scholarly journals Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

2021 ◽  
Vol 12 ◽  
Author(s):  
Alison Smith ◽  
Adam Norman ◽  
Haydn Kuchel ◽  
Brian Cullis
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Subsequent Paper ◽  
Environment Interaction ◽  
Fundamental Factor ◽  
Factor Analytic ◽  
Graphical Tool ◽  
Variety Selection ◽  
Variety Performance

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

scholarly journals Genomic Selection Using Environmental Covariates Within an Integrated Factor Analytic Linear Mixed Model

2021 ◽  
Author(s):  
Daniel Tolhurst ◽  
R. Chris Gaynor ◽  
Brian Gardunia ◽  
John Hickey ◽  
Gregor Gorjanc
Keyword(s):  
Genomic Selection ◽  
Genetic Variance ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Single Stage ◽  
Environment Interaction ◽  
Latent Factors ◽  
Environmental Covariates ◽  
Factor Analytic ◽  
Special Factor

Abstract This paper introduces a single-stage genomic selection approach which directly integrates environmental covariates within a special factor analytic framework. The factor analytic approach of Smith et al. (2001) is an effective method of analysis for multi-environment trial (MET) datasets, but has limited biological interpretation since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes directly interpretable, and thence predictable. This paper develops a model for both predictable and observable GEI in terms of a joint set of known and latent factors, as well as non-genetic sources of variation within trials and environments. This single-stage approach is referred to as the integrated factor analytic linear mixed model (IFA-LMM). The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer Crop Science. The results show that the environmental covariates explain 34.6% of the genetic variance across environments (compared to only 23.3% for a conventional regression model). This represents 92.7% of the crossover GEI. The latent factors then explain 40.7% of the genetic variance, which represents 87.6% of the non-crossover GEI. This demonstrates the ability of the IFA-LMM to model crossover and non-crossover GEI in a manner that is both informative and practical to plant breeding.

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

scholarly journals The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness

2020 ◽  
Vol 25 (4) ◽  
pp. 553-578 ◽  
Author(s):  
Brian R. Cullis ◽  
Alison B. Smith ◽  
Nicole A. Cocks ◽  
David G. Butler
Keyword(s):  
Plant Breeding ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Early Stage ◽  
Genetic Effects ◽  
Breeding Programs ◽  
Model Based ◽  
Selection Decisions ◽  
Design Generation

Abstract The use of appropriate statistical methods has a key role in improving the accuracy of selection decisions in a plant breeding program. This is particularly important in the early stages of testing in which selections are based on data from a limited number of field trials that include large numbers of breeding lines with minimal replication. The method of analysis currently recommended for early-stage trials in Australia involves a linear mixed model that includes genetic relatedness via ancestral information: non-genetic effects that reflect the experimental design and a residual model that accommodates spatial dependence. Such analyses have been widely accepted as they have been found to produce accurate predictions of both additive and total genetic effects, the latter providing the basis for selection decisions. In this paper, we present the results of a case study of 34 early-stage trials to demonstrate this type of analysis and to reinforce the importance of including information on genetic relatedness. In addition to the application of a superior method of analysis, it is also critical to ensure the use of sound experimental designs. Recently, model-based designs have become popular in Australian plant breeding programs. Within this paradigm, the design search would ideally be based on a linear mixed model that matches, as closely as possible, the model used for analysis. Therefore, in this paper, we propose the use of models for design generation that include information on genetic relatedness and also include non-genetic and residual models based on the analysis of historic data for individual breeding programs. At present, the most commonly used design generation model omits genetic relatedness information and uses non-genetic and residual models that are supplied as default models in the associated software packages. The major reasons for this are that preexisting software is unacceptably slow for designs incorporating genetic relatedness and the accuracy gains resulting from the use of genetic relatedness have not been quantified. Both of these issues are addressed in the current paper. An updating scheme for calculating the optimality criterion in the design search is presented and is shown to afford prodigious computational savings. An in silico study that compares three types of design function across a range of ancillary treatments shows the gains in accuracy for the prediction of total genetic effects (and thence selection) achieved from model-based designs using genetic relatedness and program specific non-genetic and residual models. Supplementary materials accompanying this paper appear online.

scholarly journals Family ties: the multilevel effects of households and kinship on the networks of individuals

2018 ◽  
Vol 5 (4) ◽  
pp. 172159 ◽  
Author(s):  
Jeremy Koster
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Strong Support ◽  
Group Level ◽  
Family Ties ◽  
Individual Household ◽  
Household Members

Among social mammals, humans uniquely organize themselves into communities of households that are centred around enduring, predominantly monogamous unions of men and women. As a consequence of this social organization, individuals maintain social relationships both within and across households, and potentially there is conflict among household members about which social ties to prioritize or de-emphasize. Extending the logic of structural balance theory, I predict that there will be considerable overlap in the social networks of individual household members, resulting in a pattern of group-level reciprocity. To test this prediction, I advance the Group-Structured Social Relations Model, a generalized linear mixed model that tests for group-level effects in the inter-household social networks of individuals. The empirical data stem from social support interviews conducted in a community of indigenous Nicaraguan horticulturalists, and model results show high group-level reciprocity among households. Although support networks are organized around kinship, covariates that test predictions of kin selection models do not receive strong support, potentially because most kin-directed altruism occurs within households, not between households. In addition, the models show that households with high genetic relatedness in part from children born to adulterous relationships are less likely to assist each other.

The analysis of quantitative trait loci in multi-environment trials using a multiplicative mixed model

2003 ◽  
Vol 54 (12) ◽  
pp. 1395 ◽  
Author(s):  
A. P. Verbyla ◽  
P. J. Eckermann ◽  
R. Thompson ◽  
B. R. Cullis
Keyword(s):  
Program Analysis ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Genetic Model ◽  
Random Effect ◽  
Doubled Haploid Population ◽  
Environment Interaction ◽  
Factor Analytic Model ◽  
Factor Analytic ◽  
Trait Locus

A new approach for multi-environment quantitative trait locus (QTL) analysis based on an appropriate genetic model is presented. To accommodate a multi-environment analysis, the size of a QTL effect is assumed to be a random effect. The approach results in a multiplicative mixed model for QTL × environment interaction of the factor analytic type. The full genetic model may also include a factor analytic model for the residual genotype × environment interaction, whereas the environmental model for the non-genetic variation involves local, global, and extraneous variation. The approach is used to determine QTLs for yield in the Arapiles × Franklin doubled haploid population of the National Barley Molecular Marker Program. Analysis leads to the determination of 8 QTLs. Many of these QTLs are associated with other traits.

scholarly journals Linear Mixed Models

2022 ◽  
pp. 141-170
Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Mixed Models ◽  
Prediction Performance ◽  
Environment Interaction ◽  
Model Framework ◽  
Genotype Environment Interaction

AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

2019 ◽  
Vol 136 (4) ◽  
pp. 279-300 ◽  
Author(s):  
Daniel J. Tolhurst ◽  
Ky L. Mathews ◽  
Alison B. Smith ◽  
Brian R. Cullis
Keyword(s):  
Plant Breeding ◽  
Genomic Selection ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Factor Analytic

scholarly journals Climatic drivers of genotype–environment interactions in lodgepole pine based on multi-environment trial data and a factor analytic model of additive covariance

2018 ◽  
Vol 48 (7) ◽  
pp. 835-854 ◽  
Author(s):  
Nicholas K. Ukrainetz ◽  
Alvin D. Yanchuk ◽  
Shawn D. Mansfield
Keyword(s):  
Climate Change ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Lodgepole Pine ◽  
Future Climate Change ◽  
Climate Modelling ◽  
Mixed Model Analysis ◽  
Factor Analytic Model ◽  
Factor Analytic ◽  
Breeding Groups

The optimum deployment of select material from tree breeding programs is affected by the presence of genotype–environment interactions (G × E) and further complicated by future climate change. Here, we analyzed tree height data from 28 progeny test sites in a multi-environment trial (MET) dataset for two testing cycles of the lodgepole pine (Pinus contorta Douglas ex Loudon) breeding program in British Columbia to characterize one approach to investigating the climatic variables influencing G × E and the potential impacts of climate change. Linear mixed model analysis was conducted using an approximate reduced animal model with a factor analytic (FA) variance model to estimate the complex additive (co)variance structure. Test sites were grouped according to patterns of G × E, and climate modelling was employed to project historical and future deployment zones for each group. Based on these findings, it appears that breeding groups with historically wide deployment zones from northern environments will become less important as the climate warms, and therefore investment should be directed toward southern breeding groups, which will be useful across a very wide geographic range in the near to mid-term future.

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