Original article

Consequences of reducing a full modelof variance analysis in tree breeding experiments

M Giertych H Van De Sype2

1 Institute of Dendrology, 62-035 Kornik, Poland; 2 INRA, Station d’Améliorationdes Arbres Forestiers, Ardon, 45160 Olivet, France

(Received 20 July 1988; accepted 30 June 1989)

Summary — An analysis of variance was performed on height measurement of 11-year-old trees (7in the field), using the results of a non-orthogonal progeny within provenance experiment establi-shed for Norway spruce (Picea abies (L.) Karst.) at 2 locations in Poland. The full model includinglocations, provenances, progenies within provenances, blocks within locations and trees within plotsis used assuming all sources of variation to be random. This model is compared with various modelsreduced by 1 factor or the other within the model. Theoretical modifications of estimated variancecomponents and heritabilities are tested with experimental data. By referring to the original model itis shown how changes came to be and where the losses of information occurred. A method is pro-posed to reduce the factor level number without bias. The general conclusion is that it pays to makethe effort and work with the full model.

Piceas abies / height / provenance / progeny / variance analysis / method / genetic parameter

Résumé — Conséquences de la réduction d’un modèle complet d’analyse de variance pourdes expériences d’amélioration forestière. La hauteur totale à 11 ans, après 7 ans de plantation,a été mesurée en Pologne dans deux sites pour 12 provenances d’Epicéa commun originaires dePologne, avec environ 8 familles par provenance. Les différents termes et indices sont explicitésdans le tableau 1. L’analyse de la variance selon un modèle complet (localité, bloc dans localité,provenance, famille dans provenance, et les diverses interactions) a été réalisée en considérantles facteurs comme aléatoires (tableau 2). Elle est comparée à des analyses selon des modèlessimplifiés qui ignorent successivement les niveaux provenance, famille ou bloc, ou les valeurs indi-viduelles. Dans le cas du modèle simplifié sans facteur provenance, les nouvelles espérances descarrés moyens (tableau 3) peuvent être strictement comparées à celles obtenues avec le modèlecomplet. Les modifications théoriques ont été calculées et sont présentées de façon schématiquepour l’estimation des composantes de la variance (tableau 4) et des paramètres génétiques(tableau 5). Les résultats théoriques associés aux autres modèles sont également reportés dansces deux derniers tableaux. En outre, l’implication du nombre de niveaux par facteur sur les biaisentraînés a été précisée. En général, les simplifications surestiment fortement les composantes dela variance et augmentent de façon illicite les gains espérés. Les résultats obtenus avec les don-nées expérimentales montrent effectivement des changements au niveau des composantes de lavariance ou des tests associés (tableau 7) et de légères modifications pour les paramètres géné-

*Correspondence and reprints.

tiques (tableau 8). Les biais que de telles simplifications peuvent entraîner dans un programmed’amélioration forestière sont discutés. En conclusion, une proposition est formulée pour réduirepar étapes mais de façon fiable le nombre de niveaux à étudier.

Picea abies / hauteur / provenance / descendance / analyse de variance / méthode / para-mètre génétique

INTRODUCTION

In complicated tree breeding experiments,particularly when one deals with non-

orthogonal and unbalanced design, andthis is often the case, the temptationarises to reduce the model to only thoseparts that are of particular interest at a

given time. Such reductions from the full

model create certain consequences that

we are not always fully aware of. The aimof the present paper is to show on one

experiment how different reductions of theexperimental model affect the results andconclusions derived from them.

MATERIAL

The experiment discussed here is a Norwayspruce (Picea abies (L.) Karst.) progeny withinprovenance study established at 2 locations in

Poland, in Kornik and in Goldap, in 1976 using2+2 seedlings raised in a nursery in Kornik. Theexperiment includes half-sib progenies from 12 provenances from the North Eastern range ofthe spruce in Poland. Originally, cones werecollected from 10, randomly selected trees fromeach of the provenances. However, due to an

inadequate number of seeds or seedlings per

progeny, the experiment was established in an

incomplete block design. Not only were thematernal trees selected at random, but the pro-venances were also a random choice of ForestDistricts in the area and cone collections werecarried out from fellings which were being madein the Forest District at the time we arrivedthere for cone collection.

Since all our Polish experiments were

concentrated in regions near Kornik and Gol-dap, the choice of locations could also be consi-dered as random. The blocks in our locationsare just part of the areas, and therefore coverall variations of the site, and may also be consi-dered as random. Details of the study were pre-sented in an earlier paper (Giertych and Kroli-kowski, 1982). The designations used in the

study are shown in table I.As the design is far from orthogonal, ana-

lyses of data (height in 1983) were performed inFrance using the Amance ANOVA programs(Bachacou et al., 1981). Furthermore, the num-ber of factor levels was larger than the compu-ter capacity, and accordingly analyses weredone in several stages.

ANALYSES WITH DIFFERENT MODELS

The full model

The full model has been used to extract

the maximum amount of information from

the material (symbols are explained in

table I):

Since all elements of the experimentwere considered to be random, the

degrees of freedom and expected meansquares for the variance analysis are asshown in table II obtained through the pro-cedure described by Hicks (1973). Thetheoretical degrees of freedom for an

orthogonal model and the expected meansquares are shown in table II.

On the basis of this full model, it is pos-sible to calculate heritabilities by the for-mula proposed by Nanson (1970) for:- provenances: h2P = σ2P / VP, where:

- and families within provenance: h2F = σ2F / VF, where:

In an orthogonal system, these heritabi-lities can be estimated from the Fvalue ofthe Snedecor’s test by 1 -(1/F). In fact,due to non-orthogonality and unbalanceddesign, they were calculated from the

variance components.For this half-sib experiment, another

approach is to calculate single tree herita-bility (narrow sense) based on the bet-

ween-families’ additive variance and the

phenotypic variance (VPh):h2S = 4 σ2F /VPh where:

Heritability (h2), variance (V), selectionintensity (i) and expected genotypic gain(ΔG = i h2 &jadnr;V) depend on the aim of theselection and the type of material used.

For example, it is possible to estimate thegenotypic gain which will be expected forreforestation with the same seeds which

gave the material selected in this experi-ment. The best provenance may be selec-ted from a total of twelve, so the expectedgain will be estimated with heritability and

phenotypic variance at the provenancelevel, and i = 1.840. The selection of the 2best families within each provenance willuse family parameters andi = 1.289. At the end of these 2 steps, 2families within the best provenance will be

selected; this will be compared to a 1 stepselection with i = 2.417. Another method isto select the 50 best individuals from the9122 trees of this experiment, to propaga-te them, and to establish a seed orchard.The expected genetic gain of the seedorchard offsprings will be estimated from

phenotypic variance (VPh), narrow senseheritability (h2s) and i = 2.865. It is assu-med here that these last values are onesthat utilize the maximum amount of dataand are therefore the best that can beobtained.

Let us now examine the changes pro-duced with simpler models when a part ofthe information is not used.

Model ignoring the provenance factor

For increasing estimation of genetic para-meters, it may be tempting to treat the

families, altogether, disregarding the split-

up of families into provenances. In a fullyorthogonal model with p provenances andf families (within provenances), the num-ber of families is pf with a new subscript k’instead of k(i). The model now becomes:

The distribution of the degrees of free-dom and the expected mean squares areas shown in table III. In order to use thevariance components estimated from thefull model, we must combine the sum ofsquares from table II as follows:

Degrees of freedomand SS from model 2 SS from model 1

The total sum of squares remains unaf-fected. Working from the bottom of this listwe can identify, on the left hand-side, the

new sum of squares with the expectedmean squares multiplied by the degrees offreedom indicated above (and in table III),and on the right hand-side, the combina-tion of sums of squares with their expec-ted mean squares multiplied by their owndegrees of freedom from table II. The pro-cedure is shown for the 2 first factors.1/ new residualThe degrees of freedom are Ibpf(x-1) forboth sides of the equation. The equationSSE’ = SSE is transformed as Ibpf(x-1)σ2E’= Ibpf(x-1)σ2E, thus leads to:σ2E’ = σ2E (relation 1).2/ new family x block interactionThe degrees of freedom are I(b-1)(pf-1)for the new expected mean square andI(b-1)p(f-1) for the full model. SSF’B’ =

SSFB + SSPB becomes:

considering 1 (σ2E’ = σ2E) and simplifyingby (pf-1)x gives:

The same procedure is followed forother equalities of sums of squares. Tosummarize, when we decide to speak offamilies only, instead of provenances andfamilies (within provenances), we obtainthe following changes in variance compo-nents:

This implies modifications for variancecomponents and total variance as shownin table IV. The true variance componentsof interactions between provenance and

locality (σ2PL) or block (σ2PB) are eachsplit in 2 parts. The largest part enters inthe component of interactions between

family and locality (σ2F’L’) or block (σ2F’B’),and the smallest one enters in the locality(σ2L’) or block (σ2B’) components. For thetrue variance component for provenance(σ2P,), the largest part enters in the familycomponent (σ2F’) and the smallest one islost altogether, so the total variance (VT’)is lowered by σ2P (f-1)/(pf-1).

Compared to the full model, ignoringthe provenance level introduces modifica-tions for estimation of genetic parameters(table V). The mean family variance(VF) isincreased by the largest part of all the

components of provenance effect and

interactions (σ2P + σ2PL/I + σ2PB/Ib)(p-1)f/(pf-1). The family heritability (h2F)decreases slightly and the expected gainis higher (ΔGF). At the individual level, thephenotypic variance (VPh) is lowered bythe smallest part of variance componentsfor provenance effects (σ2P + σ2PL +

σ2PB)(f-1)/(pf-1). The narrow sense heri-tability (h2s) and the expected genetic gainfor additive effect (ΔG) are increased by apart of the non-additive effects, originatingfrom provenance variations.

One point of interest is to observe the

changes which occur in relation to thenumber of provenances (p) or families perprovenance (f). For the same total numberof families (pf),the larger the number ofprovenances, the lower the loss of total

variance (VT’) and the larger phenotypicvariances, heritabilities and expectedgains at family or individual levels. Byincreasing the total number of families (pf),the same modifications occur.

Ignoring the family factor

When comparing provenances, it seemseasier to ignore the family variation, and toreduce the experiment to a simple prove-nance trial. We then obtain the followingmodel, where a new subscript (n’) is usedinstead of (k) family and of (n) tree ones:

As with the previous model, expectedmean squares can be constructed with thenew sums of squares and new degrees offreedom and then compared to the originalfull model 1. Change is observed for theresidual level only, which now includes allfamily-dependent variations: SSE’ = SSE +

SSFB + SSFL + SSF. The degrees of free-dom become Ibp(fx-1) = Ibpf(x-1) +

I(b-1)p(f-1) + (I-1)p(f-1) + p(f-1) with fxthe new number of trees per plot. Usingthe same procedure as for model 2°, weobtained the following values for the newvariance components in terms of those ofthe original full model:

The total variance and the locality andblock (within locality) variance compo-nents remain unaffected (table IV). Prove-nance and provenance-locality variance

components are increased respectively bythe smallest part of family and family-loca-lity components. The provenance-blockinteraction is modified by a small part of acombination of all family componentswhile the main part is included in the resi-dual. For genetic estimations (table V), themean provenance variance (VP) remainsunchanged and the provenance heritability(h2P) is higher with the increase of thevariance component of provenance.Consequently, the expected gain for a pro-venance selection is higher. All thesemodifications depend on the number offamilies per provenance only (f). The

higher the number, the lower the bias.

Model ignoring block effect

Sometimes authors have no interest in thevariation between blocks and they placeall block effects into the residual. A new

subscript n’ must be used instead of m forblock and n for tree, the model will thenbe:

The new sums of squares, compared tothe original ones, will change for residualonly: SSE’ = SSE + SSFB + SSPB + SSB.bx now becomes the new number of trees

per element of the experiment and thenew degrees of freedom for the error termbecome:

Following the same procedure as befo-re, we obtain new values of variance com-

ponents:

When ignoring the block effect, the totalvariance and the variance components forprovenance and family levels remain unaf-fected (table IV). The variance compo-nents at locality or provenance-localitylevels include a small part of the block orprovenance-block component. The mainpart of the block and block-interactionvariance components enters into the resi-dual. For genetic parameters (table V),variance of means, heritability and expec-ted gain are not changed for provenanceor family levels. At the individual level, thephenotypic variance is increased by themain part of the block component (σ2B(b-1)/b), so the single tree heritability is

lower than that in the full model and the

expected genetic gain decreases. The

more blocks (b), the smaller the bias forvariance components, and the larger formass selection option.

Model with plot averages

Another method used is to work on plotaverages only. This is generally used fortraits such as mortality or productivity perunit area. In these cases, SSE is not avai-

lable and the only approach is to use SSFBas the new residual. It is therefore impos-sible to estimate the true variance compo-

nent for the family x block interaction

(σ2FB). The model becomes:

The number of trees per plot (x) is the

new unit of measurement and does not

enter into the degrees of freedom. Sincethe analysis is performed on the basis ofplot means (sums per plot divided by x),original sums of squares must be dividedby x2. The new sums of squares will beconstructed as below:

Degrees of freedom

Considering 3 and simplifying:

Computations and results are similar

for all remaining variance components,thus:

The decrease of variance componentsdepends on the number of trees per plot(x), and reflects the use of plot means ascompared with individual data (table IV).For the total variance, a part of losses is a«logical» reduction due to a lower numberof data (VT/x). Another part is a loss ofinformation (σ2E (x-1)/x2). The higher thenumber of trees per plot (x), the lower thetotal variance (VT’), also lower is the relati-ve loss due to lack of information (σ2E).

For provenance and family levels, in

comparison with the full model, heritabili-ties are unaffected while means variancesare divided by x, and expected gains aredivided by &jadnr;x (table V). Without individualdata, phenotypic variance (VPh) and singletree heritability (h2s) cannot be estimated.

Experimental data

Experimental data (tree height at 7 yearsin the field) were analyzed with the 5

models. The experimental factor levels areindicated in table I.

With the full model (model 1), the resultof the analysis of variance (table VI)shows that 3 factors are in significant. It is

very surprising that no locality effect andno provenance x locality interaction effect,exists. The climatic conditions in Kornikand Goldap are very different, but grandmeans are identical for the 2 locations

(232.8 cm and 234.6 cm respectively).Unfortunately, locality and provenance x

locality variance components have negati-ve values (they are indicated betweenbrackets in tables VI and VII), but areconsidered as zero for the estimation of

genetic parameters. The non-significanceof the provenance effect is not unders-tood. For these reasons, the demonstrati-ve aspect of our experimental data will beweaker. Consequences for improvementare indicated in table VIII. The choice ofthe best provenance is uncertain heresince FP is not significant. Reforestationwith the best 2 families, if seed supply is

sufficient, would give an expected gain of17 cm. With the seed orchard option, the

expected genetic gain would be 18 cm (or8%).

With model 2, ignoring the provenancelevel, the total variance (VT’) calculated,including the negative values for some

variances, declined from 6842.4 to 6839.0,which corresponds exactly to the lost partof the provenance variance (σ2P(f-1)/(pf-1) = 46.5 * 0.074 = 3.4). Changesobserved at the family or family interactionlevels depend on values of variance com-ponents at provenance or provenanceinteraction levels (table VII). Thus, theF-test can increase (family x block level)or decrease (family x locality level from1 % probability level to non-significant).Compared to the full model, ignoring theprovenance level introduces large modifi-cations for estimation of the genetic para-meters (table VIII). At the family level, theheritability (h2F) decreases slightly whilethe expected genotypic gain (ΔGF)becomes higher. At the individual level,the narrow sense heritability (h2s) and theexpected genetic gain for additive effects(ΔG) are increased by a part of the non-additive effects originating from provenan-ce variations.

For the third model, without the familylevel, changes are not large and F-testshave the same probability level (table VII).Provenance heritability (h2P) increasesfrom 0.27 to 0.40 with a part of the familyheritability (table VIII). Consequently, theexpected gain for provenance selection is

higher (ΔGP: 6.6 to 8.9 cm) even if thechoice for provenance is uncertain (F-testfor provenance is not significant).

Ignoring the block level (model 4), intro-duces changes for the provenance x loca-lity level (F-test from non-significant to

significant at 0.1 % level) and for the fami-ly x locality level (table VII). As was expec-ted in table V, most genetic parametersremain the same (table VIII). However,changes are observed at the individuallevel. The phenotypic variance (VPh) isincreased by the main part of the block

component, so the single tree heritability(h2s) and the expected genetic gaindecrease (ΔG).

For the last model, with plot means ins-tead of individual data, the family x localityinteraction becomes more significant (from1 % to 0.1 %) due to the change of deno-minator level (new residual instead of

family x block level). For the total variance(VT’ = 339), the reduction of information

([σ2E (x-1)/x2] = 466) represents morethan half of the «logical» variance (VT /x =

805). The decrease of genetic parametersis important but corresponds to the reduc-tion in the number of measured data.

DISCUSSION AND CONCLUSION

In practice, reduction of a full model to asimpler one is often due to computationlimitations. This may result in the calcula-tion for unbalanced design with non-ortho-gonal data which necessitates special pro-cedures such as Amance programs usedfor this study. Another possible limitation isthe kind of design treated by a computerprogram, such as nested and cross classi-fication. Another difficulty is the maximumnumber of levels which is compatible withcomputer capacity; in this experiment, thenumber is 1188. All these limitations inciteauthors to reduce their statistical model toa simpler one which they are able to ana-lyse.

The material used here is just an

example. On the one hand, it gives nosignificant effects for locality, provenanceand provenance x locality interaction and2 variance components are negative. Onthe other hand, our experimental data

gives a very low single tree heritability (h2s= 0.080) and a small expected geneticgain (+ 8 %) for total height at 7 years inthe field (11 from the seed). It is possiblethat the experimental design, described byGiertych and Krolikowski (1982), is not

suited to give the best estimation of gene-tic parameters. In spite of the weak

demonstrative value or our experimentaldata, our results clearly indicate that anydeviation from the full model introduces

significant changes. For example, interac-tion estimations are very different, and

may lead to opposite conclusions depen-ding on the adopted model.

Ignoring the provenance level leads toa loss of information (total variance

decrease) and an unjustified increase ofsingle tree heritability and consequentlyexpected genetic gain. We have shownthat these increases result in the addition

of non-additive variance from provenanceto an additive one from half-sib families.

Thus, this method can only be used if pro-venances originate from the same ecotypeand if genetic structures are comparable.This can best be tested by Bartlett’s or

Hartley’s tests for 1 trait or by comparisonof variance-covarience matrix with Kull-

back’s test (1967) for multitrait analysis.Furthermore, we must bear in mind that

the bias is lower when the numbers of pro-venances and of families per provenanceare low.

By ignoring the family level, provenan-ce heritability increases because it

includes a part of the family variance. Thecorrelated expected gain is also higher.The bias is low with a large number offamilies per provenance and is unaffected

by the number of provenances. Comparedwith others, this model introduces less

modification. When ignoring the block

level, heritability and expected gains at

family or provenance levels remain the

same. At the single tree level, the modifi-cation appears to have few conse-

quences. In fact, with this procedure, theexpected genotypic gain is obtained byselection of the best trees, located in the

richest part of the site. Accordingly, the

selected material does not necessarilyhave the best genetic value, and the reali-

zed gain can be very low compared withthe expected one.

Working on plot means, all variance

components are changed in the same

ratio but heritabilities for provenance and

family remain the same, while genotypicgains are reduced. This method causes avery important loss of information, but maybe necessary for traits like mortality or pro-ductivity per unit area.

Since every model reduction leads to

modifications, it can prove useful to find

way to change the model in order to obtainthe technical means to treat it. One possi-bility consists of discarding blocks byadjusting individual data to the block

effect. A first step can be an analysiswithout the family level, which is the onlymodel to give good estimates of localityand block variance components. The

second step consists of adjusting indivi-

dual data to block and/or locality effect. Atthe same time, the total degrees of free-dom must be reduced by those for blockand/or locality. The next steps, comprisinginteraction studies, can be achieved by dif-ferent analyses, with more facility for com-putation. In any case, expected mean

squares of simplified models must be

compared with the full model ones.Working on a full model may involve

extra work, and require some stepwiseprocedures, due to the limited capacity ofcomputers, but the effort is worthwhilebecause otherwise unreliable or incomple-te results would be obtained.

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