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PubMed2006

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.

Chromosome Mapping↗
PubMed2003

Rank-based statistical methodologies for quantitative trait locus mapping.

This article addresses the identification of genetic loci (QTL and elsewhere) that influence nonnormal quantitative traits with focus on experimental crosses. QTL mapping is typically based on the assumption that the traits follow normal distributions, which may not be true in practice. Model-free tests have been proposed. However, nonparametric estimation of genetic effects has not been studied. We propose an estimation procedure based on the linear rank test statistics. The properties of the new procedure are compared with those of traditional likelihood-based interval mapping and regression interval mapping via simulations and a real data example. The results indicate that the nonparametric method is a competitive alternative to the existing parametric methodologies.

Crosses, Genetic↗
PubMed2005

Improving quantitative trait loci mapping resolution in experimental crosses by the use of genotypically selected samples.

One of the key factors contributing to the success of a quantitative trait locus (QTL) mapping experiment is the precision with which QTL positions can be estimated. We show, using simulations, that QTL mapping precision for an experimental cross can be increased by the use of a genotypically selected sample of individuals rather than an unselected sample of the same size. Selection is performed using a previously described method that optimizes the complementarity of the crossover sites within the sample. Although the increase in precision is accompanied by a decrease in QTL detection power at markers distant from QTL, only a modest increase in marker density is needed to obtain equivalent power over the whole map. Selected samples also show a slight reduction in the number of false-positive QTL. We find that two features of selected samples independently contribute to these effects: an increase in the number of crossover sites and increased evenness in crossover spacing. We provide an empirical formula for crossover enrichment in selected samples that is useful in experimental design and data analysis. For QTL studies in which the phenotyping is more of a limiting factor than the generation of individuals and the scoring of genotypes, selective sampling is an attractive strategy for increasing genome-wide QTL map resolution.

Chromosome Mapping↗
PubMed2001

Consequences of recombination rate variation on quantitative trait locus mapping studies. Simulations based on the Drosophila melanogaster genome.

We examine the effect of variation in gene density per centimorgan on quantitative trait locus (QTL) mapping studies using data from the Drosophila melanogaster genome project and documented regional rates of recombination. There is tremendous variation in gene density per centimorgan across this genome, and we observe that this variation can cause systematic biases in QTL mapping studies. Specifically, in our simulated mapping experiments of 50 equal-effect QTL distributed randomly across the physical genome, very strong QTL are consistently detected near the centromeres of the two major autosomes, and few or no QTL are often detected on the X chromosome. This pattern persisted with varying heritability, marker density, QTL effect sizes, and transgressive segregation. Our results are consistent with empirical data collected from QTL mapping studies of this species and its close relatives, and they explain the "small X-effect" that has been documented in genetic studies of sexual isolation in the D. melanogaster group. Because of the biases resulting from recombination rate variation, results of QTL mapping studies should be taken as hypotheses to be tested by additional genetic methods, particularly in species for which detailed genetic and physical genome maps are not available.

Animals↗
PubMed1992

A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.

The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.

Animals↗
PubMed1997

Combining information within and between pedigrees for mapping complex traits.

This paper is concerned with efficient strategies for gene mapping using pedigrees containing small numbers of affecteds and identity-by-descent data from closely spaced markers throughout the genome. Particular attention is paid to additive traits involving phenocopies and/or locus heterogeneity. For a sample of pedigrees containing a particular configuration of affecteds, e.g., pairs of siblings together with a first cousin, we use a likelihood analysis to find 1-df statistics that are very efficient over a broad range of penetrances and allele frequencies. We identify configurations of affecteds that are particularly powerful for detecting linkage, and we show how pedigrees containing different numbers and configurations of affecteds can be efficiently combined in an overall test statistic.

Chromosome Mapping↗
PubMed1999

Allele dose analysis in recombinant inbred strains: a tool for multiple phenotype analysis with implications for quantitative trait loci mapping.

A considerable investment in genetic mapping is necessary to confirm linkages of quantitative trait loci for complex traits such as ethanol sensitivity, a significant predictor of alcoholism. Before embarking on such intensive mapping efforts in large intercrosses, we suggest an approach based on genetic marker data in recombinant strains that yield a rationale for selecting a battery of related phenotypes for confirmation studies of quantitative trait loci action. Using this approach with selected strains of mice, we retrospectively consider the relationship between ethanol sensitivity and neurotensin levels in several mammalian brain regions.

Alcoholism↗
PubMed2003

Mapping quantitative trait loci associated with resistance to coccidiosis and growth.

To map QTL associated with disease resistance to avian coccidiosis and growth, two commercial broiler lines with different degrees of resistance to the disease were crossed to generate an F1 generation that was intercrossed to produce 314 F2 generation offspring. The F2 offspring were inoculated with sporulated oocysts of Eimeria maxima. Five disease-associated phenotypes were measured after the infection. Intertrait comparisons revealed that oocyst shedding was a good parameter for evaluating disease resistance or susceptibility. One hundred nineteen microsatellite markers, covering 80% of the chicken genome with an average marker interval of 25 cM, were used for genotyping of F1 parents and F2 offspring. Statistical analysis based on the data of four families revealed a locus on chromosome 1 associated with oocyst shedding (logarithm of odds = 3.46). The genetic mechanism of this locus appeared additive. The genomic scan also identified three potential growth QTL on chromosomes 1, 6, and 8. These results provide the foundation for further investigation to validate the QTL.

Animals↗
PubMed1999

Mapping quantitative trait loci for seizure response to a GABAA receptor inverse agonist in mice.

To define the genetic contributions affecting individual differences in seizure threshold, a beta carboline [methyl-beta-carboline-3-carboxylate (beta-CCM)]-induced model of generalized seizures was genetically dissected in mice. beta-CCM is a GABAA receptor inverse agonist and convulsant. By measuring the latency to generalized seizures after beta-CCM administration to A/J and C57BL6/J mice and their progeny, we estimated a heritability of 0.28 +/- 0.10. A genome wide screen in an F2 population of these parental strains (n = 273) mapped quantitative trait loci (QTLs) on proximal chromosome 7 [logarithm of the likelihood for linkage (LOD) = 3.71] and distal chromosome 10 (LOD = 4.29) for seizure susceptibility, explaining approximately 22 and 25%, respectively, of the genetic variance for this seizure trait. The best fitting logistic regression model suggests that the A/J allele at each locus increases the likelihood of seizures approximately threefold. In a subsequent backcross population (n = 223), we mapped QTLs on distal chromosome 4 (LOD = 2.88) and confirmed the distal chromosome 10 QTLs (LOD = 4.36). In the backcross, the C57BL/6J allele of the chromosome 10 QTL decreases the risk of seizures approximately twofold. These QTLs may ultimately lead to the identification of genes influencing individual differences in seizure threshold in mice and the discovery of novel anticonvulsant agents. The colocalization on distal chromosome 10 of a beta-CCM susceptibility QTL and a QTL for open field ambulation and vertical movement suggests the existence of a single, pleiotropic locus, which we have named Exq1.

Animals↗
PubMed2003

A general statistical framework for mapping quantitative trait loci in nonmodel systems: issue for characterizing linkage phases.

Because of uncertainty about linkage phases of founders, linkage mapping in nonmodel, outcrossing systems using molecular markers presents one of the major statistical challenges in genetic research. In this article, we devise a statistical method for mapping QTL affecting a complex trait by incorporating all possible QTL-marker linkage phases within a mapping framework. The advantage of this model is the simultaneous estimation of linkage phases and QTL location and effect parameters. These estimates are obtained through maximum-likelihood methods implemented with the EM algorithm. Extensive simulation studies are performed to investigate the statistical properties of our model. In a case study from a forest tree, this model has successfully identified a significant QTL affecting wood density. Also, the probability of the linkage phase between this QTL and its flanking markers is estimated. The implications of our model and its extension to more general circumstances are discussed.

Chromosome Mapping↗
PubMed2005

Quantitative trait locus mapping and DNA array hybridization identify an FLM deletion as a cause for natural flowering-time variation.

Much of the flowering time variation in wild strains of Arabidopsis thaliana is due to allelic variation at two epistatically acting loci, FRIGIDA (FRI) and FLOWERING LOCUS C (FLC). FLC encodes a MADS (MCM1/AGAMOUS/DEFICIENS/SRF1) domain transcription factor that directly represses a series of flowering-promoting genes. FRI and FLC, however, do not explain all of the observed variation, especially when plants are grown in short days. To identify loci that act in addition to FRI and FLC in controlling flowering of natural accessions, we have analyzed a recombinant inbred line population derived from crosses of accession Niederzenz (Nd) to Columbia, both of which contain natural FRI lesions. Quantitative trait locus mapping and genomic DNA analysis by microarray hybridization were used to identify candidate genes affecting variation in flowering behavior. In both long and short days, the quantitative trait locus of largest effect, termed FLOWERING 1 (FLW1), was found to be associated with a Nd-specific deletion of FLOWERING LOCUS M (FLM), which encodes a floral repressor closely related to FLC. Analysis of near isogenic lines and quantitative transgenic complementation experiments confirmed that the FLM deletion is, in large part, responsible for the early flowering of the Nd accession.

Alleles↗
PubMed2004

Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits.

From simulation studies it is known that the allocation of experimental resources has a crucial effect on power of QTL detection as well as on accuracy and precision of QTL estimates. In this study, we used a very large experimental data set composed of 976 F(5) maize testcross progenies evaluated in 19 environments and cross-validation to assess the effect of sample size (N), number of test environments (E), and significance threshold on the number of detected QTL, the proportion of the genotypic variance explained by them, and the corresponding bias of estimates for grain yield, grain moisture, and plant height. In addition, we used computer simulations to compare the usefulness of two cross-validation schemes for obtaining unbiased estimates of QTL effects. The maximum, validated genotypic variance explained by QTL in this study was 52.3% for grain moisture despite the large number of detected QTL, thus confirming the infinitesimal model of quantitative genetics. In both simulated and experimental data, the effect of sample size on power of QTL detection as well as on accuracy and precision of QTL estimates was large. The number of detected QTL and the proportion of genotypic variance explained by QTL generally increased more with increasing N than with increasing E. The average bias of QTL estimates and its range were reduced by increasing N and E. Cross-validation performed well with respect to yielding asymptotically unbiased estimates of the genotypic variance explained by QTL. On the basis of our findings, recommendations for planning of QTL mapping experiments and allocation of experimental resources are given.

Chromosome Mapping↗
PubMed2001

Multi-trait QTL mapping in barley using multivariate regression.

Many studies of QTL locations record several different traits on the same population, but most analyses look at this information on a trait-by-trait basis. In this paper we show how the regression approach to QTL mapping of Haley & Knott (1992) may be extended to a multi-trait analysis via multivariate regression, easily programmed in statistical packages. A procedure for identifying QTL locations using forward selection and bootstrapping is proposed. The method is applied to examine the locations for QTLs for six yield characters (the number of fertile stems, the grain number of the main stem, the main stem grain weight, the single plant yield, the plot yield and the thousand grain weight) in a doubled haploid population of spring barley. Several chromosomal locations with effects on more than one trait are found. The method is also suitable for examining a single trait measured in different years or environments, and is used here to examine data on heading date, a highly heritable trait, and plot yield, a trait with moderate heritability and showing QTL-environment interactions.

Chromosome Mapping↗
PubMed2005

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

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

Alleles↗
PubMed1998

Quantitative trait locus mapping in dairy cattle by means of selective milk DNA pooling using dinucleotide microsatellite markers: analysis of milk protein percentage.

"Selective DNA pooling" accomplishes quantitative trait locus (QTL) mapping through densitometric estimates of marker allele frequencies in pooled DNA samples of phenotypically extreme individuals. With poly(TG) microsatellites, such estimates are confounded by "shadow" ("stutter") bands. A correction procedure was developed on the basis of an observed linear regression between shadow band intensity and allele TG repeat number. Using this procedure, a selective DNA pooling study with respect to milk protein percentage was implemented in Israel-Holstein dairy cattle. Pools were prepared from milk samples of high and low daughters of each of seven sires and genotyped with respect to 11 markers. Highly significant associations with milk protein percentage were found for 5 of the markers; 4 of these markers confirmed previous reports. Selective DNA pooling accessed 80.6 and 48.3%, respectively, of the information that would have been available through individual selective genotyping or total population genotyping. In effect, the statistical power of 45,600 individual genotypings was obtained from 328 pool genotypings. This methodology can make genome-wide mapping of QTL accessible to moderately sized breeding organizations.

Animals↗
PubMed2004

Quantitative trait loci mapping for ethanol sensitivity and neurotensin receptor density in an F2 intercross derived from inbred high and low alcohol sensitivity selectively bred rat lines.

BACKGROUND: Genetic variance in initial sensitivity to ethanol has been implicated as a risk factor for the development of alcoholism. Identification of the genes that confer differential initial sensitivity is an important goal for the development of new treatment strategies and for a comprehensive understanding of the mechanism of ethanol's action. Quantitative trait loci (QTL) mapping for initial sensitivity and other ethanol-related behavioral traits in model organisms has become an important first step for the ultimate identification of genes that contribute to variation in ethanol responses. METHODS: An F(2) intercross was made from the Inbred High and Low Alcohol Sensitivity rat lines (IHAS and ILAS). The F(2) rats were tested for duration of the loss of righting reflex test (LORR); blood ethanol concentration at regain of righting reflex (BECrrr); BEC at the first time to reach criterion on the rotarod after 1.6 g/kg of ethanol (BEC1); acute functional tolerance on the rotarod (AFT); and high-affinity neurotensin receptor (NTR1) density in the nucleus accumbens (NAc), caudate putamen (CP), and ventral midbrain (VMB). A full genome scan with an average marker spacing of 16.8 cM for interval QTL mapping was conducted on the F(2) rats (N = 363). RESULTS: Seven significant or suggestive QTL were detected for LORR, one for BECrrr, three for BEC1, two for NTR1 binding in the CP, and one for binding in the NAc, but none were mapped for AFT or NTR1 binding density in the VMB. Effect size of the seven LORR QTL, the trait for which the parental strains were selected, ranged from 3 to 4%, with all accounting for approximately 22% of the total phenotypic variation. One of the LORR QTL on chromosome 2 (approximately 87 cM) was significant, and a second QTL on chromosome 5 (approximately 37 cM) was suggestive for both LORR and BECrrr. CONCLUSIONS: The results indicate that segregating populations derived from the IHAS and ILAS strains can be used for mapping ethanol sensitivity QTL. The chromosome 2 LORR QTL may confer variation in ethanol metabolism, whereas the chromosome 5 LORR/BECrrr QTL likely mediates central nervous system ethanol sensitivity. The small number or absence of QTL for BEC1, AFT, and NTR1 receptor density suggests that genetic variation for these traits is minimal in the IHAS/ILAS strains and/or the effect size of QTL for these traits is too small to be mapped efficiently in this sample of F(2) rats. The ultimate identification of genes underlying these alcohol sensitivity QTL will contribute to our understanding of the actions of alcohol in the central nervous system if not to a deeper understanding of the genetic risk factors for alcoholism.

Alcohol Drinking↗
PubMed2005

Semiparametric methods for mapping quantitative trait loci with censored data.

Statistical methods for the detection of genes influencing quantitative traits with the aid of genetic markers are well developed for normally distributed, fully observed phenotypes. Many experiments are concerned with failure-time phenotypes, which have skewed distributions and which are usually subject to censoring because of random loss to follow-up, failures from competing causes, or limited duration of the experiment. In this article, we develop semiparametric statistical methods for mapping quantitative trait loci (QTLs) based on censored failure-time phenotypes. We formulate the effects of the QTL genotype on the failure time through the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model and derive efficient likelihood-based inference procedures. In addition, we show how to assess statistical significance when searching several regions or the entire genome for QTLs. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. Applications to two animal studies are provided.

Animals↗
PubMed2002

Efficient algorithms for quantitative trait loci mapping problems.

Rapid advances in molecular genetics push the need for efficient data analysis. Advanced algorithms are necessary for extracting all possible information from large experimental data sets. We present a general linear algebra framework for quantitative trait loci (QTL) mapping, using both linear regression and maximum likelihood estimation. The formulation simplifies future comparisons between and theoretical analyses of the methods. We show how the common structure of QTL analysis models can be used to improve the kernel algorithms, drastically reducing the computational effort while retaining the original analysis results. We have evaluated our new algorithms on data sets originating from two large F(2) populations of domestic animals. Using an updating approach, we show that 1-3 orders of magnitude reduction in computational demand can be achieved for matrix factorizations. For interval-mapping/composite-interval-mapping settings using a maximum likelihood model, we also show how to use the original EM algorithm instead of the ECM approximation, significantly improving the convergence and further reducing the computational time. The algorithmic improvements makes it feasible to perform analyses which have previously been deemed impractical or even impossible. For example, using the new algorithms, it is reasonable to perform permutation testing using exhaustive search on populations of 200 individuals using an epistatic two-QTL model.

Algorithms↗