SAS Output

GEV_03_GENETIC-GENOMIC MODELS_COMPLETE OR MISSING GENOTYPES_October-31-2014_a November 1, 2014

Model_30_Animal_GEV_03_1T_Polygenic_October-31-2014_a November 1, 2014

GENETIC AND GENOMIC EVALUATION NOTES

CHAPTER GEV_03 ALL MODELS

MULTIPLE TRAIT GENETIC AND GENOMIC MODELS WITH:

1) UNEQUAL RESIDUAL, ADDITIVE GENETIC, AND NONADDITIVE GENETIC COVARIANCE MATRICES ACROSS BREED GROUPS

2) EQUAL RESIDUAL COVARIANCE MATRIX, UNEQUAL ADDITIVE AND NONADDITIVE GENETIC COVARIANCE MATRICES

3) EQUAL RESIDUAL AND ADDITIVE GENETIC COVARIANCE MATRICES, UNEQUAL NONADDITIVE GENETIC COVARIANCE MATRICES

4) EQUAL RESIDUAL AND ADDITIVE GENETIC COVARIANCE MATRICES, NO RANDOM NONADDITIVE GENETIC EFFECTS

Mauricio A. Elzo, University of Florida, maelzo@ufl.edu

Read input dataset (SAS file)

datmat = matrix of input data

datmat
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18	COL19	COL20	COL21	COL22	COL23	COL24	COL25	COL26	COL27	COL28	COL29	COL30	COL31	COL32	COL33	COL34	COL35	COL36	COL37	COL38	COL39	COL40	COL41	COL42	COL43	COL44	COL45	COL46	COL47	COL48	COL49	COL50	COL51	COL52	COL53	COL54	COL55	COL56	COL57	COL58	COL59	COL60	COL61	COL62	COL63	COL64	COL65	COL66	COL67	COL68	COL69	COL70	COL71	COL72	COL73	COL74	COL75	COL76
ROW1	1	0	0	1	0	1	0	1	0	1	0	1	0	1	33	289	2	1	1	2	1	1	2	2	0	1	1	1	0	1	2	1	2	0	2	2	0	2	2	0	1	0	0	0	1	0	2	1	0	1	0	1	1	2	2	1	0	2	1	1	0	1	2	1	1	1	0	2	0	2	0	0	0	0	2	2
ROW2	2	0	0	0	1	0	1	0	1	0	1	0	1	2	29	245	0	1	2	0	0	1	2	2	1	2	2	0	1	2	1	1	2	1	0	2	0	2	2	2	1	1	1	2	2	1	2	2	1	1	0	1	1	1	2	2	2	1	2	0	1	0	0	2	0	2	2	1	1	0	0	1	0	1	2	1
ROW3	3	0	2	0.5	0.5	1	0	0	1	0	1	0	1	2	32	256	1	2	0	0	1	1	1	2	2	2	1	0	1	0	2	1	2	1	2	2	0	2	2	0	1	1	0	2	1	1	2	2	1	0	0	0	2	0	1	2	0	1	1	0	0	2	1	2	2	1	0	1	0	1	0	0	0	1	1	2
ROW4	4	1	0	0.5	0.5	1	0	0	1	0	1	0	1	2	30	261	1	2	0	0	0	2	2	0	0	2	1	2	0	1	1	0	2	2	1	0	0	1	1	1	1	1	0	2	2	1	1	1	1	1	1	1	1	0	2	2	0	1	2	0	1	1	1	2	2	1	2	1	0	1	0	1	0	1	2	1
ROW5	5	1	2	0.5	0.5	1	0	0	1	0	1	0	1	1	38	292	1	2	0	1	1	1	1	1	1	2	2	2	0	1	1	1	2	0	2	2	0	2	1	1	0	0	1	1	0	0	2	2	0	0	0	2	2	2	2	1	2	2	2	0	0	0	2	2	1	2	0	1	0	1	0	1	0	2	2	1
ROW6	6	1	3	0.75	0.25	1	0	0.5	0.5	1	0	0	1	1	35	286	2	1	2	2	1	2	2	1	1	2	1	1	2	0	2	0	2	1	0	1	0	1	2	2	0	0	0	2	1	0	2	2	1	1	1	0	2	0	1	1	0	2	1	0	0	1	2	1	1	2	1	1	1	2	0	1	0	1	2	1
ROW7	7	0	3	0.25	0.75	0	1	0.5	0.5	1	0	0	1	1	28	272	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
ROW8	8	1	4	0.75	0.25	1	0	0.5	0.5	1	0	0	1	2	31	264	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
ROW9	9	5	8	0.625	0.375	0.5	0.5	0.75	0.25	1	0	0.5	0.5	2	30	270	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
ROW10	10	5	3	0.5	0.5	0.5	0.5	0.5	0.5	1	0	0	1	1	33	278	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
ROW11	11	6	0	0.375	0.625	0.75	0.25	0	1	0	1	0	1	2	27	259	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
ROW12	12	6	2	0.375	0.625	0.75	0.25	0	1	0	1	0	1	1	32	280	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.

Read allele frequencies input dataset (SAS file)

ntsnp
60

snpfreq
1	0.1509
2	0.4252
3	0.1842
4	0.5314
5	0.6242
6	0.4292
7	0.2036
8	0.3518
9	0.5454
10	0.1048
11	0.3338
12	0.3284
13	0.006
14	0.502
15	0.2263
16	0.4706
17	0.0808
18	0.7216
19	0.026
20	0.3271
21	0.8718
22	0.0948
23	0.3825
24	0.0561
25	0.5401
26	0.6809
27	0.785
28	0.3758
29	0.0067
30	0.7891
31	0.0581
32	0.1429
33	0.6041
34	0.7196
35	0.9386
36	0.6335
37	0.4312
38	0.0033
39	0.2717
40	0.2203
41	0.5794
42	0.2023
43	0.5134
44	0.755
45	0.5648
46	0.518
47	0.3458
48	0.4806
49	0.3258
50	0.3117
51	0.7503
52	0.4132
53	0.743
54	0.6061
55	0.9933
56	0.7377
57	0.9399
58	0.4419
59	0.1295
60	0.0928

Enter Parameters for Current Run

Enter restronsol = 1 to impose restrictions on solutions to solve the MME, else = 0 if not

restronsol
0

No restrictions imposed on solutions to solve MME

Enter nt = Number of traits

nt
1

Enter nfixpol = Number of fixed environmental and polygenic genetic effects

nfixpol
6

Define nbr for the computation of gene content

nbr
2

Enter nrec = Number of records

nrec
12

Enter number of first non-genotyped animal (non-genotyped animals are last in the datafile)

nongenanim1
7

Enter nanim = Number of animals

nanim
12

Enter 1 if model combines additive genetic and genomic relationships, else enter 0

Enter nsnp = number of fixed marker locus genomic effects in the model

nsnp
0

Enter 1 if random marker genomic effects in the model, else enter zero

ranma
0

Enter 1 if random additive polygenic genetic effects in the model, else enter zero

addpol
1

Enter 1 if random additive genomic marker effects in the model, else enter zero

addma
0

Enter 1 if random nonadditive polygenic genetic effects in the model, else enter zero

nadpol
0

Enter 1 if zma values are [0,1,2] if zma values are [VanRaden(2009)]

zmaval
1

Enter 1 if igenomebv are to be computed, else enter zero

Enter 1 if icompmissgenot are to be computed, else enter zero

Compute nf = Number of equations for fixed effects in the MME

nf
6

Compute nma = Number of equations for marker locus additive genetic effects in the MME

nma
0

Compute nga = Number of equations for random animal additive polygenic effects in the MME

nga
12

nga
12

Compute ngn = Number of equations for random polygenic nonadditive genetic effects in the MME

ngn
0

Compute neq = nf+nma+nga+ngn = total number of equations in the MME

neq
18

Define pedigf = pedigree file with breed composition of animals, sires, and dams

pedigf
1	0	0	1	0	1	0	1	0	1	0	1	0
2	0	0	0	1	0	1	0	1	0	1	0	1
3	0	2	0.5	0.5	1	0	0	1	0	1	0	1
4	1	0	0.5	0.5	1	0	0	1	0	1	0	1
5	1	2	0.5	0.5	1	0	0	1	0	1	0	1
6	1	3	0.75	0.25	1	0	0.5	0.5	1	0	0	1
7	0	3	0.25	0.75	0	1	0.5	0.5	1	0	0	1
8	1	4	0.75	0.25	1	0	0.5	0.5	1	0	0	1
9	5	8	0.625	0.375	0.5	0.5	0.75	0.25	1	0	0.5	0.5
10	5	3	0.5	0.5	0.5	0.5	0.5	0.5	1	0	0	1
11	6	0	0.375	0.625	0.75	0.25	0	1	0	1	0	1
12	6	2	0.375	0.625	0.75	0.25	0	1	0	1	0	1

Construct xf = matrix of fixed and random effects

Construct fixed effects in matrix xf

Construct random polygenic additive genetic effects in matrix xf

xf
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	1	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
ROW2	1	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0
ROW3	1	0.5	0.5	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0
ROW4	1	0.5	0.5	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0
ROW5	1	0.5	0.5	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0
ROW6	1	0.75	0.25	0.5	1	0	0	0	0	0	0	1	0	0	0	0	0	0
ROW7	1	0.25	0.75	0.5	1	0	0	0	0	0	0	0	1	0	0	0	0	0
ROW8	1	0.75	0.25	0.5	0	1	0	0	0	0	0	0	0	1	0	0	0	0
ROW9	1	0.625	0.375	0.5	0	1	0	0	0	0	0	0	0	0	1	0	0	0
ROW10	1	0.5	0.5	0.5	1	0	0	0	0	0	0	0	0	0	0	1	0	0
ROW11	1	0.375	0.625	0.75	0	1	0	0	0	0	0	0	0	0	0	0	1	0
ROW12	1	0.375	0.625	0.75	1	0	0	0	0	0	0	0	0	0	0	0	0	1

Make x = xf, i.e., use computed xf

x
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	1	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
ROW2	1	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0
ROW3	1	0.5	0.5	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0
ROW4	1	0.5	0.5	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0
ROW5	1	0.5	0.5	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0
ROW6	1	0.75	0.25	0.5	1	0	0	0	0	0	0	1	0	0	0	0	0	0
ROW7	1	0.25	0.75	0.5	1	0	0	0	0	0	0	0	1	0	0	0	0	0
ROW8	1	0.75	0.25	0.5	0	1	0	0	0	0	0	0	0	1	0	0	0	0
ROW9	1	0.625	0.375	0.5	0	1	0	0	0	0	0	0	0	0	1	0	0	0
ROW10	1	0.5	0.5	0.5	1	0	0	0	0	0	0	0	0	0	0	1	0	0
ROW11	1	0.375	0.625	0.75	0	1	0	0	0	0	0	0	0	0	0	0	1	0
ROW12	1	0.375	0.625	0.75	1	0	0	0	0	0	0	0	0	0	0	0	0	1

Enter intrabreed and interbreed environmental variances

veaa	vebb	veab
49	16	25

Compute vef = block-diagonal matrix of multibreed residual covariance matrices for individual animals

pedigf
1	0	0	1	0	1	0	1	0	1	0	1	0
2	0	0	0	1	0	1	0	1	0	1	0	1
3	0	2	0.5	0.5	1	0	0	1	0	1	0	1
4	1	0	0.5	0.5	1	0	0	1	0	1	0	1
5	1	2	0.5	0.5	1	0	0	1	0	1	0	1
6	1	3	0.75	0.25	1	0	0.5	0.5	1	0	0	1
7	0	3	0.25	0.75	0	1	0.5	0.5	1	0	0	1
8	1	4	0.75	0.25	1	0	0.5	0.5	1	0	0	1
9	5	8	0.625	0.375	0.5	0.5	0.75	0.25	1	0	0.5	0.5
10	5	3	0.5	0.5	0.5	0.5	0.5	0.5	1	0	0	1
11	6	0	0.375	0.625	0.75	0.25	0	1	0	1	0	1
12	6	2	0.375	0.625	0.75	0.25	0	1	0	1	0	1

vef
49	0	0	0	0	0	0	0	0	0	0	0
0	16	0	0	0	0	0	0	0	0	0	0
0	0	32.5	0	0	0	0	0	0	0	0	0
0	0	0	32.5	0	0	0	0	0	0	0	0
0	0	0	0	32.5	0	0	0	0	0	0	0
0	0	0	0	0	47	0	0	0	0	0	0
0	0	0	0	0	0	30.5	0	0	0	0	0
0	0	0	0	0	0	0	47	0	0	0	0
0	0	0	0	0	0	0	0	47.5625	0	0	0
0	0	0	0	0	0	0	0	0	45	0	0
0	0	0	0	0	0	0	0	0	0	33.0625	0
0	0	0	0	0	0	0	0	0	0	0	33.0625

Make r = vef

r = block-diagonal matrix of residual covariance matrices for individual animals

r
49	0	0	0	0	0	0	0	0	0	0	0
0	16	0	0	0	0	0	0	0	0	0	0
0	0	32.5	0	0	0	0	0	0	0	0	0
0	0	0	32.5	0	0	0	0	0	0	0	0
0	0	0	0	32.5	0	0	0	0	0	0	0
0	0	0	0	0	47	0	0	0	0	0	0
0	0	0	0	0	0	30.5	0	0	0	0	0
0	0	0	0	0	0	0	47	0	0	0	0
0	0	0	0	0	0	0	0	47.5625	0	0	0
0	0	0	0	0	0	0	0	0	45	0	0
0	0	0	0	0	0	0	0	0	0	33.0625	0
0	0	0	0	0	0	0	0	0	0	0	33.0625

invr = inverse of block-diagonal matrix of residual covariance matrices for individual animals

invr
0.0204082	0	0	0	0	0	0	0	0	0	0	0
0	0.0625	0	0	0	0	0	0	0	0	0	0
0	0	0.0307692	0	0	0	0	0	0	0	0	0
0	0	0	0.0307692	0	0	0	0	0	0	0	0
0	0	0	0	0.0307692	0	0	0	0	0	0	0
0	0	0	0	0	0.0212766	0	0	0	0	0	0
0	0	0	0	0	0	0.0327869	0	0	0	0	0
0	0	0	0	0	0	0	0.0212766	0	0	0	0
0	0	0	0	0	0	0	0	0.021025	0	0	0
0	0	0	0	0	0	0	0	0	0.0222222	0	0
0	0	0	0	0	0	0	0	0	0	0.0302457	0
0	0	0	0	0	0	0	0	0	0	0	0.0302457

Read yf = vector of records

yf
289
245
256
261
292
286
272
264
270
278
259
280

Make y = yf, i.e., use read yf

y
289
245
256
261
292
286
272
264
270
278
259
280

Compute xtinvr = x transpose times r

xtinvr
0.0204082	0.0625	0.0307692	0.0307692	0.0307692	0.0212766	0.0327869	0.0212766	0.021025	0.0222222	0.0302457	0.0302457
0.0204082	0	0.0153846	0.0153846	0.0153846	0.0159574	0.0081967	0.0159574	0.0131406	0.0111111	0.0113422	0.0113422
0	0.0625	0.0153846	0.0153846	0.0153846	0.0053191	0.0245902	0.0053191	0.0078844	0.0111111	0.0189036	0.0189036
0	0	0.0307692	0.0307692	0.0307692	0.0106383	0.0163934	0.0106383	0.0105125	0.0111111	0.0226843	0.0226843
0.0204082	0	0	0	0.0307692	0.0212766	0.0327869	0	0	0.0222222	0	0.0302457
0	0.0625	0.0307692	0.0307692	0	0	0	0.0212766	0.021025	0	0.0302457	0
0.0204082	0	0	0	0	0	0	0	0	0	0	0
0	0.0625	0	0	0	0	0	0	0	0	0	0
0	0	0.0307692	0	0	0	0	0	0	0	0	0
0	0	0	0.0307692	0	0	0	0	0	0	0	0
0	0	0	0	0.0307692	0	0	0	0	0	0	0
0	0	0	0	0	0.0212766	0	0	0	0	0	0
0	0	0	0	0	0	0.0327869	0	0	0	0	0
0	0	0	0	0	0	0	0.0212766	0	0	0	0
0	0	0	0	0	0	0	0	0.021025	0	0	0
0	0	0	0	0	0	0	0	0	0.0222222	0	0
0	0	0	0	0	0	0	0	0	0	0.0302457	0
0	0	0	0	0	0	0	0	0	0	0	0.0302457

Compute xtinvrx = x transpose times r times x

xtinvrx
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	0.3542946	0.1536096	0.200685	0.1969699	0.1577088	0.1965858	0.0204082	0.0625	0.0307692	0.0307692	0.0307692	0.0212766	0.0327869	0.0212766	0.021025	0.0222222	0.0302457	0.0302457
ROW2	0.1536096	0.0917455	0.0618642	0.0953487	0.0824002	0.0712094	0.0204082	0	0.0153846	0.0153846	0.0153846	0.0159574	0.0081967	0.0159574	0.0131406	0.0111111	0.0113422	0.0113422
ROW3	0.200685	0.0618642	0.1388208	0.1016212	0.0753086	0.1253763	0	0.0625	0.0153846	0.0153846	0.0153846	0.0053191	0.0245902	0.0053191	0.0078844	0.0111111	0.0189036	0.0189036
ROW4	0.1969699	0.0953487	0.1016212	0.155981	0.0915964	0.1053736	0	0	0.0307692	0.0307692	0.0307692	0.0106383	0.0163934	0.0106383	0.0105125	0.0111111	0.0226843	0.0226843
ROW5	0.1577088	0.0824002	0.0753086	0.0915964	0.1577088	0	0.0204082	0	0	0	0.0307692	0.0212766	0.0327869	0	0	0.0222222	0	0.0302457
ROW6	0.1965858	0.0712094	0.1253763	0.1053736	0	0.1965858	0	0.0625	0.0307692	0.0307692	0	0	0	0.0212766	0.021025	0	0.0302457	0
ROW7	0.0204082	0.0204082	0	0	0.0204082	0	0.0204082	0	0	0	0	0	0	0	0	0	0	0
ROW8	0.0625	0	0.0625	0	0	0.0625	0	0.0625	0	0	0	0	0	0	0	0	0	0
ROW9	0.0307692	0.0153846	0.0153846	0.0307692	0	0.0307692	0	0	0.0307692	0	0	0	0	0	0	0	0	0
ROW10	0.0307692	0.0153846	0.0153846	0.0307692	0	0.0307692	0	0	0	0.0307692	0	0	0	0	0	0	0	0
ROW11	0.0307692	0.0153846	0.0153846	0.0307692	0.0307692	0	0	0	0	0	0.0307692	0	0	0	0	0	0	0
ROW12	0.0212766	0.0159574	0.0053191	0.0106383	0.0212766	0	0	0	0	0	0	0.0212766	0	0	0	0	0	0
ROW13	0.0327869	0.0081967	0.0245902	0.0163934	0.0327869	0	0	0	0	0	0	0	0.0327869	0	0	0	0	0
ROW14	0.0212766	0.0159574	0.0053191	0.0106383	0	0.0212766	0	0	0	0	0	0	0	0.0212766	0	0	0	0
ROW15	0.021025	0.0131406	0.0078844	0.0105125	0	0.021025	0	0	0	0	0	0	0	0	0.021025	0	0	0
ROW16	0.0222222	0.0111111	0.0111111	0.0111111	0.0222222	0	0	0	0	0	0	0	0	0	0	0.0222222	0	0
ROW17	0.0302457	0.0113422	0.0189036	0.0226843	0	0.0302457	0	0	0	0	0	0	0	0	0	0	0.0302457	0
ROW18	0.0302457	0.0113422	0.0189036	0.0226843	0.0302457	0	0	0	0	0	0	0	0	0	0	0	0	0.0302457

Enter intrabreed and interbreed additive genetic covariance matrices

vaaa	vabb	vaab
36	44	22

Compute the inverse of the additive polygenic covariance matrix

Compute vaf = multibreed additive genetic covariance matrices for individual animals

vaf
36	0	0	0	0	0	0	0	0	0	0	0
0	44	0	0	0	0	0	0	0	0	0	0
0	0	40	0	0	0	0	0	0	0	0	0
0	0	0	40	0	0	0	0	0	0	0	0
0	0	0	0	40	0	0	0	0	0	0	0
0	0	0	0	0	43.5	0	0	0	0	0	0
0	0	0	0	0	0	47.5	0	0	0	0	0
0	0	0	0	0	0	0	43.5	0	0	0	0
0	0	0	0	0	0	0	0	48.625	0	0	0
0	0	0	0	0	0	0	0	0	51	0	0
0	0	0	0	0	0	0	0	0	0	45.125	0
0	0	0	0	0	0	0	0	0	0	0	45.125

Compute diagonals of additive relationship matrix

Animals MUST be ordered from oldest to youngest

Base animals have unknown parents

Additive relationship of each animal with itself

addrel
1
1
1
1
1
1
1
1.25
1.1875
1.125
1
1.125

Compute daf = block-diagonal matrix of residual additive genetic covariance matrices

Recall: (Ga)-1 = (I - 1/2 P') (Block-diagonal Da)-1 (I - 1/2 P) for [dai]-1 blocks

Accounting for multibreed inbreeding completely (Elzo, 1990)

	i	sqvii	vii
animal i	1	36	6

i	j	tvii	vii	uii
1	1	6	6	36

i	j	tvii	vii	uii
1	2	0	0	0

i	j	tvii	vii	uii
1	3	0	0	0

i	j	tvii	vii	uii
1	4	3	3	9

i	j	tvii	vii	uii
1	5	3	3	9

i	j	tvii	vii	uii
1	6	3	3	9

i	j	tvii	vii	uii
1	7	0	0	0

i	j	tvii	vii	uii
1	8	4.5	4.5	20.25

i	j	tvii	vii	uii
1	9	3.75	3.75	14.0625

i	j	tvii	vii	uii
1	10	1.5	1.5	2.25

i	j	tvii	vii	uii
1	11	1.5	1.5	2.25

i	j	tvii	vii	uii
1	12	1.5	1.5	2.25

i	j	vmat	umat
1	13	6	36
		0	0
		0	0
		3	9
		3	9
		3	9
		0	0
		4.5	20.25
		3.75	14.0625
		1.5	2.25
		1.5	2.25
		1.5	2.25

	i	sqvii	vii
animal i	2	44	6.6332496

i	j	tvii	vii	uii
2	2	6.6332496	6.6332496	44

i	j	tvii	vii	uii
2	3	3.3166248	3.3166248	11

i	j	tvii	vii	uii
2	4	0	0	0

i	j	tvii	vii	uii
2	5	3.3166248	3.3166248	11

i	j	tvii	vii	uii
2	6	1.6583124	1.6583124	2.75

i	j	tvii	vii	uii
2	7	1.6583124	1.6583124	2.75

i	j	tvii	vii	uii
2	8	0	0	0

i	j	tvii	vii	uii
2	9	1.6583124	1.6583124	2.75

i	j	tvii	vii	uii
2	10	3.3166248	3.3166248	11

i	j	tvii	vii	uii
2	11	0.8291562	0.8291562	0.6875

i	j	tvii	vii	uii
2	12	4.145781	4.145781	17.1875

i	j	vmat	umat
2	13	6	36
		6.6332496	44
		3.3166248	11
		0	9
		3.3166248	20
		1.6583124	11.75
		1.6583124	2.75
		0	20.25
		1.6583124	16.8125
		3.3166248	13.25
		0.8291562	2.9375
		4.145781	19.4375

	i	sqvii	vii
animal i	3	29	5.3851648

i	j	tvii	vii	uii
3	3	5.3851648	5.3851648	29

i	j	tvii	vii	uii
3	4	0	0	0

i	j	tvii	vii	uii
3	5	0	0	0

i	j	tvii	vii	uii
3	6	2.6925824	2.6925824	7.25

i	j	tvii	vii	uii
3	7	2.6925824	2.6925824	7.25

i	j	tvii	vii	uii
3	8	0	0	0

i	j	tvii	vii	uii
3	9	0	0	0

i	j	tvii	vii	uii
3	10	2.6925824	2.6925824	7.25

i	j	tvii	vii	uii
3	11	1.3462912	1.3462912	1.8125

i	j	tvii	vii	uii
3	12	1.3462912	1.3462912	1.8125

i	j	vmat	umat
3	13	6	36
		6.6332496	44
		5.3851648	40
		0	9
		0	20
		2.6925824	19
		2.6925824	10
		0	20.25
		0	16.8125
		2.6925824	20.5
		1.3462912	4.75
		1.3462912	21.25

	i	sqvii	vii
animal i	4	31	5.5677644

i	j	tvii	vii	uii
4	4	5.5677644	5.5677644	31

i	j	tvii	vii	uii
4	5	0	0	0

i	j	tvii	vii	uii
4	6	0	0	0

i	j	tvii	vii	uii
4	7	0	0	0

i	j	tvii	vii	uii
4	8	2.7838822	2.7838822	7.75

i	j	tvii	vii	uii
4	9	1.3919411	1.3919411	1.9375

i	j	tvii	vii	uii
4	10	0	0	0

i	j	tvii	vii	uii
4	11	0	0	0

i	j	tvii	vii	uii
4	12	0	0	0

i	j	vmat	umat
4	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		0	20
		0	19
		0	10
		2.7838822	28
		1.3919411	18.75
		0	20.5
		0	4.75
		0	21.25

	i	sqvii	vii
animal i	5	20	4.472136

i	j	tvii	vii	uii
5	5	4.472136	4.472136	20

i	j	tvii	vii	uii
5	6	0	0	0

i	j	tvii	vii	uii
5	7	0	0	0

i	j	tvii	vii	uii
5	8	0	0	0

i	j	tvii	vii	uii
5	9	2.236068	2.236068	5

i	j	tvii	vii	uii
5	10	2.236068	2.236068	5

i	j	tvii	vii	uii
5	11	0	0	0

i	j	tvii	vii	uii
5	12	0	0	0

i	j	vmat	umat
5	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		0	19
		0	10
		0	28
		2.236068	23.75
		2.236068	25.5
		0	4.75
		0	21.25

	i	sqvii	vii
animal i	6	24.5	4.9497475

i	j	tvii	vii	uii
6	6	4.9497475	4.9497475	24.5

i	j	tvii	vii	uii
6	7	0	0	0

i	j	tvii	vii	uii
6	8	0	0	0

i	j	tvii	vii	uii
6	9	0	0	0

i	j	tvii	vii	uii
6	10	0	0	0

i	j	tvii	vii	uii
6	11	2.4748737	2.4748737	6.125

i	j	tvii	vii	uii
6	12	2.4748737	2.4748737	6.125

i	j	vmat	umat
6	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		4.9497475	43.5
		0	10
		0	28
		0	23.75
		0	25.5
		2.4748737	10.875
		2.4748737	27.375

	i	sqvii	vii
animal i	7	37.5	6.1237244

i	j	tvii	vii	uii
7	7	6.1237244	6.1237244	37.5

i	j	tvii	vii	uii
7	8	0	0	0

i	j	tvii	vii	uii
7	9	0	0	0

i	j	tvii	vii	uii
7	10	0	0	0

i	j	tvii	vii	uii
7	11	0	0	0

i	j	tvii	vii	uii
7	12	0	0	0

i	j	vmat	umat
7	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		4.9497475	43.5
		6.1237244	47.5
		0	28
		0	23.75
		0	25.5
		0	10.875
		0	27.375

	i	sqvii	vii
animal i	8	24.5	4.9497475

i	j	tvii	vii	uii
8	8	4.9497475	4.9497475	24.5

i	j	tvii	vii	uii
8	9	2.4748737	2.4748737	6.125

i	j	tvii	vii	uii
8	10	0	0	0

i	j	tvii	vii	uii
8	11	0	0	0

i	j	tvii	vii	uii
8	12	0	0	0

i	j	vmat	umat
8	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		4.9497475	43.5
		6.1237244	47.5
		4.9497475	52.5
		2.4748737	29.875
		0	25.5
		0	10.875
		0	27.375

	i	sqvii	vii
animal i	9	25.5	5.0497525

i	j	tvii	vii	uii
9	9	5.0497525	5.0497525	25.5

i	j	tvii	vii	uii
9	10	0	0	0

i	j	tvii	vii	uii
9	11	0	0	0

i	j	tvii	vii	uii
9	12	0	0	0

i	j	vmat	umat
9	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		4.9497475	43.5
		6.1237244	47.5
		4.9497475	52.5
		5.0497525	55.375
		0	25.5
		0	10.875
		0	27.375

	i	sqvii	vii
animal i	10	31	5.5677644

i	j	tvii	vii	uii
10	10	5.5677644	5.5677644	31

i	j	tvii	vii	uii
10	11	0	0	0

i	j	tvii	vii	uii
10	12	0	0	0

i	j	vmat	umat
10	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		4.9497475	43.5
		6.1237244	47.5
		4.9497475	52.5
		5.0497525	55.375
		5.5677644	56.5
		0	10.875
		0	27.375

	i	sqvii	vii
animal i	11	34.25	5.85235

i	j	tvii	vii	uii
11	11	5.85235	5.85235	34.25

i	j	tvii	vii	uii
11	12	0	0	0

i	j	vmat	umat
11	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		4.9497475	43.5
		6.1237244	47.5
		4.9497475	52.5
		5.0497525	55.375
		5.5677644	56.5
		5.85235	45.125
		0	27.375

	i	sqvii	vii
animal i	12	23.25	4.8218254

i	j	tvii	vii	uii
12	12	4.8218254	4.8218254	23.25

i	j	vmat	umat
12	13	6	36
		6.6332496	44
		5.3851648	40
		5.5677644	40
		4.472136	40
		4.9497475	43.5
		6.1237244	47.5
		4.9497475	52.5
		5.0497525	55.375
		5.5677644	56.5
		5.85235	45.125
		4.8218254	50.625

Block-diagonal matrix da for populations with inbred animals

da
36	0	0	0	0	0	0	0	0	0	0	0
0	44	0	0	0	0	0	0	0	0	0	0
0	0	29	0	0	0	0	0	0	0	0	0
0	0	0	31	0	0	0	0	0	0	0	0
0	0	0	0	20	0	0	0	0	0	0	0
0	0	0	0	0	24.5	0	0	0	0	0	0
0	0	0	0	0	0	37.5	0	0	0	0	0
0	0	0	0	0	0	0	24.5	0	0	0	0
0	0	0	0	0	0	0	0	25.5	0	0	0
0	0	0	0	0	0	0	0	0	31	0	0
0	0	0	0	0	0	0	0	0	0	34.25	0
0	0	0	0	0	0	0	0	0	0	0	23.25

Compute dainv = inverse of da

dainv = inverse of block-diagonal matrix of residual additive genetic covariance matrices

dainv
0.0277778	0	0	0	0	0	0	0	0	0	0	0
0	0.0227273	0	0	0	0	0	0	0	0	0	0
0	0	0.0344828	0	0	0	0	0	0	0	0	0
0	0	0	0.0322581	0	0	0	0	0	0	0	0
0	0	0	0	0.05	0	0	0	0	0	0	0
0	0	0	0	0	0.0408163	0	0	0	0	0	0
0	0	0	0	0	0	0.0266667	0	0	0	0	0
0	0	0	0	0	0	0	0.0408163	0	0	0	0
0	0	0	0	0	0	0	0	0.0392157	0	0	0
0	0	0	0	0	0	0	0	0	0.0322581	0	0
0	0	0	0	0	0	0	0	0	0	0.0291971	0
0	0	0	0	0	0	0	0	0	0	0	0.0430108

Compute gainv = inverse of the matrix of multibreed additive genetic covariances

Using algorithm to compute gainv directly; Elzo (1990a),JAS 68:1215-1228

gainv
0.0687505	0.0125	0.0102041	-0.005925	-0.025	-0.020408	0	-0.020408	0	0	0	0
0.0125	0.0546007	-0.017241	0	-0.025	0.0107527	0	0	0	0	0	-0.021505
0.0102041	-0.017241	0.059418	0	0.0080645	-0.020408	-0.013333	0	0	-0.016129	0	0
-0.005925	0	0	0.0424621	0	0	0	-0.020408	0	0	0	0
-0.025	-0.025	0.0080645	0	0.0678684	0	0	0.0098039	-0.019608	-0.016129	0	0
-0.020408	0.0107527	-0.020408	0	0	0.0588683	0	0	0	0	-0.014599	-0.021505
0	0	-0.013333	0	0	0	0.0266667	0	0	0	0	0
-0.020408	0	0	-0.020408	0.0098039	0	0	0.0506202	-0.019608	0	0	0
0	0	0	0	-0.019608	0	0	-0.019608	0.0392157	0	0	0
0	0	-0.016129	0	-0.016129	0	0	0	0	0.0322581	0	0
0	0	0	0	0	-0.014599	0	0	0	0	0.0291971	0
0	-0.021505	0	0	0	-0.021505	0	0	0	0	0	0.0430108

gainv
0.069	0.013	0.010	-0.006	-0.025	-0.020	0.000	-0.020	0.000	0.000	0.000	0.000
0.013	0.055	-0.017	0.000	-0.025	0.011	0.000	0.000	0.000	0.000	0.000	-0.022
0.010	-0.017	0.059	0.000	0.008	-0.020	-0.013	0.000	0.000	-0.016	0.000	0.000
-0.006	0.000	0.000	0.042	0.000	0.000	0.000	-0.020	0.000	0.000	0.000	0.000
-0.025	-0.025	0.008	0.000	0.068	0.000	0.000	0.010	-0.020	-0.016	0.000	0.000
-0.020	0.011	-0.020	0.000	0.000	0.059	0.000	0.000	0.000	0.000	-0.015	-0.022
0.000	0.000	-0.013	0.000	0.000	0.000	0.027	0.000	0.000	0.000	0.000	0.000
-0.020	0.000	0.000	-0.020	0.010	0.000	0.000	0.051	-0.020	0.000	0.000	0.000
0.000	0.000	0.000	0.000	-0.020	0.000	0.000	-0.020	0.039	0.000	0.000	0.000
0.000	0.000	-0.016	0.000	-0.016	0.000	0.000	0.000	0.000	0.032	0.000	0.000
0.000	0.000	0.000	0.000	0.000	-0.015	0.000	0.000	0.000	0.000	0.029	0.000
0.000	-0.022	0.000	0.000	0.000	-0.022	0.000	0.000	0.000	0.000	0.000	0.043

Compute lhs = left hand side of the MME

Add gainv to lhs

lhs
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	0.3542946	0.1536096	0.200685	0.1969699	0.1577088	0.1965858	0.0204082	0.0625	0.0307692	0.0307692	0.0307692	0.0212766	0.0327869	0.0212766	0.021025	0.0222222	0.0302457	0.0302457
ROW2	0.1536096	0.0917455	0.0618642	0.0953487	0.0824002	0.0712094	0.0204082	0	0.0153846	0.0153846	0.0153846	0.0159574	0.0081967	0.0159574	0.0131406	0.0111111	0.0113422	0.0113422
ROW3	0.200685	0.0618642	0.1388208	0.1016212	0.0753086	0.1253763	0	0.0625	0.0153846	0.0153846	0.0153846	0.0053191	0.0245902	0.0053191	0.0078844	0.0111111	0.0189036	0.0189036
ROW4	0.1969699	0.0953487	0.1016212	0.155981	0.0915964	0.1053736	0	0	0.0307692	0.0307692	0.0307692	0.0106383	0.0163934	0.0106383	0.0105125	0.0111111	0.0226843	0.0226843
ROW5	0.1577088	0.0824002	0.0753086	0.0915964	0.1577088	0	0.0204082	0	0	0	0.0307692	0.0212766	0.0327869	0	0	0.0222222	0	0.0302457
ROW6	0.1965858	0.0712094	0.1253763	0.1053736	0	0.1965858	0	0.0625	0.0307692	0.0307692	0	0	0	0.0212766	0.021025	0	0.0302457	0
ROW7	0.0204082	0.0204082	0	0	0.0204082	0	0.0891586	0.0125	0.0102041	-0.005925	-0.025	-0.020408	0	-0.020408	0	0	0	0
ROW8	0.0625	0	0.0625	0	0	0.0625	0.0125	0.1171007	-0.017241	0	-0.025	0.0107527	0	0	0	0	0	-0.021505
ROW9	0.0307692	0.0153846	0.0153846	0.0307692	0	0.0307692	0.0102041	-0.017241	0.0901873	0	0.0080645	-0.020408	-0.013333	0	0	-0.016129	0	0
ROW10	0.0307692	0.0153846	0.0153846	0.0307692	0	0.0307692	-0.005925	0	0	0.0732314	0	0	0	-0.020408	0	0	0	0
ROW11	0.0307692	0.0153846	0.0153846	0.0307692	0.0307692	0	-0.025	-0.025	0.0080645	0	0.0986377	0	0	0.0098039	-0.019608	-0.016129	0	0
ROW12	0.0212766	0.0159574	0.0053191	0.0106383	0.0212766	0	-0.020408	0.0107527	-0.020408	0	0	0.0801449	0	0	0	0	-0.014599	-0.021505
ROW13	0.0327869	0.0081967	0.0245902	0.0163934	0.0327869	0	0	0	-0.013333	0	0	0	0.0594536	0	0	0	0	0
ROW14	0.0212766	0.0159574	0.0053191	0.0106383	0	0.0212766	-0.020408	0	0	-0.020408	0.0098039	0	0	0.0718968	-0.019608	0	0	0
ROW15	0.021025	0.0131406	0.0078844	0.0105125	0	0.021025	0	0	0	0	-0.019608	0	0	-0.019608	0.0602407	0	0	0
ROW16	0.0222222	0.0111111	0.0111111	0.0111111	0.0222222	0	0	0	-0.016129	0	-0.016129	0	0	0	0	0.0544803	0	0
ROW17	0.0302457	0.0113422	0.0189036	0.0226843	0	0.0302457	0	0	0	0	0	-0.014599	0	0	0	0	0.0594428	0
ROW18	0.0302457	0.0113422	0.0189036	0.0226843	0.0302457	0	0	-0.021505	0	0	0	-0.021505	0	0	0	0	0	0.0732565

lhs
0.354	0.154	0.201	0.197	0.158	0.197	0.020	0.063	0.031	0.031	0.031	0.021	0.033	0.021	0.021	0.022	0.030	0.030
0.154	0.092	0.062	0.095	0.082	0.071	0.020	0.000	0.015	0.015	0.015	0.016	0.008	0.016	0.013	0.011	0.011	0.011
0.201	0.062	0.139	0.102	0.075	0.125	0.000	0.063	0.015	0.015	0.015	0.005	0.025	0.005	0.008	0.011	0.019	0.019
0.197	0.095	0.102	0.156	0.092	0.105	0.000	0.000	0.031	0.031	0.031	0.011	0.016	0.011	0.011	0.011	0.023	0.023
0.158	0.082	0.075	0.092	0.158	0.000	0.020	0.000	0.000	0.000	0.031	0.021	0.033	0.000	0.000	0.022	0.000	0.030
0.197	0.071	0.125	0.105	0.000	0.197	0.000	0.063	0.031	0.031	0.000	0.000	0.000	0.021	0.021	0.000	0.030	0.000
0.020	0.020	0.000	0.000	0.020	0.000	0.089	0.013	0.010	-0.006	-0.025	-0.020	0.000	-0.020	0.000	0.000	0.000	0.000
0.063	0.000	0.063	0.000	0.000	0.063	0.013	0.117	-0.017	0.000	-0.025	0.011	0.000	0.000	0.000	0.000	0.000	-0.022
0.031	0.015	0.015	0.031	0.000	0.031	0.010	-0.017	0.090	0.000	0.008	-0.020	-0.013	0.000	0.000	-0.016	0.000	0.000
0.031	0.015	0.015	0.031	0.000	0.031	-0.006	0.000	0.000	0.073	0.000	0.000	0.000	-0.020	0.000	0.000	0.000	0.000
0.031	0.015	0.015	0.031	0.031	0.000	-0.025	-0.025	0.008	0.000	0.099	0.000	0.000	0.010	-0.020	-0.016	0.000	0.000
0.021	0.016	0.005	0.011	0.021	0.000	-0.020	0.011	-0.020	0.000	0.000	0.080	0.000	0.000	0.000	0.000	-0.015	-0.022
0.033	0.008	0.025	0.016	0.033	0.000	0.000	0.000	-0.013	0.000	0.000	0.000	0.059	0.000	0.000	0.000	0.000	0.000
0.021	0.016	0.005	0.011	0.000	0.021	-0.020	0.000	0.000	-0.020	0.010	0.000	0.000	0.072	-0.020	0.000	0.000	0.000
0.021	0.013	0.008	0.011	0.000	0.021	0.000	0.000	0.000	0.000	-0.020	0.000	0.000	-0.020	0.060	0.000	0.000	0.000
0.022	0.011	0.011	0.011	0.022	0.000	0.000	0.000	-0.016	0.000	-0.016	0.000	0.000	0.000	0.000	0.054	0.000	0.000
0.030	0.011	0.019	0.023	0.000	0.030	0.000	0.000	0.000	0.000	0.000	-0.015	0.000	0.000	0.000	0.000	0.059	0.000
0.030	0.011	0.019	0.023	0.030	0.000	0.000	-0.022	0.000	0.000	0.000	-0.022	0.000	0.000	0.000	0.000	0.000	0.073

Compute rhs = right hand side of the MME

rhs
94.879904
42.100491
52.779413
53.35649
44.532301
50.347603
5.8979592
15.3125
7.8769231
8.0307692
8.9846154
6.0851064
8.9180328
5.6170213
5.6767411
6.1777778
7.8336484
8.4688091

rhs
94.88
42.10
52.78
53.36
44.53
50.35
5.90
15.31
7.88
8.03
8.98
6.09
8.92
5.62
5.68
6.18
7.83
8.47

Compute ginvlhs = generalized inverse of the left hand side of the MME

ginvlhs
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	7.010421	5.981304	1.029117	-9.493370	3.773432	3.236989	-8.098097	-8.148935	-5.527533	-4.653024	-7.282614	-8.172574	-5.819226	-7.923921	-8.308871	-7.684764	-5.066929	-7.495720
ROW2	5.981304	35.086344	-29.10504	-9.325371	-1.714712	7.696016	-19.19255	11.434982	0.388825	-10.28266	-2.473859	-11.02563	7.506292	-20.25523	-14.38865	-1.675587	-3.547344	3.324370
ROW3	1.029117	-29.10504	30.134157	-0.167999	5.488144	-4.459026	11.094455	-19.58392	-5.916358	5.629632	-4.808755	2.853052	-13.32552	12.331307	6.079782	-6.009177	-1.519585	-10.82009
ROW4	-9.493370	-9.325371	-0.167999	32.882037	-4.332850	-5.160520	4.570016	6.326752	-4.199428	-4.690788	-1.364305	-0.544679	-1.028616	2.166987	1.693709	-0.777573	-3.393082	-1.289042
ROW5	3.773432	-1.714712	5.488144	-4.332850	7.775667	-4.002235	-4.326298	-5.034449	-2.797078	0.813790	-6.514578	-5.947458	-7.835064	0.143193	-1.582116	-7.353465	-1.108852	-7.801226
ROW6	3.236989	7.696016	-4.459026	-5.160520	-4.002235	7.239224	-3.771799	-3.114486	-2.730455	-5.466814	-0.768036	-2.225116	2.015838	-8.067114	-6.726754	-0.331299	-3.958077	0.305506
ROW7	-8.098097	-19.19255	11.094455	4.570016	-4.326298	-3.771799	32.307837	1.152090	6.524530	13.572270	15.064514	19.890694	5.112128	22.211500	18.212837	12.178881	9.314516	10.000618
ROW8	-8.148935	11.434982	-19.58392	6.326752	-5.034449	-3.114486	1.152090	32.850818	14.569265	4.827659	14.590161	8.013278	15.316563	3.402771	8.752394	14.381787	9.330710	18.763251
ROW9	-5.527533	0.388825	-5.916358	-4.199428	-2.797078	-2.730455	6.524530	14.569265	25.772890	9.547394	12.024475	15.460520	13.922075	9.416191	11.283292	16.569363	11.408635	15.019519
ROW10	-4.653024	-10.28266	5.629632	-4.690788	0.813790	-5.466814	13.572270	4.827659	9.547394	27.568736	9.882567	12.644874	4.640997	20.148062	15.631491	9.223941	10.216426	8.306272
ROW11	-7.282614	-2.473859	-4.808755	-1.364305	-6.514578	-0.768036	15.064514	14.590161	12.024475	9.882567	27.928985	16.005362	13.011579	11.403591	17.019335	19.219646	10.549007	16.724586
ROW12	-8.172574	-11.02563	2.853052	-0.544679	-5.947458	-2.225116	19.890694	8.013278	15.460520	12.644874	16.005362	34.172222	11.744265	16.940769	16.479377	16.852904	15.087229	19.353409
ROW13	-5.819226	7.506292	-13.32552	-1.028616	-7.835064	2.015838	5.112128	15.316563	13.922075	4.640997	13.011579	11.744265	32.232246	3.497323	6.987132	14.939891	8.017477	16.176497
ROW14	-7.923921	-20.25523	12.331307	2.166987	0.143193	-8.067114	22.211500	3.402771	9.416191	20.148062	11.403591	16.940769	3.497323	39.033719	24.424351	10.511581	11.413431	8.467573
ROW15	-8.308871	-14.38865	6.079782	1.693709	-1.582116	-6.726754	18.212837	8.752394	11.283292	15.631491	17.019335	16.479377	6.987132	24.424351	37.384723	13.762694	11.863274	11.625276
ROW16	-7.684764	-1.675587	-6.009177	-0.777573	-7.353465	-0.331299	12.178881	14.381787	16.569363	9.223941	19.219646	16.852904	14.939891	10.511581	13.762694	36.810579	10.745083	17.429099
ROW17	-5.066929	-3.547344	-1.519585	-3.393082	-1.108852	-3.958077	9.314516	9.330710	11.408635	10.216426	10.549007	15.087229	8.017477	11.413431	11.863274	10.745083	27.575226	11.710059
ROW18	-7.495720	3.324370	-10.82009	-1.289042	-7.801226	0.305506	10.000618	18.763251	15.019519	8.306272	16.724586	19.353409	16.176497	8.467573	11.625276	17.429099	11.710059	33.832558

ginvlhs
7.010	5.981	1.029	-9.493	3.773	3.237	-8.098	-8.149	-5.528	-4.653	-7.283	-8.173	-5.819	-7.924	-8.309	-7.685	-5.067	-7.496
5.981	35.086	-29.11	-9.325	-1.715	7.696	-19.19	11.435	0.389	-10.28	-2.474	-11.03	7.506	-20.26	-14.39	-1.676	-3.547	3.324
1.029	-29.11	30.134	-0.168	5.488	-4.459	11.094	-19.58	-5.916	5.630	-4.809	2.853	-13.33	12.331	6.080	-6.009	-1.520	-10.82
-9.493	-9.325	-0.168	32.882	-4.333	-5.161	4.570	6.327	-4.199	-4.691	-1.364	-0.545	-1.029	2.167	1.694	-0.778	-3.393	-1.289
3.773	-1.715	5.488	-4.333	7.776	-4.002	-4.326	-5.034	-2.797	0.814	-6.515	-5.947	-7.835	0.143	-1.582	-7.353	-1.109	-7.801
3.237	7.696	-4.459	-5.161	-4.002	7.239	-3.772	-3.114	-2.730	-5.467	-0.768	-2.225	2.016	-8.067	-6.727	-0.331	-3.958	0.306
-8.098	-19.19	11.094	4.570	-4.326	-3.772	32.308	1.152	6.525	13.572	15.065	19.891	5.112	22.211	18.213	12.179	9.315	10.001
-8.149	11.435	-19.58	6.327	-5.034	-3.114	1.152	32.851	14.569	4.828	14.590	8.013	15.317	3.403	8.752	14.382	9.331	18.763
-5.528	0.389	-5.916	-4.199	-2.797	-2.730	6.525	14.569	25.773	9.547	12.024	15.461	13.922	9.416	11.283	16.569	11.409	15.020
-4.653	-10.28	5.630	-4.691	0.814	-5.467	13.572	4.828	9.547	27.569	9.883	12.645	4.641	20.148	15.631	9.224	10.216	8.306
-7.283	-2.474	-4.809	-1.364	-6.515	-0.768	15.065	14.590	12.024	9.883	27.929	16.005	13.012	11.404	17.019	19.220	10.549	16.725
-8.173	-11.03	2.853	-0.545	-5.947	-2.225	19.891	8.013	15.461	12.645	16.005	34.172	11.744	16.941	16.479	16.853	15.087	19.353
-5.819	7.506	-13.33	-1.029	-7.835	2.016	5.112	15.317	13.922	4.641	13.012	11.744	32.232	3.497	6.987	14.940	8.017	16.176
-7.924	-20.26	12.331	2.167	0.143	-8.067	22.211	3.403	9.416	20.148	11.404	16.941	3.497	39.034	24.424	10.512	11.413	8.468
-8.309	-14.39	6.080	1.694	-1.582	-6.727	18.213	8.752	11.283	15.631	17.019	16.479	6.987	24.424	37.385	13.763	11.863	11.625
-7.685	-1.676	-6.009	-0.778	-7.353	-0.331	12.179	14.382	16.569	9.224	19.220	16.853	14.940	10.512	13.763	36.811	10.745	17.429
-5.067	-3.547	-1.520	-3.393	-1.109	-3.958	9.315	9.331	11.409	10.216	10.549	15.087	8.017	11.413	11.863	10.745	27.575	11.710
-7.496	3.324	-10.82	-1.289	-7.801	0.306	10.001	18.763	15.020	8.306	16.725	19.353	16.176	8.468	11.625	17.429	11.710	33.833

Compute gl = ginvlhs*lhs = matrix of expectations of solutions

gl
0.500	0.250	0.250	0.000	0.250	0.250	0.000	0.000	-0.000	0.000	0.000	-0.000	-0.000	0.000	-0.000	-0.000	0.000	0.000
0.250	0.625	-0.375	0.000	0.125	0.125	-0.000	-0.000	0.000	0.000	0.000	0.000	-0.000	0.000	-0.000	-0.000	0.000	0.000
0.250	-0.375	0.625	0.000	0.125	0.125	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
-0.000	-0.000	-0.000	1.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	0.000	0.000	-0.000	0.000	-0.000	-0.000	0.000
0.250	0.125	0.125	-0.000	0.625	-0.375	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	0.000
0.250	0.125	0.125	0.000	-0.375	0.625	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000
-0.000	0.000	-0.000	-0.000	0.000	0.000	1.000	0.000	0.000	-0.000	-0.000	0.000	0.000	-0.000	0.000	0.000	-0.000	-0.000
-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	1.000	-0.000	0.000	-0.000	0.000	-0.000	-0.000	-0.000	-0.000	0.000	-0.000
-0.000	0.000	-0.000	-0.000	-0.000	0.000	-0.000	-0.000	1.000	-0.000	0.000	0.000	0.000	-0.000	0.000	-0.000	0.000	-0.000
0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	-0.000
0.000	0.000	-0.000	0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	1.000	0.000	0.000	-0.000	0.000	0.000	-0.000	-0.000
-0.000	0.000	-0.000	-0.000	0.000	0.000	0.000	0.000	0.000	-0.000	-0.000	1.000	0.000	-0.000	0.000	0.000	0.000	-0.000
0.000	0.000	-0.000	0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	-0.000	0.000	1.000	-0.000	0.000	-0.000	0.000	-0.000
-0.000	0.000	-0.000	-0.000	-0.000	0.000	0.000	0.000	0.000	-0.000	-0.000	0.000	0.000	1.000	0.000	0.000	0.000	-0.000
0.000	0.000	-0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	-0.000	-0.000	0.000	0.000	-0.000	1.000	0.000	-0.000	-0.000
-0.000	0.000	-0.000	-0.000	-0.000	0.000	-0.000	0.000	-0.000	-0.000	-0.000	0.000	0.000	-0.000	0.000	1.000	0.000	-0.000
-0.000	0.000	-0.000	-0.000	-0.000	-0.000	-0.000	0.000	-0.000	-0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	-0.000
0.000	0.000	0.000	0.000	0.000	0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	0.000	-0.000	0.000	0.000	0.000	1.000

Notice that lg = gl (i.e., lhs*ginvlhs = lhs*ginvlhs)

lg
0.500	0.250	0.250	0.000	0.250	0.250	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
0.250	0.625	-0.375	0.000	0.125	0.125	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
0.250	-0.375	0.625	0.000	0.125	0.125	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
-0.000	0.000	-0.000	1.000	-0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
0.250	0.125	0.125	-0.000	0.625	-0.375	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
0.250	0.125	0.125	0.000	-0.375	0.625	0.000	0.000	-0.000	0.000	0.000	0.000	-0.000	0.000	0.000	0.000	0.000	-0.000
-0.000	0.000	0.000	-0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	-0.000	0.000	-0.000	0.000	-0.000
0.000	-0.000	0.000	0.000	-0.000	-0.000	-0.000	1.000	-0.000	0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000
0.000	0.000	-0.000	0.000	-0.000	0.000	-0.000	-0.000	1.000	-0.000	0.000	-0.000	0.000	-0.000	0.000	-0.000	-0.000	-0.000
-0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	-0.000	0.000	1.000	0.000	-0.000	-0.000	0.000	0.000	-0.000	0.000	0.000
0.000	0.000	-0.000	-0.000	-0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	-0.000	0.000
-0.000	-0.000	0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000
0.000	-0.000	-0.000	-0.000	-0.000	-0.000	0.000	-0.000	0.000	0.000	-0.000	0.000	1.000	0.000	0.000	-0.000	0.000	-0.000
-0.000	-0.000	-0.000	0.000	-0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000
-0.000	0.000	0.000	0.000	0.000	-0.000	-0.000	0.000	0.000	0.000	-0.000	-0.000	-0.000	-0.000	1.000	0.000	0.000	0.000
0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	0.000	-0.000	-0.000	-0.000	-0.000	0.000	-0.000	0.000	1.000	0.000	-0.000
0.000	0.000	-0.000	0.000	0.000	0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	0.000	-0.000	-0.000	1.000	-0.000
0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	-0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000

Verify that lgl = lhs (i.e., lhs*ginvlhs*lhs = lhs => generalized inverse is correct)

lgl
0.354	0.154	0.201	0.197	0.158	0.197	0.020	0.063	0.031	0.031	0.031	0.021	0.033	0.021	0.021	0.022	0.030	0.030
0.154	0.092	0.062	0.095	0.082	0.071	0.020	0.000	0.015	0.015	0.015	0.016	0.008	0.016	0.013	0.011	0.011	0.011
0.201	0.062	0.139	0.102	0.075	0.125	0.000	0.063	0.015	0.015	0.015	0.005	0.025	0.005	0.008	0.011	0.019	0.019
0.197	0.095	0.102	0.156	0.092	0.105	-0.000	0.000	0.031	0.031	0.031	0.011	0.016	0.011	0.011	0.011	0.023	0.023
0.158	0.082	0.075	0.092	0.158	0.000	0.020	0.000	0.000	0.000	0.031	0.021	0.033	0.000	0.000	0.022	0.000	0.030
0.197	0.071	0.125	0.105	-0.000	0.197	-0.000	0.063	0.031	0.031	-0.000	-0.000	-0.000	0.021	0.021	0.000	0.030	-0.000
0.020	0.020	0.000	0.000	0.020	0.000	0.089	0.013	0.010	-0.006	-0.025	-0.020	0.000	-0.020	0.000	-0.000	0.000	-0.000
0.062	-0.000	0.063	-0.000	-0.000	0.063	0.013	0.117	-0.017	0.000	-0.025	0.011	0.000	-0.000	0.000	-0.000	-0.000	-0.022
0.031	0.015	0.015	0.031	-0.000	0.031	0.010	-0.017	0.090	0.000	0.008	-0.020	-0.013	0.000	0.000	-0.016	0.000	-0.000
0.031	0.015	0.015	0.031	-0.000	0.031	-0.006	-0.000	0.000	0.073	0.000	-0.000	-0.000	-0.020	0.000	-0.000	0.000	0.000
0.031	0.015	0.015	0.031	0.031	0.000	-0.025	-0.025	0.008	0.000	0.099	0.000	0.000	0.010	-0.020	-0.016	-0.000	-0.000
0.021	0.016	0.005	0.011	0.021	0.000	-0.020	0.011	-0.020	-0.000	-0.000	0.080	0.000	-0.000	0.000	-0.000	-0.015	-0.022
0.033	0.008	0.025	0.016	0.033	0.000	0.000	0.000	-0.013	0.000	-0.000	-0.000	0.059	-0.000	-0.000	-0.000	0.000	-0.000
0.021	0.016	0.005	0.011	-0.000	0.021	-0.020	0.000	-0.000	-0.020	0.010	0.000	-0.000	0.072	-0.020	0.000	0.000	-0.000
0.021	0.013	0.008	0.011	0.000	0.021	0.000	0.000	0.000	0.000	-0.020	-0.000	-0.000	-0.020	0.060	0.000	-0.000	0.000
0.022	0.011	0.011	0.011	0.022	0.000	-0.000	0.000	-0.016	-0.000	-0.016	0.000	0.000	0.000	0.000	0.054	0.000	-0.000
0.030	0.011	0.019	0.023	-0.000	0.030	-0.000	0.000	-0.000	0.000	-0.000	-0.015	0.000	0.000	-0.000	-0.000	0.059	-0.000
0.030	0.011	0.019	0.023	0.030	0.000	-0.000	-0.022	-0.000	0.000	0.000	-0.022	-0.000	0.000	0.000	0.000	0.000	0.073

Compute ranklhs = rank of the MME = trace of ginvlhs*lhs

ranklhs
16

Compute sol = vector of solutions for the MME

sol
132.92958
76.625398
56.304178
7.3226024
78.016381
54.913195
2.0823513
0.6294069
-3.040196
0.4273981
3.8830874
-0.225963
-2.883137
1.8791854
5.1880816
-1.003531
0.8301802
-0.031249

sol
132.93
76.63
56.30
7.32
78.02
54.91
2.08
0.63
-3.04
0.43
3.88
-0.23
-2.88
1.88
5.19
-1.00
0.83
-0.03

Compute sesol = standard error of solutions

sesol
2.65
5.92
5.49
5.73
2.79
2.69
5.68
5.73
5.08
5.25
5.28
5.85
5.68
6.25
6.11
6.07
5.25
5.82

Computation of Additive, Nonadditive, and Total Genetic Predictions

Using matrix computations

Define ka = coefficient matrix of multiple trait additive genetic predictions deviated from B

ka
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	0	1	-1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ROW2	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ROW3	0	0.5	-0.5	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ROW4	0	0.5	-0.5	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ROW5	0	0.5	-0.5	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ROW6	0	0.75	-0.75	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ROW7	0	0.25	-0.25	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ROW8	0	0.75	-0.75	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
ROW9	0	0.625	-0.625	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ROW10	0	0.5	-0.5	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ROW11	0	0.375	-0.375	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
ROW12	0	0.375	-0.375	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1


1.00	-1.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.50	-0.50	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.50	-0.50	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.50	-0.50	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.75	-0.75	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00
0.25	-0.25	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00
0.75	-0.75	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00
0.63	-0.63	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00
0.50	-0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00
0.38	-0.38	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00
0.38	-0.38	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00

Compute kagl = ka*ginvlhs*lhs to check if functions in matrix ka are estimable

(kagl = ka if functions in ka are estimable)

kagl
0.00	1.00	-1.00	0.00	-0.00	0.00	1.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	1.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.50	-0.50	0.00	-0.00	0.00	-0.00	-0.00	1.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.50	-0.50	0.00	-0.00	0.00	-0.00	-0.00	0.00	1.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.50	-0.50	0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	1.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.75	-0.75	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.25	-0.25	0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.75	-0.75	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	1.00	-0.00	0.00	0.00	-0.00
0.00	0.63	-0.63	0.00	-0.00	0.00	-0.00	0.00	0.00	0.00	-0.00	0.00	-0.00	-0.00	1.00	0.00	0.00	-0.00
0.00	0.50	-0.50	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	1.00	0.00	-0.00
-0.00	0.38	-0.38	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	1.00	-0.00
0.00	0.38	-0.37	0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	1.00

difkaglka
0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	0.00	-0.00
0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00
0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00
-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00
0.00	0.00	0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00

Compute uaka = vector of multibreed additive genetic predictions

uaka
22.40
0.63
7.12
10.59
14.04
15.01
2.20
17.12
17.89
9.16
8.45
7.59

Compute vepuaka = matrix of variance of errors of additive genetic predictions

vepuaka
95.16	32.17	59.40	44.23	63.97	75.87	49.23	59.48	55.96	63.08	42.22	59.07
32.17	32.85	30.08	20.34	30.10	31.28	23.07	26.67	28.14	29.89	20.96	30.40
59.40	30.08	62.94	35.60	47.20	59.54	41.34	44.14	43.56	52.75	35.90	47.60
44.23	20.34	35.60	42.51	33.95	40.06	26.51	38.21	34.02	34.29	26.38	32.55
63.97	30.10	47.20	33.95	61.12	57.10	39.44	43.15	46.82	53.41	33.55	47.82
75.87	31.28	59.54	40.06	57.10	82.78	47.04	51.52	50.31	59.45	43.08	59.47
49.23	23.07	41.34	26.51	39.44	47.04	50.36	34.12	34.18	41.87	26.89	39.10
59.48	26.67	44.14	38.21	43.15	51.52	34.12	59.58	46.56	43.75	32.39	41.57
55.96	28.14	43.56	34.02	46.82	50.31	34.18	46.56	60.01	44.81	31.85	41.72
63.08	29.89	52.75	34.29	53.41	59.45	41.87	43.75	44.81	72.00	34.50	49.27
42.22	20.96	35.90	26.38	33.55	43.08	26.89	32.39	31.85	34.50	43.41	33.61
59.07	30.40	47.60	32.55	47.82	59.47	39.10	41.57	41.72	49.27	33.61	61.80

Compute sepuaka = vector of standard errors of additive genetic predictions

sepuaka
9.76
5.73
7.93
6.52
7.82
9.10
7.10
7.72
7.75
8.49
6.59
7.86

Define kn = coefficient matrix of direct and maternal nonadditive genetic predictions

Assume that males will be mated to (1/2A 1/2B) females and viceversa

kn
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW2	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW3	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW4	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW5	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW6	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW7	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW8	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW9	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW10	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW11	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ROW12	0	0	0	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	0

kn
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Compute kngl = kn*ginvlhs*lhs to check if functions in matrix kn are estimable

(kngl = kn if functions in kn are estimable)

kngl
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00

difknglkn
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00

Compute uakn = vector of multibreed nonadditive genetic predictions

uakn
3.66
3.66
3.66
3.66
3.66
3.66
3.66
3.66
3.66
3.66
3.66
3.66

Compute vepuakn = matrix of variance of errors of nonadditive genetic predictions

vepuakn
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22
8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22	8.22

Compute sepuakn = vector of standard errors of nonadditive genetic predictions

sepuakn
2.87
2.87
2.87
2.87
2.87
2.87
2.87
2.87
2.87
2.87
2.87
2.87

Define kt = coefficient matrix of total direct and maternal genetic predictions

Assume that males will be mated to (1/2A 1/2B) females and viceversa

kt
	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
ROW1	0	1	-1	0.5	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ROW2	0	0	0	0.5	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ROW3	0	0.5	-0.5	0.5	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ROW4	0	0.5	-0.5	0.5	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ROW5	0	0.5	-0.5	0.5	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ROW6	0	0.75	-0.75	0.5	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ROW7	0	0.25	-0.25	0.5	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ROW8	0	0.75	-0.75	0.5	0	0	0	0	0	0	0	0	0	1	0	0	0	0
ROW9	0	0.625	-0.625	0.5	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ROW10	0	0.5	-0.5	0.5	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ROW11	0	0.375	-0.375	0.5	0	0	0	0	0	0	0	0	0	0	0	0	1	0
ROW12	0	0.375	-0.375	0.5	0	0	0	0	0	0	0	0	0	0	0	0	0	1


1.00	-1.00	0.50	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.50	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.50	-0.50	0.50	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.50	-0.50	0.50	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.50	-0.50	0.50	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.75	-0.75	0.50	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00
0.25	-0.25	0.50	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00
0.75	-0.75	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00
0.63	-0.63	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00
0.50	-0.50	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00
0.38	-0.38	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00
0.38	-0.38	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00

Compute ktgl = kt*ginvlhs*lhs to check if functions in matrix kt are estimable

(ktgl = kt if functions in kt are estimable)

ktgl
-0.00	1.00	-1.00	0.50	-0.00	0.00	1.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	-0.00	-0.00	0.50	-0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00
-0.00	0.50	-0.50	0.50	-0.00	0.00	-0.00	-0.00	1.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.50	-0.50	0.50	-0.00	0.00	-0.00	-0.00	0.00	1.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00
-0.00	0.50	-0.50	0.50	-0.00	0.00	-0.00	-0.00	0.00	0.00	1.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.75	-0.75	0.50	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.25	-0.25	0.50	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	0.00	1.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.75	-0.75	0.50	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	1.00	-0.00	0.00	0.00	-0.00
0.00	0.63	-0.63	0.50	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	1.00	0.00	0.00	-0.00
-0.00	0.50	-0.50	0.50	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	1.00	0.00	-0.00
-0.00	0.38	-0.38	0.50	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	1.00	-0.00
0.00	0.38	-0.38	0.50	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	1.00

difktglkt
-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00
-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00
-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	0.00	-0.00
0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00
-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00
-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00
0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	0.00	0.00	-0.00

Compute uakt = vector of multibreed total genetic predictions

uakt
26.06
4.29
10.78
14.25
17.70
18.68
5.86
20.78
21.55
12.82
12.11
11.25

Compute vepuakt = matrix of variance of errors of total genetic predictions

vepuakt
98.80	41.26	60.94	45.52	66.93	78.09	53.50	63.06	59.87	66.33	44.73	62.64
41.26	47.40	37.07	27.09	38.51	38.95	32.80	35.70	37.51	38.60	28.93	39.42
60.94	37.07	62.38	34.80	48.06	59.66	43.52	45.62	45.38	53.90	36.32	49.07
45.52	27.09	34.80	41.47	34.57	39.94	28.43	39.44	35.60	35.20	26.55	33.78
66.93	38.51	48.06	34.57	63.40	58.65	43.03	46.05	50.05	55.98	35.39	50.70
78.09	38.95	59.66	39.94	58.65	83.59	49.90	53.69	52.81	61.29	44.18	61.62
53.50	32.80	43.52	28.43	43.03	49.90	55.27	38.33	38.72	45.75	30.04	43.30
63.06	35.70	45.62	39.44	46.05	53.69	38.33	63.10	50.42	46.95	34.84	45.08
59.87	37.51	45.38	35.60	50.05	52.81	38.72	50.42	64.21	48.34	34.64	45.56
66.33	38.60	53.90	35.20	55.98	61.29	45.75	46.95	48.34	74.87	36.63	52.45
44.73	28.93	36.32	26.55	35.39	44.18	30.04	34.84	34.64	36.63	44.81	36.06
62.64	39.42	49.07	33.78	50.70	61.62	43.30	45.08	45.56	52.45	36.06	65.30

Compute sepuakt = vector of standard errors of total genetic predictions

sepuakt
9.94
6.88
7.90
6.44
7.96
9.14
7.43
7.94
8.01
8.65
6.69
8.08

Obs	animal	sire	dam	afa	afb	sfa	sfb	dfa	dfb	mgsfa	mgsfb	mgdfa	mgdfb	sex	bw	ww	snp01	snp02	snp03	snp04	snp05	snp06	snp07	snp08	snp09	snp10	snp11	snp12	snp13	snp14	snp15	snp16	snp17	snp18	snp19	snp20	snp21	snp22	snp23	snp24	snp25	snp26	snp27	snp28	snp29	snp30	snp31	snp32	snp33	snp34	snp35	snp36	snp37	snp38	snp39	snp40	snp41	snp42	snp43	snp44	snp45	snp46	snp47	snp48	snp49	snp50	snp51	snp52	snp53	snp54	snp55	snp56	snp57	snp58	snp59	snp60
1	1	0	0	1.000	0.000	1.00	0.00	1.00	0.00	1	0	1.0	0.0	1	33	289	2	1	1	2	1	1	2	2	0	1	1	1	0	1	2	1	2	0	2	2	0	2	2	0	1	0	0	0	1	0	2	1	0	1	0	1	1	2	2	1	0	2	1	1	0	1	2	1	1	1	0	2	0	2	0	0	0	0	2	2
2	2	0	0	0.000	1.000	0.00	1.00	0.00	1.00	0	1	0.0	1.0	2	29	245	0	1	2	0	0	1	2	2	1	2	2	0	1	2	1	1	2	1	0	2	0	2	2	2	1	1	1	2	2	1	2	2	1	1	0	1	1	1	2	2	2	1	2	0	1	0	0	2	0	2	2	1	1	0	0	1	0	1	2	1
3	3	0	2	0.500	0.500	1.00	0.00	0.00	1.00	0	1	0.0	1.0	2	32	256	1	2	0	0	1	1	1	2	2	2	1	0	1	0	2	1	2	1	2	2	0	2	2	0	1	1	0	2	1	1	2	2	1	0	0	0	2	0	1	2	0	1	1	0	0	2	1	2	2	1	0	1	0	1	0	0	0	1	1	2
4	4	1	0	0.500	0.500	1.00	0.00	0.00	1.00	0	1	0.0	1.0	2	30	261	1	2	0	0	0	2	2	0	0	2	1	2	0	1	1	0	2	2	1	0	0	1	1	1	1	1	0	2	2	1	1	1	1	1	1	1	1	0	2	2	0	1	2	0	1	1	1	2	2	1	2	1	0	1	0	1	0	1	2	1
5	5	1	2	0.500	0.500	1.00	0.00	0.00	1.00	0	1	0.0	1.0	1	38	292	1	2	0	1	1	1	1	1	1	2	2	2	0	1	1	1	2	0	2	2	0	2	1	1	0	0	1	1	0	0	2	2	0	0	0	2	2	2	2	1	2	2	2	0	0	0	2	2	1	2	0	1	0	1	0	1	0	2	2	1
6	6	1	3	0.750	0.250	1.00	0.00	0.50	0.50	1	0	0.0	1.0	1	35	286	2	1	2	2	1	2	2	1	1	2	1	1	2	0	2	0	2	1	0	1	0	1	2	2	0	0	0	2	1	0	2	2	1	1	1	0	2	0	1	1	0	2	1	0	0	1	2	1	1	2	1	1	1	2	0	1	0	1	2	1
7	7	0	3	0.250	0.750	0.00	1.00	0.50	0.50	1	0	0.0	1.0	1	28	272	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
8	8	1	4	0.750	0.250	1.00	0.00	0.50	0.50	1	0	0.0	1.0	2	31	264	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
9	9	5	8	0.625	0.375	0.50	0.50	0.75	0.25	1	0	0.5	0.5	2	30	270	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
10	10	5	3	0.500	0.500	0.50	0.50	0.50	0.50	1	0	0.0	1.0	1	33	278	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
11	11	6	0	0.375	0.625	0.75	0.25	0.00	1.00	0	1	0.0	1.0	2	27	259	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
12	12	6	2	0.375	0.625	0.75	0.25	0.00	1.00	0	1	0.0	1.0	1	32	280	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.