scholarly journals Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Thinh Tuan Chu ◽  
Mark Henryon ◽  
Just Jensen ◽  
Birgitte Ask ◽  
Ole Fredslund Christensen
Keyword(s):  
Statistical Model ◽  
Statistical Models ◽  
Genetic Effects ◽  
Response To Selection ◽  
Heritable Variation ◽  
Fixed Rate ◽  
Random Group ◽  
Breeding Programs ◽  
The Difference ◽  
Family Groups

Abstract Background Social genetic effects (SGE) are the effects of the genotype of one animal on the phenotypes of other animals within a social group. Because SGE contribute to variation in economically important traits for pigs, the inclusion of SGE in statistical models could increase responses to selection (RS) in breeding programs. In such models, increasing the relatedness of members within groups further increases RS when using pedigree-based relationships; however, this has not been demonstrated with genomic-based relationships or with a constraint on inbreeding. In this study, we compared the use of statistical models with and without SGE and compared groups composed at random versus groups composed of families in genomic selection breeding programs with a constraint on the rate of inbreeding. Results When SGE were of a moderate magnitude, inclusion of SGE in the statistical model substantially increased RS when SGE were considered for selection. However, when SGE were included in the model but not considered for selection, the increase in RS and in accuracy of predicted direct genetic effects (DGE) depended on the correlation between SGE and DGE. When SGE were of a low magnitude, inclusion of SGE in the model did not increase RS, probably because of the poor separation of effects and convergence issues of the algorithms. Compared to a random group composition design, groups composed of families led to higher RS. The difference in RS between the two group compositions was slightly reduced when using genomic-based compared to pedigree-based relationships. Conclusions The use of a statistical model that includes SGE can substantially improve response to selection at a fixed rate of inbreeding, because it allows the heritable variation from SGE to be accounted for and capitalized on. Compared to having random groups, family groups result in greater response to selection in the presence of SGE but the advantage of using family groups decreases when genomic-based relationships are used.

scholarly journals On the properties of the asymptotic incompatibility measure in multiparameter quantum estimation

2021 ◽  
Vol 54 (48) ◽  
pp. 485301
Author(s):  
Alessandro Candeloro ◽  
Matteo G A Paris ◽  
Marco G Genoni
Keyword(s):  
Statistical Model ◽  
Statistical Models ◽  
Quantum Dynamics ◽  
Logarithmic Derivative ◽  
Single Mode ◽  
Continuous Variable ◽  
Quantum Systems ◽  
Unknown Parameters ◽  
Quantum Statistical ◽  
The Difference

Abstract We address the use of asymptotic incompatibility (AI) to assess the quantumness of a multiparameter quantum statistical model. AI is a recently introduced measure which quantifies the difference between the Holevo and the symmetric logarithmic derivative (SLD) scalar bounds, and can be evaluated using only the SLD operators of the model. At first, we evaluate analytically the AI of the most general quantum statistical models involving two-level (qubit) and single-mode Gaussian continuous-variable quantum systems, and prove that AI is a simple monotonous function of the state purity. Then, we numerically investigate the same problem for qudits (d-dimensional quantum systems, with 2 < d ⩽ 4), showing that, while in general AI is not in general a function of purity, we have enough numerical evidence to conclude that the maximum amount of AI is attainable only for quantum statistical models characterized by a purity larger than μ min = 1 / ( d − 1 ) . In addition, by parametrizing qudit states as thermal (Gibbs) states, numerical results suggest that, once the spectrum of the Hamiltonian is fixed, the AI measure is in one-to-one correspondence with the fictitious temperature parameter β characterizing the family of density operators. Finally, by studying in detail the definition and properties of the AI measure we find that: (i) given a quantum statistical model, one can readily identify the maximum number of asymptotically compatible parameters; (ii) the AI of a quantum statistical model bounds from above the AI of any sub-model that can be defined by fixing one or more of the original unknown parameters (or functions thereof), leading to possibly useful bounds on the AI of models involving noisy quantum dynamics.

scholarly journals The quantitative genetics of the endemic prevalence of infectious diseases: Indirect Genetic Effects dominate heritable variation and response to selection

2021 ◽  
Author(s):  
Piter Bijma ◽  
Andries Hulst ◽  
Mart C. M. de Jong
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Status ◽  
Host Population ◽  
Genetic Effects ◽  
Breeding Value ◽  
Response To Selection ◽  
Heritable Variation ◽  
Genetic Theory ◽  
Quantitative Genetic

AbstractPathogens have profound effects on life on earth, both in nature and agriculture. Despite the availability of well-established epidemiological theory, however, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of the transmission dynamics of infectious diseases for heritable variation and response to selection of the host population, our current theoretical framework of quantitative genetics does not include these dynamics. As a consequence, we do not know which genetic effects of the host population determine the prevalence of an infectious disease, and have no concepts of breeding value and heritable variation for endemic prevalence.Here we propose a quantitative genetic theory for the endemic prevalence of infectious diseases. We first identify the genetic factors that determine the prevalence of an infectious disease, using an approach founded in epidemiological theory. Subsequently we investigate the population level effects of individual genetic variation on R0 and on the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for both prevalence and individual binary disease status, and show how these parameters depend on the endemic prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases when prevalence approaches zero, while heritability of individual disease status goes to zero. We show that response of prevalence to selection accelerates considerably when prevalence goes down, in contrast to predictions based on classical genetic models. Finally, we show that most of the heritable variation in the endemic prevalence of the infection is due to indirect genetic effects, suggestion a key role for kin-group selection both in the evolutionary history of current populations and for genetic improvement strategies in animals and plants.

scholarly journals The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to Indirect Genetic Effects dominates heritable variation and response to selection

Genetics ◽  
2021 ◽  
Author(s):  
Piter Bijma ◽  
Andries D Hulst ◽  
Mart C M de Jong
Keyword(s):  
Genetic Variation ◽  
Infectious Diseases ◽  
Quantitative Genetics ◽  
Disease Status ◽  
Genetic Effects ◽  
Breeding Value ◽  
Response To Selection ◽  
Heritable Variation ◽  
Genetic Theory ◽  
Quantitative Genetic

Abstract Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently we investigate the population level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.

scholarly journals Tower Running—Participation, Performance Trends, and Sex Difference

2020 ◽  
Vol 17 (6) ◽  
pp. 1902 ◽  
Author(s):  
Daniel Stark ◽  
Stefania Di Gangi ◽  
Caio Victor Sousa ◽  
Pantelis Nikolaidis ◽  
Beat Knechtle
Keyword(s):  
Sex Differences ◽  
Statistical Model ◽  
Sex Difference ◽  
Random Effects ◽  
Age Groups ◽  
Repeated Measurements ◽  
Stair Climbing ◽  
Age Group ◽  
Performance Trends ◽  
The Difference

Though there are exhaustive data about participation, performance trends, and sex differences in performance in different running disciplines and races, no study has analyzed these trends in stair climbing and tower running. The aim of the present study was therefore to investigate these trends in tower running. The data, consisting of 28,203 observations from 24,007 climbers between 2014 and 2019, were analyzed. The effects of sex and age, together with the tower characteristics (i.e., stairs and floors), were examined through a multivariable statistical model with random effects on intercept, at climber’s level, accounting for repeated measurements. Men were faster than women in each age group (p < 0.001 for ages ≤69 years, p = 0.003 for ages > 69 years), and the difference in performance stayed around 0.20 km/h, with a minimum of 0.17 at the oldest age. However, women were able to outperform men in specific situations: (i) in smaller buildings (<600 stairs), for ages between 30 and 59 years and >69 years; (ii) in higher buildings (>2200 stairs), for age groups <20 years and 60–69 years; and (iii) in buildings with 1600–2200 stairs, for ages >69 years. In summary, men were faster than women in this specific running discipline; however, women were able to outperform men in very specific situations (i.e., specific age groups and specific numbers of stairs).

scholarly journals On the Accuracy of the Sine Power Lomax Model for Data Fitting

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 78-104
Author(s):  
Vasili B. V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Statistical Model ◽  
Simulation Study ◽  
Statistical Models ◽  
Goodness Of Fit ◽  
Data Fitting ◽  
Applied Science ◽  
Survival Distribution ◽  
Lomax Distribution ◽  
Model Based

Every day, new data must be analysed as well as possible in all areas of applied science, which requires the development of attractive statistical models, that is to say adapted to the context, easy to use and efficient. In this article, we innovate in this direction by proposing a new statistical model based on the functionalities of the sinusoidal transformation and power Lomax distribution. We thus introduce a new three-parameter survival distribution called sine power Lomax distribution. In a first approach, we present it theoretically and provide some of its significant properties. Then the practicality, utility and flexibility of the sine power Lomax model are demonstrated through a comprehensive simulation study, and the analysis of nine real datasets mainly from medicine and engineering. Based on relevant goodness of fit criteria, it is shown that the sine power Lomax model has a better fit to some of the existing Lomax-like distributions.

ADDITIVE, NONADDITIVE AND MATERNAL GENETIC EFFECTS ON ADIPOSITY IN MICE FED DIFFERENT LEVELS OF FAT

1982 ◽  
Vol 24 (3) ◽  
pp. 347-360 ◽  
Author(s):  
A. de Padua Deodato ◽  
E. J. Eisen ◽  
J. M. Leatherwood
Keyword(s):  
Body Weight ◽  
Dietary Fat ◽  
Subcutaneous Fat ◽  
Genetic Effects ◽  
Major Portion ◽  
Female Mice ◽  
Ad Libitum ◽  
The Difference ◽  
Fat Depot ◽  
Different Levels

Polygenic obese (M16), nonobese (ICR) and reciprocal crossbred (M16 male × ICR female and ICR male × M16 female) mice were fed ad libitum diets containing 1, 5 or 25% fat from 3 to 10 weeks of age. Epididymal and subcutaneous fat depot weights (E, S) and depot weights as a proportion of empty body weight (E%, S%) were used as measures of adiposity at 6 and 10 weeks of age. Genetic differences in adiposity among the four populations were partitioned into average direct (a), average maternal (m) and direct heterotic (h) effects. Line M16 was greater than ICR at both 6 and 10 weeks in E (81% at 6 weeks and 114% at 10 weeks), S (82%, 73%), E% (27%, 37%) and S% (26%, 12%). Average direct genetic effects, as determined by a, accounted for 60% of the M16 vs. ICR line difference in E and S at six weeks, the remainder of the difference being due to m. The major portion of the line difference in E% and S% at 6 weeks was accounted for by m. At ten weeks of age, most of the line difference in E, S, E% and S% was due to additive direct genetic effects while the contribution of maternal genetic effects was negligible. Heterosis was sizeable for all measures of adiposity, varying from 10.8% in S% at 10 weeks to 26.8% in E at six weeks, possibly indicating the presence of directional dominance. E and E% increased significantly with the increase in dietary fat percent, but S and S% were not affected. Interactions of genotype with level of dietary fat percent were not significant for the epididymal or subcutaneous fat depot weights or proportional weights.

scholarly journals Diffeological Statistical Models, the Fisher Metric and Probabilistic Mappings

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 167
Author(s):  
Hông Vân Lê
Keyword(s):  
Positive Integer ◽  
Statistical Model ◽  
Statistical Models ◽  
Probabilistic Mapping ◽  
Fisher Metric

We introduce the notion of a C k -diffeological statistical model, which allows us to apply the theory of diffeological spaces to (possibly singular) statistical models. In particular, we introduce a class of almost 2-integrable C k -diffeological statistical models that encompasses all known statistical models for which the Fisher metric is defined. This class contains a statistical model which does not appear in the Ay–Jost–Lê–Schwachhöfer theory of parametrized measure models. Then, we show that, for any positive integer k , the class of almost 2-integrable C k -diffeological statistical models is preserved under probabilistic mappings. Furthermore, the monotonicity theorem for the Fisher metric also holds for this class. As a consequence, the Fisher metric on an almost 2-integrable C k -diffeological statistical model P ⊂ P ( X ) is preserved under any probabilistic mapping T : X ⇝ Y that is sufficient w.r.t. P. Finally, we extend the Cramér–Rao inequality to the class of 2-integrable C k -diffeological statistical models.

Beat-to-beat stroke volume estimation from aortic pressure waveform in conscious rats: comparison of models

2001 ◽  
Vol 281 (3) ◽  
pp. H1148-H1155 ◽  
Author(s):  
C. Cerutti ◽  
M. P. Gustin ◽  
P. Molino ◽  
C. Z. Paultre
Keyword(s):  
Stroke Volume ◽  
Statistical Model ◽  
Statistical Models ◽  
Correlation Coefficients ◽  
Aortic Pressure ◽  
Pulse Contour ◽  
Volume Estimation ◽  
Good Precision ◽  
Pressure Waveform ◽  
Contour Models

Several methods for estimating stroke volume (SV) were tested in conscious, freely moving rats in which ascending aortic pressure and cardiac flow were simultaneously (beat-to-beat) recorded. We compared two pulse-contour models to two new statistical models including eight parameters extracted from the pressure waveform in a multiple linear regression. Global as well as individual statistical models gave higher correlation coefficients between estimated and measured SV ( model 1, r = 0.97; model 2, r= 0.96) than pulse-contour models ( model 1, r = 0.83; model 2, r = 0.91). The latter models as well as statistical model 1 used the pulsatile systolic area and thus could be applied to only 47 ± 17% of the cardiac beats. In contrast, statistical model 2 used the pressure-increase characteristics and was therefore established for all of the cardiac beats. The global statistical model 2 applied to data sets independent of those used to establish the model gave reliable SV estimates: r= 0.54 ± 0.07, a small bias between −8% to +10%, and a mean precision of 7%. This work demonstrated the limits of pulse-contour models to estimate SV in conscious, unrestrained rats. A multivariate statistical model using eight parameters easily extracted from the aortic waveform could be applied to all cardiac beats with good precision.

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