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Rönnegård, L. (2019). The evolution of peer-reviewed papers.. Journal of Animal Breeding and Genetics, 136(2), 77-78
Open this publication in new window or tab >>The evolution of peer-reviewed papers.
2019 (English)In: Journal of Animal Breeding and Genetics, ISSN 0931-2668, E-ISSN 1439-0388, Vol. 136, no 2, p. 77-78Article in journal, Editorial material (Other academic) Published
National Category
Probability Theory and Statistics Social Sciences Interdisciplinary
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29577 (URN)10.1111/jbg.12385 (DOI)000458954400002 ()30773713 (PubMedID)
Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2019-03-07Bibliographically approved
Marjanovic, J., Mulder, H. A., Rönnegård, L. & Bijma, P. (2018). Modelling the co-evolution of indirect genetic effects and inherited variability. Heredity, 121, 631-647
Open this publication in new window or tab >>Modelling the co-evolution of indirect genetic effects and inherited variability
2018 (English)In: Heredity, ISSN 0018-067X, E-ISSN 1365-2540, Vol. 121, p. 631-647Article in journal (Refereed) Published
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.

National Category
Biological Sciences Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27449 (URN)10.1038/s41437-018-0068-z (DOI)000449427300011 ()29588510 (PubMedID)
Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2018-11-22Bibliographically approved
Saqlain, M., Alam, M., Brandt, D., Rönnegård, L. & Westin, J. (2018). Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease. In: : . Paper presented at The 40th Conference on Stochastic Processes and their Applications – SPA 2018, June 11-15 2018, Gothenburg.
Open this publication in new window or tab >>Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease
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2018 (English)Conference paper, Poster (with or without abstract) (Other academic)
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.

Keywords
levodopa, parkinson's disease, pharmacokinetic model, stochastic modelling, PSM.
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
urn:nbn:se:du-28268 (URN)
Conference
The 40th Conference on Stochastic Processes and their Applications – SPA 2018, June 11-15 2018, Gothenburg
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-12-17Bibliographically approved
Bring, J. & Rönnegård, L. (2018). Åldersbedömningar - en statistisk utmaning. Folkvett (1), 7-13
Open this publication in new window or tab >>Åldersbedömningar - en statistisk utmaning
2018 (Swedish)In: Folkvett, ISSN 0283-0795, no 1, p. 7-13Article in journal (Other (popular science, discussion, etc.)) Published
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
urn:nbn:se:du-27778 (URN)
Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2018-09-10Bibliographically approved
Zan, Y., Sheng, Z., Lillie, M., Rönnegård, L., Honaker, C. F., Siegel, P. B. & Carlborg, Ö. (2017). Artificial selection response due to polygenic adaptation from a multilocus, multiallelic genetic architecture. Molecular biology and evolution, 34(10), 2678-2689
Open this publication in new window or tab >>Artificial selection response due to polygenic adaptation from a multilocus, multiallelic genetic architecture
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2017 (English)In: Molecular biology and evolution, ISSN 0737-4038, E-ISSN 1537-1719, Vol. 34, no 10, p. 2678-2689Article in journal (Refereed) Published
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.

Keywords
epistasis, genetic architecture, genetic variation, multiallelic, multilocus, polygenic adaptation
National Category
Biological Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-26369 (URN)10.1093/molbev/msx194 (DOI)000411814800019 ()28957504 (PubMedID)2-s2.0-85030711152 (Scopus ID)
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2017-11-20Bibliographically approved
Lee, Y., Rönnegård, L. & Noh, M. (2017). Data Analysis Using Hierarchical Generalized Linear Models with R. Boca Raton: CRC Press
Open this publication in new window or tab >>Data Analysis Using Hierarchical Generalized Linear Models with R
2017 (English)Book (Other academic)
Place, publisher, year, edition, pages
Boca Raton: CRC Press, 2017. p. 322
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-25940 (URN)9781138627826 (ISBN)
Available from: 2017-09-01 Created: 2017-09-01 Last updated: 2017-09-01Bibliographically approved
Nelson, R. M., Temnykh, S. V., Johnson, J. L., Kharlamova, A. V., Vladimirova, A. V., Shepeleva, D. V., . . . Kukekova, A. V. (2017). Genetics of interactive behavior in silver foxes (Vulpes vulpes). Behavior Genetics, 47(1), 88-101
Open this publication in new window or tab >>Genetics of interactive behavior in silver foxes (Vulpes vulpes)
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2017 (English)In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Vol. 47, no 1, p. 88-101Article in journal (Refereed) Published
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.

Keywords
Aggression; Behavior genetics; Canis familiaris; Domestication; Epistasis; Quantitative trait loci; Social behavior; Vulpes vulpes
National Category
Biological Sciences Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-23278 (URN)10.1007/s10519-016-9815-1 (DOI)000392185800008 ()27757730 (PubMedID)
Available from: 2016-10-25 Created: 2016-10-25 Last updated: 2017-11-29Bibliographically approved
Silva, C. N., McFarlane, S. E., Hagen, I. J., Rönnegård, L., Billing, A. M., Kvalnes, T., . . . Husby, A. (2017). Insights into the genetic architecture of morphological traits in two passerine bird species. Heredity, 119(3), 197-205
Open this publication in new window or tab >>Insights into the genetic architecture of morphological traits in two passerine bird species
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2017 (English)In: Heredity, ISSN 0018-067X, E-ISSN 1365-2540, Vol. 119, no 3, p. 197-205Article in journal (Refereed) Published
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.

National Category
Biological Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-25251 (URN)10.1038/hdy.2017.29 (DOI)000407362100008 ()28613280 (PubMedID)
Available from: 2017-06-20 Created: 2017-06-20 Last updated: 2017-08-31Bibliographically approved
Rönnegård, L., McFarlane, S. E., Husby, A., Kawakami, T., Ellegren, H. & Qvarnström, A. (2016). Increasing the power of genome wide association studies in natural populations using repeated measures: evaluation and implementation. Methods in Ecology and Evolution, 7(7), 792-799
Open this publication in new window or tab >>Increasing the power of genome wide association studies in natural populations using repeated measures: evaluation and implementation
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2016 (English)In: Methods in Ecology and Evolution, ISSN 2041-210X, E-ISSN 2041-210X, Vol. 7, no 7, p. 792-799Article in journal (Refereed) Published
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.

National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-23162 (URN)10.1111/2041-210X.12535 (DOI)
Available from: 2016-09-21 Created: 2016-09-21 Last updated: 2017-11-21Bibliographically approved
Sivertsen, T. R., Åhman, B., Steyaert, S. M. J., Rönnegård, L., Frank, J., Segerström, P., . . . Skarin, A. (2016). Reindeer habitat selection under the risk of brown bear predation during calving season. Ecosphere, 7(11), Article ID e01583.
Open this publication in new window or tab >>Reindeer habitat selection under the risk of brown bear predation during calving season
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2016 (English)In: Ecosphere, ISSN 2150-8925, E-ISSN 2150-8925, Vol. 7, no 11, article id e01583Article in journal (Refereed) Published
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. 

Keywords
brown bears, habitat selection, predation risk, predator–prey interactions, Rangifer, resource selection functions
National Category
Ecology
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
urn:nbn:se:du-23712 (URN)10.1002/ecs2.1583 (DOI)000392207600030 ()
Available from: 2016-12-22 Created: 2016-12-22 Last updated: 2017-11-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1057-5401

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