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Mouresan, E. F., Selle, M. & Rönnegård, L. (2019). Genomic Prediction Including SNP-Specific Variance Predictors. G3: Genes, Genomes, Genetics, Article ID g3.400381.2019.
Open this publication in new window or tab >>Genomic Prediction Including SNP-Specific Variance Predictors
2019 (English)In: G3: Genes, Genomes, Genetics, ISSN 2160-1836, E-ISSN 2160-1836, article id g3.400381.2019Article in journal (Refereed) Epub ahead of print
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.

Keywords
BLUP, CodataGS, external information, genomic selection, hglm
National Category
Probability Theory and Statistics Bioinformatics (Computational Biology)
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30685 (URN)10.1534/g3.119.400381 (DOI)31467030 (PubMedID)
Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-09-12
Saqlain, M., Alam, M., Rönnegård, L. & Westin, J. (2019). Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease. European journal of drug metabolism and pharmacokinetics
Open this publication in new window or tab >>Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease
2019 (English)In: European journal of drug metabolism and pharmacokinetics, ISSN 0378-7966, E-ISSN 2107-0180Article in journal (Refereed) Epub ahead of print
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.

National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30962 (URN)10.1007/s13318-019-00580-w (DOI)31595429 (PubMedID)
Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-10-16
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1057-5401

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