Multiple trait analysis of genetic mapping for quantitative trait loci

QTL studies reveal little relevance of chilling-related seedling traits for yield in maize

Prolonged low temperature phases and short-term cold spells often occur in spring during the crucial stages of early maize (Zea mays L.) development. The effect of low temperature-induced growth retardation at the seedling stage on final yield is poorly studied. Therefore, the aim was to identify genomic regions associated with morpho-physiological traits at flowering and harvest stage and their relationship to previously identified quantitative trait loci (QTLs) for photosynthesis and morpho-physiological traits from the same plants at seedling stage. Flowering time, plant height and shoot biomass components at harvest were measured in a dent mapping population for cold tolerance studies, which was sown in the Swiss Midlands in early and late spring in two consecutive years. Early-sown plants exhibited chilling stress during seedling stage, whereas late-sown plants grew under favorable conditions. Significant QTLs, which were stable across environments, were found for plant height and for the time of flowering. The QTLs for flowering were frequently co-localized with QTLs for plant height or ear dry weight. The comparison with QTLs detected at seedling stage revealed only few common QTLs. A pleiotropic effect was found on chromosome 3 which revealed that a good photosynthetic performance of the seedling under warm conditions had a beneficial effect on plant height and partially on biomass at harvest. However, a high chilling tolerance of the seedling seemingly had an insignificant or small negative effect on the yield.

Mapping interspecific genetic architecture in a host-parasite interaction system

Under a hypothesis that the host-parasite interaction system is governed by genome-for-genome interaction, we propose a genetic model that integrates genetic information from both the host and parasite genomes. The model can be used for mapping quantitative trait loci (QTL) conferring the interaction between host and parasite and detecting interactions among these QTL. A one-dimensional genome-scan strategy is used to map QTL in both the host and parasite genomes simultaneously conditioned on selected pairs of markers controlling the background genetic variation; a two-dimensional genome-scan procedure is conducted to search for epistasis within the host and parasite genomes and interspecific QTL-by-QTL interactions between the host and parasite genomes. A permutation test is adopted to calculate the empirical threshold to control the experimentwise false-positive rate of detected QTL and QTL interactions. Monte Carlo simulations were conducted to examine the reliability and the efficiency of the proposed models and methods. Simulation results illustrated that our methods could provide reasonable estimates of the parameters and adequate powers for detecting QTL and QTL-by-QTL interactions.

Quantitative genetic analysis of life-history traits of Caenorhabditis elegans in stressful environments

BACKGROUND

Organisms live in environments that vary. For life-history traits that vary across environments, fitness will be maximised when the phenotype is appropriately matched to the environmental conditions. For the free-living nematode Caenorhabditis elegans, we have investigated how two major life-history traits, (i) the development of environmentally resistant dauer larvae and (ii) reproduction, respond to environmental stress (high population density and low food availability), and how these traits vary between lines and the genetic basis of this variation.

RESULTS

We found that lines of C. elegans vary in their phenotypic plasticity of dauer larva development, i.e. there is variation in the likelihood of developing into a dauer larva for the same environmental change. There was also variation in how lifetime fecundity and the rate of reproduction changed under conditions of environmental stress. These traits were related, such that lines that are highly plastic for dauer larva development also maintain a high population growth rate when stressed. We identified quantitative trait loci (QTL) on two chromosomes that control the dauer larva development and population size phenotypes. The QTLs affecting the dauer larva development and population size phenotypes on chromosome II are closely linked, but are genetically separable. This chromosome II QTL controlling dauer larva development does not encompass any loci previously identified to control dauer larva development. This chromosome II region contains many predicted 7-transmembrane receptors. Such proteins are often involved in information transduction, which is clearly relevant to the control of dauer larva development.

CONCLUSION

C. elegans alters both its larval development and adult reproductive strategy in response to environmental stress. Together the phenotypic and genotypic data suggest that these two major life-history traits are co-ordinated responses to environmental stress and that they are, at least in part, controlled by the same genomic regions.

Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability

Grain yield is a major goal for the improvement of durum wheat, particularly in drought-prone areas. In this study, the genetic basis of grain yield (GY), heading date (HD), and plant height (PH) was investigated in a durum wheat population of 249 recombinant inbred lines evaluated in 16 environments (10 rainfed and 6 irrigated) characterized by a broad range of water availability and GY (from 5.6 to 58.8 q ha(-1)). Among the 16 quantitative trait loci (QTL) that affected GY, two major QTL on chromosomes 2BL and 3BS showed significant effects in 8 and 7 environments, with R2 values of 21.5 and 13.8% (mean data of all 16 environments), respectively. In both cases, extensive overlap was observed between the LOD profiles of GY and PH, but not with those for HD. QTL specific for PH were identified on chromosomes 1BS, 3AL, and 7AS. Additionally, three major QTL for HD on chromosomes 2AS, 2BL, and 7BS showed limited or no effects on GY. For both PH and GY, notable epistasis between the chromosome 2BL and 3BS QTL was detected across several environments.

A systems biology approach to drug discovery

Common human diseases like obesity and diabetes are driven by complex networks of genes and any number of environmental factors. To understand this complexity in hopes of identifying targets and developing drugs against disease, a systematic approach is required to elucidate the genetic and environmental factors and interactions among and between these factors, and to establish how these factors induce changes in gene networks that in turn lead to disease. The explosion of large-scale, high-throughput technologies in the biological sciences has enabled researchers to take a more systems biology approach to study complex traits like disease. Genotyping of hundreds of thousands of DNA markers and profiling tens of thousands of molecular phenotypes simultaneously in thousands of individuals is now possible, and this scale of data is making it possible for the first time to reconstruct whole gene networks associated with disease. In the following sections, we review different approaches for integrating genetic expression and clinical data to infer causal relationships among gene expression traits and between expression and disease traits. We further review methods to integrate these data in a more comprehensive manner to identify common pathways shared by the causal factors driving disease, including the reconstruction of association and probabilistic causal networks. Particular attention is paid to integrating diverse information to refine these types of networks so that they are more predictive. To highlight these different approaches in practice, we step through an example on how Insig2 was identified as a causal factor for plasma cholesterol levels in mice.

Clustering and principal-components approach based on heritability for mapping multiple gene expressions

When the number of phenotypes in a genetic study is on the scale of thousands, such as in studies concerning thousands of gene expression levels, the single-trait analysis is computationally intensive, and heavy adjustment of multiple comparisons is required. Traditional multivariate genetic linkage analysis for quantitative traits focuses on mapping only a few phenotypes and is not feasible for a large number of traits. To cope with high-dimensional phenotype data, clustering analysis and principal-component analysis (PCA) are proposed to reduce the data dimensionality and to map shared genetic contributions for multiple traits. However, standard clustering analysis and PCA are applicable for independent observations. In most genetic studies, where family data are collected, these standard analyses can only be applied to founders and can lead to the loss of information. Here, we proposed a clustering method that can exploit family structure information and applied the method to 29 gene expression levels mapped to a reported hot spot on chromosome 14. We then used a PCA approach based on heritability applicable to small number of traits to combine phenotypes in the clusters. Lastly, we used a penalized PCA approach based on heritability applicable to arbitrary number of traits to combine 150 gene expression levels with the highest heritability. Genome-wide multipoint linkage analysis was carried out on the individual traits and on the combined traits. Two previously reported peaks on chromosomes 14 and 20 were identified. Linkage evidence was stronger for traits derived from methods that incorporate family structure information.

Characterisation of lanthanum toxicity for root growth of Arabidopsis thaliana from the aspect of natural genetic variation

The mechanism of lanthanum (La³⁺) toxicity on root growth of Arabidopsis was studied by physiological and genetic approaches using Landsberg erecta (Ler) × Columbia (Col) recombinant inbred lines (RILs) and other natural accessions. Quantitative trait locus (QTL) analyses revealed regulation of La³⁺ tolerance of the Ler × Col RILs by multiple genetic factors consisted of three significant QTLs and seven epistatic interacting loci pairs. The La content in the root tip was not correlated with La³⁺ tolerance in the RILs, indicating that the observed La³⁺ rhizotoxicity was not related to direct toxicity of La³⁺ in the symplast. The La³⁺ tolerance of root growth in the RILs was not correlated with Al³⁺ and Cu²⁺ tolerances, but was correlated with tolerances for other rare earth elements, including Gd³⁺, a known Ca²⁺ channel antagonist, and verapamil, a Ca²⁺ channel blocker. The genetic architecture of verapamil tolerance in root growth, which was identified by QTL analysis, was closely related to that of La³⁺ tolerance. La³⁺ tolerance and verapamil tolerance or Gd³⁺ tolerance in natural accessions of Arabidopsis also showed a positive correlation. These results indicate that the major La³⁺ toxicity on the root growth of Arabidopsis may involve its action as a Ca²⁺ channel antagonist.

Gene-based modelling for rice: An opportunity to enhance the simulation of rice growth and development?

Process-based crop simulation models require employment of new knowledge for continuous improvement. To simulate growth and development of different genotypes of a given crop, most models use empirical relationships or parameters defined as genetic coefficients to represent the various cultivar characteristics. Such a loose introduction of different cultivar characteristics can result in bias within a simulation, which could potentially integrate to a high simulation error at the end of the growing season when final yield at maturity is predicted. Recent advances in genetics and biomolecular analysis provide important opportunities for incorporating genetic information into process-based models to improve the accuracy of the simulation of growth and development and ultimately the final yield. This improvement is especially important for complex applications of models. For instance, the effect of the climate change on the crop growth processes in the context of natural climatic and soil variability and a large range of crop management options (e.g., N management) make it difficult to predict the potential impact of the climate change on the crop production. Quantification of the interaction of the environmental variables with the management factors requires fine tuning of the crop models to consider differences among different genotypes. In this paper we present this concept by reviewing the available knowledge of major genes and quantitative trait loci (QTLs) for important traits of rice for improvement of rice growth modelling and further requirements. It is our aim to review the assumption of the adequacy of the available knowledge of rice genes and QTL information to be introduced into the models. Although the rice genome sequence has been completed, the development of gene-based rice models still requires additional information than is currently unavailable. We conclude that a multidiscipline research project would be able to introduce this concept for practical applications.

A conceptual framework for mapping quantitative trait Loci regulating ontogenetic allometry

Although ontogenetic changes in body shape and its associated allometry has been studied for over a century, essentially nothing is known about their underlying genetic and developmental mechanisms. One of the reasons for this ignorance is the unavailability of a conceptual framework to formulate the experimental design for data collection and statistical models for data analyses. We developed a framework model for unraveling the genetic machinery for ontogenetic changes of allometry. The model incorporates the mathematical aspects of ontogenetic growth and allometry into a maximum likelihood framework for quantitative trait locus (QTL) mapping. As a quantitative platform, the model allows for the testing of a number of biologically meaningful hypotheses to explore the pleiotropic basis of the QTL that regulate ontogeny and allometry. Simulation studies and real data analysis of a live example in soybean have been performed to investigate the statistical behavior of the model and validate its practical utilization. The statistical model proposed will help to study the genetic architecture of complex phenotypes and, therefore, gain better insights into the mechanistic regulation for developmental patterns and processes in organisms.

Genetic dissection of cytonuclear epistasis in line crosses

Dissection of cytonuclear interactions is fundamentally important for understanding the genetic architecture of complex traits. Here we propose a mating design based on reciprocal crosses and extend the existing QTL mapping method to evaluate the contribution of cytoplasm and QTL x cytoplasm interactions to the phenotypic variation. Efficiency of the design and method is demonstrated via simulated data.

Genetics of microenvironmental canalization in Arabidopsis thaliana

Canalization is a fundamental feature of many developmental systems, yet the genetic basis for this property remains elusive. We examine the genetic basis of microenvironmental canalization in the model plant Arabidopsis thaliana, focusing on differential developmental stability between genotypes in one fitness and four quantitative morphological traits. We measured developmental stability in genetically identical replicates of two populations of recombinant inbred (RI) lines and one population of geographically widespread accessions of A. thaliana grown in two different photoperiod-controlled environments. We were able to map quantitative trait loci associated with developmental stability. We also identified a candidate gene, ERECTA, that may contribute to microenvironmental canalization in rosette leaf number under long-day photoperiods, and analysis of mutant lines indicates that the er-105 allele results in increased canalization for this trait. ERECTA, which encodes a signaling protein, appears to act as an ecological amplifier by transducing developmental noise (e.g., microenvironmental variation) into phenotypic differentiation. We also measured genotypic selection on four plant architecture traits and find evidence for selection for both increased and decreased canalization at various traits.

Indirect genetic effects from ecological interactions in Arabidopsis thaliana

Indirect genetic effects arise when genes expressed in one individual affect the expression of traits in other individuals. The importance of indirect genetic effects has been recognized for a diversity of evolutionary processes including kin selection, sexual selection, community structure and multilevel selection, but data regarding their genetic architecture and prevalence throughout the genome remain scarce, especially for interactions between unrelated individuals. Using a set of 411 Bay-0 x Shahdara Arabidopsis recombinant inbred lines grown with Landsberg neighbours, we examined quantitative trait loci (QTL) having direct and indirect effects on size, developmental, and fitness related traits. Using an interval mapping approach, we identified 15 QTL with direct effects and found that 13 of these QTL had significant indirect effects on trait expression in neighbouring plants. These results suggest widespread pleiotropy, as nearly all direct effect QTL have associated pleiotropic indirect effects. Paradoxically, most indirect effects were of the same sign as direct effects, creating a pattern of nearly universal positive pleiotropy that makes most covariances between direct and indirect effects positive. These results are consistent with a complex genetic basis for intraspecific interactions, but suggest that interactions between neighbouring plants are largely positive, rather than negative as would be expected for competition. In addition to their evolutionary and ecological importance, these pleiotropic relationships between DGE and IGE loci have implications for quantitative genetic studies of natural populations as well as experimental design considerations. Additionally, studies that ignore IGEs may over- or underestimate quantitative genetic parameters, as well as the effect of and variance contributed by QTL.

Bayesian mapping of quantitative trait loci for multiple complex traits with the use of variance components

Complex traits important for humans are often correlated phenotypically and genetically. Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits. Compared with single-trait analysis, joint mapping addresses more questions and has advantages for power of QTL detection and precision of parameter estimation. Some statistical methods have been developed to map QTLs underlying multiple traits, most of which are based on maximum-likelihood methods. We develop here a multivariate version of the Bayes methodology for joint mapping of QTLs, using the Markov chain-Monte Carlo (MCMC) algorithm. We adopt a variance-components method to model complex traits in outbred populations (e.g., humans). The method is robust, can deal with an arbitrary number of alleles with arbitrary patterns of gene actions (such as additive and dominant), and allows for multiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mixtures of continuous traits and discrete traits). Under a Bayesian framework, parameters--including the number of QTLs--are estimated on the basis of their marginal posterior samples, which are generated through two samplers, the Gibbs sampler and the reversible-jump MCMC. In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy. The performance of our method is evaluated in simulations with full-sib families. The results show that our proposed Bayesian joint-mapping method performs well for mapping multiple QTLs in situations of either bivariate continuous traits or mixed data types. Compared with the analysis for each trait separately, Bayesian joint mapping improves statistical power, provides stronger evidence of QTL detection, and increases precision in estimation of parameter and QTL position. We also applied the proposed method to a set of real data and detected a coincident linkage responsible for determining bone mineral density and areal bone size of wrist in humans.

Mapping QTLs with digenic epistasis under multiple environments and predicting heterosis based on QTL effects

Mixed linear model approach was proposed for mapping QTLs with the digenic epistasis and QTL by environment (QE) interaction as well as additive and dominant effects. Monte Carlo simulations indicated that the proposed method could provide unbiased estimations for both positions and genetic main effects of QTLs, as well as unbiased predictions for QE interaction effects. A method was suggested for predicting heterosis based on individual QTL effects. The immortalized F(2) (IF(2)) population constructed by random mating among RI or DH lines is appropriate for mapping QTLs with epistasis and their QE interaction. Based on the models and methodology proposed, we developed a QTL mapping software, QTLMapper 2.0 on the basis of QTLmapper 1.0, which is suitable for analyzing populations of DH, RIL, F(2) and IF(2). Data of thousand grain weight of IF(2) population with 240 lines derived from elite hybrid rice Shanyou 63 were analyzed as a worked example.

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone.

Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms

BACKGROUND

Genetic variation explains a considerable part of observed phenotypic variation in gene expression networks. This variation has been shown to be located both locally (cis) and distally (trans) to the genes being measured. Here we explore to which degree the phenotypic manifestation of local and distant polymorphisms is a dynamic feature of regulatory design.

RESULTS

By combining mathematical models of gene expression networks with genetic maps and linkage analysis we find that very different network structures and regulatory motifs give similar cis/trans linkage patterns. However, when the shape of the cis-regulatory input functions is more nonlinear or threshold-like, we observe for all networks a dramatic increase in the phenotypic expression of distant compared to local polymorphisms under otherwise equal conditions.

CONCLUSION

Our findings indicate that genetic variation affecting the form of cis-regulatory input functions may reshape the genotype-phenotype map by changing the relative importance of cis and trans variation. Our approach combining nonlinear dynamic models with statistical genetics opens up for a systematic investigation of how functional genetic variation is translated into phenotypic variation under various systemic conditions.

A computational approach for functional mapping of quantitative trait loci that regulate thermal performance curves

Whether and how thermal reaction norm is under genetic control is fundamental to understand the mechanistic basis of adaptation to novel thermal environments. However, the genetic study of thermal reaction norm is difficult because it is often expressed as a continuous function or curve. Here we derive a statistical model for dissecting thermal performance curves into individual quantitative trait loci (QTL) with the aid of a genetic linkage map. The model is constructed within the maximum likelihood context and implemented with the EM algorithm. It integrates the biological principle of responses to temperature into a framework for genetic mapping through rigorous mathematical functions established to describe the pattern and shape of thermal reaction norms. The biological advantages of the model lie in the decomposition of the genetic causes for thermal reaction norm into its biologically interpretable modes, such as hotter-colder, faster-slower and generalist-specialist, as well as the formulation of a series of hypotheses at the interface between genetic actions/interactions and temperature-dependent sensitivity. The model is also meritorious in statistics because the precision of parameter estimation and power of QTLdetection can be increased by modeling the mean-covariance structure with a small set of parameters. The results from simulation studies suggest that the model displays favorable statistical properties and can be robust in practical genetic applications. The model provides a conceptual platform for testing many ecologically relevant hypotheses regarding organismic adaptation within the Eco-Devo paradigm.

QTL mapping of agronomic traits in tef [Eragrostis tef (Zucc) Trotter]

BACKGROUND

Tef [Eragrostis tef (Zucc.) Trotter] is the major cereal crop in Ethiopia. Tef is an allotetraploid with a base chromosome number of 10 (2n = 4x = 40) and a genome size of 730 Mbp. The goal of this study was to identify agronomically important quantitative trait loci (QTL) using recombinant inbred lines (RIL) derived from an inter-specific cross between E. tef and E. pilosa (30-5).

RESULTS

Twenty-two yield-related and morphological traits were assessed across eight different locations in Ethiopia during the growing seasons of 1999 and 2000. Using composite interval mapping and a linkage map incorporating 192 loci, 99 QTLs were identified on 15 of the 21 linkage groups for 19 traits. Twelve QTLs on nine linkage groups were identified for grain yield. Clusters of more than five QTLs for various traits were identified on seven linkage groups. The largest cluster (10 QTLs) was identified on linkage group 8; eight of these QTLs were for yield or yield components, suggesting linkage or pleotrophic effects of loci. There were 15 two-way interactions of loci to detect potential epistasis identified and 75% of the interactions were derived from yield and shoot biomass. Thirty-one percent of the QTLs were observed in multiple environments; two yield QTLs were consistent across all agro-ecology zones. For 29.3% of the QTLs, the alleles from E. pilosa (30-5) had a beneficial effect.

CONCLUSION

The extensive QTL data generated for tef in this study will provide a basis for initiating molecular breeding to improve agronomic traits in this staple food crop for the people of Ethiopia.

Knockdown resistance to heat stress and slow recovery from chill coma are genetically associated in a quantitative trait locus region of chromosome 2 inDrosophila melanogaster

In insects, two ecologically relevant traits of thermal adaptation are knockdown resistance to high temperature (KRHT) and chill-coma recovery (CCR). Chromosome 2 of Drosophila melanogaster was tested for quantitative trait loci (QTL) affecting both CCR and KRHT in backcrosses between homosequential lines that are fixed for the standard (noninverted) sequence of this autosome. These lines were obtained by artificial selection on KRHT and subsequent inbreeding from a stock that was derived from a single wild population. Heat-induced expression of the 70KD heat-shock protein (Hsp70) was also examined for variation between the lines. Composite interval mapping was performed for each trait on each reciprocal backcross, identifying one QTL region in the middle of chromosome 2 for both KRHT and CCR. The largest estimates of additive effects were found in pericentromeric regions of chromosome 2, accounting for 10-14% (CCR) and 10-17% (KRHT) of the phenotypic variance in BC populations. No QTL was found in the region of the heat-shock factor (hsf) gene. However, the two parental lines have diverged in the heat-induced Hsp70 expression. Distribution of KRHT QTL on chromosome 2 was similar between this study based on crosses between lines selected from a single wild population and previous work based on crosses between selection lines from different continents. Colocalized QTL showed a trade-off association between CCR and KRHT, which should be the result of either multiple, tightly linked trait-specific genes or a single gene with pleiotropic effects on the traits. We discuss candidate loci contained within the QTL regions.

A ridge penalized principal-components approach based on heritability for high-dimensional data

OBJECTIVE

To develop a ridge penalized principal-components approach based on heritability that can be applied to high-dimensional family data.

METHODS

The first principal component of heritability for a trait constellation is defined as a linear combination of traits that maximizes the heritability, which is equivalent to maximize the family-specific variation relative to the subject-specific variation. To analyze high-dimensional data and prevent overfitting, we propose a penalized principal-components approach based on heritability by adding a ridge penalty to the subject-specific variation. We choose the optimal regularization parameter by cross-validation.

RESULTS

The principal-components approach based on heritability with and without ridge penalty was compared to the usual principal-components analysis in four settings. The penalized principal-components of heritability analysis had substantially larger coefficients for the traits with genetic effect than for the traits with no genetic effect, while the non-regularized analysis failed to identify the genetic traits. In addition, linkage analysis on the combined traits showed that the power of the proposed methods was higher than the usual principal-components analysis and the non-regularized principal-components of heritability analysis.

CONCLUSIONS

The penalized principal-components approach based on heritability can effectively handle large number of traits with family structure and provide power gain for linkage analysis. The cross-validation procedure performs well in choosing optimal magnitude of penalty.

A growth QTL (Pbwg1) region of mouse chromosome 2 contains closely linked loci affecting growth and body composition

Previous QTL studies have identified 24 QTLs for body weight and growth from 3 to 10 weeks after birth in an intersubspecific backcross mouse population between C57BL/6J and wild Mus musculus castaneus that has 60% of the body size of C57BL/6J. The castaneus allele at the most potent QTL (Pbwg1) on proximal chromosome 2 retards growth. In this study we have developed a congenic strain with a 44.1-Mb interval containing the castaneus allele at Pbwg1 by recurrent backcrossing to C57BL/6J. The congenic mouse developed was characterized by significantly higher body weight gain between 1 and 3 weeks of age and lower weight of white fat pads at 10 weeks of age than C57BL/6J. However, no clear difference in body weight at 1-10 weeks of age was observed between congenic and C57BL/6J strains. QTL analysis with 269 F(2) mice between the two strains did not identify any QTLs for body weight at 1, 3, 6, and 10 weeks of age, but it discovered eight closely linked QTLs affecting body weight gain from 1 to 3 weeks of age, lean body weight, weight of white fat pads, and body length within the Pbwg1 region. The castaneus alleles at all fat pad QTLs reduced the phenotypes, whereas at the remaining growth and body composition QTLs, they increased the trait values. These results illustrate that Pbwg1, which initially appeared to be a single locus, was resolved into several loci with opposite effects on the composition traits of overall body weight. This gives a reason for the loss of the Pbwg1 effect found in the original backcross population.

Bayesian mapping of genomewide interacting quantitative trait loci for ordinal traits

Development of statistical methods and software for mapping interacting QTL has been the focus of much recent research. We previously developed a Bayesian model selection framework, based on the composite model space approach, for mapping multiple epistatic QTL affecting continuous traits. In this study we extend the composite model space approach to complex ordinal traits in experimental crosses. We jointly model main and epistatic effects of QTL and environmental factors on the basis of the ordinal probit model (also called threshold model) that assumes a latent continuous trait underlies the generation of the ordinal phenotypes through a set of unknown thresholds. A data augmentation approach is developed to jointly generate the latent data and the thresholds. The proposed ordinal probit model, combined with the composite model space framework for continuous traits, offers a convenient way for genomewide interacting QTL analysis of ordinal traits. We illustrate the proposed method by detecting new QTL and epistatic effects for an ordinal trait, dead fetuses, in a F(2) intercross of mice. Utility and flexibility of the method are also demonstrated using a simulated data set. Our method has been implemented in the freely available package R/qtlbim, which greatly facilitates the general usage of the Bayesian methodology for genomewide interacting QTL analysis for continuous, binary, and ordinal traits in experimental crosses.

Principal component analysis of quantitative trait loci for immune response to adenovirus in mice

Data on the duration of transgene expression in the liver, the presence of cytotoxic T lymphocytes (CTLs) against adenovirus, and serum cytokines from 18 strains of C57BL/6 x DBA/2 (B x D) recombinant inbred mice were analyzed. Our aim was to detect quantitative trait loci (QTLs) that may have causal relationship with the duration of adenovirus-mediated transgene expression in the liver. Information from beta-galactosidase (LacZ) expression; CTL production; and serum levels of gamma interferon, tumor necrosis factor-alpha, and interleukin-6 30 days after intravenous injection of liver LacZ were summarized by principal component analysis and analyzed using maximum likelihood interval mapping implemented in the QTL cartographer software. Two principal component (PC) scores explained 82.5% of the phenotypic variance in the original variables and identified QTLs not identified by analysis of individual traits. The distribution of original variables among PCs was such that variables in PC1 were predominantly cytokines with little CTL response whereas LacZ and CTL were the predominant contributors to PC2 with practically no contribution from cytokines. PC1 was significantly associated with two QTLs on chromosomes 7 and 9 located at 57.5 cM and 41.01 cM, respectively. Five QTLs were significantly associated with PC2 on chromosomes 12 (23.01 and 31.01 cM) and 15 (29.21, 36.01, and 56.31 cM). These results illustrate the use of principal component analysis in mapping QTLs using multiple correlated traits.

Classical genetic and quantitative trait loci analyses of heterosis in a maize hybrid between two elite inbred lines

The exploitation of heterosis is one of the most outstanding advancements in plant breeding, although its genetic basis is not well understood yet. This research was conducted on the materials arising from the maize single cross B73 x H99 to study heterosis by procedures of classical genetic and quantitative trait loci (QTL) analyses. Materials were the basic generations, the derived 142 recombinant inbred lines (RILs), and the three testcross populations obtained by crossing the 142 RILs to each parent and their F(1). For seedling weight (SW), number of kernels per plant (NK), and grain yield (GY), heterosis was >100% and the average degree of dominance was >1. Epistasis was significant for SW and NK but not for GY. Several QTL were identified and in most cases they were in the additive-dominance range for traits with low heterosis and mostly in the dominance-overdominance range for plant height (PH), SW, NK, and GY. Only a few QTL with digenic epistasis were identified. The importance of dominance effects was confirmed by highly significant correlations between heterozygosity level and phenotypic performance, especially for GY. Some chromosome regions presented overlaps of overdominant QTL for SW, PH, NK, and GY, suggesting pleiotropic effects on overall plant vigor.

A QTL study of growth and body shape in the inter-species hybrid of Père David's deer (Elaphurus davidianus) and red deer (Cervus elaphus)

An interspecies deer hybrid resource population developed from a cross of Père David's and red deer was used to detect QTL that account for species differences. A genome scan, coupled with composite interval mapping, was conducted to search for QTL controlling body measurements at pre-pubescent age (6 months of age) and puberty (15 months of age) in this interspecies hybrid. Five linkage groups that harbour QTL affecting morphology were identified. A joint-traits analysis was used to search for putative pleiotropic QTL on four of these linkage groups, and three were significantly associated with pleiotropic QTL for nose width and foot length (metacarpal and phalanges), which collectively accounted for 29-58% of the phenotypic difference between the two deer species. This study suggests that a few loci with large pleiotropic effects may be responsible for species-specific differences in growth and structure-related traits.

The genetic relationship among plant-height traits found using multiple-trait QTL mapping of a dent corn and popcorn cross

Plant height (PH) is one of the most important traits in maize breeding programs. In popcorn, inferior plant traits can be improved with the dent/flint corn germplasm. In the current study, a total of 259 F2:3families, developed from a cross between a dent corn inbred and a popcorn inbred, were evaluated for 4 PH traits. Quantitative trait loci (QTLs) for each trait were detected using composite interval mapping methods. In addition, genetic interrelationships were investigated using multiple-trait joint analysis for PH with ear height (EH), and for PH with top height (TH). In total, 6, 5, 2, and 6 QTLs were identified for PH, EH, TH, and TH/PH in single-trait analysis, respectively. Joint-analysis data suggest a strong and complex genetic relationship between PH and EH, and between PH and EH, with no QTLs controlling any single trait independently. In addition, 4 kinds of QTLs detected were classified as closely linked QTLs, pleiotropic QTLs, QTLs with opposite effects, and additional QTLs. It was, consequently, difficult to improve lodge resistance through selection on any individual PH trait. The current study demonstrates that multiple-trait joint analysis not only identified additional QTLs, but also revealed the genetic relationship among different highly correlated traits at the molecular level.

Multivariate segregation analysis for quantitative traits in line crosses

Segregation analysis is a method of detecting major genes for quantitative traits without using marker information. It serves as an important tool in helping investigators to plan further studies such as quantitative trait loci mapping or more sophisticated genomic analyses. However, current methods of segregation analysis for a single trait typically have low statistical power. We propose a multivariate segregation analysis (MSA) that takes advantage of the correlation structure of multiple quantitative traits to detect major genes. This method not only increases the statistical power, but allows dissection of the genetic architecture underlying the trait complex. In MSA the observed phenotypes of multiple correlated traits are fitted to a multivariate Gaussian mixture model. Model parameters are estimated under the maximum likelihood framework via the expectation-maximization algorithm. The presence of major genes is tested using likelihood ratio test statistics. Pleiotropy is distinguished from close linkage by comparing three possible models using the Bayesian information criterion. Two simulation experiments were performed based on the F(2) mating design. In the first, the statistical properties of MSA under varying heritabilities and sample sizes were investigated and the results compared with those obtained from single-trait analysis. In the second simulation the efficacy of MSA in separating pleiotropy from close linkage was demonstrated. Finally, the new method was applied to real data and detected a major gene responsible for both plant height and tiller number in rice.

Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.

Complex phenotypes such as common human diseases are caused by variations in DNA in many genes that interact in complex ways with a number of environmental factors. These multifactorial gene and environmental perturbations induce changes in molecular networks that in turn lead to phenotypic changes in the organism under study. The comprehensive monitoring of transcript abundances using gene expression microarrays in different tissues over a large number of individuals in a population can be used to reconstruct molecular networks that underlie higher-order phenotypes such as disease. The cost to generate these large-scale gene activity measurements over large numbers of individuals can be extreme. However, by integrating DNA variation and gene activity data monitored in each individual in a given population of interest, we demonstrate that the power to elucidate molecular networks that drive complex phenotypes can be significantly enhanced, without increasing the sample size. Using a biologically realistic simulation framework, we demonstrate that molecular networks reconstructed using the combined DNA variation and gene activity data are more accurate than molecular networks reconstructed from gene activity data alone, implying that adding DNA variation data might allow us to use fewer subjects to produce molecular networks that better explain complex phenotypes such as disease.

Characterization of genetic loci conferring adult plant resistance to leaf rust and stripe rust in spring wheat

Leaf (brown) and stripe (yellow) rusts, caused by Puccinia triticina and Puccinia striiformis, respectively, are fungal diseases of wheat (Triticum aestivum) that cause significant yield losses annually in many wheat-growing regions of the world. The objectives of our study were to characterize genetic loci associated with resistance to leaf and stripe rusts using molecular markers in a population derived from a cross between the rust-susceptible cultivar 'Avocet S' and the resistant cultivar 'Pavon76'. Using bulked segregant analysis and partial linkage mapping with AFLPs, SSRs and RFLPs, we identified 6 independent loci that contributed to slow rusting or adult plant resistance (APR) to the 2 rust diseases. Using marker information available from existing linkage maps, we have identified additional markers associated with resistance to these 2 diseases and established several linkage groups in the 'Avocet S' x 'Pavon76' population. The putative loci identified on chromosomes 1BL, 4BL, and 6AL influenced resistance to both stripe and leaf rust. The loci on chromosomes 3BS and 6BL had significant effects only on stripe rust, whereas another locus, characterized by AFLP markers, had minor effects on leaf rust only. Data derived from Interval mapping indicated that the loci identified explained 53% of the total phenotypic variation (R2) for stripe rust and 57% for leaf rust averaged across 3 sets of field data. A single chromosome recombinant line population segregating for chromosome 1B was used to map Lr46/Yr29 as a single Mendelian locus. Characterization of slow-rusting genes for leaf and stripe rust in improved wheat germplasm would enable wheat breeders to combine these additional loci with known slow-rusting loci to generate wheat cultivars with higher levels of slow-rusting resistance.

Morphological variation in tomato: a comprehensive study of quantitative trait loci controlling fruit shape and development

Variation in fruit morphology is a prevalent characteristic among cultivated tomato. The genetic and developmental mechanisms underlying similarities and differences in shape between the fruit of two elongated tomato varieties were investigated. Fruit from two F2 populations constructed from either Solanum lycopersicum cv. Howard German or cv. Banana Legs crossed with S. pimpinellifolium accession LA1589, and one BC1 population constructed with S. lycopersicum Howard German as the recurrent parent, were analysed for shape by using a new software program Tomato Analyzer. Quantitative trait loci (QTLs) controlling 15 individual shape attributes were mapped by both single and multitrait composite interval mapping in each population. In addition, principal components analysis and canonical discriminant analysis were conducted on these shape attributes to determine the greatest sources of variation among and between the populations. Individual principal components and canonical variates were subjected to QTL analysis to map regions of the genome influencing fruit shape in the cultivars. Common and unique regions, as well as previously known and novel QTLs, underlying fruit morphology in tomato were identified. Four major loci were found to control multiple fruit shape traits, principal components, and canonical variates in the populations. In addition, QTLs associated with the principal components better revealed regions of the genome that varied among populations than did the QTL associated with canonical variates. The QTL identified can be compared across additional populations of tomato and other fruit-bearing crop species.

Quantitative trait loci x maternal cytoplasmic environment interaction for development rate in Oncorhynchus mykiss

Effects of maternal cytoplasmic environment (MCE) on development rate in rainbow trout were evaluated within a quantitative trait loci (QTL) analysis framework. Previous research had identified QTL for development rate in doubled haploid (DH) progeny produced from a cross between the Oregon State University (OSU) and the Swanson (SW) River rainbow trout clonal lines. In this study, progeny for QTL mapping were produced from a cross between the OSU and Clearwater (CW) River clonal lines. Doubled haploids were produced from the OSU x CW F1 by androgenesis using eggs from different females (or MCEs); with androgenesis, the maternal nuclear genome was destroyed by irradiation and diploidy was restored by blocking the first embryonic cleavage by heat shock. All embryos were incubated at the same temperature and development rate quantified as time to hatch. Using a linkage map constructed primarily with AFLP markers, QTL mapping was performed, including MCE covariates and QTL x MCE effects in models for testing. The major QTL for development rate in the OSU x SW cross overlaps with the major QTL found in this OSU x CW cross; effects at this locus were the same across MCEs. Both MCE and QTL x MCE effects contribute to variability in development rate, but QTL x MCE were minor and detected only at small-effect QTL.

Genetics of global gene expression

A new field of genetic analysis of global gene expression has emerged in recent years, driven by the realization that traditional techniques of linkage and association analysis can be applied to thousands of transcript levels measured by microarrays. Genetic dissection of transcript abundance has shed light on the architecture of quantitative traits, provided a new approach for connecting DNA sequence variation with phenotypic variation, and improved our understanding of transcriptional regulation and regulatory variation.

Quantitative trait loci analysis of phenotypic traits and principal components of maize tassel inflorescence architecture

Maize tassel inflorescence architecture is relevant to efficient production of F(1) seed and yield performance of F(1) hybrids. The objectives of this study were to identify genetic relationships among seven measured tassel inflorescence architecture traits and six calculated traits in a maize backcross population derived from two lines with differing tassel architectures, and identify Quantitative Trait Loci (QTL) involved in the inheritance of those tassel inflorescence architecture traits. A Principal Component (PC) analysis was performed to examine relationships among correlated traits. Traits with high loadings for PC1 were branch number and branch number density, for PC2 were spikelet density on central spike and primary branch, and for PC3 were lengths of tassel and central spike. We detected 45 QTL for individual architecture traits and eight QTL for the three PCs. For control of inflorescence architecture, important QTL were found in bins 7.02 and 9.02. The interval phi034-ramosa1 (ral) in bin 7.02 was associated with six individual architecture trait QTL and explained the largest amount of phenotypic variation (17.3%) for PC1. Interval bnlg344-phi027 in bin 9.02 explained the largest amount of phenotypic variation (14.6%) for PC2. Inflorescence architecture QTL were detected in regions with candidate genes fasciated ear2, thick tassel dwarf1, and ral. However, the vast majority of QTL mapped to regions without known candidate genes, indicating positional cloning efforts will be necessary to identify these genes.

A meta-analysis of quantitative trait loci associated with body weight and adiposity in mice

OBJECTIVE

Cross-breeding experiments with different mouse strains have successfully been used by many groups to identify genetic loci that predispose for obesity. In order to provide a statistical assessment of these quantitative trait loci (QTL) as a basis for a systematic investigation of candidate genes, we have performed a meta-analysis of genome-wide linkage scans for body weight and body fat.

DATA

From a total of 34 published mouse cross-breeding experiments, we compiled a list of 162 non-redundant QTL for body weight and 117 QTL for fat weight and body fat percentage. Collectively, these studies include data from 42 different parental mouse strains and >14,500 individual mice.

METHODS

The results of the studies were analyzed using the truncated product method (TPM).

RESULTS

The analysis revealed significant evidence (logarithm of odds (LOD) score >4.3) for linkage of body weight and adiposity to 49 different segments of the mouse genome. The most prominent regions with linkage for body weight and body fat (LOD scores 14.8-21.8) on chromosomes 1, 2, 7, 11, 15, and 17 contain a total of 58 QTL for body weight and body fat. At least 34 candidate genes and genetic loci, which have been implicated in regulation of body weight and body composition in rodents and/or humans, are found in these regions, including CCAAT/enhancer-binding protein alpha (C/EBPA), sterol regulatory element-binding transcription factor 1 (SREBP-1), peroxisome proliferator activator receptor delta (PPARD), and hydroxysteroid 11-beta dehydrogenase 1 (HSD11B1). Our results demonstrate the presence of numerous distinct consensus QTL regions with highly significant LOD scores that control body weight and body composition. An interactive physical map of the QTL is available online at (http://www.obesitygenes.org).

Confirmation and fine mapping of ethanol sensitivity quantitative trait loci, and candidate gene testing in the LXS recombinant inbred mice

In previous studies, we have mapped quantitative trait loci (QTLs) for hypnotic sensitivity to ethanol using a small recombinant inbred (RI) panel and a large F(2) backcross. Alcohol sensitivity is a major predictor of long-term risk for alcoholism. We remapped hypnotic sensitivity using a new set of 75 RI strains, the LXS, derived from Inbred Long Sleep and Inbred Short Sleep strains. We expected to improve mapping resolution in the QTL regions and to identify novel QTLs for loss of the righting reflex due to ethanol. We used three common mapping algorithms (R/qtl, QTL Cartographer, and WebQTL) to map QTLs in the LXS, and we compared the results. Most mapping studies use only a single algorithm, an approach that may result in failure to identify minor QTLs. We confirmed most of our previously reported QTLs, although one major QTL from earlier work (Lore2) failed to replicate, possibly because it represented multiple linked genes separated by recombination in the RI strains. We also report narrowed confidence intervals, based on mapping with a new genetic resource of more than 4000 polymorphic single-nucleotide polymorphism markers. These narrowed confidence intervals will facilitate candidate gene identification and assessment of overlap with human regions specifying risk for alcoholism. Finally, we present an approach for using these RI strains to assess evidence for candidate genes in the narrowed intervals, and we apply this method to a strong candidate, the serotonin transporter.

QTL analysis of trabecular bone in BXD F2 and RI mice

A sample of 693 mice was used to identify regions of the mouse genome associated with trabecular bone architecture as measured using microCT. QTLs for bone in the proximal tibial metaphysis were identified on several chromosomes indicating regions containing genes that regulate properties of trabecular bone.

INTRODUCTION

Age-related osteoporosis is a condition of major concern because of the morbidity and mortality associated with osteoporotic fractures in humans. Osteoporosis is characterized by reduced bone density, strength, and altered trabecular architecture, all of which are quantitative traits resulting from the actions of many genes working in concert with each other and the environment over the lifespan. microCT gives accurate measures of trabecular bone architecture providing phenotypic data related to bone volume and trabecular morphology. The primary objective of this research was to identify chromosomal regions called quantitative trait loci (QTLs) that contain genes influencing trabecular architecture as measured by microCT.

MATERIALS AND METHODS

The study used crosses between C57BL/6J (B6) and DBA/2J (D2) as progenitor strains of a second filial (F2) generation (n = 141 males and 148 females) and 23 BXD recombinant inbred (RI) strains (n approximately 9 of each sex per strain). The proximal tibial metaphyses of the 200-day-old mice were analyzed by microCT to assess phenotypic traits characterizing trabecular bone, including bone volume fraction, trabecular connectivity, and quantitative measures of trabecular orientation and anisotropy. Heritabilities were calculated and QTLs were identified using composite interval mapping.

RESULTS

A number of phenotypes were found to be highly heritable. Heritability values for measured phenotypes using RI strains ranged from 0.15 for degree of anisotropy in females to 0.51 for connectivity density in females and total volume in males. Significant and confirmed QTLs, with LOD scores 4.3 in the F2 cohort and 1.5 in the corresponding RI cohort were found on chromosomes 1 (43 cM), 5 (44 cM), 6 (20 cM), and 8 (49 cM). Other QTLs with LOD scores ranging from 2.8 to 6.9 in the F2 analyses were found on chromosomes 1, 5, 6, 8, 9, and 12. QTLs were identified using data sets comprised of both male and female quantitative traits, suggesting similar genetic action in both sexes, whereas others seemed to be associated exclusively with one sex or the other, suggesting the possibility of sex-dependent effects.

CONCLUSIONS

Identification of the genes underlying these QTLs may lead to improvements in recognizing individuals most at risk for developing osteoporosis and in the design of new therapeutic interventions.

Bayesian reanalysis of a quantitative trait locus accounting for multiple environments by scaling in broilers1

A Bayesian method was developed to handle QTL analyses of multiple experimental data of outbred populations with heterogeneity of variance between sexes for all random effects. The method employed a scaled reduced animal model with random polygenic and QTL allelic effects. A parsimonious model specification was applied by choosing assumptions regarding the covariance structure to limit the number of parameters to estimate. Markov chain Monte Carlo algorithms were applied to obtain marginal posterior densities. Simulation demonstrated that joint analysis of multiple environments is more powerful than separate single trait analyses of each environment. Measurements on broiler BW obtained from 2 experiments concerning growth efficiency and carcass traits were used to illustrate the method. The population consisted of 10 full-sib families from a cross between 2 broiler lines. Microsatellite genotypes were determined on generations 1 and 2, and phenotypes were collected on groups of generation 3 animals. The model included a polygenic correlation, which had a posterior mean of 0.70 in the analyses. The reanalysis agreed on the presence of a QTL in marker bracket MCW0058-LEI0071 accounting for 34% of the genetic variation in males and 24% in females in the growth efficiency experiment. In the carcass experiment, this QTL accounted for 19% of the genetic variation in males and 6% in females.

Genetical metabolomics of flavonoid biosynthesis in Populus: a case study

Genetical metabolomics [metabolite profiling combined with quantitative trait locus (QTL) analysis] has been proposed as a new tool to identify loci that control metabolite abundances. This concept was evaluated in a case study with the model tree Populus. Using HPLC, the peak abundances were analyzed of 15 closely related flavonoids present in apical tissues of two full-sib poplar families, Populus deltoides cv. S9-2 x P. nigra cv. Ghoy and P. deltoides cv. S9-2 x P. trichocarpa cv. V24, and correlation and QTL analysis were used to detect flux control points in flavonoid biosynthesis. Four robust metabolite quantitative trait loci (mQTL), associated with rate-limiting steps in flavonoid biosynthesis, were mapped. Each mQTL was involved in the flux control to one or two flavonoids. Based on the identities of the affected metabolites and the flavonoid pathway structure, a tentative function was assigned to three of these mQTL, and the corresponding candidate genes were mapped. The data indicate that the combination of metabolite profiling with QTL analysis is a valuable tool to identify control points in a complex metabolic pathway of closely related compounds.

Structural model analysis of multiple quantitative traits

We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.

Disease states are often associated with multiple, correlated traits that may result from shared genetic and nongenetic factors. Genetic analysis of multiple traits can reveal a network of effects in which each trait is influenced by more than one genetic locus (heterogeneity) and different traits share one or more loci in common (pleiotropy). Physiological interactions independent of genetic factors may also contribute to the observed correlations. Structural equation modeling is proposed as a statistical method to characterize the architecture of multiple trait genetic systems. Application of structural equation modeling to body size, adiposity, and bone geometry traits illustrates how the effects of a genetic locus can be decomposed along direct and indirect paths that may be mediated through interactions with other traits. Using this technique the authors identify adiposity loci that act independently of loci affecting overall body size.

Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice

To comprehensively investigate the genetic architecture of growth and obesity, we performed Bayesian analyses of multiple epistatic quantitative trait locus (QTL) models for body weights at five ages (12 days, 3, 6, 9 and 12 weeks) and body composition traits (weights of two fat pads and five organs) in mice produced from a cross of the F1 between M16i (selected for rapid growth rate) and CAST/Ei (wild-derived strain of small and lean mice) back to M16i. Bayesian model selection revealed a temporally regulated network of multiple QTL for body weight, involving both strong main effects and epistatic effects. No QTL had strong support for both early and late growth, although overlapping combinations of main and epistatic effects were observed at adjacent ages. Most main effects and epistatic interactions had an opposite effect on early and late growth. The contribution of epistasis was more pronounced for body weights at older ages. Body composition traits were also influenced by an interacting network of multiple QTLs. Several main and epistatic effects were shared by the body composition and body weight traits, suggesting that pleiotropy plays an important role in growth and obesity.

Mapping quantitative trait loci for longitudinal traits in line crosses

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.

Multitrait Quantitative Trait Loci Mapping for Milk Production Traits in Danish Holstein Cattle

The aims of this study were (1) to confirm previously identified quantitative trait loci (QTL) on bovine chromosomes 6, 11, 14, and 23 in the Danish Holstein cattle population, (2) to assess the pleiotropic nature of each QTL on milk production traits by building multitrait and multi-QTL models, and (3) to include pedigree information on nongenotyped individuals to improve the estimation of genetic parameters underlying the random QTL model. Nineteen grandsire families were analyzed by single-trait (ST) and multitrait (MT) QTL mapping methods. The variance component-based QTL mapping model was implemented via restricted maximum likelihood (REML) to estimate QTL position and parameters. Segregation of the previously identified QTL was confirmed on bovine chromosomes 6, 11, and 14, but not on 23. A highly significant (1% chromosome-wise level) QTL was found on chromosome 6, between 37 and 73 cM. This QTL had a strong effect on protein percentage (PP) and fat percentage (FP) according to ST analyses, and effects on PP, FP, milk yield (MY), fat yield (FY), and protein yield (PY) in MT analyses. A QTL affecting PP was detected on chromosome 11 (at 70 cM) using ST analysis. The MT analysis revealed a second QTL (at 67 cM) approaching significance with an effect on MY. The ST analysis identified a QTL for MY and FP on chromosome 14, between 10 and 24 cM. The extended pedigree (nongenotyped animals) was included to estimate genetic parameters underlying the random QTL model; that is, additive polygenic and QTL variances. In general, the estimates of the QTL variance components were smaller but more precise when the extended pedigree was considered in the analysis.

Causal inference of regulator-target pairs by gene mapping of expression phenotypes

Background

Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. In this paper, we consider the problem of identifying individual pairs of genes in a direct or indirect, causal, trans-acting relationship.

Results

Inspired by epistatic models of multi-locus quantitative trait (QTL) mapping, we propose a unified model of expression and genotype to identify quantitative trait genes (QTG) by extending the conventional linear model to include both genotype and expression of regulator genes and their interactions. The model provides mapping of specific genes in contrast to standard linkage approaches that implicate large QTL intervals typically containing tens of genes. In simulations, we found that the method can often detect weak trans-acting regulators amid the background noise of thousands of traits and is robust to transcription models containing multiple regulator genes. We reanalyze several pleiotropic loci derived from a large set of yeast matings and identify a likely alternative regulator not previously published. However, we also found that many regulators can not be so easily mapped due to the presence of cis-acting QTLs on the regulators, which induce close linkage among small neighborhoods of genes. QTG mapped regulator-target pairs linked to ARN1 were combined to form a regulatory module, which we observed to be highly enriched in iron homeostasis related genes and contained several causally directed links that had not been identified in other automatic reconstructions of that regulatory module. Finally, we also confirm the surprising, previously published results that regulators controlling gene expression are not enriched for transcription factors, but we do show that our more precise mapping model reveals functional enrichment for several other biological processes related to the regulation of the cell.

Conclusion

By incorporating interacting expression and genotype, our QTG mapping method can identify specific regulator genes in contrast to standard QTL interval mapping. We have shown that the method can recover biologically significant regulator-target pairs and the approach leads to a general framework for inducing a regulatory module network topology of directed and undirected edges that can be used to identify leads in pathway analysis.

A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits

Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.

Gene by environment QTL mapping through multiple trait analyses in blood pressure salt-sensitivity: identification of a novel QTL in rat chromosome 5

BACKGROUND

The genetic mechanisms underlying interindividual blood pressure variation reflect the complex interplay of both genetic and environmental variables. The current standard statistical methods for detecting genes involved in the regulation mechanisms of complex traits are based on univariate analysis. Few studies have focused on the search for and understanding of quantitative trait loci responsible for gene x environmental interactions or multiple trait analysis. Composite interval mapping has been extended to multiple traits and may be an interesting approach to such a problem.

METHODS

We used multiple-trait analysis for quantitative trait locus mapping of loci having different effects on systolic blood pressure with NaCl exposure. Animals studied were 188 rats, the progenies of an F2 rat intercross between the hypertensive and normotensive strain, genotyped in 179 polymorphic markers across the rat genome. To accommodate the correlational structure from measurements taken in the same animals, we applied univariate and multivariate strategies for analyzing the data.

RESULTS

We detected a new quantitative train locus on a region close to marker R589 in chromosome 5 of the rat genome, not previously identified through serial analysis of individual traits. In addition, we were able to justify analytically the parametric restrictions in terms of regression coefficients responsible for the gain in precision with the adopted analytical approach.

CONCLUSION

Future work should focus on fine mapping and the identification of the causative variant responsible for this quantitative trait locus signal. The multivariable strategy might be valuable in the study of genetic determinants of interindividual variation of antihypertensive drug effectiveness.

Functional mapping - how to map and study the genetic architecture of dynamic complex traits

The development of any organism is a complex dynamic process that is controlled by a network of genes as well as by environmental factors. Traditional mapping approaches for analysing phenotypic data measured at a single time point are too simple to reveal the genetic control of developmental processes. A general statistical mapping framework, called functional mapping, has been proposed to characterize, in a single step, the quantitative trait loci (QTLs) or nucleotides (QTNs) that underlie a complex dynamic trait. Functional mapping estimates mathematical parameters that describe the developmental mechanisms of trait formation and expression for each QTL or QTN. The approach provides a useful quantitative and testable framework for assessing the interplay between gene actions or interactions and developmental changes.