Strategies for mapping and cloning quantitative trait genes in rodents

Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis

The genetic architecture of transcript-level variation is largely unknown. The genetic determinants of transcript-level variation were characterized in a recombinant inbred line (RIL) population (n = 211) of Arabidopsis thaliana using whole-genome microarray analysis and expression quantitative trait loci (eQTL) mapping of transcript levels as expression traits (e-traits). Genetic control of transcription was highly complex: one-third of the quantitatively controlled transcripts/e-traits were regulated by cis-eQTL, and many trans-eQTL mapped to hotspots that regulated hundreds to thousands of e-traits. Several thousand eQTL of large phenotypic effect were detected, but almost all (93%) of the 36,871 eQTL were associated with small phenotypic effects (R(2) < 0.3). Many transcripts/e-traits were controlled by multiple eQTL with opposite allelic effects and exhibited higher heritability in the RILs than their parents, suggesting nonadditive genetic variation. To our knowledge, this is the first large-scale global eQTL study in a relatively large plant mapping population. It reveals that the genetic control of transcript level is highly variable and multifaceted and that this complexity may be a general characteristic of eukaryotes.

Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits

A central challenge in evolutionary biology is to identify genes underlying ecologically important traits and describe the fitness consequences of naturally occurring variation at these loci. To address this goal, several novel approaches have been developed, including 'population genomics,' where a large number of molecular markers are scored in individuals from different environments with the goal of identifying markers showing unusual patterns of variation, potentially due to selection at linked sites. Such approaches are appealing because of (1) the increasing ease of generating large numbers of genetic markers, (2) the ability to scan the genome without measuring phenotypes and (3) the simplicity of sampling individuals without knowledge of their breeding history. Although such approaches are inherently applicable to non-model systems, to date these studies have been limited in their ability to uncover functionally relevant genes. By contrast, quantitative genetics has a rich history, and more recently, quantitative trait locus (QTL) mapping has had some success in identifying genes underlying ecologically relevant variation even in novel systems. QTL mapping, however, requires (1) genetic markers that specifically differentiate parental forms, (2) a focus on a particular measurable phenotype and (3) controlled breeding and maintenance of large numbers of progeny. Here we present current advances and suggest future directions that take advantage of population genomics and quantitative genetic approaches - in both model and non-model systems. Specifically, we discuss advantages and limitations of each method and argue that a combination of the two provides a powerful approach to uncovering the molecular mechanisms responsible for adaptation.

Fragmentation of Two Quantitative Trait Loci Controlling Collagen-Induced Arthritis Reveals a New Set of Interacting Subloci

Linkage analysis of F(2) crosses has led to identification of large numbers of quantitative trait loci (QTL) for complex diseases, but identification of the underlying genes has been more difficult. Reasons for this could be complications that arise from separation of interacting or neighboring loci. We made a partial advanced intercross (PAI) to characterize and fine-map linkage to collagen-induced arthritis in two chromosomal regions derived from the DBA/1 strain and crossed into the B10.Q strain: Cia7 on chromosome 7 and a locus on chromosome 15. Only Cia7 was detected by a previous F(2) cross. Linkage analysis of the PAI revealed a different linkage pattern than the F(2) cross, adding multiple loci and strong linkage to the previously unlinked chromosome 15 region. Subcongenic strains derived from animals in the PAI confirmed the loci and revealed additional subloci. In total, no less than seven new loci were identified. Several loci interacted and three loci were protective, thus partly balancing the effect of the disease-promoting loci. Our results indicate that F(2) crosses do not reveal the full complexity of identified QTLs, and that detection is more dependent on the genetic context of a QTL than the potential effect of the underlying gene.

The mouse as a model for human biology: a resource guide for complex trait analysis

The mouse has been a powerful force in elucidating the genetic basis of human physiology and pathophysiology. From its beginnings as the model organism for cancer research and transplantation biology to the present, when dissection of the genetic basis of complex disease is at the forefront of genomics research, an enormous and remarkable mouse resource infrastructure has accumulated. This review summarizes those resources and provides practical guidelines for their use, particularly in the analysis of quantitative traits.

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.

The endophenotype concept in psychiatric genetics

The idea that some phenotypes bear a closer relationship to the biological processes that give rise to psychiatric illness than diagnostic categories has attracted considerable interest. Much effort has been devoted to finding such endophenotypes, partly because it is believed that the genetic basis of endophenotypes will be easier to analyse than that of psychiatric disease. This belief depends in part on the assumption that the effect sizes of genetic loci contributing to endophenotypes are larger than those contributing to disease susceptibility, hence increasing the chance that genetic linkage and association tests will detect them. We examine this assumption by applying meta-analytical techniques to genetic association studies of endophenotypes. We find that the genetic effect sizes of the loci examined to date are no larger than those reported for other phenotypes. A review of the genetic architecture of traits in model organisms also provides no support for the view that the effect sizes of loci contributing to phenotypes closer to the biological basis of disease is any larger than those contributing to disease itself. While endophenotype measures may afford greater reliability, it should not be assumed that they will also demonstrate simpler genetic architecture.

Genetic study of variation in normal mouse iron homeostasis reveals ceruloplasmin as an HFE-hemochromatosis modifier gene

BACKGROUND & AIMS

Genetic hemochromatosis is one of the most common genetic disorders, with progressive tissue iron overload leading to severe clinical complications. In Northern European populations, genetic hemochromatosis is usually caused by homozygosity for the C282Y mutation in the HFE protein. However, penetrance of this mutation is incomplete, suggesting that other genetic and environmental factors contribute to its differential biologic or clinical expression.

METHODS

To identify genes modifying iron homeostasis, we screened the 27 recombinant congenic strains of the C3H/DiSnA-C57BL/10ScSnA/Dem series for tissue and serum iron indices and genotyped 18 microsatellite markers in (C3H/DiSnA x HcB-2) F2 hybrid mice.

RESULTS

We identified 1 locus encompassing the Ceruloplasmin (Cp) gene with a strong linkage with liver iron, serum iron, and transferrin levels but not with spleen iron. Sequencing of Cp showed an R435X nonsense mutation in exon 7 in C3H/DiSnA mice. To evaluate whether Cp might act as a modifier gene of genetic hemochromatosis, we intercrossed C3H Hfe(-/-) and C3HDiSnA Cp(R435X/R435X) mice. As expected, we found that double-mutant mice deposited more iron in the liver than mice defective for either one or both genes. In contrast, Hfe(-/-) x Cp(R435/R435X) or Cp(R435X/R435X) x Hfe(+/-) showed 30% decrease in liver iron when compared with single mutant mice.

CONCLUSIONS

This study highlights the existence of complex interactions between Cp and HFE and represents the first example of a modifier gene with a protective effect, in which heterozygosity reduces the iron load in the context of HFE deficiency.

Can pharmacology possibly have a role for bioinformatics?

In today's information-driven culture, there is virtually no walk of life that is not impacted on by computing. As a bridging discipline in the health sciences with activities that span both basic science and clinical interests, modern pharmacology is no exception. As the plethora of data and databases spawned by the 'omics' generation expand in number and complexity, bioinformatics is necessary to manage, integrate and exploit this cohort of data so that the appropriate links to molecular pathology and therapeutic response can be made. Bioinformatics is now an integral part of drug discovery and development. This article reviews the use of bioinformatics in this process, from target identification and validation, to pharmacogenomics, toxicogenomics and systems biology.

Application of nutrigenomic concepts to Type 2 diabetes mellitus

The genetic makeup that individuals inherit from their ancestors is responsible for variation in responses to food and susceptibility to chronic diseases such as Type 2 diabetes mellitus (T2DM). Common variations in gene sequences, such as single nucleotide polymorphisms, produce differences in complex traits such as height or weight potential, food metabolism, food-gene interactions, and disease susceptibilities. Nutritional genomics, or nutrigenomics, is the study of how foods affect the expression of genetic information in an individual and how an individual's genetic makeup affects the metabolism and response to nutrients and other bioactive components in food. Since both diet and genes alter one's health and susceptibility to disease, identifying genes that are regulated by diet and that cause or contribute to chronic diseases could result in the development of diagnostic tools, individualized intervention, and eventually strategies for maintaining health. Translating this research through clinical studies promises contributions to the development of personalized medicine that includes nutritional as well as drug interventions. Reviewed here are the key nutrigenomic concepts that help explain aspects of the development and complexity of T2DM.

The genetic landscape of type 2 diabetes in mice

Inbred mouse strains provide genetic diversity comparable to that of the human population. Like humans, mice have a wide range of diabetes-related phenotypes. The inbred mouse strains differ in the response of their critical physiological functions, such as insulin sensitivity, insulin secretion, beta-cell proliferation and survival, and fuel partitioning, to diet and obesity. Most of the critical genes underlying these differences have not been identified, although many loci have been mapped. The dramatic improvements in genomic and bioinformatics resources are accelerating the pace of gene discovery. This review describes how mouse genetics can be used to discover diabetes-related genes, summarizes how the mouse strains differ in their diabetes-related phenotypes, and describes several examples of how loci identified in the mouse may directly relate to human diabetes.

R/qtlDesign: inbred line cross experimental design

An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html .

Mouse SNP Miner: an annotated database of mouse functional single nucleotide polymorphisms

Background

The mapping of quantitative trait loci in rat and mouse has been extremely successful in identifying chromosomal regions associated with human disease-related phenotypes. However, identifying the specific phenotype-causing DNA sequence variations within a quantitative trait locus has been much more difficult. The recent availability of genomic sequence from several mouse inbred strains (including C57BL/6J, 129X1/SvJ, 129S1/SvImJ, A/J, and DBA/2J) has made it possible to catalog DNA sequence differences within a quantitative trait locus derived from crosses between these strains. However, even for well-defined quantitative trait loci (<10 Mb) the identification of candidate functional DNA sequence changes remains challenging due to the high density of sequence variation between strains.

Description

To help identify functional DNA sequence variations within quantitative trait loci we have used the Ensembl annotated genome sequence to compile a database of mouse single nucleotide polymorphisms (SNPs) that are predicted to cause missense, nonsense, frameshift, or splice site mutations (available at ). For missense mutations we have used the PolyPhen and PANTHER algorithms to predict whether amino acid changes are likely to disrupt protein function.

Conclusion

We have developed a database of mouse SNPs predicted to cause missense, nonsense, frameshift, and splice-site mutations. Our analysis revealed that 20% and 14% of missense SNPs are likely to be deleterious according to PolyPhen and PANTHER, respectively, and 6% are considered deleterious by both algorithms. The database also provides gene expression and functional annotations from the Symatlas, Gene Ontology, and OMIM databases to further assess candidate phenotype-causing mutations. To demonstrate its utility, we show that Mouse SNP Miner successfully finds a previously identified candidate SNP in the taste receptor, Tas1r3, that underlies sucrose preference in the C57BL/6J strain. We also use Mouse SNP Miner to derive a list of candidate phenotype-causing mutations within a previously uncharacterized QTL for response to morphine in the 129/Sv strain.

Rodent models: utility for candidate gene studies in human attention-deficit hyperactivity disorder (ADHD)

Attention-deficit hyperactivity disorder (ADHD) is a common neurobehavioral disorder defined by symptoms of developmentally inappropriate inattention, impulsivity and hyperactivity. Behavioral genetic studies provide overwhelming evidence for a significant genetic role in the pathogenesis of the disorder. Rodent models have proven extremely useful in helping understand more about the genetic basis of ADHD in humans. A number of well-characterized rodent models have been proposed, consisting of inbred strains, selected lines, genetic knockouts, and transgenic animals, which have been used to inform candidate gene studies in ADHD. In addition to providing information about the dysregulation of known candidate genes, rodents are excellent tools for the identification of novel ADHD candidate genes. While not yet widely used to identify genes for ADHD-like behaviors in rodents, quantitative trait loci (QTL) mapping approaches using recombinant inbred strains, heterogeneous stock mice, and chemically mutated animals have the potential to revolutionize our understanding of the genetic basis of ADHD.

Fine mapping of collagen-induced arthritis quantitative trait loci in an advanced intercross line

The generation of advanced intercross lines (AIL) is a powerful approach for high-resolution fine mapping of quantitative trait loci (QTLs), because they accumulate much more recombination events compared with conventional F2 intercross and N2 backcross. However, the application of this approach is severely hampered by the requirements of excessive resources to maintain such crosses, i.e., in terms of animal care, space, and time. Therefore, in this study, we produced an AIL to fine map collagen-induced arthritis (CIA) QTLs using comparatively limited resources. We used only 308 (DBA/1 x FVB/N)F11/12 AIL mice to refine QTLs controlling the severity and onset of arthritis as well as the Ab response and T cell subset in CIA, namely Cia2, Cia27, and Trmq3. These QTLs were originally identified in (DBA/1 x FVB/N)F2 progeny. The confidence intervals of the three QTLs were refined from 40, 43, and 48 Mb to 12, 4.1, and 12 Mb, respectively. The data were complemented by the use of another QTL fine-mapping approach, haplotype analysis, to further refine Cia2 into a 2-Mb genomic region. To aid in the search for candidate genes for the QTLs, genome-wide expression profiling was performed to identify strain-specific differentially expressed genes within the confidence intervals. Of the 1396 strain-specific differentially expressed genes, 3, 3, and 12 genes were within the support intervals of the Cia2, Cia27, and Trmq3, respectively. In addition, this study revealed that Cia27 and Trmq3 controlling anti-CII IgG2a Ab and CD4:CD8 T cell ratio, respectively, also regulated CIA clinical phenotypes.

A simplified QTL mapping approach for screening and mapping of novel AFLP markers associated with beef marbling

Genome screening of quantitative trait loci (QTL) for a complex trait is usually costly and highly laborious, as it requires a large number of markers spanning the whole genome. Here we present a simplified approach for screening and mapping of QTL-linked markers for beef marbling using a WagyuxLimousin F(2) reference population. This simplified approach involves integration of the amplified fragment length polymorphism (AFLP) with DNA pooling and selective genotyping and comparative bioinformatics tools. AFLP analysis on two high and two low marbling DNA pools yielded ten visually different markers. Among them, four were confirmed based on individual AFLP validation. Sequencing and in silico characterization assigned two of these AFLP markers to bovine chromosomes 1 (BTA1) and 13 (BTA13), which are orthologous to human chromosomes HSA21q22.2 and HSA10p11.23 with both regions harboring QTL for obesity-related phenotypes. Both AFLP markers showed significantly large additive genetic effects (0.28+/-0.11 on BTA1 and 0.54+/-0.21 on BTA13) on beef-marbling score (BMS) (P<0.05). Overall, this approach is less time consuming, inexpensive and in particular, suitable for screening and mapping QTL-linked markers when targeting one or a few complex traits.

Candidate genes of anxiety-related behavior in HAB/LAB rats and mice: Focus on vasopressin and glyoxalase-I

Two animal models of trait anxiety, HAB/LAB rats and mice, are described, representing inborn extremes in anxiety-related behavior. The comprehensive phenotypical characterization included basal behavioral features, stress-coping strategies and neuroendocrine responses upon stressor exposure with HAB animals being hyper-anxious, preferring passive coping, emitting more stressor-induced ultrasonic vocalization calls and showing typical peculiarities of the hypothalamic-pituitary-adrenocortical axis and line-specific patterns of Fos expression in the brain indicative of differential neuronal activation. In most cases, unselected Wistar rats and CD1 mice, respectively, displayed intermediate behaviors. In both HAB/LAB rats and mice, the behavioral phenotype has been found to be significantly correlated with the expression of the neuropeptide arginine vasopressin (AVP) at the level of the hypothalamic paraventricular nucleus (PVN). Additional receptor antagonist approaches in HABs confirmed that intra-PVN release of AVP is likely to contribute to hyper-anxiety and depression-like behavior. As shown exemplarily in HAB rats and LAB mice, single nucleotide polymorphisms (SNPs) in regulatory structures of the AVP gene underlie AVP-mediated phenotypic phenomena; in HAB rats, a SNP in the promoter of the AVP gene leads to reduced binding of the transcriptional repressor CBF-A, thus causing AVP overexpression and overrelease. Conversely, in LAB mice, a SNP in the AVP gene seems to cause an amino acid exchange in the signal peptide, presumably leading to a deficit in bioavailable AVP likely to underlie the total hypo-anxiety of LAB mice in combination with signs of central diabetes insipidus. Another feature of LAB mice is overexpression of glyoxalase-I. The functional characterization of this enzyme will determine its involvement in anxiety-related behavior beyond that of a reliable biomarker. The further identification of quantitative trait loci, candidate genes (and their products) and SNPs will not only help to explain inter-individual variation in emotional behavior, but will also reveal novel targets for anxiolytic and antidepressive interventions.

Fine mapping reveals sex bias in quantitative trait loci affecting growth, skeletal size and obesity-related traits on mouse chromosomes 2 and 11

Previous speed congenic analysis has suggested that the expression of growth and obesity quantitative trait loci (QTL) on distal mouse chromosomes (MMU) 2 and 11, segregating between the CAST/EiJ (CAST) and C57BL/6J-hg/hg (HG) strains, is dependent on sex. To confirm, fine map, and further evaluate QTL x sex interactions, we constructed congenic by recipient F2 crosses for the HG.CAST-(D2Mit329-D2Mit457)N(6) (HG2D) and HG.CAST-(D11Mit260-D11Mit255)N(6) (HG11) congenic strains. Over 700 F2 mice were densely genotyped and phenotyped for a panel of 40 body and organ weight, skeletal length, and obesity-related traits at 9 weeks of age. Linkage analysis revealed 20 QTL affecting a representative subset of phenotypes in HG2DF2 and HG11F2 mice. The effect of sex was quantified by comparing two linear models: the first model included sex as an additive covariate and the second incorporated sex as an additive and an interactive covariate. Of the 20 QTL, 8 were sex biased, sex specific, or sex antagonistic. Most traits were regulated by single QTL; however, two closely linked loci were identified for five traits in HG2DF2 mice. Additionally, the confidence intervals for most QTL were significantly reduced relative to the original mapping results, setting the stage for quantitative trait gene (QTG) discovery. These results highlight the importance of assessing the contribution of sex in complex trait analyses.

Genetic dissection of ethanol tolerance in the budding yeast Saccharomyces cerevisiae

Uncovering genetic control of variation in ethanol tolerance in natural populations of yeast Saccharomyces cerevisiae is essential for understanding the evolution of fermentation, the dominant lifestyle of the species, and for improving efficiency of selection for strains with high ethanol tolerance, a character of great economic value for the brewing and biofuel industries. To date, as many as 251 genes have been predicted to be involved in influencing this character. Candidacy of these genes was determined from a tested phenotypic effect following gene knockout, from an induced change in gene function under an ethanol stress condition, or by mutagenesis. This article represents the first genomics approach for dissecting genetic variation in ethanol tolerance between two yeast strains with a highly divergent trait phenotype. We developed a simple but reliable experimental protocol for scoring the phenotype and a set of STR/SNP markers evenly covering the whole genome. We created a mapping population comprising 319 segregants from crossing the parental strains. On the basis of the data sets, we find that the tolerance trait has a high heritability and that additive genetic variance dominates genetic variation of the trait. Segregation at five QTL detected has explained approximately 50% of phenotypic variation; in particular, the major QTL mapped on yeast chromosome 9 has accounted for a quarter of the phenotypic variation. We integrated the QTL analysis with the predicted candidacy of ethanol resistance genes and found that only a few of these candidates fall in the QTL regions.

Behavioral genomics of honeybee foraging and nest defense

The honeybee has been the most important insect species for study of social behavior. The recently released draft genomic sequence for the bee will accelerate honeybee behavioral genetics. Although we lack sufficient tools to manipulate this genome easily, quantitative trait loci (QTLs) that influence natural variation in behavior have been identified and tested for their effects on correlated behavioral traits. We review what is known about the genetics and physiology of two behavioral traits in honeybees, foraging specialization (pollen versus nectar), and defensive behavior, and present evidence that map-based cloning of genes is more feasible in the bee than in other metazoans. We also present bioinformatic analyses of candidate genes within QTL confidence intervals (CIs). The high recombination rate of the bee made it possible to narrow the search to regions containing only 17–61 predicted peptides for each QTL, although CIs covered large genetic distances. Knowledge of correlated behavioral traits, comparative bioinformatics, and expression assays facilitated evaluation of candidate genes. An overrepresentation of genes involved in ovarian development and insulin-like signaling components within pollen foraging QTL regions suggests that an ancestral reproductive gene network was co-opted during the evolution of foraging specialization. The major QTL influencing defensive/aggressive behavior contains orthologs of genes involved in central nervous system activity and neurogenesis. Candidates at the other two defensive-behavior QTLs include modulators of sensory signaling (Am5HT₇ serotonin receptor, AmArr4 arrestin, and GABA-B-R1 receptor). These studies are the first step in linking natural variation in honeybee social behavior to the identification of underlying genes.

Individual QTLs controlling quantitative variation in blood pressure inherited in a Mendelian mode

We studied three possible genotypes at 10 well-defined blood pressure (BP) QTLs using congenic rat lines. The central question was whether the hypertensive or normotensive allele is dominant, or whether there is partial dominance. The congenic strains were employed to investigate the BP effects of alleles originating from normotensive rats in the background of hypertensive Dahl salt-sensitive (DSS) rats. The normotensive alleles at eight QTLs were fully dominant over DSS alleles, which we tentatively interpreted as indicating that DSS rats incurred a loss of function at these loci and that the QTLs produced BP-reducing agents. In contrast, the normotensive allele of only one QTL was recessive over its DSS counterpart, implying a gain of function at this QTL or a null allele involved in generating a BP-elevating agent. Only one locus, C17QTL, had alleles exhibiting partial dominance. These estimates of dominance differ considerably from those obtained by QTL analysis in a F2 cross. This disagreement demonstrates the importance of establishing a cause-effect relationship between a QTL and its phenotypic effect via congenic strains. The dominance relationships suggest pertinent strategies for gene identification and pharmaceutical intervention.

Incorporating genome-scale tools for studying energy homeostasis

Mammals have evolved complex regulatory systems that enable them to maintain energy homeostasis despite constant environmental challenges that limit the availability of energy inputs and their composition. Biological control relies upon intricate systems composed of multiple organs and specialized cell types that regulate energy up-take, storage, and expenditure. Because these systems simultaneously perform diverse functions and are highly integrated, they are extremely difficult to understand in terms of their individual component contributions to energy homeostasis. In order to provide improved treatments and clinical options, it is important to identify the principle genetic and molecular components, as well as the systemic features of regulation. To begin, many of these features can be discovered by integrating experimental technologies with advanced methods of analysis. This review focuses on the analysis of transcriptional data derived from microarrays and how it can complement other experimental techniques to study energy homeostasis.

Pharmacogenomic evaluation of the antidepressant citalopram in the mouse tail suspension test

The identification of genetic variants regulating antidepressant response in human patients would allow for more individualized, rational, and successful drug treatments. We have previously identified the BALB/cJ inbred mouse strain as highly responsive to the selective serotonin reuptake inhibitor (SSRI) citalopram in the tail suspension test (TST), a widely used and well-established screening paradigm for detecting compounds with antidepressant activity. In contrast, A/J mice did not show a significant response to citalopram in this test despite exposure to equivalent plasma levels of the drug. To identify genetic determinants of this differential response, 506 F2 mice from an intercross between BALB/cJ and A/J mice were phenotyped. Composite interval mapping of 92 mice from the phenotypic extremes revealed three loci on chromosomes 7, 12, and 19 affecting citalopram response in the TST. The quantitative trait locus (QTL) at the telomeric end of chromosome 19 showed the greatest level of significance. Three candidate genes residing in this locus include those for vesicular monoamine transporter 2 (VMAT2, slc18a2), alpha 2A adrenergic receptor (adra2a), and beta 1 adrenergic receptor (adrb1). The protein coding regions of these three genes in BALB/cJ and A/J mice were sequenced and two polymorphisms were found in VMAT2 (Leu117Pro and Ser505Pro), while the transcribed regions of adra2a and adrb1 were of identical sequence between strains. Follow-up studies are needed to determine if the VMAT2 polymorphisms are functional and if they could explain the chromosome 19 QTL. The present quantitative trait study suggests possible candidate genes for human pharmacogenetic studies of therapeutic responses to SSRIs such as citalopram.

Heritability and tissue specificity of expression quantitative trait loci

Variation in gene expression is heritable and has been mapped to the genome in humans and model organisms as expression quantitative trait loci (eQTLs). We applied integrated genome-wide expression profiling and linkage analysis to the regulation of gene expression in fat, kidney, adrenal, and heart tissues using the BXH/HXB panel of rat recombinant inbred strains. Here, we report the influence of heritability and allelic effect of the quantitative trait locus on detection of cis- and trans-acting eQTLs and discuss how these factors operate in a tissue-specific context. We identified several hundred major eQTLs in each tissue and found that cis-acting eQTLs are highly heritable and easier to detect than trans-eQTLs. The proportion of heritable expression traits was similar in all tissues; however, heritability alone was not a reliable predictor of whether an eQTL will be detected. We empirically show how the use of heritability as a filter reduces the ability to discover trans-eQTLs, particularly for eQTLs with small effects. Only 3% of cis- and trans-eQTLs exhibited large allelic effects, explaining more than 40% of the phenotypic variance, suggestive of a highly polygenic control of gene expression. Power calculations indicated that, across tissues, minor differences in genetic effects are expected to have a significant impact on detection of trans-eQTLs. Trans-eQTLs generally show smaller effects than cis-eQTLs and have a higher false discovery rate, particularly in more heterogeneous tissues, suggesting that small biological variability, likely relating to tissue composition, may influence detection of trans-eQTLs in this system. We delineate the effects of genetic architecture on variation in gene expression and show the sensitivity of this experimental design to tissue sampling variability in large-scale eQTL studies.

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.

Confirmation of quantitative trait loci for ethanol sensitivity and neurotensin receptor density in crosses derived from the inbred high and low alcohol sensitive selectively bred rat lines

RATIONALE

Genetically influenced alcohol sensitivity is thought to be an important risk factor for the development of alcoholism. An effective first step for identifying genes that mediate variation in alcohol sensitivity is through quantitative trait loci (QTL) mapping in model organisms.

OBJECTIVE

Fourteen provisional QTLs related to alcohol sensitivity were previously mapped in an F2 derived from the IHAS1 and ILAS1 rat lines. The objective of the current study was to confirm those QTLs in an independently derived F2 and in congenics that were bred for two of the loci.

MATERIALS AND METHODS

IHAS1 X ILAS1 F2 (n=450) were tested for alcohol-induced loss of righting reflex (LORR), blood ethanol concentration at regain of righting reflex (BECRR), sensitivity and acute tolerance on the Rotarod, and neurotensin receptor density (NTR1). Rats were genotyped at the 14 candidate loci and QTL mapping was conducted. Reciprocal congenic strains were bred for loci on chromosomes 2 and 5 and tested for LORR and BECRR.

RESULTS

Four LORR QTLs were mapped at the suggestive or significant level (chromosomes 2, 5, 12, and 13). BECRR was mapped to chromosomes 5, 12, and 13 either in the original or current experiment. Results of the congenic experiment also support QTLs for LORR and BECRR on chromosomes 2 and 5. QTLs for NTR1 density and behavior on the Rotarod were not confirmed.

CONCLUSIONS

QTL mapping in crosses derived from the IHAS1 and ILAS1 has successfully identified loci related to alcohol sensitivity. Recombinant congenics are now being bred to more finely map the confirmed QTLs.

Novel method for high-throughput phenotyping of sleep in mice

Assessment of sleep in mice currently requires initial implantation of chronic electrodes for assessment of electroencephalogram (EEG) and electromyogram (EMG) followed by time to recover from surgery. Hence, it is not ideal for high-throughput screening. To address this deficiency, a method of assessment of sleep and wakefulness in mice has been developed based on assessment of activity/inactivity either by digital video analysis or by breaking infrared beams in the mouse cage. It is based on the algorithm that any episode of continuous inactivity of > or =40 s is predicted to be sleep. The method gives excellent agreement in C57BL/6J male mice with simultaneous assessment of sleep by EEG/EMG recording. The average agreement over 8,640 10-s epochs in 24 h is 92% (n = 7 mice) with agreement in individual mice being 88-94%. Average EEG/EMG determined sleep per 2-h interval across the day was 59.4 min. The estimated mean difference (bias) per 2-h interval between inactivity-defined sleep and EEG/EMG-defined sleep was only 1.0 min (95% confidence interval for mean bias -0.06 to +2.6 min). The standard deviation of differences (precision) was 7.5 min per 2-h interval with 95% limits of agreement ranging from -13.7 to +15.7 min. Although bias significantly varied by time of day (P = 0.0007), the magnitude of time-of-day differences was not large (average bias during lights on and lights off was +5.0 and -3.0 min per 2-h interval, respectively). This method has applications in chemical mutagenesis and for studies of molecular changes in brain with sleep/wakefulness.

The p47 GTPases Igtp and Irgb10 map to the Chlamydia trachomatis susceptibility locus Ctrq-3 and mediate cellular resistance in mice

Infections caused by the bacteria Chlamydia trachomatis contribute to diverse pathologies in a variety of human populations. We previously used a systemic model of C. trachomatis infection in mice to map three quantitative trait loci that influence in vivo susceptibility differences between the C57BL/6J and C3H/HeJ inbred strains of mouse. One of these quantitative trait loci, Ctrq-3, influences an IFN-gamma-dependent susceptibility difference in primary embryonic fibroblasts isolated from these strains. Here we use fine structure mapping in congenic fibroblasts carrying DNA from the susceptible parent to localize the effect of Ctrq-3 to a 1.2-megabase interval of genomic DNA that contains Irgb10 and Igtp, two members of the IFN-gamma-inducible p47 family of GTPases. This class of proteins has been widely implicated in resistance to intracellular pathogens in mice. We analyzed expression of Irgb10 and Igtp in parental and congenic embryonic fibroblasts treated with IFN-gamma and found that relatively resistant fibroblasts express more Irgb10 than relatively susceptible fibroblasts. However, we also found that abolishing the expression of either Irgb10 or Igtp increases susceptibility of embryonic fibroblasts to C. trachomatis. Thus, we conclude that, although a difference in Irgb10 expression is likely responsible for the effect of Ctrq-3 on susceptibility to C. trachomatis, both genes play a role in intracellular resistance to C. trachomatis.

Geometric morphometrics defines shape differences in the cortical area map of C57BL/6J and DBA/2J inbred mice

Background

We previously described planar areal differences in adult mouse visual, somatosensory, and neocortex that collectively discriminated C57BL/6J and DBA/2J inbred strain identity. Here we use a novel application of established methods of two-dimensional geometric morphometrics to examine shape differences in the cortical area maps of these inbred strains.

Results

We used Procrustes superimposition to align a reliable set of landmarks in the plane of the cortical sheet from tangential sections stained for the cytochrome oxidase enzyme. Procrustes superimposition translates landmark configurations to a common origin, scales them to a common size, and rotates them to minimize an estimate of error. Remaining variation represents shape differences. We compared the variation in shape between C57BL/6J and DBA/2J relative to that within each strain using a permutation test of Goodall's F statistic. Significant differences in shape in the posterior medial barrel subfield (PMBSF), as well as differences in shape across primary sensory areas, characterize the cortical area maps of these common inbred, isogenic strains.

Conclusion

C57BL/6J and DBA/2J have markedly different cortical area maps, in both size and shape. These differences suggest polymorphism in genetic factors underlying cortical specification, even between common isogenic strains. Comparing cortical phenotypes between normally varying inbred mice or between genetically modified mice can identify genetic contributions to cortical specification. Geometric morphometric analysis of shape represents an additional quantitative tool for the study of cortical development, regardless of whether it is studied from phenotype to gene or gene to phenotype.

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.

New ENU-induced semidominant mutation, Ali18, causes inflammatory arthritis, dermatitis, and osteoporosis in the mouse

Inflammation is a complex cellular and humoral response against trauma and infection, and its presence leads to destruction of tissue in humans. The mechanisms that initiate inflammatory diseases remain largely unknown because of complex interactions between multiple genetic and environmental factors during pathogenesis. Animal models for human diseases offer dissection of complex pathogenesis by inbred genetic backgrounds and controlled circumstances. In this article we report a chemically induced new mutation, Ali18 (Abnormal limb), as a mouse model for inflammatory arthritis and dermatitis. Ali18/+ mice exhibit rubor and swelling of footpads in hindlimbs in adults. In Ali18/Ali18 mice, the digits in forelimbs and hindlimbs and tails were necrotic and/or deformed by severe swelling. Histologic analysis revealed infiltration of mixed populations of inflammatory cells into bone marrow, peripheral joints, and skin in the affected areas of Ali18/Ali18 mice. In addition, generalized osteoporosis-like phenotypes were confirmed by dual energy X-ray absorptiometry (DXA), microcomputed tomography (muCT), and peripheral quantitative computed tomography (pQCT) in homozygous animals. Whereas the Ali18 mutation was mapped to a single locus, the phenotype presentation was altered by complex modifier effects from other inbred genetic backgrounds. Detailed analysis of the Ali18 phenotype and identification of the mutation and its modifier genes may provide molecular insights into the complex nature of inflammatory diseases and the relationship between inflammation and bone metabolism.

Cochlear developmental defect and background-dependent hearing thresholds in the Jackson circler (jc) mutant mouse

Jackson circler (jc) is a spontaneous, recessive mouse mutation that results in circling behavior and an impaired acoustic startle response. In this study, we refined the phenotypic and genetic parameters of the original jc mutation and characterized a new mutant allele, jc(2J). In open-field behavior tests, homozygous jc mutants exhibited abnormal circling and ambulatory behavior that was indistinguishable from that of phenotypically similar mutants with defects in the vestibule of the inner ear. The jc/jc and jc(2J)/jc(2J) mice had stable elevated auditory-evoked brainstem response (ABR) thresholds at the 16kHz stimulus of 88+/-9dB sound pressure levels (SPL) and 43+/-11dB SPL, respectively. Peak latencies and peak time intervals were normal in jc mutants. The jc mice showed no measurable distortion-product otoacoustic emissions (DPOAEs) above the system noise floor. In the mutant cochlea, the apical turn failed to form due to the developmental growth arrest of the cochlear duct at the level of the first turn at gestational day 13.5. In a large intrasubspecific intercross, jc localized to a 0.2cM interval at position 25cM on chromosome 10, which is homologous to the human 6q21 region. On CZECHII/Ei and CAST/Ei backgrounds jc/jc mutant hearing thresholds at the 16kHz stimulus were significantly lower than those observed on the C57BL/6J background, with means of 62+/-22dB SPL and 55+/-18dB SPL, respectively. Genome-wide linkage scans of backcross, intercross, and congenic progeny revealed a complex pattern of genetic and stochastic effects.

Sequencing, expression, and functional analyses support the candidacy of Ncf2 in susceptibility to Salmonella typhimurium infection in wild-derived mice

A recessive Salmonella Typhimurium susceptibility locus (immunity to Typhimurium (Ity3) was reported previously on distal mouse chromosome 1 using a cross between C57BL/6J and wild-derived MOLF/Ei mice. This quantitative trait locus is located in a genomic region spanning 84 Mb, rich in candidate genes for which a role in host resistance to Salmonella infection is either known or can be envisioned. In this study, we report the evaluation of neutrophil cytosolic factor 2 (Ncf2) as a candidate Salmonella susceptibility gene for Ity3. Ncf2 encodes p67phox, a subunit of the multiprotein enzyme complex NADPH oxidase, known to be responsible for the generation of superoxides. Congenic mice carrying the Ity3 region from MOLF/Ei, B6.MOLF-Ity/Ity3 were more susceptible to infection compared with control mice heterozygous at Ity3, B6.MOLF-Ity/Ity3(MOLF/B6), confirming the existence of a recessive Salmonella susceptibility locus on distal chromosome 1. Spleen Ncf2 expression levels were lower in infected congenic mice homozygous for the MOLF/Ei allele at Ity3 compared with mice heterozygous at Ity3. C57BL/6J and MOLF/Ei Ncf2 sequence comparisons revealed one nonconservative amino acid change (R394Q) in the functional and highly conserved Phox and Bem1 domain of the protein. Functional analysis revealed that the MOLF/Ei allele had reduced PMA- and Salmonella-induced superoxide induction as compared with their wild-type counterparts ex vivo. The R394Q substitution seems to occur on an amino acid involved in electrostatic interactions with p40phox, crucial in its activation. Moreover, a human mutation in the corresponding R395W, resulting in chronic granulatomous disease, is known to lead to reduced superoxide levels. These results support the candidacy of Ncf2 as the gene underlying Ity3.

Genome-wide genetic association of complex traits in heterogeneous stock mice

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.

Replication of Small Effect Quantitative Trait Loci for Behavioral Traits Facilitated by Estimation of Effect Size from Independent Cohorts

Quantitative trait locus (QTL) mapping is often done in a single segregating population, such as a backcross or an intercross. Both QTL location and effect size are then estimated from the same dataset. This approach results in an over-estimate of effect size for two reasons: (1) LOD scores, which are maximized over numerous point-wise tests, are correlated with estimated effect size and (2) small effect QTLs are often undetected in underpowered experiments, yielding inflated effect sizes for detected QTLs (the Beavis effect). When it is impractical to maintain or generate large population sizes, an alternative is to use two populations, one for initial detection and localization and a second for a locus-matched estimate of effect size, not conditioned on significance. Recombinant inbred (RI) panels are eminently suitable for this approach, as each strain genotype can be sampled repeatedly. We present mapping results from the LXS RI panel for two behavioral phenotypes relating to ethanol response: low-dose ethanol activation and loss of righting following high-dose injection. Both the phenotypes were measured in two or three independent cohorts, which were then used to re-estimate effect size. Many small-effect QTLs replicated using this approach, but in all cases, effect size, in the replicate cohorts, was reduced from the initial estimate, often substantially. Such a reduction will have important consequences for power analyses in which sample sizes are determined for subsequent confirmation studies.

Genetics of hearing loss: Allelism and modifier genes produce a phenotypic continuum

Recent genetic and genomic studies have greatly advanced our knowledge of the structure and function of genes involved in hearing loss. We are starting to recognize, however, that many of these genes do not appear to follow traditional Mendelian expression patterns and are subject to the effects of allelism and modifier genes. This review presents two genes illustrative of this concept that have varied expression pattern such that they may produce either syndromic or nonsyndromic hearing loss. One of these genes, cadherin 23, produces a spectrum of phenotypic traits, including presbycusis, nonsyndromic prelingual hearing loss (DFNB12), and syndromic hearing loss as part of Usher syndrome (Usher 1D). Missense mutations in CDH23 have been associated with presbycusis and DFNB12, whereas null alleles cause the majority of Usher 1D. Modifier gene products that interact with cadherin 23 also affect the phenotypic spectrum. Similarly, allelsim in the gene encoding wolframin (WFS1) causes either a nonsyndromic dominant low-frequency hearing loss (DFNA6/14/38) or Wolfram syndrome. Missense mutations within a defined region are associated with DFNA6/14/38, while more severe mutations spanning WFS1 are found in Wolfram syndrome patients. The phenotypic spectrum of Wolfram syndrome is also hypothesized to be influenced by modifier genes products. These studies provide increasing evidence for the importance of modifier genes in elucidating the functional pathways of primary hearing loss genes. Characterizing modifier genes may result in better treatment options for patients with hearing loss and define new diagnostic and therapeutic targets.

Identification of QTLs controlling gene expression networks defined a priori

Background

Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses.

Results

We describe a statistical approach that is capable of assessing higher-order a priori defined gene network response, as measured by microarrays. This analysis detected significant network variation between two Arabidopsis thaliana accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 a priori-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara.

Conclusion

This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of a priori definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses.

A comprehensive mouse IBD database for the efficient localization of quantitative trait loci

Traditional fine-mapping approaches in mouse genetics that go from a linkage region to a candidate gene are very costly and time consuming. Shared ancestry regions, along with the combination of genetics and genomics approaches, provide a powerful tool to shorten the time and effort required to identify a causative gene. In this article we present a novel methodology that predicts IBD (identical by descent) regions between pairs of inbred strains using single nucleotide polymorphism (SNP) maps. We have validated this approach by comparing the IBD regions, estimated using different algorithms, to the results derived using the sequence information in the strains present in the Celera Mouse Database. We showed that based on the current publicly available SNP genotypes, large IBD regions (>1 Mb) can be identified successfully. By assembling a list of 21,514 SNPs in 61 common inbred strains, we inferred IBD regions between all pairs of strains and confirmed, for the first time, that existing quantitative trait genes (QTG) and susceptibility genes all lie outside of IBD regions. We also illustrated how knowledge of IBD structures can be applied to strain selection for future crosses. We have made our results available for data mining and download through a public website ( http://www.mouseibd.florida.scripps.edu ).

Prediction of cis-QTLs in a pair of inbred mouse strains with the use of expression and haplotype data from public databases

Cis-QTLs are important candidates for any other trait that maps to the same locus. In this article we have used publicly available databases and a small microarray data set to "map" cis-QTLs in the ILS and ISS inbred mouse strains without the need to generate microarray data from an ILSXISS segregating population. Expression data were obtained from brains of C57BL/6, DBA/2, ILS, and ISS. Cis-QTLs were mapped for the 760 transcripts found to be differentially expressed between the C57BL/6 and DBA/2 using expression data previously obtained from the BXD RIs. The 469 detected cis-QTLs were then examined for SNP haplotypes and expression patterns that could relate the ILS and ISS to the C57BL/6 and DBA/2. Of the 338 cis-QTL transcripts that had informative haplotypes, 189 were significantly different between the ILS and ISS with 184 showing segregation of haplotype with expression. These were considered to be probable cis-QTLs in the ILS and ISS. There were almost certainly additional ILS/ISS cis-QTLs among the other transcripts with informative haplotypes, but in the absence of an ILS/ISS expression difference, the level of confidence was reduced. Several of the putative ILS/ISS cis-QTLs are considered important candidate genes because they are linked to ILS/ISS behavioral QTLs. A potential ascertainment bias related to strain-dependent target sequences was observed suggesting that as much as 35% of the cis-QTLs were hybridization artifacts. Nonetheless, the results suggest that this approach is an economical and widely applicable method for mapping cis-QTLs in a strain pair of interest.

Integrated gene expression profiling and linkage analysis in the rat

The combined application of genome-wide expression profiling from microarray experiments with genetic linkage analysis enables the mapping of expression quantitative trait loci (eQTLs) which are primary control points for gene expression across the genome. This approach allows for the dissection of primary and secondary genetic determinants of gene expression. The cis-acting eQTLs in practice are easier to investigate than the trans-regulated eQTLs because they are under simpler genetic control and are likely to be due to sequence variants within the gene itself or its neighboring regulatory elements. These genes are therefore candidates both for variation in gene expression and for contributions to whole-body phenotypes, particularly when these are located within known and relevant physiologic QTLs. Multiple trans-acting eQTLs tend to cluster to the same genetic location, implying shared regulatory control mechanisms that may be amenable to network analysis to identify gene clusters within the same metabolic pathway. Such clusters may ultimately underlie development of individual complex, whole-body phenotypes. The combined expression and linkage approach has been applied successfully in several mammalian species, including the rat which has specific features that demonstrate its value as a model for studying complex traits.

Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice

The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as “genetical genomics”) is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of “key driver” genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases.

A hitchhiker's guide to behavioral analysis in laboratory rodents

Genes and environment are both essential and interdependent determinants of behavioral responses. Behavioral genetics focuses on the role of genes on behavior. In this article, we aim to provide a succinct, but comprehensive, overview of the different means through which behavioral analysis may be performed in rodents. We give general recommendations for planning and performing behavioral experiments in rats and mice, followed by brief descriptions of experimental paradigms most commonly used for the analysis of reflexes, sensory function, motor function and exploratory, social, emotional and cognitive behavior. We end with a discussion of some of the shortcomings of current concepts of genetic determinism and argue that the genetic basis of behavior should be analyzed in the context of environmental factors.

The genetic basis of emotional behaviour in mice

The last decade has witnessed a steady expansion in the number of quantitative trait loci (QTL) mapped for complex phenotypes. However, despite this proliferation, the number of successfully cloned QTL has remained surprisingly low, and to a great extent limited to large effect loci. In this review, we follow the progress of one complex trait locus; a low magnitude moderator of murine emotionality identified some 10 years ago in a simple two-strain intercross, and successively resolved using a variety of crosses and fear-related phenotypes. These experiments have revealed a complex underlying genetic architecture, whereby genetic effects fractionate into several separable QTL with some evidence of phenotype specificity. Ultimately, we describe a method of assessing gene candidacy, and show that given sufficient access to genetic diversity and recombination, progression from QTL to gene can be achieved even for low magnitude genetic effects.

PAPPA2, an enzyme that cleaves an insulin-like growth-factor-binding protein, is a candidate gene for a quantitative trait locus affecting body size in mice

Identifying genes responsible for quantitative variation remains a major challenge. We previously identified a quantitative trait locus (QTL) affecting body size that segregated between two inbred strains of mice. By fine mapping, we have refined the location of this QTL to a genomic region containing only four protein-coding genes. One of these genes, PAPPA2, is a strong candidate because it codes for an enzyme that cleaves insulin-like growth-factor-binding protein 5 (IGFBP-5), an important stimulator of bone formation. Among littermates that segregate only for the four-gene region, we show that the QTL has a significant effect on the circulating levels of IGFBP-5 and IGFBP-3 (the latter subject to limited degradation by PAPPA2), but not on levels of IGFBP-2 and IGFBP-4, which are not cleaved by PAPPA2. There are 14 nonsynonymous SNPs among QTL alleles, which may affect the activity of the translated protein. The refinement of the target region to four genes and the finding that the QTL affects IGFBP-5 levels suggest that PAPPA2 may be involved with normal postnatal growth. Our mapping results also illustrate the potentially fractal nature of QTL: as we mapped our QTL with increasing resolution, what appeared to be a single QTL resolved into three closely linked QTL (previous work), and then one of these was further dissected into two in this study.

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.

The molecular genetic architecture of human personality: beyond self-report questionnaires

Molecular genetic studies of personality began with two high impact papers in 1996 that showed provisional associations between the dopamine DRD4 exon III repeat region and Novelty Seeking/Extraversion. These first two reports were shortly followed by an investigation linking Neuroticism/Harm Avoidance with the serotonin transporter (SLC6A4) promoter region polymorphism (5-HTTLPR). In the ensuing decade, thousands of subjects have been studied for association between these genes and personality, assessed by using self-report questionnaires, with erratic success in replication of the first findings for Novelty Seeking (DRD4) and Harm Avoidance (5-HTTLPR). Small effect sizes characteristic of non-Mendelian traits, polygenic patterns of inheritance and true heterogeneity between studies confound attempts to reach a consensus regarding the role of common polymorphisms in contributing to personality domains. Nevertheless, the current state of personality genetics is far from being bleak. Several new paradigms especially functional neuroimaging or 'imaging genomics' have strengthened the connection between 5-HTTLPR and anxiety-related personality traits. The demonstrations that early environmental information can considerably strengthen and even uncover associations between genes and behavior (Caspi's seminal studies and more recently the demonstration that early environment impacts on DRD4 and Novelty Seeking) are notable and herald a new era of personality genetics. Finally, consideration of the broader phenotypic expression of common polymorphisms (e.g. the 'social brain', altruism, etc.) and the use of new experimental paradigms including neurophysiological, neuropsychological and computer games that go beyond the narrow self-report questionnaire design will enable a deeper understanding of how common genetic polymorphisms modulate human behavior. Human personality, defined by Webster as the quality or state of being a person or the complex of characteristics that distinguishes an individual, surely requires a more encompassing view towards understanding its complex molecular genetic architecture.

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.