| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
* Departamento de Mejora Genética Animal, INIA, Carretera La Coruña km 7, 28040 Madrid, Spain;
and
Departament de Ciència Animal i dels Aliments, Universidad Autónoma de Barcelona, 08193 Bellaterra, Barcelona, Spain;
and
Area de Producció Animal, Centre UdLl-IRTA, 25198 Lleida, Spain; and
and
Centre de Tecnología de la Carn, IRTA, 17121 Monells, Girona, Spain
2 Correspondence:
phone: 34-91-3471490; fax: 34-91-3572293; E-mail:
ovilo{at}inia.es.
 |
Abstract |
|---|
 Top Abstract IntroductionMaterial and MethodsResultsDiscussionImplicationsLiterature Cited |
|---|
Key Words: Meat Quality • Pigs • Quantitative Trait Loci
|
Introduction |
|---|
|
TopAbstractIntroductionMaterial and MethodsResultsDiscussionImplicationsLiterature Cited |
|---|
We have developed an F2 cross between Iberian and Landrace pigs in order to map QTL that affect growth, carcass, meat quality and histochemical traits. Preliminary and partial analyses regarding carcass and intramuscular fat for chromosomes 4 and 6 have been reported elsewhere (Ovilo et al., 2000b; Pérez-Enciso et al., 2000). The objective of this paper is to present results of the whole genome scan for meat quality traits. The number of genotyped animals has increased compared to previous reports. The present work also reports analyses using a more complex model fitting several chromosomes with epistatic effects to account for possible interactions between QTL mapped on different chromosomes.
|
Material and Methods |
|---|
|
TopAbstractIntroductionMaterial and MethodsResultsDiscussionImplicationsLiterature Cited |
|---|
Three Guadyerbas boars were mated to 31 Landrace sows, which had the genotype N/N for the RYR1 locus (Iberian breed is free of the mutation in this gene). Six males and 72 females from their F1 progeny produced 577 F2 animals. All the animals were reared under normal intensive conditions in the experimental farm of Nova Genètica. Feeding was ad libitum and males were not castrated. The pigs were slaughtered in seven batches between December 1997 and March 1998 following a commercial protocol. The average age at slaughter was 175.5 ± 0.3 d.
Several meat quality traits were measured in the longissimus thoracis muscle in a sample that was taken from the last rib. Intramuscular fat content (IMF) was estimated with the NIT (Near Infrared Transmittance) technique (Davies and Grant, 1987). Muscle pH was measured at 24 h postmortem (pH 24 h) using a CRISON portable pH meter. Muscle color Minolta (Chromameter CR-2000; Osaka, Japan) measurements (L* for lightness, a* for redness and b* for yellowness) were taken at 24 h postmortem on the exposed cut surface of the muscle. A 2.5-cm thick chop was cut and placed in a polystyrene tray, covered with plastic film and stored for one hour at 4°C before measuring. The hue angle (H* = arctan(b*/a*)) and chroma (C* = ((a*)2 + (b*)2)0.5) parameters were calculated. Pigment content was measured as µg acid hematin/g fresh tissue (haematin pigments). The means and SD of the traits analyzed are presented in Table 1
.
|
Statistical Analyses
Linkage analysis used the CriMap version 2.4, option "build" (Green et al., 1990). Marker information contents were obtained following Knott et al. (1998). The underlying statistical model assumes that the putative QTL is diallelic with alternative alleles fixed in each parental breed (Haley et al., 1994), here QQ for the Iberian genotype (with effect a), qq for the Landrace genotype (with effect -a), and Qq for the F1 (with effect d). The probability of every F2 individual being each of the three possible QTL genotypes is calculated conditional upon the markers at 1 cM intervals along the genome. These probabilities are used in a least squares framework to calculate the additive and dominance effects of the putative QTL at each position. The statistical model is
 | [1] |
where yijk is the phenotype, Si is the sex effect, Fj is the is the full-sib family effect (63 levels), and the covariate (Cijk) was carcass weight for all analyzed traits. Coefficients ca and cd are calculated as follows:
 |
where pr(QQ), pr(qq) and pr(Qq) are the respective probabilities that the QTL alleles are homozygous from Iberian or Landrace origin, or are heterozygous.
Model (1) was fitted every centimorgan for each chromosome, regressing the phenotypes onto the coefficients ca and cd. At each location an F ratio was calculated comparing the model with a QTL to the equivalent model without QTL. Estimates for a and d were calculated at the best estimated position with the highest F-ratio. Genome and chromosome-wise levels of significance were calculated using permutation techniques (Churchill and Doerge, 1994). A total number of 20,000 permutations of phenotypes were calculated, maintaining the data structure by sex and families. In each permutation, the highest F-values obtained from the analyses of each one of the 18 autosomes were saved to calculate chromosome threshold values. The highest F-value of each permutation was also saved and their distribution allowed to establish threshold values of 8.53, 10.39 and 13.07 for genome-wise levels of significance at 5%, 1%, and 0.1%. Confidence intervals (CI) for the location of the most significant QTL were obtained using the 
2 drop approximation (Mangin et al., 1994). An F statistics is equal to 2p/p with p being the number of parameters estimated (additive and dominance effects in this case). The 95% threshold being 22,95 = 3.85, the 95% CI limits were obtained at the chromosome locations where the F-statistics decreased 1.92 units starting in both directions from the position corresponding to the maximum F.
A bidimensional scan at 10-cM intervals along the genome was also performed for the traits Minolta measurements a*, b* and L*, IMF, pH 24 h and Haematin content, fitting two different models. The first model includes the effects of two QTL but it does not allow for interaction between them. The statistical model was:
 | [2] |
The effects a1 and a2 are the additive effects and d1 and d2 are dominance effects for each QTL. Coefficients ca1, cd1, ca2 and cd2 were calculated conditional upon the markers, as follows:
 |
were pr1 and pr2 are the probabilities for configurations QQ, Qq and qq in locations 1 and 2.
The second model allows for epistasis:
 | [3] |
where Iaxa, Iaxd, Idxa and Idxd are the additive x additive, additive x dominance, dominance x additive and dominance x dominance epistatic interaction effects, respectively. Regression coefficients caxa, caxd, cdxa and cdxd express the joint probabilities at both QTL positions and were calculated at 10 cM intervals along the genome, as follows:
 |
Several contrasts have been performed between these nested models based on F statistics computed from sums of squares explained by the additive, dominance and epistatic coefficients. The contrast of model [2] against the one QTL model was performed by an F test with 2 df in the numerator to detect QTL given the additive and dominance effects of a second location in the genome. The contrast of the model [3] against the model without any QTL coefficient was performed by an F test with 8 df in the numerator to detect a joint effect on the analyzed trait of two positions of the genome and their interaction. Finally, models [3] and [2] were compared to test epistatic interactions using an F test with 4 df in the numerator.
Genome and bichromosome-wise levels of significance were calculated for two QTL models using permutation techniques (Churchill and Doerge, 1994). A total of 20,000 permutations were calculated, maintaining the data structure by sex and families. For the contrast of the model [3] vs the model without QTL coefficients, the genome-wise significance thresholds were 5.00, 5.56 and 6.56 for the 5, 1 and 0.1% levels. For the contrast of the model [3] vs [2], the corresponding threshold values were 8.16, 9.11 and 10.74. Bi-chromosome-wise levels of significance were also calculated for this contrast, the average values among the 153 two-chromosome combinations were 4.86, 5.96, and 7.56 for the 5, 1, and 0.1% levels, respectively.
|
Results |
|---|
|
TopAbstractIntroductionMaterial and MethodsResultsDiscussionImplicationsLiterature Cited |
|---|
presents a summary of the markers information. Considering all the chromosomes together, 49% of the analyzed microsatellites had information content values contained in the range from 0.9 to 1, 16% from 0.8 to 0.9, 18% from 0.6 to 0.8, 16% from 0.4 to 0.6 and only 1% of the microsatellites had information content values lower than 0.4. The map length was greater in the female sex than in the male sex. The order of markers and the distances between them are in agreement with previous maps (USDA-MARC Swine Genome Map, http://www.genome.iastate.edu/pig). The average length of the maps obtained also agree with previous ones, except for chromosomes 1, 3, 15, 16, and 18, which are approximately 30 to 40% shorter than published maps, due to the lack of informative markers at the ends of this chromosomes for this cross. The corresponding maps for chromosomes 3, 4, 6, 7, and 8 were (in cM) as follows, chromosome 3: Sw72(0.0)- S0206(25.6)- S0216(55.1)- S0002(77.5)- Sw349(86); chromosome 4: Sw2404(0.0)- S0301(40.8)- S0001(59.5)- Sw839(72.8)- DECR(78.8)- S0214(95.1)- Sw445(116.8)- S0097(134.4); chromosome 6: S0035(0.0)- Sw1057(44.3)- S0087(57.7)- Sw316(81.2)- S0228(96.0)- Sw1881(108.7)- Sw2419(145.3); chromosome 7: S0025(0.0)- S0064(40.1)- TNFB(68.9)- S0066(87.8)- Sw632(111.9)- S0101(137.7)- Sw764(160.3); chromosome 8: Sw2410(0.0)- Sw905(26.0)- Swr110(44.7)- S0017(66.5)- S0225(86.1)- Sw61(101.9).
|
shows the location corresponding to the highest test statistics and the parameter estimates obtained using the standard model [1]. Genome-wise significant QTL were located in SSC4, SSC6 and SSC7. No significant effect was detected for Minolta b*, H* (hue angle) and C* (chroma) parameters in any of the analyzed autosomes.
|
). There is evidence for Minolta L* value at position 109 cM (CI bounds = 101–118 cM) at the genome-wise significant level of 0.1%, and for Minolta a* value at 89 cM (CI bounds = 77–112 cM) at the chromosome-wise significance level of the 1%. For the haematin pigments content the position is also 109 cM (CI bounds = 97–121 cM) at the genome-wise significance level of 5%. The fraction of phenotypic variance explained by the QTL ranges from 2.7 to 10.8% and the gene action is additive for all the traits. A highly significant QTL affecting intramuscular fat was found on SSC6. This QTL was previously described with a subset of this population by Ovilo et al. (2000b). According to the present results, which are based on a larger sample of genotyped animals, this QTL maps at position 101 cM (CI bounds = 95–106 cM), within the interval between microsatellites S0228 and Sw1881, and explains the 18% of the phenotypic variance (h2QTL = 0.181). The gene action is partially dominant with the heterozygote being closer to the homozygous Landrace genotype.
In SSC7 a QTL significant at the 5% genome-wise level was found for the haematin pigments at position 87 cM (CI = 76–97 cM) that explains 2.4% of the phenotypic variance. This QTL is contained in the bracket between microsatellites TNFB and S0066. The gene action is dominant with the heterozygote being closer to the Iberian homozygote. There is also suggestive evidence of a QTL in the same region (77 cM, CI = 56–86 cM) that affects Minolta L* value and a QTL affecting Minolta a* value in an overlapping region (50 cM, CI = 26–66 cM).
A QTL significant at the 1% chromosome-wise level was found for the Minolta a* value at 81 cM (CI = 69–101 cM) of SSC8, that explains 5.7% of the phenotypic variance. The gene action is additive. A QTL for pH 24 h was found in SSC3, explaining 6.4% of the phenotypic variance. This QTL is located between the markers S0216 and S0206 and is significant at the 1% chromosome-wise level.
Two-Dimensional Genome Scan
In order to account for possible variation caused by different chromosomes, the traits Minolta a*, b* and L*, IMF, pH 24 h and Haematin content were analyzed with the model (2), fitting one QTL on each pair of positions at 10-cM intervals along the genome. Results from the test of the model [2] vs the model [1] were similar to those of the single QTL analysis. Significant QTL appear at the same locations identified in the one QTL analysis, with a similar level of significance. None of the suggestive QTL identified in the single QTL analysis reach the genome-wise significance level after the inclusion in the model of the additive and dominance coefficients of a second location in the genome.
A more complete model was also used to test the presence of epistatic interactions between different genomic locations. Results of the contrast of model [3] against the no QTL model showed a lot of pairs of chromosome positions with a joint significant effect, but most of these pairs include at least one of the two most important QTL mapped on SSC4 and SSC6 in the previously described genome scan. Other two pairs of chromosome positions were found with joint significant effects at 1% genome-wise level. The first pair maps on SSC8 (30 cM) and SSC12 (60 cM) with joint significant effects on Minolta a* (F = 5.61). At SSC8, the estimated additive and dominance effects were a1 = -1.20 ± 0.25 and d1 = 1.00 ± 0.39. At SSC12, the respective values were a2 = 0.41 ± 0.27 and d2 = 0.47 ± 0.43. The four interaction effects were Iaxa = 0.04 ± 0.33, Iaxd = 1.31 ± 0.42, Idxa = -0.14 ± 0.38 and Idxd = -2.06 ± 0.63. The F value of the test of the model [3] against the model [2] was 5.00, exceeding the 5% bichromosome-wise level of significance. The second pair maps on SSC6 (130 cM) and SSC9 (20 cM) with joint significant effects on IMF content (F = 6.38). In this case, the F value of the test of the model [3] against the model [2] was 6.15, exceeding the 1% bichromosome-wise level of significance and the results are shown in Table 4.
|
. |
Discussion |
|---|
|
TopAbstractIntroductionMaterial and MethodsResultsDiscussionImplicationsLiterature Cited |
|---|
In this work we have analyzed traits useful for predicting technological as well as eating quality of pork. The pH value 24 h postmortem has a direct relation with water binding ability, texture and color, with a higher value resulting in a darker and juicier meat. Color affects appearance and consumer acceptability of meat and it is mainly determined by its pigment content. Measurement of color requires the description of its three dimensions: hue angle, which describes the kind of color; chroma or saturation, which describes depth of color or the extent to which the hue is diluted with black; and lightness (L*), which reflects the extent to which the hue is diluted with white. Also, a* and b* measurements reflect the redness and yellowness of the meat, respectively. Moreover, intramuscular fat content is generally considered as having a favourable influence on eating quality. However, there are contradictory results with several analyses failing to find a significant positive correlation between intramuscular fat content and meat quality. Also, an excess of visible fatness could reduce consumer acceptability of fresh meat and cured cooked products (Fernández et al., 1999a,b, 2000). Consequently, increasing the level of IMF without increasing the level of visible fat, could be a way to improve the acceptability of fresh pork, and therefore an optimum range of IMF content has been suggested (2–3%). For dry-cured products the optimal intramuscular fat content is higher (López-Bote, 1998).
There is presently ample evidence of QTL affecting growth, fat deposition and fatty acid metabolism on SSC4 (Knott et al., 1998; Walling et al., 2000; Pérez-Enciso et al., 2000). A QTL with a major effect on fatness has been named FAT1 (Marklund et al., 1999). In a previous study on the present experimental cross, Pérez-Enciso et al. (2000) observed effects on fat deposition and composition in the interval 71–90 cM of this chromosome. The possible SSC4 QTL found in this study, with significant effects on muscle color Minolta L* value and pigment content (97–121 cM) does not overlap. Nevertheless, this region is coincident with the position of the suggestive QTL for color described by Wang et al. (1998). The direction of QTL effects detected in the present experiment were in agreement with the phenotypic differences between parental lines (Serra et al., 1998). The Iberian allele increased the pigment content and the Minolta a* value for meat redness, diminishing the Minolta L* value related to lightness (i.e., darker color), but there is no evidence of an effect on the Minolta b* value.
Significant effects in the expected direction were also found on SSC7 for Minolta a*, L* and haematin pigment content. It is not clear if the three effects correspond to the same QTL, as the confidence intervals are not totally overlapping, and they also do not overlap with the suggestive QTL for color score on this chromosome described by Wang et al. (1998).
The present results also confirm the QTL affecting IMF previously described in this population by Ovilo et al. (2000b), and locates it in the interval 95–106 cM of chromosome 6. This QTL has been also described in other recent studies (de Koning et al., 1999; Gerbens et al., 2000; Grindflek et al., 2001), but differences can be observed related to the location of the QTL and the existence of an effect on backfat thickness. In the works of Gerbens et al. (2000) and Grindflek et al. (2001) the H-FABP gene was postulated as positional candidate gene for this QTL, due to its role in the cellular fatty acid transport and its coincident position. Moreover, some polymorphisms in this gene have been associated with the amount of IMF (Gerbens et al., 1999). However, the effect of the gene on different fatness traits varies among experiments, the favorable allele is also different between populations (Gerbens et al., 2000; Chen et al., 2000) and finally, Gerbens et al. (2001) could not associate the variation of intramuscular fat content with differences in H-FABP mRNA and protein expression levels. These results suggest that the polymorphisms analyzed are not the causal mutations, but instead are markers linked to them in this or other closely linked genes. According to our results, the H-FABP gene maps at 84 cM on SSC6, 11 cM outside the confidence interval of the QTL (Ovilo et al., 2000a).
The results obtained in other chromosomes related with pH 24 h (SSC3) and Minolta a* (SSC8) are very interesting. Differences for both traits were evidenced in the comparison of the parental lines, being higher in the Guadyerbas line (Serra et al., 1998). The effect of Iberian alleles on pH 24 h was in the expected direction but the effect on Minolta a* corresponds to a cryptic allele.
The results from the two dimensional genomic scan fitting model [2] suggest that cofactors or two QTL analyses do not improve substantially the statistical analysis of a single QTL in this F2 experiment. A two-dimensional genomic scan is indicated to detect possible interactions between loci. Non-allelic interactions between loci are not usually fitted in the models for QTL detection in livestock populations, although there are reports in mice (Routman et al., 1997). In two cases, the combination of additive, dominance and interaction effects of two locations of the genome reached the 1% genome-wise level of significance. But the evidence for these QTL must be cautiously considered, because the threshold levels of significance were calculated on a single trait basis and several traits were separately analyzed. In the test between the models [3] and [2] to detect epistatic effects, there was not any combination significant at the genome-wise level. Possible epistatic effects in the analyzed traits are not relevant enough to be detected with the statistical power of the present experiment. The lack of epistasis between the detected QTL facilitates their possible use for pig meat quality improvement, because of the epistasis would complicate the application of QTL to marker assisted introgression in commercial lines.
Some of the results obtained in this work agree with previous findings, mainly confirming the presence of QTL on SSC4, SSC6 and SSC7, but there is also new and interesting information regarding less studied meat quality traits like Minolta parameters and haematin pigment content. Our results have detected a small number of genomic regions explaining part of the phenotypic variation of the experimental cross (i.e., 2 or 3 QTL account for almost a 20% of the variance for IMF and Minolta a*). The significance of these results might be improved by genotyping more F2 animals or other new crosses with more informative meiosis, with additional markers in the more interesting regions. It can be argued that regions with small effect will never be uncovered with this approach, but it is beyond doubt that very important differences may be due to small number of genetic changes (e.g., Hal or RN).
An alternative approach could be the identification and analysis of positional candidate genes located in these regions and biologically related with the quality traits affected. For the QTL for IMF mapped in SSC 6, the search of new mutations on H-FABP gene is indicated. For SSC4 and SSC7 the information contained in current pig genetic maps do not allow the identification of genes potentially involved in the quality traits affected by both QTL. However, useful information can be obtained from human genetic maps (http://www.ncbi.nlm.nih.gov/). One positional candidate gene related with the meat pigment content was identified in the human chromosome regions (HSA1, HSA8) homologous to the confidence interval of QTL mapped on SSC4. The Protoporphyrinogen oxidase gene (PPO, HSA1q22) participates in the heme biosynthetic pathway, the main component of most meat pigments, and is then possibly related with pig meat color traits. Differences in meat color could also be influenced by the proportions of the different muscular fiber types, with different myoglobin contents. Histochemical composition of the muscle has also been recorded and analyzed in a small sample of 160 animals of this experiment, but no significant effect (at the genome-wise test) on the proportion of fiber types has been detected on any of the chromosomes that show effect on color traits, explained in this report (our unpublished observations).
|
Implications |
|---|
|
TopAbstractIntroductionMaterial and MethodsResultsDiscussionImplicationsLiterature Cited |
|---|
|
Footnotes |
|---|
Received for publication August 13, 2001. Accepted for publication April 30, 2002.
|
Literature Cited |
|---|
|
TopAbstractIntroductionMaterial and MethodsResultsDiscussionImplicationsLiterature Cited |
|---|
This article has been cited by other articles:
|
|
 |
|
  Y.-Y. Duan, J.-W. Ma, F. Yuan, L.-B. Huang, K.-X. Yang, J.-P. Xie, G.-Z. Wu, and L.-S. Huang Genome-wide identification of quantitative trait loci for pork temperature, pH decline, and glycolytic potential in a large-scale White Duroc x Chinese Erhualian resource population J Anim Sci, January 1, 2009; 87(1): 9 - 16. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Estelle, F. Gil, J. M. Vazquez, R. Latorre, G. Ramirez, M. C. Barragan, J. M. Folch, J. L. Noguera, M. A. Toro, and M. Perez-Enciso A quantitative trait locus genome scan for porcine muscle fiber traits reveals overdominance and epistasis J Anim Sci, December 1, 2008; 86(12): 3290 - 3299. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Russo, L. Fontanesi, E. Scotti, F. Beretti, R. Davoli, L. Nanni Costa, R. Virgili, and L. Buttazzoni Single nucleotide polymorphisms in several porcine cathepsin genes are associated with growth, carcass, and production traits in Italian Large White pigs J Anim Sci, December 1, 2008; 86(12): 3300 - 3314. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. F. Cardoso, G. J. M. Rosa, J. P. Steibel, C. W. Ernst, R. O. Bates, and R. J. Tempelman Selective Transcriptional Profiling and Data Analysis Strategies for Expression Quantitative Trait Loci Mapping in Outbred F2 Populations Genetics, November 1, 2008; 180(3): 1679 - 1690. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Uemoto, Y. Nagamine, E. Kobayashi, S. Sato, T. Tayama, Y. Suda, T. Shibata, and K. Suzuki Quantitative trait loci analysis on Sus scrofa chromosome 7 for meat production, meat quality, and carcass traits within a Duroc purebred population J Anim Sci, November 1, 2008; 86(11): 2833 - 2839. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. B. Edwards, C. W. Ernst, N. E. Raney, M. E. Doumit, M. D. Hoge, and R. O. Bates Quantitative trait locus mapping in an F2 Duroc x Pietrain resource population: II. Carcass and meat quality traits J Anim Sci, February 1, 2008; 86(2): 254 - 266. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Lei, C. Luo, X. Peng, M. Fang, Q. Nie, D. Zhang, G. Yang, and X. Zhang Polymorphism of Growth-Correlated Genes Associated with Fatness and Muscle Fiber Traits in Chickens Poult. Sci., May 1, 2007; 86(5): 835 - 842. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
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]
|
|
|
|
|
|
H. J. van Wijk, B. Dibbits, E. E. Baron, A. D. Brings, B. Harlizius, M. A. M. Groenen, E. F. Knol, and H. Bovenhuis Identification of quantitative trait loci for carcass composition and pork quality traits in a commercial finishing cross J Anim Sci, April 1, 2006; 84(4): 789 - 799. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Estelle, A. Mercade, J. L. Noguera, M. Perez-Enciso, C. Ovilo, A. Sanchez, and J. M. Folch Effect of the porcine IGF2-intron3-G3072A substitution in an outbred Large White population and in an Iberian x Landrace cross J Anim Sci, December 1, 2005; 83(12): 2723 - 2728. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. M. Stearns, J. E. Beever, B. R. Southey, M. Ellis, F. K. McKeith, and S. L. Rodriguez-Zas Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. II. Multivariate and principal component analyses J Anim Sci, November 1, 2005; 83(11): 2471 - 2481. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. M. Stearns, J. E. Beever, B. R. Southey, M. Ellis, F. K. McKeith, and S. L. Rodriguez-Zas Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. I. Univariate outbred F2 and sib-pair analyses J Anim Sci, July 1, 2005; 83(7): 1481 - 1493. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
O. Vidal, J. L. Noguera, M. Amills, L. Varona, M. Gil, N. Jimenez, G. Davalos, J. M. Folch, and A. Sanchez Identification of carcass and meat quality quantitative trait loci in a Landrace pig population selected for growth and leanness J Anim Sci, February 1, 2005; 83(2): 293 - 300. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Nii, T. Hayashi, S. Mikawa, F. Tani, A. Niki, N. Mori, Y. Uchida, N. Fujishima-Kanaya, M. Komatsu, and T. Awata Quantitative trait loci mapping for meat quality and muscle fiber traits in a Japanese wild boar x Large White intercross J Anim Sci, February 1, 2005; 83(2): 308 - 315. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Varona, L. Gomez-Raya, W. M. Rauw, A. Clop, C. Ovilo, and J. L. Noguera Derivation of a Bayes Factor to Distinguish Between Linked or Pleiotropic Quantitative Trait Loci Genetics, February 1, 2004; 166(2): 1025 - 1035. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
