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

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.

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 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 Use of Molecular Markers to Locate Quantitative Trait Loci Linked to High Soluble Solids Content in a Hybrid of Lycopersicon cheesmanii

1992 ◽  
Vol 117 (3) ◽  
pp. 497-499 ◽  
Author(s):  
T. Casey Garvey ◽  
John D. Hewitt
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Soluble Solids ◽  
Starch Gel Electrophoresis ◽  
Soluble Solids Content ◽  
Morphological Marker ◽  
Trait Loci ◽  
Marker Loci ◽  
Solids Content ◽  
Lycopersicon Cheesmanii

An interspecific hybrid was made between an accession of Lycopersicon cheesmanii f. minor Riley (LA 1508) from the Galapagos Islands, Ecuador, and L. pennellii (Corr.) D'Arcy (LA 716). LA 1508 was used because of its high soluble solids content (SSC). It was crossed with LA 716 to test for linkage between isozymes and morphological markers and loci conditioning high SSC. For both accessions, chromosome numbers are equal and there are large differences between SSC and no barriers to crossing. Modified BC1 populations derived from the hybridization were assayed for isozyme markers using starch gel electrophoresis. Associations between marker loci and quantitative-trait loci (QTL) conditioning high SSC were determined using analysis of variance. Six isozymes located on five chromosomes and one morphological marker had significant associations with SSC, indicating linkage to QTL. Digenic epistatic interactions between pairs of independent markers did not appear to play an important role in the interactions between QTL that condition SSC.

Molecular Markers Linked to Quantitative Trait Loci for Anthracnose Resistance in Tomato

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 515a-515 ◽  
Author(s):  
John R. Stommel ◽  
Yiping Zhang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Rapd Markers ◽  
Random Amplified Polymorphic Dna ◽  
Marker Assisted Breeding ◽  
Anthracnose Resistance ◽  
Trait Loci ◽  
Marker Loci ◽  
Single Factor Analysis ◽  
Potential Use

Random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) markers linked to quantitative trait loci (QTL) involved in tomato anthracnose resistance were identified in an F2 population of tomato (Lycopersicon esculentum) segregating for anthracnose resistance. The F2 population was developed from the cross of an unadapted and small-fruited, but highly anthracnose-resistant L. esculentum accession and an adapted, but anthracnose-susceptible processing type tomato. Resistance to anthracnose caused by the fungal pathogen Colletotrichum coccodes is estimated to be controlled by at least three genes or chromosomal regions in this cross. One-thousand RAPD random primers and 64 AFLP primer pairs were screened for polymorphisms between the parental lines. Primers or primer pairs which differentiated the anthracnose resistant and susceptible parents were utilized to screen the F2 population for detection of QTL. Using single-factor analysis of variance, a number of markers, including six unmapped RAPD markers were identified that were significantly associated with resistance. Mapping of marker loci and their potential use in marker assisted breeding will be discussed.

scholarly journals Models for mapping quantitative trait loci (QTL) in progeny of non-inbred parents and their behaviour in presence of distorted segregation ratios

1996 ◽  
Vol 67 (1) ◽  
pp. 43-54 ◽  
Author(s):  
R. Schäfer-Pregl ◽  
F. Salamini ◽  
C. Gebhardt
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Marker Locus ◽  
Common Allele ◽  
Distorted Segregation ◽  
Segregation Ratios ◽  
Trait Loci ◽  
Marker Loci ◽  
The Common ◽  
Allele Model

SummaryIn plants, models for mapping quantitative trait loci (QTL) based on flanking markers have been mainly developed for progenies of inbred lines. We propose twoflanking marker models for QTL mapping in F1 progenies of non-inbred parents. The first is based on the segregation of four different scorable alleles at a marker locus (the four-allele model) and the second (the commonallele model) on one scorable allele per marker locus segregating in both parents. These models are suitable for the majority of the allelic configurations which may occur in crosses between heterozygous parents. For both cases, when four scorable or one common-allele per marker locus segregate, additional algorithms were developed to estimate the recombination frequency between two marker loci. Tests carried out with simulated populations of various sizes indicate that the models provide a good estimate of QTL genotypic means and of recombination frequencies between flanking markers and between the marker loci and the QTL.The estimates of QTL genotypic means have a higher precision than the estimates of recombination frequencies. The four-allele model shows a higher ability to detect QTLs than the common-allele model. If segregation ratios are distorted, the power of both models and the precision of the estimates of recombination frequencies are reduced, whereas the accuracy of estimates of QTL genotype means is not affected by distorted segregation ratios. The power of the common-allele model is substantially reduced if QTL genotypic means depend on additive allelic interactions, whereas the four-allele model is less affected by the non-additive behaviour of QTL alleles.

Role of genetic markers in fisheries and aquaculture: useful tools or stamp collecting?

1998 ◽  
Vol 55 (7) ◽  
pp. 1553-1563 ◽  
Author(s):  
Moira M Ferguson ◽  
Roy G Danzmann
Keyword(s):  
Quantitative Trait Loci ◽  
Genetic Markers ◽  
Family Relationships ◽  
Quantitative Trait ◽  
Genome Structure ◽  
Restriction Endonuclease Analysis ◽  
Randomly Amplified Polymorphic Dna ◽  
Trait Loci ◽  
Marker Loci

We comment on the role of genetic markers in fisheries and aquaculture with a view to the future. Our goal is to encourage researchers to evaluate the molecular markers they need to deploy and shift their thinking away from analyses of stock structure towards more aggressive pursuit of questions related to genome structure and function. Examples illustrate that no one marker type is appropriate for all applications. Choice should be based on the evolutionary genetic attributes of both the species and the marker loci themselves. We evaluate three relatively new marker types: mitochondrial DNA (mtDNA) sequences, randomly amplified polymorphic DNA, and hypervariable nuclear loci. We conclude that (i) sequences of mtDNA do not necessarily detect greater polymorphism than restriction endonuclease analysis, (ii) the technical ease of randomly amplified polymorphic DNA is offset by questionable repeatability, and (iii) simulations illustrate that even new marker systems with large numbers of alleles need not detect differences among closely related yet significantly differentiated populations. Increasing the number of alleles per locus did not increase the probability of detecting significant differences. Finally, we consider the roles of genetic markers in helping to determine family relationships in pooled lots of fishes and locate genes that control an organism's phenotype (quantitative trait loci). We discuss how knowledge of quantitative trait loci can help us to understand the basis of individual differences in performance.

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