# Linear Models for the Prediction of Animal Breeding Values, 3rd Edition.
# Raphael Mrode
# Example 11.1 p180
간단한 설명은 다음 참조
Fixed Effect Model for SNP Effects, unweighted analysis
# Linear Models for the Prediction of Animal Breeding Values, 3rd Edition. # Raphael Mrode # Example 11.1 p180 SNP effect를 고정효과로 모형에 넣어 SNP 추정하고 개체의 genotype을 이용..
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Data
13 0 0 1 558 9 0.00179211 1.3571429 -0.3571429 0.2857143
14 0 0 1 722 13.4 0.00138504 0.3571429 -0.3571429 -0.7142857
15 13 4 1 300 12.7 0.00333333 0.3571429 0.6428571 1.2857143
16 15 2 1 73 15.4 0.01369863 -0.6428571 -0.3571429 1.2857143
17 15 5 1 52 5.9 0.01923077 -0.6428571 0.6428571 0.2857143
18 14 6 1 87 7.7 0.01149425 0.3571429 0.6428571 -0.7142857
19 14 9 1 64 10.2 0.01562500 -0.6428571 -0.3571429 0.2857143
20 14 9 1 103 4.8 0.00970874 -0.6428571 0.6428571 0.2857143
1 ~ 3 : animal, sire, dam
4 : general mean
5 : EDC(using weight)
6 : Fat DYD
7 : EDC 역수
8 - 10 : SNP1 ~ SNP3의 coding하고 평균을 0으로 scaling한 값
(7 - 10 컬럼은 원래의 자료에서 계산을 하여 입력하여야 한다.)
Pedigree
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 0 0
11 0 0
12 0 0
13 0 0
14 0 0
15 13 4
16 15 2
17 15 5
18 14 6
19 14 9
20 14 9
21 1 3
22 14 8
23 14 11
24 14 10
25 14 7
26 14 12
Renumf90 Parameter File
# Parameter file for program renf90; it is translated to parameter
# file for BLUPF90 family programs.
DATAFILE
fem_snp_data2.txt
TRAITS
6
FIELDS_PASSED TO OUTPUT
WEIGHT(S)
RESIDUAL_VARIANCE
245
EFFECT
4 cross alpha
EFFECT
8 cov
EFFECT
9 cov
EFFECT
10 cov
EFFECT
1 cross alpha
RANDOM
animal
FILE
fem_snp_pedi.txt
FILE_POS
1 2 3
PED_DEPTH
0
(CO)VARIANCES
35.241
OPTION solv_method FSPAK
실행
명령창에서 다음과 같은 명령어로 renumf90을 실행한다.
renumf90 renumf90_fem_snp_uw.par | tee renumf90_fem_snp_uw_01.log
Renumf90 실행 로그
RENUMF90 version 1.145
renumf90_fem_snp_uw.par
datafile:fem_snp_data2.txt
traits: 6
R
245.0
Processing effect 1 of type cross
item_kind=alpha
Processing effect 2 of type cov
Processing effect 3 of type cov
Processing effect 4 of type cov
Processing effect 5 of type cross
item_kind=alpha
pedigree file name "fem_snp_pedi.txt"
positions of animal, sire, dam, alternate dam, yob, and group 1 2 3 0 0 0 0
all pedigrees to be included
Reading (CO)VARIANCES: 1 x 1
Maximum size of character fields: 20
Maximum size of record (max_string_readline): 800
Maximum number of fields for input file (max_field_readline): 100
Pedigree search method (ped_search): convention
Order of pedigree animals (animal_order): default
Order of UPG (upg_order): default
Missing observation code (missing): 0
hash tables for effects set up
first 3 lines of the data file (up to 70 characters)
13 0 0 1 558 9 0.00179211 1.3571429 -0.3571429 0.2857143
14 0 0 1 722 13.4 0.00138504 0.3571429 -0.3571429 -0.7142857
15 13 4 1 300 12.7 0.00333333 0.3571429 0.6428571 1.2857143
read 8 records
table with 1 elements sorted
added count
Effect group 1 of column 1 with 1 levels
table expanded from 10000 to 10000 records
added count
Effect group 5 of column 1 with 8 levels
wrote statistics in file "renf90.tables"
Basic statistics for input data (missing value code is '0')
Pos Min Max Mean SD N
6 4.8000 15.400 9.8875 3.7434 8
8 -0.64286 1.3571 -0.17857E-01 0.74402 8
9 -0.35714 0.64286 0.14286 0.53452 8
10 -0.71429 1.2857 0.28571 0.75593 8
Correlation matrix
6 8 9 10
6 1.00 0.12 -0.60 0.35
8 0.12 1.00 -0.18 -0.25
9 -0.60 -0.18 1.00 0.00
10 0.35 -0.25 0.00 1.00
Counts of nonzero values (order as above)
8 8 8 8
8 8 8 8
8 8 8 8
8 8 8 8
random effect 5
type:animal
opened output pedigree file "renadd05.ped"
read 26 pedigree records
loaded 18 parent(s) in round 0
Pedigree checks
Number of animals with records = 8
Number of parents without records = 18
Total number of animals = 26
Wrote parameter file "renf90.par"
Wrote renumbered data "renf90.dat" 8 records
Renumf90 실형 결과 파일
renf90.tables
Effect group 1 of column 1 with 1 levels, effect # 1
Value # consecutive number
1 8 1
renadd05.ped
26 3 20 1 0 2 0 0 0 26
1 0 0 3 0 0 1 1 0 13
21 9 11 1 0 2 0 0 0 21
13 0 0 3 0 0 0 0 1 5
2 3 17 1 0 2 1 0 0 19
3 0 0 3 0 0 1 8 0 14
22 3 16 1 0 2 0 0 0 22
11 0 0 3 0 0 0 0 1 3
16 0 0 3 0 0 0 0 1 8
4 1 12 1 0 2 1 2 0 15
23 3 19 1 0 2 0 0 0 23
18 0 0 3 0 0 0 0 1 10
9 0 0 3 0 0 0 1 0 1
14 0 0 3 0 0 0 0 1 6
5 4 10 1 0 2 1 0 0 16
24 3 18 1 0 2 0 0 0 24
19 0 0 3 0 0 0 0 1 11
12 0 0 3 0 0 0 0 1 4
6 4 13 1 0 2 1 0 0 17
17 0 0 3 0 0 0 0 2 9
25 3 15 1 0 2 0 0 0 25
20 0 0 3 0 0 0 0 1 12
7 3 17 1 0 2 1 0 0 20
10 0 0 3 0 0 0 0 1 2
8 3 14 1 0 2 1 0 0 18
15 0 0 3 0 0 0 0 1 7
설명은 이전 포스트 참조
renf90.dat
9 1 1.3571429 -0.3571429 0.2857143 1
13.4 1 0.3571429 -0.3571429 -0.7142857 3
12.7 1 0.3571429 0.6428571 1.2857143 4
15.4 1 -0.6428571 -0.3571429 1.2857143 5
5.9 1 -0.6428571 0.6428571 0.2857143 6
7.7 1 0.3571429 0.6428571 -0.7142857 8
10.2 1 -0.6428571 -0.3571429 0.2857143 2
4.8 1 -0.6428571 0.6428571 0.2857143 7
renf90.par
# BLUPF90 parameter file created by RENUMF90
DATAFILE
renf90.dat
NUMBER_OF_TRAITS
1
NUMBER_OF_EFFECTS
5
OBSERVATION(S)
1
WEIGHT(S)
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
2 1 cross
3 1 cov
4 1 cov
5 1 cov
6 26 cross
RANDOM_RESIDUAL VALUES
245.00
RANDOM_GROUP
5
RANDOM_TYPE
add_animal
FILE
renadd05.ped
(CO)VARIANCES
35.241
OPTION solv_method FSPAK
BLUPF90 실행
다음과 같은 명령어로 blupf90을 실행
blupf90 renf90.par | tee blupf90_fem_snp_uw_01.log
실행 화면
blupf90 실행 로그
renf90.par
BLUPF90 ver. 1.68
Parameter file: renf90.par
Data file: renf90.dat
Number of Traits 1
Number of Effects 5
Position of Observations 1
Position of Weight (1) 0
Value of Missing Trait/Observation 0
EFFECTS
# type position (2) levels [positions for nested]
1 cross-classified 2 1
2 covariable 3 1
3 covariable 4 1
4 covariable 5 1
5 cross-classified 6 26
Residual (co)variance Matrix
245.00
Random Effect(s) 5
Type of Random Effect: additive animal
Pedigree File: renadd05.ped
trait effect (CO)VARIANCES
1 5 35.24
REMARKS
(1) Weight position 0 means no weights utilized
(2) Effect positions of 0 for some effects and traits means that such
effects are missing for specified traits
* The limited number of OpenMP threads = 4
* solving method (default=PCG):FSPAK
Data record length = 6
# equations = 30
G
35.241
read 8 records in 6.2500000E-02 s, 50
nonzeroes
read 26 additive pedigrees
finished peds in 6.2500000E-02 s, 103 nonzeroes
solutions stored in file: "solutions"
blupf90 실행 결과 : solutions
trait/effect level solution
1 1 1 9.89535730
1 2 1 0.60686511
1 3 1 -4.08027436
1 4 1 1.93415480
1 5 1 -0.29881631
1 5 2 -0.09223369
1 5 3 0.25587182
1 5 4 0.14242845
1 5 5 0.25423609
1 5 6 -0.08517366
1 5 7 -0.18078055
1 5 8 0.27054660
1 5 9 0.00000000
1 5 10 0.12201458
1 5 11 0.00000000
1 5 12 0.19455774
1 5 13 -0.10425859
1 5 14 0.09507379
1 5 15 0.00000000
1 5 16 0.00000000
1 5 17 -0.26444303
1 5 18 0.00000000
1 5 19 0.00000000
1 5 20 0.00000000
1 5 21 0.00000000
1 5 22 0.12793591
1 5 23 0.12793591
1 5 24 0.12793591
1 5 25 0.12793591
1 5 26 0.12793591
2, 3, 4 effect가 SNP effect이다. 이들 SNP를 이용하여 DGV를 계산하는 것은 다음 포스트를 참조한다. 5 effect가 polygenic effect이다.
Fixed Effect Model for SNP Effects, unweighted analysis
# Linear Models for the Prediction of Animal Breeding Values, 3rd Edition. # Raphael Mrode # Example 11.1 p180 SNP effect를 고정효과로 모형에 넣어 SNP 추정하고 개체의 genotype을 이용..
bhpark.tistory.com
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