Dekkers BLUP Paper

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Commercial application of marker- and gene-assisted selectionin 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 oppor-

tunities to enhance response to selection, in particular

for traits that are difficult to improve by conventional

selection (low heritability or traits for which measure-

ment 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 mark-

ers in population-wide linkage disequilibrium with the

QTL (LD markers), and linked markers in population-

wide equilibrium with the QTL (LE markers). In gen-

eral, although molecular genetic information has been

Key Words: Breeding Programs, Markers-Assisted Selection, Quantitative Trait Loci

2004 American Society of Animal Science. All rights reserved. J. Anim. Sci. 2004. 82(E. Suppl.):E313–E328

Introduction

Substantial advances have been made over the pastdecades through the application of molecular genetics

1Information was provided by the following people and organiza-

tions: D. Funk (American Breeders Service), J. McEwan (AgRe-

search), 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 Com-

pany), G. T. Nieuwhof (Meat and Livestock Commission), M. Lohuis

andJ. Veenhuizen(Monsanto), G. Plastow(Sygen Int.),E. Knol (TOP-

IGS), 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), andK. Weigel (Univ.

of Wisconsin). Financial support from the State of Iowa, Hatch and

Multi-State Research funds.2This article was presented at the 2003 Joint ADSA-ASAS-AMPA

meeting as part of the Breeding and Genetics symposium “Molecu-

lar Genetics.”3Correspondence: 225CKildee Hall (phone: 515-294-7509;fax: 515-

294-9150; e-mail: [email protected]).

Received October 16, 2003.

Accepted February 11, 2004.

E313

used in industry programs for several decades and isgrowing, the extent of use has not lived up to initialexpectations. Most applications to date have been inte-grated in existing programs on an ad hoc basis. Directmarkers are preferred for effective implementation of marker-assisted selection, followed by LD and LEmarkers, the latter requiring within-family analysisand selection. Ease of application and potential for ex-tra-genetic gain is greatest for direct markers, followedby 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 havebeen hampered by logistical requirements, which aresubstantial, in particular for LE markers. Opportuni-ties for the use of molecular information exist, but theirsuccessful implementation requires a comprehensiveintegrated strategy that is closely aligned with businessgoals. The current attitude toward marker-assisted se-lection is therefore one of cautious optimism.

in the identification of loci and chromosomal regions

that contain loci that affect traits of importance inlivestock production (Andersson, 2001). This has en-

abled opportunities to enhance genetic improvementprograms in livestock by direct selection on genes orgenomic regions that affect economic traits through

marker-assisted selection and gene introgression(Dekkers and Hospital, 2002). To this end, many theo-retical studies have been conducted over the past sev-

eral decades to evaluate strategies for the use of molec-ular 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 geneinformation has been used in commercial livestock im-provement programs, to assess the successes and limi-

tations that have been experienced in such applica-tions, and to discuss strategies to overcome these limi-

tations. I will start with a discussion of the principles



DekkersE314

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 im-

provement relies on the ability to genotype individuals

for specific genetic loci. For these purposes, three types

of observable polymorphic genetic loci can be distin-

guished: 1) direct markers: loci that code for the func-

tional mutation; 2) LD markers: loci that are in popu-

lation-wide linkage disequilibrium with the functional

mutation; 3) LE markers: loci that are in population-

wide 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 readilydetected 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 infor-

mativeness and genotyping costs; Darvasi et al., 1993)

to detect most QTL of moderate to large effects. Many

examples of successful applications of this methodol-

ogy 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 suffi-

cient 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 his-

tory). The LD markers can be identified using candi-

date genes (Rothschild and Soller, 1997) or fine-map-

ping approaches (Andersson, 2001). Direct markers

(i.e., polymorphisms that code for the functional muta-

tions) 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 ongenotype across the population because of the consis-

tent 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 ge-

netic defects, genetic disorders, and appearance. For

the purposes of QTL detection and application, quanti-

tative traits can be categorized into a) routinely re-

corded traits; b) difficult to record traits (feed intake,

product quality); and c) unrecorded traits (disease re-

sistance). 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 of-

ten 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 fromMAS 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 selectionwithin a cross. A specific form of the latter is marker-

assisted 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 applica-

tions 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 certainalleles 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 pheno-

type, 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



Marker-assisted selection E315

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 ina BLUP animal model genetic evaluation model (e.g.,

Van Arendonk et al., 1999), is equivalent to index se-

lection (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 thepopulation for a comprehensive multiple-trait breed-

ing 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 empha-

sis on one trait diverts emphasis away from other com-

ponents, but the joint effect on all traits determines

the success of the breeding program. The challenge of

thedesign of a breeding program is to balance selection

emphasis among traits to maximize response in the

overall objective. With the availability of genetic mark-ers and tests, this is further complicated by the need

to balance emphasis on molecular vs. quantitative ge-

netic information. This also holds for selection against

genetic defects, the emphasis on which must be bal-

anced against selection on quantitative traits. Extra

genetic gains from MAS, therefore, depend on the ef-

fect 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 re-

sults 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 molecu-

lar score are optimized (e.g., Dekkers and van Aren-

donk, 1998). Figure 1 illustrates differences in re-

sponse from tandem vs. index selection on direct mark-

ers. These results apply to selection on a single trait

and selection on an overall breeding goal. For the lat-

ter, 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 increasedbecause tandem selection is equivalent to index selec-

tion 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 incorpora-

tion 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 andmay 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 mo-

lecular information in a given stage of selection. If

selection of candidates is over multiple stages, the im-

pact 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 avail-

able to distinguish selection candidates (preselection,

Strategy III; Lande and Thompson, 1990). An example

is preselection among full-sib dairy bulls for entry intoprogeny testing programs (Kashi et al., 1990; Mackin-

non 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 within-

family basis. This strategy also minimizes the effect

on other selection stages and therefore minimizes the

risk of losing response to routine selection on pheno-

typic 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 se-

lection 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-



DekkersE316

Figure 1. Response lost over one, two, and three generations and in cumulative discounted response over 10generations at 10% interest (CDR10) from tandem vs. index selection on a QTL with an initial frequency of thefavorable allele of 0.1, and additive effect a (in genetic standard deviations, σg). Selection is for a quantitative traitwith 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 particu-

lar, if multiple genes or QTL regions must be intro-

gressed simultaneously, the requirement that selected

parents carry the target allele for all QTL is infeasible

in livestock and not necessary for successful introgres-

sion (Chaiwong et al., 2002).

Industry Application of Marker-Assisted Selection

Examples of Commercially Availableor 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 cate-

gorized 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 oc-

curred prior to the era of molecular genetics, including

selection on observable genetic defects and appear-

ance, 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 poul-

try, 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 gen-

otyping services. To date, the majority of publicly

available tests are for direct or LD markers.

Although there are a large number of scientific re-

ports on detection of QTL for livestock (e.g., Bidanel

and Rothschild, 2002; Bovenhuis and Schrooten, 2002;

Hocking, 2003), most of these were identified in experi-

mental 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 ex-

ample 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




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 typeof marker

Linkage Linakge

disequilibrium e quilibrium

Trait category Direct marker marker marker

Congenital defects BLAD (Da)

Citrulinaemia (D,Bb)

DUMPS (Dc)CVM (Dd)

Maple syrup urine (D,Be)

Mannosidosis (D,Bf )

RYR (Pg ) RYR (Ph)

Appearance CKIT (Pi) Polled (Bn)

MC1R/MSHR (P j,Bk,Dl)

MGF (Bm)

Milk quality κ-Casein (Do)

β -lactoglobulin (Do)

FMO3 (Dp)

Meat quality RYR (Pg ) RYR (Ph)

RN/PRKAG3 (Pq) RN/PRKAG3 (Pr)

A-FABP/FABP4 (Ps)

H-FABP/FABP3 (Pt)

CAST (Pu

, B v

)>15 PICmarq (Pw)‡

THYR (Bx)

Leptin (By)

Feed intake MC4R (Pz)

Disease Prp (Saa) B blood group (Cbb)

F18 (Pcc) K88 (Pdd)

Reproduction Booroola (See) Booroola (Sff )

Inverdale(Sgg ) ESR (Phh)

Hanna (Sii) PRLR (P jj)

RBP4 (Pkk)

Gro wth and composition M C4R (Pz) CAST (Pu) QTL (Pll)

IGF-2 (Pmm) IGF-2 (Pnn)

Myostatin (Boo) QTL (Bpp)

Callipyge (Sqq) Carwell (Srr)

Milk yield and composition DGAT (Dss) PRL (Dtt) QTL (Duu)

GRH (D vv)

κ-Casein (Do)

aShuster et al. (1992); bDennis et al. (1989); cSchwenger et al. (1993); dBorchersen (2001); eDennis andHealy (1999); f Berg et al. (1997), Leipprandt et al. (1999); g Fuji et al. (1991); hHanset et al. (1995); iMarklundet al. (1998); jKijas et al. (1998); kKlungland et al. (1995); lJoerg et al. (1996); mSeitz et al. (1999); nSchmutzet al. (1995); oMedrano and Aquilar-Cordova (1990), Rincon and Medrano (2003); pLunden et al. (2002);qMilan 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 etal. (2001); yBuchanan et al. (2002); zKim et al. (2000); aaBelt et al. (1995); bbHansen et al. (1967), Hansenand Law (1970); cc Vogeli e t al. (1997), Meijerink et al. (2000); ddJørgensen et al. (2004); eeWilson et al. (2001);ff Lord et al. (1998); gg Galloway et al. (2000); hhRothschild et al. (1996); iiMcNatty et al. (2001); jj Vincent etal. (1998); kkRothschild et al. (2000); llM. Lohuis (Monsanto Co., St. Louis, MO, personal communication);mmGeorges et al. (2003); nnJeon et al. (1999), Nezer et al. (1999); ooGrobet et al. (1998); ppJ. Hetzel (GeneticSolutions, Brisbane, Australia, personal communication); qqFreking et al. (2002); rrNicoll et al. (1998);ssGrisart 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 alterna-

tive 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 (Ev-

ans et al., 2003) and has been used in beef cattle (J.

Hetzel, Genetic Solutions, Brisbane, Australia, per-

sonal 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



DekkersE318

et al., 2004; D. Funk, American Breeders Service, De-

Forrest, WI, personal communication; E. Mullaart,

Holland Genetics, Arnheim, The Netherlands, per-

sonal communication). Several issues related to the

transfer of initial results from genome scans to appli-

cations in breeding programs were discussed in Spel-

man and Bovenhuis (1998).

Examples of Applications of MAI or MAS Marker-Assisted Introgression Programs. Marker-as-

sisted 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 Pietrain 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 marker-

assisted background selection to speed up the recovery

of the broiler genome when introgressing the naked-

neck 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 re-

sistance or tolerance genes are being considered for

cattle (J. Gibson, ILRI, Nairobi, Kenya, personal com-

munication).

Marker-Assisted Selection. The main application andpotential for use of markers to enhance genetic improve-

ment in livestock is through within-breed selection.

This requires markers that trace within-breed variabil-

ity. 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 mark-

ers have been primarily used, as evidenced by their

abundance among available tests (Table 1). Direct and

LD markers allow selection on markers across the popu-

lation, which facilitates their use. Some of the earlierapplications of MAS in livestock were prior to the era

of molecular genetics (e.g., selection for disease resis-

tance in poultry using an Elisa tests for the B-blood

group as an LD marker; J. Arthur personal communica-

tion) and selection against the halothane gene in pigs

using the halothane test. Subsequently, several genetic

tests have been used to select against carriers of reces-

sive 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 com-

mercial 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 therecent 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 im-

portant QTL regions. In dairy cattle, in addition to di-

rect markers for genetic defects and milk protein vari-

ants (Table 1), an LD marker near the prolactin gene

(Cowan et al., 1990) that is segregating in one promi-

nent Holstein sire family, has been used for preselection

of young bulls since (Cowan et al., 1997). M. Cowan

(Genetic Visions, Middleton, WI, personal communica-

tion) reported the use of several additional direct, LD,

and LE markers for selection of bull dams and prese-

lection of young bulls. In-house selection programs us-ing 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 re-

ported by Spelman et al. (1996), Arranz et al. (1998)

and Coppieters et al. (1998). Establishment of national

genetic evaluation programs using LE markers to pro-

vide information for in-house use by dairy cattle breed-

ing organizations has been reported for France (Boich-

ard et al., 2002) and Germany (Bennewitz et al., 2003).In beef cattle, several direct and LD markers are com-

mercially available (Table 1) and used by individual

breeders (J. Hetzel, S. Schmutz, University of Saskatch-

ewan, 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, per-

sonal 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 condi-

tions, because differences in response are not expected

to be large, especially when considering that traits se-

lected for using MAS are part of a multiple-trait breed-




ing goal, and appropriate controls are often not avail-

able. Responses to MAS or MAI can, however, be evalu-

ated 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 fre-

quencies at the marker locus reflect the ability to capi-

talize 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 popula-

tion 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 suc-

cesses at this level are commercial lines that are spe-

cifically 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 limitationsof implementing such selection, have been documented

(e.g., Spelman, 2002) and will be discussed later.

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. Be-

cause 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. Suc-

cess 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- T a b l e

2 . E v a l u a t i o n o f t h e s u c c e s s o f m a r k e r - a s s i s t e d s e l e c t i o n i n

b r e e d i n g p r o g r a m s f o r d i f f e r e n t t y

p e s o f m a r k e r s

L e v e

l o

f e v a

l u a

t i o n

D i r e c t m a r k e r

L i n k a g e

d i s e q u

i l i b r i u m m a r k e r

L i n k a g

e e q u

i l i b r i u m m a r k e r

F r e q u e n c y o

f m a r k e r

l o c u s

D i r e c t p o p u

l a t i o n e s t i m a

t e

D i r e c t p o p u

l a t i o n e s t i m

a t e

W i t h i n

- f a m i l y a s s e s s m e n

t

F r e q u e n c y o

f t a r g e

t l o c u s

D i r e c t p o p u

l a t i o n e s t i m a

t e

P o p u

l a t i o n - w

i d e m a r k e r - t r a

i t a s s o c i a

t i o n

W i t h i n

- f a m i l y m a r k e r - t r a

i t a s s o c i a

t i o n

P h e n o

t y p

i c e

f f e c t o

f t a r g e

t l o c u s

P o p u

l a t i o n - w


i t a s s o c

i a t i o n

P o p u

l a t i o n - w


i t a s s o c i a

t i o n

W i t h i n

- f a m i l y m a r k e r - t r a

i t a s s o c i a

t i o n

G e n e

t i c m e r i t o

f p o p u

l a t i o n

L i n e c o m p a r i s o n





DekkersE320

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 ob-

served for some well-studied genes in livestock. For

example, for the ESR gene for litter size in pigs (Roth-

schild et al., 1996), significant associations were demon-

strated in multiple commercial lines in one of the

largest studies of a candidate gene in livestock con-ducted to date (Short et al., 1997). However, some sub-

sequent 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, ge-

notype × environment interactions, and epistatic effects

(Beavis, 1994).

Effects of the target locus or region in the targetpopulation can be readily evaluated for major genetic

defects. For example, Rothschild and Plastow (1999)

reported a reduction in mortality to zero and an im-

provement in meat quality from removal of the halo-

thane 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 mark-

ers, 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 modifyQTL effect estimates and selection criteria. Thus, im-

plementation 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 pre-

viously, MAS diverts selection emphasis away from

polygenes and traits without marked QTL, and the ulti-

mate 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 tan-

dem selection, followed by index selection, and prese-

lection. It is unclear to what extent each of these strate-

gies is used in commercial applications of MAS.

Because appropriate controls are often not available,

success of MAS based on improvements in overall ge-

netic merit of the population or selected individuals is

very difficult to quantify in commercial breeding pro-

grams, 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 nu-

cleus herds. This, however, represented the increase in

genetic superiority for litter size of selected animals

over a relatively short period of time, with limited ac-

curacy.

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 exam-ple, 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 organiza-

tions. To date, such studies have not yet been conducted

in a comprehensive manner but several indirect assess-

ments have been made. For example, Cowan et al.

(1997) found an increase in mean EBV and in the num-

ber 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 similarimpact 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 communi-

cation), 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 morefully 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 in-

crease full-sib family size from bull dams. Similar limi-

tations 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 esti-

mates, but also on the ability to integrate technologies

that are required to effectively implement a MAS pro-

gram (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 to-

ward the application of LE, LD, or direct markers. Table

3 summarizes the relative requirements and opportuni-

ties for detection and application of these three types




Table 3. Requirements and opportunities for the implementation of linkage equilibrium(LE) vs. linkage disequilibrium (LD) vs. direct (D) markers

Requirement or Relative

opportunity requirements

QTL detection requirements LE < LD << D

Marker development LE < LD << D

Phenotyping and data structure LE >> LD ∼ D

Genome-wide analysis opportunities LE >> LD >> D

Within-line confirmation requirements LE >> LD > DRoutine genetic evaluation requirements LE >> LD > D

Phenotyping and data structure LE >> LD ∼> D

Genotyping LE >> LD ∼> D

Genetic evaluation models LE >> LD ∼> D

Implementation logistics LE >> LD > D

Genetic gain opportunities (for given QTL) LE < LD ∼< D

Marketing opportunities (patents, product differentiation) LE << LD < D

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. Require-

ments 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 link-

age with the causative mutation, either identified as

targeted candidate gene polymorphisms or by high-den-

sity marker maps. In addition, LE markers allow for

genome-wide analysis of QTL based on a limited num-

ber of markers at 15- to 50-cM spacings. Genome-wide

analysis is also possible for LD markers, but this willrequire 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 popula-

tions (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 pheno-

typed and genotyped individuals, without a specific pop-

ulation 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 inoutbred populations. The latter requires large numbers

of phenotyped individuals with a specific family struc-

ture (e.g., Weller et al., 1990). The same is true for

estimation and confirmation of LE vs. LD marker ef-

fects 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-

tion procedures are also much greater for LE than forLD or direct markers, both with regard to requirementsfor the number and which individuals that must bephenotyped and genotyped, and with regard to methodsof analysis (Table 3). Use of LE markers in an outbredpopulation requires the phenotyping and genotyping of selection candidates and/or their relatives becauseeffects must be estimated on a within-family basis. Theextent of family data needed depends on recombinationrates between markers and QTL. Less data will beneeded and can be from more distant relatives if recom-bination rates are low. Direct and LD markers requirethe genotyping of only selection candidates because es-timates of genotype effects can be obtained from priorinformation or from a sample of individuals that haveboth genotype and phenotype information.

Data from LE markers can be incorporated intoBLUP animal model genetic evaluations using the ap-proach of Fernando and Grossman (1989), by fitting random effects for each QTL and allowing for differentQTL effects within families. This method, or extensionsthereof, has been applied to several commercial situa-tions in dairy cattle (Boichard et al., 2002; Bennewitzet al., 2003; E. Mullaart, personal communication). Ap-plication of these procedures requires substantial modi-fication of existing animal model genetic evaluation pro-cedures, estimation of variance components, and exten-sive computing resources. Data from LD or directmarkers on the other hand, can be incorporated in ex-isting genetic evaluation procedures as fixed genotypeor haplotype effects (Van Arendonk et al., 1999). If notall animals are genotyped, which will be the case inpractice, marker data must be supplemented with geno-type probabilities, which can be derived using pedigreeand marker data (e.g., Israel and Weller, 2002). Never-theless, computational requirements for incorporating LD or direct markers in genetic evaluation are muchless than for LE markers. Genetic evaluation require-ments are slightly greater for LD than for direct mark-ers because LD markers require identification and anal-ysis of marker haplotypes and confirmation of marker-QTL linkage phases.



DekkersE322

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 mark-

ers. 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 lim-

ited number of individuals. Costs of genotyping limitsthe application of high-density genotyping at present,

but these are expected to decrease in the future.

Implementation Logistics. Because of the greater re-

quirements for phenotyping, genotyping, genetic evalu-

ation, and within-family selection, the logistical de-

mands for implementation of MAS arealso 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 commer-

cial programs focusing on the use of LD or direct instead

of LE markers Spelman, 2002; Plastow, 2003; M. Lo-

huis, personal communication).

Genetic Gain. Opportunities for increases in geneticgain through MAS on a given QTL differ depending on

whether the QTL is marked by LE, LD, or direct mark-

ers. 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. Differ-

ences were reduced but far from eliminated when

marker spacing was reduced to 1 or even 0.05 cM. Add-ing 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 particu-

lar 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 esti-

mated. 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 vari-

ance 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 in-

creases accuracy, but also increases the number of ef-

fects 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 opportu-nities for marketing and protection. A detailed discus-

sion of intellectual property issues related to molecular

genetics in livestock is found in Rothschild and New-

man (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 mark-

ers. 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 Selectionin Breeding Programs

Whereas initial applications of MAS in livestock pop-

ulations may have been on an ad hoc basis, it is clear

that successful implementation of a MAS program re-

quires a comprehensive integrated approach that is

closely aligned with business goals and markets. Com-

ponents 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 fordata analysis (Figure 2). This must be supported by a

systematic approach to quality control and must sup-

port day-to-day decision making (e.g., on which animals

to genotype or regenotype in case of errors, which ani-

mals 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 lat-

ter will be mostly driven by genetic gain but are ulti-

mately determined by economics.

Economic Aspects of MAS

Commercial application of MAS requires careful con-

sideration of economic aspects and business risks. Eco-

nomic analysis of MAS requires a comprehensive ap-

proach that aims to evaluate the economic feasibility

and optimal implementation of MAS. An excellent ex-

ample of such an analysis is in Hayes and Goddard

(2003), who conducted a comprehensive economic anal-

ysis of the implementation of LE-MAS in the nucleus




Figure 2. Components of an integrated system for the use of molecular genetic information in breeding programsfor marker-assisted selection (MAS).

breeding program of an integrated pig production enter-

prise. 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 imple-

mentation 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 setduring the QTL

detection phase such that genotyping costs during the

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 businessgoals, using analysis tools to estimate breeding values (EBV), molecular scores, and genotypes (or genotype probabili-ties). Solid and broken arrows indicate the flow of information for polygenic and monogenic traits, respectively.

implementation phase arereduced and selection of false

positives is minimized. An economic analysis of intro-

gression of the Booroola gene into dairy sheep breeds

is given in Gootwine et al. (2001) and of MAS prese-

lection in dairy cattle in Brascamp et al. (1993), Mackin-

non and Georges (1998), and Spelman and Garrick

(1998).

Whereas Hayes and Goddard (2003) evaluated eco-

nomic returns from MAS from increased profit at the

production level, which is proportional to extra genetic

gain, most commercial breeding programs derive profit



DekkersE324

Figure 4. Effect (%) of 50% preselection of young bulls for entry into a progeny-test program on genetic gain (meanEBV of the top 10% progeny-tested bulls) and market share (number of bulls in top 10% and top 1%). Preselectionis on an index of genotype for a QTL with additive effect a (in genetic standard deviations, σg) and a polygenic EBVthat 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 germ-

plasm. 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 re-ceived 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 esti-

mated with true polygenic breeding values among

young bulls of 0.0 and 0.1 are evaluated and prese-

lection 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

notimply 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 objec-

tive, 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 economicreturns from increased market share were less than

from increased production for a preselection situation

similar to that considered here. Nevertheless, it is im-

portant that economic analysis is conducted in relation

to business and market realities and goals. Computa-

tional approaches using genetic algorithms (e.g., King-

horn et al., 2002) can be used to develop selection and

mating strategies based on multiple sources of informa-

tion, including markers that meet multiple business

goals and constraints. Weller (1994) provides further

discussion of alternative criteria to economically evalu-

ate alternative breeding programs.

Other Opportunities

Optimal implementation of MAS involves careful con-

sideration of alternative selection strategies, business

goals, and integration of molecular with other technol-

ogies (e.g., reproductive technologies following Georges

and Massey, 1991). Opportunities also exist to imple-

ment 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 addi-

tion, strategies must be developed to estimate gene ef-




fects at the commercial level for nucleus breeding pro-

grams, 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 inbreed-

ing, parental verification, and product tracing. Pedigree

verification is an important aspect of the use of molecu-

lar markers in several breeding programs (e.g., Spel-

man, 2002; M. Cowan, personal communication) andcan lead to substantial opportunities for increasing ac-

curacy 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 (Plas-

tow, 2003).

Implications

Marker-assisted selection is used in the livestock

breeding industry, primarily through gene-assisted se-

lection and linkage disequilibrium markers-assisted se-

lection. Use of linkage equilibrium markers-assistedselection has been limited and is hampered by imple-

mentation issues. Success of commercial application of

marker-assisted selection is unclear and undocumented

and will depend on the ability to integrate marker infor-

mation in selection and breeding programs. Opportuni-

ties for the application of marker-assisted selection ex-

ist, 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 require-

ments. Regardless of the strategy, successful applica-

tion of marker-assisted selection requires a comprehen-

sive integrated approach with continued emphasis onphenotypic recording programs to enable quantitative

trait loci detection, estimation and confirmation of ef-

fects, 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 op-

timism.

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 publi-

cation number WO02064820. Available: http://ep.espacenet.-

com/. Accessed Feb. 9, 2004.

Beavis, W. D. 1994. The power and deceit of QTL experiments: Les-

sons from comparative QTLstudies. Pages 250–266 in Proc. 49th

Annu. Corn and Sorghum Res. Conf., Am. Seed Trade Assoc.,

Alexandria, VA.

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 GermanHolstein dairy cattlebreeding:Out-

line of the program and marker-assisted breeding value estima-

tion. Page 5 in Bookof 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-Kuntzel, 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 substitu-

tionin thetransmembrane domainof thebovine growthhormone

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 se-

lection in French dairy cattle. Electronic communication 22–03

in Proc. 7th World Cong. Genet. Appl. Livest. Prod., Montpel-

lier, 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 locifor milk

production traits in dairy cattle. Electronic communication 9:7

in Proc. 7th World Cong. Genet. Appl. Livest. Prod., Montpel-

lier, 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, andM. F. Rothschild.

2002. Introgressing multiple QTL in backcross breeding pro-

grams of limited size. Electronic communication 22:08 in Proc.

7thWorld Cong. Genet. Appl. Livest. Prod., Montpellier,France.

Ciobanu, D., J. Bastiaansen, M. Malek, J. Helm, G. Plastow, J. Wool-

lard, and M. Rothschild. 2001. Evidence for new alleles in the

protein kinase adenosine monophosphate activated gamma3-

subunit gene associatedwith low glycogen content in pigskeletal

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 quanti-

tative 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



DekkersE326

V. Available: http://www.intl-pag.org/pag/5/abstracts/p-5j-

296.html. Accessed Feb. 9, 2004.

Darvasi, A., andM. Soller.1994.Optimum spacingof genetic markers

for determining linkage between marker loci and quantitative

trait loci. Theor. Appl. Genet. 89:351–357.

Dekkers, J. C. M. 1992.Asymptoticresponseto 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 selec-

tion for quantitative traits with information on an identified

locus in outbred populations. Genet. Res. 71:257–275.

Dekkers, J. C. M., andF. Hospital.2002. Theuse of molecular geneticsin improvement of agricultural populations. Nat. Rev. Genet.

3:22–32.

Dennis, J. A.,and P. J. Healy.1999.Definitionof themutationrespon-

sible for maple syrup urine disease in Poll Shorthorns and geno-

typing 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 de-

ficiency. Proc. Nat. Acad. Sci. USA. 86:7947–7951.

Evans, G. J., E. Giuffra, A. Sanchez, S. Kerje, G. Davalos, O. Vidal,

S. Illan, 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 traitloci 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. Ge-

nome 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

thesingle base change causingthe 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 malig-

nant 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 dos-

age-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 my-

ostatin 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 productionin 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. VanLaere, andL. Anders-

son. 2003. Genetic dissection of an imprinted QTL mapping to

proximal SSC2. Abstract W237 in Proc. Plant and Animal Ge-

nome 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.Meuwis-

sen, 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 infor-

mation for genetic improvement in low and medium input sys-

tems. 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. Geneticand economic analysis of introgressionthe 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. Cambi-

sano, M. Mni, S. Reid, R. Spelman, M. Georges, and R. Snell.

2002. Positional candidate cloning of a QTL in dairy cattle: iden-

tification 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. Mic-

haux, F. Menissier, M. Zanotti, S. Dunner, and M. Georges.

1998. Molecular definition of an allelic series of mutations dis-

rupting 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 Pietrain de l’allele nor-

mal aux locus de sensibilite 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 disequi-

librium 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 Neth-

erlands.

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.

7thWorld Cong. Genet. Appl. Livest. Prod., Montpellier,France.

Israel, C., and J. I. Weller, 2000. Effect of misidentification on genetic

gain andestimation of breeding value in dairycattlepopulations.

J. Dairy Sci. 83:181–187.

Israel, C., and J. I. Weller, 2002. Estimation of quantitative trait locieffects 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. Lundstrom,

and L. Andersson. 1999. A paternally expressed QTL affecting

skeletal and cardiac muscle mass in pigs mapsto 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. Raud-

sepp, 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).




Kashi, Y., E. Hallerman, and M. Soller. 1990. Marker assisted selec-

tion 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 animalbreeding. Electronic commu-

nication 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, andA. Lacoste. 2002.Simulta-

neous 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-manno-

sidosis mutation and detection of two beta-mannosidase pseu-

dogenes. 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., andM. A. J. Georges. 1998. Marker-assisted prese-

lection 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 se-

quences 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 poly-

morphism influencing alpha(1,2)fucosyltransferase activity of

the pig FUT1 enzyme determines susceptibility of small intesti-

nal epithelium to Escherichia coli F18 adhesion. Immunogenet-

ics 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.

Chardon, and L. Andersson. 2000. A mutation in PRKAG3 asso-

ciated with excess glycogen content in pig skeletal muscle. Sci-

ence 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 tothe Carwell locus for rib-eye muscling in sheep. Pages 529–532

in Proc. 6th World Cong. Genet. Appl. Livest. Prod., Armi-

dale, 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. vander Steen, A. Mileham, andG. Plastow. 1996.

The estrogen receptor locus is associated with a major geneinfluencing 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. South-

wood, 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., andS. Newman. 2002. Intellectual Property Rights

in Animal Breeding and Genetics. CABI Publishing, Wall-

ingford, 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 IntellectualProperty Rightsin AnimalBreeding and Genetics.

M. F. Rothschild and S. Newman, ed. CABI Publishing, Wall-ingford, U.K.

Rothschild, M. F., and M. Soller. 1997. Candidate gene analysis to

detect genes controlling traits of economic importance in domes-

tic 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. Electroniccommunication 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

theestrogen receptor locus on reproductionand productiontraits

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.



DekkersE328

Spelman, R. J., and H. Bovenhuis. 1998. Moving from QTL experi-

mentalresults to theutilisation of QTLin 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 Holstein-

Friesian population. Genetics 144:1799–1808.

Spelman, R. J., and D. J. Garrick. 1998. Genetic and economic re-

sponses 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 Geno-

mics: Visions for Animal Breeding and Genetics. J. C. M. Dek-

kers, 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 andopportunities. Pages 407–414 in Proc. 6thWorld

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., Armi-

dale, 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. Me ijerink, R. Frie s, 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 andgranddaughter 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 do-

main 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 markers-

assisted selection utilizing linkage disequilibrium. Theor. Appl.

Genet. 83:813–820.

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