g x e: genotype-by- environment...
TRANSCRIPT
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G x E: Genotype-by-
environment interactions
Much more detail in the two online WL chapters (32
and esp. 33)
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G x E• Introduction to G x E
– Basics of G x E
– Finlay-Wilkinson regressions
• SVD-based methods– The singular value decomposition (SVD)
– AMMI models
• Factorial regressions
• Mixed-Model approaches– BLUP
– Structured covariance models
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Yield in Environment 2
Yield in Environment 1
Genotype 1
Genotype 2
G11
G12
G21
G22
E1
E2
E2E1 G1 G2
Ei = mean value in environment i
Overall means
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G11 G12G21 G22
E2E1 G1 G2
Gij = mean of genotype i in environment j
Under base model of Quantitative Genetics,
Gij = µ + Gi + Ej
When G x E present, there is an interaction between
a particular genotype and a particular environment so that
Gij is no longer additive, Gij = µ + Gi + Ei + GEij
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G11 G12G21 G22
E2E1 G1 G2
µ
Components measured as deviations
from the mean µ
GEij = gij - gi - ej
e2e1
g1
g11g12
g2
g22g21
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Which genotype is the best?
G11 G12G21 G22
E2E1 G1 G2
Depends: If the genotypes are grown in both environments,
G2 has a higher mean
If the genotypes are only grown in environment 1, G2 has a
higher mean
If the genotypes are only grown in environment 2, G1 has a
higher mean
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G x E: Both a problem and an
opportunity
• A line with little G x E has stability acrossenvironments.
• However, a line with high G x E mayoutperform all others in specificenvironments.
• G x E implies the opportunity to fine-tunespecific lines to specific environments
• High !2(GE) implies high G x E in at leastsome lines in the sample.
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Major vs. minor environments• An identical genotype will display slightly different traits
values even over apparently identical environments due tolow micro-environmental variation and developmental noise
• However, macro-environments (such as different locationsor different years <such as a wet vs. a dry year>) can showsubstantial variation, and genotypes (pure lines) maydifferentially perform over such macro-environments (G xE).
• Problem: The mean environment of a location may besomewhat predictable (e.g., corn in the tropics vs. temperateNorth American), but year-to-year variation at the samelocation is essentially unpredictable.
• Decompose G x E into components
– G x Elocations + G x Eyears + G x Eyears x locations
– Ideal: strong G x E over locations, high stability over years.
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Genotypes vs. individuals
• Much of the G x E theory is developed forplant breeders who are using pure (= fullyinbred) lines, so that every individual hasthe same genotype
• The same basic approaches can be used bytaking family members as the replicatesfor outbred species. Here the “genotype”over the family members is some compositevalue.
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Estimating the GE term• While GE can be estimated directly from the mean in a cell
(i.e., Gi in Ej) we can usually get more information (and abetter estimate) by considering the entire design anexploiting structure in the GE terms
• This approach also allows us to potentially predict the GEterms in specific environments
• Basic idea: replace GEij by "i#j or more generally by $k "ki#kj
These are called biadditive or bilinear models. This (at firstsight) seems more complicated. Why do this?
• With nG genotypes and nE environments, we have
– nG nE GE terms (assuming no missing values)
– nG + nE "i and #i unique terms
– k(nG + nE) unique terms in $k "ki#kj .
• Suppose 50 genotypes in 10 environments
– 500 GEij terms, 60 unique "i and #i terms, and (for k=3), 180unique "ki and #ki terms.
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Finlay-Wilkinson RegressionAlso called a joint regression or regression on an
environmental index.
Let µ + Gi be the mean of the ith genotype over all
the environments, and µ + Ej be the average yield of
all genotypes in environment j
The FW regression estimates GEij by the regression GEij = %iEj+ &ij.
The regression coefficient is obtained for each genotype from
the slope of the regression of the Gij over the Ej. &ij is the
residual (lack of fit). If !2(GE) >> !2(&) , then the regression
accounted for most of the variation in GE.
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Application
• Yield in lines of wheat over differentenvironments was examined by Calderiniand Slafer (1999). The lines they examinedwhere lines from different eras ofbreeding (for four different countries)
• Newer lines had larger values, but also hadhigher slopes (large %i values), indicatingless stability over mean environmentalconditions than see in older lines
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Regression slope for each genotype is %i
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SVD approaches
• In Finlay-Wilkinson, the GEij term was estimatedby %iEj, where Ej was observed. We could alsohave used #jGi, where #j is the regression ofgenotype values over the j-th environment. AgainGi is observable.
• Singular-value decomposition (SVD) approachesconsider a more general approach, approximatingGEij by $k "ki#kj where the "ki and #kj aredetermined by the first k terms in the SVD of thematrix of GE terms.
• The SVD is a way to obtain the best approximationof a full matrix by some matrix of lower dimension
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A data set for soybeans grown in New York (Gauch 1992) gives the
GE matrix as
Where GEij = value for
Genotype i in envir. j
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For example, the rank-1 SVD approximation for GE32 is
g31'1e12 = 746.10*(-0.66)*0.64 = -315
While the rank-2 SVD approximation is g31'2e12 + g32'2e22 =
746.10*(-0.66)*0.64 + 131.36* 0.12*(-0.51) = -323
Actual value is -324
Generally, the rank-2 SVD approximation for GEij is
gi1'1e1j + gi2'2e2j
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AMMI modelsAdditive main effects, multiplicative interaction (AMMI)
models use the first m terms in the SVD of GE:
Giving
AMMI is actually a family of models, with AMMIm
denoting AMMI with the first m SVD terms
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AMMI models
Fit main effects
Fit principal components
to the interaction term
(SVD is a generalization
of PC methods)
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Why do AMMI?
• One can plot the SVD terms (#ki, (kj) tovisualize interactions– Called biplots (see online notes Chapter 33 for
details)
• AMMI can better predict mean values ofGEij than just using the cell value (theobserved mean of Genotype i inEnvironment j)
• A huge amount more on AMMI in the onlinenotes (Chapter 33)!
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Factorial Regressions
• While AMMI models attempt to extract informationabout how G x E interactions are related across setsof genotypes and environments, factorial regressionsincorporate direct measures of environmentalfactors in an attempt to account for the observedpattern of G x E.
• The power of this approach is that if we candetermine which genotypes are more (or less)sensitive to which environmental features, thebreeder may be able to more finely tailor a line to aparticular environment without necessarily requiringtrials in the target environment.
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Suppose we have a series of m measured values from
the environments of interest (such as average rainfall,
maximum temperature, etc.) Let xkj denote the value
of the k-th environmental variable in environment j
Factorial regressions then model the GE term as
the sensitivity )ki of environmental
value k to genotype i, (this is a regression slope to be
estimated from the data)
Note that the Finlay-Wilkinson regression is a special
case where m=1 and xj is the mean trait value (over
all genotypes) in that environment.
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Mixed model analysis of G x E• Thus far, our discussion of estimating GE has be set in terms of
fixed effects.
• Mixed models are a powerful alternative, as they easily handlemissing data (i.e., not all combinations of G and E explored).
• As with all mixed models, key is the assumed covariancestructure
– Structured covariance models
• Compound symmetry
• Finlay-Wilkinson
• Factor-analytic models (closely related to AMMI)
• Much more complete treatment and discussion in WLChapter 33
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Basic GxE Mixed model• Typically, we assume either G or E is fixed,
and the other random (making GE random)
• Taking E as fixed, basic model becomes
• z = X% + Z1g + Z2ge + e– The vector % of fixed effects includes estimates of the
Ej. The vector g contains estimates of the Gi values,while the vector ge contains estimates of all the GEij.
– Typically we assume e ~ 0, !e2I, and
independent of g and ge.
– Models significantly differ on thevariance/covariance structure of g and ge.
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ExampleWe have two genotypes and three environments. Let zijk denote
the k-th replicate of genotype i in environment j. Suppose we
have single replicates of genotype 1 in all three environments, two
replicates of genotype 2 in environment 1, and one in environment 3
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Compound Symmetry assumption
• To proceed further on the analysis of themixed model, we need covarianceassumptions on g and ge.
• The compound symmetry assumption is– !2(Gi) = !G
2 , !2(GEij) = !2GE
– Under this assumption, the covariance of anygenotype across any two (different)environments is the same.
– Likewise, the genetic variance within anyenvironment is constant across environments
– Net result, the genetic covariance is the samebetween any two environments
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Expected genetic variance within a given environment
Genetic correlation across environments is constant
Genetic covariance of the same genotype across environments
For our example, the resulting covariance matrix becomes
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Mixed-model allows for missing values.
Under fixed-model model, estimate of µij = zij.
BLUP estimates under mixed-model (E fixed, G random)
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BLUP shrinks (regresses) the BLUE estimate
back towards zero
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Modification of the residual
covariance
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Extending genetic covariances
• Shukla’s model: starts with the compoundsymmetry model, but allows for different Gx E variances over genotypes,– GEij ~ N(0, !2
GiE)
– Gi ~ N(0, !G2)
– Cov(ge) = Diagonal (!2G1E , …, !2
GnE)
– The covariance of a genotype acrossenvironments is still !G
2
• Structured covariance models allow morecomplicated (and more general) covariancematrices
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Covariances based on Finlay-Wilkinson
We treat Gi and %i as fixed effects, &ij and eijk as
random (fixed genetic effects, random environmental
effects)
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Factor-analytic covariance structures allow one to consider more
general covariance structures informed by the data, rather than
assumed by the investigator
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AMMI-based structured covariance models
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Chapter 33 goes into detail on further analysis of this
model, and its connection to stability analysis and the
response to selection.
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Summary: Structured Covariance models