Download Commercial application of marker- and gene

Commercial application of marker- and gene-assisted selection
in livestock: Strategies and lessons1,2
J. C. M. Dekkers3
Department of Animal Science, Iowa State University, Ames 50011-3150
ABSTRACT: During the past few decades, advances
in molecular genetics have led to the identification of
multiple genes or genetic markers associated with
genes that affect traits of interest in livestock, including
genes for single-gene traits and QTL or genomic regions
that affect quantitative traits. This has provided opportunities to enhance response to selection, in particular
for traits that are difficult to improve by conventional
selection (low heritability or traits for which measurement of phenotype is difficult, expensive, only possible
late in life, or not possible on selection candidates).
Examples of genetic tests that are available to or used
in industry programs are documented and classified
into causative mutations (direct markers), linked markers in population-wide linkage disequilibrium with the
QTL (LD markers), and linked markers in populationwide equilibrium with the QTL (LE markers). In general, although molecular genetic information has been
used in industry programs for several decades and is
growing, the extent of use has not lived up to initial
expectations. Most applications to date have been integrated in existing programs on an ad hoc basis. Direct
markers are preferred for effective implementation of
marker-assisted selection, followed by LD and LE
markers, the latter requiring within-family analysis
and selection. Ease of application and potential for extra-genetic gain is greatest for direct markers, followed
by LD markers, but is antagonistic to ease of detection,
which is greatest for LE markers. Although the success
of these applications is difficult to assess, several have
been hampered by logistical requirements, which are
substantial, in particular for LE markers. Opportunities for the use of molecular information exist, but their
successful implementation requires a comprehensive
integrated strategy that is closely aligned with business
goals. The current attitude toward marker-assisted selection is therefore one of cautious optimism.
Key Words: Breeding Programs, Markers-Assisted Selection, Quantitative Trait Loci
2004 American Society of Animal Science. All rights reserved.
Introduction
Substantial advances have been made over the past
decades through the application of molecular genetics
1
Information was provided by the following people and organizations: D. Funk (American Breeders Service), J. McEwan (AgResearch), J. Hetzel (Genetic Solutions), M. Cowan (Genetic Visions),
N. Buys (Gentec), E. Mullaart (Holland Genetics), J. Fulton and J.
Arthur (Hy-Line Int.), R. Spelman (Livestock Improvement Company), G. T. Nieuwhof (Meat and Livestock Commission), M. Lohuis
and J. Veenhuizen (Monsanto), G. Plastow (Sygen Int.), E. Knol (TOPIGS), S. Dominik (CSIRO), R. Fernando (Iowa State Univ.), J. Gibson
(ILRI), B. Hayes (Victorian Inst. Anim. Sci.), M. Rothschild (Iowa
State Univ.), S. Schmutz (Univ. Saskatchewan), and K. Weigel (Univ.
of Wisconsin). Financial support from the State of Iowa, Hatch and
Multi-State Research funds.
2
This article was presented at the 2003 Joint ADSA-ASAS-AMPA
meeting as part of the Breeding and Genetics symposium “Molecular Genetics.”
3
Correspondence: 225C Kildee Hall (phone: 515-294-7509; fax: 515294-9150; e-mail: [email protected]).
Received October 16, 2003.
Accepted February 11, 2004.
J. Anim. Sci. 2004. 82(E. Suppl.):E313–E328
in the identification of loci and chromosomal regions
that contain loci that affect traits of importance in
livestock production (Andersson, 2001). This has enabled opportunities to enhance genetic improvement
programs in livestock by direct selection on genes or
genomic regions that affect economic traits through
marker-assisted selection and gene introgression
(Dekkers and Hospital, 2002). To this end, many theoretical studies have been conducted over the past several decades to evaluate strategies for the use of molecular genetic information in selection programs. The
extra responses to selection that have been predicted
by several studies (e.g., Meuwissen and Goddard,
1996) have resulted in great optimism for the use of
molecular genetic information in industry breeding
programs. Objectives of this paper are to assess the
extent to which and in which ways marker and gene
information has been used in commercial livestock improvement programs, to assess the successes and limitations that have been experienced in such applications, and to discuss strategies to overcome these limitations. I will start with a discussion of the principles
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of the use of molecular genetic information in genetic
improvement, which will set the stage for the analysis
of marker-assisted selection in commercial breeding
programs.
Principles of Marker-Assisted Selection
Types of Genetic Markers
Application of molecular genetics for genetic improvement relies on the ability to genotype individuals
for specific genetic loci. For these purposes, three types
of observable polymorphic genetic loci can be distinguished: 1) direct markers: loci that code for the functional mutation; 2) LD markers: loci that are in population-wide linkage disequilibrium with the functional
mutation; 3) LE markers: loci that are in populationwide linkage equilibrium with the functional mutation
in outbred populations.
Methods to detect these types of loci were described
in Andersson (2001). The LE markers can be readily
detected on a genome-wide basis by using breed
crosses or analysis of large half-sib families within the
breed. Such genome scans require only sparse marker
maps (15 to 50 cM spacing, depending on marker informativeness and genotyping costs; Darvasi et al., 1993)
to detect most QTL of moderate to large effects. Many
examples of successful applications of this methodology for detection of QTL regions are available in the
literature (see Andersson, 2001). The LD markers are
by necessity close to the functional mutation for sufficient population-wide LD between the marker and
QTL to exist (within 1 to 5 cM, depending the extent
of LD, which depends on population structure and history). The LD markers can be identified using candidate genes (Rothschild and Soller, 1997) or fine-mapping approaches (Andersson, 2001). Direct markers
(i.e., polymorphisms that code for the functional mutations) are the most difficult to detect because causality
is difficult to prove and, as a result, a limited number
of examples are available, except for single-gene traits
(Andersson, 2001).
The three types of marker loci differ not only in
methods of detection, but also in their application in
selection programs. Whereas direct markers and, to
a lesser degree, LD markers, allow for selection on
genotype across the population because of the consistent association between genotype and phenotype, use
of LE markers must allow for different linkage phases
between markers and QTL from family to family.
Thus, the ease and ability to use markers in selection
is opposite to their ease of detection and increases from
direct markers to LD markers and LE markers. In
what follows, selection on these three types of markers
will be referred to as gene-assisted selection (GAS),
LD markers-assisted selection (LD-MAS), and LE
marker-assisted selection (LE-MAS).
Traits
Molecular markers have been used to identify loci
or chromosomal regions that affect single-gene traits
and quantitative traits. Single-gene traits include genetic defects, genetic disorders, and appearance. For
the purposes of QTL detection and application, quantitative traits can be categorized into a) routinely recorded traits; b) difficult to record traits (feed intake,
product quality); and c) unrecorded traits (disease resistance). Each of these can be further subdivided into
traits that are i) recorded on both sexes; ii) sex-limited
traits; and iii) traits that are recorded late in life. The
ability to detect QTL depends on the availability of
phenotypic data and decreases in the order a, b, c and
within each of those in the order i, ii, and iii. For
related reasons, genome scans, which require more
phenotypic data than candidate gene analyses, are often used to detect QTL for traits in Category a,
whereas candidate gene approaches are more often
used to identify QTL for traits that are not routinely
recorded (b and c). Potential extra genetic gains from
MAS or GAS are in inverse proportion to the ability
to make genetic progress using conventional methods
and are greatest for traits in Category c and lowest
for traits in Category a, in particular for traits that
are routinely recorded on both sexes prior to selection
(Meuwissen and Goddard, 1996).
Strategies for Use of Molecular Data in Selection
For the purpose of genetic improvement, markers
can be used to enhance within-breed selection based
on GAS, LD-MAS, or LE-MAS, or to enhance programs
to capitalize on between-breed variation by selection
within a cross. A specific form of the latter is markerassisted introgression (MAI), which will be discussed
later. Because of the extensive LD in crosses, markers
can be used as LD markers without requiring close
linkage (Lande and Thompson, 1990). Most applications of markers in livestock, however, are based on
within-breed selection.
In principle, all applications of molecular genetic
information for genetic improvement involve selection
on a molecular score, although the composition of this
score differs from application to application (Dekkers
and Hospital, 2002). For example, the molecular score
could be based on the presence or absence of certain
alleles or genotypes, as for MAI, or on estimates of
marker or QTL effects, which can be summed over loci
when multiple QTL regions are selected for (Lande
and Thompson, 1990). In general then, three strategies
for can be distinguished for the use of the molecular
score (MS) in selection, in combination with phenotype, or EBV derived from phenotypic information.
These apply to GAS, LD-MAS, and LE-MAS, and to
MAS using within- and between-breed variation: I)
tandem selection, with selection of candidates on MS
followed by selection on phenotype or EBV; II) index
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selection on a combination of MS and phenotype or
EBV: I = b1MS + b2EBV (Lande and Thompson, 1990);
III) preselection on MS (or an index of MS and EBV)
at a young age, followed by selection on an updated
EBV at a later age (Lande and Thompson, 1990).
Selection on total EBV, as the sum of an estimate
of the breeding or genetic value for the QTL and an
estimate of polygenic EBV, as would be obtained from
including molecular data as fixed or random effects in
a BLUP animal model genetic evaluation model (e.g.,
Van Arendonk et al., 1999), is equivalent to index selection (Strategy II) with index weights equal to one
(I = MS + EBV). For other cases, and if the objective
is to maximize response over multiple generations,
index weights will differ from one (e.g., Dekkers and
van Arendonk, 1998). This is further complicated by
the fact that many genes or QTL affect multiple traits
and that selection most often is for multiple traits.
Use of MAS for multiple trait selection was addressed
by Lande and Thompson (1990) and Weller (2001).
Extra Response from Marker-Assisted Selection. The
basic objective of selection programs is to improve the
population for a comprehensive multiple-trait breeding goal. This goal can, in principle, be formulated as
a combination of genetic traits. To affect progress in
the overall goal, a finite amount of available selection
pressure, which is limited by characteristics of the
breeding program and population (e.g., reproductive
rate), must be divided among traits. Increased emphasis on one trait diverts emphasis away from other components, but the joint effect on all traits determines
the success of the breeding program. The challenge of
the design of a breeding program is to balance selection
emphasis among traits to maximize response in the
overall objective. With the availability of genetic markers and tests, this is further complicated by the need
to balance emphasis on molecular vs. quantitative genetic information. This also holds for selection against
genetic defects, the emphasis on which must be balanced against selection on quantitative traits. Extra
genetic gains from MAS, therefore, depend on the effect of direct selection on individual loci on genetic
progress at other loci (polygenes) and for other traits
that affect overall genetic merit. This is the case even
for selection against genetic defects and in the absence
of pleitropic effects of such loci.
How much response in polygenes and other traits
is affected by selection on markers depends on the
selection strategy used. Although tandem selection results in the most rapid fixation of the gene(s) that
are targeted by the molecular score, it results in the
greatest loss in response for polygenes and for traits
that are not included in the molecular score and may
therefore result in less response in the trait and the
overall breeding goal. In theory, index selection results
in the greatest overall response to selection for a given
selection stage, in particular if weights on the molecular score are optimized (e.g., Dekkers and van Arendonk, 1998). Figure 1 illustrates differences in re-
sponse from tandem vs. index selection on direct markers. These results apply to selection on a single trait
and selection on an overall breeding goal. For the latter, effects of the gene are expressed in terms of genetic
standard deviations for the breeding goal.
Response lost can be as high as 60% with tandem
selection on a direct marker of low initial frequency
(0.1) if the gene has no effect (Figure 1). Lost response
decreased to zero as the effect of the gene increased
because tandem selection is equivalent to index selection if the QTL has a very large effect. These results
indicate that it is important to incorporate molecular
information in an index and to avoid tandem selection.
This also holds for selection against genetic defects
and single-gene traits, which requires assigning an
economic value to such traits to enable their incorporation into an overall breeding goal.
The choice between tandem and index selection (and
other alternatives) also depends on other factors, such
as market and cost considerations. For example, rapid
fixation of the targeted gene (e.g., by tandem selection)
will reduce costs of genotyping over generations and
may be desirable from a marketing perspective. This
can, however, also be achieved by increasing the
weight on the molecular score in an index, as has been
demonstrated by Settar et al. (2002).
Tandem and index selection apply to the use of molecular information in a given stage of selection. If
selection of candidates is over multiple stages, the impact on response for polygenes and other traits can be
minimized if molecular information is used at an early
age when limited or no phenotypic information is available to distinguish selection candidates (preselection,
Strategy III; Lande and Thompson, 1990). An example
is preselection among full-sib dairy bulls for entry into
progeny testing programs (Kashi et al., 1990; Mackinnon and Georges, 1998). Application of MAS at this
level, however, requires family sizes large enough that
selection room is available to apply MAS on a withinfamily basis. This strategy also minimizes the effect
on other selection stages and therefore minimizes the
risk of losing response to routine selection on phenotypic information and has been the preferred approach
for initial applications of LE-MAS in dairy cattle.
Strategies
for
Marker-Assisted
Introgression.
Marker-assisted introgression programs are based on
tandem selection in a multigenerational backcrossing
program, in which a MS based on the presence of donor
breed alleles at or around the target gene is used in
the first selection step (foreground selection), followed
by background selection on a MS based on presence
or absence of recipient alleles at markers spread over
the genome, on phenotype, or an index of the two (e.g.,
Visscher et al., 1996). Although tandem selection has
been implicit to gene introgression programs, the selection on an index of molecular score and phenotypic
information in these programs should be considered,
especially for quantitative traits, unless the gene has
a very large effect. Although this could result in selec-
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Figure 1. Response lost over one, two, and three generations and in cumulative discounted response over 10
generations at 10% interest (CDR10) from tandem vs. index selection on a QTL with an initial frequency of the
favorable allele of 0.1, and additive effect a (in genetic standard deviations, σg). Selection is for a quantitative trait
with selected proportions 10 and 25%, and accuracies of polygenic EBV of 0.8 and 0.5 for sires and dams, respectively.
tion of some parents that do not carry the target allele,
overall response is expected to be greater. In particular, if multiple genes or QTL regions must be introgressed simultaneously, the requirement that selected
parents carry the target allele for all QTL is infeasible
in livestock and not necessary for successful introgression (Chaiwong et al., 2002).
Industry Application of MarkerAssisted Selection
Examples of Commercially Available
or Utilized Genetic Tests
A nonexhaustive summary of gene or marker tests
that are currently available or used in commercial
breeding programs is given in Table 1, with tests categorized by the type of trait and the type of marker. A
substantial number of genetic tests are available.
Some applications of selection for individual genes occurred prior to the era of molecular genetics, including
selection on observable genetic defects and appearance, the halothane test as a physical test for the RYR
gene, and use of the B-blood group as a physiological
LD marker for selection for disease resistance in poultry, which started in the 1960s (Hansen et al., 1967;
Hansen and Law, 1970; J. Arthur, Hy-Line Int., Dallas
Center, IA, personal communication). Several tests are
used for within-house selection only (e.g., PICmarq
markers used by the Pig Improvement Co., G. Plastow,
Sygen Int., Berkeley, CA, personal communication),
whereas others are available through commercial genotyping services. To date, the majority of publicly
available tests are for direct or LD markers.
Although there are a large number of scientific reports on detection of QTL for livestock (e.g., Bidanel
and Rothschild, 2002; Bovenhuis and Schrooten, 2002;
Hocking, 2003), most of these were identified in experimental populations using crosses between breeds or
lines (Andersson, 2001). Such studies identify QTL
that differ in frequency between breeds but results
cannot be used directly for selection within breeds.
They can, however, provide an important stepping
stone for identification of LD markers for QTL that
segregate within breeds using positional candidate
gene approaches (Rothschild and Soller, 1997). An example is the detection of additional mutations in the
RN gene, known as PRKAG3, which have been found to
segregate in commercial lines of pigs using a positional
candidate gene approach in a QTL region that was
detected in a cross between two commercial breeds
(Ciobanu et al., 2001). The use of experimental crosses
explains the abundance of the use of direct and LD
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Table 1. Examples of gene tests used in commercial breeding for different species (D =
dairy cattle, B = beef cattle, C = poultry, P = pigs, S = sheep) by trait category and type
of marker
Linkage
disequilibrium
marker
Trait category
Direct marker
Congenital defects
BLAD (Da)
Citrulinaemia (D,Bb)
DUMPS (Dc)
CVM (Dd)
Maple syrup urine (D,Be)
Mannosidosis (D,Bf)
RYR (Pg)
CKIT (Pi)
MC1R/MSHR (Pj,Bk,Dl)
MGF (Bm)
κ-Casein (Do)
β-lactoglobulin (Do)
FMO3 (Dp)
RYR (Pg)
RN/PRKAG3 (Pq)
Appearance
Milk quality
Meat quality
Linakge
equilibrium
marker
RYR (Ph)
Polled (Bn)
RYR (Ph)
RN/PRKAG3 (Pr)
A-FABP/FABP4 (Ps)
H-FABP/FABP3 (Pt)
CAST (Pu, Bv)
>15 PICmarq (Pw)‡
THYR (Bx)
Leptin (By)
Feed intake
Disease
Reproduction
Growth and composition
Milk yield and composition
MC4R (Pz)
Prp (Saa)
F18 (Pcc)
Booroola (See)
Inverdale(Sgg)
Hanna (Sii)
MC4R (Pz)
IGF-2 (Pmm)
Myostatin (Boo)
Callipyge (Sqq)
DGAT (Dss)
GRH (Dvv)
κ-Casein (Do)
B blood group (Cbb)
K88 (Pdd)
Booroola (Sff)
ESR (Phh)
PRLR (Pjj)
RBP4 (Pkk)
CAST (Pu)
IGF-2 (Pnn)
QTL (Pll)
QTL (Bpp)
Carwell (Srr)
PRL (Dtt)
QTL (Duu)
a
Shuster et al. (1992); bDennis et al. (1989); cSchwenger et al. (1993); dBorchersen (2001); eDennis and
Healy (1999); fBerg et al. (1997), Leipprandt et al. (1999); gFuji et al. (1991); hHanset et al. (1995); iMarklund
et al. (1998); jKijas et al. (1998); kKlungland et al. (1995); lJoerg et al. (1996); mSeitz et al. (1999); nSchmutz
et al. (1995); oMedrano and Aquilar-Cordova (1990), Rincon and Medrano (2003); pLunden et al. (2002);
q
Milan et al. (2000); rCiobanu et al. (2001); sGerbens et al. (1998); tGerbens et al. (1999); uCiobanu et al.
(2004); vBarendse (2001); wG. Plastow (Sygen Int., Berkeley, CA, personal communication); xBarendse et
al. (2001); yBuchanan et al. (2002); zKim et al. (2000); aaBelt et al. (1995); bbHansen et al. (1967), Hansen
and Law (1970); ccVogeli et al. (1997), Meijerink et al. (2000); ddJørgensen et al. (2004); eeWilson et al. (2001);
ff
Lord et al. (1998); ggGalloway et al. (2000); hhRothschild et al. (1996); iiMcNatty et al. (2001); jjVincent et
al. (1998); kkRothschild et al. (2000); llM. Lohuis (Monsanto Co., St. Louis, MO, personal communication);
mm
Georges et al. (2003); nnJeon et al. (1999), Nezer et al. (1999); ooGrobet et al. (1998); ppJ. Hetzel (Genetic
Solutions, Brisbane, Australia, personal communication); qqFreking et al. (2002); rrNicoll et al. (1998);
ss
Grisart et al. (2002); ttCowan et al. (1990); uuSpelman et al. (1996), Arranz et al. (1998), Coppieters et al.
(1998), Georges et al. (1995), Zhang et al. (1998); vvBlott et al. (2003).
‡Applies to both direct and linkage disequilibrium columns.
markers compared with LE markers for species such
as pigs, beef cattle, and poultry (Table 1). An alternative is to follow a breed-cross QTL analysis with an
LE QTL analysis within commercial lines in identified
regions, which has shown to be successful in pigs (Evans et al., 2003) and has been used in beef cattle (J.
Hetzel, Genetic Solutions, Brisbane, Australia, personal communication). This results in identification of
LE markers that can be used for selection.
An important exception to the use of experimental
crosses for first-phase QTL detection is dairy cattle,
for which genome scans are based primarily on the
large paternal half-sib families that are available in
the industry, using the daughter or grand-daughter
designs (Weller et al., 1990; Bovenhuis and Schrooten,
2002). This has resulted in the availability and use of
LE markers for several QTL regions (Boichard et al.,
2002; Spelman, 2002; Bennewitz et al., 2003; Khatkar
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Dekkers
et al., 2004; D. Funk, American Breeders Service, DeForrest, WI, personal communication; E. Mullaart,
Holland Genetics, Arnheim, The Netherlands, personal communication). Several issues related to the
transfer of initial results from genome scans to applications in breeding programs were discussed in Spelman and Bovenhuis (1998).
Examples of Applications of MAI or MAS
Marker-Assisted Introgression Programs. Marker-assisted introgression has been the main approach for
utilization of genetic markers in plant breeding and
successes and limitations of these applications have
been documented by Hospital et al. (2002). Because of
longer generation intervals, lower reproductive rates,
and greater rearing costs, introgression is only feasible
in livestock for genes of large effect. However, some
examples of MAI in livestock are available. Hanset et
al. (1995) reported on the successful introgression of
the halothane normal allele into a Piétrain line that
had a high frequency of the halothane-positive allele.
They used LD foreground selection on markers linked
to the RYR locus. Yancovich et al. (1996) used markerassisted background selection to speed up the recovery
of the broiler genome when introgressing the nakedneck gene from a rural low-BW breed into a commercial
broiler line. Gootwine et al. (1998) reported on MAI of
the Booroola gene (FecB) into dairy sheep breeds using
LD markers for foreground selection. In developing
countries, programs for the introgression of disease resistance or tolerance genes are being considered for
cattle (J. Gibson, ILRI, Nairobi, Kenya, personal communication).
Marker-Assisted Selection. The main application and
potential for use of markers to enhance genetic improvement in livestock is through within-breed selection.
This requires markers that trace within-breed variability. Although several genetic tests are available to effect
such selection, as documented in Table 1, the extent to
which they are used in commercial breeding programs
is unclear, as is the manner in which they are used
(i.e., Strategies I, II, or III), and whether their use leads
to greater responses to selection. Direct and LD markers have been primarily used, as evidenced by their
abundance among available tests (Table 1). Direct and
LD markers allow selection on markers across the population, which facilitates their use. Some of the earlier
applications of MAS in livestock were prior to the era
of molecular genetics (e.g., selection for disease resistance in poultry using an Elisa tests for the B-blood
group as an LD marker; J. Arthur personal communication) and selection against the halothane gene in pigs
using the halothane test. Subsequently, several genetic
tests have been used to select against carriers of recessive genetic defects in livestock species, as reflected in
Table 1. One of the first examples of use of an LD
marker for a quantitative trait was the test for the
estrogen receptor gene (ESR; Rothschild et al., 1996;
Short et al., 1997), which has been used in several commercial lines to enhance selection for litter size (G.
Plastow, personal communication). Plastow et al.
(2003) and G. Plastow (personal communication) also
reported the use of more than 15 proprietary direct
and LD markers (PICmarq) for traits associated with
reproduction, feed intake, growth, body composition,
and meat quality in pigs. M. Lohuis (Monsanto, Co.,
St. Louis, MO, personal communication) reported the
recent in-house use of a combination of LE and LD
markers in commercial lines of pigs, and fine-mapping
efforts to replace LE markers with LD markers for important QTL regions. In dairy cattle, in addition to direct markers for genetic defects and milk protein variants (Table 1), an LD marker near the prolactin gene
(Cowan et al., 1990) that is segregating in one prominent Holstein sire family, has been used for preselection
of young bulls since (Cowan et al., 1997). M. Cowan
(Genetic Visions, Middleton, WI, personal communication) reported the use of several additional direct, LD,
and LE markers for selection of bull dams and preselection of young bulls. In-house selection programs using LE markers have been conducted in several dairy
cattle breeding programs, including for pre-selection of
young bulls in the US based on QTL studies reported
by Georges et al. (1995) and Zhang et al. (1998) (D.
Funk, personal communication) and in New Zealand
(Spelman, 2002) and The Netherlands (E. Mullaart,
personal communication) based on QTL results reported by Spelman et al. (1996), Arranz et al. (1998)
and Coppieters et al. (1998). Establishment of national
genetic evaluation programs using LE markers to provide information for in-house use by dairy cattle breeding organizations has been reported for France (Boichard et al., 2002) and Germany (Bennewitz et al., 2003).
In beef cattle, several direct and LD markers are commercially available (Table 1) and used by individual
breeders (J. Hetzel, S. Schmutz, University of Saskatchewan, Saskatoon, Canada, personal communication).
Programs for LE MAS are being initiated in some cases
(J. Hetzel, personal communication). In sheep, J.
McEwan (AgResearch, Invermay, New Zealand, personal communication) reported on an LD-MAS program
for a 5-cM region around the Carwell gene (Nicoll et
al., 1998). An animal model with the Carwell genotype
included as a fixed effect is used. In addition, several
direct or LD markers associated with reproduction and
disease, including scrapie, are being used (S. Dominik
CSIRO, Armidale, Australia, G. J. Nieuwhof Meat and
Livestock Commission, Milton Keynes, U.K., personal
communication).
Evaluation of the Success of Commercial MAS
The effect of MAS or MAI on genetic improvement is
difficult to quantify, even under experimental conditions, because differences in response are not expected
to be large, especially when considering that traits selected for using MAS are part of a multiple-trait breed-
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Within-family assessment
Within-family marker-trait association
Within-family marker-trait association
Line comparison
Direct population estimate
Population-wide marker-trait association
Population-wide marker-trait association
Line comparison
Direct population estimate
Direct population estimate
Population-wide marker-trait association
Line comparison
Frequency of marker locus
Frequency of target locus
Phenotypic effect of target locus
Genetic merit of population
Linkage disequilibrium marker
Direct marker
Level of evaluation
Changes in Gene Frequencies for the Target Locus.
For direct markers, changes in marker frequencies are
equivalent to frequency changes in the target locus. For
LD markers, effects on frequencies of alleles at the
targeted locus will depend on the extent of LD between
the marker(s) and the causative locus, which can differ
between populations and can change over generations
because of recombination. These associations, and
therefore the effect of LD-MAS on allele frequencies at
the target locus, can only be evaluated indirectly based
on marker-trait associations. The impact of selection
on LE markers on the target locus can also only be
evaluated indirectly through marker-trait analysis. Because of the need to evaluate effects on a within-family
basis, monitoring marker-trait associations requires
much more data for LE than for LD markers.
Phenotypic Effect of the Target Locus or Region. Success of MAS also depends on the consistency of QTL
effects across populations and environments. The effect
of the target locus or region on the trait can differ in
the selected or target population from its initial effect
or its estimate before selection. Evaluation of introgres-
Table 2. Evaluation of the success of marker-assisted selection in breeding programs for different types of markers
ing goal, and appropriate controls are often not available. Responses to MAS or MAI can, however, be evaluated at different levels, as summarized in Table 2: 1)
changes in gene frequencies for the selected marker
locus; 2) changes in gene frequencies in the targeted
locus (if different from the selected loci); 3) effect of
the targeted locus or region on the trait in the target
population; and 4) improvement of the population or
selected individuals in overall genetic level for trait(s)
of interest.
Changes in Marker Frequencies. Changes in gene frequencies at the marker locus reflect the ability to capitalize on opportunities for selection on the marker. In
addition to accuracy and other technical specifics of the
genetic test, marker frequency changes depend on the
ability to effect selection on the marker in the breeding
program. Changes in marker frequencies can be readily
evaluated for direct and LD markers based on population estimates and have been documented for some
cases, in particular for genetic defects. For example, for
bovine leukocyte adhesion deficiency in Holstein dairy
cattle, the development of a genetic test (Shuster et al.,
1992) led to rapid elimination of carriers in U.S. bull
studs, from over 150 in 1988 to less than five in 1992
(K. Weigel, University of Wisconsin, Madison, personal
communication). Other examples of documented successes at this level are commercial lines that are specifically marketed based on fixation of a particular gene
or marker, for example the RYR and the RN genes in
pigs (e.g., see Knap et al., 2002).
For LE markers, the effect of MAS on the marker
locus cannot be evaluated by its population frequencies
because the desired marker allele differs by family.
Some examples of the ability to select on LE marker(s)
on a within-family basis, and the logistical limitations
of implementing such selection, have been documented
(e.g., Spelman, 2002) and will be discussed later.
Linkage equilibrium marker
Marker-assisted selection
E320
Dekkers
sion programs in plants has found that effects tend to
be consistent for major genes that control simple traits
but not for QTL for complex traits (e.g., yield; Hospital
et al., 2002). Inconsistent effects have also been observed for some well-studied genes in livestock. For
example, for the ESR gene for litter size in pigs (Rothschild et al., 1996), significant associations were demonstrated in multiple commercial lines in one of the
largest studies of a candidate gene in livestock conducted to date (Short et al., 1997). However, some subsequent studies have found no effect of this LD marker
on the trait and interactions with line and environment
have been identified also (Rothschild and Plastow,
2002). Potential reasons for inconsistent results across
studies and populations include statistical anomalies
such as false positive or negative results (small sample
sizes) and overestimation of significant QTL effects, as
well as true effects, such as inconsistent marker-QTL
linkage phases across populations for LD markers, genotype × environment interactions, and epistatic effects
(Beavis, 1994).
Effects of the target locus or region in the target
population can be readily evaluated for major genetic
defects. For example, Rothschild and Plastow (1999)
reported a reduction in mortality to zero and an improvement in meat quality from removal of the halothane stress gene. Effects, however, require careful
analysis of marker–trait associations for more complex
traits. This can be done at the population level using
a random nonpedigreed sample for direct and LD markers, but it must be done on a within-family basis for
LE markers. The latter requires substantially more
data and a defined pedigree structure. Such analyses
are not only needed to evaluate and monitor the success
of MAS, but are also required to develop and modify
QTL effect estimates and selection criteria. Thus, implementation of MAS requires continuous monitoring
and reevaluation of gene or QTL effects in the target
population and environment. This requires continuous
emphasis on phenotypic recording in both nucleus and
field populations.
Genetic Merit of the Population. As described previously, MAS diverts selection emphasis away from
polygenes and traits without marked QTL, and the ultimate success of MAS is determined by its impact on
total genetic merit. It has also been shown that the
impact of MAS on other loci and traits differs between
the three selection strategies, and is greatest for tandem selection, followed by index selection, and preselection. It is unclear to what extent each of these strategies is used in commercial applications of MAS.
Because appropriate controls are often not available,
success of MAS based on improvements in overall genetic merit of the population or selected individuals is
very difficult to quantify in commercial breeding programs, let alone at an experimental level. Because of
this and other reasons, few reports are available and
these are not well documented. For example, Rothschild
and Plastow (1999) reported an increase in response
by up to 30% in litter size by incorporating the ESR
genotype in selection indices for dam lines in PIC nucleus herds. This, however, represented the increase in
genetic superiority for litter size of selected animals
over a relatively short period of time, with limited accuracy.
Use of markers in preselection, as for entry of young
dairy bulls into progeny test programs, does provide
opportunities to assess the success of MAS. For example, by correlating EBV following progeny test with
the preselection criterion, or by comparing progeny test
EBV of preselected bulls to those of their full brothers,
which may have been progeny tested by other organizations. To date, such studies have not yet been conducted
in a comprehensive manner but several indirect assessments have been made. For example, Cowan et al.
(1997) found an increase in mean EBV and in the number of progeny tested dairy bulls returned to service by
preselection of young bulls based on the κ-casein locus
and on the prolactin marker (Cowan et al., 1990). This
was, however, based on limited numbers. Recently, M.
Cowan (personal communication) reported a similar
impact on graduation rates from subsequent use of
these and other LE and LD markers for preselection of
young bulls and bull dams. D. Funk (personal communication), however, reported limited initial evidence of an
effect on the number of bulls returned to service from
one of the first applications of LE-MAS for preselection
based on QTL regions identified by Georges et al. (1995)
and Zhang et al. (1998) of 70 now progeny-tested dairy
bulls. Apart from small numbers, this apparent lack
of success may reflect the limitations of the data and
markers used in this early application of MAS for
within-family selection, rather than the potential of
MAS (D. Funk, personal communication). In a more
fully documented example, Spelman (2002) described
limited success from 2 yr of preselection of young bulls
using 25 LE markers for six QTL regions in New
Zealand. Lack of success was due to the limited number
of bulls that could be preselected because of lack of
selection room within families, which was due to the
limited success of reproductive technologies used to increase full-sib family size from bull dams. Similar limitations in creating selection room for MAS have been
identified in other programs (D. Funk and M. Cowan,
personal communication) and indicates that the success
of MAS depends not only on the accuracy of QTL estimates, but also on the ability to integrate technologies
that are required to effectively implement a MAS program (see Integration of Marker-Assisted Selection in
Breeding Programs).
LE vs. LD vs. Direct Markers
An important consideration for the use of molecular
genetics in breeding programs is whether to work toward the application of LE, LD, or direct markers. Table
3 summarizes the relative requirements and opportunities for detection and application of these three types
E321
Marker-assisted selection
Table 3. Requirements and opportunities for the implementation of linkage equilibrium
(LE) vs. linkage disequilibrium (LD) vs. direct (D) markers
Requirement or
opportunity
Relative
requirements
QTL detection requirements
Marker development
Phenotyping and data structure
Genome-wide analysis opportunities
Within-line confirmation requirements
Routine genetic evaluation requirements
Phenotyping and data structure
Genotyping
Genetic evaluation models
Implementation logistics
Genetic gain opportunities (for given QTL)
Marketing opportunities (patents, product differentiation)
of markers. These comparisons, which will be further
discussed below, also provide insight into the reasons
for the extent of success and limitations that have been
experienced in different commercial applications of
MAS.
Marker Development and QTL Detection. Requirements for marker development are least for LE markers
and greatest for direct markers (Table 3; Andersson
2001). Whereas LE markers can be random anonymous
markers, direct markers require identification of the
causative mutation, and LD markers require close linkage with the causative mutation, either identified as
targeted candidate gene polymorphisms or by high-density marker maps. In addition, LE markers allow for
genome-wide analysis of QTL based on a limited number of markers at 15- to 50-cM spacings. Genome-wide
analysis is also possible for LD markers, but this will
require a very dense marker map, depending on the
extent of LD in the population (e.g., Meuwissen et al.,
2001). The latter seems to be wide in livestock populations (Farnir et al., 2000; McRae et al., 2002), such that
informative markers every 1 or 2 cM may be sufficient
to detect most QTL.
Associations between direct or LD markers and traits
can be identified based on a limited number of phenotyped and genotyped individuals, without a specific population or family structure. Detection of QTL using LE
markers, however, requires the presence of LD that
extend over 20 or more centimorgan. Such LD can be
created by crossing lines or found within families in
outbred populations. The latter requires large numbers
of phenotyped individuals with a specific family structure (e.g., Weller et al., 1990). The same is true for
estimation and confirmation of LE vs. LD marker effects in other (outbred) populations (e.g., Spelman and
Bovenhuis, 1998), resulting in greater phenotyping and
genotyping requirements at this stage for LE markers,
in particular if the initial QTL detection was based on
a cross between lines.
Marker-Assisted Genetic Evaluation. Requirements
for integration of marker data in routine genetic evalua-
LE
LE
LE
LE
LE
LE
LE
LE
LE
LE
LE
LE
< LD << D
< LD << D
>> LD ∼ D
>> LD >> D
>> LD > D
>> LD > D
>> LD ∼> D
>> LD ∼> D
>> LD ∼> D
>> LD > D
< LD ∼< D
<< LD < D
tion procedures are also much greater for LE than for
LD or direct markers, both with regard to requirements
for the number and which individuals that must be
phenotyped and genotyped, and with regard to methods
of analysis (Table 3). Use of LE markers in an outbred
population requires the phenotyping and genotyping
of selection candidates and/or their relatives because
effects must be estimated on a within-family basis. The
extent of family data needed depends on recombination
rates between markers and QTL. Less data will be
needed and can be from more distant relatives if recombination rates are low. Direct and LD markers require
the genotyping of only selection candidates because estimates of genotype effects can be obtained from prior
information or from a sample of individuals that have
both genotype and phenotype information.
Data from LE markers can be incorporated into
BLUP animal model genetic evaluations using the approach of Fernando and Grossman (1989), by fitting
random effects for each QTL and allowing for different
QTL effects within families. This method, or extensions
thereof, has been applied to several commercial situations in dairy cattle (Boichard et al., 2002; Bennewitz
et al., 2003; E. Mullaart, personal communication). Application of these procedures requires substantial modification of existing animal model genetic evaluation procedures, estimation of variance components, and extensive computing resources. Data from LD or direct
markers on the other hand, can be incorporated in existing genetic evaluation procedures as fixed genotype
or haplotype effects (Van Arendonk et al., 1999). If not
all animals are genotyped, which will be the case in
practice, marker data must be supplemented with genotype probabilities, which can be derived using pedigree
and marker data (e.g., Israel and Weller, 2002). Nevertheless, computational requirements for incorporating
LD or direct markers in genetic evaluation are much
less than for LE markers. Genetic evaluation requirements are slightly greater for LD than for direct markers because LD markers require identification and analysis of marker haplotypes and confirmation of markerQTL linkage phases.
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Dekkers
Whereas the previous refers to requirements for a
given QTL, LE-MAS allows for genome-wide analysis
and evaluation of QTL with a limited number of markers. This is also possible for LD-MAS with high-density
genotyping. Meuwissen et al. (2001) demonstrated that
EBV of high accuracy could be obtained based on a
Bayesian mixed-model analysis of marker haplotypes
with high-density genotyping and phenotyping of a limited number of individuals. Costs of genotyping limits
the application of high-density genotyping at present,
but these are expected to decrease in the future.
Implementation Logistics. Because of the greater requirements for phenotyping, genotyping, genetic evaluation, and within-family selection, the logistical demands for implementation of MAS are also considerably
greater for LE than for LD or direct markers. Logistical
problems associated with implementation of LE-MAS
were described previously and has led several commercial programs focusing on the use of LD or direct instead
of LE markers Spelman, 2002; Plastow, 2003; M. Lohuis, personal communication).
Genetic Gain. Opportunities for increases in genetic
gain through MAS on a given QTL differ depending on
whether the QTL is marked by LE, LD, or direct markers. Villanueva et al. (2002) showed that even when
all individuals in the population are phenotyped and
genotyped, extra genetic gains from MAS are lower for
LE markers than for direct markers. The difference is
caused by the accuracy of estimates of the molecular
score, which is lower for LE markers because of the
limited information that is available to estimate effects
on a within-family basis, whereas for direct markers,
effects are estimated from data across families. Differences were reduced but far from eliminated when
marker spacing was reduced to 1 or even 0.05 cM. Adding prior data to QTL effect estimates resulted in nearly
equivalent gains for LE and direct markers, indicating
that accuracy of molecular scores was the causative
factor (Villanueva et al., 2002). Prior data could come
from previous generations if marker–QTL distances are
short. Greater differences between the two types of
markers are expected if phenotypic and/or genotypic
data is not available on all individuals, which will limit
the accuracy of molecular scores based on LE markers
for individuals in families with limited data, in particular if marker-QTL distances are considerable.
The LD markers also enable use of phenotypic and
genotypic data across families to estimate marker
scores but accuracies may be slightly lower than for
direct markers as a result of incomplete marker–QTL
LD and a greater number of effects that must be estimated. Hayes et al. (2001) found that haplotypes of 4
and 11 markers in a 10-cM region that captures the
QTL were associated with 64 and 98% of the QTL variance for levels of LD that may be expected in livestock
populations. Accuracy of estimates of molecular scores
based on data from 1,000 individuals were 0.66 and
0.79 for haplotypes of 4 and 11 markers. Increasing the
number of markers from 4 to 11 increased accuracy,
but to a greater degree if more progeny were evaluated.
Increasing the number of markers increases the extent
of LD between the haplotype and the QTL, which increases accuracy, but also increases the number of effects to be estimated, which decreases accuracy (Hayes
et al., 2001). The latter is less important if the number
of individuals evaluated is greater.
Commercialization. Final considerations regarding
the use of LE vs. LD vs. direct markers involve opportunities for marketing and protection. A detailed discussion of intellectual property issues related to molecular
genetics in livestock is found in Rothschild and Newman (2002). It is clear that opportunities for intellectual
property protection through patents are greatest for
direct markers, substantial for LD markers (especially
if based on candidate genes), and limited for LE markers. Direct markers and, to a lesser degree, LD markers
for candidate genes, also enable product differentiation
in the market based on presence or absence of specific
genotypes. These opportunities are again nearly absent
for LE markers because knowledge on identity of the
QTL is limited.
Integration of Marker-Assisted Selection
in Breeding Programs
Whereas initial applications of MAS in livestock populations may have been on an ad hoc basis, it is clear
that successful implementation of a MAS program requires a comprehensive integrated approach that is
closely aligned with business goals and markets. Components of such an approach are illustrated in Figures
2 and 3. Implementation of MAS requires development
and integration of procedures and logistics for DNA
collection and storage, genotyping and storage, and for
data analysis (Figure 2). This must be supported by a
systematic approach to quality control and must support day-to-day decision making (e.g., on which animals
to genotype or regenotype in case of errors, which animals to phenotype, etc.).
In practice, all three types of markers are available
for the categories of traits described previously, and a
comprehensive approach is needed to collect, integrate,
and analyze data on phenotypes for multiple traits, for
direct, LD, and LE markers, and to develop selection
strategies that meet business goals (Figure 3). The latter will be mostly driven by genetic gain but are ultimately determined by economics.
Economic Aspects of MAS
Commercial application of MAS requires careful consideration of economic aspects and business risks. Economic analysis of MAS requires a comprehensive approach that aims to evaluate the economic feasibility
and optimal implementation of MAS. An excellent example of such an analysis is in Hayes and Goddard
(2003), who conducted a comprehensive economic analysis of the implementation of LE-MAS in the nucleus
Marker-assisted selection
E323
Figure 2. Components of an integrated system for the use of molecular genetic information in breeding programs
for marker-assisted selection (MAS).
breeding program of an integrated pig production enterprise. Detection of QTL and MAS on identified QTL
regions for a multitrait breeding goal and associated
genotyping costs and extra returns from the production
phase of the integrated enterprise were considered in
the economic assessment. They concluded that implementation of LE-MAS was feasible for the assumed
cost and price parameters. They also found that, in
particular if QTL detection was based on small sample
sizes, stringent thresholds should be set during the QTL
detection phase such that genotyping costs during the
implementation phase are reduced and selection of false
positives is minimized. An economic analysis of introgression of the Booroola gene into dairy sheep breeds
is given in Gootwine et al. (2001) and of MAS preselection in dairy cattle in Brascamp et al. (1993), Mackinnon and Georges (1998), and Spelman and Garrick
(1998).
Whereas Hayes and Goddard (2003) evaluated economic returns from MAS from increased profit at the
production level, which is proportional to extra genetic
gain, most commercial breeding programs derive profit
Figure 3. Integration of phenotypic and molecular data on polygenic and monogenic traits, including data on direct
(D), linkage disequilibrium (LD), and linkage equilibrium (LE) markers, in a selection program that will meet business
goals, using analysis tools to estimate breeding values (EBV), molecular scores, and genotypes (or genotype probabilities). Solid and broken arrows indicate the flow of information for polygenic and monogenic traits, respectively.
E324
Dekkers
Figure 4. Effect (%) of 50% preselection of young bulls for entry into a progeny-test program on genetic gain (mean
EBV of the top 10% progeny-tested bulls) and market share (number of bulls in top 10% and top 1%). Preselection
is on an index of genotype for a QTL with additive effect a (in genetic standard deviations, σg) and a polygenic EBV
that has a correlation (r) with true polygenic breeding values among young bulls of 0.0 or 0.1.
from increased market share of breeding stock or germplasm. In general, implementation of MAS will have a
greater impact on market share than on genetic gain.
An example is given in Figure 4, which evaluates the
effect of GAS preselection of young dairy bulls in a
competitive market. A deterministic model of a mixture
of two normal distributions to represent sons that received alternate QTL alleles from their heterozygous
sire was used. Extra response from preselection of sons
from heterozygous sires depends on the variation that
is still present among selection candidates for polygenic
EBV for the overall selection criterion, which is based
on pedigree information only. If stringent selection on
EBV has been applied to bull dams and bull sires, this
variation will be limited and the accuracy of polygenic
EBV to further differentiate selection candidates will
be small (Dekkers, 1992). In Figure 4, correlations estimated with true polygenic breeding values among
young bulls of 0.0 and 0.1 are evaluated and preselection is on an index of the MS and polygenic EBV.
For a QTL with a substitution effect of 0.3 genetic
standard deviations and a polygenic EBV accuracy of
0.0, preselection increased genetic gain of selected (top
10%) progeny-tested bulls by 7%, but the number of
bulls in the top 10 and 1% increased by 20 and 30%,
respectively (Figure 3). Mean EBV and market share
increased with effect of the QTL and decreased with
increasing accuracy of the polygenic EBV at the time
of preselection. Effects on market share were, however,
always greater than effects on mean EBV. This does
not imply that the economic feasibility of MAS is greater
in a competitive market than when returns are derived
from commercial production, as was evaluated by
Hayes and Goddard (2003). Economic feasibility not
only depends on the proportional increase in the objective, but also on the absolute returns associated with
a percentage increase in genetic gain vs. market share;
in fact, Brascamp et al. (1993) showed that economic
returns from increased market share were less than
from increased production for a preselection situation
similar to that considered here. Nevertheless, it is important that economic analysis is conducted in relation
to business and market realities and goals. Computational approaches using genetic algorithms (e.g., Kinghorn et al., 2002) can be used to develop selection and
mating strategies based on multiple sources of information, including markers that meet multiple business
goals and constraints. Weller (1994) provides further
discussion of alternative criteria to economically evaluate alternative breeding programs.
Other Opportunities
Optimal implementation of MAS involves careful consideration of alternative selection strategies, business
goals, and integration of molecular with other technologies (e.g., reproductive technologies following Georges
and Massey, 1991). Opportunities also exist to implement LD-MAS in synthetic lines, capitalizing on the
extensive disequilibrium that exists in crosses and their
power to detect QTL (Zhang and Smith, 1992). In addition, strategies must be developed to estimate gene ef-
Marker-assisted selection
fects at the commercial level for nucleus breeding programs, in particular if they involve crossbreeding. This
also opens opportunities to use markers to capitalize on
nonadditive effects and assignment of specific matings.
Genetic markers can also be used to control inbreeding, parental verification, and product tracing. Pedigree
verification is an important aspect of the use of molecular markers in several breeding programs (e.g., Spelman, 2002; M. Cowan, personal communication) and
can lead to substantial opportunities for increasing accuracy of EBV and genetic gain (e.g., Van Arendonk et
al., 1998; Israel and Weller, 2000), but these are beyond
the focus of this article. The use of markers for product
tracing has been implemented in some industries (Plastow, 2003).
Implications
Marker-assisted selection is used in the livestock
breeding industry, primarily through gene-assisted selection and linkage disequilibrium markers-assisted selection. Use of linkage equilibrium markers-assisted
selection has been limited and is hampered by implementation issues. Success of commercial application of
marker-assisted selection is unclear and undocumented
and will depend on the ability to integrate marker information in selection and breeding programs. Opportunities for the application of marker-assisted selection exist, in particular for gene-assisted selection and linkage
disequilibrium markers-assisted selection and, to a
lesser degree, for linkage equilibrium markers-assisted
selection because of greater implementation requirements. Regardless of the strategy, successful application of marker-assisted selection requires a comprehensive integrated approach with continued emphasis on
phenotypic recording programs to enable quantitative
trait loci detection, estimation and confirmation of effects, and use of estimates in selection. Although initial
expectations for the use of marker-assisted selection
were high, the current attitude is one of cautious optimism.
Literature Cited
Andersson, L. 2001. Genetic dissection of phenotypic diversity in farm
animals. Nat. Rev. Genet. 2:130–138.
Arranz, J.-J., W. Coppieters, P. Berzi, N. Cambisano, B. Grisart, L.
Karim, F. Marcq, J. Riquet, P. Simon, P. Vanmanshoven, D.
Wagenaar, and M. Georges. 1998. A QTL affecting milk yield
and composition maps to bovine chromosome 20: A confirmation
Anim. Genet. 29:107–115.
Barendse, W., R. Bunch, M. Thomas, S. Armitage, S. Baud, and
N. Donaldson. 2001. The TG5 DNA marker test for marbling
capacity in Australian feedlot cattle. Pages 52–57 in Proc. Beef
Quality CRC Marbling Symp., Coffs Harbour, Australia.
Barendse, W. 2001. DNA markers for meat tenderness. Patent publication number WO02064820. Available: http://ep.espacenet.com/. Accessed Feb. 9, 2004.
Beavis, W. D. 1994. The power and deceit of QTL experiments: Lessons from comparative QTL studies. Pages 250–266 in Proc. 49th
Annu. Corn and Sorghum Res. Conf., Am. Seed Trade Assoc.,
Alexandria, VA.
E325
Belt, P. B. G. M., I. H. Muileman, B. E. C. Schreuder, J. Bosderuijter,
A. L. J. Gielkens, and M. A. Smits. 1995. Identification of five
allelic variants of the sheep PrP gene and their association with
natural scrapie. J. Gen. Virol. 76:509–517.
Bennewitz, J., N. Reinsch, J. Szyda, F. Reinhardt, C. Kuhn, M.
Schwerin, G. Erhardt, C. Weimann, and E. Kalm. 2003. Marker
assisted selection in German Holstein dairy cattle breeding: Outline of the program and marker-assisted breeding value estimation. Page 5 in Book of Abstr. 54th Annu. Mtg. Eur. Assoc. Anim.
Prod. Y. van der Honing, ed. Wageningen Academic Publishers,
Wageningen, The Netherlands.
Berg, T., P. J. Healy, O. K. Tollersrud, and O. Nilssen. 1997. Molecular
heterogeneity for bovine alpha-mannosidosis—PCR based
assays for detection of breed-specific mutations. Res. Vet. Sci.
63:279–282.
Bidanel, J. P., M. Rothschild. 2002. Current status of quantitative
trait locus mapping in pigs. Pig News Info. 23:39N–53N.
Blott S., J.-J. Kim, S. Moisio, A. Schmidt-Küntzel, A. Cornet, P. Berzi,
N. Cambisano, C. Ford, B. Grisart, D. Johnson, L. Karim, P.
Simon, R. Snell, R. Spelman, J. Wong, J. Vilkki, M. Georges,
F. Farnir, and W. Coppieters. 2003. Molecular dissection of a
quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone
receptor is associated with a major effect on milk yield and
composition. Genetics 163:253–266.
Boichard, D., S. Fritz, M. N. Rossignol, M. Y. Boscher, A. Malafosse,
and J. J. Colleau. 2002. Implementation of marker-assisted selection in French dairy cattle. Electronic communication 22–03
in Proc. 7th World Cong. Genet. Appl. Livest. Prod., Montpellier, France.
Borchersen, S. 2001. Danish scientists reveal the gene responsible
for CVM, a lethal heritable defect in Holstein Cattle. Danish
Cattle Breeding, press release 2001 08 17. Available: http://
www.lr.dk/kvaeg/diverse/PRESS-uk.htm. Accessed Feb. 9, 2004.
Bovenhuis H., and C. Schrooten. 2002. Quantitative trait loci for milk
production traits in dairy cattle. Electronic communication 9:7
in Proc. 7th World Cong. Genet. Appl. Livest. Prod., Montpellier, France.
Brascamp, E. W, J. A. M. van Arendonk, and A. F. Groen, 1993.
Economic appraisal of the utilization of genetic markers in dairy
cattle breeding. J. Dairy Sci. 76:1204–1214.
Buchanan, F. C., C. J. Fitzsimmons, A. G. Van Kessel, T. D. Thue,
D. C. Winkelman-Sim, and S. M. Schmutz. 2002. A missense
mutation in the bovine leptin gene is correlated with carcass fat
content and leptin mRNA levels. Genet. Select. Evol. 34:1–12.
Chaiwong, N., J. C. M. Dekkers, R. L. Fernando, and M. F. Rothschild.
2002. Introgressing multiple QTL in backcross breeding programs of limited size. Electronic communication 22:08 in Proc.
7th World Cong. Genet. Appl. Livest. Prod., Montpellier, France.
Ciobanu, D., J. Bastiaansen, M. Malek, J. Helm, G. Plastow, J. Woollard, and M. Rothschild. 2001. Evidence for new alleles in the
protein kinase adenosine monophosphate activated gamma3subunit gene associated with low glycogen content in pig skeletal
muscle and improved meat quality. Genetics 158:1151–1162.
Ciobanu, D. C., J. W. M. Bastiaansen, S. M. Lonergan, H. Thomsen,
J. C. M. Dekkers, G. S. Plastow, and M. F. Rothschild. 2004.
New alleles in calpastatin gene are associated with meat quality
traits in pigs. J. Anim. Sci. (In press).
Coppieters, W., J. Riquet, J. J. Arranz, P. Berzi, N. Cambisano, B.
Grisart, L. Karim, F. Marcq, L. Moreau, C. Nezer, P. Simon, P.
Vanmanshoven, D. Wagenaar, and M. Georges. 1998. A QTL
with major effect on milk yield and composition maps to bovine
Chromosome 14. Mamm. Genome 9:540–544.
Cowan, C. M., M. R Dentine, R. L. Ax, and L. A. Schuler. 1990.
Structural variation around the prolactin gene linked to quantitative traits in an elite Holstein sire family. Theor. Appl. Genet.
79:577–582.
Cowan, C. M., O. M. Meland, D. C. Funk, and D. F. Erf. 1997. Realized
genetic gain following marker-assisted selection of progeny test
dairy bulls. Abstract P296 in Proc. Plant and Animal Genome
E326
Dekkers
V.
Available:
http://www.intl-pag.org/pag/5/abstracts/p-5j296.html. Accessed Feb. 9, 2004.
Darvasi, A., and M. Soller. 1994. Optimum spacing of genetic markers
for determining linkage between marker loci and quantitative
trait loci. Theor. Appl. Genet. 89:351–357.
Dekkers, J. C. M. 1992. Asymptotic response to selection on best linear
unbiased predictors of breeding value. Anim. Prod. 54:351–360.
Dekkers, J. C. M., and J. A. M. van Arendonk. 1998. Optimum selection for quantitative traits with information on an identified
locus in outbred populations. Genet. Res. 71:257–275.
Dekkers, J. C. M., and F. Hospital. 2002. The use of molecular genetics
in improvement of agricultural populations. Nat. Rev. Genet.
3:22–32.
Dennis, J. A., and P. J. Healy. 1999. Definition of the mutation responsible for maple syrup urine disease in Poll Shorthorns and genotyping Poll Shorthorns and Poll Herefords for maple syrup urine
disease alleles. Res. Vet. Sci. 67:1–6.
Dennis, J. A., P. J. Healy, A. L. Beaudet, and W. E. Obrien. 1989.
Molecular definition of bovine argininosuccinate synthetase deficiency. Proc. Nat. Acad. Sci. USA. 86:7947–7951.
Evans, G. J., E. Giuffra, A. Sanchez, S. Kerje, G. Davalos, O. Vidal,
S. Illán, J. L. Noguera, L. Varona, I. Velander, O. I. Southwood,
D.-J. de Koning, C. S. Haley, G. S. Plastow, and L. Andersson.
2003. Identification of quantitative trait loci for production traits
in commercial pig populations. Genetics 164:621–627.
Farnir, F., W. Coppieters, J.-J. Arranz, P. Berzi, N. Cambisano, G.
Bernard, L. Karim, F. Marcq, L. Moreau, M. Mni, C. Nezer, P.
Simon, P. Vanmanshoven, D. Wagenaar, and M. Georges. 2000.
Extensive genome-wide linkage disequilibrium in cattle. Genome Res. 10:220–227.
Fernando, R. L., and M. Grossman. 1989. Marker-assisted selection
using best linear unbiased prediction. Genet. Select. Evol.
21:467–477.
Freking B. A., S. K. Murphy, A. A. Wylie, S. J. Rhodes, J. W. Keele, K.
A. Leymaster, R. L. Jirtle, and T. P. Smith. 2002. Identification of
the single base change causing the callipyge muscle hypertrophy
phenotype, the only known example of polar overdominance in
mammals. Genome Res. 10:1496–1506.
Fuji, J., K. Otsu, F. Zorzato, S. De Leon, V. K. Khanna, J. E. Weiler,
P. J. O’Brien, and D. H. Maclennan. 1991. Identification of a
mutation in porcine ryanodine receptor associated with malignant hyperthermia. Science 253:448–451.
Galloway, S. M., K. P. McNatty, L. M. Cambridge, M. P. E. Laitinen,
J. L. Juengel, T. S. Jokiranta, R. J. McLaren, K. Luiro, K. G.
Dodds, G. W. Montgomery, A. E. Beattie, G. H. Davis, and O.
Ritvos. 2000. Mutations in an oocyte-derived growth factor gene
(BMP15) cause increased ovulation rate and infertility in a dosage-sensitive manner. Nat. Genet. 25:279–283.
Georges, M., L. Grobet, D. Poncelet, L. J. Royo, D. Pirottin, and B.
Brouwers. 1998. Positional candidate cloning of the bovine mh
locus identifies an allelic series of mutations disrupting the myostatin function and causing double-muscling in cattle. Pages
195–204 in Proc. 6th World Cong. Genet. Appl. Livest. Prod.,
Armidale, Australia.
Georges, M., and J. M. Massey. 1991. Velogenetics, or the synergistic
use of marker assisted selection and germ-line manipulation.
Theriogenology 25:151–159.
Georges, M., D. Nielsen, M. Mackinnon, A. Mishra, R. Okimoto, A.
T. Pasquino, L. S. Sargeant, A. Sorensen, M. R. Steele, X. Zhao,
J. E. Womack, and I. Hoeschele. 1995. Mapping quantitative
trait loci controlling milk production in dairy cattle by exploiting
progeny testing. Genetics 139:907–920.
Georges, M., G. Andersson, M. Braunschweig, N. Buys, C. Collette,
L. Moreau, C. Nezer, M. Nguyen, A.-S. Van Laere, and L. Andersson. 2003. Genetic dissection of an imprinted QTL mapping to
proximal SSC2. Abstract W237 in Proc. Plant and Animal Genome XI. Available: http://www.intl-pag.org/11/abstracts/
W52_W327_XI.html. Accessed Feb. 9, 2004.
Gerbens, F., A. Jansen, A. J. M. Van Erp, F. Harders, T. H. E. Meuwissen, G. Rettenberger, J. H. Veerkamp, and M. F. W. te Pas,
M.F.W. 1998. The adipocyte fatty acid-binding protein locus:
Characterization and association with intramuscular fat content
in pigs. Mamm. Genome 9:1022–1026.
Gerbens, F., A. J. M. Van Erp, F. L. Harders, F. J. Verburg, T. H.
E. Meuwissen, J. H. Veerkamp, and M. F. W. te Pas. 1999. Effect
of genetic variants of the heart fatty acid-binding protein gene
on intramuscular fat and performance traits in pigs. J. Anim.
Sci. 77:846–852.
Gibson, J. 2002. A coherent model for use of molecular genetic information for genetic improvement in low and medium input systems. Proc. 10th Asian-Australas. Assoc. Anim. Prod. Cong.,
New Dehli, India.
Gootwine, E., S. Yossefi, A. Zenou, and A. Bor. 1998. Marker assisted
selection for FecB carriers in Booroola Awassi crosses. Pages
161–164 in Proc. 6th World Cong. Genet. Appl. Livest. Prod.,
Armidale, Australia.
Gootwine, E., A. Zenu, A. Bor, S. Yossafi, A. Rosov, and G. E. Pollott.
2001. Genetic and economic analysis of introgression the B allele
of the FecB (Booroola) gene into the Awassi and Assaf dairy
breeds. Livest. Prod. Sci. 71:49–58.
Grisart, B., W. Coppieters, F. Farnir, L. Karim, C. Ford, N. Cambisano, M. Mni, S. Reid, R. Spelman, M. Georges, and R. Snell.
2002. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene
with major effect on milk yield and composition. Genome Res.
12:222–231.
Grobet, L., D. Poncelet, L. J. Royo, B. Brouwers, D. Pirottin, C. Michaux, F. Menissier, M. Zanotti, S. Dunner, and M. Georges.
1998. Molecular definition of an allelic series of mutations disrupting the myostatin function and causing double-muscling in
cattle. Mamm. Genome 9:210–213.
Hansen M. P., and G. R. J. Law. 1970. Transfer of a specific blood
group allele and its effect on performance. Pages 77–81 in Proc.
XIV World Poult. Cong., Madrid, Spain.
Hansen, M. P., G. R. J. Law, and J. N. Van Zandt. 1967. Differences
in susceptibility to Marek’s disease in chickens carrying two
different B locus blood group alleles. Poult. Sci. 46:1268.
Hanset, R., C. Dasnoi, S. Scalais, C. Michaux, and L. Grobet. 1995.
Effets de l’introgression dons le genome Piétrain de l’allele normal aux locus de sensibilité a l’halothane. Genet. Select. Evol.
27:77–88.
Hayes, B., and M. E. Goddard. 2003. Evaluation of marker assisted
selection in pig enterprises. Livest. Prod. Sci. 81:197–211.
Hayes, B., P. J. Bowman, and M. E. Goddard. 2001. Linkage disequilibrium and accuracy of predicting breeding values from marker
haplotypes. Pages 269–272 in Proc. Assoc. Advmt. Anim. Breed.
Genet., Queenstown, New Zealand.
Hocking, P. M. Review of QTL mapping results in poultry. Abstract
in Proc. 3rd Eur. Poult. Genet. Symp., Wageningen, The Netherlands.
Hospital, F., A. Bouchez, L. Lecomte, M. Causse, and A. Charcosset.
2002. Use of markers in plant breeding: Lessons from genotype
building experiments. Electronic communication 22:05 in Proc.
7th World Cong. Genet. Appl. Livest. Prod., Montpellier, France.
Israel, C., and J. I. Weller, 2000. Effect of misidentification on genetic
gain and estimation of breeding value in dairy cattle populations.
J. Dairy Sci. 83:181–187.
Israel, C., and J. I. Weller, 2002. Estimation of quantitative trait loci
effects in dairy cattle populations. J. Dairy Sci. 85:1285–1297.
Jeon, J., O. Carlborg, A. Tornsten, E. Giuffra, V. Amarger, P. Chardon,
L. Andersson-Eklund, K. Andersson, I. Hansson, K. Lundström,
and L. Andersson. 1999. A paternally expressed QTL affecting
skeletal and cardiac muscle mass in pigs maps to the IGF2 locus.
Nature Genetics 21:157–158.
Joerg, H., H. R. Fries, E. Meijerink, G. F. Stranzinger. 1996. Red
coat color in Holstein cattle is associated with a deletion in the
MSHR gene. Mamm. Genome 7:317–318.
Jørgensen, C. B., S. Cirera, S. I. Anderson, A. L. Archibald, T. Raudsepp, B. Chowdhary, I. Edfors-Lilja, L. Andersson, and M.
Fredholm. 2004. Linkage and comparative mapping of the gene
responsible for susceptibility towards E. coli F4ab/ac diarrhoea
in pigs. Cytogenet. Genome Res. (In press).
Marker-assisted selection
Kashi, Y., E. Hallerman, and M. Soller. 1990. Marker assisted selection of candidate bulls for progeny testing programmes. Anim.
Prod. 51:63–74.
Khatkar, M. S., P. C. Thomson, I. Tammen, and H. W. Raadsma.
2004. Quantitative trait loci mapping in dairy cattle: Review
and meta-analysis. Genet. Select. Evol. (In press).
Kijas, J. M. H., R. Wales, A. Tornsten, P. Chardon, M. Moller, and
L. Andersson. 1998. Melanocortin receptor 1 (MC1R) mutations
and coat color in pigs. Genetics 150:1177–1185.
Kim, K. S., N. Larsen, T. Short, G. Plastow, and M. F. Rothschild.
2000. A missense variant of the porcine melanocortin-4 receptor
(MC4R) gene is associated with fatness, growth and feed intake
traits. Mamm. Genome 11:131–135.
Kinghorn, B. P., S. A. Meszaros, and R. D. Vagg. 2002. Dynamic
tactical decision systems for animal breeding. Electronic communication 23–07 in Proc. 7th World Cong. Genet. Appl. Livest.
Prod., Montpellier, France.
Klungland, H., D. I. Vage, L. Gomez-raya, S. Adalsteinsson, and S.
Lien. 1995. The role of melanocyte-stimulating hormone (msh)
receptor in bovine coat color determination. Mamm. Genome
6:636–639.
Knap, P. W., A. A. Sosnicki, R. E. Klont, and A. Lacoste. 2002. Simultaneous Improvement of Meat Quality and Growth and Carcass
Traits in Pigs. Electronic communication 11:07 in Proc. 7th
World Cong. Genet. Appl. Livest. Prod., Montpellier, France.
Lande, R., and R. Thompson. 1990. Efficiency of marker-assisted
selection in the improvement of quantitative traits. Genetics
124:743–756.
Leipprandt, J. R., H. Chen, J. E. Horvath, X. T. Qiao, M. Z. Jones,
and K. H. Friderici. 1999. Identification of a bovine beta-mannosidosis mutation and detection of two beta-mannosidase pseudogenes. Mamm. Genome 10:1137–1141.
Lord, E. A., G. H. Davis, K. G. Dodds, H. M. Henry, J. M. Lumsden,
and G. W. Montgomery. 1998. Identification of Booroola carriers
using microsatellite markers. Wool Technol. Sheep Breed.
46:245–249.
Lunden, A., S. Marklund, V. Gustafsson, and L. Andersson. 2002. A
nonsense mutation in the FMO3 gene underlies fishy off-flavor
in cow’s milk. Genome Res. 12:1885–1888.
Mackinnon, M. J., and M. A. J. Georges. 1998. Marker-assisted preselection of young dairy sires prior to progeny-testing. Livest. Prod.
Sci. 54:229–250.
Marklund, S., J. Kijas, H. Rodriguezmartinez, L. Ronnstrand, K.
Funa, M. Moller, D. Lange, I. Edforslilja, and L. Andersson.
1998. Molecular basis for the dominant white phenotype in the
domestic pig. Genome Res. 8:826–833.
McNatty, K. P., J. L. Jeungel, T. Wilson, S. M. Galloway, and G. H.
Davis. 2001. Genetic mutations influencing ovulation rate in
sheep. Reprod. Fertil. Dev. 13:549–555.
McRae, A. F., J. C. McEwan, K. G. Dodds, T. Wilson, A. M. Crawford,
and J. Slate, 2002. Linkage disequilibrium in domestic sheep.
Genetics 160:1113–1122.
Medrano, J. F., and E. Aquilar-Cordova. 1990. Polymerase chain
reaction amplification of bovine β-lactoglobulin genomic sequences and identification of genetic variants by RFLP analysis.
Anim. Biotech. 1:73–77.
Meijerink, E., S. Neuenschwander, R. Fries, A. Dinter, H. U.
Bertschinger, G. Stranzinger, and P. Vogeli. 2000. A DNA polymorphism influencing alpha(1,2)fucosyltransferase activity of
the pig FUT1 enzyme determines susceptibility of small intestinal epithelium to Escherichia coli F18 adhesion. Immunogenetics 52:129–136.
Meuwissen, T. H. E., and M. E. Goddard. 1996. The use of marker
haplotypes in animal breeding schemes. Genet. Select. Evol.
28:161–176.
Meuwissen, T. H. E., B. Hayes, and M. E. Goddard. 2001. Prediction
of total genetic value using genome-wide dense marker maps.
Genetics 157:1819–1829.
Milan, D., J. T. Jeon, C. Looft, V. Amarger, A. Robic, M. Thelander,
C. Rogel-Gaillard, S. Paul, N. Iannuccelli, L. Rask, H. Ronne,
K. Lundstrom, N. Reinsch, J. Gellin, E. Kalm, P. Le Roy, P.
E327
Chardon, and L. Andersson. 2000. A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science 288:1248–1251.
Nezer, C., L. Moreau, B. Brouwers, W. Coppieters, J. Detillieux, R.
Hanset, L. Karim, A. Kvasz, P. LeRoy, and M. Georges. 1999.
An imprinted QTL with major effect on muscle mass and fat
deposition maps to the IGF2 locus in pigs. Nat. Genet.
21:155–156.
Nicoll, G. B., H. R. Burkin, T. E. Broad, N. B. Jopson, G. J. Greer,
W. E. Bain, C. S. Wright, K. G. Dodds, P. F. Fennessy, and J.
C. McEwan. 1998. Genetic linkage of microsatellite markers to
the Carwell locus for rib-eye muscling in sheep. Pages 529–532
in Proc. 6th World Cong. Genet. Appl. Livest. Prod., Armidale, Australia.
Plastow, G. S. 2003. The changing world of genomics and its impact
on the pork chain. Adv. Pork Prod. 14:67–71.
Plastow, G., S. Sasaki, T-P. Yu, N. Deeb, G. Prall, K. Siggens, and E.
Wilson. 2003. Practical application of DNA markers for genetic
improvement. Pages 151–154 in Proc. 28th Annu. Mtg. Natl.
Swine Improve. Fed., Iowa State Univ., Ames.
Rinson, G., and J. F. Medrano, 2003. Single nucleotide polymorphism
genotyping of bovine milk protein genes using the tetra-primer
ARMS-PCR. J. Anim. Breed. Genet. 120:333–337.
Rothschild, M. F., C. Jacobson, D. Vaske, C. Tuggle, L. Wang, T.
Short, G. Eckhart, S. Sasaki, A. Vincent, D. G. McLaren, O. I.
Southwood, H. van der Steen, A. Mileham, and G. Plastow. 1996.
The estrogen receptor locus is associated with a major gene
influencing litter size in pigs. Proc. Natl. Acad. Sci. USA
93:201–205.
Rothschild, M. F., L. A. Messer, A. Day, R. Wahs, T. Short, O. Southwood, and G. Plastow. 2000. Investigation of the retinol binding
protein (RBP4) gene as a candidate gene for litter size in the
pig. Mamm. Genome 11:75–77.
Rothschild, M. F., and S. Newman. 2002. Intellectual Property Rights
in Animal Breeding and Genetics. CABI Publishing, Wallingford, U.K.
Rothschild, M. F. and G. S. Plastow. 1999. Advances in pig genomics
and industry applications. AgBioTechNet 10:1–8.
Rothschild, M. F., and G. S. Plastow. 2002. Development of a genetic
marker for litter size in the pig: A case study. Pages 179–196
in Intellectual Property Rights in Animal Breeding and Genetics.
M. F. Rothschild and S. Newman, ed. CABI Publishing, Wallingford, U.K.
Rothschild, M. F., and M. Soller. 1997. Candidate gene analysis to
detect genes controlling traits of economic importance in domestic livestock. Probe 8:13–20.
Schmutz, S. M., F. L. S. Marquess, T. G. Berryere, and J. S. Moker.
1995. DNA marker-assisted selection of the polled condition in
Charolais cattle. Mamm. Genome 6:710–713.
Schwenger, B., S. Schober, and D. Simon. 1993. DUMPS cattle carry
a point mutation in the uridine monophosphate synthase gene.
Genomics 16:241–244.
Seitz, J. J., S. M. Schmutz, T. D. Thue, and F. C. Buchanan. 1999.
A missense mutation in the bovine MGF gene is associated with
the roan phenotype in Belgian Blue and Shorthorn cattle.
Mamm. Genome 10:710–712.
Settar, P., J. C. M. Dekkers, and H. A. M. van der Steen. 2002.
Control of QTL frequency in breeding populations. Electronic
communication 23–04 in Proc. 7th World Congr. Genet. Appl.
Livest. Prod., Montpellier, France.
Short, T. H., M. F. Rothschild, O. I. Southwood, D. G. McLaren, A.
de Vries, H. van der Steen, G. R. Eckardt, C. K. Tuggle, J. Helm,
D. A. Vaske, A. J. Mileham, and G. S. Plastow. 1997. Effect of
the estrogen receptor locus on reproduction and production traits
in four commercial pig lines. J. Anim. Sci. 75:3138–3142.
Shuster, D. E., M. E. Kehrli, Jr., M. R. Ackermann, and R. O. Gilbert.
1992. Identification and prevalence of a genetic defect that
causes leukocyte adhesion deficiency in Holstein cattle. Proc.
Natl. Acad. Sci. USA. 89:9225–9229.
Spelman, R. J., 2002. Utilization of molecular information in dairy
cattle breeding. Electronic communication 22–02 in Proc. 7th
World Congr. Genet. Appl. Livest. Prod., Montpellier, France.
E328
Dekkers
Spelman, R. J., and H. Bovenhuis. 1998. Moving from QTL experimental results to the utilisation of QTL in breeding programmes.
Anim. Genet. 29:77–84.
Spelman, R. J., W. Coppieters, L. Karim, J. A. M. van Arendonk, and
H. Bovenhuis. 1996. Quantitative trait loci analysis for five milk
production traits on chromosome six in the Dutch HolsteinFriesian population. Genetics 144:1799–1808.
Spelman, R. J., and D. J. Garrick. 1998. Genetic and economic responses for within-family markers-assisted selection in dairy
cattle breeding schemes. J. Dairy Sci. 81:2942–2950.
Van Arendonk, J. A. M., M. C. A. M. Bink, P. M. Bijma, H. Bovenhuis,
D.-J. de Koning, and E. W. Brascamp. 1999. Use of molecular
data for genetic evaluation of livestock. From Jay Lush to Genomics: Visions for Animal Breeding and Genetics. J. C. M. Dekkers, S. J. Lamont, M. F. Rothschild, ed. Dept. Animal Science,
Iowa State Univ., Ames. Available http://www.agbiotechnet.com/proceedings/4_johan.pdf. Accessed Feb. 9, 2004.
Van Arendonk, J. A. M., R. S. Spelman, E. H. van der Waaij, P.
Bijma, and H. Bovenhuis. 1998. Livestock breeding schemes:
Challenges and opportunities. Pages 407–414 in Proc. 6th World
Congr. Genet. Appl. Livest. Prod., Armidale, Australia.
Villanueva, B., R. Pong-Wong, and J. A. Woolliams. 2002. Marker
assisted selection with optimised contributions of candidates for
selection. Genetics Selection Evolution 34:679–703.
Vincent, A. L., G. Evans, T. H. Short, O. I. Southwood, G. S. Plastow,
C. K. Tuggle, and M. F. Rothschild. 1998. The prolactin receptor
gene is associated with increased litter size in pigs. Pages 15–
18 in Proc. 6th World Congr. Genet. Appl. Livest. Prod., Armidale, Australia.
Visscher, P. M., C. S. Haley, and R. Thompson. 1996. Marker-assisted
introgression in backcross breeding programs. Genetics
144:1923–1932.
Vogeli, P., E. Meijerink, R. Fries, S. Neuenschwander, N. Vorlander,
G. Stranzinger, and H. U. Bertschinger. 1997. A molecular test
for the identification of E. coli F18 receptors—A break-through
in the battle against porcine oedema disease and post-weaning
diarrhoea. (In German) Schweiz. Archiv. Tierheilkd. 139:479–
484.
Weller, J. I. 1994. Economic Aspects of Animal Breeding. Chapman
and Hall, London, U.K.
Weller, J. I. 2001. Quantitative Trait Loci Analysis in Animals. CABI
Publishing, Wallingford, U.K.
Weller, J. I., Y. Kashi, and M. Soller. 1990. Power of daughter and
granddaughter designs for determining linkage between marker
loci and quantitative trait loci in dairy cattle. J. Dairy Sci.
73:2525–2537.
Wilson, T., X. Y. Wu, J. L. Juengel, I. K. Ross, J. M. Lumsden, E. A.
Lord, K. G. Dodds, G. A. Walling, J. C. McEwan, A. R. O’Connell,
K. P. McNatty, and G. W. Montgomery. 2001. Highly prolific
Booroola sheep have a mutation in the intracellular kinase domain of bone morphogenetic protein IB receptor (ALK-6) that
is expressed in both oocytes and granulosa cells. Biol. Reprod.
64:1225–1235.
Yancovich, A., I. Levin, A. Cahaner, and J. Hillel. 1996. Introgression
of the avian naked neck gene assisted by DNA fingerprints.
Anim. Genet. 27:149–155.
Zhang, Q., D. Boichard, I. Hoeschele, C. Ernst, A. Eggen, B. Murkve,
M. Pfister-Genskow, L. E. Witte, F. E. Grignola, P. Uimari, G.
Thaller, and M. D. Bishop. 1998. Mapping quantitative trait loci
for milk production and health of dairy cattle in a large outbred
pedigree. Genetics 149:1959–1973.
Zhang, W., and C. Smith. 1992. Computer simulation of markersassisted selection utilizing linkage disequilibrium. Theor. Appl.
Genet. 83:813–820.