Animal model genetic evaluation of type traits for five dairy cattle breeds.
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Biomedical subjects
G R Wiggans
Publications and source records attributed to G R Wiggans.
Method R estimates of heritability for milk, fat, and protein yields of United States dairy cattle.
Heritabilities for milk, fat, and protein yields were estimated from first lactation data used for USDA-Dairy Herd Improvement Association (DHIA) genetic evaluations. Contemporary group assignments and standard deviations within herd-year were determined with the procedure used for national evaluations. Pedigree data were included for animals born since 1970; yield data were included for cows born since 1980. Lactation records were divided into four mutually exclusive data sets based on standard deviations. Ranges for standard deviations were chosen so that data sets were approximately equal in size. Method R was used to estimate heritability with 25 different random samples of half of the data for each data set. Because of the large number of Holstein observations, estimates of heritability for Holsteins were based on random subsets of the complete data file; each subset included approximately 5% of the data. Mean heritability estimates increased with standard deviations, and estimates ranged from 0.18 to 0.51 across breeds. Repeatability estimates for milk yield of Holsteins were approximately 0.50 and did not change with standard deviation. These heritability estimates were higher than those previously used in the USDA-DHIA genetic evaluation. Heritability used in the USDA-DHIA genetic evaluation have been increased based on these results.
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Equity of elite cow status across states.
Cows with superior genetic merit, based on an economic index for milk, fat, and yields (milk-fat-protein dollars), have been designated as elite by USDA. Because of the concern that adjustment for heterogeneous variance may have resulted in inequitable state representation for elite cows, the percentages of US cows with elite status were compared by state using evaluations of registered Holstein cows from 1990, 1991, and 1997. The numbers and percentages of eligible cows and the proportions of those cows that were designated as elite were determined by state from May 1997 evaluations of 772,302 registered and 1,499,729 grade Holstein cows; means and standard deviations for milk-fat-protein dollars were computed. Correlations were computed among the number of cows that were eligible for elite status, the number of cows that were designated as elite, the percentage of eligible cows that were designated as elite, and the mean and the standard deviation for milk-fat-protein dollars. Models were examined to for ability to predict the percentage of elite cows by state from mean and standard deviation for milk-fat-protein dollars. The number of elite cows for a state was highly correlated to the number of cows that were eligible for elite status. States with > 1.0% of eligible cows designated as elite had mean milk-fat-protein dollars that were higher than the US mean of $44, but standard deviations were equal to or slightly lower than the US standard deviation of $71. The mean value for milk-fat-protein dollars was associated with the state percentage of elite cows, but variation of the index was not related. However, the standard deviation for milk-fat-protein dollars was important in explaining the percentage of elite cows if the model also contained the mean value. Differences in the variation of lactation records across states or adjustments for those differences did not appear to cause inequity in designating elite status. Corresponding results for grade cows supported findings for registered cows.
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Use of linear type and production data to supplement early predicted transmitting abilities for productive life.
Genetic evaluations for the length of productive life based on actual DHIA culling data have been available in the US since January 1994. Although substantial genetic variation in productive life exists, the reliability of selection is often low, particularly for recently progeny-tested bulls having daughters that have not yet had an opportunity to be culled. Correlated production and conformation traits, which have higher heritability than productive life and are available earlier in life, may be used to enhance evaluations of productive life for young bulls that have little or no direct culling information available. Genetic correlations between productive life and milk, fat, dairy form, and udder traits ranged from +0.22 to +0.46. The maximum reliability of the indirect prediction of productive life from 16 correlated type and production traits was 0.56, and the maximum reliability from a subset of 10 traits was 0.51. Indirect information about productive life that was derived from type and production traits was combined with actual culling information to increase the total amount of available information for many recently progeny-tested bulls. The procedures described herein for enhancing direct evaluations for the productive life of dairy sires with indirect information about production and type were implemented by the USDA Animal Improvement Programs Laboratory and the Holstein Association USA in July 1994.
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Variance component estimation and multitrait genetic evaluation for type traits of dairy goats.
Covariance components for final score and 13 linear type traits of dairy goats were estimated by multitrait REML using canonical transformation with an animal model. Data were 10,932 type appraisals from 1988 through 1994 from herds with > or = 40 appraisals. Heritabilities were estimated as 0.27 for final score, 0.52 for stature, 0.29 for strength, 0.24 for dairyness, 0.38 for teat diameter, 0.21 for rear legs, 0.32 for rump angle, 0.27 rump width, 0.25 for fore udder attachment, 0.25 for rear udder height, 0.19 for rear udder arch, 0.25 for udder depth, 0.33 for suspensory ligament, and 0.36 for teat placement. Genetic correlations of linear type traits and final score were positive except for dairyness (-0.15) and teat diameter (-0.10); the largest correlations with final score were 0.66 for fore udder attachment, 0.44 for rear udder arch, 0.36 for rump width, and 0.30 for strength. The largest positive correlation among linear traits was 0.63 for stature and rump width; the largest negative correlation was -0.51 for strength and dairyness. Multitrait evaluations were calculated with data from all herds. Correlations between PTA calculated with animal and sire models ranged from 0.44 to 0.70 for bucks that had a PTA with a reliability of > or = 30%.
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Relationship of yield during early lactation and days open during current lactation with 305-day yield.
To measure and to partition the effect of pregnancy on yield, the relationships among milk, fat, and protein yields during early lactation, current days open, and 305-d yields were investigated using sample day records of 247,310 Holstein cows. The model included fixed effects of calving herd-year-season, calving age, and days open; the continuous variable of early cumulative yield to 80, 100, 120, or 140 d; and a random residual effect. As days open during first lactation increased from 30 to 100 d, 305-d milk yield increased by 876 kg; as days open increased from 100 to 200 d, milk yield increased by only 172 kg. The impact of current days open was greater on second lactation than on first; the difference in 305-d milk yield between cows open 40 and 290 d was 1199 kg for first lactation and 1613 kg for second lactation. If early yield to 120 d was included in the model, the corresponding difference was reduced to 860 kg for first lactation and 1001 kg for second lactation. Inclusion of early yield in the model reduced regression coefficients for days open during first lactation by 22% for 80-d yield, 24% for 100-d yield, 27% for 120-d yield, and 30% for 140-d yield and by 31, 35, 38, and 41%, respectively, for second lactation. Statistical models to derive adjustment factors should account for early lactation yield so that those factors can remove effects of pregnancy but not correlations between yield and fertility caused by early yield.
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A computationally feasible test day model for genetic evaluation of yield traits in the United States.
A multitrait analysis of test day yields is proposed that includes 60 traits [3 yield traits (milk, fat, and protein), 2 parity groups (first and later) per yield trait, and 10 stages of lactation per parity]. To reduce the computations needed for the 60 traits, test day effects are estimated within the herd before analysis across herds, the rank of t he genetic (co)variance matrix is reduced, a canonical transformation is used with missing values replace by their expectations, and a repeatability model is applied to allow inclusion of parities after second. Historical 305-d records are included through their correlations with test day effects. Possible benefits from this model include 1) more accurate estimation of environmental effects from including the influence of particular days of recording, 2) optimal use of information from all test days (especially for lactations with long intervals from calving to first test or between tests), 3) improved accuracy of evaluations for component yields through contributions from information for milk yield, and 4) greater stability of bull evaluations from accounting for genetic differences among daughters in the shape of lactation curve and maturity rate.
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Estimation of (co)variance components for Jersey type traits using a repeatability model.
(Co)variance components for final score and 15 linear types traits of Jersey cows were estimated by multitrait REML using multiple diagonalization and a repeatability model with 34,999 records of 22,354 cows. Multiple diagonalization gave relative off-diagonals (ratio of squared off-diagonals to the product of diagonals) of < 0.1%. Heritabilities and repeatabilities, respectively, were estimated as 0.29 and 0.48 for final score, 0.40 and 0.57 for stature, 0.26 and 0.39 for strength, 0.28 and 0.43 for dairy form, 0.13 and 0.25 for foot angle, 0.13 and 0.25 for rear legs (side view), 0.27 and 0.41 for body depth, 0.31 and 0.52 for rump angle, 0.22 and 0.33 for thurl width, 0.22 and 0.36 for fore under attachment, 0.28 and 0.46 for rear udder height, 0.26 and 0.42 for rear udder width, 0.32 and 0.48 for udder depth, 0.20 and 0.36 for udder cleft, 0.29 and 0.46 for front teat placement, and 0.31 and 0.48 for teat length. Estimates of heritability generally were higher, and estimates of repeatability were lower, than values used previously for USDA genetic evaluations, which were based on data from the 1970s and early 1980s. Final score was highly correlated both genetically and phenotypically with dairy form and rear udder traits. These estimates of heritabilities and (co)variance components are necessary for multitrait genetic evaluation of linear type traits of US Jerseys.
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Relationship between United States and Canadian genetic evaluations of longevity and somatic cell score.
Canadian and US evaluations of Holstein bulls were compared for longevity measures (433 bulls) and somatic cell score (354 bulls). Bulls were required to have a birth year of > or = 1975, daughter information from > or = 20 herds, and a reliability of > or = 50% in both Canada and the US. The number of bulls with longevity evaluations was greater for early years because longevity information was available from lactation data and daughters were required to be > or = 3 yr of age for US evaluations; evaluations for somatic cell score required additional collection of data and did not have corresponding numbers of bulls until the 1980s. Correlation between longevity measures in the US (productive life) and Canada (herd life) was 0.60. This low correlation was expected because US productive life includes yield information, but yield is excluded from Canadian herd life. For evaluations for somatic cell score, the correlation between the two countries was 0.82. Genetic correlations with productive life were estimated to be 0.69 for herd life and 0.81 for herd life combined with protein yield. Conversion equations were developed to predict a US evaluation for somatic cell score from a Canadian evaluation for somatic cell score and to predict a US evaluation for productive life from Canadian evaluations for herd life and yield.
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Consistency of international genetic evaluations of Holstein bulls.
International genetic evaluations from August 1995 and February 1996 for Holstein bulls from Canada, France, Germany, Italy, The Netherlands, and the US were evaluated for consistency across time. Mean evaluations, expressed on a US basis, were unchanged for US bulls; evaluations for bulls from France, Germany, Italy, and The Netherlands increased about 14 kg for milk and 0.4 kg for fat and protein. Mean genetic merit of US parents of bulls sampled in Canada, France, Germany, The Netherlands, and the US overestimated bull merit. Solutions for country of bull generally were not different for other countries relative to the US; however, evaluations for German and Netherlands bulls were higher than evaluations for US bulls with the same parent merit. French bulls that were full brothers to US bulls had higher evaluations for milk and protein, regardless of country of evaluation. Intercepts for conversion equations to a US basis increased by birth year and decreased for conversions from a US basis. Future international evaluations generally were predicted more accurately by prior international evaluations than by more recently converted national evaluations; however, converted evaluations with substantial increases in data could be better predictors, depending on country. The continued use of the latest international evaluations is recommended. Improvements in methodology that increase the consistency of evaluations across time and location may be possible. Alternatively, users may need to accept some uncertainty and error in international evaluations because of limitations in available data and methodology.
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Application of canonical transformation with missing values to multitrait evaluation of Jersey type.
A multitrait animal model was used to calculate predicted transmitting ability and reliabilities for final score and 15 linear type traits of 225,632 US Jersey cows. Records were adjusted for age and stage of lactation before analysis. The model contained effects for interactions of herd and date scored; year scored, parity, and age; and herd and sire; effects of permanent environment and additive genetics were also included. Of the 381,511 records included, some observations were missing for final score (8%), body depth (43%), and teat length (33%). The evaluation system used a canonical transformation, included several random effects, and estimated missing values with each iteration. Inbreeding was considered in the computations. Convergence was achieved in approximately 50 rounds of iteration. Correlations between animal and sire model predicted transmitting ability ranged from 0.56 to 0.95 and generally were higher for bulls than for cows and for more recent birth years. Genetic trend was strongly positive for dairy form, final score, and rear udder traits (height and width) and negative for udder depth. For other traits, genetic trend was small. This methodology should improve the accuracy of genetic evaluations for type traits of US Jerseys.
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Productive life evaluations: calculation, accuracy, and economic value.
A preliminary national evaluation of productive life was computed from records of 11 million Holsteins. Data were a mixture of projected and completed months in milk by 7 yr of age. Data were analyzed with the animal model programs currently used for yield traits. Genetic trend for productive life was positive as a correlated response to past selection for yield and type traits. Phenotypic trend was slightly negative. Active AI bulls had PTA that ranged from -1.7 to +4.3 mo and mean reliability of 56%; 3-yr-old cows had mean reliability of 29%. Yield and type PTA were almost uncorrelated with productive life PTA for active AI bulls but had positive correlations (.25 to .36) for all bulls born since 1980. An index that combines yield and productive life PTA with relative weights 2.5:1 can increase economic progress by up to 4%. Nearly identical progress can result from an index that combines yield and productive life adjusted for yield. Multitrait evaluations might produce higher reliabilities for productive life by inclusion of correlated traits measured earlier in life such as yield, type, and somatic cell score.
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Calculation and use of inbreeding coefficients for genetic evaluation of United States dairy cattle.
Inbreeding coefficients are calculated routinely for all animals included in national genetic evaluations for yield traits. The base population for inbreeding is defined as animals born during 1960. Animals with unknown parents are assumed to have inbreeding coefficients that are equal to the mean of coefficients for animals with known parents born during the same year. Mean inbreeding coefficients reached .03 to .04 for recent years, and coefficients for some animals exceeded .50. The annual increase in level of inbreeding was highest for Milking Shorthorns, but the rate of change of that increase was greatest for Holsteins. Accounting for inbreeding in calculation of the inverse of the relationship matrix had only a small effect on evaluations. For Jersey, the maximum change in breeding value was 73 kg of milk for cows and 40 kg of milk for bulls with > or = 10 daughters. Estimates of inbreeding depression were similar across breeds for production traits and were -29.6 kg of milk, -1.08 kg of fat, and -.97 kg of protein per 1% of inbreeding for Holsteins. In January 1994, the USDA began considering the percentage of inbreeding when calculating inverses of relationship matrices, the largest matrix representing over 20 million Holsteins; this inbreeding percentage was released to the dairy industry for bulls.
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Standardization of lactation means of somatic cell scores for calculation of genetic evaluations.
Sample day records of means of somatic cell scores were analyzed to develop adjustments to standardize records for length of lactation. Estimates for effect of DIM were from a model that included random lactation and cow effects for lactations to date of 753,929 Holsteins and 21,842 Jerseys calving in Pennsylvania or Wisconsin from 1987 to 1991. Lactation, cow, and residual variances were estimated using REML. Lactation and cow variances relative to a phenotypic variance of 1.00 were .57 and .31, respectively, for Holsteins and .52 and .35, respectively, for Jerseys. Estimates of effect of DIM were used to compute additive adjustments. Final lactation mean of somatic cell score at < or = 305 DIM for 1,857,532 Holsteins and 113,998 Jerseys from all participating states were standardized for lactation length and analyzed to determine the national effects of calving age and the regional effects of calving month. Multiplicative adjustments were developed for calving age and additive adjustments for calving month. Sample day records of lactation means of somatic cell scores were used to estimate weights based on number of somatic cell sample days to account for the lower accuracy of short records for genetic evaluation.
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Genetic variation in lactation means of somatic cell scores for six breeds of dairy cattle.
Lactation means of somatic cell scores from sample days were used to estimate the components of variation for additive genetic, permanent environmental, and herd-sire interaction effects. Data included records of 4314 Ayrshire, 7845 Brown Swiss, 18,115 Guernsey, 1,135,752 Holstein, 67,862 Jersey, and 787 Milking Shorthorn cows from across the US. Records were preadjusted for length of lactation. Fixed effects of herd-year, calving age, and calving month were included in animal models for estimation of variance components. Additive genetic estimates from REML relative to a phenotypic variance of 1.00 were .07 for Ayrshires, .07 for Brown Swiss, .11 for Guernseys, .09 for Holsteins, .09 for Jerseys, and .08 for Milking Shorthorns; permanent environmental estimates were .25, .26, .22, .21, .20, and .35; and herdsire interaction estimates were .04, .02, .00, .02, .02, and .01. Effects of calving age were similar for all regions of the US but differed for Jerseys and Holsteins. Effects of calving month were similar for all breeds. Cows calving during summer had the highest lactation means for somatic cell score from sample days. Impact of calving month was greatest in the Southeast.
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Effect of sampling status and adjustment for heterogeneous variance on bias in bull evaluations.
Holstein bulls were assigned to sampling categories (AI stud, AI nonstud, or non-AI) based on bull code, controller number, and age at semen distribution. The AI stud bulls were sampled through traditional progeny-testing programs of 13 AI organizations; AI nonstud bulls had AI semen collection reported by another organization or by multiple organizations. The non-AI bulls had no reported AI semen collection. Actual daughter yield deviations for these three groups of bulls were compared with expected performance (parent average) to provide an indication of whether evaluations were free from bias for daughter yield deviations. Mean difference of daughter yield deviation from parent average was close to 0 kg for animal model evaluations of all 22,930 bulls but was positively biased by 46 kg of milk for AI nonstud bulls. Mean PTA and reliabilities for parents were highest for AI stud bulls and lowest for non-AI bulls. The AI stud bulls varied least and were intermediate for mean management, approximated as mean daughter yield minus bull PTA. Management was highest for AI nonstud bulls, which suggested that adjustment for heterogeneous variance might reduce bias. However, the effect of this adjustment on mean difference of daughter yield deviation from parent average was small.
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Meeting the needs at the national level for genetic evaluation and health monitoring.
National dairy data can contribute to improved management decisions. Animals can be ranked nationally for selection decisions, and success of various management practices and environments in supporting profitable production and in minimizing disease incidence can be determined. Baseline information is useful for analyzing farm business status. Genetic evaluations depend on accurate recording of parentage, production, and environmental factors. Epidemiological data should be representative of the entire population. Data collection can be accomplished through periodic contribution of producer records to a central site or by national record keeping through service agencies. In addition to DHIA supervisors, who presently collect most dairy data, consultants and veterinarians also should contribute information. Surveys may be needed for some health and financial data. Collection of data from many sources can be made efficient by standardizing formats for data exchange. Present processing power of personal computers and availability of electronic linkages enable on-farm record processing. Regional processing centers could emphasize accumulation of national summary data and development and support of on-farm systems. With appropriate organization, the cost of contributing on-farm information to a national data-base should be small. Resulting management, genetic, and health information provided to the dairy industry should provide benefits sufficient to motivate financial and time investments by dairy producers.
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Transformation of measurements percentage of white coat color for Holsteins and estimation of heritability.
Percentage of white coat color was measured on registration certificates of 4293 Holstein heifers on eight dairy farms in Florida. Measurements of white percentage were by visual evaluation on one side of the upper body (head, neck, and trunk) only and obtained in increments of 5%. Mean, median, mode, standard deviation, and skewness of white percentage were 25.6, 15, 0, 26.9, and 1.03. Distribution of white percentages showed lack of normality. Original data were transformed using an extension of the Box-Cox transformation to approach normality and to provide maximum likelihood estimators of the transformed parameters. Heritability estimates for percentages of white coat color were computed using derivative-free REML with an animal model. Estimates of heritability were .715 from untransformed data and .779 for transformed. Standard errors of estimates were slightly lower (.032 vs. .035) following transformation. Additional study to find an improved transformation procedure still seems warranted.
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