scholarly journals The Genetic Architecture of Quantitative Traits Cannot Be Inferred From Variance Component Analysis

2016 ◽  
Author(s):  
Wen Huang ◽  
Trudy F.C. Mackay
Keyword(s):  
Genetic Variation ◽  
Variance Components ◽  
Variance Component ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Component Analysis ◽  
Genetic Models ◽  
Variance Component Analysis ◽  
Components Of Variance

AbstractClassical quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals. The genetic variance components are defined in terms of genotypic values reflecting underlying genetic architecture (additive, dominance and epistatic genotypic effects) and allele frequencies. However, the dependency of the definition of genetic variance components on the underlying genetic models is not often appreciated. Here, we show how the partitioning of additive and non-additive genetic variation is affected by the genetic models and parameterization of allelic effects. We show that arbitrarily defined variance components often capture a substantial fraction of total genetic variation regardless of the underlying genetic architecture in simulated and real data. Therefore, variance component analysis cannot be used to infer genetic architecture of quantitative traits. The genetic basis of quantitative trait variation in a natural population can only be defined empirically using high resolution mapping methods followed by detailed characterization of QTL effects.

scholarly journals Variance Component Analysis of Parthenocarpic Character in a Processing Cucumber (Cucumis sativus L.) population

HortScience ◽  
2004 ◽  
Vol 39 (4) ◽  
pp. 881A-881
Author(s):  
Zhanyong Sun* ◽  
Richard L. Lower ◽  
Jack E. Staub
Keyword(s):  
Cucumis Sativus ◽  
Variance Component ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Component Analysis ◽  
Variance Component Analysis ◽  
Individual Plant ◽  
Normal Leaf ◽  
Dominance Genetic Variance ◽  
Plant Basis

The incorporation of genes for parthenocarpy (production of fruit without fertilization) has potential for increasing yield in pickling cucumber (Cucumis sativus L.). The inheritance of parthenocarpy in cucumber is not well understood, and thus a genetic analysis was performed on F3 cross-progeny resulting from a mating between the processing cucumber inbred line 2A (P1, gynoecious, parthenocarpic, indeterminate, normal leaf) and Gy8 (P2, gynoecious, non-parthenocarpic, indeterminate, normal leaf). A variance component analysis was performed to fruit yield data collected at two locations (designated E-block and G-block) at Hancock, WI in 2000. The relative importance of additive genetic variance compared to dominance genetic variance changed across environments. The additive genetic variance was 0.5 and 4.3 times of dominance genetic variance in E-block and G-block, respectively. The estimated environmental variance accounted for ≈90% of the total phenotypic variance on an individual plant basis in both locations. Narrow-sense heritability estimated on an individual plant basis ranged from 0.04 (E-block) to 0.12 (G-block). Broad-sense heritability estimated on an individual plant basis ranged from 0.12 (E-block) to 0.15 (G-block). The minimum number of effective factors controlling parthenocarpy was estimated to range between 5 (G-block) to 13 (E-block). These results suggest that the response to direct selection of individual plants for improving parthenocarpy character will likely be slow and difficult. Experiment procedures that minimize the effect of environment on the expression of parthenocarpy will likely maximize the likelihood of gain from selection.

scholarly journals Genetic Variance Component and Heritability Estimates of Freedom from Weevil Injury to Sweetpotato

1994 ◽  
Vol 119 (3) ◽  
pp. 620-623 ◽  
Author(s):  
P.G. Thompson ◽  
John C. Schneider ◽  
Boyett Graves
Keyword(s):  
Variance Component ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Component Analysis ◽  
Variance Component Analysis ◽  
Breeding Cycle ◽  
Individual Plant ◽  
Root Number ◽  
Narrow Sense Heritability ◽  
Heritability Estimates

Narrow-sense heritability for component traits of freedom from weevil injury and yield of sweetpotato were estimated by parent-offspring regression and variance component analysis. Heritability estimates by variance component analysis based on half-sib families for percent and number of uninjured roots were 0.25 and 0.83, respectively. Individual plant heritability estimates for uninjured root percent and number were 0.03 and 0.13, respectively. Heritability estimates by parent-offspring regression for uninjured root percent and number were 0.35 and 0.52, respectively. Genetic variance was mostly additive for all traits except stem diameter. Genetic correlations between total root number, uninjured root number, and percent uninjured roots ranged from 0.66 to 0.87, indicating that selection for uninjured root number should most effectively increase uninjured root number and percent, as well as total root numbers. Predicted gains in uninjured root percent and number were 8.8% and 0.87 in the progeny derived from intermating the highest four out of 19 families for uninjured root number. The 0.87 gain in uninjured root number equals a 24% increase in one breeding cycle.

scholarly journals Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing among full-sibs

2021 ◽  
Author(s):  
Antoine Fraimout ◽  
Zitong Li ◽  
Mikko J. Sillanpää ◽  
Pasi Rastas ◽  
Juha Merilä
Keyword(s):  
Natural Selection ◽  
Variance Components ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Identity By Descent ◽  
Quantitative Genetic ◽  
Genome Wide ◽  
Quantitative Genetic Parameters

ABSTRACTAdditive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most wild animal populations. Using estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci, we estimated quantitative genetic parameters for traits known to be under directional natural selection in nine-spined sticklebacks (Pungitius pungitius) and compared these to traditional pedigree-based estimators. Using four different datasets, with varying sample sizes and pedigree complexity, we further assessed the performance of different Genomic Relationship Matrices (GRM) to estimate additive and dominance variance components. Large variance in IBD relationships allowed accurate estimation of genetic variance components, and revealed significant heritability for all measured traits, with negligible dominance contributions. Genome-partitioning analyses revealed that all traits have a polygenic basis and are controlled by genes at multiple chromosomes. The results demonstrate how large full-sib families of highly fecund vertebrates can be used to obtain accurate estimates quantitative genetic parameters to provide insights on genetic architecture of quantitative traits in non-model organisms from the wild. This approach should be particularly useful for studies requiring estimates of genetic variance components from multiple populations as for instance when aiming to infer the role of natural selection as a cause for population differentiation in quantitative traits.

scholarly journals GENETIC MODELS FOR THE ANALYSIS OF DATA FROM THE FAMILIES OF IDENTICAL TWINS

Genetics ◽  
1976 ◽  
Vol 83 (4) ◽  
pp. 811-826
Author(s):  
Walter E Nance ◽  
Linda A Corey
Keyword(s):  
Least Squares ◽  
Maternal Effects ◽  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Weighted Least Squares ◽  
Identical Twins ◽  
Genetic Models ◽  
Family Unit ◽  
Least Squares Estimates

ABSTRACT Genetic models are described which exploit the unique relationships that exist within the families of identical twins to obtain weighted least squares estimates of additive, dominance and epistatic components of genetic variance as well as estimates of the contributions of X-linked genes, maternal effects and three sources of environmental variation. Since all of the relationships required to achieve a resolution of these variance components are contained within each family unit, the model would appear to be superior to previous approaches to the analysis of quantitative traits in man.

COMPARISON OF PREDICTED AND SIMULATED RESPONSES TO S1 SELECTION IN A DIPLOID, CROSS-FERTILIZED SPECIES

1981 ◽  
Vol 61 (1) ◽  
pp. 9-15 ◽  
Author(s):  
T. M. CHOO ◽  
L. W. KANNENBERG
Keyword(s):  
Coefficient Of Variation ◽  
Variance Component ◽  
Genetic Variance ◽  
Recurrent Selection ◽  
Genetic Models ◽  
Variance Component Analysis ◽  
Narrow Sense Heritability ◽  
Complete Dominance ◽  
Per Se ◽  
Good Agreement

Computer simulation was used to test the accuracy of mathematical formulae for predicting mean response and variance of response to S1 per se recurrent selection under both additive and complete dominance genetic models. S1 selection was simulated at two levels of selection intensity (5 and 25%) and two levels of narrow sense heritability (0.2 and 0.6) for 15 cycles. In each cycle, 400 S1 families were evaluated in simulated trials consisting of one replication of 10-plant plots at each of four locations. The character under selection was controlled by 40 independently assorted loci. For both genetic models, variance component analysis provided good estimates of genetic variance and the predicted gains were in good agreement with the simulated gains. The simulated coefficient of variation of response was small and was very close to the predicted coefficient of variation in each of the four selection regimes under both models.

scholarly journals Cumulants and Partition Lattices V. Calculating Generalized k-Statistics

1988 ◽  
Vol 44 (2) ◽  
pp. 171-196 ◽  
Author(s):  
T. P. Speed ◽  
H. L. Silcock
Keyword(s):  
Variance Component ◽  
Random Variables ◽  
Component Analysis ◽  
Variance Component Analysis ◽  
Components Of Variance ◽  
Partition Lattices

AbstractA method is developed for obtaining compact, easily computed and statistically interpretable expressions for the generalized k-statistics associated with multiply-indexed arrays of random variables such as those which arise in variance component analysis. These expressions will be used in the next paper in this series to give formulae for variances and covariances of estimates of components of variance.

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