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opencc-zeroJul 2020View details →

Data from: Genetic and environmental canalization are not correlated among altitudinally varying populations of Drosophila melanogaster

Organisms are exposed to environmental and mutational effects influencing both mean and variance of phenotypes. Potentially deleterious effects arising from this variation can be reduced by the evolution of buffering (canalizing) mechanisms, ultimately reducing phenotypic variability. There has been interest regarding the conditions enabling the evolution of canalization. Under some models, the circumstances under which genetic canalization evolves is limited, despite apparent empirical evidence for it. It has been argued that genetic canalization evolves as a correlated response to environmental canalization (congruence model). Yet, empirical evidence has not consistently supported predictions of a correlation between genetic and environmental canalization. In a recent study, a population of Drosophila adapted to high altitude showed evidence of genetic decanalization relative to those from low altitudes. Using strains derived from these populations, we tested if they varied for multiple aspects of environmental canalization We observed the expected differences in wing size, shape, cell (trichome) density and mutational defects between high- and low-altitude populations. However, we observed little evidence for a relationship between measures of environmental canalization with population or with defect frequency. Our results do not support the predicted association between genetic and environmental canalization.

opencc-zeroDec 2020View details →

Maternal and genetic correlations between morphology and physical performance traits in a small captive primate, Microcebus murinus

Physical performance traits are key components of fitness and direct targets of selection. Maternal effects are important components of integrated phenotypes in a variety of species. Yet their contribution to variation in performance, and phenotypes closely associated with performance, remains poorly understood. We used an animal model approach to quantify the contribution of maternal effects to performance trait variation (in bite force and pull strength) and the relationships between performance and the relevant underlying morphology in Microcebus murinus. We show that bite force is heritable (h2~0.23), and that maternal effects are also important source of variation, resulting in a medium inclusive heritability (IH2~0.47). Grip strength presented a rather low and non-significant narrow-sense heritability suggesting a higher selective pressure on this trait. Genetic correlations between performance traits and their associated morphometric traits were significant and high (0.47 bite force-head width; 0.48 grip strength-radius length), as was the maternal correlation for bite force-head width (0.75). Further studies evaluating the heritability of performance for other taxa and the role of maternal effects are badly needed to better understand the drivers of variation in performance ultimately allowing for a better understanding of the importance of these types of traits in an evolutionary context.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 15. Drawing of autocorrelation function and partial correlation of the residues for males and females primary stage students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for males and females of primary stage students, through applying the compute (Ljung-Box Q) to check the model accommodation on the Function level 0.05 so the Q value occurs of males and females of primary stage students: Ljung-Box Q' = 1.10306, With p-value = P(Chi-square(1) > 1.10306) = 0.2936 Note that the Tabulated value   equals 3.841 while the Q value is less than   Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 13. Drawing of autocorrelation Function and partial correlation of the residues for primary stage males students

It's possible to notice the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for males primary stage, in which the residues  value is located within the confidence interval limits which means the residues series is random and the Evaluated Model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm- Figure 12. Drawing of autocorrelation function and partial correlation for females primary stage students

We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 10. Drawing of autocorrelation function and partial correlation for Males and Females primary stage students

The instability of the time series is recognized, and to be more accurate, we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (10).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 14. Drawing of autocorrelation function and partial correlation of the residues for primary stage males students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for primary stage female students, through applying the compute (Ljung- Box Q) to check the model accommodation on the function level 0.05 so the Q value occurs of primary stage female students: Ljung-Box Q' = 0.966626, With p-value = P(Chi-square(1) > 0.966626) = 0.3255 However, the Tabulated value   equals 3.841 whilst the Q value is less than Tabulated value, so it accepts the Null Hypothesis which indicates the emptiness of the evaluated model out of the contrast accordance trouble. It's possible to notice the two parameters functions (Autocorrelation and Partial Correlation Functions)  of the residues for  females primary stage students, in which the residues  value is located within the confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is shown.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation function and partial correlation for females primary stage students

The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 4. Drawing of autocorrelation function and partial correlation for males primary stage students

We get to notice the stability of the time series, and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure (4).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 8. Drawing of autocorrelation function and partial correlation for Females primary stage students

The stability of the time series is observed and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (8).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 2. Drawing of autocorrelation function and partial correlation for males primary stage

We get to notice the instability of the time series, and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure 2.

opencc-by-4.0Nov 2024View details →

Supplementary table for "Deciphering the Relationship Between Circulating Metabolites and Osteoarthritis: A Comprehensive Genetic Correlation and Mendelian Randomization Studies"

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publicFeb 2022View details →

Maternal and genetic correlations between morphology and physical performance traits in a small captive primate, Microcebus murinus

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