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15 Examples 302Animal modelIn this section we will illustrate the use of .ped files to define the genetic pedigreestructure between animals. This is an alternate method of estimating additivegenetic variance for these data. The variance matrix of the animals (sires, damsand lambs) for which we only have data on lambs is given byvar (u A ) = Σ A ⊗ A −1where A −1 is the inverse of the genetic relationship matrix. There are a totalof 10696 = 92 + 3561 + 7043 animals in the pedigree. The ASReml input file ispresented below. Note that this model is not equivalent to the sire/dam/littermodel with respect to the animal/litter components for gfw, fd and fat.Multivariate Animal modeltag !Psiredam !Pgrp 49sexbrr 4litter 4871age wwt !m0 ywt !m0 # !M0 identifies missing valuesgfw !m0 fdm !m0 fat !m0coop.fmt # read pedigree from first three fieldscoop.fmt !DOPATH $1 !CONTINUE !MAXIT 20 !STEP 0.01# $1 allows selection of PATH as a command line argument!PATH 3!EXTRA 4 # Force 4 more iterations after convergence criterion met!PATHwwt ywt gfw fdm fat ~ Trait Tr.age Tr.brr Tr.sex Tr.age.sex,!r Tr.tag ,at(Tr,1).dam, at(Tr,2).dam, -at(Tr,3).dam .003,at(Tr,1).lit, at(Tr,2).lit, at(Tr,3).lit, at(Tr,4).lit,at(Trait,1).age.grp .0024,at(Trait,2).age.grp .0019,at(Trait,4).age.grp .0020,at(Trait,5).age.grp .00026,at(Trait,1).sex.grp .93,at(Trait,2).sex.grp 16.0,at(Trait,3).sex.grp .28,at(Trait,5).sex.grp 1.18,!f Tr.grp1 2 3 # One multivariate R structure, 3 G structures0 0 0 # No structure across lamb records# First zero lets ASReml count te number of recordsTr 0 US#General structure across traits7.66
15 Examples 3035.33 13.18 .66 .10.78 2.1 .27 3.2.73 2.02 .08 .20 1.44Tr.tag 2# Direct animal effects.!PATH 2Tr 0 FA1 !GP0.5 0.5 -.01 -.01 0.12.4 5.2 0.06 .8 .14!PATH 3Tr 0 US2.48002.8 6.40.0128 0.03 0.06-.1 -.22 -.0011 0.720.24 0.55 0.0026 -0.0202 0.14!PATHtagat(Tr,1).dam 2!PATH 22 0 CORGH !GFU.991.6 2.54!PATH 32 0 US !GU1.1 .58 .31!PATHdam# Maternal effects.at(Tr,1).lit 2# Litter effects.!PATH 24 0 FA1 !GP # Factor Analytic.5 .5 .01 .1 .014.95 4.63 0.037 0.941 0.102!PATH 34 0 US # Unstructured5.0733.545 3.9140.1274 0.08909 0.028650.07277 0.05090 0.001829 1.019!PATHlitThe term Tr.tag now replaces the sire (and part of dam) terms in the half-sibanalysis. This analysis uses information from both sires and dams to estimate
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ASRemlUser GuideRelease 2.0A R Gilm
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Published by:E-mail:Website:VSN Int
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PrefaceiiASReml is one of several u
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ContentsPrefaceiList of TablesxviiL
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Contentsvi3.3 The ASReml data file
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ContentsviiiCombining rows from sep
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Contentsx7.6 Variance structure qua
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Contentsxii12.2 The command line .
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Contentsxiv15 Examples 24115.1 Intr
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List of Tablesxvi7.3 Details of the
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List of Figuresxix15.8 Rice bloodwo
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1 Introduction 21.1 What ASReml can
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1 Introduction 41.4 How to use this
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2 Some theoryThe linear mixed model
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2 Some theory 8laid out in a rectan
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2 Some theory 10product of matrices
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2 Some theory 12where H = R + ZGZ
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2 Some theory 14where y i is the
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2 Some theory 162.4 Combining varia
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2 Some theory 18balanced one-way cl
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2 Some theory 20L, r × p, and l, r
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2 Some theory 22the terms A, B, C,
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2 Some theory 24Kenward and Roger A
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3 A guided tourIntroductionNebraska
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3 A guided tour 28column rectangula
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3 A guided tour 30(see Chapter 2) t
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3 A guided tour 32els. Similarly fo
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3 A guided tour 34mand to run nin89
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3 A guided tour 36* SYNTAX change:
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3 A guided tour 38repl 3 -0.8713 1.
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3 A guided tour 40title linenin all
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4 Data file preparation 424.1 Intro
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4 Data file preparation 44Fixed for
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5 Command file: Reading the data 46
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6 Command file: Specifying theterms
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6 Command file: Specifying the term
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7 Command file: Specifying variance
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8 Command file: Multivariate analys
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9 Command file: Genetic analysisInt
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9 Command file: Genetic analysis 15
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10 Tabulation of the data andpredic
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10 Tabulation of the data and predi
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11 Functions of variancecomponentsI
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11 Functions of variance components
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11 Functions of variance components
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12 Command file: Running the jobInt
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12 Command file: Running the job 17
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13 Description of output filesIntro
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13 Description of output files 190A
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13 Description of output files 192i
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13 Description of output files 194f
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13 Description of output files 196.
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13 Description of output files 198o
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13 Description of output files 200E
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13 Description of output files 202t
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13 Description of output files 204|
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NIN alliance trial 1989 9a 1Residua
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13 Description of output files 208w
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13 Description of output files 210T
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14 Error messagesIntroductionCommon
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14 Error messages 214filename!last
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14 Error messages 216variety !AQUAL
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14 Error messages 2188. misspelt va
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14 Error messages 220Missing/faulty
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14 Error messages 222Last line read
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14 Error messages 224Model specific
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14 Error messages 226:11 column 11
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14 Error messages 22814.5 Informati
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14 Error messages 230List of warnin
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14 Error messages 232Alphabetical l
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15 Examples 24215.1 IntroductionIn
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15 Examples 244For simple variance
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15 Examples 2460.2_cwt Marvellous 1
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15 Examples 248The input file conta
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15 Examples 250Source Model terms G
Page 274 and 275: 15 Examples 252ltage example 5−3
Page 276 and 277: 15 Examples 254The focus is modelli
Page 278 and 279: 15 Examples 256y1 y3 y5 y7 y10 ~ Tr
Page 280 and 281: 15 Examples 258power model shown in
Page 282 and 283: 15 Examples 260Residual US=UnStr 2
Page 284 and 285: 15 Examples 262the current model).
Page 286 and 287: 15 Examples 264Slate Hall example f
Page 288 and 289: 15 Examples 266Predicted values of
Page 290 and 291: 15 Examples 268wheat.asd !skip 1 !D
Page 292 and 293: Tullibigeal trial i2 1Variogram of
Page 294 and 295: 15 Examples 272column evaluated at
Page 296 and 297: 15 Examples 274this is for the pair
Page 298 and 299: 15 Examples 276Table 15.8: Estimate
Page 300 and 301: 15 Examples 278(and in fact necessa
Page 302 and 303: 15 Examples 280Tr.run 22 0 US .79 .
Page 304 and 305: 15 Examples 282Interpretation of re
Page 306 and 307: 15 Examples 284The independence of
Page 308 and 309: 15 Examples 286Note that the data f
Page 310 and 311: 15 Examples 288Table 15.11 Orange d
Page 312 and 313: 15 Examples 290217Predicted values
Page 314 and 315: 15 Examples 292105Residual0-5-10200
Page 316 and 317: 15 Examples 294Table 15.13: REML es
Page 318 and 319: 15 Examples 29615*0. #Asreml will e
Page 320 and 321: 15 Examples 298Covariance/Variance/
Page 322 and 323: 15 Examples 300Eigen values 4.382 0
Page 326 and 327: 15 Examples 304additive genetic var
Page 328 and 329: BibliographyAbramowitz, M. and Steg
Page 330 and 331: Bibliography 308Gilmour, A. R., Cul
Page 332 and 333: Bibliography 310Schall, R. (1991).
Page 334 and 335: IndexABORTASR.NOW, 65FINALASR.NOW,
Page 336 and 337: Index 314free format, 42functions o
Page 338 and 339: Index 316!==, 53!>, 53!>=, 53!*, 53
Page 340 and 341: Index 318!STEP, 71!SUBSET, 71!SUB,
Page 342: Index 320parameter, 7variance compo
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