Evaluation of sugarcane morphological descriptors using variance component analysis

1997 ◽  
Vol 48 (6) ◽  
pp. 769 ◽  
Author(s):  
D. J. Gallacher
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Environmental Effect ◽  
Error Variance ◽  
Morphological Characters ◽  
Variance Component Analysis ◽  
Broad Sense Heritability ◽  
Morphological Descriptors ◽  
Quantitative Characters ◽  
High Level

Genotypic, environmental, interaction, and error variances were studied for each of 24 qualitative and 142 quantitative morphological characters. Data were collected in 3 character sets: vegetative macromorphological, inflorescence, and epidermal. Broad-sense heritability was estimated for quantitative characters, and variance components were estimated. Non-zero significance was determined for variance components of all characters. Many characters were identified as having a large genetic component to their variation. Optimising the descriptive power of vegetative macromorphology characters requires the use of test clones to quantify the environmental effect, and a high level of sampling to reduce error variance. Less work is required for inflorescence characters, which are mostly highly heritable. Some valuable epidermal characters were identified.

scholarly journals A variance component analysis of the moral circle

2021 ◽  
Author(s):  
Bastian Jaeger ◽  
Matti Wilks
Keyword(s):  
Multilevel Modeling ◽  
Variance Components ◽  
Variance Component ◽  
The United States ◽  
Variance Component Analysis ◽  
Moral Concern ◽  
The United Kingdom ◽  
Country Differences ◽  
Cross Country ◽  
Target Characteristics

People’s treatment of others—humans, animals, or other targets—often depends on whether they think the entity is worthy of moral consideration. Recent work has begun to examine which factors determine whether an entity is included in people’s moral circle. Here, we rely on multilevel modeling to map the variance components of the moral circle. We examine how much variance in moral concern is explained by who is being judged (i.e., between-target differences), by who is making the judgment (i.e., between-judge differences), and by their interaction. Two studies with participants from the Netherlands, the United States, the United Kingdom, and Australia (N = 836) show that all three components explain substantial amounts of variance in judgments of moral concern. Few cross-country differences emerged. Thus, to accurately predict when people grant moral standing to a target, characteristics of the target, characteristics of the judge, and their interaction need to be considered.

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.

THE EFFECT OF ADJUSTING FOR YEARLY SELECTION TRENDS ON VARIANCE COMPONENT ESTIMATES

1989 ◽  
Vol 69 (2) ◽  
pp. 487-490
Author(s):  
W. W. GEARHEART ◽  
M. E. DAVIS ◽  
W. R. HARVEY
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Variance Components ◽  
Variance Component ◽  
Error Variance ◽  
Component Selection ◽  
Variance Component Estimates ◽  
Selection Of

Computer-generated beef cattle data were used to investigate the effect of accounting for the yearly selection of parents on the bias and precision of sire and error variance component estimates. The adjustment for yearly selection reduced the biases of estimated sire variance components, but resulted in losses of precision of up to 25%. Key words: Beef cattle, variance component, selection

scholarly journals Overview of the Genetic Diversity of African Macrotermes (Termitidae: Macrotermitinae) and Implications for Taxonomy, Ecology and Food Science

Insects ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 518
Author(s):  
Bronwyn Egan ◽  
Zwannda Nethavhani ◽  
Barbara van Asch
Keyword(s):  
Negative Impact ◽  
Phylogenetic Reconstruction ◽  
Morphological Characters ◽  
Food Science ◽  
Cryptic Diversity ◽  
African Species ◽  
Genetic Groups ◽  
Wide Range ◽  
Coi Sequences ◽  
High Level

Macrotermes termites play important ecological roles and are consumed by many communities as a delicacy and dietary complement throughout Africa. However, lack of reliable morphological characters has hampered studies of Macrotermes diversity in a wide range of scientific fields including ecology, phylogenetics and food science. In order to place our preliminary assessment of the diversity of Macrotermes in South Africa in context, we analysed a comprehensive dataset of COI sequences for African species including new and publicly available data. Phylogenetic reconstruction and estimates of genetic divergence showed a high level of incongruity between species names and genetic groups, as well as several instances of cryptic diversity. We identified three main clades and 17 genetic groups in the dataset. We propose that this structure be used as a background for future surveys of Macrotermes diversity in Africa, thus mitigating the negative impact of the present taxonomic uncertainties in the genus. The new specimens collected in Limpopo fell into four distinct genetic groups, suggesting that the region harbours remarkable Macrotermes diversity relative to other African regions surveyed in previous studies. This work shows that African Macrotermes have been understudied across the continent, and that the genus contains cryptic diversity undetectable by classic taxonomy. Furthermore, these results may inform future taxonomic revisions in Macrotermes, thus contributing to advances in termitology.

scholarly journals Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Variance Components ◽  
Variance Component ◽  
Predictive Accuracy ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Effects ◽  
Additive Variance ◽  
Additive Genetic Effects

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

scholarly journals Genetic and Genome-Wide Association Analysis of Yearling Weight Gain in Israel Holstein Dairy Calves

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 708
Author(s):  
Moran Gershoni ◽  
Joel Ira Weller ◽  
Ephraim Ezra
Keyword(s):  
Weight Gain ◽  
Milk Fat ◽  
Variance Components ◽  
Complete Data ◽  
Variance Component Analysis ◽  
Coefficient Of Determination ◽  
Birth Date ◽  
Analysis Model ◽  
Data Set ◽  
Genetic Evaluations

Yearling weight gain in male and female Israeli Holstein calves, defined as 365 × ((weight − 35)/age at weight) + 35, was analyzed from 814,729 records on 368,255 animals from 740 herds recorded between 1994 and 2021. The variance components were calculated based on valid records from 2008 through 2017 for each sex separately and both sexes jointly by a single-trait individual animal model analysis, which accounted for repeat records on animals. The analysis model also included the square root, linear, and quadratic effects of age at weight. Heritability and repeatability were 0.35 and 0.71 in the analysis of both sexes and similar in the single sex analyses. The regression of yearling weight gain on birth date in the complete data set was −0.96 kg/year. The complete data set was also analyzed by the same model as the variance component analysis, including both sexes and accounting for differing variance components for each sex. The genetic trend for yearling weight gain, including both sexes, was 1.02 kg/year. Genetic evaluations for yearling weight gain was positively correlated with genetic evaluations for milk, fat, protein production, and cow survival but negatively correlated with female fertility. Yearling weight gain was also correlated with the direct effect on dystocia, and increased yearling weight gain resulted in greater frequency of dystocia. Of the 1749 Israeli Holstein bulls genotyped with reliabilities >50%, 1445 had genetic evaluations. As genotyping of these bulls was performed using several single nucleotide polymorhphism (SNP) chip platforms, we included only those markers that were genotyped in >90% of the tested cohort. A total of 40,498 SNPs were retained. More than 400 markers had significant effects after permutation and correction for multiple testing (pnominal < 1 × 10−8). Considering all SNPs simultaneously, 0.69 of variance among the sires’ transmitting ability was explained. There were 24 markers with coefficients of determination for yearling weight gain >0.04. One marker, BTA-75458-no-rs on chromosome 5, explained ≈6% of the variance among the estimated breeding values for yearling weight gain. ARS-BFGL-NGS-39379 had the fifth largest coefficient of determination in the current study and was also found to have a significant effect on weight at an age of 13–14 months in a previous study on Holsteins. Significant genomic effects on yearling weight gain were mainly associated with milk production quantitative trait loci, specifically with kappa casein metabolism.

scholarly journals Estimates of (co)variance components and genetic parameters for body weights and growth efficiency traits in the New Zealand white rabbit

2017 ◽  
Vol 25 (4) ◽  
pp. 329 ◽  
Author(s):  
M. Sakthivel ◽  
D. Balasubramanyam ◽  
P. Kumarasamy ◽  
H. Gopi ◽  
A. Raja ◽  
...  
Keyword(s):  
Body Weight ◽  
New Zealand ◽  
Variance Components ◽  
Environmental Effect ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Genetic Effect ◽  
Growth Efficiency ◽  
Body Weights ◽  
Heritability Estimates

The genetic parameters of growth traits in the New Zealand White rabbits kept at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India were estimated by partitioning the variance and covariance components. The (co)variance components of body weights at weaning (W42), post-weaning (W70) and marketing (W135) age and growth efficiency traits viz., average daily gain (ADG), relative growth rate (RGR) and Kleiber ratio (KR) estimated on a daily basis at different age intervals (42 to 70 d; 70 to 135 d and 42 to 135 d) from weaning to marketing were estimated by restricted maximum likelihood, fitting 6 animal models with various combinations of direct and maternal effects. Data were collected over a period of 15 yr (1998 to 2012). A log-likelihood ratio test was used to select the most appropriate univariate model for each trait, which was subsequently used in bivariate analysis. Heritability estimates for W42, W70 and W135 were 0.42±0.07, 0.40±0.08 and 0.27±0.07, respectively. Heritability estimates of growth efficiency traits were moderate to high (0.18 to 0.42). Of the total phenotypic variation, maternal genetic effect contributed 14 to 32% for early body weight traits (W42 and W70) and ADG1. The contribution of maternal permanent environmental effect varied from 6 to 18% for W42 and for all the growth efficiency traits except for KR2. Maternal permanent environmental effect on most of the growth efficiency traits was a carryover effect of maternal care during weaning. Direct maternal genetic correlations, for the traits in which maternal genetic effect was significant, were moderate to high in magnitude and negative in direction. Maternal effect declined as the age of the animal increased. The estimates of total heritability and maternal across year repeatability for growth traits were moderate and an optimum rate of genetic progress seems possible in the herd by mass selection. The genetic and phenotypic correlations among body weights and between growth efficiency traits were also estimated. Moderate to high heritability and higher genetic correlation in body weight traits promise good scope for genetic improvement provided measures are taken to keep the inbreeding at the lowest level.

scholarly journals The effect of the photoperiod on the level of endogenous growth regulators in pine (Pinus silvestris L.) seedlings

2014 ◽  
Vol 51 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Krystyna Kriesel ◽  
Sławomir Ciesielska
Keyword(s):  
Abscisic Acid ◽  
Growth And Development ◽  
Morphological Characters ◽  
Axillary Buds ◽  
Pinus Silvestris ◽  
Low Intensity ◽  
Pine Seedlings ◽  
Terminal Bud ◽  
Development Processes ◽  
High Level

The investigations were performed on pine seedlings growing under 12, 16 and 20 hour photoperiods. In 4 succesive stages of seedling development i.e. after 2, 12, 18 and 30 weeks of culture morphological characters of the seedlings were measured and the levels of auxins-, gibberellins-, cytokininsand abscisic acid-like inhibitor were determined. The intensity of growth and development of juvenile leaves, needles and of the shoot was the lowest in plants growing under 12 hour photoperiod conditions. As the length of the photoperiod increased so did the intensity of these processes. Under the 12 hour photoperiod the development of scale leaves, axillary buds and the formation of the terminal bud started earliest. This process reached completion under the 12 hour photoperiod and the bud remained in a state of dormancy. Seedlings growing under the 12 hour photoperiod were characterized by a low level of stimulators, and at the same time by a high level of inhibitors. On the other hand in seedlings grown at 16 and 20 hour photoperiods the content of stimulators was higher and that of inhibitors lower. A high intensity of growth and development processes was correlated with a high level of stimulators while a high level of inhibitors was correlated with a low intensity of these processes.The obtained results suggest the participation of gibberellins and cytokinins in the processes of regulation of the initiation of scale leaves and axillary buds, and the participation of these hormones and of abscisic acid in the regulation of needle elongation.

scholarly journals On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component Model

2011 ◽  
Vol 1 (3) ◽  
pp. 280-285 ◽  
Author(s):  
Lars Sjöberg
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Component Model ◽  
Variance Component Model ◽  
Minimum Bias ◽  
Unbiased Estimators ◽  
Independent Observations ◽  
Special Case

On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component ModelVariance components (VCs) in linear adjustment models are usually successfully computed by unbiased estimators. However, for many unbiased VC techniques estimated variance components might be negative, a result that cannot be tolerated by the user. This is, for example, the case with the simple additive VC model aσ2/1 + bσ2/2 with known coefficients a and b, where either of the unbiasedly estimated variance components σ2/1 + σ2/2 may frequently come out negative. This fact calls for so-called non-negative VC estimators. Here the Best Quadratic Minimum Bias Non-negative Estimator (BQMBNE) of a two-variance component model is derived. A special case with independent observations is explicitly presented.

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