Contents
Genetic correlation estimation procedure
Estimation of genetic correlations among countries takes place in testruns and only when new or modified data are submitted from a country, according to the following procedure (as per Interbull technical workshop of January 2004, Uppsala, Sweden):
Step 1: Estimation of correlations
Data for estimation of genetic correlations are deregressed breeding values for all AI bulls that have daughters in at least 10 herds. For mastitis and calving traits an additional requirement is that bulls have at least 50 daughters.
Correlations are estimated using the software package developed at Holstein Association USA (Klei & Weigel, 1998). Correlations are estimated simultaneously for all countries, except for Holstein, where subsets of usually 78 countries are considered. Countries are grouped into triplets (sometimes quadruplets) according to their number of common bulls and a per analysis correlations are estimated between the countries belonging to two triplets plus a fixed set of countries, varying from trait to trait but always including USA, which are used as linked providers. Genetic correlation estimates for all country pairs are obtained by considering all possible combinations of triplets.
For each analysis only records from common bulls and bulls belonging to ¾sib families with evaluations in multiple countries are used. Pedigree information is traced back until 1970; parents of ancestors born before 1970 are treated as missing and assigned to phantom parent groups. Phantom parent groups are defined according to origin, birth year of the bull and path of selection. Small groups are merged, where the first priority is given to combining birth years, and next to combining countries of origin. Genetic groups are treated as random effects.
Starting correlations for the REML procedure are the previously used correlations, and iterations are stopped when the relative change for all λ = Gij/√(Ri*Rj) is less than 106, where Gij is the sire covariance between country i and j, and Ri and Rj the residual variance in country i and j, respectively, or when the maximum change in correlation is less than 106. Aitken acceleration is used to speed up convergence.
Due to the country subsetting for Holstein, multiple estimates are obtained for the genetic correlation between some country pairs. The correlation matrix used in the next step (postprocessing) is a combination of matrix of the maximum and average correlation estimates, weighted such to obtain the matrix with the highest smallest eigenvalue.
Step 2:Post processing
The following information sources are considered:
 The correlation estimated from step 1
 The correlation used in the previous run.
own expectations Magnitude of changes tested
 Correlations from Holsteins (only for nonHolstein breeds)
Estimates are required to fall within certain windows. For milk production traits, for example, separate windows are maintained depending on the climate and whether or not countries predominantly have grazing system. Two countries with a similar climate and production system (grazing vs. nongrazing) are expected to be more correlated with each other than two countries with different climate or production system. If estimates are higher than the maximum (or lower than the minimum) window's value, they are set equal to the the maximum (or minimum) window's value specified for that given group. In addition, estimates are regressed towards a mean correlation within groups, the regression depending on the number of common bulls. Trait specific windows' parameters are given below.
For breeds other than Holstein, and for some traits (production and udder health), estimates are combined with genetic correlations for Holstein and weighted by both the number of common bulls between the two countries and the prior (HOL) common bulls. If a specific country is not among the HOL evaluation, the prior correlations used are equal to the minimum value of all nonmissing countries . The approach to follow is similar to the one for Red Dairy Cattle conformation.
The two values (i.e. The results from the preceding step and the previously used correlations) are combined into a weighted average to avoid large changes in correlations between consecutive test runs, weighted by the number of common bulls. If the national evaluations for two countries have not changed, then the genetic correlation between these two countries is not expected to change much. However, if one of the countries has introduced changes in their national evaluations, the genetic correlation between two countries may change. An increase in number of common bulls is expected to yield a more precise estimate of the genetic correlation, and more weight is given to the current estimate. This is done by increasing the weight on the current estimate proportionally to the increase in number of common bulls.
Type of Changes Tested
Weight on Previous Correlations
No changes
3
Minor change in at least one country (e.g., data edit, pedigree improvement)
1
Major change in at least one country (e.g., new model or parameters)
0
 Finally, the updated (co)variance matrix is bended, using the bending procedure described by Jorjani et al. (2003).
Trait specific windows' parameters:
Production
Minimum size of phantom parent groups: 30
Grouping of countries:
 Other Countries
 Israel (climate)
 Australia, Ireland, New Zealand (grazing)
Windows:

Minimum 
Maximum 
Median 


G1 
G2 
G3 
G1 
G2 
G3 
G1 
G2 
G3 
G1 



0,99 
0,99 
0,99 



G2 



0,99 
0,99 
0,99 



G3 



0,99 
0,99 
0,99 



Regression:
r = (CBij · rGij + 10 · µij) /CBij + 10
where CBij is the number of common bulls between country i and j, rGij the genetic correlation between country i and j, and μij is either 0.92 or 0.82, depending on whether countries i and j belong to the same or different groups, respectively.
Conformation
Minimum size of phantom parent groups: 30
Windows:
OCS
 Other countries
 AUS, NZL, IRL






G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


OUS
 Other countries
 NZL, AUS, IRL

Minimum 
Maximum 
Median 


G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


OFL
 Other countries
 AUS, IRL

Minimum 
Maximum 
Median 


G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


Other Conformation Traits
 All Countries

Minimum 
Maximum 
Median 
ANG 

0,99 

BCS 

0,99 

BDE 

0,99 

CWI 

0,99 

FAN 

0,99 

FTL 

0,99 

FTP 

0,99 

FUA 

0,99 

LOC 

0,99 

RAN 

0,99 

RLR 

0,99 

RLS 

0,99 

RTP 

0,99 

RUH 

0,99 

RWI 

0,99 

STA 

0,99 

UDE 

0,99 

USU 

0,99 

BSW additional traits
 All Countries
Trait 
Minimum 
Maximum 
Median 
hde 
0,75 
0,99 

ruh 
0,5 
0,99 

ofr 
0,76 
0,99 

tpl 
0,88 
0,99 

oru 
0,44 
0,99 

rle 
0,47 
0,99 

pwi 
0,57 
0,99 

thp 
0,56 
0,99 

hoq 
0,77 
0,99 

ful 
0,30 
0,99 

udb 
0,71 
0,99 

tdi 
0,90 
0,99 

tth 
0,86 
0,99 

Udder health
Minimum size of phantom parent groups: 30
Windows:
SCS:
 Somatic Cells (SCS): Other Countries
 Somatic Cells (SCS): Israel (climate)
 Somatic Cells (SCS): Australia, Ireland, New Zealand (grazing)

Minimum 
Maximum 
Median 


G1 
G2 
G3 
G1 
G2 
G3 
G1 
G2 
G3 
G1 



0,99 
0,99 
0,99 



G2 



0,99 
0,99 
0,99 



G3 



0,99 
0,99 
0,99 



MAS:
 Somatic Cells (SCS): Other Countries
 Somatic Cells (SCS): Israel (climate)
 Somatic Cells (SCS): Australia, Ireland, New Zealand (grazing)
 Mastitis (MAS): country using real MAS data

Minimum 
Maximum 
Median 


G1 
G2 
G3 
G4 
G1 
G2 
G3 
G4 
G1 
G2 
G3 
G4 
G1 




0,99 
0,99 
0,99 
0,99 




G2 




0,99 
0,99 
0,99 
0,99 




G3 




0,99 
0,99 
0,99 
0,99 




G4 




0,99 
0,99 
0,99 
0,99 




Regression:
r = (CBij · rGij + 10 · µij) /CBij + 10
where CBij is the number of common bulls between country i and j, rGij the genetic correlation between country i and j, and μij is:
 0.92 if countries i and j belong to the same group (SCS)
 0.90 if countries i and j belong to the same group (MAS)
 0.82 if countries i and j belong to different groups (SCS)
 0.68 if countries i and j belong to different groups and traits
Longevity
Minimum size of phantom parent groups: 30
Grouping of countries:
 All Countries
Windows:

Minimum 
Maximum 
Median 
G1 

0,99 

No regression applied.
Calving
Minimum size of phantom parent groups: 30
Windows:
DCE
 Other
 Australia (grazing)

Minimum 
Maximum 
Median 


G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


MCE
 All Countries

Minimum 
Maximum 
Median 
G1 

0,99 

DSB
 Australia (grazing)
 Countries with DCE information
 Countries with DSB information

Minimum 
Maximum 
Median 


G1 
G2 
G3 
G1 
G2 
G3 
G1 
G2 
G3 
G1 



0,99 
0,99 
0,99 



G2 



0,99 
0,99 
0,99 



G3 



0,99 
0,99 
0,99 



MSB
 Countries with MCE information
 Countries with MSB information

Minimum 
Maximum 
Median 


G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


Regression:
r = (CBij · rGij + 10 · µij) /CBij + 10
where CBij is the number of common bulls between country i and j, rGij the genetic correlation between country i and j, and μij is:
Female Fertility
Minimum size of phantom parent groups: 30
Windows:
HCO
 Countries providing NR
 Countries providing CR

Minimum 
Maximum 
Median 


G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


CRC
 Countries providing CI/DO
 Countries providing CF
 Countries providing PM

Minimum 
Maximum 
Median 


G1 
G2 
G3 
G1 
G2 
G3 
G1 
G2 
G3 
G1 



0,99 
0,99 
0,99 



G2 



0,99 
0,99 
0,99 



G3 



0,99 
0,99 
0,99 



CC1
 Countries providing NR
 Countries providing CR

Minimum 
Maximum 
Median 


G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


CC2
 Countries providing CI/DO
 Countries providing FC/FL
 Countries providing NR

Minimum 
Maximum 
Median 


G1 
G2 
G3 
G1 
G2 
G3 
G1 
G2 
G3 
G1 



0,99 
0,99 
0,99 



G2 



0,99 
0,99 
0,99 



G3 



0,99 
0,99 
0,99 



INT
 Countries providing CI/DO
 Countries providing RC

Minimum 
Maximum 
Median 


G1 
G2 
G1 
G2 
G1 
G2 
G1 


0,99 
0,99 


G2 


0,99 
0,99 


Regression
r = (CBij · rGij + 10 · µij) /CBij + 10
Where CBij is the number of common bulls between country i and j, rGij is the genetic correlation between country i and j, and μij is the mean correlation indicated above.
Workability Traits
Minimum size of phantom parent groups: 30
Windows:
MSP
 All Countries

Minimum 
Maximum 
Median 
G1 

0,99 

TEM
 All Countries

Minimum 
Maximum 
Median 
G1 

0,99 

Regression:
r = (CBij · rGij + 10 · µij) /CBij + 10
where CBij is the number of common bulls between country i and j, rGij the genetic correlation between country i and j, and μij is:
 0.92 if countries i and j belong to the same group (MSP)
 0.85 if countries i and j belong to the same group (TEM)