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Results for “Quantitative Trait Loci”

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PubMed2006

Locus-specific heritability estimation via the bootstrap in linkage scans for quantitative trait loci.

BACKGROUND/AIMS: In genome-wide linkage analysis of quantitative trait loci (QTL), locus-specific heritability estimates are biased when the original data are used to both localize linkage and estimate effects, due to maximization of the LOD score over the genome. Positive bias is increased by adoption of stringent significance levels to control genome-wide type I error. We propose multi-locus bootstrap resampling estimators for bias reduction in the situation in which linkage peaks at more than one QTL are of interest. METHODS: Bootstrap estimates were based on repeated sample splitting in the original dataset. We conducted simulation studies in nuclear families with 0 to 5 QTLs and applied the methods in a genome-wide analysis of a blood pressure phenotype in extended pedigrees from the Framingham Heart Study (FHS). RESULTS: Compared to naïve estimates in the original simulation samples, bootstrap estimates had reduced bias and smaller mean squared error. In the FHS pedigrees, the bootstrap yielded heritability estimates as much as 70% smaller than in the original sample. CONCLUSIONS: Because effect estimates obtained in an initial study are typically inflated relative to those expected in an independent replication study, successful replication will be more likely when sample size requirements are based on bias-reduced estimates.

Cardiovascular Diseases↗

PubMedDate not supplied

Teaching a new dog old tricks: identifying quantitative trait loci using lessons from plants.

Locating quantitative trait loci (QTL) in mammalian systems has proven difficult due to the lack of genetic control and reproducibility, as well as the expense of maintaining sufficiently large populations for genotyping and phenotyping. In plants, populations of recombinant inbred lines (progeny bred to homozygosity from a single cross) do not have these problems. Methods developed to identify QTL in a recombinant inbred soybean population provide a basis for analysis of a suitable mammalian population, such as Portuguese water dogs in the United States. The more than 6,000 dogs have accurate pedigrees, available phenotypic data and samples for genotyping, as well as interesting quantitative trait variation. The computer program Georgie allows us to choose large subpopulations with desirable characteristics such as high degrees of consanguinity that capture some of the benefits of recombinant inbred lines in plants. Computer simulations extending methods developed for simpler plant populations indicate that QTL with realistic effects can be identified from such subpopulations. Currently we are developing markers and collecting phenotypic and genotypic data from this population to begin the process of unraveling the genetic basis of quantitative traits in dogs.

Animals↗

PubMed2004

A comparison between methods for linkage disequilibrium fine mapping of quantitative trait loci.

We present a maximum likelihood method for mapping quantitative trait loci that uses linkage disequilibrium information from single and multiple markers. We made paired comparisons between analyses using a single marker, two markers and six markers. We also compared the method to single marker regression analysis under several scenarios using simulated data. In general, our method outperformed regression (smaller mean square error and confidence intervals of location estimate) for quantitative trait loci with dominance effects. In addition, the method provides estimates of the frequency and additive and dominance effects of the quantitative trait locus.

Chromosome Mapping↗

PubMed2003

Quantitative trait loci variation for growth and obesity between and within lines of pigs (Sus scrofa).

The hypothesis that quantitative trait loci (QTL) that explain variation between divergent populations also account for genetic variation within populations was tested using pig populations. Two regions of the porcine genome that had previously been reported to harbor QTL with allelic effects that differed between the modern pig and its wild-type ancestor and between the modern pig and a more distantly related population of Asian pigs were studied. QTL for growth and obesity traits were mapped using selectively genotyped half-sib families from five domesticated modern populations. Strong support was found for at least one QTL segregating in each population. For all five populations there was evidence of a segregating QTL affecting fatness in a region on chromosome 7. These findings confirm that QTL can be detected in highly selected commercial populations and are consistent with the hypothesis that the same chromosome locations that account for variation between populations also explain genetic variation within populations.

Alleles↗

PubMed2005

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

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

Animals↗

PubMed1995

Detection and positional cloning of blood pressure quantitative trait loci: is it possible? Identifying the genes for genetic hypertension.

Identification of the quantitative trait loci that influence blood pressure and cause genetic hypertension is a major challenge. Several genetically hypertensive rat strains exist and can be used to locate by linkage analysis broad chromosomal regions containing blood pressure quantitative trait loci. Such broad chromosomal regions, and the narrower subregions, can be moved among strains (ie, production of congenic strains and congenic substrains) to identify small chromosomal regions containing the blood pressure quantitative trait loci. However, ultimate positional cloning of the quantitative trait loci presents a major difficulty because the genetic variants involved are likely to result in subtle changes in function rather than the blatant loss of function characteristic of all mendelian disease genes discovered so far by positional cloning.

Animals↗

PubMed2006

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Chromosome Mapping↗

PubMed2004

Modifying the Schwarz Bayesian information criterion to locate multiple interacting quantitative trait loci.

The problem of locating multiple interacting quantitative trait loci (QTL) can be addressed as a multiple regression problem, with marker genotypes being the regressor variables. An important and difficult part in fitting such a regression model is the estimation of the QTL number and respective interactions. Among the many model selection criteria that can be used to estimate the number of regressor variables, none are used to estimate the number of interactions. Our simulations demonstrate that epistatic terms appearing in a model without the related main effects cause the standard model selection criteria to have a strong tendency to overestimate the number of interactions, and so the QTL number. With this as our motivation we investigate the behavior of the Schwarz Bayesian information criterion (BIC) by explaining the phenomenon of the overestimation and proposing a novel modification of BIC that allows the detection of main effects and pairwise interactions in a backcross population. Results of an extensive simulation study demonstrate that our modified version of BIC performs very well in practice. Our methodology can be extended to general populations and higher-order interactions.

Bayes Theorem↗

PubMed2004

Comparing linkage disequilibrium-based methods for fine mapping quantitative trait loci.

Recently, a method for fine mapping quantitative trait loci (QTL) using linkage disequilibrium was proposed to map QTL by modeling covariance between individuals, due to identical-by-descent (IBD) QTL alleles, on the basis of the similarity of their marker haplotypes under an assumed population history. In the work presented here, the advantage of using marker haplotype information for fine mapping QTL was studied by comparing the IBD-based method with 10 markers to regression on a single marker, a pair of markers, or a two-locus haplotype under alternative population histories. When 10 markers were genotyped, the IBD-based method estimated the position of the QTL more accurately than did single-marker regression in all populations. When 20 markers were genotyped for regression, as single-marker methods do not require knowledge of haplotypes, the mapping accuracy of regression in all populations was similar to or greater than that of the IBD-based method using 10 markers. Thus for populations similar to those simulated here, the IBD-based method is comparable to single-marker regression analysis for fine mapping QTL.

Alleles↗

PubMed2001

A genome scan to identify quantitative trait loci affecting economically important traits in a US Holstein population.

Quantitative trait loci affecting economically important traits were studied for eight large US Holstein grandsire families by using the granddaughter design. A total of 155 microsatellite markers located throughout the bovine genome were selected for the scan. The data analyzed include genotypes for 50 markers not previously reported. Results analyses of 105 marker genotypes reported previously were updated. Effects of marker alleles were analyzed for 38 traits including traits for milk production, somatic cell score, productive life, conformation, calving ease, and 16 canonical traits derived from conformation and production traits. Permutation tests were used to calculate empirical traitwise error rates. A traitwise critical value of P = 0.1 was used to determine significance. Ten putative quantitative trait loci associated with seven of the new markers were identified within specific families. One marker on chromosome 14 was associated with differences in fat yield, fat percentage, and a canonical production trait in two families. Markers on chromosomes 18 and 22 were associated with differences in rump angle in the same family. Markers were associated with differences in udder depth and fore udder attachment on chromosomes 16 and 20, respectively. One marker on chromosome 27 was associated with a difference in the dairy capacity composite index, and another marker on chromosome 13 was associated with a difference in a canonical conformation trait. These additional markers complete our genome scan to identify quantitative trait loci affecting economically important traits in a selected commercial Holstein population. The quantitative trait loci identified in this genome scan may be useful for marker-assisted selection to increase the rate of genetic improvement on traits such as disease resistance and conformation traits associated with fitness while accelerating genetic improvement for production.

Alleles↗

PubMed2003

The nature and identification of quantitative trait loci: a community's view.

This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Animals↗

PubMed2006

On the generalized poisson regression mixture model for mapping quantitative trait loci with count data.

Statistical methods for mapping quantitative trait loci (QTL) have been extensively studied. While most existing methods assume normal distribution of the phenotype, the normality assumption could be easily violated when phenotypes are measured in counts. One natural choice to deal with count traits is to apply the classical Poisson regression model. However, conditional on covariates, the Poisson assumption of mean-variance equality may not be valid when data are potentially under- or overdispersed. In this article, we propose an interval-mapping approach for phenotypes measured in counts. We model the effects of QTL through a generalized Poisson regression model and develop efficient likelihood-based inference procedures. This approach, implemented with the EM algorithm, allows for a genomewide scan for the existence of QTL throughout the entire genome. The performance of the proposed method is evaluated through extensive simulation studies along with comparisons with existing approaches such as the Poisson regression and the generalized estimating equation approach. An application to a rice tiller number data set is given. Our approach provides a standard procedure for mapping QTL involved in the genetic control of complex traits measured in counts.

Algorithms↗

PubMed2005

Bayesian model selection for genome-wide epistatic quantitative trait loci analysis.

The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.

Algorithms↗

PubMed1999

Multiple interval mapping for quantitative trait loci.

A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from approximately 1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed approximately 10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0. 3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).

Chromosome Mapping↗

PubMed1998

Mapping quantitative trait loci for ordered categorical traits in four-way crosses.

Many quantitative traits of economical importance are ordinal in nature. Although methods of mapping quantitative trait loci (QTLs) for continuous quantitative characters are well developed, such methods for ordinal characters are generally lacking. In this paper, we develop a method based on the framework of a generalized linear model using four-way cross populations. The method estimates and tests the average effects of a gene substitution in the parents. All markers in the same linkage group are simultaneously used to infer the allelic transmission of a putative QTL. General results of the method are demonstrated by a few simulation experiments. We discuss extensions of the method to QTL mapping in full-sib families.

Animals↗

PubMed2005

Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452x'AC Domain'.

Relatively little is known about the genetic control of agronomic traits in common wheat (Triticum aestivum L.) compared with traits that follow Mendelian segregation patterns. A doubled-haploid population was generated from the cross RL4452x'AC Domain' to study the inheritance of the agronomic traits: plant height, time to maturity, lodging, grain yield, test weight, and 1000-grain weight. This cross includes the genetics of 2 western Canadian wheat marketing classes. Composite interval mapping was conducted with a microsatellite linkage map, incorporating 369 loci, and phenotypic data from multiple Manitoba environments. The plant height quantitative trait loci (QTLs), QHt.crc-4B and QHt.crc-4D, mapped to the expected locations of Rht-B1 and Rht-D1. These QTLs were responsible for most of the variation in plant height and were associated with other agronomic traits. An additional 25 agronomic QTLs were detected in the RL4452x'AC Domain' population beyond those associated with QHt.crc-4B and QHt.crc-4D. 'AC Domain' contributed 4 alleles for early maturity, including a major time to maturity QTL on 7D. RL4452 contributed 2 major alleles for increased grain yield at QYld.crc-2B and QYld.crc-4A, which are potential targets for marker-assisted selection. A key test weight QTL was detected on 3B and prominent 1000-grain weight QTLs were identified on 3D and 4A.

Chromosome Mapping↗

PubMed2005

Quantitative trait loci for novelty/stress-induced locomotor activation in recombinant inbred (RI) and recombinant congenic (RC) strains of mice.

The objective of the present study was to map and compare quantitative trait loci (QTLs) for an anxiety-related trait (novelty/stress-induced activation) in the AXB/BXA recombinant inbred (RI) and AcB/BcA recombinant congenic (RC) strains of mice derived from the A/J and C57BL/6J inbred progenitor strains. Activational responses to a novel open field (OF) were measured under identical stressful conditions (no prior handling or exposure to testing procedures) in both the RI and RC strains. Naive male and female mice were weighed, injected with IP saline and locomotor activity was monitored in a computerized OF apparatus for 15 min. Measures obtained from this experimental design included: (1) total activity scores, (2) time course of response (5 min time blocks over the 15 min session). Data for the RI strains were subjected to a QTL analysis using composite interval mapping. Significant loci were identified on chr 5 (D5Mit356, 41 cM), chr 8 (D8Mit305, 37 cM) and chr 14 (D14Mit36, 6 3cM). Single locus association analysis of the AcB/BcA RC strains identified 15 putative regions, 7 of which overlapped regions independently mapped in the RI strains on chr 1 (58.5-63.1cM), chr 4 (21.9-28.6 cM), chr 5 (19-45 & 74-86 cM), chr 6 (0.5-20.4cM), chr 9 (15-38 cM), chr 13 (47cM) and chr 19 (47cM). The loci identified on chr 5 near D5Mit356 (41cM) in both the AXB/BXA RIS and AcB/BcA RCS maps to a region containing the genes for several GABA(A) receptor subunits. Additionally, the present study provides further confirmation of a frequently identified QTL on chromosome 1. The results are discussed in the context of previous QTL studies of anxiety-related traits that have used genetic crosses that include the A or B6 progenitors.

Analysis of Variance↗

PubMed1998

Further investigation on the regression method of mapping quantitative trait loci.

The simple regression method of mapping quantitative trait loci (QTL) is further investigated in comparison with the mixture model maximum likelihood method under high heritabilities, dominant and missing markers. No significant difference between the two methods is detected in terms of errors of parameter estimation and statistical powers, with the exception that the estimation of residual variance provided by the regression method is confounded with part of the QTL variance. The test statistic profiles show some difference between the two methods, but the difference is only detectable at the micro level. An alternative method, referred to as iteratively reweighted least squares, is proposed, which can correct the deficiency of parameter confounding in the regression method yet retains the properties of simplicity and rapidity of the ordinary regression method. Like the existing regression method, the weighted least squares method can be useful in QTL mapping in conjunction with the permutation tests and construction of confidence intervals by bootstrapping.

Chromosome Mapping↗