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  • 1. Alvarez-Castro, J.M.
    et al.
    Carlborg, Ö.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Estimation and interpretation of genetic effects with epistasis using the NOIA model2012Ingår i: Quantitative trait loci (QTL): Methods and Protocols / [ed] Scott A. Rifkin, Humana Press, 2012, s. 191-204Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    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.

  • 2. Besnier, Francois
    et al.
    Wahlberg, Per
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Ek, Weronika
    Andersson, Leif
    Siegel, Paul
    Carlborg, Örjan
    Fine mapping and replication of QTL in outbred chicken advanced intercross lines2011Ingår i: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 43, artikel-id 3Artikel i tidskrift (Refereegranskat)
    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.

  • 3. Mulder, Han A.
    et al.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Fikse, W Freddy
    Veerkamp, R F
    Strandberg, E
    Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models2013Ingår i: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 45, artikel-id 23Artikel i tidskrift (Refereegranskat)
    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.

  • 4.
    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 genetics2013Ingår i: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 193, nr 4, s. 1255-1268Artikel i tidskrift (Refereegranskat)
    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.

  • 5.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Swedish University of Agricultural Sciences.
    Carlborg, Örjan
    Swedish University of Agricultural Sciences.
    Beware of risk for increased false positive rates in genome-wide association studies for phenotypic variability2013Ingår i: Frontiers in Genetics, ISSN 1664-8021, E-ISSN 1664-8021, nr 4Artikel i tidskrift (Refereegranskat)
  • 6. 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 analysis2014Ingår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, nr 7, s. 548-557Artikel i tidskrift (Refereegranskat)
    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.

  • 7.
    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 thaliana2012Ingår i: PLOS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 8, nr 8, artikel-id e1002839Artikel i tidskrift (Refereegranskat)
    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.

  • 8.
    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 variability2013Ingår i: F1000Research, ISSN 2046-1402, Vol. 2, nr 200Artikel i tidskrift (Refereegranskat)
    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.

  • 9. 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)2013Ingår i: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 45, artikel-id 41Artikel i tidskrift (Refereegranskat)
    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.

  • 10.
    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 quality2013Ingår i: Advances in Animal Biosciences, ISSN 2040-4719, Vol. 4, nr 3, s. 606-611Artikel i tidskrift (Refereegranskat)
    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.

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