Should genetic groups be fitted in BLUP evaluation? Practical answer for the French AI beef sire evaluation

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Some analytical and simulated criteria were used to determine whether a priori genetic differences among groups, which are not accounted for by the relationship matrix, ought to be fitted in models for genetic evaluation, depending on the data structure and the accuracy of the evaluation. These criteria were the mean square error of some extreme contrasts between animals, the true genetic superiority of animals selected across groups, i.e. the selection response, and the magnitude of selection bias (difference between true and predicted selection responses). The different statistical models studied considered either fixed or random genetic groups (based on six different years of birth) versus ignoring the genetic group effects in a sire model. Including fixed genetic groups led to an overestimation of selection response under BLUP selection across groups despite the unbiasedness of the estimation, i.e. despite the correct estimation of differences between genetic groups. This overestimation was extremely important in numerical applications which considered two kinds of within-station progeny test designs for French purebred beef cattle AI sire evaluation across years: the reference sire design and the repeater sire design. When assuming a priori genetic differences due to the existence of a genetic trend of around 20% of genetic standard deviation for a trait with h 2 = 0.4, in a repeater sire design, the overestimation of the genetic superiority of bulls selected across groups varied from about 10% for an across-year selection rate p = 1/6 and an accurate selection index (100 progeny records per sire) to 75% for p = 1/2 and a less accurate selection index (20 progeny records per sire). This overestimation decreased when the genetic trend, the heritability of the trait, the accuracy of the evaluation or the connectedness of the design increased. Whatever the data design, a model of genetic evaluation without groups was preferred to a model with genetic groups when the genetic trend was in the range of likely values in cattle breeding programs (0 to 20% of genetic standard deviation). In such a case, including random groups was pointless and including fixed groups led to a large overestimation of selection response, smaller true selection response across groups and larger variance of estimation of the differences between groups. Although the genetic trend was correctly predicted by a model fitting fixed genetic groups, important errors in predicting individual breeding values led to incorrect ranking of animals across groups and, consequently, led to lower selection response.

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Selection bias

Accuracy

Connection

Beef cattle

Informations

Publié par	biomed
Publié le	01 janvier 2004
Nombre de lectures	10
Langue	English

Extrait

Genet. Sel. Evol. 36 (2004) 325–345 325
c INRA, EDP Sciences, 2004
DOI: 10.1051/gse:2004004
Original article
Should genetic groups be ﬁtted in BLUP
evaluation? Practical answer for the French
AI beef sire evaluation
∗Florence P,DenisL¨
Station de génétique quantitative et appliquée, Institut national de la recherche agronomique,
78352 Jouy-en-Josas Cedex, France
(Received 27 February 2003; accepted 29 December 2003)
Abstract – Some analytical and simulated criteria were used to determine whether apriori ge-
netic diﬀerences among groups, which are not accounted for by the relationship matrix, ought
to be ﬁtted in models for genetic evaluation, depending on the data structure and the accuracy of
the evaluation. These criteria were the mean square error of some extreme contrasts between an-
imals, the true genetic superiority of animals selected across groups, i.e. the selection response,
and the magnitude of selection bias (diﬀerence between true and predicted selection responses).
The diﬀerent statistical models studied considered either ﬁxed or random genetic groups (based
on six diﬀerent years of birth) versus ignoring the genetic group eﬀects in a sire model. Includ-
ing ﬁxed genetic groups led to an overestimation of selection response under BLUP selection
across groups despite the unbiasedness of the estimation, i.e. despite the correct estimation of
diﬀerences between genetic groups. This overestimation was extremely important in numerical
applications which considered two kinds of within-station progeny test designs for French pure-
bred beef cattle AI sire evaluation across years: the reference sire design and the repeater sire
design. When assuming apriori genetic diﬀerences due to the existence of a genetic trend of
2around 20% of genetic standard deviation for a trait with h = 0.4, in a repeater sire design, the
overestimation of the genetic superiority of bulls selected across groups varied from about 10%
for an across-year selection rate p= 1/6 and an accurate selection index (100 progeny records
per sire) to 75% for p= 1/2 and a less accurate selection index (20 progeny records per sire).
This overestimation decreased when the genetic trend, the heritability of the trait, the accuracy
of the evaluation or the connectedness of the design increased. Whatever the data design, a
model of genetic evaluation without groups was preferred to a model with genetic groups when
the genetic trend was in the range of likely values in cattle breeding programs (0 to 20% of ge-
netic standard deviation). In such a case, including random groups was pointless and including
∗ Corresponding author: laloe@dga.jouy.inra.fr326 F. Phocas, D. Laloë
ﬁxed groups led to a large overestimation of selection response, smaller true selection response
across groups and larger variance of estimation of the diﬀerences between groups. Although the
genetic trend was correctly predicted by a model ﬁtting ﬁxed genetic groups, important errors
in predicting individual breeding values led to incorrect ranking of animals across groups and,
consequently, led to lower selection response.
selection bias/ accuracy/ genetic trend/ connection/ beef cattle
1. INTRODUCTION
More and more often, genetic evaluations deal with heterogeneous popula-
tions, dispersed over time and space. The reference method to get an accurate
and unbiased prediction of breeding values of animals with records made at
diﬀerent time periods and in diﬀerent environments (herds, countries...) is the
best linear unbiased prediction (BLUP) under a mixed model including all in-
formation and pedigree from a base population where animals with unknown
parents are unselected and sampled from a normal distribution with a zero
mean and a variance equal to twice the Mendelian variance [4]. Considering
the breeding values of animals in a mixed model as random eﬀects from a
homogeneous distribution implies the assumption that the breeding values of
base animals have the same expectation, whatever their age or their geograph-
ical origin. A violation of this assumption can lead to an underestimation of
genetic trend and to a biased prediction of breeding values. Including all data
and pedigree information upon which selection is based, is often impossible in
the practical world. Including ﬁxed genetic groups overcomes the assumption
of equality of expectations of breeding values across space and time [6], but
the way to distinguish between the environmental and genetic parts of perfor-
mance across diﬀerent environments is not obvious [12]. Laloë and Phocas [9]
showed that as soon as there is some confounding between genetic and envi-
ronmental eﬀects, the prediction of genetic trend may be strongly regressed
towards a zero value when the average reliability of the evaluation is not large
enough in well connected data designs of beef cattle breeding programs. In-
cluding ﬁxed genetic groups in the evaluation leads to an unbiased estimation
of diﬀerences between these groups, but also leads to less accurate estimated
breeding values. In order to decide whether or not genetic groups ought to be
considered in sire evaluation, two criteria have been proposed: the level of ac-
curacy of comparisons between sires within the same group and between two
sires in diﬀerent groups [2] and the mean square error (MSE) of diﬀerences
between groups [7]. Kennedy [7] showed that, in terms of minimising MSE,Genetic groups in BLUP evaluation 327
an operational model that ignores genetic groups is preferable to a model that
accounts for diﬀerences between if the true diﬀerence between
genetic groups is not large enough. He proved that ignoring genetic groups
leads to smaller MSE of the genetic contrasts across groups than the PEV un-
der a model with genetic groups, as soon as the true genetic diﬀerence is less
than the standard error of estimation of this between group diﬀerence. How-
ever, the proof could not be extended over two groups. Kennedy’s argument
was related to the classical statistical problem about accuracy versus bias. A
more practical argument will be based on the eﬃciency of selection (by trun-
cation on the estimated breeding values) induced by the evaluation model. In
this paper, both kinds of criteria will be used to decide whether or not groups
should be included in a genetic evaluation.
The numerical application concerns two kinds of progeny test design for sire
evaluation in French beef cattle breeds [9]. Although these designs are really
speciﬁc to France, they are quite illustrative of the problem of connectedness
met with any beef cattle genetic evaluation because of the practical limitations
of semen exchanges in many beef cattle herds. Indeed, some confounding may
often be encountered between herd-year eﬀects and genetic values of some an-
imals like natural service bulls used within a herd and year. In the French AI
beef sire evaluation, most of the bulls have their progeny performance recorded
within a single year and only a few connecting bulls had progeny in diﬀerent
years in order to ensure some genetic links across years. The genetic group
deﬁnition is based on the year of birth of the sires, assuming that no pedi-
gree and records for sires are available and the sires are sampled from a se-
lected base population. The genetic groups will be included as either random
or ﬁxed eﬀects in the statistical model. Usually, genetic groups are considered
as ﬁxed eﬀects, but some authors (e.g. [3]) advocate treating genetic groups as
random eﬀects when small amounts of data and pedigree information are avail-
able. In our numerical application, sire relationships were ignored, because re-
lationships are not numerous in the open breeding nuclei of the French beef
cattle breeds. Moreover, accounting for relationships may confuse the issue
and do not allow a clear interpretation, because the results may strongly vary
according to the degree of the relationships [4, 8]. Pollak and Quaas [11] have
explained that the grouping of base animals is the only relevant grouping and
they have shown that diﬀerences between groups decrease as more information
is included in the relationship matrix. Empirical evidence has shown, however,
that the use of relationships between sires does not completely account for
the large existing genetic diﬀerences between groups when migration occurs
without tracing back the common ancestors of animals in diﬀerent areas [7,12].328 F. Phocas, D. Laloë
In this paper, we will not formally consider phantom parent grouping strate-
gies [13] because relationships are not taken into account. However, ignoring
relationships will not remove anything to the generality of our conclusions,
since this paper deals with the problem of grouping of base animals.
The aim of this research was to answer the following question: does a model
that includes groups lead to a more eﬃcient ranking of animals across groups
and consequently a higher selection response? Criteria based on the analytical
derivation of the selection bias under a model including genetic groups and on
empirical expectations of true and predicted responses to selection are devel-
oped to determine whether apriori diﬀerences among genetic groups ought to
be included in genetic evaluation.
2. METHODS
2.1. Models and notations
Let us consider the following mixed mode