# Fitting genotype by environment models in sommer

## Giovanny Covarrubias-Pazaran

### 2026-07-08

The sommer package was developed to provide R users a powerful and reliable multivariate mixed model solver. The package is focused on two approaches: 1) p > n (more effects to estimate than observations) using the mmes() function, and 2) n > p (more observations than effects to estimate) using the mmes() function. The core algorithms are coded in C++ using the Armadillo library. This package allows the user to fit mixed models with the advantage of specifying the variance-covariance structure for the random effects, specifying heterogeneous variances, and obtaining other parameters such as BLUPs, BLUEs, residuals, fitted values, variances for fixed and random effects, etc.

The purpose of this vignette is to show how to fit different genotype by environment (GxE) models using the sommer package:

1. Single environment model
2. Multienvironment model: Main effect model
3. Multienvironment model: Diagonal model (DG)
4. Multienvironment model: Compund symmetry model (CS)
5. Multienvironment model: Unstructured model (US)
6. Multienvironment model: Random regression model (RR)
7. Multienvironment model: Other covariance structures for GxE
8. Multienvironment model: Finlay-Wilkinson regression
9. Multienvironment model: Factor analytic (reduced rank) model (FA)
10. Two stage analysis

When the breeder decides to run a trial and apply selection in a single environment (whether because the amount of seed is a limitation or there’s no availability for a location) the breeder takes the risk of selecting material for a target population of environments (TPEs) using an environment that is not representative of the larger TPE. Therefore, many breeding programs try to base their selection decision on multi-environment trial (MET) data. Models could be adjusted by adding additional information like spatial information, experimental design information, etc. In this tutorial we will focus mainly on the covariance structures for GxE and the incorporation of relationship matrices for the genotype effect.

## 1) Single environment model

A single-environment model is the one that is fitted when the breeding program can only afford one location, leaving out the possible information available from other environments. This will be used to further expand to GxE models.

    library(sommer)

    ## Loading required package: Matrix

    ## Loading required package: MASS

    ## Loading required package: crayon

    ## Loading required package: enhancer

    data(DT_example, package="enhancer")
    DT <- DT_example
    A <- A_example

    ansSingle <- mmes(Yield~1,
                  random= ~ vsm(ism(Name), Gu=A),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansSingle)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC      BIC Method Converge
    ## Value -78.80875 159.6175 162.8378     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##              VarComp VarCompSE Zratio Constraint
    ## Name:A:mu:mu   6.529     2.202  2.965   Positive
    ## units:mu:mu   13.868     1.633  8.494   Positive
    ## ============================================================
    ## Fixed effects:
    ##             Estimate Std.Error t.value
    ## (Intercept)    11.74    0.4876   24.07
    ## ============================================================
    ## Use the '$' sign to access results and parameters

    # if setting henderson=TRUE provide the inverse
    # Ai <- solve(A)
    # Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
    # attr(Ai, "inverse")=TRUE

In this model, the only term to be estimated is the one for the germplasm (here called `Name`). For the sake of example we have added a relationship matrix among the levels of the random effect `Name`. This is just a diagonal matrix with as many rows and columns as levels present in the random effect `Name`, but any other non-diagonal relationship matrix could be used.

## 2) MET: main effect model

A multi-environment model is the one that is fitted when the breeding program can afford more than one location. The main effect model assumes that GxE doesn’t exist and that the main genotype effect plus the fixed effect for environment is enough to predict the genotype effect in all locations of interest.

    ansMain <- mmes(Yield~Env,
                  random= ~ vsm(ism(Name), Gu=A),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansMain)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC      BIC Method Converge
    ## Value -32.59421 71.18842 80.84949     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##              VarComp VarCompSE Zratio Constraint
    ## Name:A:mu:mu   4.856    1.5233  3.188   Positive
    ## units:mu:mu    8.109    0.9615  8.434   Positive
    ## ============================================================
    ## Fixed effects:
    ##           Estimate Std.Error t.value
    ## Intercept   16.385    0.5849  28.012
    ## CA.2012     -5.688    0.5741  -9.908
    ## CA.2013     -6.218    0.6107 -10.182
    ## ============================================================
    ## Use the '$' sign to access results and parameters

    # if setting henderson=TRUE provide the inverse
    # Ai <- solve(A)
    # Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
    # attr(Ai, "inverse")=TRUE

## 3) MET: diagonal model (DG)

A multi-environment model is the one that is fitted when the breeding program can afford more than one location. The diagonal model assumes that GxE exists and that the genotype variation is expressed differently at each location, therefore fitting a variance component for the genotype effect at each location. The main drawback is that this model assumes no covariance among locations, as if genotypes were independent (despite the fact that is the same genotypes). The fixed effect for environment plus the location-specific BLUP is used to predict the genotype effect in each locations of interest.

    ansDG <- mmes(Yield~Env,
                  random= ~ vsm(dsm(Env),ism(Name), Gu=A),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansDG)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC      BIC Method Converge
    ## Value -21.04157 48.08314 57.74421     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##                            VarComp VarCompSE Zratio Constraint
    ## Env:Name:A:CA.2011:CA.2011  17.493    6.1104  2.863   Positive
    ## Env:Name:A:CA.2012:CA.2012   5.337    1.7669  3.021   Positive
    ## Env:Name:A:CA.2013:CA.2013   7.884    2.5527  3.088   Positive
    ## units:mu:mu                  4.381    0.6491  6.748   Positive
    ## ============================================================
    ## Fixed effects:
    ##           Estimate Std.Error t.value
    ## Intercept   16.621    0.9481  17.532
    ## CA.2012     -5.958    1.0454  -5.699
    ## CA.2013     -6.662    1.0981  -6.067
    ## ============================================================
    ## Use the '$' sign to access results and parameters

    # if setting henderson=TRUE provide the inverse
    # Ai <- solve(A)
    # Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
    # attr(Ai, "inverse")=TRUE

## 4) MET: compund symmetry model (CS)

A multi-environment model is the one that is fitted when the breeding program can afford more than one location. The compound symmetry model assumes that GxE exists and that a main genotype variance-covariance component is expressed across all location. In addition, it assumes that a main genotype-by-environment variance is expressed across all locations. The main drawback is that the model assumes the same variance and covariance among locations. The fixed effect for environment plus the main effect for BLUP plus genotype-by-environment effect is used to predict the genotype effect in each location of interest.

    E <- diag(length(unique(DT$Env)));rownames(E) <- colnames(E) <- unique(DT$Env)
    Ei <- solve(E)
    Ai <- solve(A)
    EAi <- kronecker(Ei,Ai, make.dimnames = TRUE)
    Ei <- as(as(as( Ei,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
    Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
    EAi <- as(as(as( EAi,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
    attr(Ai, "inverse")=TRUE
    attr(EAi, "inverse")=TRUE
    ansCS <- mmes(Yield~Env,
                  random= ~ vsm(ism(Name), Gu=Ai) + vsm(ism(Env:Name), Gu=EAi),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansCS)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC      BIC Method Converge
    ## Value -20.14538 46.29075 55.95182     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##                    VarComp VarCompSE Zratio Constraint
    ## Name:Ai:mu:mu        3.682     1.691  2.177   Positive
    ## Env:Name:EAi:mu:mu   5.173     1.495  3.460   Positive
    ## units:mu:mu          4.366     0.647  6.748   Positive
    ## ============================================================
    ## Fixed effects:
    ##           Estimate Std.Error t.value
    ## Intercept   16.496    0.6855  24.065
    ## CA.2012     -5.777    0.7558  -7.643
    ## CA.2013     -6.380    0.7960  -8.015
    ## ============================================================
    ## Use the '$' sign to access results and parameters

## 5) MET: unstructured model (US)

A multi-environment model is the one that is fitted when the breeding program can afford more than one location. The unstructured model is the most flexible model assuming that GxE exists and that an environment-specific variance exists in addition to as many covariances for each environment-to-environment combinations. The main drawback is that is difficult to make this models converge because of the large number of variance components, the fact that some of these variance or covariance components are zero, and the difficulty in choosing good starting values. The fixed effect for environment plus the environment specific BLUP (adjusted by covariances) is used to predict the genotype effect in each location of interest.

    ansUS <- mmes(Yield~Env,
                  random= ~ vsm(usm(Env),ism(Name), Gu=A),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansUS)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC      BIC Method Converge
    ## Value -14.20949 34.41898 44.08005     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##                            VarComp VarCompSE Zratio Constraint
    ## Env:Name:A:CA.2011:CA.2011 15.9937    5.3811 2.9722   Positive
    ## Env:Name:A:CA.2011:CA.2012  6.1726    2.5041 2.4650   Unconstr
    ## Env:Name:A:CA.2012:CA.2012  5.2741    3.0689 1.7186   Positive
    ## Env:Name:A:CA.2011:CA.2013  6.3662    1.7509 3.6359   Unconstr
    ## Env:Name:A:CA.2012:CA.2013  0.3749    1.5350 0.2442   Unconstr
    ## Env:Name:A:CA.2013:CA.2013  7.6895    2.4907 3.0873   Positive
    ## units:mu:mu                 4.3862    0.6499 6.7492   Positive
    ## ============================================================
    ## Fixed effects:
    ##           Estimate Std.Error t.value
    ## Intercept   16.341    0.8141  20.072
    ## CA.2012     -5.696    0.7406  -7.692
    ## CA.2013     -6.286    0.8202  -7.664
    ## ============================================================
    ## Use the '$' sign to access results and parameters

    # if setting henderson=TRUE provide the inverse
    Ai <- solve(A)
    Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
    attr(Ai, "inverse")=TRUE

## 6) MET: random regression model

A multi-environment model is the one that is fitted when the breeding program can afford more than one location. The random regression model assumes that the environment can be seen as a continuous variable and therefore a variance component for the intercept and a variance component for the slope can be fitted. The number of variance components will depend on the order of the Legendre polynomial fitted.

    library(orthopolynom)
    DT$EnvN <- as.numeric(as.factor(DT$Env))

    ansRR <- mmes(Yield~Env,
                  random= ~ vsm(dsm(leg(EnvN,1)),ism(Name)),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansRR)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC      BIC Method Converge
    ## Value -27.70316 61.40633 71.06739     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##                     VarComp VarCompSE Zratio Constraint
    ## EnvN:Name:leg0:leg0  10.394    3.1487  3.301   Positive
    ## EnvN:Name:leg1:leg1   2.080    0.9802  2.122   Positive
    ## units:mu:mu           6.296    0.8439  7.461   Positive
    ## ============================================================
    ## Fixed effects:
    ##           Estimate Std.Error t.value
    ## Intercept   16.541    0.6771  24.430
    ## CA.2012     -5.832    0.6425  -9.077
    ## CA.2013     -6.472    0.8240  -7.854
    ## ============================================================
    ## Use the '$' sign to access results and parameters

In addition, an unstructured, diagonal or other variance-covariance structure can be put on top of the polynomial model:

    library(orthopolynom)
    DT$EnvN <- as.numeric(as.factor(DT$Env))

    ansRR <- mmes(Yield~Env,
                  random= ~ vsm(usm(leg(EnvN,1)),ism(Name)),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansRR)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC      BIC Method Converge
    ## Value -25.56966 57.13931 66.80038     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##                     VarComp VarCompSE Zratio Constraint
    ## EnvN:Name:leg0:leg0  10.792    3.2757  3.295   Positive
    ## EnvN:Name:leg0:leg1  -2.428    1.3708 -1.772   Unconstr
    ## EnvN:Name:leg1:leg1   2.287    1.0414  2.196   Positive
    ## units:mu:mu           6.259    0.8418  7.435   Positive
    ## ============================================================
    ## Fixed effects:
    ##           Estimate Std.Error t.value
    ## Intercept   16.501    0.7779  21.214
    ## CA.2012     -5.791    0.6705  -8.637
    ## CA.2013     -6.476    0.8555  -7.570
    ## ============================================================
    ## Use the '$' sign to access results and parameters

## 7) Other GxE covariance structures

Although not very commonly used in GxE models, the autoregressive of order 1 (AR1) and other covariance structures could be used in the GxE modeling. Here we show how to do it (not recommending it).

    E <- AR1(DT$Env) # can be AR1() or CS(), etc.
    rownames(E) <- colnames(E) <- unique(DT$Env)
    EA <- kronecker(E,A, make.dimnames = TRUE)
    ansCS <- mmes(Yield~Env,
                  random= ~ vsm(ism(Name), Gu=A) + vsm(ism(Env:Name), Gu=EA),
                  rcov= ~ units,
                  data=DT, verbose = FALSE)
    summary(ansCS)

    ## ============================================================
    ##          Multivariate Linear Mixed Model fit by REML
    ## **********************  sommer 4.4  **********************
    ## ============================================================
    ##          logLik      AIC     BIC Method Converge
    ## Value -19.39067 44.78134 54.4424     AI     TRUE
    ## ============================================================
    ## Variance-Covariance components:
    ##                   VarComp VarCompSE Zratio Constraint
    ## Name:A:mu:mu        2.225    1.7536  1.269   Positive
    ## Env:Name:EA:mu:mu   6.424    1.8293  3.512   Positive
    ## units:mu:mu         4.334    0.6418  6.752   Positive
    ## ============================================================
    ## Fixed effects:
    ##           Estimate Std.Error t.value
    ## Intercept   16.484    0.6735  24.474
    ## CA.2012     -5.780    0.7365  -7.848
    ## CA.2013     -6.372    0.7799  -8.170
    ## ============================================================
    ## Use the '$' sign to access results and parameters

## 8) Finlay-Wilkinson regression

    data(DT_h2, package="enhancer")
    DT <- DT_h2

    ## build the environmental index
    ei <- aggregate(y~Env, data=DT,FUN=mean)
    colnames(ei)[2] <- "envIndex"
    ei$envIndex <- ei$envIndex - mean(ei$envIndex,na.rm=TRUE) # center the envIndex to have clean VCs
    ei <- ei[with(ei, order(envIndex)), ]

    ## add the environmental index to the original dataset
    DT2 <- merge(DT,ei, by="Env")

    # numeric by factor variables like envIndex:Name can't be used in the random part like this
    # they need to come with the vsm() structure
    DT2 <- DT2[with(DT2, order(Name)), ]
    mix2 <- mmes(y~ envIndex, henderson=TRUE,
                 random=~ Name + vsm(dsm(envIndex),ism(Name)), data=DT2,
                 rcov=~vsm(dsm(Name),ism(units)),
                 tolParConvNorm = .0001,
                 nIters = 50, verbose = FALSE
    )
    # summary(mix2)$varcomp

    b=mix2$uList$`vsm(dsm(envIndex), ism(Name))` # adaptability (b) or genotype slopes
    mu=mix2$uList$`sommer::vsm( sommer::ism( Name ) ` # general adaptation (mu) or main effect
    e=sqrt(summary(mix2)$varcomp[-c(1:2),1]) # error variance for each individual

    ## general adaptation (main effect) vs adaptability (response to better environments)
    plot(mu[,1]~b[,1], ylab="general adaptation", xlab="adaptability")
    text(y=mu[,1],x=b[,1], labels = rownames(mu), cex=0.5, pos = 1)

    ## prediction across environments
    Dt <- mix2$Dtable
    Dt[1,"average"]=TRUE
    Dt[2,"include"]=TRUE
    Dt[3,"include"]=TRUE
    pp <- predict(mix2,Dtable = Dt, D="Name")
    preds <- pp$pvals
    # preds[with(preds, order(-predicted.value)), ]
    ## performance vs stability (deviation from regression line)
    plot(preds[,2]~e, ylab="performance", xlab="stability")
    text(y=preds[,2],x=e, labels = rownames(mu), cex=0.5, pos = 1)

## 9) Factor analytic (reduced rank) model

When the number of environments where genotypes are evaluated is big and we want to consider the genetic covariance between environments and location-specific variance components we cannot fit an unstructured covariance in the model since the number of parameters is too big and the matrix can become non-full rank leading to singularities. In those cases is suggested a dimensionality reduction technique. Among those the factor analytic structures proposed by many research groups (Piepho, Smith, Cullis, Thompson, Meyer, etc.) are the way to go. Sommer has a reduced-rank factor analytic implementation available through the rrm() function. Here we show an example of how to fit the model:

    data(DT_h2, package="enhancer")
    DT <- DT_h2
    DT=DT[with(DT, order(Env)), ]
    head(DT)

    ##          Name     Env Loc Year     Block  y
    ## 67   MSL007-B CA.2011  CA 2011 CA.2011.2  5
    ## 105  MSL007-B CA.2011  CA 2011 CA.2011.1  6
    ## 308  MSK061-4 CA.2011  CA 2011 CA.2011.2  9
    ## 393  MSK061-4 CA.2011  CA 2011 CA.2011.1 10
    ## 469 MSR169-8Y CA.2011  CA 2011 CA.2011.1 11
    ## 471     NY148 CA.2011  CA 2011 CA.2011.1 11

    indNames <- na.omit(unique(DT$Name))
    A <- diag(length(indNames))
    rownames(A) <- colnames(A) <- indNames

    # fit diagonal model first to produce H matrix
    ansDG <- mmes(y~Env, henderson=TRUE,
                  random=~ vsm(dsm(Env), ism(Name)),
                  rcov=~units, nIters = 100,
                  data=DT, verbose = FALSE)

    H0 <- ansDG$uList$`vsm(dsm(Env), ism(Name))` # GxE table

    # reduced rank model
    ansFA <- mmes(y~Env, henderson=TRUE,
                  random=~vsm( usm(rrm(Env, H = H0, nPC = 3)) , ism(Name)) + # rr
                    vsm(dsm(Env), ism(Name)), # diag
                  rcov=~units,
                  # we recommend giving more iterations to these models
                  nIters = 100, verbose = FALSE,
                  # we recommend giving more EM iterations at the beggining
                  data=DT)

    vcFA <- ansFA$theta[[1]]
    vcDG <- ansFA$theta[[2]]

    loadings=with(DT, rrm(Env, nPC = 3, H = H0, returnGamma = TRUE) )$Gamma
    scores <- ansFA$uList[[1]]

    vcUS <- loadings %*% vcFA %*% t(loadings)
    G <- vcUS + vcDG
    # colfunc <- colorRampPalette(c("steelblue4","springgreen","yellow"))
    # hv <- heatmap(cov2cor(G), col = colfunc(100), symm = TRUE)

    uFA <- scores %*% t(loadings)
    uDG <- ansFA$uList[[2]]
    u <- uFA + uDG

As can be seen genotype BLUPs for all environments can be recovered by multiplying the loadings (Gamma) by the factor scores. This is a parsomonious way to model an unstructured covariance.

## 10) Two stage analysis

It is common then to fit a first model that accounts for the variation of random design elements, e.g., locations, years, blocks, and fixed genotype effects to obtain the estimated marginal means (EMMs) or best linear unbiased estimators (BLUEs) as adjusted entry means. These adjusted entry means are then used as the phenotype or response variable in GWAS and genomic prediction studies.

    ##########
    ## stage 1
    ## use mmes for dense field trials
    ##########
    data(DT_h2, package="enhancer")
    DT <- DT_h2
    head(DT)

    ##                 Name     Env Loc Year     Block y
    ## 1            W8822-3 FL.2012  FL 2012 FL.2012.1 2
    ## 2            W8867-7 FL.2012  FL 2012 FL.2012.2 2
    ## 3           MSL007-B MO.2011  MO 2011 MO.2011.1 3
    ## 4         CO00270-7W FL.2012  FL 2012 FL.2012.2 3
    ## 5 Manistee(MSL292-A) FL.2013  FL 2013 FL.2013.2 3
    ## 6           MSM246-B FL.2012  FL 2012 FL.2012.2 3

    envs <- unique(DT$Env)
    BLUEL <- list()
    XtXL <- list()
    for(i in 1:length(envs)){
      ans1 <- mmes(y~Name-1,
                   random=~Block,
                   verbose=FALSE,
                   data=droplevels(DT[which(DT$Env == envs[i]),]
                   )
      )
      ans1$Beta$Env <- envs[i]

      BLUEL[[i]] <- data.frame( Effect=factor(rownames(ans1$b)),
                                Estimate=ans1$b[,1],
                                Env=factor(envs[i]))
      # to be comparable to 1/(se^2) = 1/PEV = 1/Ci = 1/[(X'X)inv]
      XtXL[[i]] <- solve(ans1$Ci[1:nrow(ans1$b),1:nrow(ans1$b)])
    }

    DT2 <- do.call(rbind, BLUEL)
    OM <- do.call(adiag1,XtXL)

    ##########
    ## stage 2
    ## use mmes for sparse equation
    ##########
    m <- matrix(1/var(DT2$Estimate, na.rm = TRUE))
    ans2 <- mmes(Estimate~Env, henderson=TRUE,
                 random=~ Effect + Env:Effect,
                 rcov=~vsm(ism(units,thetaC = matrix(3), theta = m)),
                 W=OM,
                 verbose=FALSE,
                 data=DT2
    )

    ## Using the weights matrix

    summary(ans2)$varcomp

    ##                   VarComp  VarCompSE    Zratio Constraint
    ## Effect:mu:mu     2.077462 0.07336063  28.31848   Positive
    ## Env:Effect:mu:mu 3.327513 0.03083364 107.91824   Positive
    ## units:NA         1.000000 0.01098721  91.01495      Fixed

## Literature

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