The effect of genetic parameters on inheritance of the first pod hight in snap bean - Phaseolus vulgaris L.

Analysis of genetic variance components for number of leaves and branches per plant and stem diameter was done according to the method of HAYMAN (1954). Heritability in narrow (h2a) and broad (h2b) sense was determined for the same traits, using the method of Mather and Jinks (1971). Non-additive component of genetic variance was greater than additive component in all three studied traits. Dominant and recessive genes were not equally distributed in parent genotypes, with dominant genes prevailing. Ratio (H1/D)1/2 was higher than 1 in all three tested traits. Calculated values for heritability in narrow sense showed that stem diameter and number of branches per plant are traits with low heritability, and number of leaves per plant a trait with the high heritability. Heritability in a broad sense Was high for all three tested traits.

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Corrigendum - Response to selection in Australian Merino sheep. II. Estimates of phenotypic and genetic parameters for some production traits in Merino ewes and an analysis of the possible effects of selection on them

Australian Journal of Agricultural Research ◽

10.1071/ar9680303c ◽

1968 ◽

Vol 19 (2) ◽

pp. 303

Author(s):

GH Brown ◽

HN Turner

Keyword(s):

Genetic Parameters ◽

Genetic Variance ◽

Statistical Significance ◽

Additive Genetic Variance ◽

Genetic Correlations ◽

Production Traits ◽

Average Value ◽

Selection History ◽

Order Of Magnitude ◽

Selection For

Estimates of heritabilities and of phenotypic and genetic correlations are given, based on extensive measurements on medium Peppin Merino ewes at 15–16 months of age. In general these substantiate results obtained by other workers and, in particular, confirm the high heritabilities of the traits measured. An effort has been made to try to detect possible changes in additive genetic variance for the trait under selection (clean wool weight). Estimates are obtained for data from animals at different stages of selection: (A) either unselected, or with little selection history, and (B and C) with varying amounts of selection. For stage A data the average estimated additive genetic variance was 0.31. There are problems involved in estimating from stage (B+C) data but an upper limit average value of 0.22 was obtained. Thus, although a decrease in additive genetic variance has occurred, its statistical significance is unknown and conclusions about the decrease must necessarily be tentative. In practically all cases the estimates of phenotypic and genetic correlations are of the same order of magnitude, and for the genetic correlations may be summarized as: See PDFAll other combinations of traits have negligible genetic correlations (in the range –0.20 to + 0.2).

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

10.1101/2021.03.01.432833 ◽

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.

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What can genetic variation tell us about the evolution of senescence?

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2009.0183 ◽

2009 ◽

Vol 276 (1665) ◽

pp. 2271-2278 ◽

Cited By ~ 30

Author(s):

Jacob A. Moorad ◽

Daniel E.L. Promislow

Keyword(s):

Genetic Variation ◽

Variance Components ◽

Genetic Parameters ◽

Genetic Variance ◽

Mutation Accumulation ◽

Antagonistic Pleiotropy ◽

Age Classes ◽

Dominance Variance ◽

Quantitative Genetic ◽

Genetic Techniques

Quantitative genetic approaches have been developed that allow researchers to determine which of two mechanisms, mutation accumulation (MA) or antagonistic pleiotropy (AP), best explain observed variation in patterns of senescence using classical quantitative genetic techniques. These include the creation of mutation accumulation lines, artificial selection experiments and the partitioning of genetic variances across age classes. This last strategy has received the lion's share of empirical attention. Models predict that inbreeding depression (ID), dominance variance and the variance among inbred line means will all increase with age under MA but not under those forms of AP that generate marginal overdominance. Here, we show that these measures are not, in fact, diagnostic of MA versus AP. In particular, the assumptions about the value of genetic parameters in existing AP models may be rather narrow, and often violated in reality. We argue that whenever ageing-related AP loci contribute to segregating genetic variation, polymorphism at these loci will be enhanced by genetic effects that will also cause ID and dominance variance to increase with age, effects also expected under the MA model of senescence. We suggest that the tests that seek to identify the relative contributions of AP and MA to the evolution of ageing by partitioning genetic variance components are likely to be too conservative to be of general value.

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Response to selection in Australian Merino sheep. II. Estimates of phenotypic and genetic parameters for some production traits in Merino ewes and an analysis of the possible effects of selection on them

Australian Journal of Agricultural Research ◽

10.1071/ar9680303 ◽

1968 ◽

Vol 19 (2) ◽

pp. 303

Author(s):

GH Brown ◽

HN Turner

Keyword(s):

Genetic Parameters ◽

Genetic Variance ◽

Statistical Significance ◽

Additive Genetic Variance ◽

Genetic Correlations ◽

Production Traits ◽

Average Value ◽

Selection History ◽

Order Of Magnitude ◽

Selection For

Estimates of heritabilities and of phenotypic and genetic correlations are given, based on extensive measurements on medium Peppin Merino ewes at 15–16 months of age. In general these substantiate results obtained by other workers and, in particular, confirm the high heritabilities of the traits measured. An effort has been made to try to detect possible changes in additive genetic variance for the trait under selection (clean wool weight). Estimates are obtained for data from animals at different stages of selection: (A) either unselected, or with little selection history, and (B and C) with varying amounts of selection. For stage A data the average estimated additive genetic variance was 0.31. There are problems involved in estimating from stage (B+C) data but an upper limit average value of 0.22 was obtained. Thus, although a decrease in additive genetic variance has occurred, its statistical significance is unknown and conclusions about the decrease must necessarily be tentative. In practically all cases the estimates of phenotypic and genetic correlations are of the same order of magnitude, and for the genetic correlations may be summarized as: See PDFAll other combinations of traits have negligible genetic correlations (in the range –0.20 to + 0.2).

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Biochemical and Anatomical Characters of Snap Bean (Phaseolus vulgaris L.) Pods under Furrow and Drip Irrigation System at Harvest and during Postharvest

Suez Canal University Journal of Food Sciences ◽

10.21608/scuj.2016.6655 ◽

2016 ◽

Vol 5 (1) ◽

pp. 13-22

Author(s):

El-tahan M. ◽

M. El-hamahmy ◽

A. Elewa ◽

A. Baz

Keyword(s):

Phaseolus Vulgaris ◽

Drip Irrigation ◽

Irrigation System ◽

Drip Irrigation System ◽

Phaseolus Vulgaris L ◽

Snap Bean ◽

Anatomical Characters

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Sapling and coppice biomass heritabilities and potential gains from Eucalyptus polybractea progeny trials

Tree Genetics & Genomes ◽

10.1007/s11295-021-01499-7 ◽

2021 ◽

Vol 17 (2) ◽

Author(s):

Beren Spencer ◽

Richard Mazanec ◽

Mark Gibberd ◽

Ayalsew Zerihun

Keyword(s):

Western Australia ◽

Growth Rates ◽

Genetic Parameters ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Short Rotation ◽

Sapling Growth ◽

Genetic And Phenotypic Correlations ◽

Breeding Selections ◽

Cross Site

AbstractEucalyptus polybractea has been planted as a short-rotation coppice crop for bioenergy in Western Australia. Historical breeding selections were based on sapling biomass and despite a long history as a coppice crop, the genetic parameters of coppicing are unknown. Here, we assessed sapling biomass at ages 3 and 6 from three progeny trials across southern Australia. After the second sapling assessment, all trees were harvested. Coppice biomass was assessed 3.5 years later. Mortality following harvest was between 1 and 2%. Additive genetic variance for the 6-sapling estimate at one site was not significant. Sapling heritabilities were between 0.06 and 0.36 at 3 years, and 0.18 and 0.20 at 6 years. The heritability for the coppice biomass was between 0.07 and 0.17. Within-site genetic and phenotypic correlations were strong between all biomass assessments. Cross-site correlations were not different from unity. Selections based on net breeding values revealed positive gains in sapling and coppice biomass. Lower or negative gains were estimated if 3-year sapling selections were applied to the coppice assessments (−7.1% to 3.4%) with useful families culled. Positive gains were obtained if 6-year sapling selections were applied to the coppice assessment (6.4% to 9.3%) but these were lower than those obtained by applying coppice selections to the coppice assessment (8.4% to 14.8%). Removal of poor performing families and families that displayed fast sapling growth rates but under-performed as coppice will benefit potential coppice production. These results indicate that selections should be made using coppice data.

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A note on genetic parameters and accuracy of estimated breeding values in honey bees

Genetics Selection Evolution ◽

10.1186/s12711-019-0510-6 ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 6

Author(s):

Evert W. Brascamp ◽

Piter Bijma

Keyword(s):

Honey Bees ◽

Variance Components ◽

Genetic Parameters ◽

Additive Genetic Variance ◽

Breeding Value ◽

Phenotypic Variance ◽

Breeding Values ◽

Estimated Breeding Values ◽

Additive Genetic Relationship ◽

The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

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