scholarly journals Using known QTLs to detect directional epistatic interactions

2012 ◽  
Vol 94 (1) ◽  
pp. 39-48 ◽  
Author(s):  
MONTGOMERY SLATKIN ◽  
MARK KIRKPATRICK
Keyword(s):  
Genetic Variance ◽  
Additive Model ◽  
Quantitative Character ◽  
Gene Interactions ◽  
Ratio Test ◽  
Linkage Equilibrium ◽  
Response To Selection ◽  
Epistatic Interactions ◽  
New Strategy ◽  
Trait Locus

SummaryEpistasis plays important roles in evolution, for example in the evolution of recombination, but each of the current methods to study epistasis has limitations. Here, we propose a new strategy. If a quantitative trait locus (QTL) affecting a quantitative character has been identified, individuals who have the same genotype at that QTL can be regarded as comprising a subpopulation whose response to selection depends in part on interactions with other loci affecting the character. We define the marginal differences to be the differences in the average phenotypes of individuals with different genotypes of that QTL. We show that the response of the marginal differences to directional selection on the quantitative character depends on epistatic gene interactions. For a model with no interactions, the marginal differences do not differ on average from their starting values once linkage equilibrium has been re-established. If there is directional epistasis, meaning that interactions between the QTL and other loci tend to increase or decrease the character more than under an additive model, then the marginal differences will tend to increase or decrease accordingly when larger values of the character are selected for. We develop a likelihood ratio test for significant changes in the marginal differences and show that it has some power to detect directional epistasis for realistic sample sizes. We also show that epistatic interactions which affect the evolution of the marginal differences do not necessarily result in a substantial epistatic component of the genetic variance.

The Equation for Response to Selection and Its Use for Prediction

1997 ◽  
Vol 5 (3) ◽  
pp. 303-346 ◽  
Author(s):  
Heinz Mühlenbein
Keyword(s):  
Genetic Algorithms ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Linkage Equilibrium ◽  
Realized Heritability ◽  
Response To Selection ◽  
Infinite Population ◽  
Livestock Breeding ◽  
Genotype Frequencies ◽  
Tournament Selection

The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA, and the average fitness. We compare Mendelian two-parent recombination with gene-pool recombination, which belongs to a special class of genetic algorithms that we call univariate marginal distribution (UMD) algorithms. UMD algorithms keep the genotypes in linkage equilibrium. For UMD algorithms, an exact RS equation is proven that can be used for long-term prediction. Empirical and theoretical evidence is provided that indicates that Mendelian two-parent recombination is also mainly exploiting the additive genetic variance. We compute an exact RS equation for binary tournament selection. It shows that the two classical methods for estimating realized heritability—the regression heritability and the heritability in the narrow sense—may give poor estimates. Furthermore, realized heritability for binary tournament selection can be very different from that of proportionate selection. The paper ends with a short survey about methods that extend standard genetic algorithms and UMD algorithms by detecting interacting variables in nonlinear fitness functions and using this information to sample new points.

scholarly journals How informative is Wright's estimator of the number of genes affecting a quantitative character?

Genetics ◽  
1990 ◽  
Vol 126 (1) ◽  
pp. 235-247 ◽  
Author(s):  
Z B Zeng ◽  
D Houle ◽  
C C Cockerham
Keyword(s):  
Genetic Variance ◽  
Estimation Procedure ◽  
Expected Value ◽  
Allele Frequencies ◽  
Quantitative Character ◽  
Base Population ◽  
Single Population ◽  
Number Of Genes ◽  
Selection Limit ◽  
The Difference

Abstract S. Wright suggested an estimator, m, of the number of loci, m, contributing to the difference in a quantitative character between two differentiated populations, which is calculated from the phenotypic means and variances in the two parental populations and their F1 and F2 hybrids. The same method can also be used to estimate m contributing to the genetic variance within a single population, by using divergent selection to create differentiated lines from the base population. In this paper we systematically examine the utility and problems of this technique under the influences of unequal allelic effects and initial allele frequencies, and linkage, which are known to lead m to underestimate m. In addition, we examine the effects of population size and selection intensity during the generations of selection. During selection, the estimator m rapidly approaches its expected value at the selection limit. With reasonable assumptions about unequal allelic effects and initial allele frequencies, the expected value of m without linkage is likely to be on the order of one-third of the number of genes. The estimates suffer most seriously from linkage. The practical maximum expectation of m is just about the number of chromosomes, considerably less than the "recombination index" which has been assumed to be the upper limit. The estimates are also associated with large sampling variances. An estimator of the variance of m derived by R. Lande substantially underestimates the actual variance. Modifications to the method can ameliorate some of the problems. These include using F3 or later generation variances or the genetic variance in the base population, and replicating the experiments and estimation procedure. However, even in the best of circumstances, information from m is very limited and can be misleading.

scholarly journals Quantitative genetic variability maintained by mutation-stabilizing selection balance: sampling variation and response to subsequent directional selection

1989 ◽  
Vol 54 (1) ◽  
pp. 45-58 ◽  
Author(s):  
Peter D. Keightley ◽  
William G. Hill
Keyword(s):  
Genetic Variance ◽  
Directional Selection ◽  
Quantitative Character ◽  
Stabilizing Selection ◽  
Effective Population ◽  
Selection Responses ◽  
Quantitative Genetic ◽  
Before And After ◽  
Cage Population ◽  
Selection Of

SummaryA model of genetic variation of a quantitative character subject to the simultaneous effects of mutation, selection and drift is investigated. Predictions are obtained for the variance of the genetic variance among independent lines at equilibrium with stabilizing selection. These indicate that the coefficient of variation of the genetic variance among lines is relatively insensitive to the strength of stabilizing selection on the character. The effects on the genetic variance of a change of mode of selection from stabilizing to directional selection are investigated. This is intended to model directional selection of a character in a sample of individuals from a natural or long-established cage population. The pattern of change of variance from directional selection is strongly influenced by the strengths of selection at individual loci in relation to effective population size before and after the change of regime. Patterns of change of variance and selection responses from Monte Carlo simulation are compared to selection responses observed in experiments. These indicate that changes in variance with directional selection are not very different from those due to drift alone in the experiments, and do not necessarily give information on the presence of stabilizing selection or its strength.

scholarly journals A network view of microRNA and gene interactions in different pathological stages of colon cancer

2019 ◽  
Vol 12 (S7) ◽  
Author(s):  
Jia Wen ◽  
Benika Hall ◽  
Xinghua Shi
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Gene Interactions ◽  
New Strategy ◽  
Pathological Stages

Abstract Background Colon cancer is one of the common cancers in human. Although the number of annual cases has decreased drastically, prognostic screening and translational methods can be improved. Hence, it is critical to understand the molecular mechanisms of disease progression and prognosis. Results In this study, we develop a new strategy for integrating microRNA and gene expression profiles together with clinical information toward understanding the regulation of colon cancer. Particularly, we use this approach to identify microRNA and gene expression networks that are specific to certain pathological stages. To demonstrate the application of our method, we apply this approach to identify microRNA and gene interactions that are specific to pathological stages of colon cancer in The Cancer Genome Atlas (TCGA) datasets. Conclusions Our results show that there are significant differences in network connections between miRNAs and genes in different pathological stages of colon cancer. These findings point to a hypothesis that these networks signify different roles of microRNA and gene regulation in the pathogenesis and tumorigenesis of colon cancer.

scholarly journals Marker assisted selection for genetic improvement of animal populations when a single QTL is marked

1995 ◽  
Vol 66 (1) ◽  
pp. 71-83 ◽  
Author(s):  
J. Ruane ◽  
J. J. Colleau
Keyword(s):  
Marker Assisted Selection ◽  
Genetic Variance ◽  
Genetic Model ◽  
Polymorphic Markers ◽  
Linkage Phase ◽  
Monte Carlo Simulation Study ◽  
Animal Populations ◽  
Single Character ◽  
Selection For ◽  
Trait Locus

SummaryA Monte Carlo simulation study to evaluate the benefits of marker assisted selection (MAS) in small populations with one marked bi-allelic quantitative trait locus (QTL) is described. In the base generation, linkage phase equilibrium between the markers, QTL and polygenes was assumed and frequencies of 0·5 for the two QTL alleles were used. Six discrete generations of selection for a single character measured on both sexes followed. An additive genetic model was used with the QTL positioned midway between two highly polymorphic markers. Schemes were simulated with a distance of 10 cM between the QTL and either of the two markers and with the QTL explaining 1/8 of the total genetic variance in the base generation. Values of 0·5, 0·25 or 0·1 were assumed for the heritability. Eight males and 16, 32 or 64 females were selected each generation with each dam producing four sons and four daughters as candidates for the next generation. Animals were evaluated with a conventional BLUP animal model or with a model using marker information. MAS resulted in substantially higher QTL responses (4–54%), especially with low heritabilities, than conventional BLUP but lower polygenic responses (up to 4%) so that the overall effect on the total genetic response, although in the majority of cases favourable, was relatively small. With QTLs of larger size (explaining 25% of the genetic variance) comparable results were found. When the distance between the QTL and the markers was reduced to 2 cM, genetic responses were increased very slightly with a heritability of 0·5 whereas with a heritability of 0·1 responses were increased by up to 10%, compared with conventional BLUP. Results emphasize that MAS should be most useful for lowly heritable traits and that once QTLs for such traits have been identified the search for closely linked polymorphic markers should be intensified.

scholarly journals Explaining the heritability of an ecologically significant trait in terms of individual quantitative trait loci

2011 ◽  
Vol 7 (6) ◽  
pp. 896-898 ◽  
Author(s):  
Alison G. Scoville ◽  
Young Wha Lee ◽  
John H. Willis ◽  
John K. Kelly
Keyword(s):  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Variance ◽  
Balancing Selection ◽  
Natural Populations ◽  
Population Based ◽  
Population Variation ◽  
Trait Locus ◽  
Genomic Regions

Most natural populations display substantial genetic variation in behaviour, morphology, physiology, life history and the susceptibility to disease. A major challenge is to determine the contributions of individual loci to variation in complex traits. Quantitative trait locus (QTL) mapping has identified genomic regions affecting ecologically significant traits of many species. In nearly all cases, however, the importance of these QTLs to population variation remains unclear. In this paper, we apply a novel experimental method to parse the genetic variance of floral traits of the annual plant Mimulus guttatus into contributions of individual QTLs. We first use QTL-mapping to identify nine loci and then conduct a population-based breeding experiment to estimate V Q , the genetic variance attributable to each QTL. We find that three QTLs with moderate effects explain up to one-third of the genetic variance in the natural population. Variation at these loci is probably maintained by some form of balancing selection. Notably, the largest effect QTLs were relatively minor in their contribution to heritability.

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