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PubMed1990

Estimates of genetic parameters in turkeys. 3. Sexual dimorphism and its implications in selection procedures.

Live, carcass, and skeletal data taken at 16 wk of age on 504 female and 584 male turkeys from 34 sires and 168 dams were utilized to evaluate sex differences in genetic parameter estimates. Data were transformed to common mean and variance to evaluate possible scaling effects. Genetic parameters were estimated from transformed and untransformed data. Further analyses were conducted with a model that included sire by sex and dams within sire by sex interactions, and the variance estimates were used to calculate genetic correlations between the sexes and genetic regression parameters. Heritability estimates from transformed and untransformed data were similar, indicating that sex differences were present in the genetic parameters, but scaling effects were not an important factor. Genetic correlation estimates from paternal (PHS) and maternal (MHS) half-sib estimates were close to unity for BW (1.14, PHS; 1.09, MHS), shank width (.99, PHS; .93, MHS), breast muscle weight (1.23, PHS; 1.04, MHS), and shank length (1.09, PHS; .97, MHS). However, abdominal fat (.79, PHS; .59 MHS), total drumstick muscle weight (.75, PHS; 1.14, MHS), rough cleaned shank weight (.78, PHS; not estimatable, MHS), and shank bone density (1.00, PHS; .53, MHS) estimates were somewhat lower. The estimates suggest that the measurement of these latter "traits" at the same age in the two sexes may, in fact, be measuring different genetic effects and that selection procedures in turkeys need to take these correlations into account in order to make optimum progress. The genetic regression parameters indicated that more intense selection in the sex that has the smaller genetic variation could be practiced to make greater gains in the opposite sex.

Analysis of Variance↗

PubMed2005

Genetic parameters for carcass composition and pork quality estimated in a commercial production chain.

Breeding goals in pigs are subject to change and are directed much more toward retail carcass yield and meat quality because of the high economic value of these traits. The objective of this study was to estimate genetic parameters of growth, carcass, and meat quality traits. Carcass components included ham and loin weights as primal cuts, which were further dissected into boneless subprimal cuts. Meat quality traits included pH, drip loss, purge, firmness, and color and marbling of both ham and loin. Phenotypic measurements were collected on a commercial crossbred pig population (n = 1,855). Genetic parameters were estimated using REML procedures applied to a bivariate animal model. Heritability estimates for carcass traits varied from 0.29 to 0.51, with 0.39 and 0.51 for the boneless subprimals of ham and loin, respectively. Heritability estimates for meat quality traits ranged from 0.08 to 0.28, with low estimates for the water holding capacity traits and higher values for the color traits: Minolta b*(0.14), L* (0.15), a* (0.24), and Japanese color scale (0.25). Heritability estimates differed for marbling of ham (0.14) and loin (0.31). Neither backfat nor ADG was correlated with loin depth (r(g) = 0.0), and their mutual genetic correlation was 0.27. Loin primal was moderately correlated with ham primal (r(g) = 0.31) and more strongly correlated with boneless ham (r(g) = 0.58). Backfat was negatively correlated with (sub)primal cut values. Average daily gain was unfavorably correlated with subprimals and with most meat quality characteristics measured. Genetic correlations among the color measurements and water-holding capacity traits were high (average r(g) = 0.70), except for Minolta a* (average r(g) = 0.17). The estimated genetic parameters indicate that meat quality and valuable cut yields can be improved by genetic selection. The estimated genetic parameters make it possible to predict the response to selection on performance, carcass, and meat quality traits and to design an effective breeding strategy fitting pricing systems based on retail carcass and quality characteristics.

Adipose Tissue↗

PubMedDate not supplied

Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects.

Simulations were used to study the influence of model adequacy and data structure on the estimation of genetic parameters for traits governed by direct and maternal effects. To test model adequacy, several data sets were simulated according to different underlying genetic assumptions and analysed by comparing the correct and incorrect models. Results showed that omission of one of the random effects leads to an incorrect decomposition of the other components. If maternal genetic effects exist but are neglected, direct heritability is overestimated, and sometimes more than double. The bias depends on the value of the genetic correlation between direct and maternal effects. To study the influence of data structure on the estimation of genetic parameters, several populations were simulated, with different degrees of known paternity and different levels of genetic connectedness between flocks. Results showed that the lack of connectedness affects estimates when flocks have different genetic means because no distinction can be made between genetic and environmental differences between flocks. In this case, direct and maternal heritabilities are under-estimated, whereas maternal environmental effects are overestimated. The insufficiency of pedigree leads to biased estimates of genetic parameters.

Animals↗

PubMed2002

Comparison of models for estimation of genetic parameters for mature weight of Hereford cattle.

Genetic parameters of mature weight are needed for effective selection and genetic evaluation. Data for estimating these parameters were collected from 1963 to 1985 and consisted of 32,018 mature weight records of 4,175 Hereford cows that were in one control and three selection lines that had been selected for weaning weight, for yearling weight, or for an index combining yearling weight and muscle score for 22 yr. Several models and subsets of the data were considered. The mature weight records consisted of a maximum of three seasonal weights taken each year, at brand clipping (February and March), before breeding (May and June), and at palpation (August and September). Heritability estimates were high (0.49 to 0.86) for all models considered, which suggests that selection to change mature weight could be effective. The model that best fit the data included maternal genetic and maternal permanent environmental effects in addition to direct genetic and direct permanent environmental effects. Estimates of direct heritability with this model ranged from 0.53 to 0.79, estimates of maternal heritability ranged from 0.09 to 0.21, and estimates of the genetic correlation between direct and maternal effects ranged from -0.16 to -0.67 for subsets of the data based on time of year that mature weight was measured. For the same subsets, estimates of the proportions of variance due to direct permanent environment and maternal permanent environment ranged from 0.00 to 0.09 and 0.00 to 0.06, respectively. Using a similar model that combined all records and included an added fixed effect of season of measurement of mature weight, direct heritability, maternal heritability, genetic correlation between direct and maternal effects, proportion of variance due to direct permanent environmental effects, and proportion of variance due to maternal permanent environmental effects were estimated to be 0.69, 0.13, -0.65, 0.00, and 0.04, respectively. Mature weight is a highly heritable trait that could be included in selection programs and maternal effects should not be ignored when analyzing mature weight data.

Animals↗

PubMed2005

Estimation of quantitative genetic parameters.

This paper gives a short review of the development of genetic parameter estimation over the last 40 years. This shows the development of more statistically and computationally efficient methods that allow the fitting of more biologically appropriate models. Methods have evolved from direct methods based on covariances between relatives to methods based on individual animal models. Maximum-likelihood methods have a natural interpretation in terms of best linear unbiased predictors. Improvements in iterative schemes to give estimates are discussed. As an example, a recent estimation of genetic parameters for a British population of dairy cattle is discussed. The development makes a connection to relevant work by Bill Hill.

Analysis of Variance↗

PubMed1988

The ascertainment sampling problem and estimation of genetic parameters when parental haplotypes are known.

Comparisons of bias and standard errors of genetic parameter estimates arising from various estimation procedures are made by using contrived genetic data in which parental haplotypes are known in addition to the haplotypes of affected sibs. These comparisons show that knowledge of parental haplotypes can reduce standard errors of genetic parameter estimates by between 10% and 45% as compared to estimates when parental information is not available. The added information does not appear to change the standard errors of ascertainment-assumption-free estimates relative to those from classical methods. This implies that the decision to accept possible bias arising from a classical estimation method or to accept slightly increased standard errors under an ascertainment-assumption-free approach is comparable to that involved when parental haplotypes are unknown.

Biometry↗

PubMed1988

Genetic parameters for fertility of dairy bulls.

Genetic parameters for male fertility and fertility ratings of AI bulls were obtained by analyzing 298,013 service records of cows with successive calving records. Cows were mated to 746 service bulls, which were progeny of 126 sires. The model for variance component estimation accounted for fixed effects of herd-year-seasons, sire of the service bull, age of mates, and random effects of service bull and residual error. Estimates of variances of service bulls and residual error components for bull fertility indicated almost 10% of the phenotypic variation for fertility is among AI bulls. Best linear unbiased prediction of fertility ratings of individual bulls with inclusion of sire and maternal grandsire relationships on these data permitted the evaluation of 886 AI bulls for bull fertility. Heritability for bull fertility computed as twice the regression of son on sire was .158. Differences in fertility ratings of AI bulls ranged from -.29 to .19. Prediction of fertility of young AI bulls and more accurate rating of proven bulls might be useful to the industry.

Animals↗

PubMed2000

Estimating quantitative genetic parameters using sibships reconstructed from marker data.

Previous techniques for estimating quantitative genetic parameters, such as heritability in populations where exact relationships are unknown but are instead inferred from marker genotypes, have used data from individuals on a pairwise level only. At this level, families are weighted according to the number of pairs within which each family appears, hence by size rather than information content, and information from multiple relationships is lost. Estimates of parameters are therefore not the most efficient achievable. Here, Markov chain Monte Carlo techniques have been used to partition the population into complete sibships, including, if known, prior knowledge of the distribution of family sizes. These pedigrees have then been used with restricted maximum likelihood under an animal model to estimate quantitative genetic parameters. Simulations to compare the properties of parameter estimates with those of existing techniques indicate that the use of sibship reconstruction is superior to earlier methods, having lower mean square errors and showing nonsignificant downward bias. In addition, sibship reconstruction allows the estimation of population allele frequencies that account for the relationships within the sample, so prior knowledge of allele frequencies need not be assumed. Extensions to these techniques allow reconstruction of half sibships when some or all of the maternal genotypes are known.

Algorithms↗

PubMed1998

Estimation of genetic parameters for scrotal circumference, age at puberty in heifers, and hip height in Brahman cattle.

Genetic parameters were estimated for scrotal circumference (SC; n=287), age at puberty in heifers (AP; n=292), and hip height in both sexes (HH; n=684) for Brahman cattle born from 1984 to 1994 at the Subtropical Agricultural Research Station, Brooksville, Florida. Age at puberty was defined as the age (days) at first detected ovulatory estrus. Measurements of SC and HH were taken at 18 mo of age. Fixed effects considered in the SC model were year of birth (YOB), age of dam (AOD), and age at measurement (AGE) as a linear covariate. Fixed effects fitted to the AP model were YOB and AOD. Fixed effects in the HH model were YOB, sex, AOD, and AGE as a linear covariate. Variances and covariances were estimated using REML with a derivative-free algorithm and fitting a multiple trait animal model. Estimates of heritability for SC, AP, and HH were .28, .42, and .65, respectively. Estimates of genetic correlations between SC and AP, SC and HH, and AP and HH were -.32, .19, and .25, respectively. Estimates of environmental correlations were .19 between SC and HH, and -.13 between AP and HH. Estimates of genetic parameters indicate a favorable genetic relationship between SC in Brahman bulls and AP in Brahman heifers under subtropical conditions. There was also evidence that selecting Brahman bulls for HH would not adversely affect SC but would have some detrimental effect on AP in female progeny.

Age Factors↗

PubMed1997

Nonlinear genetic relationships between traits and their implications on the estimation of genetic parameters.

To estimate nonlinear genetic relationships between traits, formulas based on the paternal halfsib analysis theory were derived. To illustrate the usefulness of the formulas, a series of data sets with halfsib structure for some preselected parameters and sample size situations were generated by means of Monte Carlo techniques. When a nonlinear relationship in the form of a polynomial relationship of degree two is present, the linear and quadratic regression coefficients can be estimated from a paternal halfsib analysis without bias. Some of the traditional linear genetic parameters need correction; however, their value is limited if the relationship between two traits is nonlinear. Although regression coefficients may be estimated appropriately in many situations, the application of the described method is restricted to situations in which the causal flow between the traits involved is clear and the heritability of the determining trait is larger than approximately .10. Further work should be directed to investigation of possibilities for including such parameters in selection decisions in a formalized way.

Analysis of Variance↗

PubMed2006

Genetic parameters for functional traits in dairy cattle from daily random regression models.

The objective of the research was to estimate genetic parameters, such as heritabilities and genetic correlations, using daily test day data for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) using random regression methodology. Data were from first lactation dairy cows (n = 320) from the Chamau research farm of the Swiss Federal Institute of Technology, Switzerland over the period from April 1994 to 2004. All traits were recorded daily using automated machines. Estimated heritabilities (h(2)) varied from 0.18 to 0.30 (mean h(2) = 0.24) for MY, 0.003 to 0.098 (mean h(2) = 0.03) for MS, 0.22 to 0.53 (mean h(2) = 0.43) for BW, and 0.12 to 0.34 (mean h(2) = 0.23) for DMI. A permanent environmental effect was included in both the univariate and bivariate models, but was assumed constant in estimating some genetic correlations because of convergence problems. Estimated genetic correlations varied from 0.31 to 0.41 between MY and MS, from -0.47 to 0.29 between MY and DMI, from -0.60 to 0.54 between MY and BW, from 0.17 to 0.26 between MS and DMI, from -0.18 to 0.25 between MS and BW, and from -0.89 to 0.29 between DMI and BW. Genetic correlations for MY, MS, DMI, and BW from calving to midlactation decreased similarly to 0.40, 0.36, 0.14, and 0.36 and, at the end of the lactation, decreased to -0.06, 0.23, -0.07, and 0.09, respectively. Daily genetic variance-covariance of many functional traits are reported for the first time and will be useful when constructing selection indexes for more than one trait based on longitudinal genetic parameters.

Animals↗

PubMed1987

[Interpretation of various ratios of genetic parameters in Hayman's diallelic analysis].

The genetical algebra of diallele analysis was used to interpret some relations of genetic parameters which were not earlier applied in research of genetic control (GC). It was discovered that in the case of gene correlation, the relation H2 greater than H1 is possible, meaning the regression among frequencies of alleles in loci. We demonstrated, in what way to distinguish two reasons for changes in H1, H2 and h2, when an external treatment is applied: Due to frequency changes of dominant and recessive alleles in the majority of loci, in case new loci are involved in GC; Due to changes in dominance contributions. The latter could provide new information about a situation in loci by analysing the changes in h2 and the correlation coefficient of parental trait levels with their dominant properties as well as changes in D. A few experimental examples illustrate the theory implication.

Alleles↗

PubMed1999

Genetic parameters of lactation cell counts and milk and protein yields in dairy ewes.

A total of 3231 lactation records of somatic cell counts (SCC), milk yield, and protein percentage for 2379 Spanish Churra ewes from 10 flocks were used to estimate genetic and environmental parameters. Genetic parameters were estimated by REML with a multitrait repeatability animal model. A lactation measure of SCC was obtained as the mean of test day log SCC adjusted for stage of lactation. Heritabilities for SCC, milk yield, and protein percentage were 0.12, 0.24, and 0.17, respectively. The corresponding repeatabilities were 0.35, 0.49, and 0.38. Heritability and repeatability estimates of SCC obtained from this study fell within the range frequently reported for dairy cows. Therefore, as practiced for dairy cattle, future possibilities for sire evaluation to improve udder health status using lactation measures of SCC for dairy sheep are not rejected, although hygienic practices are regarded as more important. Genetic correlations of SCC with milk yield and protein percentage were -0.15 and -0.03, respectively. The genetic correlation between milk yield and protein percentage was -0.47. The low genetic correlations of SCC with milk yield and protein percentage may indicate that breeding decisions to improve milk and protein yields of Churra ewes are not expected to have an effective correlated response in SCC.

Analysis of Variance↗

PubMed1992

Phenotypic plasticity for life-history traits in Drosophila melanogaster. III. Effect of the environment on genetic parameters.

We estimated genetic and environmental variance components for developmental time and dry weight at eclosion in Drosophila melanogaster raised in ten different environments (all combinations of 22, 25 and 28 degrees C and 0.5, 1 and 4% yeast concentration, and 0.25% yeast at 25 degrees C). We used six homozygous lines derived from a natural population for complete diallel crosses in each environment. Additive genetic variances were consistently low for both traits (h2 around 10%). The additive genetic variance of developmental time was larger at lower yeast concentrations, but the heritability did not increase because other components were also larger. The additive genetic effects of the six parental lines changed ranks across environments, suggesting a mechanism for the maintenance of genetic variation in heterogenous environments. The variance due to non-directional dominance was small in most environments. However, there was directional dominance in the form of inbreeding depression for both traits. It was pronounced at high yeast levels and temperatures but disappeared when yeast or temperature were decreased. This meant that the heterozygous flies were more sensitive to environmental differences than homozygous flies. Because dominance effects are not heritable, this suggests that the evolution of plasticity can be constrained when dominance effects are important as a mechanism for plasticity.

Adaptation, Physiological↗

PubMed2000

Genetic parameters among weight, prolificacy, and wool traits of Columbia, Polypay, Rambouillet, and Targhee sheep.

Genetic parameters for Columbia, Polypay, Rambouillet, and Targhee sheep were estimated using REML with animal models for prolificacy, weight, and wool traits. All bivariate analyses included a covariance between additive genetic effects for the two traits plus appropriate additional covariances. Number of observations by breed ranged from 5,140 to 7,095 for prolificacy traits, from 7,750 to 9,530 for weight traits, and from 4,603 to 34,746 for wool traits. Heritability estimates ranged from .03 to .11 for prolificacy traits (litter size at birth and litter size at weaning), from .09 to .26 for weight traits (birth weight and average daily gain), and from .25 to .53 for wool traits (fleece weight, fleece grade and staple length). Estimates of direct genetic correlations among prolificacy and among weight traits were positive and ranged from .58 to 1.00 and .18 to 1.00, respectively. Estimates of direct genetic correlation between fleece weight and staple length were positive (.50 to .70) but were negative between fleece weight and fleece grade (-.60 to -.34) and between staple length and fleece grade (-.72 and -.40). Prolificacy and wool traits were essentially uncorrelated. Weight and prolificacy traits were slightly positively correlated. Weight traits had a moderate positive direct genetic correlation with fleece weight and staple length, but were uncorrelated with fleece grade. These estimates of genetic parameters between prolificacy, weight, and wool traits can be used to construct multiple-trait selection indexes for dual-purpose sheep.

Age Factors↗

PubMed2003

Genetic parameters for feet and leg traits evaluated in different environments.

The objective of this study was to test for genotype x environmental interaction (GXE) for feet and leg traits scored in different environments. Genetic correlations of seven feet and leg traits were estimated across different management systems: free versus tie stalls, slatted versus solid flooring, and intact versus trimmed hooves. Data were records from first-lactation Holstein cattle. Traits were claw uniformity, depth of heel, rear leg rear view, foot angle, bone quality, rear leg side view, and overall feet and legs. Different subsets of data were used for each comparison, resulting in 147,400; 53,550; and 145,160 records for housing, flooring, and hoof trimming management systems, respectively. Genetic parameters were estimated using REML and two-trait models in which for each animal a given trait was observed in one environment and missing in the other. Phenotypic scores were lower with tie stalls, slatted floors, and no trimming. Heritabilities tended to be greater in herds with tie stalls and slatted floors. Trimming had little effect on genetic parameters. The genetic correlations of feet and leg traits across pairs of management systems were > or = 0.85, except for rear legs, rear view. Therefore, effects of GXE were assumed to be of little importance and modification of genetic evaluation procedures on the basis of housing, flooring, and hoof conditions seems unnecessary.

Animal Husbandry↗

PubMed1981

Some genetic parameters involving poststress performance traits in chickens.

Genetic parameters of several economic traits associated with an 18 week period of poststress egg production were estimated in a randombred population of 1800 White Leghorn hens. Genetic correlations among 6 of 11 traits associated with poststress egg production were different (P less than .05) between the control and stress groups. Earlier maturing birds recovered from stress sooner than later maturing birds and had higher egg production. Early egg production (20 to 40 weeks of age) was a better indicator of poststress egg production (55 to 73 weeks of age) in the stress group than in the control group. Rate of lay from the 9th to the 18th week after stress was higher in the stress group, but overall poststress rate of lay was higher in the control group.

Animals↗

PubMed2004

[Using VCE4.0 package to estimate genetic parameters on growth traits in Landrace].

It is an accurate and quick methods to apply VCE4.0 to estimating genetic parameter. It makes full use of all information, analyzing selection and culling effects. The Genetic parameters for the age to 30kg(AGE30), age to 100kg (AGE100), and average daily gain from 30kg to 100kg (ADG) and backfat thickness at 100kg(probed,FAT) are estimated using VCE4.0 applied to REML with a multivariate individual animal model in Landrace pigs. Estimates of heritabilities for AGE30, AGE100,ADG and FAT are 0.207,0.396,0.304 and 0.493, respectively. Genetic correlations for FAT/ADG, FAT/AGE100, ADG/AGE100, ADG /AGE30 and AGE30/AGE100 are -0.343,0.180,-0.941,-0.48,and 0.745, respectively, its phenotypic correlations are -0.139,0.138,-0.82,-0.026, and 0.565, respectively. The common litter environment effects for AGE30,AGE100, ADG and FAT are 0.194,0.156,0.157 and 0.043, respectively.

Animals↗