bivariate binomial-normal analysis

Dear all,

I'm working with pre-weaning calf mortality (1=death; 0=alive).

Calf birth weight seems to be an important characteristic regarding
mortality (average optimum), and show possibility to be used as an indicator
trait.

For these reason, I want to fit a bivariate analysis, using mortality and
birth weight as traits.

Initially, I've done univariate analysis, in order of get heritability
estimates, as well as starting values for bivariate analysis.

The model I used for the bivariate analysis, regarding sire model, is as
follows:

Analise bivariada da mortalidade ONM210 PN sire model

anim !P # calf
number

 pai !P # sire
number

 mae !P # dam
number

 nmae 3576 !I # dam number
with number of dams in file

 idv 4 !I # dam
age class (4)

 sx !I # calf
sex

 gc 68 !I #
contemporary group

 onm210 !M -9 #missing = -9 # pre-weaning mortality

 pn !M -9 #missing = -9 # calf birth weight

ped_mtger.dat # pedigree file

mt_to.dat !TYPEIIISS !EXTRA 50 !REPORT !ASUV #!CONTINUE #data file

onm210 pn !BIN !LOGIT ~ Trait Trait.idv Trait.sx !r Trait.pai !f Trait.gc

1 2 1

0

Trait 0 US 3.29 3.32 13.40 !GPFP # in fact, the value in .asr for "3.29" was
"1", but considering I used a logit link, I put the error as 3.29 (is that
correct?)

Trait.pai 2

Trait 0 US 0.04 0.15 1.96 !GP

pai 0 AINV

and the .pin file:

F phenvar 1:3 + 4:6

F addvar 4:6 * 4

H heritA 10 7

H heritB 12 9

R phencorr 7 8 9

R gencor 4:6

.pvc file

   7 phenvar 1 1.131 0.2906E-01

   8 phenvar 2 3.226 0.5354E-01

   9 phenvar 3 95.39 0.5863

  10 addvar 4 0.2804E-01 0.1074

  11 addvar 5 -0.3762 0.2141

  12 addvar 6 5.337 0.8535

     heritA = addvar 10/phenvar 7= 0.0248 0.0944

     heritB = addvar 12/phenvar 9= 0.0560 0.0088

     phencorr = phenvar /SQR[phenvar *phenvar ]= 0.3106 0.0068

     gencor 2 1 = Trait 5/SQR[Trait 4*Trait 6]= -0.9725 1.8858

The program converged, but I don't know if that's the right (or better)
model.

Should I have similar heritability values between uni and bivariate
analysis? (because they are quite different).

Is there a reason (and a way of dealing with) the high SE found in genetic
correlation?

I thank all in advance for the attention and help.
Regards

Anita Schmidek

 
Received on Fri May 07 2009 - 09:37:32 EST