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  • 1.
    Carlsson, Tomas
    et al.
    Högskolan Dalarna, Akademin Utbildning, hälsa och samhälle, Idrotts- och hälsovetenskap.
    Carlsson, Magnus
    Högskolan Dalarna, Akademin Utbildning, hälsa och samhälle, Idrotts- och hälsovetenskap.
    Felleki, Majbritt
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Hammarström, Daniel
    Högskolan Dalarna, Akademin Utbildning, hälsa och samhälle, Idrotts- och hälsovetenskap.
    Heil, Daniel
    Montana State University.
    Malm, Christer
    Umeå Universitet, Idrottsmedicin.
    Tonkonogi, Michail
    Högskolan Dalarna, Akademin Utbildning, hälsa och samhälle, Idrotts- och hälsovetenskap.
    Scaling maximal oxygen uptake to predict performance in elite-standard men cross-country skiers2013Ingår i: Journal of Sports Sciences, ISSN 0264-0414, E-ISSN 1466-447X, Vol. 31, nr 16, s. 1753-1760Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The purpose of this study was to: 1) establish the optimal body-mass exponent for maximal oxygen uptake (O2max) to indicate performance in elite-standard men cross-country skiers; and 2) evaluate the influence of course inclination on the body-mass exponent. Twelve elite-standard men skiers completed an incremental treadmill roller-skiing test to determine O2max and performance data came from the 2008 Swedish National Championship 15-km classic-technique race. Log-transformation of power-function models was used to predict skiing speeds. The optimal models were found to be: Race speed = 7.86 · O2max · m −0.48 and Section speed = 5.96 · O2max · m −(0.38 + 0.03 · α) · e−0.003 · Δ (where m is body mass, α is the section's inclination and Δ is the altitude difference of the previous section), that explained 68% and 84% of the variance in skiing speed, respectively. A body-mass exponent of 0.48 (95% confidence interval: 0.19 to 0.77) best described O2max as an indicator of performance in elite-standard men skiers. The confidence interval did not support the use of either “1” (simple ratio-standard scaled) or “0” (absolute expression) as body-mass exponents for expressing O2max as an indicator of performance. Moreover, results suggest that course inclination increases the body-mass exponent for O2max.

  • 2.
    Felleki, Majbritt
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Swedish University of Agricultural Sciences.
    Genetic Heteroscedasticity for Domestic Animal Traits2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    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.

  • 3.
    Felleki, Majbritt
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Chalkias, Helena
    A Double Hierarchical Generalized Linear Model For Teat Number In Pigs2010Konferensbidrag (Övrigt vetenskapligt)
  • 4.
    Felleki, Majbritt
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Lee, Dongwhan
    Department of Statistics, Seoul National University, Seoul 151-747, Korea .
    Lee, Youngjo
    Department of Statistics, Seoul National University, Seoul 151-747, Korea .
    Gilmour, Arthur R.
    School of Mathematics and Applied Statistics, Faculty of Informatics, University of Wollongong, Wollongong, NSW 2522, Australia.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models2012Ingår i: Genetics Research, ISSN 0016-6723, Vol. 94, nr 6, s. 307-317Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being −0·52 for IRWLS and −0·62 in Sorensen & Waagepetersen (2003).

  • 5.
    Felleki, Majbritt
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Sveriges Lantbruksuniversitet.
    Lundeheim, Nils
    Sveriges Lantbruksuniversitet.
    Genetic Control of Residual Variance for Teat Number in Pigs2013Ingår i: Proc. Assoc. Advmt. Anim. Breed. Genet., AAABG , 2013, s. 538-541Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    The genetic improvement in litter size in pigs has been substantial during the last 10-15 years. The number of teats on the sow must increase as well to meet the needs of the piglets, because each piglet needs access to its own teat. We applied a genetic heterogeneity model on teat numberin sows, and estimated medium-high heritability for teat number (0.5), but low heritability for residual variance (0.05), indicating that selection for reduced variance might have very limited effect. A numerically positive correlation (0.8) between additive genetic breeding values for mean and for variance was found, but because of the low heritability for residual variance, the variance will increase very slowly with the mean.

  • 6.
    Felleki, Majbritt
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Lundeheim, Nils
    Sveriges lantbruksuniversitet, Institutionen för husdjursgenetik.
    Genetic Heteroscedasticity for Teat Count in PigsManuskript (preprint) (Övrigt vetenskapligt)
  • 7.
    Felleki, Majbritt
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Lundeheim, Nils
    SLU.
    Genetic heteroscedasticity of teat count in pigs2015Ingår i: Journal of Animal Breeding and Genetics, ISSN 0931-2668, E-ISSN 1439-0388, Vol. 132, nr 5, s. 392-398Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The genetic improvement in pig litter size has been substantial. The number of teats on the sowmust thus increase as well to meet the needs of the piglets, because each piglet needs access to itsown teat. We applied a genetic heterogeneity model to teat counts in pigs, and estimated a mediumheritability for teat counts (0.35), but found a low heritability for residual variance (0.06),indicating that selection for reduced residual variance might have a limited effect. A numericallypositive correlation (0.8) was estimated between the breeding values for the mean and the residualvariance. However, because of the low heritability of the residual variance, the residual variance will probably increase very slowly with the mean.

  • 8.
    Rönnegård, Lars
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Felleki, Majbritt
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Fikse, Freddy
    Mulder, Herman A.
    Strandberg, Erling
    Genetic heterogeneity of residual variance: estimation of variance components using double hierarchical generalized linear models2010Ingår i: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 42, artikel-id 8Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 9.
    Rönnegård, Lars
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Swedish Univ Agr Sci, Dept Anim Breeding & Genet, S-75007 Uppsala, Sweden.
    Felleki, Majbritt
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Swedish Univ Agr Sci, Dept Anim Breeding & Genet, S-75007 Uppsala, Sweden.
    Fikse, W. F.
    Mulder, H. A.
    Strandberg, E.
    Variance component and breeding value estimation for genetic heterogeneity of residual variance in Swedish Holstein dairy cattle2013Ingår i: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198, Vol. 96, nr 4, s. 2627-2636Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Trait uniformity, or micro-environmental sensitivity, may be studied through individual differences in residual variance. These differences appear to be heritable, and the need exists, therefore, to fit models to predict breeding values explaining differences in residual variance. The aim of this paper is to estimate breeding values for micro-environmental sensitivity (vEBV) in milk yield and somatic cell score, and their associated variance components, on a large dairy cattle data set having more than 1.6 million records. Estimation of variance components, ordinary breeding values, and vEBV was performed using standard variance component estimation software (ASReml), applying the methodology for double hierarchical generalized linear models. Estimation using ASReml took less than 7 d on a Linux server. The genetic standard deviations for residual variance were 0.21 and 0.22 for somatic cell score and milk yield, respectively, which indicate moderate genetic variance for residual variance and imply that a standard deviation change in vEBV for one of these traits would alter the residual variance by 20%. This study shows that estimation of variance components, estimated breeding values and vEBV, is feasible for large dairy cattle data sets using standard variance component estimation software. The possibility to select for uniformity in Holstein dairy cattle based on these estimates is discussed.

  • 10.
    Strandberg, E
    et al.
    SLU.
    Felleki, Majbritt
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Fikse, W F
    SLU.
    Franzén, J
    Stockholms Universitet.
    Mulder, H A
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Urioste, J I
    Windig, J J
    Statistical tools to select for robustness and milk quality2013Ingår i: Advances in Animal Biosciences, ISSN 2040-4719, Vol. 4, nr 3, s. 606-611Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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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