scholarly journals Genetic Control of O3 Sensitivity in a Cross Between Two Cultivars of Snap Bean

2000 ◽  
Vol 125 (2) ◽  
pp. 222-227 ◽  
Author(s):  
Richard A. Reinert ◽  
Gwen Eason
Keyword(s):  
Genetic Control ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Phenotypic Variance ◽  
Narrow Sense Heritability ◽  
Snap Bean ◽  
Reciprocal Crosses ◽  
Blue Lake ◽  
Environmental Component ◽  
Variance Estimates

Identification of genetic control of ozone (O3) sensitivity is desirable for selection of plant cultivars which are indicators of O3 stress. A cross was made between two cultivars of snap bean (Phaseolus vulgaris L.), `Oregon 91' (P1) and `Wade Bush' (P2), an O3-sensitive and O3-insensitive cultivar, respectively. Ten genetic populations (generations), `Oregon 91' (P1), `Wade Bush' (P2), F1, F2, backcrosses to both parents, and all reciprocal crosses, were field planted in each of two summers and evaluated for injury to O3. Ozone responses for the reciprocal crosses were not significantly different for any generation, so injury ratings from the reciprocal crosses were combined for each generation to provide six populations (P1, P2, F1, F2, BC1, and BC2) for analysis. When components of genetic variation were estimated from the six generations, additive genetic variance was the most important component in the total genetic variance available, although dominance variance was also a significant component. There was an inconsistency in the magnitude and the direction of the factors contributing to the dominance effects and also a large environmental component making up the phenotypic variance. Estimates of broad-sense heritability and narrow-sense heritability were 60% and 44%, respectively. Results suggest that O3-sensitive and O3-insensitive selections could be screened and evaluated in an ambient O3 environment. Several generations will be necessary, however, to develop `Bush Blue Lake' type selections that vary only in sensitivity to O3.

GENETIC VARIANCES IN AN EIGHT PARENT HALF DIALLEL OF OATS

1976 ◽  
Vol 18 (3) ◽  
pp. 419-427 ◽  
Author(s):  
D. R. Sampson ◽  
I. Tarumoto
Keyword(s):  
Grain Yield ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Heading Date ◽  
Phenotypic Variance ◽  
Narrow Sense ◽  
Narrow Sense Heritability ◽  
Additive Component ◽  
Tests Of Fit ◽  
Half Diallel

Twenty-eight progenies with their eight parent cultivars of Avena saliva L. (2n = 6x = 42) were grown in F1, F2 and F3 in separate years; the F1 as spaced plants, the F2 and F3 as dense seeded populations. Additive genetic variance constituted most of the phenotypic variance of eight traits (heading date, plant height, stem diameter, grain yield and four components of yield) according to a Griffing Method 4, Model II analysis. Similarly, additive × year interactions were more important than nonadditive × year interactions. A Hayman-Jinks analysis of the same material but with the parents included showed that the additive component was 2 to 16 times larger than the dominance components in the F1 However in the F2 and F3 the dominance components became larger than the additive components for most traits instead of declining in importance as expected. Further, tests of fit to the hypotheses underlying the Hayman-Jinks analysis were negative in 8 of 24 cases. It is postulated that these discrepancies result from epistatic variance which caused an upward bias in the dominance estimates. The calculation and uses of two estimates of narrow-sense heritability are discussed.

scholarly journals Selection strategies of segregant soybean populations for resistance to Asian rust

2009 ◽  
Vol 44 (11) ◽  
pp. 1452-1459 ◽  
Author(s):  
Aliny Simony Ribeiro ◽  
José Francisco Ferraz de Toledo ◽  
Magno Antonio Patto Ramalho
Keyword(s):  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Infected Leaf ◽  
Good Resistance ◽  
Narrow Sense Heritability ◽  
Growing Seasons ◽  
Selection Strategies ◽  
Line Selection ◽  
Soybean Resistance ◽  
Selection Of

The objective of this work was to identify the best selection strategies for the more promising parental combinations to obtain lines with good resistance to soybean Asian rust (Phakopsora pachyrhizi). Two experiments were carried out in the field during the 2006/2007 and 2007/2008 growing seasons, to determine the percentage of infected leaf area of individual plants of five parents and their segregant F2 and F3 populations. The data obtained indicates that additive genetic variance predominates in the control of soybean resistance to Asian rust, and that the year and time of assessment do not significantly influence the estimates of the genetic parameters obtained. The narrow-sense heritability (h²r) ranged from 23.12 to 55.83%, and indicates the possibility of successful selection of resistant individuals in the early generations of the breeding program. All the procedures used to select the most promising populations to generate superior inbred lines for resistance to P. pachyrhizi presented similar results and identified the BR01-18437 x BRS 232 population as the best for inbred line selection.

scholarly journals Effects on phenotypic variability of directional selection arising through genetic differences in residual variability

2004 ◽  
Vol 83 (2) ◽  
pp. 121-132 ◽  
Author(s):  
WILLIAM G. HILL ◽  
XU-SHENG ZHANG
Keyword(s):  
Genetic Variance ◽  
Environmental Variability ◽  
Phenotypic Variability ◽  
Additive Genetic Variance ◽  
Directional Selection ◽  
Frequency Change ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Genetic Covariance ◽  
Mean And Variance

In standard models of quantitative traits, genotypes are assumed to differ in mean but not variance of the trait. Here we consider directional selection for a quantitative trait for which genotypes also confer differences in variability, viewed either as differences in residual phenotypic variance when individual loci are concerned or as differences in environmental variability when the whole genome is considered. At an individual locus with additive effects, the selective value of the increasing allele is given by ia/σ+½ixb/σ2, where i is the selection intensity, x is the standardized truncation point, σ2 is the phenotypic variance, and a/σ and b/σ2 are the standardized differences in mean and variance respectively between genotypes at the locus. Assuming additive effects on mean and variance across loci, the response to selection on phenotype in mean is iσAm2/σ+½ixcovAmv/σ2 and in variance is icovAmv/σ+½ixσ2Av/σ2, where σAm2 is the (usual) additive genetic variance of effects of genes on the mean, σ2Av is the corresponding additive genetic variance of their effects on the variance, and covAmv is the additive genetic covariance of their effects. Changes in variance also have to be corrected for any changes due to gene frequency change and for the Bulmer effect, and relevant formulae are given. It is shown that effects on variance are likely to be greatest when selection is intense and when selection is on individual phenotype or within family deviation rather than on family mean performance. The evidence for and implications of such variability in variance are discussed.

scholarly journals ESTİMATİON OF GENETİC AND ENVİRONMENTAL PARAMETERS AFFECTİNG PRODUCTİVİTY İN MORKARAMAN SHEEP AND ECONOMİC EVALUATİON OF PARAMETERS

2021 ◽  
pp. 155-161
Author(s):  
Ufuk Karadavut ◽  
Burhan Bahadır ◽  
Volkan Karadavut ◽  
Galip Şimşek ◽  
Hakan İnci
Keyword(s):  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Environmental Parameters ◽  
Error Variance ◽  
Economic Importance ◽  
Phenotypic Variance ◽  
Economic Aspects ◽  
Evaluation Of Parameters ◽  
The Relationship

This study was carried out to protect the continuity of productivity in morkaraman sheep raised in Turkey and determine their economic importance. Morkaraman sheep are concentrated in the Eastern Regions of the country. The province of Bingöl, where the study was conducted, is located in this region and has an important morkaraman population. The study was carried out between 2008-2018. Sixty-eight morkaraman sheep were used during the study period out of 317 lambing lambs. In the study, the total number of lambs born per sheep (TNLBS), the number of weaned lambs (NWL), the weights of the lambs weaned per sheep (WLWS) and the total weight of the lambs weaned in the first period (TWLWFP) were determined. In addition, Additive genetic variance, Error variance, Phenotypic variance, Heritability and Ratio of error variation were determined for these variables. As a result, the correlation between the examined variables was significant and positive, except for the relationship between TNLBS and TWLWFP. The relationship between these two variables was significant but negative. Significant changes were also observed in terms of genetic parameters. It was concluded that the economic aspects of the examined variables should not be ignored in terms of sustainability. Keywords: Sheep, morkaraman, sustainability, genotypic and phenotypic variance.

scholarly journals A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance

2018 ◽  
Author(s):  
Caroline E. Thomson ◽  
Isabel S. Winney ◽  
Oceane C. Salles ◽  
Benoit Pujol
Keyword(s):  
Animal Model ◽  
Genetic Variance ◽  
Genetic Relatedness ◽  
Additive Genetic Variance ◽  
Phenotypic Traits ◽  
Phenotypic Variance ◽  
Data Types ◽  
Evolutionary Potential ◽  
Recent Developments

AbstractNon-genetic influences on phenotypic traits can affect our interpretation of genetic variance and the evolutionary potential of populations to respond to selection, with consequences for our ability to predict the outcomes of selection. Long-term population surveys and experiments have shown that quantitative genetic estimates are influenced by nongenetic effects, including shared environmental effects, epigenetic effects, and social interactions. Recent developments to the “animal model” of quantitative genetics can now allow us to calculate precise individual-based measures of non-genetic phenotypic variance. These models can be applied to a much broader range of contexts and data types than used previously, with the potential to greatly expand our understanding of nongenetic effects on evolutionary potential. Here, we provide the first practical guide for researchers interested in distinguishing between genetic and nongenetic causes of phenotypic variation in the animal model. The methods use matrices describing individual similarity in nongenetic effects, analogous to the additive genetic relatedness matrix. In a simulation of various phenotypic traits, accounting for environmental, epigenetic, or cultural resemblance between individuals reduced estimates of additive genetic variance, changing the interpretation of evolutionary potential. These variances were estimable for both direct and parental nongenetic variances. Our tutorial outlines an easy way to account for these effects in both wild and experimental populations. These models have the potential to add to our understanding of the effects of genetic and nongenetic effects on evolutionary potential. This should be of interest both to those studying heritability, and those who wish to understand nongenetic variance.

Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

2018 ◽  
Vol 156 (4) ◽  
pp. 565-569
Author(s):  
H. Ghiasi ◽  
R. Abdollahi-Arpanahi ◽  
M. Razmkabir ◽  
M. Khaldari ◽  
R. Taherkhani
Keyword(s):  
Dairy Cows ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Information Criteria ◽  
Phenotypic Variance ◽  
Dominance Variance ◽  
Dominance Genetic Variance ◽  
Estimated Heritability

AbstractThe aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

scholarly journals Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1055 ◽  
Author(s):  
Ying Liu ◽  
Lei Xu ◽  
Zezhao Wang ◽  
Ling Xu ◽  
Yan Chen ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Association Studies ◽  
Additive Genetic Variance ◽  
Additive Models ◽  
Carcass Weight ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

scholarly journals 37 Genetic aspects of livestock adaptation

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 35-35
Author(s):  
David G Riley
Keyword(s):  
Prenatal Stress ◽  
Additive Genetic Variance ◽  
Genetic Component ◽  
Random Regression ◽  
Regression Analyses ◽  
Narrow Sense Heritability ◽  
Local Conditions ◽  
Prominent Component ◽  
Differential Adaptation ◽  
Environmental Component

Abstract Livestock adaptation to less than favorable ambient has a genetic basis. Estimates of additive genetic variance and narrow sense heritability for % intramuscular fat in Hereford varied across United States geography quantifications evaluated in random regression analyses. Shedding and regrowth of winter coats of Angus cows in subtropical areas may indicate differential adaptation in cattle not adapted to the subtropics. Acquired adaptation to local conditions (multiple generations across more than 50 years) may was evidenced by performance of Florida Angus relative to prominent U.S. Angus in subtropical Florida; later sexual maturation may be a prominent component of such adaptation, and increasing milk production may be antagonistic to adaptation in natural conditions. Cattle temperament may be indicative of adaptation and is highly heritable; however, results from random regression analyses suggest that the additive genetic component appears to decrease in importance and the permanent environmental component of phenotype appears to become more important as calves age. Crossbreeding represents a proven strategy to improve adaptation almost immediately. Heterosis influences cattle body temperature maintenance, reproduction, survival, and, to a lesser extent, temperament in subtropical or other stressful environmental conditions (for example, in toxic fescue). Prenatal stress alters patterns of methylation (and likely other epigenetic mechanisms) and thereby encourages or inhibits gene expression to promote postnatal fitness. Brahman exposed to prenatal stress exhibited substantially different patterns of methylation across the genome in lymphocytes in both male and female calves; those patterns differed by sex. Female longevity may be the ultimate adaptation trait, as annual compliance to reproductive standards may be an appropriate assessment of a combination of attributes that represent adaptation. Longevity has documented heterotic influence; the additive genetic component is less well characterized but real. A simple, effective way to improve longevity may be to select bulls from aged, proven cows.

Molar Intercuspal Dimensions: Genetic Input to Phenotypic Variation

2003 ◽  
Vol 82 (5) ◽  
pp. 350-355 ◽  
Author(s):  
G. Townsend ◽  
L. Richards ◽  
T. Hughes
Keyword(s):  
Phenotypic Variation ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Mean Values ◽  
Epigenetic Events ◽  
Enamel Epithelium ◽  
Molar Teeth ◽  
Enamel Knots ◽  
Variance Estimates ◽  
Relative Variability

Molecular studies indicate that epigenetic events are important in determining how the internal enamel epithelium folds during odontogenesis. Since this process of folding leads to the subsequent arrangement of cusps on molar teeth, we hypothesized that intercuspal distances of human molar teeth would display greater phenotypic variation but lower heritabilities than overall crown diameters. Intercuspal distances and maximum crown diameters were recorded from digitized images of dental casts in 100 monozygotic and 74 dizygotic twin pairs. Intercuspal distances displayed less sexual dimorphism in mean values but greater relative variability and fluctuating asymmetry than overall crown measures. Correlations between intercuspal distances and overall crown measures were low. Models incorporating only environmental effects accounted for observed variation in several intercuspal measures. For those intercuspal variables displaying significant additive genetic variance, estimates of heritability ranged from 43 to 79%, whereas those for overall crown size were higher generally, ranging from 60 to 82%. Our finding of high phenotypic variation in intercuspal distances with only moderate genetic contribution is consistent with substantial epigenetic influence on the progressive folding of the internal enamel epithelium, following formation of the primary and secondary enamel knots.

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