scholarly journals Software update: Moving the R package sommer to multivariate mixed models for genome-assisted prediction

2018 ◽  
Author(s):  
Giovanny Covarrubias-Pazaran
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Mixed Model ◽  
R Package ◽  
Animal Breeding ◽  
Breeding Programs ◽  
Incidence Matrices ◽  
Software Update ◽  
Trial Analysis ◽  
User Friendly

AbstractIn the last decade the use of mixed models has become a pivotal part in the implementation of genome-assisted prediction in plant and animal breeding programs. Exploiting the use genetic correlation among traits through multivariate predictions has been proposed in recent years as a way to boost prediction accuracy and understand pleiotropy and other genetic and ecological phenomena better. Multiple mixed model solvers able to use relationship matrices or deal with marker-based incidence matrices have been released in the last years but multivariate versions are scarse. Such solvers have become quite popular in plant and animal breeding thanks to user-friendly platforms such as R. Among such software one of the most recent and popular is the sommer package. In this short communication we discuss the update of the package that is able to run multivariate mixed models with multiple random effects and different covariance structures at the level of random effects and trait-to-trait covariance along with other functionalities for genetic analysis and field trial analysis to enhance the genome-assisted prediction capabilities of researchers.

scholarly journals Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 362
Author(s):  
Ioannis Spyroglou ◽  
Jan Skalák ◽  
Veronika Balakhonova ◽  
Zuzana Benedikty ◽  
Alexandros G. Rigas ◽  
...  
Keyword(s):  
Time Series ◽  
Random Effects ◽  
Mixed Models ◽  
Mixed Model ◽  
Repeated Measurements ◽  
Time Series Models ◽  
Additional Time ◽  
Biotic Stresses ◽  
Viable Solution ◽  
Long Time

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

scholarly journals The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 48-76
Author(s):  
Freddy Hernández ◽  
Viviana Giampaoli
Keyword(s):  
Weibull Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Estimation Procedure ◽  
Weibull Regression ◽  
Two Parameters ◽  
The Impact

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

scholarly journals Multiple moderator meta-analysis using the R-package Meta-CART

2020 ◽  
Vol 52 (6) ◽  
pp. 2657-2673
Author(s):  
Xinru Li ◽  
Elise Dusseldorp ◽  
Xiaogang Su ◽  
Jacqueline J. Meulman
Keyword(s):  
Random Effects ◽  
Treatment Effectiveness ◽  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Tree Model ◽  
Effective Interventions ◽  
Look Ahead ◽  
Fixed And Random Effects ◽  
User Friendly

AbstractIn meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other’s effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package metacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.

A note on the analysis of germination data from complex experimental designs

2017 ◽  
Vol 27 (4) ◽  
pp. 321-327 ◽  
Author(s):  
Signe M. Jensen ◽  
Christian Andreasen ◽  
Jens C. Streibig ◽  
Eshagh Keshtkar ◽  
Christian Ritz
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Mixed Model ◽  
Experimental Designs ◽  
Germination Test ◽  
Random Effects Models ◽  
Event Time ◽  
Sources Of Variation ◽  
Germination Experiments ◽  
Time Point

AbstractIn recent years germination experiments have become more and more complex. Typically, they are replicated in time as independent runs and at each time point they involve hierarchical, often factorial experimental designs, which are now commonly analysed by means of linear mixed models. However, in order to characterize germination in response to time elapsed, specific event-time models are needed and mixed model extensions of these models are not readily available, neither in theory nor in practice. As a practical workaround we propose a two-step approach that combines and weighs together results from event-time models fitted separately to data from each germination test by means of meta-analytic random effects models. We show that this approach provides a more appropriate appreciation of the sources of variation in hierarchically structured germination experiments as both between- and within-experiment variation may be recovered from the data.

scholarly journals Adaptability and stability of maize varieties using mixed model methodology

2012 ◽  
Vol 12 (2) ◽  
pp. 111-117 ◽  
Author(s):  
Flávia Ferreira Mendes ◽  
Lauro José Moreira Guimarães ◽  
João Cândido Souza ◽  
Paulo Evaristo Oliveira Guimarães ◽  
Cleso Antônio Patto Pacheco ◽  
...  
Keyword(s):  
Mixed Models ◽  
Mixed Model ◽  
Relative Performance ◽  
Environment Interaction ◽  
Maize Breeding ◽  
Breeding Programs ◽  
Genotype X Environment ◽  
Growing Seasons ◽  
Maize Varieties ◽  
Harmonic Means

The objective of this study was to evaluate the performance, adaptability and stability of corn cultivars simultaneously in unbalanced experiments, using the method of harmonic means of the relative performance of genetic values. The grain yield of 45 cultivars, including hybrids and varieties, was evaluated in 49 environments in two growing seasons. In the 2007/2008 growing season, 36 cultivars were evaluated and in 2008/2009 25 cultivars, of which 16 were used in both seasons. Statistical analyses were performed based on mixed models, considering genotypes as random and replications within environments as fixed factors. The experimental precision in the combined analyses was high (accuracy estimates > 92 %). Despite the existence of genotype x environment interaction, hybrids and varieties with high adaptability and stability were identified. Results showed that the method of harmonic means of the relative performance of genetic values is a suitable method for maize breeding programs.

scholarly journals AlphaSimR: an R package for breeding program simulations

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
R Chris Gaynor ◽  
Gregor Gorjanc ◽  
John M Hickey
Keyword(s):  
Software Package ◽  
R Package ◽  
Animal Breeding ◽  
Breeding Program ◽  
Stochastic Simulations ◽  
Breeding Programs ◽  
Detailed Design ◽  
Building Simulations ◽  
Wide Range ◽  
Commercial Breeding

Abstract This paper introduces AlphaSimR, an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. This paper lists the main features of AlphaSimR and provides a brief example simulation to show how to use the software.

scholarly journals A unifying concept of animal breeding programs

2020 ◽  
Author(s):  
H. Simianer ◽  
A. Ganesan ◽  
L. Buettgen ◽  
N.T. Ha ◽  
T. Pook
Keyword(s):  
R Package ◽  
Companion Paper ◽  
Animal Breeding ◽  
Breeding Program ◽  
General Definition ◽  
Full Description ◽  
Genetic Progress ◽  
Breeding Programs ◽  
Definition Of ◽  
Transgenic Technologies

ABSTRACTModern animal breeding programs are constantly evolving with advances in breeding theory, biotechnology and genetics. Surprisingly, there seems to be no generally accepted succinct definition of what exactly a breeding program is, neither is there a unified language to describe breeding programs in a comprehensive, unambiguous and reproducible way. In this work, we try to fill this gap by suggesting a general definition of breeding programs that also pertains to cases where genetic progress is not achieved through selection, but e.g. through transgenic technologies, or the aim is not to generate genetic progress, but e.g. to maintain genetic diversity. The key idea of the underlying concept is to represent a breeding program in modular form as a directed graph that is composed of nodes and edges, where nodes represent cohorts of breeding units, usually individuals, and edges represent breeding activities, like ‘selection’ or ‘reproduction’. We claim, that by defining a comprehensive set of nodes and edges it is possible to represent any breeding program of arbitrary complexity by such a graph, which thus comprises a full description of the breeding program. This concept is implemented in a web-based tool (MoBPSweb, available at www.mobps.de) which is described in a companion paper, and has a link to the R-package MoBPS (Modular Breeding Program Simulator) to simulate the described breeding programs. The approach is illustrated by showcasing three different breeding programs of increasing complexity. Finally, potential limitations of the concept are indicated and extensions to other fields, like plant breeding, are discussed.

On the use of a penalized quasilikelihood information criterion for generalized linear mixed models

Biometrika ◽  
2020 ◽  
Author(s):  
Francis K C Hui
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Models ◽  
Mixed Model ◽  
Computation Time ◽  
Information Criterion ◽  
Information Criteria ◽  
Maximum Likelihood Estimates ◽  
Model Complexity ◽  
Fixed And Random Effects

Summary Information criteria are commonly used for joint fixed and random effects selection in mixed models. While information criteria are straightforward to implement, a major difficulty in applying them is that they are typically based on maximum likelihood estimates, but calculating such estimates for one candidate mixed model, let alone multiple models, presents a major computational challenge. To overcome this hurdle, we study penalized quasilikelihood estimation and use it as the basis for performing fast joint selection. Under a general framework, we show that penalized quasilikelihood estimation produces consistent estimates of the true parameters. We then propose a new penalized quasilikelihood information criterion whose distinguishing feature is the way it accounts for model complexity in the random effects, since penalized quasilikelihood estimation effectively treats the random effects as fixed. We demonstrate that the criterion asymptotically identifies the true set of important fixed and random effects. Simulations show that the quasilikelihood information criterion performs competitively with and sometimes better than common maximum likelihood information criteria for joint selection, while offering substantial reductions in computation time.

scholarly journals AFEchidna is a R package for genetic evaluation of plant and animal breeding datasets

2021 ◽  
Author(s):  
Weihua Zhang ◽  
Ruiyan Wei ◽  
Yan Liu ◽  
Yuanzhen Lin
Keyword(s):  
Random Effects ◽  
Variance Components ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Linear Models ◽  
R Package ◽  
Animal Breeding ◽  
Mixed Linear Models ◽  
Best Linear Unbiased ◽  
Genetic Assessment

Progeny tests play important roles in plant and animal breeding programs, and mixed linear models are usually performed to estimate variance components of random effects, estimate the fixed effects (Best Linear Unbiased Estimates, BLUEs) and predict the random effects (Best Linear Unbiased Predictions, BLUPs) via restricted maximum likehood (REML) methods in progeny test datasets. The current pioneer software for genetic assessment is ASReml, but it is commercial and expensive. Although there is free software such as Echidna or the R package sommer, the Echidna syntax is complex and the R package functionality is limited. Therefore, this study aims to develop a R package named AFEchidna based on Echidna software. The mixed linear models are conveniently implemented for users through the AFEchidna package to solve variance components, genetic parameters and the BLUP values of random effects, and the batch analysis of multiple traits, multiple variance structures and multiple genetic parameters can be also performed, as well as comparison between different models and genomic BLUP analysis. The AFEchidna package is free, please email us ([email protected]) to get a copy if one is interested for it. The AFEchidna package is developed to expand free genetic assessment software with the expectation that its efficiency could be close to the commercial software.

scholarly journals AlphaSimR: An R-package for Breeding Program Simulations

2020 ◽  
Author(s):  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
John M. Hickey
Keyword(s):  
Software Package ◽  
R Package ◽  
Animal Breeding ◽  
Breeding Program ◽  
Stochastic Simulations ◽  
Breeding Programs ◽  
Detailed Design ◽  
Building Simulations ◽  
Wide Range ◽  
Commercial Breeding

AbstractThis paper introduces AlphaSimR, an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. This paper lists the main features of AlphaSimR and provides a brief example simulation to show how to use the software.

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