humans.

An XML-based system for synthesis of data from disparate databases

Diverse data sets have become key building blocks of translational biomedical research. Data types captured and referenced by sophisticated research studies include high throughput genomic and proteomic data, laboratory data, data from imagery, and outcome data. In this paper, the authors present the application of an XML-based data management system to support integration of data from disparate data sources and large data sets. This system facilitates management of XML schemas and on-demand creation and management of XML databases that conform to these schemas. They illustrate the use of this system in an application for genotype-phenotype correlation analyses. This application implements a method of phenotype-genotype correlation based on phylogenetic optimization of large data sets of mouse SNPs and phenotypic data. The application workflow requires the management and integration of genomic information and phenotypic data from external data repositories and from the results of phenotype-genotype correlation analyses. Our implementation supports the process of carrying out a complex workflow that includes large-scale phylogenetic tree optimizations and application of Maddison's concentrated changes test to large phylogenetic tree data sets. The data management system also allows collaborators to share data in a uniform way and supports complex queries that target data sets.

Genomics of the future: identification of quantitative trait loci in the mouse

Positional cloning of quantitative trait loci in rodents is a common approach to identify genes involved in complex phenotypes, including genes important to human disease. However, cloning the causative genes has proved to be more difficult than determining their positions. New tools such as genomic sequence, clone libraries, and new genomic-based methods offer new approaches to identify these genes. Here we review how these new tools and approaches will improve our ability to discover the genes important in complex traits.

Bioinformatics toolbox for narrowing rodent quantitative trait loci

Quantitative trait locus (QTL) analysis is a powerful method for localizing disease genes, but identifying the causal gene remains difficult. Rodent models of disease facilitate QTL gene identification, and causal genes underlying rodent QTL are often associated with the corresponding human diseases. Recently developed bioinformatics methods, including comparative genomics, combined cross analysis, interval-specific and genome-wide haplotype analysis, followed by sequence and expression analysis, each facilitated by public databases, provide new tools for narrowing rodent QTLs. Here we discuss each tool, illustrate its application and generate a bioinformatics strategy for narrowing QTLs. Combining these bioinformatics tools with classical experimental methods should accelerate QTL gene identification.

Genetics of body weight in the LXS recombinant inbred mouse strains

This is the first phenotypic analysis of 75 new recombinant inbred (RI) strains derived from ILS and ISS progenitors. We analyzed body weight in two independent cohorts of female mice at various ages and in males at 60 days. Body weight is a complex trait which has been mapped in numerous crosses in rodents. The LXS RI strains displayed a large range of weights, transgressing those of the inbred progenitors, supporting the utility of this large panel for mapping traits not selected in the progenitors. Numerous QTLs for body weight mapped in single- and multilocus scans. We assessed replication between these and previously reported QTLs based on overlapping confidence intervals of published QTLs for body weight at 60 days and used meta-analyses to determine combined p values for three QTL regions located on Chromosomes 4, 5, and 11. Strain distribution patterns of microsatellite marker genotypes, weight, and other phenotypes are available on WebQTL (http://www.webqtl.org/search.html ) and allow genetic mapping of any heritable quantitative phenotype measured in these strains. We report one such analysis, correlating brain and body weights. Large reference panels of RI strains, such as the LXS, are invaluable for identifying genetic correlations, GXE (Gene X Environment) interactions, and replicating previously identified QTLs.

Genetic variation in laboratory mice

Characterizing the patterns of genetic variation in an organism provides fundamental insight into the evolutionary history of the organism and defines the scope and nature of studies that must be designed to correlate genotype to phenotype. Given the pre-eminent role of the inbred mouse in biomedical research, considerable effort has been undertaken in recent years to describe more fully the nature and amount of genetic variation among the numerous strains of mice that are in widest use. Here, we discuss recent studies that have contributed to an emerging understanding of the unique variation patterns found in inbred strains of mice and how they have arisen through a combination of natural evolution and human-directed breeding. These preliminary results have ramifications for genetic research into complex biomedical traits and are the basis for the development of future variation resources.

Calcification of myocardial necrosis is common in mice

Independent of the severity, phenotypes and clinical outcomes of myocardial infarction may vary considerably in patients, suggesting a strong genetic influence on healing and adaptive processes. Since little is known about these genetic determinants, we examined the tissue response to myocardial injury in seven inbred mouse strains, including those employed for gene targeting or transgenic overexpression. Myocardial necrosis was produced by non-ischemic, trans-diaphragmal freeze-thaw injury in strains C57BL/6, C3H/He, DBA/2, BALB/c, 129S1, FVB/n and A/J. Two days after injury, necrotic cardiomyocytes calcified in C3H/He, DBA/2, BALB/c and 129S1, a phenotype known as dystrophic cardiac calcinosis (DCC). The susceptibility to DCC of 129S1 was determined by Dyscalc1, a locus on chromosome 7, which was identified previously in C3H/He and DBA/2. DCC was also observed in C3H/He following ischemic injury by permanent coronary artery ligation, indicating that DCC was independent of the mode of injury. In contrast, strains C57BL/6, FVB and A/J were resistant to DCC, showing formation of a fibrous scar without calcification. The development of DCC was studied in detail in C3H/He and C57BL/6. In both strains, no calcium deposition and only little structural disintegration were noted in necrotic myocardium 24 h after injury upon calcium-sensitive fluorescence staining. Ultrastructural examination revealed calcified mitochondria in C3H/He that may have served later as a nidus for rapid intracellular calcification of cardiomyocytes. We concluded that the susceptibility to calcification of myocardial necrosis may be common among inbred strains and should be recognised as a strong genetic modifier of experimental myocardial injury.

Heritability, correlations and in silico mapping of locomotor behavior and neurochemistry in inbred strains of mice

The midbrain dopamine system mediates normal and pathologic behaviors related to motor activity, attention, motivation/reward and cognition. These are complex, quantitative traits whose variation among individuals is modulated by genetic, epigenetic and environmental factors. Conventional genetic methods have identified several genes important to this system, but the majority of factors contributing to the variation remain unknown. To understand these genetic and environmental factors, we initiated a study measuring 21 behavioral and neurochemical traits in 15 common inbred mouse strains. We report trait data, heritabilities and genetic and non-genetic correlations between pheno-types. In general, the behavioral traits were more heritable than neurochemical traits, and both genetic and non-genetic correlations within these trait sets were high. Surprisingly, there were few significant correlations between the behavioral and the individual neurochemical traits. However, striatal serotonin and one measure of dopamine turnover (DOPAC/DA) were highly correlated with most behavioral measures. The variable accounting for the most variation in behavior was mouse strain and not a specific neurochemical measure, suggesting that additional genetic factors remain to be determined to account for these behavioral differences. We also report the prospective use of the in silico method of quantitative trait loci (QTL) analysis and demonstrate difficulties in the use of this method, which failed to detect significant QTLs for the majority of these traits. These data serve as a framework for further studies of correlations between different midbrain dopamine traits and as a guide for experimental cross designs to identify QTLs and genes that contribute to these traits.

Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.)

Many commercial inbred lines are available in crops. A large amount of genetic variation is preserved among these lines. The genealogical history of the inbred lines is usually well documented. However, quantitative trait loci (QTL) responsible for the genetic variances among the lines are largely unexplored due to lack of statistical methods. In this study, we show that the pedigree information of the lines along with the trait values and marker information can be used to map QTL without the need of further crossing experiments. We develop a Monte Carlo method to estimate locus-specific identity-by-descent (IBD) matrices. These IBD matrices are further incorporated into a mixed-model equation for variance component analysis. QTL variance is estimated and tested at every putative position of the genome. The actual QTL are detected by scanning the entire genome. Applying this new method to a well-documented pedigree of maize (Zea mays L.) that consists of 404 inbred lines, we mapped eight QTL for the maize male flowering trait, growing degree day heat units to pollen shedding (GDUSHD). These detected QTL contributed >80% of the variance observed among the inbred lines. The QTL were then used to evaluate all the inbred lines using the best linear unbiased prediction (BLUP) technique. Superior lines were selected according to the estimated QTL allelic values, a technique called marker-assisted selection (MAS). The MAS procedure implemented via BLUP may be routinely used by breeders to select superior lines and line combinations for development of new cultivars.

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.

Mouse inbred strain sequence information and yin-yang crosses for quantitative trait locus fine mapping

The shared ancestry of mouse inbred strains, together with the availability of sequence and phenotype information, is a resource that can be used to map quantitative trait loci (QTL). The difficulty in using only sequence information lies in the fact that in most instances the allelic state of the QTL cannot be unambiguously determined in a given strain. To overcome this difficulty, the performance of multiple crosses between various inbred strains has been proposed. Here we suggest and evaluate a general approach, which consists of crossing the two strains used initially to map the QTL and any new strain. We have termed these crosses "yin-yang," because they are complementary in nature as shown by the fact that the QTL will necessarily segregate in only one of the crosses. We used the publicly available SNP database of chromosome 16 to evaluate the mapping resolution achievable through this approach. Although on average the improvement of mapping resolution using only four inbred strains was relatively small (i.e., reduction of the QTL-containing interval by half at most), we found a great degree of variability among different regions of chromosome 16 with regard to mapping resolution. This suggests that with a large number of strains in hand, selecting a small number of strains may provide a significant contribution to the fine mapping of QTL.

Thematic review series: The pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis

The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks.

A strategy for the integration of QTL, gene expression, and sequence analyses

Although hundreds if not thousands of quantitative trait loci (QTL) have been described for a wide variety of complex traits, only a very small number of these QTLs have been reduced to quantitative trait genes (QTGs) and quantitative trait nucleotides (QTNs). A strategy, Multiple Cross Mapping (MCM), is described for detecting QTGs and QTNs that is based on leveraging the information contained within the haplotype structure of the mouse genome. As described in the current report, the strategy utilizes the six F(2) intercrosses that can be formed from the C57BL/6J (B6), DBA/2J (D2), BALB/cJ (C), and LP/J (LP) inbred mouse strains. Focusing on the phenotype of basal locomotor activity, it was found that in all three B6 intercrosses, a QTL was detected on distal Chromosome (Chr) 1; no QTL was detected in the other three intercrosses, and thus, it was assumed that at the QTL, the C, D2, and LP strains had functionally identical alleles. These intercross data were used to form a simple algorithm for interrogating microsatellite, single nucleotide polymorphism (SNP), brain gene expression, and sequence databases. The results obtained point to Kcnj9 (which has a markedly lower expression in the B6 strain) as being the likely QTG. Further, it is suggested that the lower expression in the B6 strain results from a polymorphism in the 5'-UTR that disrupts the binding of at least three transcription factors. Overall, the method described should be widely applicable to the analysis of QTLs.

Genetic correlates of gene expression in recombinant inbred strains: a relational model system to explore neurobehavioral phenotypes

Full genome sequencing, high-density genotyping, expanding sets of microarray assays, and systematic phenotyping of neuroanatomical and behavioral traits are producing a wealth of data on the mouse central nervous system (CNS). These disparate resources are still poorly integrated. One solution is to acquire these data using a common reference population of isogenic lines of mice, providing a point of integration between the data types. Recombinant inbred (RI) mice, derived through inbreeding of progeny from an inbred cross, are a powerful tool for complex trait mapping and analysis of the challenging phenotypes of neuroscientific interest. These isogenic RI lines are a retrievable genetic resource that can be repeatedly studied using a wide variety of assays. Diverse data sets can be related through fixed and known genomes, using tools such as the interactive web-based system for complex trait analysis, www.WebQTL.org. In this report, we demonstrate the use of WebQTL to explore complex interactions among a wide variety of traits--from mRNA transcripts to the impressive behavioral and pharmacological variation among RI strains. The relational approach exploiting a common set of strains facilitates study of multiple effects of single genes (pleiotropy) without a priori hypotheses required. Here we demonstrate the power of this technique through genetic correlation of gene expression with a database of neurobehavioral phenotypes collected in these strains of mice through more than 20 years of experimentation. By repeatedly studying the same panel of mice, early data can be re-examined in light of technological advances unforeseen at the time of their initial collection.

Genomics tools for QTL analysis and gene discovery

In recent years, several new genomics resources and tools have become available that will greatly assist quantitative trait locus (QTL) mapping and cloning of the corresponding genes. Genome sequences, tens of thousands of molecular markers, microarrays, and knock-out collections are being applied to QTL mapping, facilitating the use of natural accessions for gene discovery.

Multiple cross and inbred strain haplotype mapping of complex-trait candidate genes

Identifying complex-trait candidate genes after initial low-resolution mapping has proven to be a difficult and labor-intensive undertaking, usually requiring years to develop and analyze congenic strains. As a result, to date, few complex-trait genes have been discovered. Recently it was suggested that SNP haplotype analysis in inbred strains might be useful for mapping of complex traits. In this study, we have combined medium-resolution haplotype mapping with multiple experimental cross-mapping experiments to reduce the number of potential candidate genes in a complex-trait candidate interval. Coincident mapping of a modifier gene in multiple experimental crosses using different inbred strains is consistent with the common inheritance of a modifier allele. A haplotype map was developed in four inbred strains of mice used in our complex-trait mapping crosses across the proximal 10 cM of proximal Chromosome 19 to identify haplotype blocks that segregate appropriately. Only ~23 out of >400 genes met this criteria. This strategy coupled with tissue and expression arrays, as well as our recently described common pathway analysis to reduce the number of high-priority candidates, may provide a rapid, efficient method to identify and prioritize complex-trait candidate genes without requiring construction of congenic mouse strains.

Analysis of quantitative trait loci that influence animal behavior

Behavioral differences between inbred strains of mice and rats have a genetic basis that can now be dissected using quantitative trait locus (QTL) analysis. Over the last 10 years, a large number of genetic loci that influence behavior have been mapped. In this article I review what that information has revealed about the genetic architecture of behavior. I show that most behaviors are influenced by QTL of small effect, each contributing to less than 10% of the variance of a behavioral trait. The small effect of each QTL on behavioral variation suggests that the mutational spectrum is different from that which results in Mendelian disorders. Regions of DNA should be appropriately prioritized to find the molecular variants, for instance by looking at sequences that control the level of gene expression rather than variants in coding regions. While the number of allelic loci that can contribute to a trait is large, this is not necessarily the case: the analysis of selected strains shows that a remarkably small number of QTL can explain the bulk of the genetic variation in behavior. I conclude by arguing that genetic mapping has more to offer than a starting point for positional cloning projects. With advances in multivariate analyses, mapping can also test hypotheses about the psychological processes that give rise to behavioral variation.

Genome-wide analysis of hepatic fibrosis in inbred mice identifies the susceptibility locus Hfib1 on chromosome 15

BACKGROUND & AIMS

Host genetic factors are likely to contribute to the variable course of hepatic fibrosis in response to chronic liver injury. Similarly, the fibrotic response differs among inbred mouse strains after challenge with CCl(4). Our aim was to identify unknown susceptibility loci for hepatic fibrosis in a cross between fibrosis-susceptible and -resistant inbred mice.

METHODS

Seven inbred mouse strains were treated with CCl(4), and hepatic fibrosis was phenotypically characterized by histology, hepatic hydroxyproline levels, and serum surrogate markers. F(1) hybrids of susceptible BALB/cJ and resistant A/J inbred strains were intercrossed to obtain 358 F(2) progeny. Quantitative trait loci (QTL) that determine hepatic fibrosis were identified by genome-wide interval mapping and haplotype analysis.

RESULTS

In this model, marked strain differences in fibrosis susceptibility exist, with BALB/c inbred mice being most susceptible. The hydroxyproline levels of F(1) mice resemble the resistant parental strains, indicating that fibrosis susceptibility is a recessive trait. QTL analysis identifies a susceptibility locus on chromosome 15 that significantly affects the stage of fibrosis and hydroxyproline levels. According to standard nomenclature, this locus is called Hfib1 (hepatic fibrogenic gene 1). Hfib1 is defined by genetic markers D15Mit26 and D15Mit122. A suggestive QTL on chromosome 2 colocalizes with the complement factor 5 gene, known to be mutated in the resistant strain A.

CONCLUSIONS

The set of inbred strains provides a framework for systematic analysis of fibrogenic genes. QTL mapping is useful to identify genetic susceptibility loci for hepatic fibrosis that might harbor new molecular targets for antifibrotic drug design.