scholarly journals A Penalized Likelihood Method for Mapping Epistatic Quantitative Trait Loci With One-Dimensional Genome Searches

Genetics ◽  
2002 ◽  
Vol 162 (2) ◽  
pp. 951-960 ◽  
Author(s):  
Martin P Boer ◽  
Cajo J F ter Braak ◽  
Ritsert C Jansen
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Statistical Tests ◽  
Penalized Likelihood ◽  
Likelihood Method ◽  
Likelihood Methods ◽  
One Dimensional ◽  
Statistical Framework ◽  
Multidimensional Search ◽  
Trait Loci

AbstractEpistasis is a common and important phenomenon, as indicated by results from a number of recent experiments. Unfortunately, the discovery of epistatic quantitative trait loci (QTL) is difficult since one must search for multiple QTL simultaneously in two or more dimensions. Such a multidimensional search necessitates many statistical tests, and a high statistical threshold must be adopted to avoid false positives. Furthermore, the large number of (interaction) parameters in comparison with the number of observations results in a serious danger of overfitting and overinterpretation of the data. In this article we present a new statistical framework for mapping epistasis in inbred line crosses. It is based on reducing the high dimensionality of the problem in two ways. First, epistatic QTL are mapped in a one-dimensional genome scan for high interactions between QTL and the genetic background. Second, the dimension of the search is bounded by penalized likelihood methods. We use simulated backcross data to illustrate the new approach.

A random model approach to interval mapping of quantitative trait loci.

Genetics ◽  
1995 ◽  
Vol 141 (3) ◽  
pp. 1189-1197 ◽  
Author(s):  
S Xu ◽  
W R Atchley
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Fixed Effects ◽  
Interval Mapping ◽  
Random Model ◽  
Likelihood Method ◽  
Mapping Procedure ◽  
Trait Loci ◽  
Outbred Populations ◽  
Model Approach

Abstract Mapping quantitative trait loci in outbred populations is important because many populations of organisms are noninbred. Unfortunately, information about the genetic architecture of the trait may not be available in outbred populations. Thus, the allelic effects of genes can not be estimated with ease. In addition, under linkage equilibrium, marker genotypes provide no information about the genotype of a QTL (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). To circumvent this problem, an interval mapping procedure based on a random model approach is described. Under a random model, instead of estimating the effects, segregating variances of QTLs are estimated by a maximum likelihood method. Estimation of the variance component of a QTL depends on the proportion of genes identical-by-descent (IBD) shared by relatives at the locus, which is predicted by the IBD of two markers flanking the QTL. The marker IBD shared by two relatives are inferred from the observed marker genotypes. The procedure offers an advantage over the regression interval mapping in terms of high power and small estimation errors and provides flexibility for large sibships, irregular pedigree relationships and incorporation of common environmental and fixed effects.

scholarly journals Mapping Quantitative Trait Loci by Genotyping Haploid Tissues

Genetics ◽  
1999 ◽  
Vol 152 (4) ◽  
pp. 1741-1752 ◽  
Author(s):  
R L Wu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Allelic Frequency ◽  
Likelihood Method ◽  
Parameter Estimates ◽  
Residual Variance ◽  
Population Parameters ◽  
Linkage Disequilibria ◽  
Mapping Strategy ◽  
Trait Loci

AbstractMapping strategies based on a half- or full-sib family design have been developed to map quantitative trait loci (QTL) for outcrossing species. However, these strategies are dependent on controlled crosses where marker-allelic frequency and linkage disequilibrium between the marker and QTL may limit their application. In this article, a maximum-likelihood method is developed to map QTL segregating in an open-pollinated progeny population using dominant markers derived from haploid tissues from single meiotic events. Results from the haploid-based mapping strategy are not influenced by the allelic frequencies of markers and their linkage disequilibria with QTL, because the probabilities of QTL genotypes conditional on marker genotypes of haploid tissues are independent of these population parameters. Parameter estimation and hypothesis testing are implemented via expectation/conditional maximization algorithm. Parameters estimated include the additive effect, the dominant effect, the population mean, the chromosomal location of the QTL in the interval, and the residual variance within the QTL genotypes, plus two population parameters, outcrossing rate and QTL-allelic frequency. Simulation experiments show that the accuracy and power of parameter estimates are affected by the magnitude of QTL effects, heritability levels of a trait, and sample sizes used. The application and limitation of the method are discussed.

Bayesian Mapping of Quantitative Trait Loci Under Complicated Mating Designs

Genetics ◽  
2001 ◽  
Vol 157 (4) ◽  
pp. 1759-1771 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Inbred Lines ◽  
Likelihood Method ◽  
Mcmc Algorithm ◽  
Model Framework ◽  
Multiple Alleles ◽  
Trait Loci ◽  
Bayesian Mapping

AbstractQuantitative trait loci (QTL) are easily studied in a biallelic system. Such a system requires the cross of two inbred lines presumably fixed for alternative alleles of the QTL. However, development of inbred lines can be time consuming and cost ineffective for species with long generation intervals and severe inbreeding depression. In addition, restriction of the investigation to a biallelic system can sometimes be misleading because many potentially important allelic interactions do not have a chance to express and thus fail to be detected. A complicated mating design involving multiple alleles mimics the actual breeding system. However, it is difficult to develop the statistical model and algorithm using the classical maximum-likelihood method. In this study, we investigate the application of a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm to QTL mapping under arbitrarily complicated mating designs. We develop the method under a mixed-model framework where the genetic values of founder alleles are treated as random and the nongenetic effects are treated as fixed. With the MCMC algorithm, we first draw the gene flows from the founders to the descendants for each QTL and then draw samples of the genetic parameters. Finally, we are able to simultaneously infer the posterior distribution of the number, the additive and dominance variances, and the chromosomal locations of all identified QTL.

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

2004 ◽  
Vol 83 (1) ◽  
pp. 41-47 ◽  
Author(s):  
JIHAD M. ABDALLAH ◽  
BRIGITTE MANGIN ◽  
BRUNO GOFFINET ◽  
CHRISTINE CIERCO-AYROLLES ◽  
MIGUEL PÉREZ-ENCISO
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Simulated Data ◽  
Likelihood Method ◽  
Trait Loci ◽  
Single Marker ◽  
Linkage Disequilibrium Information ◽  
Marker Regression ◽  
Trait Locus

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.

scholarly journals Maximum likelihood mapping of quantitative trait loci using full-sib families.

Genetics ◽  
1992 ◽  
Vol 132 (4) ◽  
pp. 1211-1222 ◽  
Author(s):  
S A Knott ◽  
C S Haley
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Quantitative Trait ◽  
Simulated Data ◽  
Likelihood Method ◽  
Parameter Estimates ◽  
Random Component ◽  
Test Statistic ◽  
Trait Loci ◽  
Sib Families

Abstract A maximum likelihood method is presented for the detection of quantitative trait loci (QTL) using flanking markers in full-sib families. This method incorporates a random component for common family effects due to additional QTL or the environment. Simulated data have been used to investigate this method. With a fixed total number of full sibs power of detection decreased substantially with decreasing family size. Increasing the number of alleles at the marker loci (i.e., polymorphism information content) and decreasing the interval size about the QTL increased power. Flanking markers were more powerful than single markers. In testing for a linked QTL the test must be made against a model which allows for between family variation (i.e., including an unlinked QTL or a between family variance component) or the test statistic may be grossly inflated. Mean parameter estimates were close to the simulated values in all situations when fitting the full model (including a linked QTL and common family effect). If the common family component was omitted the QTL effect was overestimated in data in which additional genetic variance was simulated and when compared with an unlinked QTL model there was reduced power. The test statistic curves, reflecting the likelihood of the QTL at each position along the chromosome, have discontinuities at the markers caused by adjacent pairs of markers providing different amounts of information. This must be accounted for when using flanking markers to search for a QTL in an outbred population.

scholarly journals Treatment of the X chromosome in mapping multiple quantitative trait loci

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Quoc Tran ◽  
Karl W Broman
Keyword(s):  
Quantitative Trait Loci ◽  
X Chromosome ◽  
Quantitative Trait ◽  
Likelihood Method ◽  
Special Treatment ◽  
Data Set ◽  
Significance Threshold ◽  
Multiple Qtl ◽  
Trait Loci ◽  
Eqtl Data

Abstract Statistical methods to map quantitative trait loci (QTL) often neglect the X chromosome and may focus exclusively on autosomal loci. But the X chromosome often requires special treatment: sex and cross-direction covariates may need to be included to avoid spurious evidence of linkage, and the X chromosome may require a separate significance threshold. In multiple-QTL analyses, including the consideration of epistatic interactions, the X chromosome also requires special care and consideration. We extend a penalized likelihood method for multiple-QTL model selection, to appropriately handle the X chromosome. We examine its performance in simulation and by application to a large eQTL data set. The method has been implemented in the package R/qtl.

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