scholarly journals ÉTUDE SUR LES PORCS DE RACE CONTRÔLÉS À LA FERME AU QUÉBEC. 2. ESTIMATION DES PARAMÈTRES GÉNÉTIQUES ET PHÉNOTYPIQUES

1988 ◽  
Vol 68 (4) ◽  
pp. 1063-1068 ◽  
Author(s):  
YVAN SAVOIE ◽  
FRANCIS MINVIELLE
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Growth Index ◽  
Minimum Variance ◽  
Unbiased Estimation ◽  
Parity Number ◽  
Variance And Covariance Components ◽  
Daily Gain

Variance and covariance components were estimated from 94 161 observations of growth and backfat taken on purebred pigs born from 8561 sows and 1431 boars. The mixed model used had herd, year-season, parents' birth years, gender and parity number as fixed effects and sire and dam as random effects. Each breed, Yorkshire, Landrace, Hampshire and Duroc, was studied separately. Several statistical methods of estimation were used but resulting values of heritability were generally quite similar. Heritability of growth traits obtained from MIVQUEO (minimum variance quadratic unbiased estimation) was between 0.21 and 0.40, depending on the breed. Heritability of backfat depth was larger, from 0.49 to 0.77. Genetic correlation of daily gain and backfat varied between 0.08 and 0.56; it was between −0.09 and −0.56 for age at 90 kg and backfat. Corresponding phenotypic correlations were smaller in absolute value but had the same sign as genetic correlations. Key words: Pigs, heritabilities, correlations, backfat, growth, index

ÉTUDE SUR LES PORCS DE RACE CONTRÔLÉS À LA FERME AU QUÉBEC. 1. SOURCES DE VARIATION

1988 ◽  
Vol 68 (4) ◽  
pp. 1051-1062 ◽  
Author(s):  
YVAN SAVOIE ◽  
FRANCIS MINVIELLE
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Genetic Correlations ◽  
Growth Data ◽  
Gender Parity ◽  
Home Test ◽  
Parity Number ◽  
Key Words Pig ◽  
Sources Of Variation ◽  
Significant Factors

Sources of variation for growth, for backfat and for a phenotypic index were studied in Quebec purebred Yorkshire, Landrace, Hampshire and Duroc pigs. A total of 94,161 records of backfat measures and of age and weight at the end of test, accumulated between 1977 and 1985, was used in this work. Weight at the end of test was found to be an important source of variation, so growth data were adjusted to 90 kg by linear regression (R2 between 31 and 52%). Gender, parity number, year-season, birth years of parents and herd were also significant factors. Therefore, they will be included as fixed effects in the mixed model to be used to evaluate heritabilities and genetic correlations. Key words: Pig, variation, environmental factors, home test

scholarly journals Estimates of genetic parameters for growth traits of local Hamra breed threatened with extinction in southwestern arid rangeland of Algeria

2018 ◽  
Vol 34 (4) ◽  
pp. 433-441
Author(s):  
Houari Yerou ◽  
Hakim Ouldouelhadj ◽  
Abdelrahmane Berber ◽  
Amine Mokadem ◽  
Bachir Koudad
Keyword(s):  
Animal Models ◽  
Data Base ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Phenotypic Correlations ◽  
Body Weights ◽  
Covariance Components ◽  
Variance And Covariance Components

The data base for body weights and growths of 411 Hamra lambs were used to estimate genetic parameters. These lambs were obtained from 31 rams and 493 ewes between 2012 and 2017. Traits analysed were weights at birth (BW), 30 days (W30), 90 days (W90) of age, and average daily gains from 10 to 30 (ADG10-30) and30-90 (ADG30-90) days. REML estimates of variance and covariance components were obtained assuming animal models that included the fixed effects for year-type of birth (2012 single, 2012 twin, . ., 2017 single, 2017 twin), sex (male, female), and ewes age at lambing (< 18, 18-30, 30-42, 42-54, >54 months). Heritabilities were 0.12, 0.06 and 0.11 respectively for BW, W3Oand W90and the average daily gains were 0.05 and 0.17 for ADG0-30and ADG30-90. The estimates of genetic correlations showed no genetic antagonisms among the growth traits. The genetic correlations estimated were positive and medium to high, except those between ADG30-90 and ADG0-30 and between ADG30-90 and W30 which were slightly negative. Phenotypic correlations were positive and ranged from 0.12 to 0.85. They were high between adjacent weights and between ADG and their corresponding weights.

PRETEST HERD EFFECTS ON STATION PERFORMANCE TEST

1986 ◽  
Vol 66 (4) ◽  
pp. 925-935 ◽  
Author(s):  
A. K. W. TONG ◽  
J. A. NEWMAN ◽  
G. W. RAHNEFELD
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Performance Test ◽  
Average Daily Gain ◽  
Research Station ◽  
Environmental Correlations ◽  
Warm Up ◽  
Covariance Components ◽  
Variance And Covariance Components ◽  
Daily Gain

Effects of herd of origin and the relationships between pretest and station test environments were examined from pre- and postweaning performance records of 1675 crossbred male and female calves of Hereford, Angus and Shorthorn dams, sired by 20 Charolais, 12 Simmental and 15 Limousin sires. The calves were born during 1969–1972 and raised to weaning in 45 herds from Manitoba, Saskatchewan and Alberta, and were then transported to the Brandon or the Lacombe Research Station for a 140-d postweaning performance test. Fixed effects and variance and covariance components were estimated from a mixed model which included fixed effects of herd-year, sex of calf and breed of sire and random sire effects. Herd-year effects were significant (P < 0.01) for all performance traits studied. Herd-year mean squares expressed as a percentage of the corrected total sum of squares accounted for 25.81% in the pretest period to a maximum of 54.26% in the warm-up period and then decreased to 6.78% in the 140-d period. Herd-year differences were relatively small in the later part of the test. Correlations of herd-year solutions ranged from −0.10 to 0.05 and of environments ranged from −0.09 to 0.02 between pretest average daily gain (ADG) and ADG at various test intervals. The low environmental correlations suggest that the performance of calves centrally tested at the Brandon and Lacombe Research Stations were not affected by the pre-test herd environment. Key words: Beef cattle, station test, pretest herd effects, environmental correlation

Estimates of genetic parameters for milk and fat yield for the first three lactations in British Friesian cows

1984 ◽  
Vol 38 (3) ◽  
pp. 313-322 ◽  
Author(s):  
Karin Meyer
Keyword(s):  
Milk Yield ◽  
Fixed Effects ◽  
Mixed Model ◽  
Genetic Correlations ◽  
Data Sets ◽  
Lactation Length ◽  
Maximum Likelihood Procedure ◽  
Fat Yield ◽  
Variance And Covariance Components ◽  
Estimate Variance

ABSTRACTA multivariate restricted maximum-likelihood procedure was used to estimate variance and covariance components between and within sires. This method, which considered all lactations simultaneously, accounted for the bias in later lactation records due to selection on dairy performance. Analysis was carried out for a mixed model with herd-year-seasons as fixed and sires as random effects, and fitting lactation length, calving age and month within season of calving as covariables. The data included 26 176 first, 19 978 second and 14 868 third lactation records for 679 test sires, and were analysed in 13 subsets. Additional records for proven sires, treated as fixed effects, were included to improve the data structure.Estimates for all components were higher in later than in earlier data sets, probably, to a large extent, because of a scale effect. Pooled estimates of heritabilities for lactations 1 to 3 were 0·28, 0·19 and 0·24 for milk yield, and 0·27, 0·21 and 0·25 for fat yield. Genetic correlations of 0·91, 0·91 and 0·95 for milk yield, and 0·91, 0·91 and 0·99 for fat yield, were found between lactations 1 and 2, 1 and 3, and 2 and 3, respectively. Results suggest that dairy performance in all lactations is almost identical genetically.

Genetic evaluation of crossbred lamb production. 4. Genetic parameters for first-cross animal performance

2007 ◽  
Vol 58 (8) ◽  
pp. 839 ◽  
Author(s):  
V. M. Ingham ◽  
N. M. Fogarty ◽  
A. R. Gilmour ◽  
R. A. Afolayan ◽  
L. J. Cummins ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Meat Quality ◽  
Mixed Models ◽  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Fibre Diameter ◽  
Quality Traits ◽  
Meat Quality Traits ◽  
Lamb Growth

The study estimated heritability for lamb growth and carcass performance, hogget ewe wool production, and worm egg count among crossbred progeny of maternal breed sires, as well as the genetic and phenotypic correlations among the traits. The data were from crossbred progeny of 91 sires from maternal breeds including Border Leicester, East Friesian, Finnsheep, Coopworth, White Suffolk, Corriedale, and Booroola Leicester. The sires were mated to Merino ewes at 3 sites over 3 years (and also Corriedale ewes at one site), with 3 common sires used at each site and year to provide genetic links. These sheep comprised part of the national maternal sire central progeny test program (MCPT) to evaluate the genetic variation for economically important production traits in progeny of maternal and dual-purpose (meat and wool) sires and the scope for genetic improvement. The matings resulted in 7846 first-cross lambs born, with 2964 wether lambs slaughtered at an average age of 214 days, and wool data from 2795 hogget ewes. Data were analysed using univariate mixed models containing fixed effects for site, year, sex and type of birth and rearing, dam source and sire breed, and random terms for sire and dam effects. Heritabilities and genetic correlations were estimated based on variances from progeny of 70 sires by fitting the same mixed models using a REML procedure in univariate and multivariate analyses. Estimates of heritability were low for lamb growth traits (0.07–0.29), meat colour and meat pH (0.10–0.23), and faecal worm egg count (0.10), moderate for carcass fat and muscle traits (0.32–0.47), and moderate to high for wool traits (0.36–0.55). Estimates of direct genetic correlations among liveweights at various ages were high and positive (0.41–0.77) and those between liveweights and most carcass and meat quality traits were small and varied in sign. Liveweights were moderately to highly positively correlated with most wool traits, except fibre diameter (–0.28–0.08). The study indicates that there is genetic variation for wool, growth, carcass, and meat quality traits, as well as for faecal worm egg count, with scope for selection within Australian maternal sire breeds of sheep.

scholarly journals Genetic parameters of meat quality, carcass composition, and growth traits in commercial swine

2019 ◽  
Vol 97 (9) ◽  
pp. 3669-3683 ◽  
Author(s):  
Piush Khanal ◽  
Christian Maltecca ◽  
Clint Schwab ◽  
Kent Gray ◽  
Francesco Tiezzi
Keyword(s):  
Meat Quality ◽  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Carcass Composition ◽  
Additive Genetic Effect ◽  
Yield Traits ◽  
Data Set ◽  
Crossbred Pigs

Abstract Swine industry breeding goals are mostly directed towards meat quality and carcass traits due to their high economic value. Yet, studies on meat quality and carcass traits including both phenotypic and genotypic information remain limited, particularly in commercial crossbred swine. The objectives of this study were to estimate the heritabilities for different carcass composition traits and meat quality traits and to estimate the genetic and phenotypic correlations between meat quality, carcass composition, and growth traits in 2 large commercial swine populations: The Maschhoffs LLC (TML) and Smithfield Premium Genetics (SPG), using genotypes and phenotypes data. The TML data set consists of 1,254 crossbred pigs genotyped with 60K SNP chip and phenotyped for meat quality, carcass composition, and growth traits. The SPG population included over 35,000 crossbred pigs phenotyped for meat quality, carcass composition, and growth traits. For TML data sets, the model included fixed effects of dam line, contemporary group (CG), gender, as well as random additive genetic effect and pen nested within CG. For the SPG data set, fixed effects included parity, gender, and CG, as well as random additive genetic effect and harvest group. Analyses were conducted using BLUPF90 suite of programs. Univariate and bivariate analyses were implemented to estimate heritabilities and correlations among traits. Primal yield traits were uniquely created in this study. Heritabilities [high posterior density interval] of meat quality traits ranged from 0.08 [0.03, 0.16] for pH and 0.08 [0.03, 0.1] for Minolta b* to 0.27 [0.22, 0.32] for marbling score, except intramuscular fat with the highest estimate of 0.52 [0.40, 0.62]. Heritabilities of primal yield traits were higher than that of primal weight traits and ranged from 0.17 [0.13, 0.25] for butt yield to 0.45 [0.36, 0.55] for ham yield. The genetic correlations of meat quality and carcass composition traits with growth traits ranged from moderate to high in both directions. High genetic correlations were observed for male and female for all traits except pH. The genetic parameter estimates of this study indicate that a multitrait approach should be considered for selection programs aimed at meat quality and carcass composition in commercial swine populations.

scholarly journals 209 Genomic selection for multiple maternal and growth traits in large white pigs using Single-Step GBLUP

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 42-42
Author(s):  
Breno Fragomeni ◽  
Zulma Vitezica ◽  
Justine Liu ◽  
Yijian Huang ◽  
Kent Gray ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Growth Traits ◽  
Computing Time ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Single Step ◽  
Genomic Information ◽  
Genomic Evaluation ◽  
Large White ◽  
Daily Gain

Abstract The objective of this study was to implement a multi-trait genomic evaluation for maternal and growth traits in a swine population. Phenotypes for preweaning mortality, litter size, weaning weight, and average daily gain were available for 282K Large White pigs. The pedigree included 314k individuals, of which 35,731 were genotyped for 45K SNPs. Variance components were estimated in a multi-trait animal model without genomic information by AIREMLF90. Genomic breeding values were estimated using the genomic information by single-step GBLUP. The algorithm for proven and young (APY) was used to reduce computing time. Genetic correlation between proportion and the total number of preweaning deaths was 0.95. A strong, positive genetic correlation was also observed between weaning weight and average daily gain (r = 0.94). Conversely, the genetic correlations between mortality and growth traits were negative, with an average of -0.7. To avoid computations by expensive threshold models, preweaning mortality was transformed from a binary trait to two linear dam traits: proportion and a total number of piglets dead before weaning. Because of the high genetic correlations within groups of traits, inclusion of only one growth and one mortality trait in the model decreases computing time and allows for the inclusion of other traits. Reduction in computing time for the evaluation using APY was up to 20x, and no differences in EPD ranking were observed. The algorithm for proven and young improves the efficiency of genomic evaluation in swine without harming the quality of predictions. For this population, a binary trait of mortality can be replaced by a linear trait of the dam, resulting in a similar ranking for the selection candidates.

Insulin-like growth factor-I measured in juvenile pigs is genetically correlated with economically important performance traits

2005 ◽  
Vol 45 (8) ◽  
pp. 783 ◽  
Author(s):  
K. L. Bunter ◽  
S. Hermesch ◽  
B. G. Luxford ◽  
H-U. Graser ◽  
R. E. Crump
Keyword(s):  
Growth Factor ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Selection Criterion ◽  
Insulin Like Growth Factor ◽  
Factor I ◽  
Igf I ◽  
Performance Traits ◽  
Growth Factor I ◽  
Daily Gain

Insulin-like growth factor-I (IGF-I) is a naturally occurring polypeptide produced in the liver, muscle and fat tissues. It is known to be associated with growth and development during the postnatal growth period. Evidence for strong genetic correlations between juvenile IGF-I and performance traits would suggest this physiological measure would be useful as an early selection criterion. This paper reports estimates of genetic parameters from 9 trials where IGF-I was measured in juvenile pigs. All trials involved populations undergoing active selection for improved performance (e.g. efficient lean meat growth). Juvenile IGF-I was moderately heritable (average h2: 0.31) and influenced by common litter effects (average c2: 0.15). Genetic correlations (rg) between juvenile IGF-I and backfat (BF), feed intake (FI) or feed conversion ratio (FCR) traits were generally large and positive: rg averaged 0.57, 0.41 and 0.65, respectively. Phenotypic correlations (rp) between juvenile IGF-I and BF, FI or FCR were much lower (rp averaged 0.21, 0.09, and 0.15, respectively) as residual correlations between IGF-I and these performance traits were low, consistent with being measured at very different times. Correlations (genetic or phenotypic) between juvenile IGF-I and growth traits (e.g. lifetime daily gain or test daily gain) were relatively low, with average values within ± 0.09 of zero. Results from the trials reported here, and several physiological studies, indicate that information on juvenile IGF-I concentration can be used as an early physiological indicator of performance traits traditionally measured later in life. There is a clear role for juvenile IGF-I to facilitate pre-selection and more accurate selection of livestock for hard to measure traits, such as FCR, in pig breeding programs.

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.

Misspecification of the random effect structure: Implications for the linear mixed model

2020 ◽  
Author(s):  
Brandon LeBeau
Keyword(s):  
Fixed Effects ◽  
Serial Correlation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Type I Error ◽  
Random Effect ◽  
Correlated Data ◽  
Unbiased Estimation ◽  
Type I ◽  
Nested Data

<p>The linear mixed model is a commonly used model for longitudinal or nested data due to its ability to account for the dependency of nested data. Researchers typically rely on the random effects to adequately account for the dependency due to correlated data, however serial correlation can also be used. If the random effect structure is misspecified (perhaps due to convergence problems), can the addition of serial correlation overcome this misspecification and allow for unbiased estimation and accurate inferences? This study explored this question with a simulation. Simulation results show that the fixed effects are unbiased, however inflation of the empirical type I error rate occurs when a random effect is missing from the model. Implications for applied researchers are discussed.</p>

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