Copyright 2003 by the Genetics Society of America
Identification of Quantitative Trait Loci for Production Traits in Commercial Pig Populations G. J. Evans,*,1 E. Giuffra,†,1,2 A. Sanchez,‡,1 S. Kerje,† G. Davalos,‡ O. Vidal,‡ S. Illa´n,§ J. L. Noguera,** L. Varona,** I. Velander,†† O. I. Southwood,* D.-J. de Koning,‡‡ C. S. Haley,‡‡ G. S. Plastow* and L. Andersson†,3 *Sygen International PLC, University of Cambridge, Department of Pathology, Cambridge CB2 1QP, United Kingdom, †Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, S-751 24 Uppsala, Sweden, ‡Department Cie`ncia Animal i dels Aliments, Universitat Auto`noma de Barcelona, Bellaterra, 08193 Catalunya, Spain, §Department Produccions Porcina, Cooperativa Agricola y Ganadera de Lleida, COPAGA, Poligono Industrial “El Segre,” Lleida, 25080 Catalunya, Spain, **Institut de Recerca i Tecnologia Agroalimenta`ries, Area de Produccio`n Animal, Centro UdL-IRTA, Lleida, 25198 Catalunya, Spain, ††Quality Genetics, S-244 24 Kaevlinge, Sweden and ‡‡Roslin Institute-RIO, Division of Genetics and Biometry, Roslin, Midlothian EH25 9PS, United Kingdom Manuscript received July 19, 2002 Accepted for publication November 22, 2002 ABSTRACT The aim of this study was to investigate methods for detecting QTL in outbred commercial pig populations. Several QTL for back fat and growth rate, previously detected in experimental resource populations, were examined for segregation in 10 different populations. Two hundred trait-by-population-by-chromosome tests were performed, resulting in 20 tests being significant at the 5% level. In addition, 53 QTL tests for 11 meat quality traits were declared significant, using a subset of the populations. These results show that a considerable amount of phenotypic variance observed in these populations can be explained by major alleles segregating at several of the loci described. Thus, despite a relatively strong selection pressure for growth and back fat traits in these populations, these alleles have not yet reached fixation. The approaches used here demonstrate that it is possible to verify segregation of QTL in commercial populations by limited genotyping of a selection of informative animals. Such verified QTL may be directly exploited in marker-assisted selection (MAS) programs in commercial populations and their molecular basis may be revealed by positional candidate cloning.
T
HE identification of polygenes or quantitative trait loci (QTL) controlling quantitative traits was pioneered in Drosophila, using external marker loci (Sax 1923; Thoday 1961). The development of large numbers of molecular markers and interval-mapping methods paved the way for QTL mapping using intercrosses of inbred experimental organisms (Paterson et al. 1988; Lander and Botstein 1989). Two different strategies have been successfully used for QTL mapping in outbred livestock. In pigs, a number of QTL have been identified using intercrosses between divergent populations, e.g., wild boar vs. European domestic, Chinese Meishan vs. European domestic, and Iberian vs. European white domestic (Andersson et al. 1994; Knott et al. 1998; Rohrer and Keele 1998a,b; De Koning et al. 1999; Ovilo et al. 2000; Perez-Enciso et al. 2000; Walling et al. 2000; Bidanel et al. 2001) and using intercrosses between commercial breeds (Nezer et al.
1
These authors contributed equally to this work. Present address: Centro Ricerche Studi Agroalimentari FPTP-CERSA, LITA, Via Fratelli Cervi 93, 20090 Segrate, Italy. 3 Corresponding author: Department of Animal Breeding and Genetics, SLU, Box 597, S-751 24 Uppsala, Sweden. E-mail:
[email protected] 2
Genetics 164: 621–627 ( June 2003)
1999; Grindflek et al. 2001; Malek et al. 2001a,b). In cattle, it has been possible to detect QTL controlling milk production traits by utilizing sire breeding values based on phenotypic data on large numbers of daughters (Georges et al. 1995). Despite the fact that several publications have demonstrated the existence of QTL for major production traits like fatness and growth at various positions in the pig genome (Rothschild and Plastow 1999; Andersson 2001), the agricultural industry has failed to apply these results to commercial populations. This is because it is not trivial to detect the presence of segregating QTL within commercial pig populations. The power in QTL mapping is reduced because only a limited proportion of parents will be heterozygous for any QTL and the heterozygosity has to be deduced using the segregation data, unlike the situation for divergent intercrosses when one can assume that all F1 animals are heterozygous at major QTL. A major hurdle for large-scale QTL mapping in commercial populations is therefore the high costs associated with the collection of samples and phenotypes and for genotyping the large number of animals required for powerful QTL mapping. The objective of this study was to establish an efficient procedure for QTL mapping in commercial populations. We
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G. J. Evans et al. TABLE 1 Commercial pig populations provided by four breeding organizations Large White
Landrace
Hampshire
Pietrain
Meishan synthetic
Company
Sires
Offspring
Sires
Offspring
Sires
Offspring
Sires
Offspring
Sires
Offspring
PIC Quality Genetics IRTA COPAGA
10 6
527 382
— 6
— 437
10 5
503 233
10 —
527 —
9 —
492 —
— 5
— 387
5 —
534 —
— —
— —
— 5
— 420
— —
— —
For each population, values refer to total number of sire half-sib families and number of offspring that are genotyped for at least one region.
have evaluated the extent to which major QTL detected in divergent intercrosses also segregate within highly selected commercial populations, using a strategy for selecting the most informative families for QTL analysis (Chatziplis and Haley 2000). Markers from 10 chromosomal regions were used to detect QTL effects on growth and fatness traits. Seven of these regions were selected because they had shown significant QTL effects in previous studies (Andersson et al. 1994; Yu et al. 1995; Casas-Carrillo et al. 1997; Andersson-Eklund et al. 1998; Knott et al. 1998; Rohrer and Keele 1998a; Jeon et al. 1999; Nezer et al. 1999). The remaining 3 were selected as control regions for which no QTL had been reported at the start of this project. The 10 loci were tested across 10 different commercial populations provided by a large multinational breeding company (PIC, United Kingdom), a regional breeding cooperative (COPAGA, Spain), a national breeding scheme (Quality Genetics, Sweden), or an agricultural research institute (IRTA, Spain). In addition, since some of the chosen populations also had meat quality trait data available we have assessed the association of variation in these traits with the targeted QTL regions. MATERIALS AND METHODS Animals and phenotypic records: Samples used in this study were from Large White, Landrace, Hampshire, Pietrain, and Meishan synthetic lines that were supplied by PIC, Quality Genetics, IRTA, and COPAGA (Table 1). Some breeds were supplied from two or three organizations, allowing comparison between different populations of Large White, Landrace, and Pietrain. All lines supplied by PIC and Quality Genetics had phenotypic measurements for growth rate and back fat recorded. Weights were recorded at birth and at slaughter, allowing calculation of lifetime daily gain (LDG, grams per day). Back fat depths (millimeters) were recorded at the last rib using an ultrasonic probe. At COPAGA and IRTA, weight (kilograms) and back fat thickness (millimeters) were recorded at 175 days of age. Back fat thickness was measured by the RENCO apparatus (A-mode equipment; Renco Corporation, Minneapolis) as the average of two ultrasonic measurements taken on each side of the spinal column, 5 cm from the middorsal line at the position of the last rib. The Spanish
Large White, Landrace, and Pietrain animals were also recorded for meat quality traits. Length and weight of the carcass were recorded while pH and conductance were measured 45 min and 24 hr after slaughter in both the semimembranaceus and longissimus dorsi muscles. Selection of informative animals for genotyping: PIC/Quality Genetics: The populations supplied from PIC and Quality Genetics are much larger than normal resource families usually used for QTL mapping. Consequently it was not possible to genotype all available animals. To optimize the chances of identifying QTL with these populations, a subset of the most informative families from each population was selected for genotyping as described by Chatziplis and Haley (2000) with some modifications. Using the thousands of individual performance records available for each of these populations, the most variable sire families from each line were selected on the basis of a normalized phenotypic index [estimated breeding value (EBV)] of growth rate and back fat giving both traits equal weight. Large sire families were identified and those with the highest mean within-full-sib family variance were selected. Full-sib families of fewer than four were rejected. Where more than the required number of progeny remained, additional full-sib families with the lowest withinfamily variance were rejected to bring the total number of progeny down to the required number. Approximately 50 offspring were represented from each sire (Table 1) and all progeny within the selected full-sib families were genotyped. COPAGA/IRTA: No preselection of animals was conducted in the populations supplied by COPAGA and IRTA since they were smaller than those of Quality Genetics and PIC. Phenotypic records were collected for ⵑ500 offspring from a total of five sires for each of the three populations supplied (Table 1). Genotyping: Seven published QTL regions and three control regions were selected for genotyping in each of the 10 populations (Table 2). The chromosome 2 region was not tested in the PIC populations because the potential commercial application of this QTL was protected by a previous patent application and it was therefore replaced by chromosome 1q. For each region, a set of microsatellite markers was chosen on the basis of their map position and proximity to the expected QTL. Markers predicted to be ⵑ10–20 cM apart were favored. Each selected sire from each population was genotyped with this panel of candidate microsatellites to assess marker heterozygosity. Two or three markers were then chosen for genotyping all offspring for each QTL to maximize the number of sires heterozygous for at least one marker for each QTL. Sires were excluded from the analyses of a region if they were uninformative for all markers in that region. Otherwise all sires were included in the calculations and all
QTL for Production Traits in Pig Populations
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TABLE 2 Chromosomal regions selected for genotyping in commercial pig populations Chr
Interval
QTL/trait reported
1p 1q 2 3 4 6 7 8 9 10 13
CGA-Sw2185 S0056-SW1301 IGF2 region Sw72-Sw251 Sw35-Sw839 Sw316-S0003 MHC-region Sw905-Sw1029 Sw983-Sw21 S0070-Sw1041 S0068-Sw398
Control QTL for fat deposition traits QTL for % lean meat and back fat thickness in WB ⫻ LW and LW ⫻ Pietrain intercrosses QTL for postweaning average daily gain Major QTL for fatness and growth confirmed in several populations Control Major QTL for fatness in crosses between Meishan and European pigs QTL for carcass traits in WB ⫻ LW intercrosses Control QTL for growth rate in a WB ⫻ LW cross QTL affecting early growth rate (from birth to 30 kg) in two independent studies
Referencea 1, 2 3, 4 5 6, 7 1, 2 8 9 6, 10
LW, Large White; WB, wild boar. a (1) Rohrer et al. (1998a); (2) Bidanel et al. (2001); (3) Nezer et al. (1999); (4) Jeon et al. (1999); (5) Casas-Carrillo et al. (1997); (6) Andersson et al. (1994); (7) Walling et al. (2000); (8) Andersson-Eklund et al. (1998); (9) Knott et al. (1998); (10) Yu et al. (1995).
across-family results presented used all sires with at least one informative marker. All individuals with paternity errors were excluded from the study. DNA was extracted from either tissue (ear or tail) or blood using genomic DNA extraction kits [QIAGEN (Valencia, CA) or Boehringer Mannheim (Mannheim, Germany)]. Genotyping was performed by PCR using fluorescently labeled microsatellite primers supplied by the U.S. Pig Genome Coordinator (http://www.genome.iastate.edu/default/pigintr.html). To increase throughput, compatible PCR products were pooled before electrophoresis, using automated sequencers (ABI310, ABI377, or ABI3700; Applied Biosystems, Foster City, CA). Statistical analyses: After genotyping, the linkage distance between each linked marker pair was calculated using CRIMAP (Green et al. 1990). The observed linkage distances were in good agreement with published maps (http://www.genome. iastate.edu/maps/marcmap.html). QTL analyses used the leastsquares within-sire (half-sib) approach (Knott et al. 1996) as implemented in the web-based QTL Express software (Seaton et al. 2002) found at http://qtl.cap.ed.ac.uk. This approach allows evidence for the presence of a segregating QTL to be accumulated across sires, but also provides a test for segregation of any QTL within each sire. Significance was judged against the pointwise nominal 5% threshold. QTL effects for individual families were standardized to phenotypic SDs by using the within-sire family variance and assuming a polygenic heritability of 0.25 for growth-related traits and 0.40 for fatness and carcass quality traits. The same heritability was used to approximate the variance explained by a putative QTL at the population level. We did not make any corrections for selective genotyping in the PIC and the Quality Genetics data because the previously suggested corrections (Bovenhuis and Spelman 2000) do not apply, the way these populations were selected. Although the individual sire estimates for these populations might be inflated, the variance explained by the QTL is hardly affected because the QTL variance and residual variance are inflated at roughly the same rate.
RESULTS
QTL for growth and back fat traits: The least-squares analyses indicated segregation of QTL in all populations
tested, except for the Large White population from COPAGA (Table 3). Four QTL were the maximum number indicated in any single population. Although some regions showed no overall effect, we did observe some individual sire families with significant QTL effects in some of these regions that were not significant overall (data not shown). Of 200 trait-by-population-by-region tests performed, 20 (10%) were significant at the 5% level. Among these, two tests were significant at the 1% and two at the 0.1% level. This clear increase over chance expectation strongly suggests that some real QTL have been detected. The statistical analysis indicated that the observed QTL explained between 6 and 29% of the residual phenotypic variance for the traits studied. The QTL significant at the 0.1% level are shown in more detail with the size of the effect detailed by sire family in Table 4. As expected, the results indicated that only some of the sires showed segregation for a given QTL. For instance, the highly significant QTL effect on chromosome 13 for back fat thickness in the Hampshire population provided by Quality Genetics is entirely due to sire family 5. The regions more consistently showing an effect across populations (different breeds and different country of origin) were on chromosomes 3 and 4. However, there was no evidence to show that a particular QTL is segregating in different populations of the same breed as shown by the lack of consistency across the various Hampshire, Large White, and Pietrain populations. Overall, there were not significantly more positive QTL results for candidate regions compared with the controls. The control regions on chromosomes 6 and 9 were both segregating QTL in two populations. QTL for carcass and meat quality traits: A subset of populations representing three breeds (Landrace, Large White, and Pietrain from IRTA and COPAGA)
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G. J. Evans et al. TABLE 3 Summary of QTL results for growth rate (G) and back fat (F) in all populations Candidate regions
Population Hampshire Hampshire Landrace Landrace Large White Large White Large White Pietrain Pietrain Meishan
1q
2
3
4
Control regions
7
8
10
PIC — G(0.12)** G(0.21)*** QG — F(0.14)* QG — F(0.06)* G(0.07)* IRTA — G(0.07)* PIC G(0.10)* — F(0.08)* QG — COP — PIC — F(0.07)* COP — F(0.07)* PIC F(0.08)* — G(0.10)* G(0.09)*
13
1p
6
9
F(0.29)*** G(0.10)*
F(0.21)** G(0.07)* G(0.08)* F(0.13)*
F(0.09)*
The values in parentheses are the proportion of phenotypic variance explained by the QTL approximated by assuming a polygenic heritability of 0.40 for fatness and 0.25 for growth. Dashes indicate region not tested. Chromosome 2 was not tested in the PIC populations and was substituted by chromosome 1q. *Significant at the 5% level, **significant at the 1% level, ***significant at the 0.1% level.
were tested for the occurrence of carcass and meat quality trait QTL at the same genome regions. All three populations showed evidence of QTL segregation (Table 5). Of 300 trait-by-population-by-region tests performed, 53 (17.6%) were declared significant at least at the 5% level and as many as 15 (5%) were significant at the 1% level; 2 tests on chromosomes 2 and 9 were significant at the 0.1% level (pH at 24 hr and 45 min in longissimus dorsi, respectively) in white breeds (Large White and Landrace). The observed QTL explained between 5 and 15% of the residual phenotypic variance
of the trait in question. As many as six of the indicated QTL concerned chromosome 6 in the Large White population from COPAGA. These QTL effects are most likely explained by the fact that one of the sires was heterozygous for the Halothane mutation, which causes the porcine stress syndrome and has large pleiotropic effects on carcass traits (Fujii et al. 1991). The selected QTL region on chromosome 6 (Sw316-S0003) is 20–40 cM distal to the calcium-release channel (RYR1) locus (http://www.thearkdb.org, June 2002) harboring the Halothane mutation.
TABLE 4 Summary of estimated effects for some of the most significant QTL (P ⬍ 0.001) Family 1
2
3
4
5
6
8
SSC7, PIC Hampshire, growth, 421 informative offspring 2.05* 1.52 2.94** 1.96 0.77 1.02 0.28 0.39
9
10
0.98
2.08* 0.27
t-test Effect
2.77** 0.37
t-test Effect
SSC13, Quality Genetics Hampshire, back fat thickness, 226 informative offspring 0.83 0.94 1.14 0.98 4.24*** 0.55
t-test Effect
t-test Effect
1.82
3.44*** 0.30
SSC2, IRTA Landrace, carcass length, 531 informative offspring 1.36 0.86 3.86*** 2.03* 0.35 0.18 SSC9, COPAGA Large White, pH in Musculus longissimus dorsi, 24 hr after slaughter, 382 informative offspring 0.22 2.93** 0.85 0.99 0.32
For each sire family, the individual t-test value is given, along with the standardized QTL effect (in phenotypic SD) assuming a heritability of 0.25 for growth and 0.40 for the other traits. *Significant at the 5% level, **significant at the 1% level, ***significant at the 0.1% level.
QTL for Production Traits in Pig Populations
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TABLE 5 Summary of QTL data for carcass and meat quality traits in populations provided by IRTA and COPAGA Candidate regions Population Landrace
2
3
7
8
IRTA P1 (0.06)* P3 (0.06)* C4 (0.06)* P1 (0.07)* L (0.09)* L (0.15)*** L (0.06)* L (0.06)* P3 (0.09)** W (0.09)* P4 (0.08)* C1 (0.08)**
Large White COP P3 (0.11)** C4 (0.06)*
Pietrain
4
Control regions
COP
10
1p
6
P1 (0.08)* P1 (0.06)* P3 (0.06)* P3 (0.05)* C2 (0.05)* C3 (0.05)* L (0.06)* C4 (0.10)** L (0.12)**
W (0.10)** C2 (0.07)* L (0.07)*
P1 (0.05)* W (0.10)** W (0.07)* C2 (0.06)* P2 (0.11)** P2 (0.10)** L (0.06)* C1 (0.07)* C2 (0.09)**
13
P4 (0.06)* P3 (0.07)* W (0.09)* P1 (0.05)* W (0.06)* L (0.15)** C3 (0.11)** C1 (0.10)* P1 (0.07)* P2 (0.11)**
9 L (0.08)** W (0.08)*
P3 (0.14)*** P4 (0.11)** C2 (0.10)* C4 (0.08)*
C1 (0.08)* W (0.07)*
Traits are as follows: pH at 45 min and 24 hr in longissimus dorsi (P1, P3) and in semimembranaceus muscles (P2, P4); conductivity at 45 min and 24 hr in longissimus dorsi (C1, C3) and semimembranaceus (C2, C4) muscles; carcass length (L ) and carcass weight (W ). The values in parentheses are the proportion of phenotypic variance explained by the QTL approximated by assuming a heritability of 0.40. COP, COPAGA. *Significant at the 5% level, **significant at the 1% level, ***significant at the 0.1% level. DISCUSSION
This study has clearly revealed more significant QTL tests than expected by chance only. In total we have screened 500 trait-by-chromosome region-by-population combinations and we thus expected to obtain 25 false positives at the 5% significance level. However, we obtained 73 significances using this threshold. The results imply that many of the QTL reported here represent true QTL effects but further studies are in most cases required to unambiguously distinguish true QTL and false positives. Although QTL have been readily identified in pigs using crosses between divergent populations it has not been clear whether these QTL have any significance for the selected populations used in commercial agriculture. A main outcome of this study is the demonstration that several of the major QTL for growth and fatness traits previously mapped in experimental crosses appear to be segregating in commercial populations. This result is in good agreement with a parallel study by Nagamine et al. (2003, this issue). They studied the same regions of chromosomes 4 and 7 in five different commercial populations and found evidence for QTL segregation within two and five populations, respectively. The two studies open important perspectives for the use of commercial populations in QTL mapping. Moreover, it is encouraging for the application of marker-assisted selection procedures in pigs. The statistical analysis indicated that the detected QTL controlled between 5 and 30% of the residual phenotypic variance for the different
traits in these populations. However, these numbers should be interpreted with caution since they represent approximate estimates and do not account for possible inflation as a result of selective genotyping. It is only the four QTL significant at the 0.1% nominal significance level that should be considered statistically significant after making corrections for the number of tests carried out in this study. A QTL for carcass length mapping to the IGF2 region of chromosome 2 was detected in the IRTA Landrace population (P ⬍ 0.001). Previous studies using divergent intercrosses showed that this region harbors a major QTL with effects on lean meat content and back fat thickness ( Jeon et al. 1999; Nezer et al. 1999). A major QTL for fatness has been identified at the major histocompatibility complex (MHC) region in crosses between Meishan and European pigs (Rohrer and Keele 1998a; Bidanel et al. 2001). A number of reports also suggest an association between the MHC region and various production traits (including growth and fatness) within commercial populations (reviewed by Schook et al. 1996). In this study the MHC region was associated with several suggestive QTL effects (P ⬍ 0.05 or P ⬍ 0.01) and one significant effect (P ⬍ 0.001) on growth in the PIC Hampshire population. The QTL on chromosome 13 with a large effect on back fat thickness in the Quality Genetics Hampshire population corresponds to a region associated with a QTL for early growth detected in two independent studies of divergent intercrosses (Andersson et al. 1994; Yu et al. 1995). The fourth QTL considered
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significant in this study was detected in the COPAGA Large White line and affected pH in muscle. This QTL does not correspond to any previously described QTL region in pigs. The regions showing the most consistent effects across populations were those on chromosomes 3 and 4, harboring, respectively, a QTL for postweaning average daily gain (Casas-Carrillo et al. 1997) and a major QTL for fatness and growth (Andersson et al. 1994; Walling et al. 2000). The QTL on chromosome 3 has been described in a population obtained by crossing sires from two lines of pigs divergently selected for rapid and slow growth. However, it was not found in later studies based on divergent crosses. We found it segregating in three populations, representing Hampshire, Landrace, and Pietrain breeds from all countries involved in the project. The QTL data for chromosome 4 are consistent with several other studies reporting a major QTL for fatness and growth on this chromosome (Andersson et al. 1994; Walling et al. 2000). This QTL was indicated for fatness traits in Hampshire, Large White, and Pietrain while in the Meishan synthetic line it was indicated for growth. The results for the Meishan synthetic line indicated the presence of QTL for fatness and growth on chromosomes 1, 4, and 7. This is in good agreement with previous studies of crosses between Meishan and European breeds, which have revealed the segregation of major QTL in these same regions (Rohrer and Keele 1998a; Bidanel et al. 2001). We did not detect segregation at the candidate QTL regions in all populations. One possibility is that, besides the major loci, considerable within-breed variability still segregates in commercial pig populations; i.e., very complex and roughly defined traits as growth and fatness could be genetically influenced by different combinations of a high number of loci. We observed segregation of some QTL occurring in the three regions originally chosen as controls. Allelic heterogeneity, low statistical power, and/or a wrong statistical QTL model might account for the missed detection of these QTL in previous studies. In fact, several recent reports have indicated a highly significant QTL for fat deposition in the region of chromosome 6 selected for this study (Ovilo et al. 2000; Grindflek et al. 2001). A second consideration is that the present results were obtained by limited genotyping of a selection of informative animals and by use of a simple statistical analysis (Knott et al. 1996). The power of these approaches needs to be compared with those of more complex segregation analyses (e.g., De Koning et al. 2002). Ultimately, full-genome scans will be required for detecting all novel QTL but the processes described here may be useful to screen many published QTL in commercial populations before verifying and resolving the location by genotyping with more markers. This study demonstrates that a considerable amount of phenotypic variance observed in commercial popula-
tions can be explained by segregation at major QTL that have not yet reached fixation through the process of artificial selection. This implies that resources already available can be used to set up large-scale studies for the comparative analysis and fine mapping of genomic regions containing genes responsible for QTL of interest. Commercial populations of livestock species may in fact provide unique opportunities for the molecular characterization of QTL. This opportunity exists because large amounts of phenotypic data are collected routinely for breeding purposes in farm animals and it is possible to study extensive, multigeneration pedigrees. Identity-by-descent mapping of major QTL haplotypes may be adopted for high-resolution mapping as recently demonstrated in a study leading to the positional candidate cloning of a major milk trait QTL in cattle (Grisart et al. 2002; Winter et al. 2002). Such studies are facilitated by the rapid development of various resources for genome research in farm animals. Moreover, the human genome draft sequence provides an invaluable resource for positional cloning in farm animals thanks to the high degree of conserved synteny between humans and farm animals (O’Brien et al. 1999; Jeon et al. 2001). We thank Kerry Harvey and Siw Johansson for expert technical assistance, Dr. Miguel Perez-Enciso for valuable comments on the article, and Dr. Matthew Binns (Animal Health Trust, United Kingdom) for use of an ABI3700 for genotyping. Microsatellite primers were supplied by the U.S. Pig Genome Coordinator. This work was partly funded by the European Community contract no. BIO4-CT972243.
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