BREEDING AND GENETICS - American Society of Animal Science

252 Genetic parameters of litter mortality and 6 related
characteristics. E. F. Knol*, Institute for Pig Genetics, Beuningen,
The Netherlands.
Still born and preweaning mortality in pigs sums to 15 to 20 % of total
piglets born Genetic improvement in litter size might even further
increase mortality. What are the possibilities of selection against mortality
Data on birth weight, crossfostering and mortality was collected
on some 200.000 individual piglets in the breeding structure of Dalland,
the Netherlands, in 2 boar lines and 2 sow lines. Piglets were born
on 12 different farms and were pure line or crosses. This dataset was
reduced to single litter records per sow for the calculation of heritabilities
and genetic correlation’s in a multitrait equal design animal model
analysis, with a model including parity, HYS, line/cross and sow as the
animal effect. Piglet mortality is addressed as a litter characteristic
of the sow, mortality of crossfostered piglets is traced back to the biological
mother. As potentially interesting other litter characteristics
litter size, litter weight, average birth weight, variation in birth weight,
gestation length and still born were taken. Heritabilities are in good
agreement with literature. Genetic correlation’s of litter mortality with
litter size (0.26), average birth weight (-0.11) and variation (0.16) are
not significantly different from zero and unexpectedly low in magnitude.
It is concluded that adding piglet mortality to the selection index of a
breeding program is worthwhile.
Table 1: Heritabilities (diagonal), genetic correlation’s (below diagonal)
and error correlation’s (above diagonal) of litter mortality and 6 related
litter characteristics.
Nr. Trait 1 2 3 4 5 6 7
1 Litter size 0.10 0.89 -0.45 0.20 -0.18 0.18 0.06
2 Litter weight 0.54 0.12 -0.05 0.16 -0.11 0.11 -0.08
3 Average birth weight -0.49 0.46 0.35 -0.17 0.20 -0.17 -0.29
4 Variation in birth weight 0.03 0.43 0.42 0.07 0.03 0.08 0.09
5 Gestation length -0.19 -0.14 0.03 -0.13 0.36 -0.05 -0.07
6 Still bor 0.16 0.22 0.09 0.09 -0.08 0.04 0.49
7 Litter mortality 0.26 0.12 -0.11 0.16 -0.32 0.57 0.05
254 Variability in female fertility in 3-in-2 accelerated
lambing system. L. Jairath* 1 , J. Wilton 1 , S. Meszaros 2 ,
G. VanderVoort 1 , and D. Kennedy 3 , 1 CGIL, University of Guelph,
Canada, 2 University of New England, Armidale, NSW, 3 OMAFRA,
Guelph, Canada.
The objective of this study was to estimate genetic and phenotypic
variability of fertility of ewes on Ontario Lamb Improvement Breeding
Scheme (OLIBS). This production system requires ewes to lamb 3 times
in 2 years resulting in a biannual production cycle. For the 3-in-2 accelerated
lambing system, breedings fall into spring (S1: March to June),
fall (S2: July to September) and winter (S3: October to December) seasons.
Not all ewes are able to breed in each season and the traits of
fertility in each season appear to be different but correlated. Data included
16,558 lambing records on 5665 ewes in 18 flocks from year 1986
to 1997. There were 7853, 9906, and 7734 observations for S1, S2 and
S3, respectively. Fertility in ewes was defined as their ability to conceive
and was treated as a binary trait. The model used was the same for all
the 3 seasons and included fixed effects of flock-year of calving, age and
breed of ewe and its mate, and random animal (ewe) additive genetic,
permanent environment and residual effects. Data were analyzed using
VCE software package with fertility considered a separate trait in each
season. The heritability of S1 and S3 fertility was same (.05), but a little
higher (.08) for S2. However, the genetic variability was observed to be
similar (and higher) for S1 and S2 than for S3. S1 had the highest environmental
variance and S3 the lowest. The genetic correlations amongst
the 3 fertility traits were positive: .39 between S1 and S2, .43 between
S1 and S3, and .08 between S2 and S3. Evidence of sufficient genetic
variability for seasonal fertility in OLIBS ewes justifies its consideration
in the OLIBS programs. Selection of ewes with high genetic merit
for seasonal fertility should result in improved productivity of Ontario
sheep flocks.
Key Words: Fertility, Sheep, Parameters
Key Words: Piglet Mortality, Genetic Parameters, Litter
253 Genetic and phenotypic correlations among measures
of neutrophil oxidative metabolism in Holstein bulls.
S. C. Kelm* 1 , M. E. Kehrli, Jr. 2 , A. E. Freeman 1 ,J.L.Burton 3 , and A.
L. Kuck 4 , 1 Iowa State University, Ames, 2 NADC-USDA-ARS, Ames,
IA, 3 Michigan State University, East Lansing, 4 21st Century Genetics,
Shawano, WI.
Relationships between measures of oxidative metabolism were determined
using phenotypic data from 60 Holstein bulls. Measures of oxidative
metabolism included opsonized-zymosan phagocytosis-associated
native chemiluminescence activity, myeloperoxidase-catalyzed iodination,
and superoxide generation. Bulls were tested in 5 groups of equal
size and sampled within the context of a glucocorticoid-induced immunosuppression
model. Dexamethasone was used as the immunologic
stressor. Days relative to dexamethasone treatment defined period 1
(days −5, −4, and −3), period 2 (days 2, 3, 4, and 5), and period 3
(days 9, 10, and 11). Neutrophil function was assessed each day for
treated and untreated control bulls. Results were expressed as a percent
of controls. Parameter estimates were obtained with MTDFREML
using a multiple-trait animal model. Separate analyses were conducted
for periods 1, 2, and 3. The model for each assay within period contained
a random animal effect and the fixed effects of group, day, and
group-day interaction. Eosinophil contamination was included as a covariate
in the model describing the neutrophil iodination assay. Genetic
correlations between measures of oxidative metabolism in period 1 were
moderately high (range .66 to .81), but lower in periods 2 and 3 (range
−.30 to .66). Phenotypic correlations between measures of oxidative
metabolism were positive in all periods (range .16 to .56). Heritability
estimates were obtained for all neutrophil measures within period
and ranged from .15 to .54. Genetic improvement of functional neutrophil
traits appears possible. Furthermore, our results indicate that
neutrophil function measured by the 3 assays of oxidative metabolism
may be under similar genetic control. Selection for improved response
in one facet of oxidative metabolism would likely result in improvement
in all measures.
Key Words: Neutrophil, Parameter Estimation, Oxidative Metabolism
255 Effect of dominance variation and population
structure on estimation of additive genetic variance. D.
Norris*, R. J. Tempelman, and I. L. Mao, Michigan State University,
East Lansing.
The impact of ignoring dominance genetic variance (V D)on the estimation
of additive genetic variance (V A)was studied. Six populations with
varying percentages of animals in full sib families,(0,2,10,20,50, and 100
population, three different combinations of V A and V D were considered:(A)
V A = 950, V D = 50, (B) V A =800,V D = 200, and (C) V A
= 500, V D = 500, thereby creating a total of 18 subpopulations, each
with 10,000 animals. In all cases, the residual variance was 2000 such
that the broad sense heritability was 33%. Variance components were
estimated using the tilde-hat approximation to REML. Estimates of V A
under a full model, that included additive and dominance genetic effects
were unbiased whereas estimates of V A under a reduced model, that included
only additive effects, were biased upwards by as much as 80%.
The large bias in V A was particularly pronounced in populations with
large number of animals in full sib families. We conclude that even in
populations where V D is relatively small, it should not be ignored in
variance component estimation if the percentage of animals in full sib
families is 10% or greater.
Percentage of animals Reduced Model Full Model
in fullsib families A B C A B C
0 951 790 508 950 790 508
2 979 830 557 949 800 497
10 1048* 920** 652** 959 837 485
20 995* 921** 826** 945 784 543
50 1012** 955** 896** 943 788 480
100 1010** 993** 905** 948 809 476
*Bias significant at P