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  • 51.
    Rönnegård, Lars
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Woolliams, J.A
    Predicted rates of inbreeding with additive maternal effects.2003Inngår i: Genetical Research, ISSN 0016-6723, E-ISSN 1469-5073, nr 82, s. 67-77Artikkel i tidsskrift (Fagfellevurdert)
  • 52.
    Rönnegård, Lars
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Woolliams, J.A.
    Danell, Ö.
    Breeding schemes in reindeer husbandry2003Inngår i: Rangifer, ISSN 1890-6729, Vol. 23, nr 2, s. 45-55Artikkel i tidsskrift (Fagfellevurdert)
  • 53.
    Saqlain, Murshid
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Alam, Moudud
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Brandt, Daniel
    Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease2018Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    The purpose of this study is to investigate a pharmacokinetic model of levodopa concentration in patients with Parkinson's disease by introducing stochasticity so that inter-individual variability may be separated into measurement and system noise. It also aims to investigate whether the stochastic differential equations (SDE) model provide better fits than its ordinary differential equations (ODE) counterpart, by using a real data set. Westin et al. developed a pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion described by four ODEs, the first three of which define the pharmacokinetic model. In this study, system noise variables are added to the aforementioned first three equations through a standard Wiener process, also known as Brownian motion. The R package PSM for mixed-effects models is used on data from previous studies for modelling levodopa concentration and parameter estimation. First, the diffusion scale parameter, σ, and bioavailability are estimated with the SDE model. Second, σ is fixed to integer values between 1 and 5, and bioavailability is estimated. Cross-validation is performed to determine whether the SDE based model explains the observed data better or not by comparingthe average root mean squared errors (RMSE) of predicted levodopa concentration. Both ODE and SDE models estimated bioavailability to be about 88%. The SDE model converged at different values of σ that were signicantly different from zero while estimating bioavailability to be about 88%. The average RMSE for the ODE model wasfound to be 0.2980, and the lowest average RMSE for the SDE model was 0.2748 when σ was xed to 4. Both models estimated similar values for bioavailability, and the non-zero σ estimate implies that the inter-individual variability may be separated. However, the improvement in the predictive performance of the SDE model turned out to be rather small, compared to the ODE model.

    Fulltekst (pdf)
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  • 54.
    Saqlain, Murshid
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Alam, Moudud
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease2020Inngår i: European journal of drug metabolism and pharmacokinetics, ISSN 0378-7966, E-ISSN 2107-0180, Vol. 45, nr 1, s. 41-49Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND AND OBJECTIVES: Levodopa concentration in patients with Parkinson's disease is frequently modelled with ordinary differential equations (ODEs). Here, we investigate a pharmacokinetic model of plasma levodopa concentration in patients with Parkinson's disease by introducing stochasticity to separate the intra-individual variability into measurement and system noise, and to account for auto-correlated errors. We also investigate whether the induced stochasticity provides a better fit than the ODE approach.

    METHODS: In this study, a system noise variable is added to the pharmacokinetic model for duodenal levodopa/carbidopa gel (LCIG) infusion described by three ODEs through a standard Wiener process, leading to a stochastic differential equations (SDE) model. The R package population stochastic modelling (PSM) was used for model fitting with data from previous studies for modelling plasma levodopa concentration and parameter estimation. First, the diffusion scale parameter (σw), measurement noise variance, and bioavailability are estimated with the SDE model. Second, σw is fixed to certain values from 0 to 1 and bioavailability is estimated. Cross-validation was performed to compare the average root mean square errors (RMSE) of predicted plasma levodopa concentration.

    RESULTS: Both the ODE and the SDE models estimated bioavailability to be approximately 75%. The SDE model converged at different values of σw that were significantly different from zero. The average RMSE for the ODE model was 0.313, and the lowest average RMSE for the SDE model was 0.297 when σw was fixed to 0.9, and these two values are significantly different.

    CONCLUSIONS: The SDE model provided a better fit for LCIG plasma levodopa concentration by approximately 5.5% in terms of mean percentage change of RMSE.

    Fulltekst (pdf)
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  • 55.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Alam, Moudud
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Fikse, Freddy
    Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    A novel generalized ridge regression method for quantitative genetics2013Inngår i: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 193, nr 4, s. 1255-1268Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    As the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithmfor situations where the number of parameters largely exceeds the number of observations. The computationally demanding parts of the method depend mainly on the number ofobservations and not the number of parameters. The algorithm was implemented in the R package bigRR based on the previously developed package hglm. Using such an approach, a heteroscedastic effects model (HEM) was also developed, implemented and tested. Theefficiency for different data sizes were evaluated via simulation. The method was tested for a bacteria-hypersensitive trait in a publicly available Arabidopsis dataset including 84 inbred lines and 216 130 SNPs. The computation of all the SNP effects required less than10 seconds using a single 2.7 GHz core. The advantage in run-time makes permutationtest feasible for such a whole-genome model, so that a genome-wide significance threshold can be obtained. HEM was found to be more robust than ordinary RR (a.k.a. SNPBLUP) in terms of QTL mapping, because SNP specific shrinkage was applied instead of acommon shrinkage. The proposed algorithm was also assessed for genomic evaluation and was shown to give better predictions than ordinary RR.

  • 56. Shen, Xia
    et al.
    Li, Ying
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Uden, Peter
    Carlborg, Orjan
    Application of a genomic model for high-dimensional chemometric analysis2014Inngår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, nr 7, s. 548-557Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The rapid development of newtechnologies for large-scale analysis of genetic variation in the genomes of individuals and populations has presented statistical geneticists with a grand challenge to develop efficient methods for identifying the small proportion of all identified genetic polymorphisms that have effects on traits of interest. To address such a "large p small n" problem, we have developed a heteroscedastic effects model (HEM) that has been shown to be powerful in high-throughput genetic analyses. Here, we describe how this whole-genome model can also be utilized in chemometric analysis. As a proof of concept, we use HEM to predict analyte concentrations in silage using Fourier transform infrared spectroscopy signals. The results show that HEM often outperforms the classic methods and in addition to this presents a substantial computational advantage in the analyses of such high-dimensional data. The results thus show the value of taking an interdisciplinary approach to chemometric analysis and indicate that large-scale genomic models can be a promising new approach for chemometric analysis that deserve to be evaluated more by experts in the field. The software used for our analyses is freely available as an R package at http://cran.r-project.org/web/packages/bigRR/. Copyright (C) 2014 JohnWiley & Sons, Ltd.

  • 57.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Pettersson, Mats
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carlborg, Örjan
    Inheritance beyond plain heritability: variance-controlling genes in Arabidopsis thaliana2012Inngår i: PLOS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 8, nr 8, artikkel-id e1002839Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The phenotypic effect of a gene is normally described by the mean-difference between alternative genotypes. A gene may, however, also influence the phenotype by causing a difference in variance between genotypes. Here, we reanalyze a publicly available Arabidopsis thaliana dataset [1] and show that genetic variance heterogeneity appears to be as common as normal additive effects on a genomewide scale. The study also develops theory to estimate the contributions of variance differences between genotypes to the phenotypic variance, and this is used to show that individual loci can explain more than 20% of the phenotypic variance. Two well-studied systems, cellular control of molybdenum level by the ion-transporter MOT1 and flowering-time regulation by the FRI-FLC expression network, and a novel association for Leaf serration are used to illustrate the contribution of major individual loci, expression pathways, and gene-by-environment interactions to the genetic variance heterogeneity.

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  • 58.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty2010Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

    Fulltekst (pdf)
    fulltext
  • 59.
    Shen, Xia
    et al.
    SLUDivision of Computational Genetics, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Division of Quantitative Genetics, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala.
    Issues with data transformation in genome-wide association studies for phenotypic variability2013Inngår i: F1000Research, ISSN 2046-1402, Vol. 2, nr 200Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The purpose of this correspondence is to discuss and clarify a few points about data transformation used in genome-wide association studies, especially for phenotypic variability. By commenting on the recent publication by Sun et al. in the American Journal of Human Genetics, we emphasize the importance of statistical power in detecting functional loci and the real meaning of the scale of the phenotype in practice.

  • 60.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carlborg, Örjan
    Hierarchical likelihood opens a new way of estimating genetic values using genome-wide dense marker maps2011Inngår i: BMC Proceedings, ISSN 1753-6561, E-ISSN 1753-6561, Proc. 14th European Workshop on QTL Mapping and Marker Assisted Selection (QTL-MAS), nr 5(Suppl 3)Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background Genome-wide dense markers have been used to detect genes and estimate relative genetic values. Among many methods, Bayesian techniques have been widely used and shown to be powerful in genome-wide breeding value estimation and association studies. However, computation is known to be intensive under the Bayesian framework, and specifying a prior distribution for each parameter is always required for Bayesian computation. We propose the use of hierarchical likelihood to solve such problems. Results Using double hierarchical generalized linear models, we analyzed the simulated dataset provided by the QTLMAS 2010 workshop. Marker-specific variances estimated by double hierarchical generalized linear models identified the QTL with large effects for both the quantitative and binary traits. The QTL positions were detected with very high accuracy. For young individuals without phenotypic records, the true and estimated breeding values had Pearson correlation of 0.60 for the quantitative trait and 0.72 for the binary trait, where the quantitative trait had a more complicated genetic architecture involving imprinting and epistatic QTL. Conclusions Hierarchical likelihood enables estimation of marker-specific variances under the likelihoodist framework. Double hierarchical generalized linear models are powerful in localizing major QTL and computationally fast.

  • 61.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carlborg, Örjan
    How to deal with genotype uncertainty in variance component quantitative trait loci analyses2011Inngår i: Genetics Research, ISSN 0016-6723, Vol. 93, nr 5, s. 333-342Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Dealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

  • 62. Shukur, Ghazi
    et al.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Framtida utmaningar för forskarutbildningen i statistik2013Inngår i: Qvintensen, ISSN 2000-1819, nr 4, s. 21-21Artikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
  • 63. Silva, C. N. S
    et al.
    McFarlane, S. E
    Hagen, I. J
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala.
    Billing, A. M
    Kvalnes, T
    Kemppainen, P
    Rønning, B
    Ringsby, T. H
    Husby, A
    Insights into the genetic architecture of morphological traits in two passerine bird species2017Inngår i: Heredity, ISSN 0018-067X, E-ISSN 1365-2540, Vol. 119, nr 3, s. 197-205Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Knowledge about the underlying genetic architecture of phenotypic traits is needed to understand and predict evolutionary dynamics. The number of causal loci, magnitude of the effects and location in the genome are, however, still largely unknown. Here, we use genome-wide single-nucleotide polymorphism (SNP) data from two large-scale data sets on house sparrows and collared flycatchers to examine the genetic architecture of different morphological traits (tarsus length, wing length, body mass, bill depth, bill length, total and visible badge size and white wing patches). Genomic heritabilities were estimated using relatedness calculated from SNPs. The proportion of variance captured by the SNPs (SNP-based heritability) was lower in house sparrows compared with collared flycatchers, as expected given marker density (6348 SNPs in house sparrows versus 38 689 SNPs in collared flycatchers). Indeed, after downsampling to similar SNP density and sample size, this estimate was no longer markedly different between species. Chromosome-partitioning analyses demonstrated that the proportion of variance explained by each chromosome was significantly positively related to the chromosome size for some traits and, generally, that larger chromosomes tended to explain proportionally more variation than smaller chromosomes. Finally, we found two genome-wide significant associations with very small-effect sizes. One SNP on chromosome 20 was associated with bill length in house sparrows and explained 1.2% of phenotypic variation (VP), and one SNP on chromosome 4 was associated with tarsus length in collared flycatchers (3% of VP). Although we cannot exclude the possibility of undetected large-effect loci, our results indicate a polygenic basis for morphological traits.

  • 64. Sivertsen, Therese R.
    et al.
    Åhman, Birgitta
    Steyaert, Sam M. J. G.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Frank, Jens
    Segerström, Peter
    Støen, Ole-Gunnar
    Skarin, Anna
    Reindeer habitat selection under the risk of brown bear predation during calving season2016Inngår i: Ecosphere, ISSN 2150-8925, E-ISSN 2150-8925, Vol. 7, nr 11, artikkel-id e01583Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The depredation of semi-domesticated reindeer by large carnivores reflects an important human-wildlife conflict in Fennoscandia. Recent studies have revealed that brown bears (Ursus arctos) may kill substantial numbers of reindeer calves (Rangifer tarandus tarandus) in forest areas in Sweden. Several authors have suggested that predation risk is an important driver of habitat selection in wild Rangifer populations where predation is a limiting factor, but little is known about these mechanisms in semi-domesticated populations. We examined the habitat selection of female reindeer in relation to spatial and temporal variations in brown bear predation risk on the reindeer calving grounds and evaluated the simultaneous responses of brown bears and reindeer to landscape characteristics. We used GPS data from 110 reindeer years (97 individuals) and 29 brown bear years (19 individuals), from two reindeer herding districts in the forest area of northern Sweden. Our results did not indicate that reindeer alter their behavior in response to spatiotemporal variation in brown bear predation risk, on the scale of the calving range. Instead, we suggest that spatiotemporal behavioral adjustments by brown bears were the main driver of prey-predator interactions in our study system. Contrasting responses by brown bears and reindeer to clear-cuts and young forest indicate that forestry can influence species interactions and possibly yield negative consequences for the reindeer herd. Even if clear-cuts may be beneficial in terms of calf survival, logging activity will eventually cause greater abundance of young regenerating forest, reducing available reindeer habitats and increasing habitat preferred by brown bears. Domestication may have made semi-domesticated reindeer in Fennoscandia less adapted to cope with predators. Areal restrictions, limiting the opportunity for dispersion and escape, possibly make the calves more susceptible to predation. Also, a generally higher population density in semi-domesticated herds compared to wild populations can make dispersion a less efficient strategy and the reindeer calves easier prey. Overall, the lack of ability of the reindeer females to reduce brown bear encounter risk on the scale of the calving range is probably an important reason for the high brown bear predation rates on reindeer calves documented in our study areas. 

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  • 65. Skarin, Anna
    et al.
    Helleman, Christian
    Sandström, Per
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Lundquist, Henrik
    Renar och vindkraft: Studie från anläggningen av två vindkraftparker i Malå sameby2013Rapport (Annet vitenskapelig)
    Abstract [sv]

    Studien undersöker hur renar påverkas under konstruktionsfasen när vindkraftverk byggs. Studien följer uppförandet av två nya vindparker i Malå kommun i Västerbotten. Sammanlagt byggdes 18 vindkraftverk i Malå samebys kalvnings- och försommarland. Inventering av renspillning samt positioner från renar med GPS-halsband visar att konstruktionen av vindkraftsparkerna har påverkat renarnas användning av området. Analysen visar att renarna under tiden för byggnationen har sökt sig bort från området. Spillningsinventeringen och GPS-data visar också att renarna undviker kraftledningar och större vägar när de ska beta.

    Rapport från kunskapsprogrammet Vindval.

  • 66.
    Skarin, Anna
    et al.
    Swedish Univ Agr Sci, Dept Anim Nutr & Management, Uppsala.
    Nellemann, Christian
    GRID Arendal, United Nations Environm Programme, Lillehammer, Norway..
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Sandstrom, Per
    Swedish Univ Agr Sci, Dept Forest Resource Management, Umea, Sweden.
    Lundqvist, Henrik
    Swedish Univ Agr Sci, Dept Anim Nutr & Management, Uppsala, Sweden.
    Wind farm construction impacts reindeer migration and movement corridors2015Inngår i: Landscape Ecology, ISSN 0921-2973, E-ISSN 1572-9761, Vol. 30, nr 8, s. 1527-1540Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Over the last decade, we have seen a massive increase in the construction of wind farms in northern Fennoscandia. Wind farms comprising hundreds of wind turbines are being built, with little knowledge of the possible cumulative adverse effects on the habitat use and migration of semi-domesticated free-ranging reindeer. We assessed how reindeer responded to wind farm construction in an already fragmented landscape, with specific reference to the effects on use of movement corridors and reindeer habitat selection. We used GPS-data from reindeer during calving and post-calving in the MalAyen reindeer herding community in Sweden. We analysed data from the pre-development years compared to the construction years of two relatively small wind farms. During construction of the wind farms, use of original migration routes and movement corridors within 2 km of development declined by 76 %. This decline in use corresponded to an increase in activity of the reindeer measured by increased step lengths within 0-5 km. The step length was highest nearest the development and declining with distance, as animals moved towards migration corridors and turned around or were observed in holding patterns while not crossing. During construction, reindeer avoided the wind farms at both regional and landscape scale of selection. The combined construction activities associated with even a few wind turbines combined with power lines and roads in or close to central movement corridors caused a reduction in the use of such corridors and grazing habitat and increased the fragmentation of the reindeer calving ranges.

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  • 67. Sonesson, Anna K
    et al.
    Odegård, Jørgen
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Genetic heterogeneity of within-family variance of body weight in Atlantic salmon (Salmo salar)2013Inngår i: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 45, artikkel-id 41Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND: Canalization is defined as the stability of a genotype against minor variations in both environment and genetics. Genetic variation in degree of canalization causes heterogeneity of within-family variance. The aims of this study are twofold: (1) quantify genetic heterogeneity of (within-family) residual variance in Atlantic salmon and (2) test whether the observed heterogeneity of (within-family) residual variance can be explained by simple scaling effects.

    RESULTS: Analysis of body weight in Atlantic salmon using a double hierarchical generalized linear model (DHGLM) revealed substantial heterogeneity of within-family variance. The 95% prediction interval for within-family variance ranged from ~0.4 to 1.2 kg2, implying that the within-family variance of the most extreme high families is expected to be approximately three times larger than the extreme low families. For cross-sectional data, DHGLM with an animal mean sub-model resulted in severe bias, while a corresponding sire-dam model was appropriate. Heterogeneity of variance was not sensitive to Box-Cox transformations of phenotypes, which implies that heterogeneity of variance exists beyond what would be expected from simple scaling effects.

    CONCLUSIONS: Substantial heterogeneity of within-family variance was found for body weight in Atlantic salmon. A tendency towards higher variance with higher means (scaling effects) was observed, but heterogeneity of within-family variance existed beyond what could be explained by simple scaling effects. For cross-sectional data, using the animal mean sub-model in the DHGLM resulted in biased estimates of variance components, which differed substantially both from a standard linear mean animal model and a sire-dam DHGLM model. Although genetic differences in canalization were observed, selection for increased canalization is difficult, because there is limited individual information for the variance sub-model, especially when based on cross-sectional data. Furthermore, potential macro-environmental changes (diet, climatic region, etc.) may make genetic heterogeneity of variance a less stable trait over time and space.

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  • 68.
    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 quality2013Inngår i: Advances in Animal Biosciences, ISSN 2040-4719, Vol. 4, nr 3, s. 606-611Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 69.
    Svenson, Kristin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Li, Yujiao
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Macuchova, Zuzana
    Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Evaluating needs of road maintenance in Sweden with the mixed proportional hazards model2016Inngår i: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, nr 2589, s. 51-58Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    National road databases often lack important information for long-term maintenance planning of paved roads. In the Swedish case, latent variables of which there are no recordings in the pavement management systems database are, for example, underlying road construction, subsoil conditions, and amount of heavy traffic measured by the equivalent single-axle load. The mixed proportional hazards model with random effects was used to capture the effect of these latent variables on a road's risk of needing maintenance. Estimation of random effects makes it possible to identify sections that have shorter or longer lifetimes than could be expected from the observed explanatory variables (traffic load, pavement type, road type, climate zone, road width, speed limit, and bearing capacity restrictions). The results indicate that the mixed proportional hazards model is useful for maintenance planning because the weakest and strongest sections in a road network can be identified. The effect of the latent variables was visualized by,plotting the random effect of each section in a map of the road network. In addition, the spatial correlation between road sections was evaluated by fitting the random effects in an intrinsic conditional autoregressive model. The spatial correlation was estimated to explain 17% of the variation in lifetimes of roads that occur because of the latent variables. The Swedish example shows that the mixed proportional hazards and intrinsic conditional autoregressive models are suitable for analyzing the effect of latent variables in national road databases.

  • 70.
    Youngjo, Lee
    et al.
    Seoul National University.
    Alam, Moudud
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Noh, Maengseok
    Pukyong National University, Korea.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Skarin, Anna
    Swedish University of Agricultural Science, Uppsala.
    Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts2016Inngår i: Ecology and Evolution, ISSN 2045-7758, E-ISSN 2045-7758, Vol. 6, nr 19, s. 7047-7056Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.

    Fulltekst (pdf)
    fulltext
  • 71. Zan, Yanjun
    et al.
    Sheng, Zheya
    Lillie, Mette
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. SLU.
    Honaker, Christa F
    Siegel, Paul B
    Carlborg, Örjan
    Artificial selection response due to polygenic adaptation from a multilocus, multiallelic genetic architecture2017Inngår i: Molecular biology and evolution, ISSN 0737-4038, E-ISSN 1537-1719, Vol. 34, nr 10, s. 2678-2689Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The ability of a population to adapt to changes in their living conditions, whether in nature or captivity, often depends on polymorphisms in multiple genes across the genome. In-depth studies of such polygenic adaptations are difficult in natural populations, but can be approached using the resources provided by artificial selection experiments. Here, we dissect the genetic mechanisms involved in long-term selection responses of the Virginia chicken lines, populations that after 40 generations of divergent selection for 56-day body weight display a 9-fold difference in the selected trait. In the F15 generation of an intercross between the divergent lines, 20 loci explained >60% of the additive genetic variance for the selected trait. We focused particularly on fine-mapping seven major QTL that replicated in this population and found that only two fine-mapped to single, bi-allelic loci; the other five contained linked loci, multiple alleles or were epistatic. This detailed dissection of the polygenic adaptations in the Virginia lines provides a deeper understanding of the range of different genome-wide mechanisms that have been involved in these long-term selection responses. The results illustrate that the genetic architecture of a highly polygenic trait can involve a broad range of genetic mechanisms, and that this can be the case even in a small population bred from founders with limited genetic diversity.

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