scholarly journals SELECTION FOR IMPROVING EGG PRODUCTION IN MANDARAH CHICKENS TO MAXIMIZE THE NET INCOME. 1- CORRELATED RESPONSES, GENETIC PARAMETERS FOR EGG PRODUCTION AND GROWTH TRAITS

2011 ◽  
Vol 2 (11) ◽  
pp. 457-470
Author(s):  
F. Abdel-Ghany ◽  
A. Abdel-Ghany
Keyword(s):  
Egg Production ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Net Income ◽  
Correlated Responses ◽  
Selection For

Selection for rate and efficiency of lean gain in Hereford cattle 1. Selection pressure applied and direct responses

1990 ◽  
Vol 51 (1) ◽  
pp. 23-34 ◽  
Author(s):  
R. A. Mrode ◽  
C. Smith ◽  
R. Thompson
Keyword(s):  
Genetic Parameters ◽  
Control Line ◽  
Correlated Responses ◽  
Direct Response ◽  
Lean Growth ◽  
Hereford Cattle ◽  
Frozen Semen ◽  
Phenotypic Standard Deviation ◽  
Selection For ◽  
Selection Of

ABSTRACTSelection of bulls for rate and efficiency of lean gain was studied in a herd of Hereford cattle. There were two selection lines, one selected for lean growth rate (LGR) from birth to 400 days and the other for lean food conversion ratio (LFCR) from 200 to 400 days of age, for a period of 8 years. A control line bred by frozen semen from foundation bulls was also maintained. Generation interval was about 2·4 years and average male selection differentials, per generation were 1·2 and — 1·1 phenotypic standard deviation units for LGR and LFCR respectively.Genetic parameters and responses to selection were estimated from the deviation of the selected lines from a control line and by restricted maximum likelihood (REML) techniques on the same material. Realized heritabilities were 0·40 (s.e. 0·12) for LGR and 0·40 (s.e. 0·13) for LFCR using the control line. Corresponding estimates from REML were 0·42 (s.e. 0·10) and 0·37 (s.e. 0·14). The estimate of the genetic correlation between LGR and LFCR was about — 0·69 (s.e. 0·12) using REML.The estimates of direct annual genetic change using deviations from the control were 3·6 (s.e. 1·3) g/day for LGR and — 0·14 (s.e. 0·07) kg food per kg lean gain for LFCR. Corrsponding estimates from REML were similar but more precisely estimated. The correlated responses for LFCR in the LGR line was higher than the direct response for LFCR.

scholarly journals Correlated Response on Growth Traits and Their Variabilities to Selection for Ovulation Rate in Rabbits Using Genetic Trends and a Cryopreserved Control Population

Animals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2591
Author(s):  
Rosa Peiró ◽  
Celia Quirino ◽  
Agustín Blasco ◽  
María Antonia Santacreu
Keyword(s):  
Growth Traits ◽  
Ovulation Rate ◽  
Control Population ◽  
Individual Growth ◽  
Correlated Response ◽  
Phenotypic Variance ◽  
Correlated Responses ◽  
Growth Variability ◽  
Selection For ◽  
Genetic Trends

The aim of this work was to estimate correlated responses in growth traits and their variabilities in an experiment of selection for ovulation rate during 10 generations in rabbits. Individual weight at 28 days old (IW28, kg) and at 63 days old (IW63, kg) was analyzed, as well as individual growth rate (IGR = IW63 − IW28, kg). The variability of each growth trait was calculated as the absolute value of the difference between the individual value and the mean value of their litter. Data were analyzed using Bayesian methodology. The estimated heritabilities of IW28, IW63 and IGR were low, whereas negligible heritabilities were obtained for growth variability traits. The common litter effect was high for all growth traits, around 30% of the phenotypic variance, whereas low maternal effect for all growth traits was obtained. Low genetic correlations between ovulation rate and growth traits were found, and also between ovulation rate and the variability of growth traits. Therefore, genetic trends methods did not show correlated responses in growth traits. A similar result was also obtained using a cryopreserved control population.

Genetic parameters and responses to selection for pre-weaning growth in suffolk and texel sheep

1995 ◽  
Vol 1995 ◽  
pp. 48-48
Author(s):  
I.W. Purvis ◽  
J.P. Hanrahan
Keyword(s):  
Growth Rate ◽  
Genetic Variation ◽  
Genetic Gain ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Present Report ◽  
Growth Data ◽  
Genetic Trend ◽  
Breeding Objective ◽  
Selection For

In order to evaluate genetic gain in populations under selection it is necessary to be able to partition the observed response into genetic and environmental components. This requires estimates of the appropriate genetic and environmental (co)variances unless appropriate genetic controls are available. Growth rate is an important component of the breeding objective for sheep breeds used as terminal sires and, whereas older estimates of the contribution of genetic variation to differences in preweaning growth indicated heritabilities of the order of 0.1, more recent studies have indicated considerably higher values. The present report concerns analyses of preweaning growth data on purebred Suffolk and Texel sheep to estimate genetic parameters for preweaning growth traits and genetic trend in growth rate from birth to weaning.

Genetic parameters for egg weight v. age curve, and other egg production and egg weight traits, in synthetic lines of chickens

1983 ◽  
Vol 34 (1) ◽  
pp. 85 ◽  
Author(s):  
BH Yoo ◽  
BL Sheldon ◽  
RN Podger
Keyword(s):  
Standard Deviation ◽  
Genetic Correlation ◽  
Egg Production ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Egg Mass ◽  
Control Line ◽  
Egg Weight ◽  
Egg Number ◽  
Selection For

An exponential curve, W = P-Qexp(- Rt), where W is egg weight at age t, was fitted to egg weights of individual pullets, and genetic parameters were estimated for P, Q and R, the residual standard deviation and other egg weight and egg production characters. The data consisted of records collected over six generations on more than 4000 pullets in two selection lines and a control line which originated from a synthetic gene pool of White Leghorn x Australorp crosses. The half-sib and offspring-on-parent regression estimates of heritability pooled over the lines were 0.23 and 0.33 for P, 0.14 and 0.20 for Q, and 0.14 and 0.25 for R. Genetic correlations were estimated to be -0.10 between P and Q, -0.46 between P and R, and 0.90 between Q and R. These estimates suggest that the egg weight v. age curve may be modified to increase the proportion of eggs in desirable weight grades and reduce the incidence of oversized eggs later in the production year. The genetic correlation between mean weight of first 10 eggs and egg weight at 62 weeks of age was estimated to be 0.68, further suggesting that early egg weight may be improved partly independently of late egg weight. The heritability estimates of egg mass output were not higher than those of egg number in spite of the highly heritable average egg weight being an important component of egg mass, probably because of the negative genetic correlation (r = -0.49) between egg number and average egg weight. The standard deviation of individual pullet's egg weights was moderately heritable and genetically correlated positively with egg weight characters and negatively with egg production; these estimates were consistent with the responses to selection for reduced egg weight variability observed elsewhere

Estimates of genetic parameters for pre-weaning and post-weaning growth traits of Chios lambs

1980 ◽  
Vol 30 (2) ◽  
pp. 271-276 ◽  
Author(s):  
A. P. Mavrogenis ◽  
A. Louca ◽  
O. W. Robison
Keyword(s):  
Body Weight ◽  
Birth Weight ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Heritability Estimate ◽  
Weaning Weight ◽  
Phenotypic Correlations ◽  
Selection For ◽  
Daily Gain

ABSTRACTData on 792 Chios lambs born during the 1972/73 and 1973/74 lambing seasons were used to estimate genetic and phenotypic parameters for birth weight, weaning weight, age at weaning, pre-weaning daily gain, body weight at 5, 10, 15 and 20 weeks of age, and postweaning daily gain. Body weight at 15 weeks of age had the highest heritability estimate (0·73 ± 0·17) and that of post-weaning daily gain was also high (0·56 ± 0·15). Selection for either weight at 15 weeks or post-weaning daily gain would be expected to yield a greater response than selection for pre-weaning daily gain or weaning weight. Genetic correlations among weights and/or gains were positive (approximately 0·20). Phenotypic correlations among weights and gains were generally higher than genetic correlations. However, the correlation between pre— and post-weaning daily gain was small (0·08). Likewise, post-weaning daily gain had low correlations with all weights before 10 weeks. Age at weaning had moderate negative associations with all weights but a very low positive correlation with post-weaning daily gain.

scholarly journals Selection for ovulation rate in rabbits: Genetic parameters and correlated responses on survival rates1

2012 ◽  
Vol 90 (2) ◽  
pp. 439-446 ◽  
Author(s):  
P. Laborda ◽  
M. L. Mocé ◽  
A. Blasco ◽  
M. A. Santacreu
Keyword(s):  
Genetic Parameters ◽  
Ovulation Rate ◽  
Correlated Responses ◽  
Selection For

scholarly journals Two-way selection for body weight in Tribolium on two levels of nutrition

1967 ◽  
Vol 9 (3) ◽  
pp. 309-330 ◽  
Author(s):  
R. T. Hardin ◽  
A. E. Bell
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Minimum Size ◽  
Direct Selection ◽  
Correlated Responses ◽  
Larval Weight ◽  
Large Size ◽  
Selection For ◽  
Selection Differentials

Parameters necessary for predicting direct and correlated responses for large and small 13-day larval weight in T. castaneum on two levels of nutrition were estimated in the base population. Larval weight in the GOOD environment was approximately twice that observed in POOR. Heritabilities (estimated from the ratio of sire component to total phenotype variance) of larval weight on the two rations were similar, 0·21 ± 0·06 and 0·19 ± 0·05 for GOOD and POOR, respectively. Heritabilities based on dam-offspring covariances were similar to these, but those obtained from full-sib covariances were much larger (0·97 ± 0·07 for GOOD and 0·69 ± 0·07 for POOR). This suggested that considerable dominance rather than maternal effects were present. The genetic correlation between growth on GOOD and growth on POOR was estimated as + 0·60 ± 0·21.The selection experiment was replicated four times with each replication extending over eight generations. Good agreement between predicted and observed values for direct selection was observed for large selection in both environments and small selection in POOR. However, response to small selection in GOOD was significantly greater than predicted in all four replications and was associated with increased selection differentials. Realized heritabilities were approximately the same for both directions in GOOD yet asymmetrical responses occurred for all replications due to unequal selection differentials. On the other hand, realized heritabilities were asymmetrical in POOR. Those observed for small selection were more than twice the size of those calculated for large lines. However, the responses in POOR were symmetrical since the selection differentials varied inversely with the realized heritabilities.Because of the asymmetry observed for heritabilities and selection differentials, correlated responses were poorly predicted. The average effective genetic correlation between growth in GOOD and growth in the POOR environment agreed remarkably well with the base estimate, yet asymmetry of the genetic correlation was a consistent phenomenon with values for the large lines being less than the base parameter while small lines were uniformly larger.Asymmetries of the various genetic parameters were not anticipated from base estimates. They were not caused by sampling or chance fluctuations since all four replications were remarkably consistent. Asymmetry for any one genetic parameter (e.g. heritability) was associated with a particular environment or direction of selection while other genetic parameters reacted asymmetrically in populations exposed to a different set of environmental treatments.For maximum performance in a single environment, these results show that selection should be practiced in that environment. With regard to mean performance in GOOD and POOR environments, selection for large size in POOR gave some 25% more gain than selection in GOOD. Selection for small size in either environment was equally effective in obtaining minimum size in both environments.

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