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  • 1.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Bayesian Cluster Analysis: Some Extensions to Non-standard Situations2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The Bayesian approach to cluster analysis is presented. We assume that all data stem from a finite mixture model, where each component corresponds to one cluster and is given by a multivariate normal distribution with unknown mean and variance. The method produces posterior distributions of all cluster parameters and proportions as well as associated cluster probabilities for all objects. We extend this method in several directions to some common but non-standard situations. The first extension covers the case with a few deviant observations not belonging to one of the normal clusters. An extra component/cluster is created for them, which has a larger variance or a different distribution, e.g. is uniform over the whole range. The second extension is clustering of longitudinal data. All units are clustered at all time points separately and the movements between time points are modeled by Markov transition matrices. This means that the clustering at one time point will be affected by what happens at the neighbouring time points. The third extension handles datasets with missing data, e.g. item non-response. We impute the missing values iteratively in an extra step of the Gibbs sampler estimation algorithm. The Bayesian inference of mixture models has many advantages over the classical approach. However, it is not without computational difficulties. A software package, written in Matlab for Bayesian inference of mixture models is introduced. The programs of the package handle the basic cases of clustering data that are assumed to arise from mixture models of multivariate normal distributions, as well as the non-standard situations.

  • 2.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Bayesian Inference for a Mixture Moddel using the Gibbs SamplerManuscript (Other academic)
  • 3.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Classification with the Possibility of a Deviant Group: An Approach to Twelve-Year-Old StudentsIn: Multivariate Behavioral ResearchArticle in journal (Refereed)
  • 4.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Implementation of the MBCA Matlab Program for Model-Based Cluster AnalysisManuscript (Other academic)
  • 5.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Longitudinal, Model-Based Clustering with Missing DataManuscript (Other academic)
  • 6.
    Franzén, Jessica
    Sveriges lantbruksuniversitet.
    Ny metod för avelsvärdering av juverhälsa2010In: Forskning special, Svensk mjölk, no 20Article, review/survey (Other (popular science, discussion, etc.))
  • 7.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Successive Clustering of Longitudinal Data: A Bayesian ApproachManuscript (Other academic)
  • 8.
    Franzén, Jessica
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics. Swedish University of Agricultural Sciences, Sweden.
    Thorburn, Daniel
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Urioste, Jorge I.
    Strandberg, Erling
    Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities2012In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 44, article id 10Article in journal (Refereed)
    Abstract [en]

    Background: Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis. Methods: Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations. Results: Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process. Conclusions: The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health.

  • 9.
    Franzén, Jessica
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Thorburn, Daniel
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Urioste, Jorge I
    Sveriges lantbruksuniversitet, Uppsala.
    Strandberg, Erling
    Sveriges lantbruksuniversitet, Uppsala.
    Use of transition probabilities for estimation of mastitis resistance2010Conference paper (Refereed)
  • 10.
    Strandberg, Erling
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Felleki, M.
    Fikse, F.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Mulder, H. A.
    Rönnegård, L
    Urioste, J.
    Windig, J.
    Statistical tools to select for robustness and milk quality2013In: Advances in Animal Biosciences, ISSN 2040-4700, Vol. 4, no 3, p. 606-611Article in journal (Refereed)
    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.

  • 11. Urioste, J I
    et al.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Windig, J J
    Strandberg, E
    Genetic variability of alternative somatic cell count traits and their relationship with clinical and subclinical mastitis2011In: Interbull Bulletin, ISSN 1011-6079, no 44, p. 204-209Article in journal (Refereed)
  • 12. Urioste, Jorge
    et al.
    Franzén, Jessica
    Strandberg, Erling
    Windig, Johannes Jacob
    Genetic relationships among mastitis and alternative somatic cell count traits in the first 3 lactations of Swedish Holsteins2012In: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198, Vol. 95, no 6, p. 3428-3434Article in journal (Refereed)
    Abstract [en]

    The objectives of this study were to estimate heritabilities of, and genetic correlations among, clinical mastitis (CM), subclinical mastitis (SCM), and alternative somatic cell count (SCC) traits in the first 3 lactations of Swedish Holstein cows, and to estimate genetic correlations for the alternative traits across lactations. Data from cows having their first calving between 2002 and 2009 were used. The alternative SCC traits were based on information on CM and monthly test-day (TD) records of SCC traits of 178,613, 116,079, and 64,474 lactations in first, second, or third parity, respectively. Sires had an average of 230, 165, or 124 daughters in the data (parities 1, 2, or 3, respectively). Subclinical mastitis was defined as the number of periods with an SCC >150,000 cell/mL and without a treatment for CM. Average TD SCC between 5 and 150 d was used as a reference trait. The alternative SCC traits analyzed were 1) presence of at least 1 TD SCC between 41,000 and 80,000 cell/mL (TD41-80), 2) at least 1 TD SCC >500,000 cells/mL, 3) standard deviation of log SCC over the lactation, 4) number of infection peaks, and 5) average days diseased per peak. The same variables in different parities were treated as distinct traits. The statistical model considered the effects of herd-year, year, month, age at calving, animal, and residual. Heritability estimates were 0.07 to 0.08 for CM, 0.12 to 0.17 for SCM, and 0.14 for SCC150. For the alternative traits, heritability estimates were 0.12 to 0.17 for standard deviation of log SCC, TD SCC >500,000 cells/mL, and average days diseased per peak, and 0.06 to 0.10 for TD41-80 and number of infection peaks. Genetic correlations between CM with SCM were 0.62 to 0.74, and correlations for these traits with the alternative SCC traits were positive and very high (0.67 to 0.82 for CM, and 0.94 to 0.99 for SCM). Trait TD41-80 was the only alternative trait that showed negative, favorable, genetic correlations with CM (-0.22 to -0.50) and SCM (-0.48 to -0.85) because it is associated with healthy cows. Genetic correlations among the alternative traits in all 3 parities were high (0.93 to 0.99, 0.92 to 0.98, and 0.78 to 0.99, respectively). The only exception was TD41-80, which showed moderate to strong negative correlations with the rest of the traits. Genetic correlations of the same trait across parities were in general positive and very high (0.83 to 0.99). In conclusion, these alternative SCC traits could be used in practical breeding programs aiming to improve udder health in dairy cattle.

  • 13.
    Urioste, Jorge I
    et al.
    Sveriges lantbruksuniversitet, Uppsala.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Strandberg, Erling
    Sveriges lantbruksuniversitet, Uppsala.
    Genetic characterization of new candidate traits derived from test-day somatic cell counts2010Conference paper (Refereed)
  • 14. Urioste, Jorge I
    et al.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Strandberg, Erling
    Phenotypic and genetic characterization of novel somatic cell count traits from weekly or monthly observations2010In: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198, Vol. 93, no 12, p. 5930-5941Article in journal (Refereed)
    Abstract [en]

    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.

  • 15.
    Urioste, Jorge I
    et al.
    Sveriges lantbruksuniversitet, Uppsala.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Strandberg, Erling
    Sveriges lantbruksuniversitet, Uppsala.
    Relationships of novel somatic cell count traits with clinical mastitis using weekly observations2010Conference paper (Refereed)
  • 16. Welderufael, B. G.
    et al.
    De Koning, D. J.
    Fikse, W. F.
    Strandberg, E.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Christensen, O. F.
    Genetic evaluation of mastitis liability and recovery through longitudinal models of simulated SCC2013In: Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Wageningen Academic Publishers, 2013, p. 522-522Conference paper (Refereed)
  • 17. Welderufael, B. G.
    et al.
    de Koning, D. J.
    Janss, L.
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Fikse, W. F.
    Longitudinal Analysis of Somatic Cell Count for Joint Genetic Evaluation of Mastitis and Recovery Liability2014In: Proceedings, 10th World Congress of Genetics Applied to Livestock Production, 2014, article id 095Conference paper (Refereed)
    Abstract [en]

    Better models of genetic evaluation for mastitis can be developed through longitudinal analysis of somatic cell count (SCC) which usually is used as a proxy for mastitis. Mastitis and recovery data with weekly observations of SCC were simulated for daughter groups of 60 and 240 per sire. Data were created to define cases: 1 if SCC was above a pre-specified boundary, else 0. A transition from below to above the boundary indicates probability to contract mastitis, and the other way indicates recovery. The MCMCglmm package was used to estimate breeding values. In the 60 daughters group, accuracies ranged from 0.53 to 0.54 for mastitis and 0.22 to 0.23 for recovery. Whereas, in the 240 daughters group accuracies ranged from 0.83 to 0.85 for mastitis and 0.57 to 0.65 for recovery. Reasonable accuracies can be achieved from SCC based estimates.

  • 18. Welderufael, B. G.
    et al.
    de Koning, D. J.
    Janss, L. L. G.
    Franzen, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Fikse, W. F.
    Simultaneous genetic evaluation of simulated mastitis susceptibility and recovery ability using a bivariate threshold sire model2016In: Acta agriculturae Scandinavica. Section A, Animal science, ISSN 0906-4702, E-ISSN 1651-1972, Vol. 66, no 3, p. 125-134Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to develop a new approach for joint genetic evaluation of mastitis and recovery. Two mastitis incidences (0.28 and 0.95) measured via somatic cell count and three between traits genetic correlations (0.0, 0.2, and -0.2) were simulated for daughter group sizes of 60 and 240. A transition model was applied to model transitions between healthy and disease state. The RJMC package in DMU was used to estimate (co)variances. Heritabilities were consistent with the simulated value (0.039) for susceptibility and a bit upward biased for recovery. Estimates of genetic correlations were -0.055, 0.205, and -0.192 for the simulated values of 0.0, 0.2, and -0.2, respectively. For daughter group size of 60, accuracies of sire EBV ranged from 0.56 to 0.69 for mastitis and from 0.26 to 0.48 for recovery. The study demonstrated that both traits can be modeled jointly and simulated correlations could be correctly reproduced.

  • 19. Welderufael, Berihu
    et al.
    De Koning, Dirk-Jan
    Fikse, Freddy
    Strandberg, Erling
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Christensen, O. F.
    Genetic Evaluation of Mastistis Liability and Recovery through Longitudinal Models of SCC2013Conference paper (Refereed)
  • 20. Welderufael, Berihu
    et al.
    Fikse, Freddy
    Franzén, Jessica
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Strandberg, Erling
    Christensen, O. F.
    De Koning, Dirk-Jan
    Genetic Evaluation of Getting and Recovering from an Intramammary Infection through Longitudinal Models of Somatic Cell Count2013Conference paper (Refereed)
1 - 20 of 20
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