scholarly journals Detection of Quantitative Trait Loci in Outbred Populations With Incomplete Marker Data

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 409-420 ◽  
Author(s):  
Marco C A M Bink ◽  
Johan A M Van Arendonk
Keyword(s):  
Quantitative Trait Loci ◽  
Bayesian Approach ◽  
Quantitative Trait ◽  
Simulated Data ◽  
Residual Variance ◽  
Monte Carlo Techniques ◽  
Marker Data ◽  
Trait Loci ◽  
Marker Loci ◽  
Outbred Populations

Abstract Augmentation of marker genotypes for ungenotyped individuals is implemented in a Bayesian approach via the use of Markov chain Monte Carlo techniques. Marker data on relatives and phenotypes are combined to compute conditional posterior probabilities for marker genotypes of ungenotyped individuals. The presented procedure allows the analysis of complex pedigrees with ungenotyped individuals to detect segregating quantitative trait loci (QTL). Allelic effects at the QTL were assumed to follow a normal distribution with a covariance matrix based on known QTL position and identity by descent probabilities derived from flanking markers. The Bayesian approach estimates variance due to the single QTL, together with polygenic and residual variance. The method was empirically tested through analyzing simulated data from a complex granddaughter design. Ungenotyped dams were related to one or more sons or grandsires in the design. Heterozygosity of the marker loci and size of QTL were varied. Simulation results indicated a significant increase in power when ungenotyped dams were included in the analysis.

scholarly journals Aspects of maximum likelihood methods for the mapping of quantitative trait loci in line crosses

1992 ◽  
Vol 60 (2) ◽  
pp. 139-151 ◽  
Author(s):  
S. A. Knott ◽  
C. S. Haley
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Quantitative Trait ◽  
Simulated Data ◽  
Interval Mapping ◽  
Likelihood Methods ◽  
Trait Loci ◽  
Marker Loci ◽  
Maximum Likelihood Methods ◽  
Distribution Testing

SummaryMaximum likelihood methods for the mapping of quantitative trait loci (QTL) have been investigated in an F2 population using simulated data. The use of adjacent (flanking) marker pairs gave improved power for the detection of QTL over the use of single markers when markers were widely spaced and the QTL effect large. The use of flanking marker loci also always gave moreaccurate and less biassed estimates for the effect and position of the QTL and made the method less sensitive to violations of assumptions, for example non-normality of the distribution. Testing the hypothesis of a linked QTL against that of no QTL is not biassed by the presence of unlinked QTL. This test is more robust and easier to obtain than the comparison of a linked with an unlinked QTL. Fixing the recombination fraction between the markers at an incorrect value in the analyses with flanking markers does not generally bias the test for QTL or estimates of their effect. The presence of multiple linked QTL bias both tests and estimates of effect with interval mapping, leading to inflated values when QTL are in association in the lines crossed and deflated values when they are in dispersion.

scholarly journals A Rank-Based Nonparametric Method for Mapping Quantitative Trait Loci in Outbred Half-Sib Pedigrees: Application to Milk Production in a Granddaughter Design

Genetics ◽  
1998 ◽  
Vol 149 (3) ◽  
pp. 1547-1555 ◽  
Author(s):  
Wouter Coppieters ◽  
Alexandre Kvasz ◽  
Frédéric Farnir ◽  
Juan-Jose Arranz ◽  
Bernard Grisart ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Real Data ◽  
Interval Mapping ◽  
Mapping Method ◽  
Residual Variance ◽  
Data Set ◽  
Wilcoxon Rank Sum Test ◽  
Holstein Friesian ◽  
Trait Loci

Abstract We describe the development of a multipoint nonparametric quantitative trait loci mapping method based on the Wilcoxon rank-sum test applicable to outbred half-sib pedigrees. The method has been evaluated on a simulated dataset and its efficiency compared with interval mapping by using regression. It was shown that the rank-based approach is slightly inferior to regression when the residual variance is homoscedastic normal; however, in three out of four other scenarios envisaged, i.e., residual variance heteroscedastic normal, homoscedastic skewed, and homoscedastic positively kurtosed, the latter outperforms the former one. Both methods were applied to a real data set analyzing the effect of bovine chromosome 6 on milk yield and composition by using a 125-cM map comprising 15 microsatellites and a granddaughter design counting 1158 Holstein-Friesian sires.

scholarly journals The contribution of quantitative trait loci and neutral marker loci to the genetic variances and covariances among quantitative traits in random mating populations.

Genetics ◽  
1995 ◽  
Vol 139 (1) ◽  
pp. 445-455 ◽  
Author(s):  
A Ruiz ◽  
A Barbadilla
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Random Mating ◽  
Unbiased Estimation ◽  
Limited Information ◽  
Trait Loci ◽  
Marker Loci ◽  
Neutral Marker ◽  
Variance Explained

Abstract Using Cockerham's approach of orthogonal scales, we develop genetic models for the effect of an arbitrary number of multiallelic quantitative trait loci (QTLs) or neutral marker loci (NMLs) upon any number of quantitative traits. These models allow the unbiased estimation of the contributions of a set of marker loci to the additive and dominance variances and covariances among traits in a random mating population. The method has been applied to an analysis of allozyme and quantitative data from the European oyster. The contribution of a set marker loci may either be real, when the markers are actually QTLs, or apparent, when they are NMLs that are in linkage disequilibrium with hidden QTLs. Our results show that the additive and dominance variances contributed by a set of NMLs are always minimum estimates of the corresponding variances contributed by the associated QTLs. In contrast, the apparent contribution of the NMLs to the additive and dominance covariances between two traits may be larger than, equal to or lower than the actual contributions of the QTLs. We also derive an expression for the expected variance explained by the correlation between a quantitative trait and multilocus heterozygosity. This correlation explains only a part of the genetic variance contributed by the markers, i.e., in general, a combination of additive and dominance variances and, thus, provides only very limited information relative to the method supplied here.

scholarly journals Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action

Genetics ◽  
1987 ◽  
Vol 116 (1) ◽  
pp. 113-125
Author(s):  
M D Edwards ◽  
C W Stuber ◽  
J F Wendel
Keyword(s):  
Quantitative Trait Loci ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Gene Action ◽  
Individual Plant ◽  
Gene Effects ◽  
Individual Gene ◽  
Trait Loci ◽  
Marker Loci

ABSTRACT Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17–20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800–1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.

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.

Linkage between Quantitative Trait Loci and Marker Loci: Resolution Power of Three Statistical Approaches in Single Marker Analysis

Biometrics ◽  
1996 ◽  
Vol 52 (2) ◽  
pp. 426 ◽  
Author(s):  
Abraham B. Korol ◽  
Yefim I. Ronin ◽  
Valery M. Kirzhner
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Single Marker Analysis ◽  
Resolution Power ◽  
Marker Analysis ◽  
Trait Loci ◽  
Marker Loci ◽  
Single Marker ◽  
Statistical Approaches

scholarly journals Mapping quantitative trait loci using four-way crosses

1996 ◽  
Vol 68 (2) ◽  
pp. 175-181 ◽  
Author(s):  
Shizhong Xu
Keyword(s):  
Regression Analysis ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Inbred Lines ◽  
Residual Variance ◽  
Environmental Variance ◽  
Trait Loci ◽  
New Strategy

SummaryIn plant species, typical gene mapping strategies use populations initiated from crosses between two inbred lines. However, schemes including more than two parents could be used. In this paper, a new approach is introduced which uses a four-way cross population derived from four inbred lines. The four-way cross design for mapping quantitative trait loci (QTLs) provides tests for QTL segregation in four lines simultaneously in one experiment. Therefore, it is a more economical strategy than oneusing line crosses between only two lines. The new strategy also increases the probability of detecting QTLs if they segregate in one line cross but not in the other. A multiple linear regression analysis is used for QTL detection. It is proven that the expected residual variance from the regression analysis differs from the pure environmental variance. Correction for the bias is proposed and verified by computer simulations.

Sequential sampling in determining linkage between marker loci and quantitative trait loci

1993 ◽  
Vol 85-85 (6-7) ◽  
pp. 658-664 ◽  
Author(s):  
U. Motro ◽  
M. Soller
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Sequential Sampling ◽  
Trait Loci ◽  
Marker Loci
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