BREEDING AND GENETICS - American Society of Animal Science

228 Comparison of univariate and bivariate models for
genetic evaluation of calving ease. I. Misztal*, L. Varona, and
J. K. Bertrand, University of Georgia, Athens.
Several models were evaluated in terms of predictive ability for calving
ease (CE). Data included birth weight (BW) and CE records for 26,006
Gelbvieh cattle and for 5 simulated populations of 6,200 animals each.
Distribution of CE scores was 71% in the first category, 21% in second,
7% in third and 2% in fourth. Included in the model were fixed age
of dam x sex interaction effects, random management groups, and random
animal direct and maternal effects. Bivariate linear-threshold (LT)
and linear-linear (LL) models for BW/CE and univariate threshold (T)
and linear (L) models for CE were applied to the data sets. For each
data set and model, one half of CE records were discarded randomly.
Predictive ability of the different models was defined by the MSE for
the difference between a deleted CE score and its prediction obtained
from the remaining data. For simulated data sets, the average MSE was
.31 for LT, .33 for LL, .39 for T and .39 for L. For the field data set,
the MSE was .32, .34,.41 and .40, respectively. Although the bivariate
models for CE/BW were more accurate than univariate models, the
threshold models showed an advantage only under the bivariate model.
In the univariate model and field data, the L model was more accurate
than the T model. One possible explanation why the theoretically more
correct T model was less accurate for categorical data is that the underlying
model for CE was simplified, e.g., by ignoring heterogeneity of
variances, and that the L model is less sensitive to incorrect assumptions
in the model. While the current evaluation for CE in beef cattle using
LT seems justified, in dairy cattle either the T model should be refined
or recording for birth weight added.
Key Words: Calving Ease, Threshold Model, Model Comparison
230 Increasing the appeal of animal breeding programs
for students. E. J. Pollak, Cornell University, Ithaca, NY.
Methods to increase appeal and interest in animal breeding for undergraduates
will be discussed. Three problems will be addressed. First,
animal science students are typically interested in veterinary medicine
and in species other than production animals. They also have been
exposed to biotechnology through newspapers, magazines, and TV, altering
to a degree their motivation for learning genetics. Second, animal
breeding is viewed as a statistical discipline that is abstract in nature.
Third, there is a stigma of limited job opportunities in the field. Animal
breeding has several strengths on which a strategy to overcome these
problems should be built. Animal agriculture will be a major player in
the application of technology, and it seems natural to capitalize on this
by refining our curriculum to emphasis this component. Including disease
characteristics and broadening the species discussed in courses has
generated increased interest. Animal breeding is a quantitative field,
which lends itself very well to computerized exercises. Computer labs
allow students to graph equations, solve problems supported by simulated
data, and do statistical manipulation of data using software such
as Matlab. These activities not only enhance the learning experience but
examples of interest to the students can be generated. This approach
brings students in contact with computer technology, a skill they recognize
as necessary, but also enables them to increase their quantitative
and analytical skills. To address the wide scope of the application of
genetics and job opportunities in the area, seminar courses are of great
value. These, however, can be expensive to run and should be shared
among institutions via distance education as is currently being done at
Cornell, Pennsylvania State University, and the State University of New
York at Cobleskill.
Key Words: Animal Breeding Curriculum, Teaching
231 Distance education in animal breeding: A Cornell
experience. P. A. Oltenacu* and E. J. Pollak, Cornell University,
Ithaca, NY.
229 Genetic analysis of productive life with censored
records. N. Vukasinovic*, J. Moll, and N. Kuenzi, Swiss Federal Institute
of Technology, Zurich, Switzerland.
A simulation study was carried out to investigate impact of censoring on
estimation of genetic parameters and accuracy of sire evaluation for the
length of productive life by means of survival analysis. Data were simulated
under a Weibull model with two fixed effects and a random sire
effect with a sire variance of 0.034, assuming a data structure typical for
dairy cattle populations. Two different family structures were investigated
- 1000 sires with 10 daughters each and 200 sires with 50 daughters
each. Sires were assumed related through the grandsires. The reference
data were generated assuming no censoring. Sire variance and sire effects
were estimated from the reference data with and without considering
the relationships among sires. The impact of censoring was investigated
by comparing variance estimates and by computing rank correlations
among estimated sire effects from the reference data and from several
different data files with increased proportion of censored records. Without
considering the relationships among sires, the estimated sire variance
was biased downwards, especially with increased proportions of censored
records. Estimates of sire effects were generally more accurate with a
large number of daughters. The rank correlations among sire effects decreased
with increased proportion of censored records. With over 40%
censored records, considering relationships among sires results in higher
rank correlations. Similar results were obtained from the analysis of real
dairy cattle data from the Swiss Braunvieh population.
In 1997 a one-credit weekly lecture course in Applied Animal Genetics
was offered to Cornell students and to students at two remote sites, State
University of New York at Cobleskill and Pennsylvania State University.
An introductory genetics course was required as prerequisite. The
course (http://www.ansci.cornell.edu/courses/as321/as321.html) presented
topics related to genetic definition and control of qualitative and
quantitative traits in various animal species. The topics were structured
into three modules: 1) genetic implications of small breeding populations,
2) breeding programs for genetic improvement of animals, and 3)
new developments in molecular genetics. The course was offered synchronously
at all locations using a PictureTel Concord 4500 videoconferencing
system with a speed of 384 kbps. As a backup for possible
failure to connect, each site had a complete set of PowerPoint slides and
a speakerphone, which allowed for “at least audio” connection if needed.
Supporting reading materials were distributed to all students one week
prior to lecture, and a handout of copies of each speaker’s slides with
space for taking notes was available on the day of each lecture. Students
at remote sites were encouraged to raise questions or contribute comments
during the lectures. The facilitator at Penn State site assisted
with student instruction. To provide Cobleskill students with a comparable
opportunity without an instructor on site, they were asked to
identify concepts that were unclear and formulate specific questions after
each lecture. These were submitted electronically to Cornell within two
days of the lecture and were discussed in a question and answer session
later in the week via the videoconferencing system. Course evaluations
were positive. The students’ comments indicated complete satisfaction
with the content and format of the course. Students at Cornell indicated
that the virtual presence of students at remote sites had a positive effect
on the quality of the learning. We plan to offer this course to 2–3 more
sites in the fall of 1998. Also, a computer-assisted lab that complements
the course will be offered to one of the remote sites.
Key Words: Distance Education, Animal Breeding
J. Anim. Sci. Vol. 76, Suppl. 1/J. Dairy Sci. Vol. 81, Suppl. 1/1998 59