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ANIMAL GENETICS |
,3,4
* IPG, Institute for Pig Genetics, P.O. Box 43, 6640 AA, Beuningen, The Netherlands; and
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Animal Breeding and Genetics Group, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONIMPLICATIONSLITERATURE CITED |
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Key Words: carcass composition • meat quality • pig • quantitative trait loci
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONIMPLICATIONSLITERATURE CITED |
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). The discovery of the Rendement Napole (Le Roy et al., 1990) and Halothane (Fujii et al., 1991) genes had by then already demonstrated the importance of allelic variation of single genes on pork quality.
Genetic improvement of meat quality by traditional breeding is difficult, and hampered by the need for extensive and expensive measurements of traits on slaughtered relatives. It is expected that for these types of traits, knowledge of the underlying genes will greatly contribute to the efficiency of selection. Many studies reported the identification of QTL in pigs for a variety of traits (e.g., Bidanel and Rothschild, 2002; Geldermann et al., 2003). However, QTL information for meat quality traits is relatively limited. In addition, most QTL studies were conducted on experimental crosses between divergent breeds. These QTL are not necessarily segregating in commercial breeds or the allelic effects might be different. Therefore, these QTL need to be verified within commercial lines before implementation. This was first done recently by Nagamine et al., (2003), Evans et al., (2003), and Vidal et al. (2005). However, information on QTL affecting body composition and meat quality traits in commercial crosses is still limited.
The objective of this study was to identify QTL for body composition and meat quality traits segregating within a commercial synthetic Piétrain/Large White boar line.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONIMPLICATIONSLITERATURE CITED |
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. Phenotypic measurements were taken from approximately 100 offspring per sire, resulting in a total of 1,855 animals recorded. This initial study was performed on 8 of the paternal half-sib families, which were randomly chosen, and encompassed 715 piglets with 77 to 103 animals per family. Their DNA was isolated from tissue samples collected from sires and offspring.
Phenotypic Records
Phenotypic measurements were recorded for 30 traits. These included traits for growth and fatness (4 traits), carcass composition (11 traits), and meat quality (15 traits; see Table 1). A detailed description of the phenotypic measurements was presented by Van Wijk et al. (2005). Summary statistics for traits measured, abbreviations used, and units of measurement are presented in Table 1.
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Primal cuts of loin and ham were weighed and further dissected into boneless subprimal cuts. For the loin, the weights for bone-in loins with skin removed and fat trimmed (LOIN) were recorded. Loins were further processed and the weights of the boneless loin with fat strap and fat cover left on (ELOIN) and the boneless loin with fat removed (DLOIN) were recorded. Hams were weighed with bone-in and skin on (HAM). Hams were subsequently skinned and defatted to obtain a boneless, 4-muscle ham weight (BHAM), and further processed into 4 subprimal cuts, the inside, outside, knuckle, and lite butt (IHAM, OHAM, KHAM, and LBHAM, respectively), which were weighed individually.
Meat quality measurements were taken on both the loin and ham. Loin Minolta L*, a*, and b* measures (LOINL, LOINA, and LOINB) were taken in the longissimus thoracis muscle on a fresh cut surface of a 2.5-cm thick chop removed from the sirloin end of the boneless center cut loin. Ham Minolta measurements were taken on the fresh cut surface of the inside ham muscle (HAML, HAMA, and HAMB). Japanese color scores also were taken on the cut surface of the loin (JCScut) and the rib-surface (JCSrib) of the defatted loin.
A marbling score was given to chops of loin (LMARB) and the outside ham (HMARB) based on the National Pork Producers Council marbling standards (NPPC, 1991). Firmness scores (FIRM) were assigned to the loin chops following the NPPC 1-to-3 scale (NPPC, 2000). Drip loss (DRIP) was expressed as a percentage loss of exudates, during 24 h of cooling of a 25-mm core taken from a second 2.5-cm loin chop. Initial pH (pHI) was measured between the 10th and 11th ribs, and an ultimate pH (pHU) score was taken 24 to 28 h postmortem in the boneless loin. Purge loss (PURGE) was determined as the percentage loss of exudates during a 6-d refrigeration of a 7.5-cm section defatted loin.
DNA Isolation
The DNA was extracted from ear or loin tissue samples using the Puregene DNA isolation kit (D-70KA, Gentra Systems, Minneapolis, MN) with minor adaptations to the manufacturer’s protocol. The isolated DNA was checked on 1.2% agarose gels for quality and adjusted in sodium chloride-Tris-EDTA buffer (5 mL of 1 M Tris-HCl (pH = 8) + 200 µL of 0.5 M EDTA (pH = 8) in 1 L of double distilled water to a final concentration of 15 ng/µL.
Genotyping and Map Construction
Before initiation of this study, an inventory was made of the QTL mapping results in pigs. Approximately 30 publications related to about 15 experimental crosses described approximately 350 significant QTL for a variety of traits (data not shown). Information from this QTL survey was made available through PigAce, which is accessible at https://acedb.asg.wur.nl. The regions examined in this study were selected with emphasis on previously described QTL affecting carcass composition and meat quality traits (Table 2). These 14 autosomal chromosome regions together represented approximately 50% of the genome.
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), and using the Kosambi mapping function. The sex-average linkage map was used in the QTL analysis.
Statistical Analyses
Before the QTL analyses, the phenotypic data were adjusted for systematic effects, using phenotypic data of the whole population (n = 1,855; Van Wijk et al., 2005). Effects were estimated using the ASReml software package (Gilmour et al., 2002). The following model was used to describe all phenotypic traits except ADG, where AGE and CCW were excluded:
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where Yijk = the trait under study; SEXi = the fixed effect of the ith sex (2 classes, barrow or gilt); GFPj = the combined fixed effect of the jth group, farm, and sample stages (20 classes); AGE = age as a covariate; CCW = cold carcass weight (kg) as a covariate; ck = the random effect of the kth litter, ck~N(0, I 
); eijk = the residual effect, eijk~N(0, I 
); b1 = the regression coefficient of Y on age; and b2 = the regression coefficient of Y on CCW.
The adjusted trait score (Y*) used in the QTL analysis represents the residual effect (i.e., the phenotypic data adjusted for the nongenetic and litter effects estimated under Model [1]). The litter effect included genetic effects of the dams along with common environmental effects.
The QTL analysis was performed using the multimarker regression approach for interval mapping in half-sib populations as applied by Knott et al. (1996) and de Koning et al. (1999). This method estimates the difference between alternative alleles transmitted by the sire. Sire haplotypes were reconstructed based on the frequency of the paternal marker allele combinations in the half-sib offspring. The most frequent haplotypes in the offspring were considered to represent the parental haplotypes. For each half-sib offspring, the probability of inheriting one of the sire’s haplotypes was calculated at 1-cM intervals along the genome, conditional upon the flanking marker genotypes. For QTL detection, the phenotypic trait scores were regressed on these probabilities. Regression was within half-sib families. An F-test statistic was calculated along the chromosome at every 1-cM interval across the half-sib families.
Chromosome-wise significance thresholds (Pchr) were determined empirically for each trait by chromosome combination using permutation as described by Churchill and Doerge (1994). The term "chromosome-wise significance" is used in this study but may not be entirely appropriate because not all regions represent whole chromosomes. Thresholds were obtained based on 10,000 permutations. Genome-wise significance thresholds were calculated by applying the Bonferroni correction following the formula: Pgen = 1 – (1 – Pchr)1/r, where r is the chromosome length divided by the total length of the 14 regions covered (de Koning et al., 1999). In this paper we report QTL exceeding the 5% chromosome-wise significance level.
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RESULTS |
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. For most of the traits, heritability and phenotypic and genetic correlation estimates were described in Van Wijk et al. (2005).
Genotyping and Map Construction
The heterozygosity of the markers ranged from 0.2 to 1 with an average of 0.7. Marker order was identical to the USDA Meat Animal Research Center (MARC) map (Rohrer et al., 1996). Marker Sw2512 at the distal end of SSC1 could not be positioned unambiguously but the best order was in agreement with USDA-MARC map (Rohrer et al., 1996). The sex-averaged linkage map spanning the 14 regions had a cumulative length of 1,129 cM (Kosambi), which is only slightly larger than the USDA-MARC map (Rohrer et al., 1996). The number of markers per selected chromosomal region varied between 3 and 10, with an average interval size of 19 cM (vs. 17 cM in USDA-MARC map). Four marker brackets exceeded 30 cM.
QTL Results
Trait by chromosome significance thresholds of the F-statistic were estimated empirically based on 10,000 permutated data sets. Thresholds differed between trait by chromosome combinations and were on average 2.29, 2.88, and 3.65 respectively for the 5, 1, and 0.1% significance level.
The QTL mapping results are summarized in Table 3. Thirty-two QTL affecting 20 of the 29 traits analyzed were identified at the 5% chromosome-wise significance level. Among these QTL, 5 were significant at the 1% chromosome-wise level. Genome-wise significance levels of all 32 QTL were in the range of P = 0.06 to 0.67. The QTL were identified for 12 of the 14 regions under study and the number of QTL per trait varied from 1 to 3. Three QTL were identified for the traits KHAM, IHAM, and DLOIN. Figure 1 shows the profile of the test statistics for the 4 chromosomes carrying QTL significant at the 1% chromosome-wise level.
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For the carcass composition traits in total, 16 QTL were identified. Eleven QTL affecting ham primal or subprimal weights were found on 7 chromosomes [i.e., SSC2 (2 QTL), SSC4, SSC5, SSC6 (3 QTL), SSC10, SSC13 (2 QTL), and SSC14]. Five QTL on chromosomes SSC5 (2 QTL), SSC11 (2 QTL), and SSC13 affected loin primal or subprimal weights. This included the most significance result obtained (i.e., the QTL for ELOIN on SSC11). The 3 QTL on SSC11 are consistent in position across the different loin traits, which also are genetically correlated with each other.
Fourteen QTL were detected for 11 meat quality traits on 10 different chromosomes (Table 3). The QTL for pH (pHI and pHU) were detected on SSC4, SSC13, and SSC18, of which the QTL on SSC13 for pHI was significant at the 1% chromosome-wise level. Regions affecting water-holding capacity traits (DRIP and PURGE) were found on SSC2 and SSC15. Five regions were found to affect meat color. Ham color was affected by QTL on SSC2, SSC4, SSC8, and SSC13. The QTL on SSC4 and SSC10 affected loin color, and the QTL on SSC4 for JCScut was significant at the 1% chromosome-wise level. Two suggestive QTL for HMARB were found on SSC6 and SSC14.
For the regions showing significant evidence for the presence of a QTL at the chromosome-wise level, individual family analyses were performed. These analyses provide family-specific F-statistics and thresholds, allowing us to determine which families contributed to the overall effects; in general, 1 or 2 of the 8 half-sib families contributed significantly. These families, which were inferred to be heterozygous for the QTL, showed average effects on the order of 0.29 to 1.96 
p (Table 3). The proportion of variance explained (R2) by individual QTL were in the range of 2.0 to 10.1%, with an average of 3.6% (Table 3). Addition of the variance explained by the individual QTL for a specific trait showed that, on average, the detected QTL explained 9% of the variance; for pHI, this was 19%.
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DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONIMPLICATIONSLITERATURE CITED |
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None of the identified QTL reached the 5% genome-wise level, although the QTL for Japanese color score on SSC4 and loin weight on SSC11 almost reached that threshold (Pgen = 0.07). These results suggest that QTL with very large effects do not seem to be segregating within this commercial line for the regions examined. This might be inherent to reduced within-breed phenotypic differences compared with between-breed differences underlying the QTL in most previous studies. Although this is expected to be true for traits that were subject to intense selection, this does not necessarily apply for meat quality traits, which were not included in the breeding goal until recently. Nevertheless, when designing studies for identification of QTL segregating within commercial pig lines it seems sensible to take into account that the probability of boars being heterozygous for the QTL is relatively low. Geldermann et al. (2003) summarized the results from QTL mapping studies for a number of traits. Table 5 of Geldermann et al. (2003) contains details of 195 QTL described in 28 publications together representing 15 different experimental crosses. Of the QTL listed, 68, 23, and 9% were significant at the Pgen < 0.01, Pgen < 0.05 and Pchr < 0.05 levels, respectively, corresponding to the identification of approximately 9 QTL at the Pgen < 0.01 per cross. Of the few studies on commercial lines available to date (de Koning et al., 2003; Evans et al., 2003; Nagamine et al., 2003; Vidal et al., 2005; this study), the majority of the QTL reported were significant at the 5% chromosome-wise level. Vidal et al. (2005) came to a similar conclusion based on a QTL mapping study in a purebred Landrace population; a small number of QTL with relatively small effects were identified.
Growth and Fatness Traits
For the growth and fatness traits, 2 QTL were identified; a QTL for LD on SSC11 and a QTL for PLEAN on SSC13. No QTL for similar traits were reported previously. However, Rohrer and Keele (1998b), Malek et al. (2001a), and Dragos-Wendrich et al. (2003b) reported QTL for LM area, and loin and neck meat weight in the same region of SSC11. In this study, the QTL on SSC11 also affected the trimmed loin weights ELOIN and DLOIN. Meat percentage (PLEAN) was calculated based on LD and BF measurements, in which the latter accounts for the largest part of the variance. The QTL for BF thickness on SSC13 were found by Malek et al. (2001a), Nezer et al. (2002), and Yue et al. (2003b). Yue et al. (2003b) also reported a QTL for fat to meat ratio in the same region.
Carcass Composition
The QTL affecting different ham weights were identified on 7 chromosomes. Although these traits have genetic correlations of up to 0.89 (Van Wijk et al., 2005), most QTL affect a single ham subprimal weight only. The KHAM QTL on SSC2 corresponds to the region of the IGF2 locus, which is known to affect muscle mass (Jeon et al., 1999; Nezer et al., 1999; van Laere et al., 2003). Analysis of IGF2, however, revealed that all boars were homozygous for the favorable IGF2 allele (IGF2-intron3-A3072A) suggesting that either an un-known mutation in the IGF2 gene or a linked gene may exist that affects ham weight.
The KHAM QTL at the end of SSC4q does not correspond to the ham weight QTL found in previous studies on SSC4, which are more centrally located near the candidate genes V-ATPase, ATP1A2, and ATP1B1 (Cepica et al., 2003). However, because of the low information content on SSC4, the estimated position of the QTL is not very precise.
The IHAM QTL on SSC5 and the telomeric region of SSC6p were not described previously. Two other QTL on SSC6 affecting OHAM and BHAM are located near the RYR1 locus. These findings agree with Grindflek et al. (2001) and Yue et al. (2003a), who both reported suggestive evidence for a QTL affecting ham weight. Because halothane-free boars were used in this study the QTL could indicate that unknown RYR1 alleles might exist.
On SSC10, a third QTL affecting KHAM was identified. There is little evidence for a QTL affecting ham weight in the literature other than a chromosome-wise evidence (P < 0.05) reported by Dragos-Wendrich et al. (2003a).
A QTL affecting BHAM and IHAM was identified on SSC13 proximal to the PIT1 gene region where a suggestive ham meat weight QTL was found in a Wild Boar x Meishan cross by Yue et al. (2003b).
Three QTL affecting loin weights were identified on chromosomes 5, 11, and 13. There is no evidence in the literature for a QTL on SSC5 affecting loin weight. The QTL on SSC11 corresponded to previous findings, although only weak evidence was reported (Rohrer and Keele, 1998b; Dragos-Wendrich et al., 2003b). A third QTL affecting loin weight was found on SSC13 near the PIT1 gene region. No previous studies reported an effect solely on loin weight, whereas several studies reported QTL affecting ham weight, carcass weight, fatness, and growth in this region (Yu et al., 1999; de Koning et al., 2001b; Malek et al., 2001a; Geldermann et al., 2003).
Meat Quality
Thirteen QTL on 9 chromosomal regions affecting 10 different meat quality traits were identified.
Ham marbling QTL were identified on SSC6 and SSC14. No previous studies reported marbling QTL in these 2 regions. On SSC6, the QTL is located in the region of RYR1 and H-FAB known from QTL for fatness and intramuscular fat.
Six QTL related to meat color were identified from which the 2 QTL on SSC8 and SSC10 were not described in previous studies. The HAMB QTL on SSC2 corresponded with meat color QTL found by Malek et al. (2001b). At the telomeric region of SSC4q, QTL were found for Minolta-L* ham (HAML, lightness) and JCSloin. The latter was among the most significant QTL found in this study and the findings correspond with findings of Malek et al. (2001b), de Koning et al. (2001a), and Ovilo et al. (2002). Ovilo et al. (2002) already proposed the protoporphyrinogen oxidase gene, which participates in the heme biosyntheses pathway, as a possible candidate gene. The glutamate-cysteine ligase modifier subunit gene, which is involved in the glutathione synthesis pathway, is another candidate gene located in the region. Myoglobin serves as an intra-cellular storage site for oxygen in muscle tissue. The oxidation of oxymyoglobin to metmyoglobin causes fresh meat discoloration; glutathione may have a reducing effect on the oxidation (Tang et al., 2003).
The HAMA QTL on SSC13 corresponds to the region where de Koning et al. (2001a) reported a QTL for lightness of the meat.
Based on the results of this study there seems little agreement between the different color measurements at the loin or ham. None of the color QTL reported affected both loin and ham color. A similar conclusion could be drawn from the publication of Malek et al. (2001b), except for the region of the PRKAG3 (Rendement Napole) gene on SSC15. We did not find any effect on color for the region of the PRKAG3 locus but found an effect on purge loss. Reduced water-holding capacity, pH, and lighter colored meat are well known effects of the unfavorable allele of the Rendement Napole gene (Le Roy et al., 1990). The unfavorable R200Q mutation, however, is Hampshire-specific (Milan et al., 2000), and it is not expected that the mutation is segregating in our cross. Ciobanu et al. (2001) reported additional alleles of the gene with an effect on meat quality, which may segregate in our population.
A second drip loss QTL (DRIP) was found on SSC2. This finding corresponds to findings of Malek et al. (2001b) who reported a similar QTL with the greatest F-value close to marker Sw766.
Loss of moisture is often associated with paler color, reduced firmness, and lower pH. In this study we did not find QTL for pH or color in the regions of the DRIP and PURGE QTL, although this may be expected based on the high genetic correlations between the traits (i.e., rg between –0.86 and –1 were found between water-holding capacity traits and pH in our data set).
Additional QTL with effects on pH were found on other chromosomes. Two QTL for pHI were found on SSC13 and SSC18 and a QTL for pHU was found on SSC4. The pHI QTL are located in a region where QTL related to water-holding capacity were described by others (Malek et al., 2001b; Bidanel and Rothschild, 2002; Dragos-Wendrich et al., 2003c). The pHU QTL in the telomeric region of SSC4q was not reported previously. This QTL is located at the same position as the Japanese color score QTL we identified. Several QTL in the central region of SSC4 with an effect on growth, fatness, and carcass composition traits have been described (reviewed by Bidanel and Rothschild, 2002; Geldermann et al., 2003). The results described in our study put further emphasis on the importance of this chromosome for the pig breeding industry. Evidence for meat quality QTL in the telomeric region of SSC4q as obtained in this study and reported by Ovilo et al. (2002) expand the region of interest on SSC4.
Experimental Design
Information obtained on within-line variation is relevant for traits under selection and could, in principle, be directly applied in marker-assisted selection programs. This makes the use of commercial populations for QTL mapping studies preferable. However, several factors negatively affect the power of QTL mapping in commercial pig populations. Analyses have to be performed within existing families. These families are often of limited size and not all of them may be segregating for the QTL.
To achieve sufficient power for the detection of QTL, we collected phenotypes of large half-sib families. Following the formula of Weller et al. (1990) and Van der Beek et al. (1996), the power of detecting QTL with effect > 0.35 p (or explaining > 15% of 
) is > 0.50 in the present experiment (under the assumptions of full marker informativeness, 8 sires with 100 progeny each, a trait with h2 of 0.30, marker-QTL recombination fraction of 0.10, heterozygosity of the QTL of 0.5, and Type I error of 0.05%). Therefore, the design of the experiment allowed for the detection of QTL with moderate to large effect. The probability of detecting QTL with effects smaller than 0.35 p was low. Although genotyping of all families could have enhanced the power of the study, 8 families were typed in this initial stage to identify the most interesting regions in this population. In a second stage, the remaining families may be typed for the most promising regions. Such a 2-stage approach saves genotyping and, with the type II error controlled at a low level, such preselection of promising regions has a very low chance of eliminating regions with a true QTL.
These results show the feasibility of detecting QTL within a selected commercial breeding line. The examined regions were selected because of described QTL affecting carcass composition and meat quality traits in those regions. This study provides additional information on whether QTL that have been identified using experimental crosses between divergent lines segregate within commercial pig populations as assessed recently by Evans et al. (2003), Nagamine et al. (2003), and Vidal et al. (2005). However, because of the limited power of the study, QTL may remain undetected. Therefore, this study may not be considered a conclusive confirmation of previous findings. Approximately half of the identified QTL are consistent with previous findings (Table 3). These QTL suggest the existence of locus and allelic homogeneity (i.e., segregation for the same QTL and the same alleles within the QTL across populations). This is consistent with the findings of Nagamine et al. (2003) and Evans et al. (2003) who confirmed that the same chromosomal regions might account for between-breed and within-breed variation. Because of the limited power of the study, QTL with smaller effects may remain undetected. In addition to confirmation of known QTL, new QTL were identified (half of the QTL identified in this study were not already known from previous studies, Table 3). Several of the regions covered showed significant QTL for a few, often correlated, traits. This is most striking for SSC11 and SSC6. These regions may carry a single QTL with pleiotropic effects on those traits. This study is one of a limited number of studies presenting carcass composition and meat quality traits recorded in a commercial slaughter cross. Thus, the study provides additional information about segregating genome regions of the studied traits within a commercial breed. The study included some less studied meat quality and carcass composition traits of relevance to the pork industry.
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IMPLICATIONS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONIMPLICATIONSLITERATURE CITED |
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Footnotes |
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3 Current address: Department of Animal Production, Escola Superior de Agricultura "Luiz de Queiroz"—USP, Av. Padua Dias 11, 13418-900, Piracicaba-SP, Brazil.
4 Current address: Institute of Animal Science, Animal Breeding and Husbandry Group, University of Bonn, Endenicher Allee 15, 53115 Bonn, Germany.
2 Corresponding author: Rik.van.Wijk{at}ipg.nl
Received for publication August 23, 2005. Accepted for publication November 14, 2005.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONIMPLICATIONSLITERATURE CITED |
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3-subunit gene associated with low glycogen content in pig skeletal muscle and improved meat quality. Genetics 159:1151–1162.This article has been cited by other articles:
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  H. J. van Wijk, H. Buschbell, B. Dibbits, S.C. Liefers, B. Harlizius, H. C. M. Heuven, E. F. Knol, H. Bovenhuis, and M. A. M. Groenen Variance component analysis of quantitative trait loci for pork carcass composition and meat quality on SSC4 and SSC11 J Anim Sci, January 1, 2007; 85(1): 22 - 30. [Abstract] [Full Text] [PDF]
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