scholarly journals Analysis of a genetically structured variance heterogeneity model using the Box–Cox transformation

2011 ◽  
Vol 93 (1) ◽  
pp. 33-46 ◽  
Author(s):  
YE YANG ◽  
OLE F. CHRISTENSEN ◽  
DANIEL SORENSEN
Keyword(s):  
Statistical Evidence ◽  
Genetic Component ◽  
Environmental Variance ◽  
Variance Model ◽  
Heterogeneous Variance ◽  
Marginal Posterior Distribution ◽  
Mean And Variance ◽  
Additive Genetic Effects ◽  
Future Work ◽  
Box Cox Transformation

SummaryOver recent years, statistical support for the presence of genetic factors operating at the level of the environmental variance has come from fitting a genetically structured heterogeneous variance model to field or experimental data in various species. Misleading results may arise due to skewness of the marginal distribution of the data. To investigate how the scale of measurement affects inferences, the genetically structured heterogeneous variance model is extended to accommodate the family of Box–Cox transformations. Litter size data in rabbits and pigs that had previously been analysed in the untransformed scale were reanalysed in a scale equal to the mode of the marginal posterior distribution of the Box–Cox parameter. In the rabbit data, the statistical evidence for a genetic component at the level of the environmental variance is considerably weaker than that resulting from an analysis in the original metric. In the pig data, the statistical evidence is stronger, but the coefficient of correlation between additive genetic effects affecting mean and variance changes sign, compared to the results in the untransformed scale. The study confirms that inferences on variances can be strongly affected by the presence of asymmetry in the distribution of data. We recommend that to avoid one important source of spurious inferences, future work seeking support for a genetic component acting on environmental variation using a parametric approach based on normality assumptions confirms that these are met.

scholarly journals Genetically controlled environmental variance for sternopleural bristles inDrosophila melanogaster– an experimental test of a heterogeneous variance model

2007 ◽  
Vol 57 (4) ◽  
pp. 196-201 ◽  
Author(s):  
Anders Christian Sørensen ◽  
Torsten Nygaard Kristensen ◽  
Volker Loeschcke ◽  
Noelia Ibáñez ◽  
Daniel Sorensen
Keyword(s):  
Experimental Test ◽  
Environmental Variance ◽  
Variance Model ◽  
Heterogeneous Variance

The Initial Return and Its Conditional Return Volatility: Evidence from the Chinese IPO Market

2014 ◽  
Vol 17 (04) ◽  
pp. 1450022 ◽  
Author(s):  
M. Monica Hussein ◽  
Zhong-Guo Zhou
Keyword(s):  
Time Series ◽  
Linear Functions ◽  
Return Volatility ◽  
Cross Sectional ◽  
Variance Model ◽  
Initial Return ◽  
Traditional Market ◽  
Mean And Variance ◽  
The Mean ◽  
Sectional Analysis

This paper investigates the monthly initial return and its conditional return volatility for Chinese IPOs. We find that the mean initial return (IR) and cross-sectional return volatility are highly auto- and cross-correlated, and time-varying. We propose a system of two simultaneous equations: a GARCH-in-mean (GARCH-M) process with an ARMA(1,1) adjustment in the residuals for the IR and an EGARCH process for the conditional return volatility, assuming that the IR and its conditional return volatility are linear functions of the same market, firm- and offer-specific characteristics. We find that the model captures both time-series and cross-sectional correlations at the mean and variance levels. Our findings suggest that the conditional return volatility affects the IR positively and significantly, in addition to the traditional market, firm- and offer-specific characteristics. IPOs with higher conditional return volatility, as a proxy for information asymmetry, tend to be underpriced more. The paper demonstrates the merit of using a conditional variance model, along with time series and cross-sectional analysis to price Chinese IPOs.

Genetic and Environmental Influences on Expression of Recurrent Headache as a Function of the Reporting Age in Twins

Twin Research ◽  
2002 ◽  
Vol 5 (4) ◽  
pp. 277-286 ◽  
Author(s):  
Dan A. Svensson ◽  
Bo Larsson ◽  
Elisabet Waldenlind ◽  
Nancy L. Pedersen
Keyword(s):  
Environmental Influences ◽  
Age Groups ◽  
Young Men ◽  
Genetic Effects ◽  
Genetic Influences ◽  
Age Group ◽  
Environmental Variance ◽  
Recurrent Headache ◽  
Old Men ◽  
Additive Genetic Effects

AbstractTo explore age-related mechanisms in the expression of recurrent headache, we evaluated whether genetic and environmental influences are a function of the reporting age using questionnaire information that was gathered in 1973 for 15- to 47-year-old Swedish twins (n =12,606 twin pairs). Liability to mixed headache (mild migraine and tension-type headache) was explained by non-additive genetic influences (49%) in men aged from 15 to 30 years and additive genetic plus shared environmental influences (28%) in men aged from 31 to 47 years. In women, the explained proportion of variance, which was mainly due to additive genetic effects, ranged from 61% in adolescent twins to 12% in twins aged from 41 to 47 years, whereas individual specific environmental variance was significantly lower in twins aged from 15 to 20 years than in twins aged from 21 to 30 years. Liability to migrainous headache (more severe migraine) was explained by non-addi-tive genetic influences in men, 32% in young men and 45% in old men, while total phenotypic variance was significantly lower in young men than in old men. In women, the explained proportion of variance ranged from 91% in the youngest age group to 37% in the oldest age group, with major contributions from non-additive effects in young and old women (15–20 years and 41–47 years, respectively) and additive genetic effects in intermediate age groups (21–40 years). While total variance showed a positive age trend, genetic variance tended to be stable across age groups, whereas individual specific environmental variance was significantly lower in adolescent women as compared to older women.

565. A study of variation in twin cattle: III. Growth

1955 ◽  
Vol 22 (1) ◽  
pp. 1-9 ◽  
Author(s):  
J. W. B. King ◽  
H. P. Donald
Keyword(s):  
Environmental Effects ◽  
Live Weight ◽  
Genetic Effects ◽  
Point Of View ◽  
Environmental Variance ◽  
Simple Assumption ◽  
Polynomial Coefficients ◽  
Straight Line ◽  
Best Fitting ◽  
Additive Genetic Effects

1. Polynomial coefficients have been fitted to data on growth in live weight to 19 months and on height at withers to 27 months of age shown by one-egg (MZ) and two-egg (DZ) twins and pairs of half-sisters (HZ). The coefficients obtained (a0, a2 and a3) have been subjected to analysis of variance.2. For growth in live weight, the ratio of intrapair variances for MZ, DZ and HZ pairs was 1:6·8:10 for a1, which gives the straight line best fitting the observed curve. Unrelated pairs, it is calculated, would have had an intrapair variance 20·9 times as great as MZ pairs. From the point of view of minimizing the intrapair variance, the advantage of the MZ pairs was usually a little less for a0, and considerably less for a2 and a3.3. For height at withers, the results were similar to those for weight.4. The contribution of environmental variance to total intrapair variance increased from a0 to a3, while that due to additive genetic effects diminished. Owing to the wide fiducial limits applicable, the results can be accommodated assuming only additive genetic effects in addition to environmental effects as estimated from one-egg twins. The extent to which HZ pairs exceeded the variance expected, however, suggests that this simple assumption may prove inadequate.

scholarly journals Phenotypic plasticity for life-history traits in Drosophila melanogaster. III. Effect of the environment on genetic parameters

1992 ◽  
Vol 60 (2) ◽  
pp. 87-101 ◽  
Author(s):  
M. D. Gebhardt ◽  
S. C. Stearns
Keyword(s):  
Drosophila Melanogaster ◽  
Variance Components ◽  
Life History Traits ◽  
Additive Genetic Variance ◽  
Dry Weight ◽  
Developmental Time ◽  
Environmental Variance ◽  
Directional Dominance ◽  
Yeast Concentration ◽  
Additive Genetic Effects

SummaryWe estimated genetic and environmental variance components for developmental time and dry weight at eclosion in Drosophila melanogaster raised in ten different environments (all combinations of 22, 25 and 28°C and 0·5, 1 and 4% yeast concentration, and 0·25% yeast at 25°C). We used six homozygous lines derived from a natural population for complete diallel crosses in each environment. Additive genetic variances were consistently low for both traits (h2 around 10%). The additive genetic variance of developmental time was larger at lower yeast concentrations, but the heritability did not increase because other components were also larger. The additive genetic effects of the six parental lines changed ranks across environments, suggesting a mechanism for the maintenance of genetic variation in heterogenous environments.The variance due to non-directional dominance was small in most environments. However, there was directional dominance in the form of inbreeding depression for both traits. It was pronounced at high yeast levels and temperatures but disappeared when yeast or temperature were decreased. This meant that the heterozygous flies were more sensitive to environmental differences than homozygous flies. Because dominance effects are not heritable, this suggests that the evolution of plasticity can be constrained when dominance effects are important as a mechanism for plasticity.

scholarly journals Variance adaptation in navigational decision making

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruben Gepner ◽  
Jason Wolk ◽  
Digvijay Shivaji Wadekar ◽  
Sophie Dvali ◽  
Marc Gershow
Keyword(s):  
Sensory Input ◽  
Variance Estimation ◽  
Temporal Dynamics ◽  
Sensory Systems ◽  
Redundant Information ◽  
Environmental Variance ◽  
Mean And Variance ◽  
The Mean ◽  
Light Signals ◽  
The Brain

Sensory systems relay information about the world to the brain, which enacts behaviors through motor outputs. To maximize information transmission, sensory systems discard redundant information through adaptation to the mean and variance of the environment. The behavioral consequences of sensory adaptation to environmental variance have been largely unexplored. Here, we study how larval fruit flies adapt sensory-motor computations underlying navigation to changes in the variance of visual and olfactory inputs. We show that variance adaptation can be characterized by rescaling of the sensory input and that for both visual and olfactory inputs, the temporal dynamics of adaptation are consistent with optimal variance estimation. In multisensory contexts, larvae adapt independently to variance in each sense, and portions of the navigational pathway encoding mixed odor and light signals are also capable of variance adaptation. Our results suggest multiplication as a mechanism for odor-light integration.

Genetic parameters for milk yield and litter size in Boutsico dairy sheep

1998 ◽  
Vol 78 (4) ◽  
pp. 525-532 ◽  
Author(s):  
A. Kominakis ◽  
E. Rogdakis ◽  
K. Koutsotolis
Keyword(s):  
Litter Size ◽  
Milk Yield ◽  
Raw Milk ◽  
Dairy Sheep ◽  
Genetic Covariance ◽  
Yield Data ◽  
Environmental Variance ◽  
Sheep Milk ◽  
Genetic And Phenotypic Correlations ◽  
Additive Genetic Effects

Lactation and litter size records (n = 2553) of Boutsico dairy sheep were used to estimate direct additive, maternal genetic, maternal and animal's permanent environmental variance components for milk yield and litter size by restricted maximum likelihood under a repeated records animal model. Raw milk yield data were transformed by log and Box–Cox (BC) transformations. Heritability estimates of direct additive effects were between 0.21 and 0.30, 0.18 and 0.24 and 0.21 and 0.27 for untransformed, log transformed and BC transformed data, respectively. Only maternal and animal's environmental effects were important for milk yield. Direct-maternal genetic covariance did not significantly (P > 0.05) influence milk yield. Repeatability for milk yield ranged from 0.32 to 0.38. Only additive genetic effects were important for litter size. Heritability and repeatability for litter size were 0.07 and 0.11, respectively. Genetic and phenotypic correlations between yield and litter size were 0.13 and 0.19, respectively. Key words: Sheep, milk yield, litter size, heritability, correlation

scholarly journals Multistep-Ahead Prediction of Urban Traffic Flow Using GaTS Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Benchao Wang ◽  
Pan Qin ◽  
Hong Gu
Keyword(s):  
Traffic Flow ◽  
Gamma Distribution ◽  
Poor Performance ◽  
Information Criterion ◽  
Urban Traffic ◽  
Stochastic Property ◽  
Proposed Model ◽  
Mean And Variance ◽  
Past Data ◽  
Box Cox Transformation

The mathematical models for traffic flow have been widely investigated for a lot of application, like planning transportation and easing traffic pressure by using statistics and machine learning methods. However, there remains a lot of challenging problems for various reasons. In this research, we mainly focused on three issues: (a) the data of traffic flow are nonnegative, and hereby, finding a proper probability distribution is essential; (b) the complex stochastic property of the traffic flow leads to the nonstationary variance, i.e., heteroscedasticity; and (c) the multistep-ahead prediction of the traffic flow is often of poor performance. To this end, we developed a Gamma distribution-based time series (GaTS) model. First, we transformed the original traffic flow observations into nonnegative real-valued data by using the Box-Cox transformation. Then, by specifying the generalized linear model with the Gamma distribution, the mean and variance of the distribution are regressed by the past data and homochronous terms, respectively. A Bayesian information criterion is used to select the proper Box-Cox transformation coefficients and the optimal model structures. Finally, the proposed model is applied to the urban traffic flow data achieved from Dalian city in China. The results show that the proposed GaTS has an excellent prediction performance and can represent the nonstationary stochastic property well.

scholarly journals Estimation of additive and dominance variance for litter size components in rabbits

2014 ◽  
Vol 59 (No. 4) ◽  
pp. 182-189 ◽  
Author(s):  
I. Nagy ◽  
J. Farkas ◽  
I. Curik ◽  
G. Gorjanc ◽  
P. Gyovai ◽  
...  
Keyword(s):  
Environmental Effects ◽  
Variance Components ◽  
Correlation Coefficients ◽  
Data File ◽  
Breeding Value ◽  
Environmental Variance ◽  
Relative Significance ◽  
Breeding Values ◽  
Dead Animal ◽  
Additive Genetic Effects

Additive, dominance, and permanent environmental variance components were estimated for the number of kits born alive, number of kits born dead, and total number of kits born of a synthetic rabbit line (called Pannon Ka). The data file consisted of 11 582 kindling records of 2620 does collected between the years 1996–2013. The total number of animals in the pedigree files was 4012. The examined traits were evaluated using single-trait and two-trait (number of kits born alive-dead) animal models containing all or part of the following effects: additive genetic effects, permanent environmental effects, dominance effects. Heritability estimates calculated using the basic single-trait and two-trait models were 0.094 ± 0.018 and 0.090 ± 0.016 for number of kits born alive, 0.037 ± 0.010 and 0.041 ± 0.012 for number of kits born dead, and 0.117 ± 0.018 for total number of kits born, respectively. The relative significance of permanent environmental effects was 0.069 ± 0.014 and 0.069 ± 0.012 for number of kits born alive, 0.025 ± 0.011 and 0.023 ± 0.010 for number of kits born dead, and 0.060 ± 0.013 for total number of kits born, respectively. Using the extended single-trait and two-trait models, the ratios of the dominance components compared to the phenotypic variances were 0.048 ± 0.008 and 0.046 ± 0.007 for number of kits born alive, 0.068 ± 0.006 and 0.065 ± 0.006 for number of kits born dead, and 0.005 ± 0.0073 for total number of kits born, respectively. Genetic correlation coefficients between number of kits born alive and number of kits born dead were 0.401 ± 0.171 and 0.521 ± 0.182, respectively. Spearman’s rank correlations between the breeding values of the different single-trait models were close to unity in all traits (0.992–0.990). Much lower breeding value stability was found for two-trait models (0.384–0.898), especially for number of kits born dead. Results showed that the dominance components for number of kits born alive and number of kits born dead were not zero and affected the ranking of the animals (based on the breeding values).  

scholarly journals Non-additive genetic effects induce novel phenotypic distributions in male mating traits of F1 hybrids

2020 ◽  
Author(s):  
Keisuke Atsumi ◽  
Malgorzata Lagisz ◽  
Shinichi Nakagawa
Keyword(s):  
Animal Species ◽  
Maternal Inheritance ◽  
Phenotypic Variability ◽  
Meta Analysis ◽  
F1 Hybrids ◽  
Male Mating ◽  
Species Pairs ◽  
Mean And Variance ◽  
Additive Genetic Effects ◽  
Phenotypic Novelty

Hybridization is a source of phenotypic novelty and variation because of increased additive genetic variation. Yet, the roles of non-additive allelic interactions in shaping phenotypic mean and variance of hybrids have been underappreciated. Here we examine the distributions of male-mating traits in F1 hybrids via a meta-analysis of 3208 effect sizes from 39 animal species pairs. Although additivity sets phenotypic distributions of F1s to be intermediate, F1s also showed dominance and maternal inheritance. F1s expressed novel phenotypes (beyond the range of both parents) in 65% of species pairs, often associated with increased phenotypic variability. Overall, however, F1s expressed smaller variation than parents in 51% of traits. While genetic divergence between parents did not impact phenotypic novelty, it increased phenotypic variability of F1s. By creating novel phenotypes with increased variability, non-additivity of heterozygotic genome may play key roles in determining mating success of F1s, and their subsequent extinction or speciation.

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