Effects Of Genetic Group Research Articles

Effect of genetic groups on estimates of additive genetic variance.

This study examined the effect of genetic grouping on REML estimates of additive genetic variance with an animal model with selected base populations. A simulated population of 40 animals (20 males and 20 females) was followed under selection or random mating conditions for 10 generations. Each population was replicated 20 or 50 times. Genetic grouping reduced estimates of additive genetic variance in populations with selected base animals, whereas grouping had little effect on the estimate in unselected populations. The effect of genetic grouping varied according to the quantity and kind of information that was missing (percentage of deleted data and pattern of deletion). When genetic grouping was completely random, the estimates were unaffected. Because including genetic groups in the model for analysis affected the estimates of additive genetic variance, the question should be considered of what value or values for heritability should be used for genetic evaluation when grouping is used to account for prior selection.

Evaluation of Various Measures of and Factors Influencing Feed Efficiency of Dairy Cattle

Early part records for milk yield and feed consumption of 2230 first lactation purebred and crossbred dairy cows were analyzed to evaluate various measures of feed efficiency. Corrected milk yield was estimated by adjusting the second 8 wk of milk yield for differences in weight of TDN consumed during wk 9 to 16, percentage of TDN derived from concentrate, and BW·75. Corrected milk yield is an estimate assuming that cows are the same size and consume the same amount of feed. Hence, it represents an expression of feed efficiency. Net feed efficiency, gross efficiency, corrected milk yield, and wk 9 to 16 milk were analyzed simultaneously. Coefficient of determination for net efficiency (.51) and gross efficiency (.72) were lower than that of milk (.82), whereas corrected milk yield had a coefficient of determination similar to that of milk. Hence, the use of ratios to define feed efficiency was less accurate than using corrected milk yield. Effects of genetic groups, stations, season of freshening, year of freshening, and heterosis were similar for gross efficiency and corrected milk yield, but different from those for milk. Therefore, corrected milk yield performed the same function as feed efficiency with higher accuracy.

Correlated Responses in Reproduction Accompanying Selection for Milk Yield in Jerseys

Reproductive traits of heifers and primiparous cows from a long-term selection project were analyzed to determine correlated response to single-trait selection for milk yield. Data were from 1056 daughters (765 selection, 291 control) of 37 bulls (17 selection, 20 control). Traits in heifers were ages at first observed estrus and at first breeding, services to conception, interval from first service to conception, and length of first gestation. Traits in primiparous cows were ages at first calving and at first breeding after calving; services to conception; length of second gestation; and intervals from calving to first observed estrus, to first breeding, and to conception, from first service to conception, and from first to second calving. Analyses for services to conception in heifers and primiparous cows were categorical using models containing genetic group and generation. Analyses of other traits were by linear mixed models using fixed effects of genetic group, generation within group, and year-season of birth. Sires were assumed random and nested within genetic group. The mean square for sires within group was used to test for group differences. No significant differences were found between genetic groups in traits measured in heifers; however, the interval from first service to conception approached significance (control superior). In primiparous cows, differences between genetic groups were significant for the intervals of calving to first breeding and Calving to conception and for length of second gestation (control superior). For other traits, reproductive performance of the control was better but not significantly different from that of the selected group. Reproductive performance should be monitored during selection for high milk yield.

Correlated Response in Growth and Body Measurements Accompanying Selection for Milk Yield in Jerseys

Growth and body measurements from a long-term selection project were analyzed to determine correlated responses to single-trait selection for milk yield. Data were from 1056 daughters (765 selection, 291 control) of 37 bulls (17 selection, 20 control) and included BW and measures of heart girth, chest depth, wither height, and length from withers to pins and from withers to hooks taken at 6 mo, 15 mo, first calving, end of first lactation, and maturity. Other data were birth weight, change in measurements and weights from first calving to end of first lactation, monthly rate of gain from 1 to 13 mo of age, and age reaching breeding weight (250kg). Principal component scores were calculated from standardized measurements at each age. The first three principal components had meaning (size, length vs. girth, and height vs. girth). All analyses used linear mixed models with fixed effects of genetic group, generation within group, year-season of birth or calving, parity of dam, and birth status (multiple or single birth). Sires were assumed to be random and nested within genetic group. Mean squares for sires was used to test for group differences. Generation did not differ in any analysis and was removed from all models. Selection cows were heavier, larger in some measurements, and had greater overall size at 6 mo of age. Selection cows had greater monthly rate of gain and attained breeding weight at an earlier age. Genetic groups did not differ for any other measurement or weight. Control cows gained more weight and increased more in some measurements between first calving and end of first lactation. Selection for milk yield did not result in an undesirable correlated response in any growth or body measurement.

Prediction of breeding values for twinning rate and ovulation rate with a multiple trait, repeated records animal model

A genetic correlation near unity between ovulation rate in heifers and later twinning frequency led to consideration of using measures of ovulation rate in heifers for each estrous cycle, beginning at puberty, to increase accuracy of selection for twinning rate. An initial evaluation with a multiple trait animal model for predicting breeding values included six genetic groups: 1) selected Scandinavian bulls, 2) transfers from other populations at the Research Center to a twinning project, 3) early-purchased Holsteins, 4) late-purchased Holsteins, 5) early purchases-other breeds, and 6) late purchases-other breeds. For ovulation and twin measures, heritabilities of .100 and .070 and repeatabilities of .120 and .092 were assumed. Assumed phenotypic correlation between ovulation and twin measures of .08 was accounted for by genetic correlation of .89 and permanent environmental correlation of .19. The number of animals evaluated was 1,745; 6,912 estrous cycles were measured for ovulation rate on 840 heifers and 1,929 parturitions were observed for occurrence of twinning on 851 cows, of which 346 had ovulation rate measured as heifers. The remaining 400 animals were foundation animals that created relationships among those with records or were sires of animals with records. The Scandinavian genetic group effect was substantially greater than that of the others. Joint evaluations were compared to evaluations using only twinning measures. For animals with twin evaluations based only on parents but with ovulation rates measured, the multiple trait evaluation increased accuracy of evaluation from .62 (twin information only) to .81.(ABSTRACT TRUNCATED AT 250 WORDS)

Effect of heterosis on performance of mice across three environments.

Performance of six genetic groups of mice was studied in three different environments in order to determine the effect of heterosis on consistency of performance in varied environments. Designed as a 3 x 6 factorial, data were analyzed using least squares analyses of variance. Genetic group effects, environmental effects, genetic group x environment interaction, and heterosis were examined for 42-d weight, age and weight at vaginal opening, age and weight at puberty, ovulation rate (determined by total corpora lutea), number of implantations, and number of fetuses at 10 d of gestation. Weight at vaginal opening, ovulation rate, implantation rate and total number of fetuses exhibited significant heterosis. Regression of these traits against an environmental index (mean performance of all genetic groups over environments) provided an estimate of stability of performance. In general, genetic groups exhibiting heterosis expressed more consistent performance across environments than pure lines did. We conclude that a stability model could be used to aid in identification of lines with consistent performance for production traits in variable environments.

Absorption of equations for non-parents for an animal model with maternal effects and genetic groups

Rules for forming the mixed-model equations for the reduced animal model with all relationships and including maternal effects have been set out by Quaas and Pollak. They also have shown how to simplify the mixed-model equations when genetic group effects are included in the model with what has become known as the Q-P transformation. Westell has given rules for calculating the coefficients for the Q-P transformed equations that are associated with the inverse of the numerator relationship matrix and genetic group effects. Those rules can be extended to include maternal effects and genetic groups for maternal as well as direct effects. As with the rules of Quaas and Pollak for the equations for the reduced animal model, a similar set of rules can be obtained for the genetic groups model after the Q-P transformation. The rules are derived easily by examining the algebraic results of absorbing the direct and maternal breeding value equations for non-parents into the parent breeding value, group and fixed effects equations. These rules involve Westell's rules and the inverse elements of the genetic (co)variance matrix for direct and maternal additive genetic effects. The rules make calculation of breeding values for parents for models including direct and maternal genetic group effects nearly as easy as for models without genetic group effects. Back solution for direct and maternal breeding values of non-parents similarly is as simple as when genetic group effects are not in the model.

Genetic and non-genetic factors affecting pre-weaning survivability in kids

ABSTRACTThe present study was carried out on 1243 kids (379 Black Bengal; 568 Jamunapari × Black Bengal and 296 Beetal × Black Bengal) born between 1981 and 1986. Data were analysed to study the influence of genetic group, sex, season of birth, type of birth, birth weight, dam's weight at kidding, filial generation and parity of dam (as covariate) on survivability of kids during the pre-weaning period (0 to 90 days of age). The overall survival rates during 0 to 15, 16 to 30, 31 to 60, 61 to 90 and 0 to 90 days of age were 81·68 (s.e. 2·89), 95·55 (s.e. 2·00), 90·77 (s.e. 2·33), 93·31 (s.e. 2·59) and 69·94 (s.e. 3·65) % respectively. Variation in survival rates from 0 to 15 days of age was significant due to genetic group (P < 0·01), birth weight (P < 0·01), dam's weight at kidding and parity of dams as a covariate (P < 0·05). Season of birth (P < 0·05), weight of kids (P < 0·01) and dam's weight at kidding (P < 0·01) had significant effects on survival rate during 0 to 90 days of age. Sex and type of birth had no significant effect on survival rates. The interaction effects of genetic groups with season of birth and sex on pre-weaning survival rates were not significant in all ages except the effect between genetic group and sex during 31 to 60 days of age. An improvement in survival rates during the period of study was noticed with the increase in filial generation number from Fj to F3, although the difference was not significant except from 61 to 90 days of age (P < 0·05). Birth weight of kids had a positive linear relationship with survivability during the pre-weaning period. Summer-born kids had a significantly lower survival rate than those born in the winter and monsoon periods from 16 to 30, 61 to 90 and from 0 to 90 days of age. A negative linear relationship was observed between dam's weight at kidding and survival rates of kids from 0 to 90 days of age except the 25 to 30 kg dams deviate. Heritability estimates of survival rates during pre-weaning periods were not significantly different from zero, indicating that improvement by simple selection is probably limited.

Alternative animal models with maternal effects and foster dams

Effects of foster dams can be included in genetic evaluations using animal models with maternal effects in several ways. The alternatives discussed involve minor changes in computing strategies from strategies used with reduced animal models that predict breeding values for direct and maternal effects. The easiest alternative is to assign foster dams to groups by breed and time period and add equations for fixed effects of breed-period. Random and, assumed, independent effects of foster dams can be nested in breed-period groups. If foster dams do not repeat, then those effects can be absorbed into equations for other fixed effects, additive direct breeding value and breed-period effects by slightly modifying least squares contributions to coefficients of those equations. A third alternative for foster dams of the same breed is to add breeding values for foster dams for direct and maternal effects to solution vectors for breeding values. Equations are similar to those without foster dams, except that least squares contributions to coefficient matrix and right-hand sides are to equations for maternal breeding values and nongenetic maternal effects of foster dams rather than biological dams. Relationships and covariance between direct and maternal effects contribute mixed-model coefficients to direct and maternal breeding value equations of biological dams. This alternative basically requires only larger solution vectors for direct and maternal breeding values to accommodate foster dams that might not be included. The fourth alternative includes a vector of maternal breeding values for foster dams of each breed of foster dams and would require using rules of Westell to calculate coefficients due to relationships and fixed maternal genetic groups within each breed of foster dam. These alternatives do not require much additional computational effort compared with full or reduced animal model equations when the transformation to predict breeding values is used with Westell's rules to calculate coefficients due to relationships and genetic group effects due to prior genetic selection.

Relationships between genotype x environment interaction and genetic correlation of the same trait measured in different environments

Relationships between genotype x environment interaction and genetic correlation of the same trait measured in different fixed environments are derived by comparing the variance-covariance structures of observations between a one-way multiple-trait linear model and a two-way single-trait mixed linear model. In the latter model, heterogeneity of interaction variances among environments and non-zero covariances among interactions are assumed, in addition to the heterogeneity of error variances and non-zero covariances between genetic-group effects and interactions that were accommodated in earlier work. The results are applicable to more than two environments and to unbalanced data. This paper is a generalization and a correction of earlier works.

Influence of Nonadditive Effects on Estimation of Genetic Parameters in Dairy Cattle

A population was simulated having progeny that descended from sires and dams with various fractions of genes from two breeds. Additive breed effects and nonadditive effects from breed crosses were simulated.Data on performance were analyzed using mixed models that accounted for fixed additive genetic group effects and random sire effects. Three additive models, with genetic groups defined according to 1) breed composition of the progeny, 2) breed composition of the sire and the dam, or 3) linear regression on breed fraction of the progeny, were compared with a nonadditive model with a linear regression on breed fraction, heterozygosity and recombination in the genome of the progeny. Variance components were estimated using restricted maximum likelihood.Additive genetic variance and heritability were overestimated for an additive model with progeny groups. Additive models gave estimates for breed difference, group effects, and breeding values that were not equal to true additive genetic values. Breed differences were overestimated when sire groups were used. Estimators for each parameter were unbiased with the nonadditive model.

Estimation of milk consumption curve parameters for different genetic groups of bovine calves

ABSTRACTMilk consumption was estimated for crossbred suckled calves from birth to weaning during 2 years. Breed groups utilized were Aberdeen Angus (A) and Charolais (C) sires on Fl Angus-Hereford (and their reciprocals) dams in the 1st year and in the 2nd year, sires A and Nelore (Bos indicus) (N) were used on the same type of dam. The weigh-suckle-weigh technique was employed twice a day to estimate milk consumption on a weekly basis. Estimations were adjusted by least-squares for the effects of genetic group, week of estimation, and their interaction, with sex of calf and calf and cow weights in the week of estimation as covariables. Then a non-lineal regression equation of the form: y = b0Wb1e -b2b2W was fitted for each individual, where y = milk consumption and W = week of estimation. Curves for A and C were very close in their shape, but with a greater consumption (P < 0·01) for C. For the 2nd year, there were differences among A and N both in the shape of the curve and in consumption at the peak of lactation (P < 0·01). Differences in time to peak lactation were also found: for A calves, it was week 9, for C calves week 10 and for N calves week 3·8. The N group showed a sharper decline in persistency than the other groups, suggesting different patterns in milk consumption.

THE ACCUMULATIVE EFFECT OF FINNSHEEP BREEDING IN CROSSBREEDING SCHEMES: MARKET LAMB PRODUCTION FROM CROSSBRED EWES

Growth and carcass data were collected on 252 multiple-born lambs representing 11 genetic groups, fed either high-energy mainly concentrate ration or low-energy mainly roughage ration and slaughtered at either 32 or 41 kg liveweight. The lambs were the progeny of Suffolk rams mated to Suffolk, Finnsheep (F), DLS (1/2 Dorset, 1/4 Leicester, 1/4 Suffolk) ewes and ewes from seven F-DLS crosses ranging between 1/8F-7/8DLS (1/8F) to 7/8F-1/8DLS (7/8F) in addition to DLS × DLS lambs. The effect of genetic group was significant for gain to 32 kg, dressing percentage, leg, loin, shoulder and kidney fat percentage of the carcass, lean, fat and bone percentage of the 12th rib and fat thickness over the loin-eye muscle. A significant linear relationship was calculated between five of the measurements and the proportion of F breeding in the lambs' dam. Finnsheep ewes produced $6.39 worth of retailed lambs for each kilogram of their liveweight, compared to $3.64 for Suffolks and $3.28 for DLS. The advantage of the F crosses in retail value compared to Suffolk ranged from 47 (7/8F) to 11% (2/8F). Lambs fed concentrate ration and slaughtered at 41 kg liveweight were significantly fatter and larger than those fed roughages and slaughtered at 32 kg. Sex of the lamb had significant effect on all the traits studied except on color, area of loin-eye and carcass dimensions. Interactions between genotype and treatments were generally nonsignificant whereas sex × slaughter weight and sex × feeding treatment were significant on many traits studies. Key words: Finnsheep corsses, growth, carcass traits, DLS sheep, Suffolk, feeding treatment, slaughter weight

Animal Model Evaluation of Ayrshire Milk Yield with All Lactations, Herd-Sire Interaction, and Groups Based on Unknown Parents

An animal model was applied to predict genetic merit for Ayrshire milk yield. The model included fixed herd-year-season (32,287) and random herd-sire interaction (32,159), permanent environment, animal, and residual effects. Animals evaluated included 119,541 cows with 301,799 records, 5762 sires, and 11,893 dams without records. Genetic groups (36) were defined for unknown parents and parents not contributing ties or records. Groups were defined by sex of parent and by birth year and sex of animal with unknown parent. Evaluations included combinations of these group effects derived from tracing each path in pedigree back to an unknown parent group. Iteration was by Gauss-Seidel for herd-year-season, permanent environment, and herd-sire interaction effects and by second-order Jacobi for animal and genetic group effects. Iteration was conducted without forming mixed model equations; instead one copy of data sorted by herd and sire was read each round. About 3.5 s of central processing unit time on a Cray X-MP/48 was required per round for the complete model; herd-sire interaction contributed. 7 s and latgr lactations, 1.6 s. Large memory requirements were reduced by evaluating most animals in groups of herds including up to 2500 cows. Information for animals with progeny outside their herd group remained in memory throughout iteration. Genetic evaluations by an animal model that includes factors presently in the national evaluation system can be computed with present computer resources. Application to major dairy cattle breeds appears possible.

Animal Model Evaluation of Ayrshire Milk Yield with All Lactations, Herd-Sire Interaction, and Groups Based on Unknown Parents

An animal model was applied to predict genetic merit for Ayrshire milk yield. The model included fixed herd-year-season (32,287) and random herd-sire interaction (32,159), permanent environment, animal, and residual effects. Animals evaluated included 119,541 cows with 301,799 records, 5762 sires, and 11,893 dams without records. Genetic groups (36) were defined for unknown parents and parents not contributing ties or records. Groups were defined by sex of parent and by birth year and sex of animal with unknown parent. Evaluations included combinations of these group effects derived from tracing each path in pedigree back to an unknown parent group. Iteration was by Gauss-Seidel for herd-year-season, permanent environment, and herd-sire interaction effects and by second-order Jacobi for animal and genetic group effects. Iteration was conducted without forming mixed model equations; instead one copy of data sorted by herd and sire was read each round. About 3.5s of central processing unit time on a Cray X-MP/48 was required per round for the complete model; herd-sire interaction contributed .7s and later lactations, 1.6s. Large memory requirements were reduced by evaluating most animals in groups of herds including up to 2500 cows. Information for animals with progeny outside their herd group remained in memory throughout iteration. Genetic evaluations by an animal model that includes factors presently in the national evaluation system can be computed with present computer resources. Application to major dairy cattle breeds appears possible.

Supercomputer for Animal Model Evaluation of Ayrshire Milk Yield

Best linear unbiased predictions of additive genetic values were obtained from first lactation Ayrshire milk records (110,278) without forming mixed model equations. The model included fixed herd-year-season (20,322) and genetic group (12) effects and random cow and residual effects; sires (7641) were included through the relationship matrix. Group was determined by birth year and whether dam was evaluated. Herd-year-season solutions were obtained by Gauss-Seidel iteration; other solutions were by second-order Jacobi iteration. Only one copy of data sorted by herd-year-season was required. Iteration was on a Cray XMP-24 supercomputer. Relaxation factors from .3 to .9 were tested on 100 rounds of iteration with optimum .83. Iteration for 400 rounds produced solutions with five-digit accuracy. Accuracy of solutions from fewer rounds was determined by comparison with these solutions. About 1s of central processing unit time was required per round, which suggested this method may be practical for larger data sets. After 100 rounds, exponential extrapolation was tested over a range of relaxation factors and achieved accuracy that normally would occur 50 to 80 rounds later. With some relaxation factors, 125 to 150 rounds were required before exponential extrapolation was beneficial. Optimum relaxation factor and benefit from exponential extrapolation may differ for other data sets.

RELATIONSHIP OF SOMATIC CELL CONCENTRATION WITH MILK YIELD IN DAIRY COWS

The relationship between somatic cell count (SCC) and milk yield was studied using the data of 2181 composite milk samples collected once every 2 mo during 1982–1983 from 665 cows of the Ottawa herd of the National Cooperative Dairy Cattle Breeding Project. SCC was treated either as a continuous (log SCC) or a classification variable. The data, when SCC was treated as a continuous variable, were analyzed separately for the first and later lactations. The model contained the effects of genetic group, stage of lactation, season of calving, and log SCC as a covariable. As a classification variable SCC was partitioned into 10 classes and the data for the first and later lactation cows were combined. The model included the effects of genetic group, parity, stage of lactation, season of calving, and SCC classification. Lactation SCC was calculated as the average of actual individual SCC tests during the lactation. The model for the lactation milk yield included the effects of genetic group and parity, with log SCC and days in milk as covaribles within parity groups. The effects of genetic group, parity, stage of lactation, season of calving and SCC were significant (P < 0.01) for daily milk yield. Average daily milk yield loss was 0.5 kg and 0.7 kg for the first and later lactation cows, respectively, when the SCC increased from 200 × 103 to 400 × 103 cell mL−1. Lactation milk yield loss per unit increase in average log SCC was 74 kg in the first lactation and 88 kg for later lactations. These relationships were linear, suggesting that loss of milk yield per unit increase in SCC was greatest when SCC was low. Key words: Somatic cell count, milk yield

A note on the factors affecting pre-weaning growth in Beetal and Black Bengal kids and their crosses

The economic value of the goat depends upon its growth and reproductive and productive efficiency. The growth determines the meat producing ability up to a marketable age i.e. 6 months. Rapid growth during the pre-weaning period minimizes the cost of rearing, thus providing more profit to the farmer. The present investigation reports the effect of genetic groups, years and season of birth, sex of kid, type of birth and dam's weight at kidding on pre-weaning growth traits, and the extent of the genetic variability and covariability among these traits.

Comparison of Iterative Procedures for Solving Equations for Sire Evaluation

Gauss-Seidel, successive overrelaxation, end-of-round relaxation, and block iteration methods of obtaining solutions for sire effects from equations rising from progeny with records in mixed model procedures were compared. Equations transformed to provide direct solutions for genetic group plus sire effects as well as constrained and unconstrained equations were compared also. Equations for milk yield for the Northeast Artificial Insemination Sire Comparison numbered 301 for Ayrshires, 325 for Brown Swiss, 6,010 for Holsteins, and 926 for Jerseys after absorption of herd-year-season effects. Numbers of coefficients were 15 to 20% less for transformed equations, which decreased computing time per round of interation about 15%. Solutions for transformed equations converged more rapidly than solutions for untransformed equations with convergence criterion the ratio of the residual norm to the norm of the right-hand sides. Successive overrelaxation generally was more efficient than Gauss-Seidel iteraton. Solutions for equations constrained to full rank converged more slowly than unconstrained equations. Block iteration was more efficient than single equation iteration.

A COMPARISON OF MASTITIS DETECTION METHODS IN DAIRY CATTLE

The potential value of electrical conductivity, somatic cell count, and California Mastitis Test (CMT) Score in composite milk samples as a screening test for subclinical mastitis was evaluated. The effect of genetic group was significant (P < 0.05) for conductivity and somatic cell count while the effects of parity and month of lactation were significant (P < 0.01) for all the three traits studied. Somatic cell count and CMT score increased from first to fourth parity while there was general trend for increasing conductivity from second to fourth parity. All the three methods identified healthy cows with a good accuracy but failed to recognize a considerable proportion of infected cows. The error rate for misclassification was lowest for CMT score (9.1%), followed by somatic cell count (13.9%) and conductivity (29.4%). Composite strict foremilk samples from cows infected with primary pathogens had significantly (P < 0.01) higher conductivity than those from uninfected cows. Staphylococcus aureus were isolated in 60% of the milk samples and appeared to be in the major organism causing mastitis in this herd. Key words: Dairy cattle, mastitis detection methods