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R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Impact of imputing markers from a low density chip on the reliability of genomic breeding values in Holstein populations
http://picasaweb.google.com/UPRA.PH/ConcoursJeune#5306011809489360786 princesse-lavache.com
Romain DassonnevilleINRA GABI G²B Institut de l’élevage
Rasmus BrøndumAarhus University
Faculty of Science and Technology
F. Guillaume, V. Ducrocq,S. Fritz, UNCEIA
B. Guldbrandtsen,M. Lund,
G. Su
T. Druet,Univ Liège
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Study of the Illumina 3K chip
EuroGenomics collaboration
Objectives :
Measure imputation error rate
Study impact on GEBV reliability
Study influence of reference population size
Previous studies 3K custom in silico chip
Our study commercially available 3K chip
Zhang and Druet , 2010
Weigel et al, 2010
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Data
Reference population
training validation
54K
54K54K 3K
imputed54K
masking
Imputation
comparison
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Data
National EuroGenomics No. of Markers
Training Validation Training Validation Training Validation
Nordic 3,058 1086 10,880 1,086 38,545 2,285
France 3,071/3,505*
966 12,078/13,947 *
966 43,582 2,635
Number of animals and number of markers used*Including bulls with partially reconstructed genotypes
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Imputation Method
• Combination of the DAGPHASE 1.1 and Beagle 2.1.3 software
• Combines :
Pedigree based family information (segregation rules)
Population Linkage Disequilibrium
Druet et Georges, 2010PHASEBOOK package
Browning and Browning, 2007
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Results
94,5
9696,1
97,9
92
93
94
95
96
97
98
99
National Eurogenomics
% a
llele
s co
rrec
tly
impu
ted
reference population
Imputation accuracy
Nordic French
91,7
95,5 95,7
90
91
92
93
94
95
96
97
98
Sire not in ref. Sire in ref. Sire and Maternal grandsire in ref.
% a
llele
s co
rrec
tly
impu
ted
Nordic population
Influence of genotyped ancestors
France: more 3K markers, more genotyped ancestors
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Traits studied :
• protein yield (h²=0.3-0.39)
• somatic cell count (SCC, h²=0.15)
• fertility (Non Return Rate or Conception rate, h²=0.02)
• udder depth (h²=0.36-0.37)
Data for genomic evaluation
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Genomic evaluation model
1 21
1 ( )nQTL
i ii
y Zu h h eµ=
= + + + +∑
Nordic:
GBLUP
French:
GMAS, QTL-BLUP combines LDLA and EN
VanRaden, 2008
Boichard et al., WCGALP 2010
1y Zg eµ= + +
u: polygenic effect based on pedigree
haplotypes
LDLA : Linkage DisequilibriumLinkage AnaysisEN : Elastic Net
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Results – Reliability of DGV
0,410,38
0,54
0,48
0
0,1
0,2
0,3
0,4
0,5
0,6
50 K 3K imp 50 K 3K imp
National ref Euro ref
relia
bilit
y
Nordic
Reliability = squared correlation (DGV, deregressed proofs) for validation populationMean over the 4 traits
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Results – Reliability of GEBV
0,460,41
0,48 0,46
0
0,1
0,2
0,3
0,4
0,5
0,6
50 K 3K imp 50 K 3K imp
National ref Euro ref
relia
bilit
y
France
Reliability = squared correlation (GEBV, DYD) for validation populationMean over the 4 traits
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Conclusion - discussion
• Imputation accuracy = 2.5-5% = close to litterature Commercially available 3K chip contains less markers after editing Bigger reference population size Beagle and DAGphase: efficient imputation softwares
• Reliability of GEBV: Reliability of GEBV based on imputed genotypes slightly lower Correlations (GEBV-50K, GEBV-3Kimp) are high (0.91-0.97)
Low density chip imputed to 50 K: Feasible alternative for pre-selection of young animals Attractive tool for a large screening of the female population
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
If you want some more details
Article published in Journal of Dairy Science, July 2011
R. Dassonneville, R. Brøndum
imputation – EAAP 2011
Impact of imputing markers from a low density chip on the reliability of genomic breeding values
in Holstein populations
http://picasaweb.google.com/UPRA.PH/ConcoursJeune#5306011809489360786 princesse-lavache.com Romain DassonnevilleINRA GABI G²B Institut de l’élevage
Rasmus BrøndumAarhus University
Faculty of Science and Technology
F. Guillaume, V. Ducrocq,S. Fritz, UNCEIA
B. Guldbrantsen,M. Lund,
G. Su
T. Druet,Univ Liège
94,5
9696,1
97,9
9293949596979899
National Eurogenomics % a
llele
s co
rrec
tly
impu
ted
reference population
Imputation accuracy
Nordic
French
0,41 0,38
0,540,48
00,10,20,30,40,50,6
50 K 3K imp 50 K 3K imp
National ref Euro ref
reliability