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ABSTRACT: A random regression model is presented as an approximation for multibreed variance model. The approximation is derived using the splitted multibreed model where the single breeding value is split to the breed specific and their segregation terms. The random regression model allows extending the multibreed information easily to genomic data models. We present the approach by a simple example.
Journal of Animal Breeding and Genetics 02/2013; 130(1):4-9. · 1.46 Impact Factor
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ABSTRACT: The current study evaluates reliability of genomic predictions in selection candidates using multi-trait random regression model, which accounts for interactions between marker effects and breed of origin in the Nordic Red dairy cattle (RDC). The population structure of the RDC is admixed. Data consisted of individual animal breed proportions calculated from the full pedigree, deregressed proofs (DRP) of published estimated breeding values (EBV) for yield traits and genotypic data for 37 595 single nucleotide polymorphic markers. The analysed data included 3330 bulls in the reference population and 812 bulls that were used for validation. Direct genomic breeding values (DGV) were estimated using the model under study, which accounts for breed effects and also with GBLUP, which assume uniform population. Validation reliability was calculated as a coefficient of determination from weighted regression of DRP on DGV (rDRP,DGV 2), scaled by the mean reliability of DRP. Using the breed-specific model increased the reliability of DGV by 2 and 3% for milk and protein, respectively, when compared to homogeneous population GBLUP. The exception was for fat, where there was no gain in reliability. Estimated validation reliabilities were low for milk (0.32) and protein (0.32) and slightly higher (0.42) for fat.
Journal of Animal Breeding and Genetics 02/2013; 130(1):10-9. · 1.46 Impact Factor
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ABSTRACT: Interest is growing in finding indicator traits for the evaluation of nutritional or tissue energy status of animals directly at the individual animal level. The development and subsequent use of such traits in practice demands a clear understanding of the genetic and phenotypic associations with the various production and functional traits. In this study, the relationships during lactation between milk fat:protein ratio (FPR) and production and functional traits were estimated for Nordic Red cattle, in which published information is scarce. The objectives of this study were to estimate genetic associations of FPR with milk yield (MY), fertility, and udder health traits during different stages of lactation. Traits included in the analyses were MY, 4 fertility traits-days from calving to insemination (DFI), days open (DO), number of inseminations (NI), and nonreturn rate to 56 d (NRR)-and 2 udder health traits-test-day somatic cell score (SCS) and clinical mastitis (CM). Data were from a total of 22,422 first-lactation cows. Random regression models were used to estimate genetic parameters and associations between traits. The mean FPR in first-lactation cows was 1.28 and ranged from 1.25 to 1.45. During first lactation, the heritability of FPR ranged from 0.14 to 0.25. Genetic correlations between FPR and MY in early lactation (until 50 d in milk) were positive and ranged from 0.05 to 0.22; later in lactation, they were close to zero or negative, indicating that cows may have come out of the negative state of energy balance. The strength of genetic associations between FPR and fertility traits varied during lactation. In early lactation, correlations between FPR and the interval fertility traits DFI and DO were positive and ranged from 0.14 to 0.28. Genetic correlations between FPR and the udder health traits SCS and CM in early lactation ranged from 0.09 to 0.20. Milk fat:protein ratio is a heritable trait and easily available from routine milk-recording schemes. It can be used as a low-cost monitoring tool of poor health and fertility in the most critical phases of lactation and as an important indicator trait to improve robustness in dairy cows through selection.
Journal of Dairy Science 12/2012; · 2.56 Impact Factor
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ABSTRACT: Multiple-trait and random regression models have multiplied the number of equations needed for the estimation of variance components. To avoid inversion or decomposition of a large coefficient matrix, we propose estimation of variance components by Monte Carlo expectation maximization restricted maximum likelihood (MC EM REML) for multiple-trait linear mixed models. Implementation is based on full-model sampling for calculating the prediction error variances required for EM REML. Performance of the analytical and the MC EM REML algorithm was compared using a simulated and a field data set. For field data, results from both algorithms corresponded well even with one MC sample within an MC EM REML round. The magnitude of the standard errors of estimated prediction error variances depended on the formula used to calculate them and on the MC sample size within an MC EM REML round. Sampling variation in MC EM REML did not impair the convergence behaviour of the solutions compared with analytical EM REML analysis. A convergence criterion that takes into account the sampling variation was developed to monitor convergence for the MC EM REML algorithm. For the field data set, MC EM REML proved far superior to analytical EM REML both in computing time and in memory need.
Journal of Animal Breeding and Genetics 12/2012; 129(6):457-468. · 1.46 Impact Factor
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ABSTRACT: Several strategies to use genomic data in predictions have been proposed. The aim of this study was to compare different genomic prediction methods. The response variables used in the genomic predictions were deregressed proofs, which were derived from 2 estimated breeding value (EBV) data sets. The full EBV data set from March 2010 included the EBV for production and mastitis traits for all Nordic red bulls. The reduced data set included the same animals as the full data set, but the EBV were predicted from a data set that excluded the last 5 yr of observations. Genomic predictions were obtained using different BLUP models: BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP). For the selection candidate bulls, the SNP-BLUP and G-BLUP models gave the same direct genomic breeding values (e.g., correlation of direct genomic breeding values between SNP-BLUP and G-BLUP for protein was 0.99), but slightly different from genomic EBV obtained from H-BLUP (correlations of SNP-BLUP or G-BLUP with H-BLUP were about 0.96). For all traits, SNP-BLUP and G-BLUP gave the same validation reliability, whereas H-BLUP led to slightly higher reliability. Therefore, the results support a slight advantage of using H-BLUP for genomic evaluation.
Journal of Dairy Science 07/2012; 95(7):4065-73. · 2.56 Impact Factor
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Acta Agriculturae Scand Section A. 03/2011; 61(1):1-6.
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Journal of Animal Breeding and Genetics 04/2010; 108(1‐6):401 - 411. · 1.46 Impact Factor
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ABSTRACT: Mink skin size in Finland, as well as in other countries, has increased considerably during last decade. However, there are signs that selection for large body size has a negative impact on litter size (LS) and also for survival of kits. Therefore, it is important to study the genetic relationships among fertility traits and animal size (AS). The variance components for age at first mating (AFM) and first three parity LS and AS were estimated using multi-trait restricted maximum likelihood animal model. Data included 82 945 animals born during 1990 to 2004, originating from nine farms. Heritability estimates for the fertility traits were from 0.10 to 0.15. For AS, heritability was estimated to be 0.18. Genetic correlation between AS and all fertility traits was estimated to be negative (varying from -0.004 to -0.38). It is important to recognize this antagonistic relationship and include the reproductive traits into breeding goals to maintain good reproductive performance when selecting for increased body size and hence larger pelts in fur animals. Genetic correlations between the traits should be accounted in breeding value evaluations by using a multi-trait model. Including AFM into breeding value estimation would also improve the accuracy of breeding value estimation for fertility, because females missing the first LS still have record on AFM.
animal 02/2010; 4(2):183-8. · 1.74 Impact Factor
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Acta Agriculturae Scand Section A. 09/2009; 59(3):131-136.
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ABSTRACT: Variance components were estimated for maturing age, first litter size, and animal size in Finnish minks. The fitted animal models had direct genetic and maternal genetic effects, litter effects, and maternal environmental effects. Multivariate analysis was performed to determine covariances between the traits. Maternal effects represented a significant source of phenotypic variance in the maturation age and animal size. For litter size, maternal effects were not as clear. Moreover, in maturation age and animal size, the covariance between the direct additive effect and the maternal additive effect was negative. In addition, litter effect variances were larger than maternal variances for all traits. Therefore, it is crucial to also estimate environmental effects common to littermates for these traits. Direct heritability and the response to selection are overestimated, especially for maturation age and also for animal size, when maternal and common litter effects are not considered.
Journal of Animal Science 07/2009; 87(10):3083-8. · 2.10 Impact Factor
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ABSTRACT: A multiplicative random regression (M-RRM) test-day (TD) model was used to analyse daily milk yields from all available parities of German and Austrian Simmental dairy cattle. The method to account for heterogeneous variance (HV) was based on the multiplicative mixed model approach of Meuwissen. The variance model for the heterogeneity parameters included a fixed region x year x month x parity effect and a random herd x test-month effect with a within-herd first-order autocorrelation between test-months. Acceleration of variance model solutions after each multiplicative model cycle enabled fast convergence of adjustment factors and reduced total computing time significantly. Maximum Likelihood estimation of within-strata residual variances was enhanced by inclusion of approximated information on loss in degrees of freedom due to estimation of location parameters. This improved heterogeneity estimates for very small herds. The multiplicative model was compared with a model that assumed homogeneous variance. Re-estimated genetic variances, based on Mendelian sampling deviations, were homogeneous for the M-RRM TD model but heterogeneous for the homogeneous random regression TD model. Accounting for HV had large effect on cow ranking but moderate effect on bull ranking.
Journal of Animal Breeding and Genetics 07/2008; 125(3):147-59. · 1.46 Impact Factor
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ABSTRACT: This paper's objectives were to estimate the genetic (co)variance components of the Gompertz growth curve parameters and to evaluate the relationship of estimated breeding values (EBV) based on average daily gain (ADG) and Gompertz growth curves. Finnish Yorkshire central test station performance data was obtained from the Faba Breeding (Vantaa, Finland). The final data set included 121,488 weight records from 10,111 pigs. Heritability estimates for the Gompertz growth parameters mature weight (alpha), logarithm of mature weight to birth weight ratio (beta) and maturation rate (kappa) were 0.44, 0.55 and 0.31, respectively. Genotypic and phenotypic correlations between the growth curve parameters were high and mainly negative. The only positive relationship was found between alpha and beta. Pearson and Spearman rank correlation coefficients between EBV for ADG and daily gain calculated from Gompertz growth curves were 0.79. The Spearman rank correlation between the sire EBV for ADG and Gompertz growth curve parameter-based ADG for all sires with at least 15 progeny was 0.86. Growth curves differ significantly between individuals and this information could be utilized for selection purposes when improving growth rate in pigs.
Journal of Animal Breeding and Genetics 07/2008; 125(3):168-75. · 1.46 Impact Factor
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ABSTRACT: In this study the genetic association during lactation of 2 clinical mastitis (CM) traits: CM1 (7 d before to 30 d after calving) and CM2 (31 to 300 d after calving) with test-day somatic cell score (SCS) and milk yield (MY) was assessed using multitrait random regression sire models. The data analyzed were from 27,557 first-lactation Finnish Ayrshire cows. Random regressions on second- and third-order Legendre polynomials were used to model the daily genetic and permanent environmental variances of test-day SCS and MY, respectively, while only the intercept term was fitted for CM. Results showed that genetic correlations between CM and the test-day traits varied during lactation. Genetic correlations between CM1 and CM2 and test-day SCS during lactation varied from 0.41 to 0.77 and from 0.34 to 0.71, respectively. Genetic correlations of test-day MY with CM1 and CM2 ranged from 0.13 to 0.51 and from 0.49 to 0.66, respectively. Correlations between CM1 and SCS were strongest during early lactation, whereas correlations between CM2 and SCS were strongest in late lactation. Genetic correlations lower than unity indicate that CM and SCS measure different aspects of the trait mastitis. Milk yield in early lactation was more strongly correlated with both CM1 and CM2 than milk yield in later lactation. This suggests that selection for higher lactation MY through selection on increased milk yield in early lactation will have a more deleterious effect on genetic resistance to mastitis than selection for higher yield in late lactation. The approach used in this study for the estimation of the genetic associations between test-day and CM traits could be used to combine information from traits with different data structures, such as test-day SCS and CM traits in a multitrait random regression model for the genetic evaluation of udder health.
Journal of Dairy Science 04/2008; 91(3):1189-97. · 2.56 Impact Factor
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ABSTRACT: In a random regression test-day model, environmental effects in addition to individual animal factors can be included and analyzed. Moreover, instead of herd-year classification of the management groups, the herd-test-day classification within the model better accounts for month-to-month short-term environmental variation in production and somatic cell count (SCC) traits. The herd management levels of milk yield (milk deviation from whole-country mean, kilograms/day), protein and fat concentration (protein and fat deviation, %), and SCC (SCC deviation, 1,000 cells/mL) are used in the dairy herd management Web application "Maitoisa" (in English, "Milky"). This management tool helps to recognize several management problems. For recognition of systematic patterns and single unusual test-days, a monthly time-trend analysis was developed to smooth the random fluctuations and display the yearly production pattern. In addition to analyzing single test-day deviations from the mean, modeled herd solutions assist users in identifying repeated phenomena and enable the forecasting of the management pattern for the subsequent year. The solutions are displayed either as tables or graphs plotted by calendar months. In addition to management effects of the farmer's own herd, he or she can request country or region percentiles to be displayed in the graphs. The Web service has been offered to farmers and dairy advisors since 2001, and it has proved to be a powerful tool for herd monitoring and planning.
Journal of Dairy Science 06/2007; 90(5):2563-8. · 2.56 Impact Factor
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ABSTRACT: In farmed salmonids, there is an unfavourable genetic relationship between high body weight and increased body lipid percent
at a fixed age. This forces breeders to control lipid deposition in order to maintain appropriate end-product quality. Here we tested
the hypothesis that this unfavourable genetic relationship can be diminished when fish are reared on a relatively low lipid and high
protein diet. To test the hypothesis, a total of 2931 rainbow trout from 210 families were reared using two diets in a split-family
design to weight of 2.5–2.7 kg. The diets were a normal protein, high lipid diet (NP) representing modern-type feed, and an
experimental high protein, low lipid diet (HP). As hypothesised, phenotypic and genetic correlations of muscle and body lipid
percent with body weight were more favourable on HP than NP diet. The correlations ranged from strongly negative to close-tozero
on HP diet but from strongly positive to close-to-zero on NP diet. These results indicate that alternative high protein, low lipid
diet partly uncouples lipid deposition from growth, providing more favourable genetic architecture for the simultaneous genetic
improvement of growth, body composition and end-product quality. The results for viscera percent from body weight, which is an
indirect estimate of visceral lipid, differed from those of muscle and body lipid percent. Phenotypic and genetic correlations
between viscera percent and body weights were negative or weakly positive without differences between diets. The correlations
differed between diets for muscle and body lipid but not for visceral lipid because these traits are genetically different.
Aquaculture 01/2007; 271:162–172. · 2.04 Impact Factor
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ABSTRACT: A test-day (TD) random regression model (RRM) was described for the genetic evaluation of somatic cell score (SCS) where first and later lactations were considered as two different but correlated traits. A two-step covariance function procedure was used to estimate variance-covariances and associated genetic parameters. Analysis of estimated breeding values (EBV), ranking of top bulls and cows and some computational aspects were used to compare RRM with TD repeatability model (RPM) and lactation average model (LAM). Residuals were analysed to assess the relative fit of TD models. Comparison between RRM and RPM showed that RRM has lower mean squared error and gave better fit to the data. For young bulls and cows, the standard deviation (SD) of EBVs was highest for RRM and lowest for LAM implying efficient utilization of information on SCS, in terms of revealing more genetic variation. A much lower correlation of EBVs ranging from 0.80 to 0.92 and significant re-ranking of top bulls and cows were observed between RRM and LAM. The lower across-lactation correlation between RRM and LAM indicated that LAM is directed to give more weight to first lactation breeding values. The RRM, where SCS in the first and later lactations was considered as two different but correlated traits was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize EBVs for first and later lactations.
Journal of Animal Breeding and Genetics 09/2006; 123(4):224-38. · 1.46 Impact Factor
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ABSTRACT: To assess the genetic potential for selection of increased feed efficiency in rainbow trout (Oncorhynchus mykiss), we estimated the heritabilities and correlations for BW, daily weight gain (DG), and daily feed intake (DFI). Body weight was recorded 5 times, and DG and DFI 3 times during a feeding trial lasting 22 mo. To test the hypothesis that phenotypic and genetic parameters were influenced by a nutritional environment, fish were fed either a modern normal protein diet (NP, 40 to 45% protein and 30 to 33% lipid) or an alternative high protein diet (HP, 50 to 56% protein, 20 to 24% lipid) in a split-family design. Results showed that there were no large differences in heritabilities between the diets. Average heritability for DFI over both diets and different fish ages was low (average h2 = 0.10), indicating that modest genetic changes in response to selection can be obtained. Average heritabilities for BW and DG over both diets and different fish ages were 0.28 and 0.33, respectively. The NP diet enabled fish to express a wide range of BW, as shown by the increased coefficients of phenotypic variation for BW. Fish fed the HP diet showed increased phenotypic variation for DFI in > 750-g fish. On the NP diet, genetic correlations of DFI with DG and BW were very strong for 750- to 2,000-g fish. In contrast, on the HP diet, the respective correlations were moderate to low, revealing more genetic potential to change growth and feed intake simultaneously in opposite directions. An analysis of the predicted selection responses showed that selection solely for high DG improved feed efficiency as a correlated genetic response. Simultaneous selection for high DG and reduced DFI, in turn, may increase genetic gain in feed efficiency by a factor of 1.2 compared with selection solely for DG. However, variation for growth and feed intake and the relationships between these traits were different in different nutritional environments, leading to divergent genetic responses on the alternative diets.
Journal of Animal Science 04/2006; 84(4):807-17. · 2.10 Impact Factor
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ABSTRACT: This paper studies whether cows with originally lower somatic cell count (SCC) are more susceptible to clinical mastitis (CM) than cows with higher somatic cell count, and evaluates the correlations between CM, SCC, and milk yield. Data were extracted from the Finnish national milk-recording database and from the health recording system. First and second lactation records of 87,861 Ayrshire cows calving between January 1998 and December 2000 were included. Traits studied were incidence of CM, test-day SCC, and test-day milk yield before and following CM. Genetic parameters were estimated using multitrait REML with a sire model. Results did not indicate that cows with genetically low SCC would be more susceptible to CM. The genetic correlation between CM in the first and second lactation was reasonably high (0.73), suggesting that susceptibility to mastitis remains similar across lactations. The genetic correlation between CM and milk yield traits was positive (from 0.38 to 0.56), confirming the genetic antagonism between production and udder health traits. The genetic correlation between SCC and milk was positive in the first lactation, but negative, or near zero in the second lactation. This indicates that breeding for lower SCC might not affect milk production in later lactations. The results of this study support the use of SCC as an indicator of mastitis and a tool for selection for mastitis resistance.
Journal of Dairy Science 03/2005; 88(2):827-33. · 2.56 Impact Factor
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ABSTRACT: The objective of this study was to estimate genetic associations of prolificacy traits with other traits under selection in the Finnish Landrace and Large White populations. The prolificacy traits evaluated were total number of piglets born, number of stillborn piglets, piglet mortality during suckling, age at first farrowing, and first farrowing interval. Genetic correlations were estimated with two performance traits (ADG and feed:gain ratio), with two carcass traits (lean percent and fat percent), with four meat quality traits (pH and L* values in longissimus dorsi and semimembranosus muscles), and with two leg conformation traits (overall leg action and buck-kneed forelegs). The data contained prolificacy information on 12,525 and 10,511 sows in the Finnish litter recording scheme and station testing records on 10,372 and 9,838 pigs in Landrace and Large White breeds, respectively. The genetic correlations were estimated by the restricted maximum likelihood method. The most substantial correlations were found between age at first farrowing and lean percent (0.19 in Landrace and 0.27 in Large White), and fat percent (-0.26 in Landrace and -0.18 in Large White), and between number of stillborn piglets and ADG (-0.38 in Landrace and -0.25 in Large White) and feed:gain (0.27 in Landrace and 0.12 in Large White). The correlations are indicative of the benefits of superior growth for piglets already at birth. Similarly, the correlations indicate that age at first farrowing is increasing owing to selection for carcass lean content. There was also clear favorable correlation between performance traits and piglet mortality from birth to weaning in Large White (r(g) was -0.43 between piglet mortality and ADG, and 0.42 between piglet mortality and feed:gain), but not in Landrace (corresponding correlations were 0.26 and -0.22). There was a general tendency that prolificacy traits were favorably correlated with performance traits, and unfavorably with carcass lean and fat percents, whereas there were no clear associations between prolificacy and meat quality or leg conformation. In conclusion, accuracy of estimated breeding values may be improved by accounting for genetic associations between prolificacy, carcass, and performance traits in a multitrait analysis.
Journal of Animal Science 09/2004; 82(8):2301-6. · 2.10 Impact Factor
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ABSTRACT: Currently, most analyses of parameters in test-day models involve two types of models: random regression, where various functions describe variability of (co)variances with regard to days in milk, and multiple traits, where observations in adjacent days in milk are treated as one trait. The methodologies used for estimation of parameters included Bayesian via Gibbs sampling, and REML in the form of derivative-free, expectation-maximization, or average-information algorithms. The first method is simpler and uses less memory but may need many rounds to produce posterior samples. In REML, however, the stopping point is well established. Because of computing limitations, the largest estimations of parameters were on fewer than 20,000 animals. The magnitude and pattern of heritabilities varied widely, which could be caused by simplifications in the model, overparameterization, small sample size, and unrepresentative samples. Patterns of heritability differ among random regression and multiple-trait models. Accurate parameters for large multi-trait random regression models may be difficult to obtain at the present time. Parameters that are sufficiently accurate in practice may be obtained outside the complete prediction model by a constructive approach, where parameters averaged over the lactation would be combined with several typical curves for (co)variances for days in milk. Obtained parameters could be used for any model, and could also aid in comparison of models.
Journal of Dairy Science 06/2000; 83(5):1125-34. · 2.56 Impact Factor
