ASReml-S reference manual - VSN International

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5.2 Pedigree, G-inverse objects and genetic groups 56> attr(harvey.ainv,"rowNames")[1] "Sire_1" "Sire_2" "Sire_3" "Sire_4" "Sire_5" "Sire_6" "Sire_7" "Sire_8"[9] "Sire_9" "101" "102" "103" "104" "105" "106" "107"[17] "108" "109" "110" "111" "112" "113" "114" "115"[25] "116" "117" "118" "119" "120" "121" "122" "123"[33] "124" "125" "126" "127" "128" "129" "130" "131"[41] "132" "133" "134" "135" "136" "137" "138" "139"[49] "140" "141" "142" "143" "144" "145" "146" "147"[57] "148" "149" "150" "151" "152" "153" "154" "155"[65] "156" "157" "158" "159" "160" "161" "162" "163"[73] "164" "165"> harvey.ainv[1:20,]Row Column Ainverse1 1 1 3.66666672 2 2 3.66666673 3 3 2.66666674 4 4 3.66666675 5 5 3.33333336 6 6 3.00000007 7 7 3.66666678 8 8 3.33333339 9 9 3.666666710 10 1 -0.666666711 10 10 1.333333312 11 1 -0.666666713 11 11 1.333333314 12 1 -0.666666715 12 12 1.333333316 13 1 -0.666666717 13 13 1.333333318 14 1 -0.666666719 14 14 1.333333320 15 1 -0.66666675.2.3 Genetic groupsIf all individuals belong to one genetic group then, as above, use 0 as the identity ofthe parents of base individuals. However, if base individuals belong to various geneticgroups, this can be specified using the groups argument to asreml.Ainverse. The pedigreedata frame must then identify these groups. All base individuals should have groupidentifiers as parents. In this case the identity 0 will only appear on the group identityrows, as in the following example where three sire lines are fitted as genetic groups.> harveyG.ped harveyG.ped[1:20,]Calf Sire Dam1 G1 0 02 G2 0 03 G3 0 04 Sire_1 G1 G15 Sire_2 G1 G16 Sire_3 G1 G17 Sire_4 G2 G28 Sire_5 G2 G29 Sire_6 G3 G310 Sire_7 G3 G311 Sire_8 G3 G3
5.3 Generating an A-inverse matrix with asreml.Ainverse() 5712 Sire_9 G3 G313 101 Sire_1 G114 102 Sire_1 G115 103 Sire_1 G116 104 Sire_1 G117 105 Sire_1 G118 106 Sire_1 G119 107 Sire_1 G120 108 Sire_1 G1It is usually appropriate to allocate a genetic group identifier where the parent is unknown.5.3 Generating an A-inverse matrix with asreml.Ainverse()asreml.Ainverse() uses the method of Meuwissen and Luo [1992] to compute the inverserelationship matrix directly from the pedigree. A complete description of the argumentsand return value of a call to asreml.Ainverse() is:asreml.Ainverse(pedigree, groups=0, selfing=NA, inBreed=NA, mgs=FALSE)Required argumentspedigreeOptional argumentsgroupsselfinginBreedmgsValuea list with the following components:A data frame with three columns that correspond to the individual,male parent and female parent, respectively. The row giving thepedigree of an individual must appear before any row where thatindividual appears as a parentincludes genetic groups in the pedigree. The first g lines of the pedigreeidentify genetic groups (with zero in both the male and femaleparent columns). All other rows must specify one of the genetic groupsas sire or dam if the actual parent is unknown.allows for partial selfing when the third field of pedigree is unknown. Itindicates that progeny from a cross where the male parent is unknownis assumed to be from selfing with probability s and from outcrossingwith probability (1 − s). This is appropriate in some forestry treebreeding studies where seed collected from a tree may have been pollinatedby the mother tree or pollinated by some other tree (Dutkowskiand Gilmour, 2001). Do not use theselfing argument in conjunctionwith inBreed or mgs.the inbreeding coefficient for base individuals. This argument generatesthe numerator relationship matrix for inbred lines. Each cross isassumed to be selfed several times to stabilize as an inbred line as isusual for cereal crops, for example, before being evaluated or crossedwith another line. Since inbreeding is usually associated with strongselection, it is not obvious that a pedigree assumption of covarianceof 0.5 between parent and offspring actually holds. The inBreed argumentcannot be used in conjunction with selfing or mgs.if TRUE, the third identity in the pedigree is the male parent of thefemale parent rather than the female parent (maternal grand-sire).
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Analysis of mixed modelsfor S langu
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D.G. ButlerQueensland Department of
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ContentsPreface . . . . . . . . . .
Page 10 and 11:
ContentsviiB Available variance mod
Page 12 and 13:
List of Figures1.1 Weekly body weig
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1.3 Data sets used 21.2.2 Help and
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1.3 Data sets used 41.3.2 Repeated
Page 18 and 19: 1.3 Data sets used 6101 Sire 1 0102
Page 20 and 21: 2.1 The linear mixed model 8Direct
Page 22 and 23: 2.2 Estimation 10There is a corresp
Page 24 and 25: 2.2 Estimation 12where H ij = ∂ 2
Page 26 and 27: 2.5 Inference for random effects 14
Page 28 and 29: 2.6 Inference for fixed effects 16T
Page 30 and 31: 2.6 Inference for fixed effects 18t
Page 32 and 33: 3Fitting the mixed model3.1 Introdu
Page 34 and 35: 3.3 Introducing the asreml function
Page 36 and 37: 3.6 Getting help3.7 The asreml func
Page 38 and 39: 3.8 Fixed terms 26na.method.Xkeep.o
Page 40 and 41: 3.8 Fixed terms 28Summary of reserv
Page 42 and 43: 3.10 Conditional factors: the at()
Page 44 and 45: Table 3.2. Families and link functi
Page 46 and 47: 3.15 Multivariate analysis 34R!trai
Page 48 and 49: Source df F_inc F_con MSource df dd
Page 50 and 51: 4.1.1 Specifying variance models in
Page 52 and 53: 4.2 A sequence of structures for th
Page 54 and 55: Σ = [σ ij ] :{σii = σ 2 , ∀i
Page 56 and 57: 4.4 Variance model functions 44init
Page 58 and 59: The Matérn class4.4 Variance model
Page 60 and 61: 4.4 Variance model functions 48Chol
Page 62 and 63: Required argumentsobj4.5 Rules for
Page 64 and 65: 4.7 Constraining variance parameter
Page 66 and 67: 5Genetic analysis5.1 IntroductionIn
Page 70 and 71: 5.4 Using Pedigree and G-inverse ob
Page 72 and 73: 6Prediction from the linear model6.
Page 74 and 75: Optional argumentslevels6.2 The pre
Page 76 and 77: 6.2 The predict method 64The predic
Page 78 and 79: 6.4 Complicated weighting 666.4 Com
Page 80 and 81: 7The asreml class and related metho
Page 82 and 83: maxiter maximum number of iteration
Page 84 and 85: 7.3 Methods and related functions 7
Page 88 and 89: pedigree7.3 Methods and related fun
Page 92 and 93: 8Examples8.1 IntroductionThis secti
Page 94 and 95: 8.2 Split Plot Design 82In this exa
Page 96 and 97: 8.3 Unbalanced nested design 84[4,]
Page 98 and 99: 8.3 Unbalanced nested design 86adju
Page 100 and 101: 8.4 Sources of variability in unbal
Page 102 and 103: 8.5 Balanced repeated measures 902
Page 104 and 105: 8.5 Balanced repeated measures 92Ta
Page 106 and 107: 8.5 Balanced repeated measures 94wh
Page 108 and 109: 8.6 Spatial analysis of a field exp
Page 110 and 111: 8.6 Spatial analysis of a field exp
Page 112 and 113: Variety predicted.value standard.er
Page 114 and 115: 8.7 Unreplicated early generation v
Page 116 and 117: 8.7 Unreplicated early generation v
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8.8 Paired Case-Control Study 106Th
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Terms added sequentially; adjusted
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8.8 Paired Case-Control Study 110[
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8.8 Paired Case-Control Study 112..
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8.9 Balanced longitudinal data - Ra
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A.3 Aliasing and singularities 122W
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Table B.1: Details of the available
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B Available variance models 126Deta
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ReferencesG. E. P. Box. Analysis of
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References 130W. W. Stroup, P. S. B
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Index 132F statistics, 17fa(,k), 49
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ASReml-S reference manual - VSN International

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