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PubMed1993

Interval mapping of multiple quantitative trait loci.

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.

Chromosome Mapping↗
PubMed1999

Comparative mapping between human chromosome 3 and porcine chromosome 13.

Zoo-FISH and somatic cell hybrid panels have earlier demonstrated extended synteny conservation between human chromosome 3 (HSA3) and pig chromosome 13 (SSC13). In the present study, eight human genes viz., ADCY5, CASR, COL7A1, COL8A1, ITIH1, RHO, SIAT1 and XPC, spread along the length of HSA3, were chosen for expanding the comparative map between the two chromosomes. Using human and rat cDNAs, or human- and porcine-specific PCR products as probes, 8 porcine lambda clones were isolated. After subcloning and partial sequence determination, identity of the clones with regards to the specific genes was established. The eight type 1 markers thus obtained were biotin labeled and FISH mapped to pig metaphase spreads. All lambda clones localized to SSC13. In combination with the hitherto published mapping data of coding sequences on SSC13, a preliminary comparative status depicting the relative organization of this chromosome with respect to HSA3 was developed. The comparative map thus obtained bears significance in searching for candidate genes of economically important traits mapped to SSC13.

Adenylyl Cyclases↗
PubMed1996

A general Monte Carlo method for mapping multiple quantitative trait loci.

In this paper we address the mapping of multiple quantitative trait loci (QTLs) in line crosses for which the genetic data are highly incomplete. Such complicated situations occur, for instance, when dominant markers are used or when unequally informative markers are used in experiments with outbred populations. We describe a general and flexible Monte Carlo expectation-maximization (Monte Carlo EM) algorithm for fitting multiple-QTL models to such data. Implementation of this algorithm is straightforward in standard statistical software, but computation may take much time. The method may be generalized to cope with more complex models for animal and human pedigrees. A practical example is presented, where a three-QTL model is adopted in an outbreeding situation with dominant markers. The example is concerned with the linkage between randomly amplified polymorphic DNA (RAPD) markers and QTLs for partial resistance to Fusarium oxysporum in lily.

Algorithms↗
PubMed2003

Statistical analysis and experimental design for mapping genes of complex traits in domestic animals.

Gene mapping for complex traits has been an active and challenging research area in humans, agricultural and laboratory species. In domestic animals and poultry, the goal of gene mapping is to find genes associated with production, reproduction and health traits. In humans, gene mapping for complex diseases has been a significant challenge. Although gene mapping results are accumulating rapidly, confirmed results are scarce. In domestic animals, gene mapping results are also accumulating rapidly and several large scale mapping projects in chickens, swine, and dairy cattle are currently in progress. Although new and more reliable results can be expected in the near future, the precise understanding of genes underlying complex traits is still some distance away. Recently, the approach of mapping genes of complex traits has been applied to gene expression data to map transcription regulatory elements, adding a new dimension to gene mapping. Statistical analysis of gene mapping data and experimental design are critical components of a gene mapping research. The purpose of this review article is to discuss current status as well as future directions and challenges in statistical analysis and experimental design for mapping genes of complex traits in domestic animals and poultry.

Animals↗
PubMed2003

Identifying loci for behavioral traits using genome-tagged mice.

Identification of behavioral loci through complex trait mapping remains a widely employed approach but suffers from poor gene localization and low replicability. Genome-tagged mice (GTMs) are overlapping sets of congenic strains spanning the whole genome and offer the possibilities of superior mapping power and reproducibility. In this study, three GTM strains each consisting of an average approximately 27 cM DBA/2J genomic intervals introgressed onto a C57BL/6J background were employed for localization of behavioral traits. These GTMs were chosen because the corresponding chromosomal regions had been previously identified as containing loci for learning and memory. Analysis of the GTMs allowed confirmation of the learning and memory loci, and one on chromosome 3 was in addition fine mapped to an 8.8-cM region of overlap between two of the GTMs. Moreover, loci for prepulse inhibition of the startle response, acoustic startle response, and spontaneous locomotor activity were also mapped. These results suggest that the GTMs should be a valuable resource for mapping and confirmation of loci contributing to complex behavioral traits in the mouse.

Animals↗
PubMed2006

The genetic basis of floral traits associated with mating system evolution in Leptosiphon (Polemoniaceae): an analysis of quantitative trait loci.

Mapping of quantitative trait loci (QTL) was used to investigate the genetic architecture of divergence in floral characters associated with the mating system, an important adaptive trait in angiosperms. Two species of Leptosiphon (Polemoniaceae), one strongly self-fertilizing (L. bicolor) and the other partially outcrossing (L. jepsonii), were crossed to produce F2 and both backcross progenies. For each crossing population, a linkage map was created using amplified fragment length polymorphism markers, and QTL were identified for several dimensions of floral size. For each of the five traits examined, three to seven QTL were detected, with independent datasets yielding congruent results in some but not all cases. The phenotypic effect of individual QTL was generally moderate. We estimated that many of the QTL were additive or showed dominance toward L. bicolor, whereas comparison of mean trait values for parental and cross progenies showed apparent overall dominance of L. jepsonii traits. Colocalization of QTL for different dimensions of floral size was consistent with high phenotypic correlations between floral traits. Substantial segregation distortion was observed in marker loci, the majority favoring alleles from the large-flowered parent. A low frequency of male sterility in the F2 population is consistent with the Dobzhansky-Muller model for the evolution of reproductive isolation.

Biological Evolution↗
PubMed2001

Using the mixed model for interval mapping of quantitative trait loci in outbred line crosses.

Interval mapping by simple regression is a powerful method for the detection of quantitative trait loci (QTLs) in line crosses such as F2 populations. Due to the ease of computation of the regression approach, relatively complex models with multiple fixed effects, interactions between QTLs or between QTLs and fixed effects can easily be accommodated. However, polygenic effects, which are not targeted in QTL analysis, cannot be treated as random effects in a least squares analysis. In a cross between true inbred lines this is of no consequence, as the polygenic effect contributes just to the residual variance. In a cross between outbred lines, however, if a trait has high polygenic heritability, the additive polygenic effect has a large influence on variation in the population. Here we extend the fixed model for the regression interval mapping method to a mixed model using an animal model. This makes it possible to use not only the observations from progeny (e.g. F2), but also those from the parents (F1) to evaluate QTLs and polygenic effects. We show how the animal model using parental observations can be applied to an outbred cross and so increase the power and accuracy of QTL analysis. Three estimation methods, i.e. regression and an animal model either with or without parental observations, are applied to simulated data. The animal model using parental observations is shown to have advantages in estimating QTL position and additive genotypic value, especially when the polygenic heritability is large and the number of progeny per parent is small.

Animals↗
PubMed1999

Multipoint genetic mapping of quantitative trait loci with dominant markers in outbred populations.

We present a multipoint algorithm for mapping quantitative trait loci (QTLs) using dominant markers. The algorithm is designed for outbred populations and is particularly suited for large families. The algorithm works with either codominant or dominant markers, either of which may be interspersed within the same linkage map. Concurrently, the algorithm also partitions dominance variance at the QTL. Computer simulations show that with large families, QTL mapping with dominant markers can be almost as powerful as with bi-allelic, codominant markers. Yet despite this, other situations show a large standard deviation in the estimate of the QTL position, thus making QTL mapping with dominant markers in outbred populations a useful detection tool, albeit limited in its resolution.

Algorithms↗
PubMed1995

Interval mapping of quantitative trait loci employing correlated trait complexes.

An approach to increase the resolution power of interval mapping of quantitative trait (QT) loci is proposed, based on analysis of correlated trait complexes. For a given set of QTs, the broad sense heritability attributed to a QT locus (QTL) (say, A/a) is an increasing function of the number of traits. Thus, for some traits x and y, H(xy)2(A/a) > or = H(x)2(A/a). The last inequality holds even if y does not depend on A/a at all, but x and y are correlated within the groups AA, Aa and aa due to nongenetic factors and segregation of genes from other chromosomes. A simple relationship connects H2 (both in single trait and two-trait analysis) with the expected LOD value, ELOD = -1/2N log(1-H2). Thus, situations could exist that from the inequality H(xy)2(A/a) > or = H(x)2(A/a) a higher resolution is provided by the two-trait analysis as compared to the single-trait analysis, in spite of the increased number of parameters. Employing LOD-score procedure to simulated backcross data, we showed that the resolution power of the QTL mapping model can be elevated if correlation between QTs is taken into account. The method allows us to test numerous biologically important hypotheses concerning manifold effects of genomic segments on the defined trait complex (means, variances and correlations).

Analysis of Variance↗
PubMed2000

Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci.

A multimarker linkage disequilibrium mapping method was developed for the fine mapping of quantitative trait loci (QTL) using a dense marker map. The method compares the expected covariances between haplotype effects given a postulated QTL position to the covariances that are found in the data. The expected covariances between the haplotype effects are proportional to the probability that the QTL position is identical by descent (IBD) given the marker haplotype information, which is calculated using the genedropping method. Simulation results showed that a QTL was correctly positioned within a region of 3, 1.5, or 0.75 cM in 70, 62, and 68%, respectively, of the replicates using markers spaced at intervals of 1, 0.5, and 0.25 cM, respectively. These results were rather insensitive to the number of generations since the QTL occurred and to the effective population size, except that 10 generations yielded rather poor estimates of the QTL position. The position estimates of this multimarker disequilibrium mapping method were more accurate than those from a single marker transmission disequilibrium test. A general approach for identifying QTL is suggested, where several stages of disequilibrium mapping are used with increasingly dense marker spacing.

Chromosome Mapping↗
PubMed2006

Statistical methods for expression quantitative trait loci (eQTL) mapping.

Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. Combining traditional quantitative trait loci (QTL) mapping methods with microarray data is a powerful approach with demonstrated utility in a number of recent biological investigations. These expression quantitative trait loci (eQTL) studies are similar to traditional QTL studies, as a main goal is to identify the genomic locations to which the expression traits are linked. However, eQTL studies probe thousands of expression transcripts; and as a result, standard multi-trait QTL mapping methods, designed to handle at most tens of traits, do not directly apply. One possible approach is to use single-trait QTL mapping methods to analyze each transcript separately. This leads to an increased number of false discoveries, as corrections for multiple tests across transcripts are not made. Similarly, the repeated application, at each marker, of methods for identifying differentially expressed transcripts suffers from multiple tests across markers. Here, we demonstrate the deficiencies of these approaches and propose a mixture over markers (MOM) model that shares information across both markers and transcripts. The utility of all methods is evaluated using simulated data as well as data from an F(2) mouse cross in a study of diabetes. Results from simulation studies indicate that the MOM model is best at controlling false discoveries, without sacrificing power. The MOM model is also the only one capable of finding two genome regions previously shown to be involved in diabetes.

Animals↗
PubMed2002

Mapping of quantitative trait loci based on growth models.

An approach called growth model-based mapping (GMM) of quantitative trait loci (QTLs) is proposed in this paper. The principle of the approach is to fit the growth curve of each individual or line with a theoretical or empirical growth model at first and then map QTLs based on the estimated growth parameters with the method of multiple-trait composite interval mapping. In comparison with previously proposed approaches of QTL mapping based on growth data, GMM has several advantages: (1) it can greatly reduce the amount of phenotypic data for QTL analysis and thus alleviate the burden of computation, particularly when permutation tests or simulation are performed to estimate significance thresholds; (2) it can efficiently analyze unbalanced phenotype data because both balanced and unbalanced data can be used for fitting growth models; and (3) it may potentially help us to better understand the genetic basis of quantitative trait development because the parameters in a theoretical growth model may often have clear biological meanings. A practical example of rice leaf-age development is presented to demonstrate the utility of GMM.

Journal Article↗
PubMed2006

Interval mapping of quantitative trait loci for time-to-event data with the proportional hazards mixture cure model.

Interval mapping using normal mixture models has been an important tool for analyzing quantitative traits in experimental organisms. When the primary phenotype is time-to-event, it is natural to use survival models such as Cox's proportional hazards model instead of normal mixtures to model the phenotype distribution. An extra challenge for modeling time-to-event data is that the underlying population may consist of susceptible and nonsusceptible subjects. In this article, we propose a semiparametric proportional hazards mixture cure model which allows missing covariates. We discuss applications to quantitative trait loci (QTL) mapping when the primary trait is time-to-event from a population of mixed susceptibility. This model can be used to characterize QTL effects on both susceptibility and time-to-event distribution, and to estimate QTL location. The model can naturally incorporate covariate effects of other risk factors. Maximum likelihood estimates for the parameters in the model as well as their corresponding variance estimates can be obtained numerically using an EM-type algorithm. The proposed methods are assessed by simulations under practical settings and illustrated using a real data set containing survival times of mice after infection with Listeria monocytogenes. An extension to multiple intervals is also discussed.

Algorithms↗
PubMed2005

Genome scans with gene-covariate interaction.

Genetic models for gene-covariate interactions are described. Methods of linkage analysis that utilize special features of these models and the corresponding score statistics are derived. Their power is compared with that of simple genome scans that ignore these special features, and substantial gains in power are observed when the gene-covariate interaction is strong. Quantitative trait mapping in randomly ascertained sibships and affected sibpair mapping are discussed. For the latter case, a simpler statistic is proposed that has similar performance to the score statistic, but does not require the estimation of nuisance parameters. Since the nuisance parameters are not estimable solely from affected sib-pair data, this statistic would be much easier to apply in practice. Similarities with linkage analysis of models for longitudinal data and multivariate phenotypes are also briefly discussed. Approximations for the P-value and power are derived under the framework of local alternatives.

Chromosome Mapping↗
PubMed1998

Multipoint genetic mapping of quantitative trait loci using a variable number of sibs per family.

We present a multipoint algorithm to map quantitative trait loci (QTLs) using families from outbred populations with a variable number of sibs. The algorithm uses information from all markers on a chromosome simultaneously to extract information of QTL segregation. A previous multipoint method (Kruglyak & Lander (1995) American Journal of Human Genetics 57, 439-454) extracts information using a hidden Markov model. However, this method is restricted to small families (< 10 sibs). We present an approximate hidden Markov model approach that can handle large sibships while retaining similar efficiency to the previous method. Computer simulations support the notion that data sampled from a small number of large families provide more power than data obtained from a large number of small families, under the constraint that the total number of individuals for the two schemes is the same. This is further reflected in simulations with variable family sizes, where variance in family size improves the statistical power of QTL detection relative to a constant size control.

Chromosome Mapping↗
PubMed2000

Markov chain Monte Carlo for mapping a quantitative trait locus in outbred populations.

A Bayesian approach is presented for mapping a quantitative trait locus (QTL) using the 'Fernando and Grossman' multivariate Normal approximation to QTL inheritance. For this model, a Bayesian implementation that includes QTL position is problematic because standard Markov chain Monte Carlo (MCMC) algorithms do not mix, i.e. the QTL position gets stuck in one marker interval. This is because of the dependence of the covariance structure for the QTL effects on the adjacent markers and may be typical of the 'Fernando and Grossman' model. A relatively new MCMC technique, simulated tempering, allows mixing and so makes possible inferences about QTL position based on marginal posterior probabilities. The model was implemented for estimating variance ratios and QTL position using a continuous grid of allowed positions and was applied to simulated data of a standard granddaughter design. The results showed a smooth mixing of QTL position after implementation of the simulated tempering sampler. In this implementation, map distance between QTL and its flanking markers was artificially stretched to reduce the dependence of markers and covariance. The method generalizes easily to more complicated applications and can ultimately contribute to QTL mapping in complex, heterogeneous, human, animal or plant populations.

Animals↗
PubMed2002

Genome-wide screening for trait conferring genes using DNA microarrays.

We report a DNA microarray-based method for genome-wide monitoring of competitively grown transformants to identify genes whose overexpression confers a specific cellular phenotype. Whereas transcriptional profiling identifies differentially expressed genes that are correlated with particular aspects of the cellular phenotype, this functional genomics approach determines genes that result in a specific physiology. This parallel gene-trait mapping method consists of transforming a strain with a genomic library, enriching the cell population in transformants containing the trait conferring gene(s), and finally using DNA microarrays to simultaneously isolate and identify the enriched gene inserts. Various methods of enrichment can be used; here, genes conferring low-level antibiotic resistance were identified by growth in selective media. We demonstrated the method by transforming Escherichia coli cells with a genomic E. coli library and selecting for transformants exhibiting a growth advantage in the presence of the anti-microbial agent Pine-Sol. Genes conferring Pine-Sol tolerance (19 genes) or sensitivity (27 genes) were identified by hybridizing, on DNA microarrays containing 1,160 E. coli gene probes, extra-chromosomal DNA isolated from transformed cells grown in the presence of various levels of Pine-Sol. Results were further validated by plating and sequencing of individual colonies, and also by assessing the Pine-Sol resistance of cells transformed with enriched plasmid library or individual resistance genes identified by the microarrays. Applications of this method beyond antibiotic resistance include identification of genes resulting in resistance to chemotherapeutic agents, genes yielding resistance to toxic products (recombinant proteins, chemical feedstocks) in industrial fermentations, genes providing enhanced growth in cell culture or high cell density fermentations, genes facilitating growth on unconventional substrates, and others.

Chromosome Mapping↗
PubMed2005

A non-stationary model for functional mapping of complex traits.

SUMMARY: Understanding the genetic control of growth is fundamental to agricultural, evolutionary and biomedical genetic research. In this article, we present a statistical model for mapping quantitative trait loci (QTL) that are responsible for genetic differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, implemented with the expectation-maximization algorithm. We incorporate mathematical aspects of growth processes to model the mean vector and structured antedependence models to approximate time-dependent covariance matrices for longitudinal traits. Our model has been employed to map QTL that affect body mass growth trajectories in both male and female mice of an F2 population derived from the Large and Small mouse strains. The results from this model are compared with those from the autoregressive-based functional mapping approach. Based on results from computer simulation studies, we suggest that these two models are alternative to one another and should be used simultaneously for the same dataset.

Aging↗