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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 E313 E314 Dekkers 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 E315 Marker-assisted selection 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- E316 Dekkers 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 E317 Marker-assisted selection 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 E318 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- E319 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. E322 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.