Phenotypic and genetic variability of estimated growth curve parameters in mice

Stochastic simulation of growth in pigs: relations between body composition and maintenance requirements as mediated through protein turn-over and thermoregulation

A dynamic model for simulation of growth in pigs, extended to describe thermoregulatory processes, was made stochastic to simulate groups of pigs with between-animal variation in mature body protein (Pα) and lipid mass (Lα), in the potential rate at which mature mass is attained (B⋆), and in the distribution of body protein and lipid over pools and depots. The resulting variation in body composition leads to variation in energy requirements for protein turn-over and thermoregulation, causing between-animal variation in maintenance requirements (MEmaint).

Simulated population means for Pα, Lα/Pαand B⋆were varied in three steps each. Excluding unrealistic parameter combinations this led to 33 – 6 = 21 simulated genotypes. Simulated within-population coefficients of variation (CV) were 7, 15 and 3%. Random replicates of each genotype were simulated five times, in climatic conditions that were in turn severely cold, mildly cold (about 5 and 1ºC below lower critical temperature), thermoneutral, mildly hot and severely hot (about 1 and 5ºC above upper critical temperature), during the entire growth period of 23 to 100 kg live weight. Simulated food intake was ad libitum.

Simulated thermoneutral within-population standard deviations of body protein and lipid content were 0·21 to 0·46 kg and 0·78 to 2·14 kg at 100 kg body weight. On average, the corresponding values in cold and hot conditions were slightly higher.

MEmaintshowed a protein-turn-over-related within-population CV of 1·5% at thermoneutrality. Thermoregulatory action contributed about 4% extra variance in cold and hot conditions but CV values were not affected. A genetic increase in the maximum protein deposition rate from 100 to 250 g/day would increase MEmaintas related to protein turn-over and thermoregulation by 11% at thermoneutrality, and by 6 to 11% in cold or hot conditions. Two relevant groups of genotypes could be distinguished based on the within-population regression coefficients of MEmainton daily or cumulative protein deposition (bdailyPdep, bcumPdep). These ranged from 0·250 to 0·428 kJ/kg0·75 per day per g/day and from 2·77 to 5·45 kJ/kg0·75per day per kg, respectively, in 12 ‘conventional’ genotypes at thermoneutrality. On average, bdailyPdepwas increased by 48%, 20%, –11% and –36% in the other climatic conditions mentioned above, respectively. The corresponding increase of bcumPdepwas 32%, 14%, 8% and 48%. Three fast-growing lean genotypes showed similar bdailyPdepand bcumPdepat thermoneutrality, but much more pronounced increases in cold and hot conditions: 137%, 49%, –12% and + 88% for bdailyPdepand 248%, 108%, 17% and 196% for bcumPdep.

It is concluded that differences in body composition traits between pig genotypes do not cause important between-genotype differences in thermoregulatory MEmaint, and that thermoregulatory processes contribute little body-composition-related variation to hot or cold MEmaintwithin most genotypes.

The inferences to be made from this with regard to experimental design are discussed. The verification of the above predictions will require a very elaborate and large-scale experiment.

Bayesian analysis of the effect of selection for residual feed intake on growth and feed intake curves in Yorkshire swine

Gompertz growth functions were fitted to longitudinal measurements of daily feed intake (DFI) and BW of 586 boars and 495 gilts from a selection experiment in Yorkshire pigs for residual feed intake (RFI). The selection experiment consists of a line selected for low residual feed intake (LRFI) for 5 generations and a randomly selected control line (CTRL). The objectives of this study were to use Bayesian methods to estimate genetic parameters of the Gompertz curve parameters for DFI and BW, to evaluate the effect of selection for reduced RFI on the Gompertz parameters and shape of curves for DFI and BW, and to develop methodology for quantifying genetic variation at the level of the original phenotypes for DFI and BW based on the Bayesian analysis of the nonlinear model. Separate analyses were done for boars and gilts and for BW and DFI. A hierarchical model was specified in 2 levels: in the first level, the Gompertz function was modeled for each pig, and at the second level, a 3-trait linear mixed model was fitted to the 3 Gompertz parameters (asymptotic value, inflection point, and decay parameter), with fixed effects of line by generation and random effects of additive genetic and environmental effects. Bayesian methods were used to combine the 2 levels of modeling. A total of 30,000 random samples of the posterior distributions after convergence of Markov chains were used for inference. Posterior means of heritability within the first level of the model for the asymptotic value, inflection point, and decay parameter for DFI were 0.74, 0.66, and 0.82 for boars and 0.79, 0.70, and 0.57 for gilts; corresponding estimates for BW were 0.64, 0.58, and 0.60 for boars and 0.46, 0.35, and 0.33 for gilts. For DFI, LRFI boars had a reduced mature DFI (2.91 vs. 3.20 kg/d) and an earlier inflection point (85 vs. 95 d) compared with CTRL boars. For BW, LRFI boars had a lighter mature BW (279 vs. 317 kg), an earlier inflection point (184 vs. 198 d), and a decreased decay parameter (127 vs. 134 d) compared with CTRL boars. In contrast, LRFI gilts had a later inflection point (225 vs. 200 d) and a greater decay parameter (172 vs. 143 d) than CTRL gilts for BW. The other Gompertz curve parameters for DFI and BW for boars and gilts were considered not different between lines, with posterior probabilities of the line differences being greater than zero ranging from 0.1 to 0.9.

A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds1

This study compared the use of various models to describe growth in lambs of 2 contrasting breeds from birth to slaughter. Live BW records (n = 7559) from 240 Texel and 231 Scottish Blackface (SBF) lambs weighed at 2-wk intervals were modeled. Biologically relevant variables were estimated for each lamb from modified versions of the logistic, Gompertz, Richards, and exponential models, and from linear regression. In both breeds, all nonlinear models fitted the data well, with an average coefficient of determination (R2) of > 0.98. The linear model had a lower average R2 than any of the nonlinear models (< 0.94). The variables used to describe the best 3 models (logistic, Gompertz, and Richards) included estimated final BW (A); maximum ADG (B); age at maximum ADG (C); position of point of inflection in relation to A (D, for Richards only). The Richards and Gompertz models provided the best fit (average R2 = 0.986 to 0.989) in both breeds. Richards estimated an extra variable, allowing increased flexibility in describing individual growth patterns, but the Akaike's information criteria value (which weighs log-likelihood by number of parameters estimated) was similar to that of the Gompertz model. Variables A, B, C, and D were moderately to highly heritable in Texel lambs (h2 = 0.33 to 0.87), and genetic correlations between variables within-model ranged from -0.80 to 0.89, suggesting some flexibility to change the shape of the growth curve when selecting for different variables. In SBF lambs, only variables from the logistic and Gompertz models had moderate heritabilities (0.17 to 0.56), but with high genetic correlations between variables within each model (< -0.88 or > 0.92). Selection on growth variables seems promising (in Texel more than SBF), but high genetic correlations between variables may restrict the possibilities to change the growth curve shape. A random regression model was also fitted to the data to allow predictions of growth rates at relevant time points. Heritabilities for growth rates differed markedly at various stages of growth and between the 2 breeds (Texel: 0.14 to 0.74; SBF: 0.07 to 0.34), with negative correlations between growth rate at 60 d of age and growth rate at finishing. Following these results, future studies should investigate genetic relationships between relevant growth curve variables and other important production traits, such as carcass composition and meat quality.

Polymorphism at the esterase isozyme locus Est10 associated with phylogenetic differentiation in rice

A new esterase isozyme locus, Est10, with 6 alleles including the null form, has been found in rice by using polyacrylamide gel electrophoresis. Thirty F(2) populations of all possible combinations between 5 different band morphs were studied. The segregation pattern indicated that bands 1, 2, 3, 4, and the null form (0) were allelic with each other. The alleles of Est10 were distributed at different frequencies among different varietal groups of rice and also between cultivated rice and its wild relatives (Oryza rufipogon Griff.). Alleles 1 and 2 were frequently found in Japonica and Indica types, respectively. Allele 3 showed a high frequency in Aus and Boro, both Indica types cultivated in South Asia. Allele 4 was frequent in wild rice O. rufipogon. Judging from the linkage between Est10 and RFLP marker RG220 and isozyme marker Est5, Est10 is located on chromosome 1. The importance of this locus in evolutionary studies of rice is discussed.

Divergent selection for shape of growth curve in Japanese quail. 1. Responses in growth parameters and food conversion

1. HG and LG quail lines selected for high and low relative weight gain between 11 and 28 d of age (RG11-28), respectively, and an unselected C line were compared. Mature body weight of both selected lines was held at that of the C line. Progeny of generation 6 were used for analysis. 2. Divergent selection for RG11-28 brought about opposite changes in the growth rates shortly after hatching. 3. Parameters of the Richards function were used to describe the growth curve. The largest differences between HG and LG lines occurred in age (t+) and body weight (y+) at the inflection point of the growth curve (on average for both sexes 28% and 20%, respectively). For HG quail, the parameter t+ was 5 d later than that for LG quail (18.6 vs 14.1 d for males and 20.6 vs 15.6 d for females, respectively), and consequently the parameter y+ was greater (90.3 vs 84.0 g for males and 104.5 vs 96.1 g for females, respectively). The shape of the growth curve expressed by the y+/A ratio was substantialy different for HG and LG quail (44.8% vs 39.6% for males and 43.5% vs 36.8% for females, respectively). 4. The food/gain ratios for the fattening period (3 to 35 d of age) were 3.21, 3.47 and 3.34 for the HG, LG and C lines, respectively. The HG quail started to utilise food more efficiently than the LG quail as early as 10 to 14 d, that is, at the age when their relative growth rate first became greater. 5. The relative deviations of the HG and LG lines from the C line are discussed.

Genetic analysis of a selection experiment on the growth curve of chickens

A selection experiment on the shape of the growth curve was performed on meat-type chickens through combined selection on juvenile and adult BW. Line X-+ was selected for low BW at 8 wk (BW8) and high BW at 36 wk (BW36). Line X+- was selected for high BW8 and low BW36. Line X++ was selected for high BW8 and BW36, and X-- was selected for low BW8 and BW36. Line X00 was maintained as an unselected control. Data on the first 14 generations (i.e., 38,693 birds) were used. The growth curve was modeled using a Gompertz function on 7,143 birds that were weighed regularly. Selection for higher BW8 increased BW from 4 to 16 wk, initial specific growth rate, and maturation rate and decreased age at inflection. Selection for higher BW36 resulted in increased BW36, asymptotic BW, and estimated BW at hatching. Body weights were more modified in Lines X++ and X--, but the growth curve parameters changed more in Lines X-+ and X-- than in Lines X++ and X+-.

Body weight and tail length divergence in mice selected for rate of development

A series of mouse lines has been produced by 19 generations of restricted index selection for rate of development during early and late ontogeny. The selection program was based on an index with the following four replicated selection treatments: E(+) and E(-) were selected to alter birth to 10-day body weight gain while holding late gain for both selection lines constant; correspondingly, L(+) and L(-) were selected to alter 28- to 56-day body weight gain holding early gain for both lines constant. Herein, we characterize response to selection for growth rate by analyzing age-specific mouse body weight and tail lengths and for growth curves using a logistics model. Selection on developmental rate has resulted in divergence in both age-specific and growth curve traits. E(+) and L(+) lines reached identical weights during the late selection interval, then diverged to unique mature weights. E(-) and L(-) lines similarly achieved identical weights during late selection and diverged to unique mature weights. However, the shapes of early and late growth curves were significantly divergent, and at least two distinct growth patterns are shown to result from selection. Response in body weight gain was accompanied by similar, though less pronounced, change in tail length traits. Significant response during intervals of restricted growth was also found, especially in lines selected for late gain. The evolution of the growth trajectory under restricted index selection is discussed in terms of drift and available additive genetic variation and covariation.

Body weight and egg weight dynamics in layers

The association between body weight-age and egg weight-age patterns was studied in a segregating population of laying hens belonging to the F3 generation of a Rhode Island Red x White Leghorn reciprocal cross. Body weight and egg weight were expressed as a function of time using the model developed by Weatherup and Foster. Each hen was characterized in terms of its asymptotic body weight (ABW), maturing rate for body weight (MBW), asymptotic egg weight (AEW), and maturing rate for egg weight (MEW) values. Four groups of hens were distinguished by means of a principal component analysis. Birds belonging to Groups 1 and 3 were discriminated for their egg weight-age pattern. Group 1 included hens laying the heaviest eggs (AEW = 66.1 g) at the lowest maturing rate (MEW = 0.922), the inverse being true for birds in Group 3 (AEW = 55.7 g and MEW = 0.737). Birds belonging to Groups 2 and 4 were distinguished for their body weight-age pattern. Hens in Group 2 showed the lowest ABW (1,893 g) and MBW (0.764) whereas the heaviest (ABW = 2,802 g) and less mature (MBW = 0.929) birds were found in Group 4. The results confirm the partial pleiotropic basis of the body weight-egg weight correlation, evincing the feasibility of applying selective pressure not only on each character separately but also on maturing rate independently of asymptotic weight within each trait. This strategy could be implemented using a biological selection index based on principal component analysis equations.

Heritabilities and genetic correlations for estimated growth curve parameters in maritime pine

Height growth curves and several other characters were measured in five maritime pine (Pinus pinaster Ait) progeny tests aged from 18 to 27 years (about half the rotation age), with sample sizes of 272-1555 trees. These curves were fitted with a reparametrized Lundqvist-Matèrn sigmoidal growth function with global estimation of two of the four parameters. Each curve was characterized by two parameters: : the maximal growth rate (r), approximately proportional to the stem height at age 16 years, and essentially determined by the height increments around age 6 years. the asymptote (A), which is an extrapolation of growth after the measurement age. A is essentially determined by the latter growth period (around age 20 years), and is also related to the shape of the observed curve. The modelling framework appeared to be well suited to the characteristics of the data studied, and the estimation standard errors of the parameters were reasonably low. The heritabilities yielded for the growth curve parameters were high, similar to the heritabilities of cumulative heights. The genetic correlation between r and A was low, pointing to a poor juvenile-mature correlation. Discrepancies from one trial to another in heritabilities and in the correlation pattern were observed, they probably originated from environmental stresses. Maritime pine is actually selected using height and butt angle of lean at age 10 years as criteria. Improvements in the breeding program are suggested.

Replicated selection for insulin-like growth factor-1 and body weight in mice

Five generations of divergent selection for plasma concentration of insulin-like growth factor-1 (IGF-1) and for 12-week body weight were carried out in mice, including randomly selected control lines for each trait. All lines were replicated once (12 lines in total). Each replicate line consisted of eight male and eight female parents per generation. Litter size was standardized to eight pups at birth. Mass selection was applied in the selected lines and within-family random selection in the control lines. Blood was taken from the orbital sinus of individual mice at 12 weeks of age for IGF-1 assay. Realized heritabilities were 0.10±0.01 for IGF-1 and 0.41 ± 0.02 for 12-week weight. The realized genetic correlation between IGF-1 and 12-week weight was 0.58 ± 0.01, with a phenotypic correlation of 0.38. Although the genetic correlation between IGF-1 and body weight in mice is moderately positive, 12-week weight responded 3.5 times as fast to weight selection as to selection for IGF-1.