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- Rönnegård, Lars, et al.
(författare)
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Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
- 2010
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Ingår i: Genetics Selection Evolution. - : Springer Science and Business Media LLC. - 0999-193X .- 1297-9686. ; 42
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Tidskriftsartikel (refereegranskat)abstract
- Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms.Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model.Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.
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| 2. |
- Strandberg, Erling, et al.
(författare)
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Statistical tools to select for robustness and milk quality
- 2013
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Ingår i: Advances in Animal Biosciences. - : Cambridge University Press. - 2040-4700 .- 2040-4719. ; 4:3, s. 606-611
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Tidskriftsartikel (refereegranskat)abstract
- This work was part of the EU RobustMilk project. In this work package, we have focused on two aspects of robustness, micro- and macro-environmental sensitivity and applied these to somatic cell count (SCC), one aspect of milk quality. We showed that it is possible to combine both categorical and continuous descriptions of the environment in one analysis of genotype by environment interaction. We also developed a method to estimate genetic variation in residual variance and applied it to both simulated and a large field data set of dairy cattle. We showed that it is possible to estimate genetic variation in both micro- and macro-environmental sensitivity in the same data, but that there is a need for good data structure. In a dairy cattle example, this would mean at least 100 bulls with at least 100 daughters each. We also developed methods for improved genetic evaluation of SCC. We estimated genetic variance for some alternative SCC traits, both in an experimental herd data and in field data. Most of them were highly correlated with subclinical mastitis (>0.9) and clinical mastitis (0.7 to 0.8), and were also highly correlated with each other. We studied whether the fact that animals in different herds are differentially exposed to mastitis pathogens could be a reason for the low heritabilities for mastitis, but did not find strong evidence for that. We also created a new model to estimate breeding values not only for the probability of getting mastitis but also for recovering from it. In a progeny-testing situation, this approach resulted in accuracies of 0.75 and 0.4 for these two traits, respectively, which means that it is possible to also select for cows that recover more quickly if they get mastitis.
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| 6. |
- Urioste, Jorge, et al.
(författare)
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Phenotypic and genetic characterization of novel somatic cell count traits from weekly or monthly observations
- 2010
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Ingår i: Journal of Dairy Science. - Urbana, Ill. : American Dairy Science Association. - 0022-0302 .- 1525-3198. ; 93:12, s. 5930-5941
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Tidskriftsartikel (refereegranskat)abstract
- The objectives of this study were (1) to explore traits that better capture weekly or monthly changes in somatic cell counts (SCC) than does the commonly used lactation-average SCC, (2) to estimate their heritabilities and relationships to clinical mastitis (CM), and (3) to determine if these traits are feasible for use in monthly testing schemes. Clinical mastitis and weekly test-day (TD) records of SCC and milk production traits from 1,006 lactations of Swedish Red and Holstein cows collected from 1989 to 2004 were used (data set W). A data subset was also created to mimic monthly recording (data set M, 980 lactations). Twenty SCC traits were defined, taking into account SCC general levels and variation along the lactation curve, time and level of infection, and time of recovery. To reduce dimensionality, cluster and stepwise logistic regression procedures were applied. In data set W, 3 traits, "standard deviation of SCC over the lactation," a discrete (0/1) indicator of "at least one TD with SCC >500,000 cells/mL", and "number of days sick in the widest SCC peak" (DWidest) were the variables kept both with cluster procedures and a stepwise logistic regression with the logit of CM as dependent variable. In data set M, DWidest was replaced by "number of SCC peaks" and "average number of days sick per peak" (ADSick). Lactation-average SCC (in the first 150 d or between 150 and 305 d) did not enter into the logistic regression. Heritability estimates obtained for these new traits under a Bayesian setting and a Gibbs sampling approach were 10 to 16% (except for ADSick: 5%). Heritabilities were at least as high in the monthly data set as in the weekly data set. Thus, these SCC traits seem promising for use in breeding programs based on monthly milk recording.
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| 7. |
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| 8. |
- Felleki, Majbritt
(författare)
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Genetic Heteroscedasticity for Domestic Animal Traits
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Animal traits differ not only in mean, but also in variation around the mean. For instance, one sire’s daughter group may be very homogeneous, while another sire’s daughters are much more heterogeneous in performance. The difference in residual variance can partially be explained by genetic differences. Models for such genetic heterogeneity of environmental variance include genetic effects for the mean and residual variance, and a correlation between the genetic effects for the mean and residual variance to measure how the residual variance might vary with the mean.The aim of this thesis was to develop a method based on double hierarchical generalized linear models for estimating genetic heteroscedasticity, and to apply it on four traits in two domestic animal species; teat count and litter size in pigs, and milk production and somatic cell count in dairy cows.The method developed is fast and has been implemented in software that is widely used in animal breeding, which makes it convenient to use. It is based on an approximation of double hierarchical generalized linear models by normal distributions. When having repeated observations on individuals or genetic groups, the estimates were found to be unbiased.For the traits studied, the estimated heritability values for the mean and the residual variance, and the genetic coefficients of variation, were found in the usual ranges reported. The genetic correlation between mean and residual variance was estimated for the pig traits only, and was found to be favorable for litter size, but unfavorable for teat count.
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| 9. |
- Ahlman, Therese, et al.
(författare)
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Culling reasons in organic and conventional dairy herds and genotype by environment interaction for longevity
- 2011
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Ingår i: Journal of Dairy Science. - : American Dairy Science Association. - 0022-0302 .- 1525-3198. ; 94, s. 1568-1575
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Tidskriftsartikel (refereegranskat)abstract
- Dairy cow longevity combines all functional traits and is thought to be especially important in organic production, which is an established, increasing part of Swedish dairy production, representing approximately 6% of the market. The aim of this study was to compare dynamics in culling reasons between organic and conventional production and to analyze genotype by environment interactions for longevity. The data contained information from all organic herds with information available from official recording (n = 402) and from approximately half of the conventional herds (n = 5,335). Records from Swedish Holsteins (n = 155,379) and Swedish Red cows (n = 160,794) that had their first calf between January 1998 and September 2003 were included. The opportunity period for longevity was at least 6 yr. Six longevity traits were defined: length of productive life; survival through first, second, and third lactations; fertility-determined survival; and udder health-determined survival. Twenty codes were used to describe the cause of culling, and these were divided into 8 groups: udder health, low fertility, low production, leg problems, metabolic diseases, other diseases, other specified causes, and unspecified cause. The main reason for culling cows in organic herds was poor udder health, whereas for cows in conventional herds it was low fertility. Furthermore, the shift in main culling reason from fertility, which was most common in first lactation regardless of production system, to udder health occurred at a lower age in organic production. Heritabilities and genetic correlations for the longevity traits expressed in organic and conventional herds were estimated from a bivariate animal model. The genetic correlations were close to unity (>0.88), except for fertility-determined survival in the Swedish Red breed (0.80). Heritabilities were low to moderate, and no clear pattern was identified for production system or breed. In general, the results indicate that farmers' culling criteria differ between organic and conventional production. Different preferences may influence the need for alternative selection indexes for organic production, with different weightings of traits, or a separate breeding program. However, no genotype by environment interaction of importance was found between the production systems.
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| 10. |
- Ahlman, Therese, et al.
(författare)
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Fertility, somatic cell count and milk production in Swedish organic and conventional dairy herds
- 2009
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Ingår i: Livestock Science. - : Elsevier BV. - 1871-1413 .- 1878-0490. ; 126, s. 176-182
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Tidskriftsartikel (refereegranskat)abstract
- The share of organic dairy production in EU is increasing and this study describes the herd structure and cow performance in organic and conventional dairy production systems in Sweden. The data included records from 2902718 lactations, collected in organic (n=471) and conventional (n = 13976) herds between 1998 and 2005. Compared with conventional herds, the organic herds were larger and increased more rapidly in size. The replacement rate was slightly lower in organic herds and fewer Swedish Holsteins were used. The statistical analysis of cow performance in the first three lactations showed lower milk, fat and protein production in organic herds, but the increase in production from first to second lactation was larger when expressed in kg milk. Fertility was better for organically managed cows compared to conventionally managed cows, but the somatic cell count (SCC) was higher. However. at a given production level the fertility was slightly worse in organic herds while there was no difference in SCC. No interactions of importance were found between production system and breed for any trait.The results showed that organic and conventional dairy production differed regarding herd structure and cow performance. However, the differences in fertility and SCC found were to a high extent explained by the lower milk yield in organic production and no breed was found to perform better in either system. (C) 2009 Elsevier B.V. All rights reserved.
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