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  • 1. Ahman, Birgitta
    et al.
    Svensson, Kristin
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    High female mortality resulting in herd collapse in free-ranging domesticated reindeer (Rangifer tarandus tarandus) in Sweden2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 10, article id e111509Article in journal (Refereed)
    Abstract [en]

    Reindeer herding in Sweden is a form of pastoralism practised by the indigenous Sami population. The economy is mainly based on meat production. Herd size is generally regulated by harvest in order not to overuse grazing ranges and keep a productive herd. Nonetheless, herd growth and room for harvest is currently small in many areas. Negative herd growth and low harvest rate were observed in one of two herds in a reindeer herding community in Central Sweden. The herds (A and B) used the same ranges from April until the autumn gathering in October-December, but were separated on different ranges over winter. Analyses of capture-recapture for 723 adult female reindeer over five years (2007-2012) revealed high annual losses (7.1% and 18.4%, for herd A and B respectively). A continuing decline in the total reindeer number in herd B demonstrated an inability to maintain the herd size in spite of a very small harvest. An estimated breakpoint for when herd size cannot be kept stable confirmed that the observed female mortality rate in herd B represented a state of herd collapse. Lower calving success in herd B compared to A indicated differences in winter foraging conditions. However, we found only minor differences in animal body condition between the herds in autumn. We found no evidence that a lower autumn body mass generally increased the risk for a female of dying from one autumn to the next. We conclude that the prime driver of the on-going collapse of herd B is not high animal density or poor body condition. Accidents or disease seem unlikely as major causes of mortality. Predation, primarily by lynx and wolverine, appears to be the most plausible reason for the high female mortality and state of collapse in the studied reindeer herding community.

  • 2.
    Alam, Moudud
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Shen, Xia
    Karolinska Institutet.
    Fitting conditional and simultaneous autoregressive spatial models in hglm2015In: The R Journal, ISSN 2073-4859, Vol. 7, no 2, p. 5-18Article in journal (Refereed)
    Abstract [en]

    We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

  • 3.
    Alam, Moudud
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Shen, Xia
    Swedish University of Agricultural Sciences, Uppsala.
    Fitting spatial models in the R package: hglm2014Report (Other academic)
    Abstract [en]

    We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

  • 4. Al-Sarraj, Razaw
    et al.
    Qie, Weigang
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Evaluation of variance component estimators based on Henderson's Method2011Report (Other academic)
  • 5. Alvarez-Castro, J.M.
    et al.
    Carlborg, Ö.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Estimation and interpretation of genetic effects with epistasis using the NOIA model2012In: Quantitative trait loci (QTL): Methods and Protocols / [ed] Scott A. Rifkin, Humana Press, 2012, p. 191-204Chapter in book (Other academic)
    Abstract [en]

    We introduce this communication with a brief outline of the historical landmarks in genetic modeling, especially concerning epistasis. Then, we present methods for the use of genetic modeling in QTL analyses. In particular, we summarize the essential expressions of the natural and orthogonal interactions (NOIA) model of genetic effects. Our motivation for reviewing that theory here is twofold. First, this review presents a digest of the expressions for the application of the NOIA model, which are often mixed with intermediate and additional formulae in the original articles. Second, we make the required theory handy for the reader to relate the genetic concepts to the particular mathematical expressions underlying them. We illustrate those relations by providing graphical interpretations and a diagram summarizing the key features for applying genetic modeling with epistasis in comprehensive QTL analyses. Finally, we briefly review some examples of the application of NOIA to real data and the way it improves the interpretability of the results.

  • 6. Besnier, Francois
    et al.
    Wahlberg, Per
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Ek, Weronika
    Andersson, Leif
    Siegel, Paul
    Carlborg, Örjan
    Fine mapping and replication of QTL in outbred chicken advanced intercross lines2011In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 43, article id 3Article in journal (Refereed)
    Abstract [en]

    Background: Linkage mapping is used to identify genomic regions affecting the expression of complex traits. However, when experimental crosses such as F2 populations or backcrosses are used to map regions containing a Quantitative Trait Locus (QTL), the size of the regions identified remains quite large, i.e. 10 or more Mb. Thus, other experimental strategies are needed to refine the QTL locations. Advanced Intercross Lines (AIL) are produced by repeated intercrossing of F2 animals and successive generations, which decrease linkage disequilibrium in a controlled manner. Although this approach is seen as promising, both to replicate QTL analyses and fine-map QTL, only a few AIL datasets, all originating from inbred founders, have been reported in the literature.

    Methods: We have produced a nine-generation AIL pedigree (n = 1529) from two outbred chicken lines divergently selected for body weight at eight weeks of age. All animals were weighed at eight weeks of age and genotyped for SNP located in nine genomic regions where significant or suggestive QTL had previously been detected in the F2 population. In parallel, we have developed a novel strategy to analyse the data that uses both genotype and pedigree information of all AIL individuals to replicate the detection of and fine-map QTL affecting juvenile body weight.

    Results: Five of the nine QTL detected with the original F2 population were confirmed and fine-mapped with the AIL, while for the remaining four, only suggestive evidence of their existence was obtained. All original QTL were confirmed as a single locus, except for one, which split into two linked QTL.

    Conclusions: Our results indicate that many of the QTL, which are genome-wide significant or suggestive in the analyses of large intercross populations, are true effects that can be replicated and fine-mapped using AIL. Key factors for success are the use of large populations and powerful statistical tools. Moreover, we believe that the statistical methods we have developed to efficiently study outbred AIL populations will increase the number of organisms for which in-depth complex traits can be analyzed.

  • 7. Bring, Johan
    et al.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Åldersbedömningar - en statistisk utmaning2018In: Folkvett, ISSN 0283-0795, no 1, p. 7-13Article in journal (Other (popular science, discussion, etc.))
  • 8.
    Carling, Kenneth
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Roszbach, Kasper
    Is Firm Interdependence within Industries Important for Portfolio Credit Risk?2004Report (Other academic)
    Abstract [en]

    A drawback of available portfolio credit risk models is that they fail to allow for default risk dependency across loans other than through common risk factors. Thereby, thesemodels ignore that close ties can exist between companies due to legal, financial and business relations. In this paper, we integrate the insights from theoretical models of default correlation into a commonly used model of default and portfolio credit risk by allowing for dependency between firm default risk through both common factors and industry specific errors in a duration model. An application using pooled data from two Swedish banks’ business loan portfolios over the period 1996-2000 shows that estimates of individual default risk are little affected by including industry specific errors. However, accounting for these industry effects increases VaR estimates by 50-200 percent. A traditional model with only systematic factors, although able to fit the broad trends in credit losses, cannot match these fluctuations because it fails to capture credit losses in bad times, when banks are typically hit by large unexpected credit losses. The model we propose manages to follow both the trend in credit losses and produce industry driven, time-varying, fluctuations in losses around that trend. Consequently, this model will better aid banks and regulators in determining the appropriate size of economic capital requirements. Capital buffers derived from our model will be larger for periods with large ”aggregate” disturbances and smaller in better times, and avoid both overcapitalization in good times and undercapitalization in bad times.

  • 9. Casals, M.
    et al.
    Langohr, K.
    Carrasco, J. L.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study2015In: SORT - Statistics and Operations Research Transactions, ISSN 1696-2281, E-ISSN 2013-8830, Vol. 39, no 2, p. 281-308Article in journal (Refereed)
    Abstract [en]

    Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are implemented in software packages. The purpose of this study is to compare the performance of three different statistical principles - marginal likelihood, extended likelihood, Bayesian analysis-via simulation studies. Real data on contact wrestling are used for illustration.

  • 10.
    Felleki, Majbritt
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    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
    Dalarna University, School of Technology and Business Studies, Statistics.
    Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models2012In: Genetics Research, ISSN 0016-6723, Vol. 94, no 6, p. 307-317Article in journal (Refereed)
    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).

  • 11.
    Han, Mengjie
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    How do neighbouring populations affect local population change over time?2013Report (Other academic)
    Abstract [en]

    This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse how local population changes are affected by neighbouring populations. To do so we use the last 200 years of local population change that redistributed population in Sweden. We use literature to identify several different processes and spatial dependencies in the redistribution between a parish and its surrounding parishes. The analysis is based on a unique unchanged historical parish division, and we use an index of local spatial correlation to describe different kinds of spatial dependencies that have influenced the redistribution of the population. To control inherent time dependencies, we introduce a non-separable spatial temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies can be observed simultaneously over time. The main conclusions are that while local population changes have been highly dependent on the neighbouring populations in the 19th century, this spatial dependence have become insignificant already when two parishes is separated by 5 kilometres in the late 20th century. Another conclusion is that the time dependency in the population change is higher when the population redistribution is weak, as it currently is and as it was during the 19th century until the start of industrial revolution.

  • 12.
    Han, Mengjie
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics. HUI Research.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Information Systems. Dalarna University, School of Technology and Business Studies, Human Geography. HUI Research, Stockholm.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics. HUI Research, Stockholm.
    To what extent do neighbouring populations affect local population growth over time?2016In: Population, Space and Place, ISSN 1544-8444, E-ISSN 1544-8452, Vol. 22, no 1, p. 68-83Article in journal (Refereed)
    Abstract [en]

    This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse to what extent local population change is affected by neighbouring populations. To do this, we focused on the last 190 years of local population change that redistributed population in Sweden. We used literature to identify several different processes in the population redistribution. The different processes implied different spatial dependencies between local population change and the surrounding populations. The analysis is based on an unchanged historical parish division, and we used an index of local spatial correlation to describe different types of spatial dependencies that influenced the redistribution of the population. To control inherent time dependencies, we introduced a non-separable spatial-temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies could be simultaneously observed over time. The main conclusions are that while local population changes have been highly dependent on neighbouring populations in the 19th century, this spatial dependence became insignificant already when two parishes are separated by 5 km in the late 20th century. It is argued that the only process that significantly redistributed the population at the end of the 20th century is the immigration to Sweden.

  • 13. Husby, Arild
    et al.
    Kawakami, Takeshi
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics. SLU.
    Smeds, Linnéa
    Ellegren, Hans
    Qvarnström, Anna
    Genome-wide association mapping in a wild avian population identifies a link between genetic and phenotypic variation in a life-history trait2015In: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 282, no 1806Article in journal (Refereed)
    Abstract [en]

    Understanding the genetic basis of traits involved in adaptation is a major challenge in evolutionary biology but remains poorly understood. Here, we use genome-wide association mapping using a custom 50 k single nucleotide polymorphism (SNP) array in a natural population of collared flycatchers to examine the genetic basis of clutch size, an important life-history trait in many animal species. We found evidence for an association on chromosome 18 where one SNP significant at the genome-wide level explained 3.9% of the phenotypic variance. We also detected two suggestive quantitative trait loci (QTLs) on chromosomes 9 and 26. Fitness differences among genotypes were generally weak and not significant, although there was some indication of a sex-by-genotype interaction for lifetime reproductive success at the suggestive QTL on chromosome 26. This implies that sexual antagonism may play a role in maintaining genetic variation at this QTL. Our findings provide candidate regions for a classic avian life-history trait that will be useful for future studies examining the molecular and cellular function of, as well as evolutionary mechanisms operating at, these loci.

  • 14. Lee, Youngjo
    et al.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Noh, M
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Skarin, Anna
    Analyzing spatially correlated counts with excessive zeros: a case of modeling the changes of reindeer distribution2013Report (Other academic)
  • 15. Lee, Youngjo
    et al.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Noh, Maengseok
    Data Analysis Using Hierarchical Generalized Linear Models with R2017Book (Other academic)
  • 16. Marjanovic, Jovana
    et al.
    Mulder, Han A
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics. SLU.
    Bijma, Piter
    Modelling the co-evolution of indirect genetic effects and inherited variability2018In: Heredity, ISSN 0018-067X, E-ISSN 1365-2540, Vol. 121, p. 631-647Article in journal (Refereed)
    Abstract [en]

    When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of IGEs and variability, as the regression coefficient can respond to selection. Our simulations show that the model results in increased variability of body weight with increasing competition. When competition decreases, i.e., cooperation evolves, variability becomes significantly smaller. Hence, our model facilitates quantitative genetic studies on the relationship between IGEs and inherited variability. Moreover, our findings suggest that we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.

  • 17.
    Mattsson Petersen, Cecilia
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Berg, Per E O
    Dalarna University, School of Technology and Business Studies, Statistics.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Quality control of waste to incineration: waste composition analysis in Lidköping2005In: Waste Management & Research, ISSN 0734-242X, E-ISSN 1096-3669, Vol. 23, no 6, p. 527-533Article in journal (Refereed)
    Abstract [en]

    In order to decrease environmental impacts in waste management the choice of treatment method must be based on the characteristics of the waste. Present sampling procedures do not provide statistically representative samples of solid waste and this provides difficulties in characterization. The objective of this study was to develop a procedure for waste component analysis and sampling of waste after collection and at plant level. A further objective was to characterize the waste delivered to an incineration plant for physical and chemical properties and to determine the amounts of delivered waste that could be classified as biofuels and fossil fuels. The proportions of recyclables and hazardous waste were also examined. Samples were taken randomly from waste trucks and divided by square implementation. Statistical analysis of the results showed that the number of sub-samples could be decreased with only a moderate increase in the confidence interval. This means that future waste composition analyses could be made more efficient and thereby less expensive. The analysis of the waste delivered to the Lidkoping incineration plant (Central Sweden) showed that 66.4% of the household waste was composed of biofuels and 21.3% of non-renewable combustibles, of which 40.3% were recyclables. In addition, 11.6% of the household waste was non-combustible and 0.6% hazardous waste. The heat value for the biofuels was 18.0-19.7 MJ kg(-1) dry mass (DM) and for the fossil fuels 28.2-33.9 MJ kg(-1) DM. The industrial waste consisted of 35.9% biofuels, 62.0% fossil fuels, 1.6% non-combustible and 0.5% hazardous waste. The heat value was 19.5 MJ kg(-1) DM for the biofuels and 31.4 MJ kg(-1) DM for the fossil fuels.

  • 18. Mischenko, Kateryna
    et al.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Holmgren, Sverker
    Mishchenko, Vladimir
    Assessing a multiple QTL search using the variance component model2010In: Computational biology and chemistry (Print), ISSN 1476-9271, E-ISSN 1476-928X, Vol. 34, no 1, p. 34-41Article in journal (Refereed)
    Abstract [en]

    Development of variance component algorithms in genetics has previously mainly focused on animal breeding models or problems in human genetics with a simple data structure. We study alternative methods for constrained likelihood maximization in quantitative trait loci (QTL) analysis for large complex pedigrees. We apply a forward selection scheme to include several QTL and interaction effects, as well as polygenic effects, with up to five variance components in the model. We show that the implemented active set and primal-dual schemes result in accurate solutions and that they are robust. In terms of computational speed, a comparison of two approaches for approximating the Hessian of the log-likelihood shows that the method using an average information matrix is the method of choice for the five-dimensional problem. The active set method, with the average information method for Hessian computation, exhibits the fastest convergence with an average of 20 iterations per tested position, where the change in variance components <0.0001 was used as convergence criterion.

  • 19. Mishchenk, Kateryna
    et al.
    Holmgren, Sverker
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Newton-type methods for REML estimation in genetic analysis of quantitative traits.2008In: Journal of Computational Methods in Sciences and Engineering, ISSN 1875-8983, Vol. 8, no 1,2, p. 53-67Article in journal (Refereed)
  • 20.
    Mouresan, Elena Flavia
    et al.
    Swedish University of Agricultural Sciences.
    Selle, Maria
    Norwegian University of Science and Technology.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Genomic Prediction Including SNP-Specific Variance Predictors2019In: G3: Genes, Genomes, Genetics, ISSN 2160-1836, E-ISSN 2160-1836, article id g3.400381.2019Article in journal (Refereed)
    Abstract [en]

    The increasing amount of available biological information on the markers can be used to inform the models applied for genomic selection to improve predictions. The objective of this study was to propose a general model for genomic selection using a link function approach within the hierarchical generalized linear model framework (hglm) that can include external information on the markers. These models can be fitted using the well-established hglm package in R. We also present an R package (CodataGS) to fit these models, which is significantly faster than the hglm package. Simulated data was used to validate the proposed model. We tested categorical, continuous and combination models where the external information on the markers was related to 1) the location of the QTLs on the genome with varying degree of uncertainty, 2) the relationship of the markers with the QTLs calculated as the LD between them, and 3) a combination of both. The proposed models showed improved accuracies from 3.8% up to 23.2% compared to the SNP-BLUP method in a simulated population derived from a base population with 100 individuals. Moreover, the proposed categorical model was tested on a dairy cattle dataset for two traits (Milk Yield and Fat Percentage). These results also showed improved accuracy compared to SNP-BLUP, especially for the Fat% trait. The performance of the proposed models depended on the genetic architecture of the trait, as traits that deviate from the infinitesimal model benefited more from the external information. Also, the gain in accuracy depended on the degree of uncertainty of the external information provided to the model. The usefulness of these type of models is expected to increase with time as more accurate information on the markers becomes available.

  • 21. Mulder, Han A.
    et al.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Fikse, W Freddy
    Veerkamp, R F
    Strandberg, E
    Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models2013In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 45, article id 23Article in journal (Refereed)
    Abstract [en]

    Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model.

    Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters.

    Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed.

    Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

  • 22. Mulder, Herman A
    et al.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Veerkamp, Roel
    Prediction of breeding values for mean and environmental variance with an iterative BLUP-procedure2010In: 9th World Congress on Genetics Applied to Livestock Production. WCGALP 2010 conference., Leipzig, Germany, 2010Conference paper (Refereed)
  • 23. Nelson, Ronald M
    et al.
    Temnykh, Svetlana V
    Johnson, Jennifer L
    Kharlamova, Anastasiya V
    Vladimirova, Anastasiya V
    Shepeleva, Darya V
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Trut, Lyudmila N
    Carlborg, Örjan
    Kukekova, Anna V
    Genetics of interactive behavior in silver foxes (Vulpes vulpes)2017In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Vol. 47, no 1, p. 88-101Article in journal (Refereed)
    Abstract [en]

    Individuals involved in a social interaction exhibit different behavioral traits that, in combination, form the individual's behavioral responses. Selectively bred strains of silver foxes (Vulpes vulpes) demonstrate markedly different behaviors in their response to humans. To identify the genetic basis of these behavioral differences we constructed a large F2 population including 537 individuals by cross-breeding tame and aggressive fox strains. 98 fox behavioral traits were recorded during social interaction with a human experimenter in a standard four-step test. Patterns of fox behaviors during the test were evaluated using principal component (PC) analysis. Genetic mapping identified eight unique significant and suggestive QTL. Mapping results for the PC phenotypes from different test steps showed little overlap suggesting that different QTL are involved in regulation of behaviors exhibited in different behavioral contexts. Many individual behavioral traits mapped to the same genomic regions as PC phenotypes. This provides additional information about specific behaviors regulated by these loci. Further, three pairs of epistatic loci were also identified for PC phenotypes suggesting more complex genetic architecture of the behavioral differences between the two strains than what has previously been observed.

  • 24.
    Nelson, Ronald Michael
    et al.
    Swedish University of Agricultural Sciences.
    Nettelblad, Carl
    Uppsala University.
    Pettersson, Mats E
    Swedish University of Agricultural Sciences.
    Shen, Xia
    Swedish University of Agricultural Sciences; Uppsala University;.
    Crooks, Lucy
    Swedish University of Agricultural Sciences.
    Besnier, Francois
    Swedish University of Agricultural Sciences.
    Alvarez-Castro, José
    Swedish University of Agricultural Sciences.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Ek, Weronica
    Swedish University of Agricultural Sciences.
    Carlborg, Örjan
    Swedish University of Agricultural Sciences.
    MAPfastR: quantitative trait loci mapping in outbred line crosses2013In: G3: Genes, Genomes, Genetics, ISSN 2160-1836, E-ISSN 2160-1836, Vol. 12, no 3, p. 2147-2149Article in journal (Refereed)
    Abstract [en]

    MAPfastR is a software package developed to analyze QTL data from inbred and outbred line-crosses. The package includes a number of modules for fast and accurate QTL analyses. It has been developed in the R language for fast and comprehensive analyses of large datasets. MAPfastR is freely available at: http://www.computationalgenetics.se/?page_id=7.

  • 25.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Selection, Maternal Effects and Inbreeding in Reindeer Husbandry2003Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In extensive grazing systems where several owners’ flocks are allowed to mix, selection strategies will also interact, due to gene flow between flocks. The aim of the thesis was to analyse breeding schemes in terms of genetic gain and rate of inbreeding (?F), given the complexity of ownership and interaction of selection strategies within a mixing reindeer population. The data, collected between 1986 and 1997 in the reindeer herding district of Ruvhten Sijte, Sweden, comprised 12,500 records of autumn calf weights. The mean phenotypic difference in calf weights between selected and non-selected flocks was 0.67 kg after 11 years of selection in Ruvhten Sijte. The genetic difference was 0.35 kg and the realized heritability was 0.2. Based on the realized heritablility, it was shown that if all owners had applied selection, the genetic response would have been 2 kg (corresponding to an annual genetic gain of 0.4% of the phenotypic mean, including the initial lag due to age structure). The relationships between life-time patterns of female weight, calving incidence and offspring weight were examined. A female rearing a calf was shown to weigh 3.1 kg less in winter than one not rearing a calf. The regression coefficient of calf autumn weight on female weight the previous winter was 0.26. Detailed life-history patterns were obtained for females aged 1 – 15 years. The expected long-term genetic contribution method to predict genetic gain and ?F in selected populations was developed to include maternal effects. It was shown that variation in inherited maternal effects influences ?F more than does variation in non-inherited maternal effects. Furthermore, population structures affect ?F much more when there are maternal effects, than direct genetic effects alone, especially in populations with large family size. This method was used to evaluate different selection schemes in reindeer husbandry, with different proportions of a population included in each scheme. It was shown that for reindeer population sizes greater than 2,000 there is no risk of inbreeding effects.

  • 26.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics. Swedish University of Agricultural Sciences.
    The evolution of peer-reviewed papers.2019In: Journal of Animal Breeding and Genetics, ISSN 0931-2668, E-ISSN 1439-0388, Vol. 136, no 2, p. 77-78Article in journal (Other academic)
  • 27.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Al-Sarraj, Razaw
    von Rosen, Dietrich
    Non-iterative variance component estimation in QTL analysis.2009In: Journal of Animal Breeding and Genetics, ISSN 0931-2668, E-ISSN 1439-0388, Vol. 126, no 1, p. 110-116Article in journal (Refereed)
    Abstract [en]

    In variance component quantitative trait loci (QTL) analysis, a mixed model is used to detect the most likely chromosome position of a QTL. The putative QTL is included as a random effect and a method is needed to estimate the QTL variance. The standard estimation method used is an iterative method based on the restricted maximum likelihood (REML). In this paper, we present a novel non-iterative variance component estimation method. This method is based on Henderson's method 3, but relaxes the condition of unbiasedness. Two similar estimators were compared, which were developed from two different partitions of the sum of squares in Henderson's method 3. The approach was compared with REML on data from a European wild boar × domestic pig intercross. A meat quality trait was studied on chromosome 6 where a functional gene was known to be located. Both partitions resulted in estimated QTL variances close to the REML estimates. From the non-iterative estimates, we could also compute good approximations of the likelihood ratio curve on the studied chromosome.

  • 28.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Besnier, Francois
    Carlborg, Örjan
    An improved method for QTL detection and identification of within-line segregation in F2 intercross designs2008In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 178, no April, p. 2315-2326Article in journal (Refereed)
  • 29.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Besnier, Francois
    Carlborg, Örjan
    Modelling dominance in a flexible intercross analysis2009In: BMC Genetics, ISSN 1471-2156, E-ISSN 1471-2156, Vol. 10, article id 30Article in journal (Refereed)
    Abstract [en]

    The aim of this paper is to develop a flexible model for analysis of quantitative trait loci (QTL) in outbred line crosses, which includes both additive and dominance effects. Our flexible intercross analysis (FIA) model accounts for QTL that are not fixed within founder lines and is based on the variance component framework. Genome scans with FIA are performed using a score statistic, which does not require variance component estimation.

    RESULTS: Simulations of a pedigree with 800 F2 individuals showed that the power of FIA including both additive and dominance effects was almost 50% for a QTL with equal allele frequencies in both lines with complete dominance and a moderate effect, whereas the power of a traditional regression model was equal to the chosen significance value of 5%. The power of FIA without dominance effects included in the model was close to those obtained for FIA with dominance for all simulated cases except for QTL with overdominant effects. A genome-wide linkage analysis of experimental data from an F2 intercross between Red Jungle Fowl and White Leghorn was performed with both additive and dominance effects included in FIA. The score values for chicken body weight at 200 days of age were similar to those obtained in FIA analysis without dominance.

    CONCLUSION: We have extended FIA to include QTL dominance effects. The power of FIA was superior, or similar, to standard regression methods for QTL effects with dominance. The difference in power for FIA with or without dominance is expected to be small as long as the QTL effects are not overdominant. We suggest that FIA with only additive effects should be the standard model to be used, especially since it is more computationally efficient.

  • 30.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Carlborg, Ö.
    A new efficient method for QTL mapping in divergent intercrosses incorporating within line variation2006In: 8th World Congress on Genetics Applied to Livestock Production, Brazil, 2006Conference paper (Other academic)
  • 31.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Carlborg, Örjan
    Separation of base allele and sampling term effects gives new insights in variance component QTL analysis2007In: BMC Genetics, ISSN 1471-2156, E-ISSN 1471-2156, Vol. 8, no 1Article in journal (Refereed)
    Abstract [en]

    Background Variance component (VC) models are commonly used for Quantitative Trait Loci (QTL) mapping in outbred populations. Here, the QTL effect is given as a random effect and a critical part of the model is the relationship between the phenotypic values and the random effect. In the traditional VC model, each individual has a unique QTL effect and the relationship between these random effects is given as a covariance structure (known as the identity-by-descent (IBD) matrix). Results We present an alternative notation of the variance component model, where the elements of the random effect are independent base generation allele effects and sampling term effects. The relationship between the phenotypic vales and the random effect is given by an incidence matrix, which results in a novel, but statistically equivalent, version of the traditional VC model. A general algorithm to estimate this incidence matrix is presented. Since the model is given in terms of base generation allele effects and sampling term effects, these effects can be estimated separately using best linear unbiased prediction (BLUP). From simulated data, we showed that biallelic QTL effects could be accurately clustered using the BLUP obtained from our model notation when markers are fully informative, and that the accuracy increased with the size of the QTL effect. We also developed a measure indicating whether a base generation marker homozygote is a QTL heterozygote or not, by comparing the variances of the sampling term BLUP and the base generation allele BLUP. A ratio greater than one gives strong support for a QTL heterozygote. Conclusion We developed a simple presentation of the VC QTL model for identification of base generation allele effects in QTL linkage analysis. The base generation allele effects and sampling term effects were separated in our model notation. This clarifies the assumptions of the model and should also enhance the development of genome scan methods.

  • 32.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Danell, Ö
    Genetic response to selection on reindeer calf weights.2003In: Rangifer, ISSN 1890-6729, Vol. 23, no 1, p. 1-13Article in journal (Refereed)
  • 33.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Danell, Ö.
    Hjordstrukturering och urval vid slakt2002In: Boazudiehtu - Nyhetsblad om forskning och utveckling i renskötseln, no 3Article in journal (Other academic)
  • 34.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Danell, Öje
    Gene flow and potential selection response in age-structured subpopulations having a common male pool.2001In: Animal Science, ISSN 0261-698X, Vol. 72, no 3, p. 427-440Article in journal (Refereed)
  • 35.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Felleki, Majbritt
    Dalarna University, School of Technology and Business Studies, Statistics.
    Fikse, Freddy
    Mulder, Herman A.
    Strandberg, Erling
    Genetic heterogeneity of residual variance: estimation of variance components using double hierarchical generalized linear models2010In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 42, article id 8Article in journal (Refereed)
    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.

  • 36.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics. Swedish Univ Agr Sci, Dept Anim Breeding & Genet, S-75007 Uppsala, Sweden.
    Felleki, Majbritt
    Dalarna University, School of Technology and Business Studies, Statistics. 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 cattle2013In: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198, Vol. 96, no 4, p. 2627-2636Article in journal (Refereed)
    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.

  • 37.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Fikse, Freddy
    Mulder, Herman
    Strandberg, E
    Breeding Value Estimation for Environmental Sensitivity on a Large Dairy Cattle Data Set2011In: Interbull Meeting, Stavanger, Norway, 2011Conference paper (Other academic)
    Abstract [en]

    Animal robustness, or environmental sensitivity, may be studied through individual differences in re-sidual variance. These differences appear to be heritable, and there is therefore a need to fit models having breeding values explaining differences in residual variance. The aim of this report is to study whether breeding value estimation for environmental sensitivity (vEBV) can be performed on a large dairy cattle data set having around 1.6 million records. Two traits were analyzed separately, somatic cell score and milk yield. Estimation of variance components, ordinary breeding values and vEBVs was performed using standard variance component estimation software (ASReml), applying the me-thodology for double hierarchical generalized linear models. Converged estimates were obtained by running ASReml iteratively 20 times, which took less than 10 days on a Linux server. The genetic coefficients of variation for environmental variance were 0.45 and 0.52, for somatic cell score and milk yield, respectively, which indicate a substantial genetic variance for environmental variance. This study shows that estimation of variance components, EBVs and vEBVs, is feasible for large dairy cattle data sets using standard variance component estimation software.

  • 38.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Forslund, Pär
    Danell, Öje
    Lifetime patterns in adult female mass, reproduction and offspring mass in semidomesticated reindeer (Rangifer tarandus tarandus).2002In: Canadian Journal of Zoology, ISSN 0008-4301, E-ISSN 1480-3283, Vol. 80, no 12, p. 2047-2055Article in journal (Refereed)
  • 39.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Hannu, U
    Urval ökar renhjordens produktivitet.2003In: Boazudiehtu - Nyhetsblad om forskning och utveckling i renskötseln, no 1Article in journal (Other academic)
  • 40.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Lee, Y
    Seoul National University, Seoul, Korea.
    Exploring the potential of hierarchical generalized linear models in animal breeding and genetics2013In: Journal of Animal Breeding and Genetics, ISSN 0931-2668, E-ISSN 1439-0388, Vol. 130, no 6, p. 415-416Article in journal (Refereed)
  • 41.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Lee, Youngjo
    Hierarchical generalized linear models have a great potential in genetics and animal breeding2010Conference paper (Refereed)
  • 42.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    McFarlane, S. Eryn
    Uppsala universitet.
    Husby, Arlid
    University of Helsinki; Norwegian University of Science and Technology.
    Kawakami, Takeshi
    Uppsala universitet.
    Ellegren, Hans
    Uppsala universitet.
    Qvarnström, Anna
    Uppsala universitet.
    Increasing the power of genome wide association studies in natural populations using repeated measures: evaluation and implementation2016In: Methods in Ecology and Evolution, ISSN 2041-210X, E-ISSN 2041-210X, Vol. 7, no 7, p. 792-799Article in journal (Refereed)
    Abstract [en]

    1. Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long-term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single-nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful.

    2. Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long-term study of collared flycatchers in Sweden.

    3. The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response.

    4. The method is available in the R package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long-term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.

  • 43.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Mishchenko, Kateryna
    Holmgren, Sverker
    Efficient implementation of the AI-REML iteration variance component QTL analysis.2007Report (Other academic)
  • 44.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Mishchenko, Kateryna
    Holmgren, Sverker
    Carlborg, Örjan
    Increasing the efficiency of variance component quantitative trait loci analysis by using reduced-rank identity-by-descent matrices.2007In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 176, no July, p. 1935-1938Article in journal (Refereed)
  • 45.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Pong-Wong, Ricardo
    Carlborg, Örjan
    Defining the assumptions underlying modelling of epistatic QTL using variance component methods.2008In: Journal of Heredity, ISSN 0022-1503, E-ISSN 1465-7333, Vol. 99, no 4, p. 421-425Article in journal (Refereed)
  • 46.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Rebreyend, Pascal
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Statistics.
    Vinterkonferens i rumslig statistik i Dalarna2011In: Qvintensen, ISSN 2000-1819, Vol. 2011, no 4Article in journal (Other academic)
  • 47.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Sand, Håkan
    Andren, Henrik
    Månsson, Johan
    Pehrson, Åke
    Evaluation of four methods used to estimate population density of moose (Alces alces)2008In: Wildlife Biology, ISSN 0909-6396, E-ISSN 1903-220X, Vol. 14, no 3, p. 358-371Article in journal (Refereed)
    Abstract [en]

    Various survey methods are used to monitor and manage ungulate popualations. The choice of optimal method depends on estimation accuracy, management objective and financial constraints. Here we compare estimates produced by four different methods for estimating population size, i.e. aerial counts, hunter observations, pellet group counts and cohort analysis. A Swedish moose Alces alces population was studied during 1973-2005 in the Grimso Wildlife Research Area (135 km(2)). The highest correlation was found between cohort analysis and aerial counts (r = 0.69. P < 0.05). and the hunter observations and the aerial counts (r = 0.76. P < 0.10). The different methods produced relatively consistent trends in population estimates over years. Pellet group counts prior to 1997 were not significantly correlated with the other methods. probably due to unrepresentative spatial sampling. A comparison of the aerial and pellet group counts in 2002 and 2006, showed that the average defecation rate was estimated at approximately 14 pellet groups per day per moose. Our results show the importance of having representative spatial sampling in pellet group surveys and indicate that hunter observations can be a useful tool for estimating long-term population trends even in moderately sized areas.

  • 48.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Shen, Xia
    Dalarna University, School of Technology and Business Studies, Statistics.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Hglm: A package for fitting hierarchical generalized linear models2010In: The R Journal, ISSN 2073-4859, E-ISSN 2073-4859, Vol. 2, no 2, p. 20-28Article in journal (Refereed)
    Abstract [en]

    We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

  • 49.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Valdar, William
    Detecting major genetic loci controlling phenotypic variability in experimental crosses2011In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 188, no 2, p. 435-447Article in journal (Refereed)
    Abstract [en]

    Traditional methods for detecting genes that affect complex diseases in humans or animal models, milk production in livestock, or other traits of interest, have asked whether variation in genotype produces a change in that trait’s average value. But focusing on differences in the mean ignores differences in variability about that mean. The robustness, or uniformity, of an individual’s character is not only of great practical importance in medical genetics and food production but is also of scienti?c and evolutionary interest (e.g., blood pressure in animal models of heart disease, litter size in pigs, ?owering time in plants). We describe a method for detecting major genes controlling the phenotypic variance, referring to these as vQTL. Our method uses a double generalized linear model with linear predictors based on probabilities of line origin. We evaluate our method on simulated F2 and collaborative cross data, and on a real F2 intercross, demonstrating its accuracy and robustness to the presence of ordinary mean-controlling QTL. We also illustrate the connection between vQTL and QTL involved in epistasis, explaining how these concepts overlap. Our method can be applied to a wide range of commonly used experimental crosses and may be extended to genetic association more generally.

  • 50.
    Rönnegård, Lars
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Valdar, William
    Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability2012In: BMC Genetics, ISSN 1471-2156, E-ISSN 1471-2156, Vol. 13, article id 63Article in journal (Refereed)
    Abstract [en]

    A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.

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