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.

scholarly journals RFLP mapping in soybean: association between marker loci and variation in quantitative traits.

Genetics ◽  
1990 ◽  
Vol 126 (3) ◽  
pp. 735-742 ◽  
Author(s):  
P Keim ◽  
B W Diers ◽  
T C Olson ◽  
R C Shoemaker
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Glycine Soja ◽  
Interspecific Cross ◽  
Field Trials ◽  
Rflp Markers ◽  
Progeny Testing ◽  
Trait Loci ◽  
Marker Loci

Abstract We have constructed a genetic map for soybean and identified associations between genetic markers and quantitative trait loci. One-hundred-fifty restriction fragment length polymorphisms (RFLPs) were used to identify genetic linkages in an F2 segregating population from an interspecific cross (Glycine max x Glycine soja). Twenty-six genetic linkage groups containing ca. 1200 recombination units are reported. Progeny-testing of F2-derived families allowed quantitative traits to be evaluated in replicated field trials. Genomic regions, which accounted for a portion of the genetic variation (R2 = 16 to 24%) in several reproductive and morphological traits, were linked to RFLP markers. Significant associations between RFLP markers and quantitative trait loci were detected for eight of nine traits evaluated. The ability to identify genes within a continuously varying trait has important consequences for plant breeding and for understanding evolutionary processes.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

Impact of the D genome and quantitative trait loci on quantitative traits in a spring durum by spring bread wheat cross

2015 ◽  
Vol 128 (9) ◽  
pp. 1799-1811 ◽  
Author(s):  
J. R. Kalous ◽  
J. M. Martin ◽  
J. D. Sherman ◽  
H.-Y. Heo ◽  
N. K. Blake ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Bread Wheat ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Spring Bread Wheat ◽  
D Genome ◽  
Trait Loci ◽  
Wheat Cross

Interaction and quantitative trait loci

2004 ◽  
Vol 44 (11) ◽  
pp. 1135 ◽  
Author(s):  
O. Mayo
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Trait Loci ◽  
Different Populations ◽  
Initial Results

Parallel searches for quantitative trait loci (QTL) for growth-related traits in different populations frequently detect sets of QTL that hardly overlap. Thus, many QTL potentially exist. Tools for the detection of QTL that interact are available and are currently being tested. Initial results suggest that epistasis is widespread. Modelling of the first recognised interaction, dominance, continues to be developed. Multigenic interaction appears to be a necessary part of any explanation. This paper covers an attempt to link some of these studies and to draw inferences about useful approaches to understanding and using the genes that influence quantitative traits.

scholarly journals Mapping quantitative trait loci with epistatic effects

2002 ◽  
Vol 79 (2) ◽  
pp. 185-198 ◽  
Author(s):  
NENGJUN YI ◽  
SHIZHONG XU
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Quantitative Traits ◽  
Threshold Model ◽  
Monte Carlo Algorithm ◽  
Epistatic Effects ◽  
Trait Loci ◽  
Robust Statistical Methods ◽  
Qtl Effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.

scholarly journals Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax

2020 ◽  
Vol 3 (2) ◽  
pp. 28 ◽  
Author(s):  
Frank M. You ◽  
Sylvie Cloutier
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Snp Markers ◽  
Genetic Maps ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Trait Loci ◽  
Genomic Regions

Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits. To date, a total of 313 QTL for 31 quantitative traits have been reported in 14 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, the scaffold sequences, or the pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but the most used ones were simple sequence repeats (SSRs) or single nucleotide polymorphisms (SNPs). To uniquely map the SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules, methods with several scripts and database files were developed to locate PCR- and SNP-based markers onto the same reference, co-locate QTL, and scan genome-wide candidate genes. Using these methods, 195 out of 200 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters; the candidate genes that co-located with these QTL clusters were also predicted. The methods and tools presented in this article facilitate marker re-mapping to a new reference, genome-wide QTL analysis, candidate gene scanning, and breeding applications in flax and other crops.

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