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Mapping Quantitative Trait Loci Affecting Body Weight and Abdominal Fat Weight on Chicken Chromosome One1
Mapping Quantitative Trait Loci Affecting Body Weight and Abdominal Fat Weight on Chicken Chromosome One1 X. Liu,* H. Li,*2 S. Wang,* X. Hu,† Y. Gao,† Q. Wang,* N. Li,† Y. Wang,* and H. Zhang* *College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; and †National Laboratories for Agribiotechnology, China Agricultural University, Beijing 100094, China wide level, and these 2 QTL were grouped to 2 different regions; and 5 QTL were identified at the suggestive level, and these 5 QTL were grouped to 4 different regions. For the abdominal fat traits, 1 QTL was identified at the 1% chromosome wide level, 1 QTL was identified at the 5% chromosome wide level, and 2 QTL were identified at the suggestive level. The QTL for BW at 12 wk of age explained 13.51% of the phenotypic variance, and 2 QTL for abdominal fat weight explained 2.53 and 3.97% of the phenotypic variance, respectively. The present study identified chromosome regions harboring significant QTL affecting BW and abdominal fat traits. The results provide a useful reference for further candidate gene research and MAS for BW and abdominal fat traits.
Key words: chicken, quantitative trait loci, body weight, abdominal fat weight, microsatellite marker 2007 Poultry Science 86:1084–1089
INTRODUCTION Genetic improvement programs for livestock and crop species can be enhanced by the use of molecular genetic information in introgression, genotype building, and recurrent selection programs. The prospects for molecular MAS are great for traits difficult to improve through conventional means because of low heritability and difficulty and expense for recording phenotypes (Dekkers and Hospital, 2002). Knowledge on position and effects of QTL would be useful for MAS as well as understanding of genetic background of traits. The chicken is not only a widely raised farm animal but also an excellent model organism, and studies on the chicken genome have an important significance in agriculture and medicine. Growth rate in broiler chickens has been intensely selected for more than half a century
©2007 Poultry Science Association Inc. Received September 15, 2006. Accepted February 10, 2007. 1 This research was supported by Program for New Century Excellent Talents in University (No. NCET-04-0343), National Natural Science Foundation Key Project (No. 30430510), and the National Basic Research Program (No. 2006CB102105). 2 Corresponding author: [email protected] or [email protected]
and will continue to be one of the most important economic traits in broiler breeding programs. Progress in rapid growth has been accompanied by an increase in fat deposition in the broiler. Fat is considered a by-product with very low commercial value, and large amounts of fat deposition can decrease feed efficiency. Measuring body fat content is expensive, and the availability of QTL for use in molecular MAS would therefore be of great value (Ikeobi et al., 2002). The combination of traditional genetic selection and modern molecular methods may be preferred for breeding chickens (Li et al., 2003). Chromosome 1 is the largest in the chicken genome. Quantitative trait loci affecting growth and fat traits were identified on chicken chromosome 1 by Van Kaam et al. (1999a,b), Tatsuda and Fujinaka (2001), Sewalem et al. (2002), Ikeobi et al. (2002, 2004), Burt and Hocking (2002), De Koning et al. (2004), Jennen et al. (2004, 2005), Nones et al. (2006), Lagarrigue et al. (2006), Gao et al. (2006), and Park et al. (2006). These studies made it possible to further study QTL and potential genes that are associated with BW and fatness. However, before attempting to identify potential genes and exploiting them in animal breeding programs by MAS, confirmation is necessary to verify the existence of QTL observed in an initial genome scan, preferably by using independent populations (Spelman and Bovenhuis, 1998; Marklund et al., 1999). The
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ABSTRACT Body weight and abdominal fat traits are complex and important economic traits that may benefit from the implementation of MAS. The objective of the current study was to identify QTL associated with BW and abdominal fat traits. The Northeast Agricultural University resource population was used in the current study. Body weight and abdominal fat weight were measured in the F2 population. A total of 369 F2 individuals produced from 4 F1 families, their parents, and F0 birds were genotyped by 23 fluorescent microsatellite markers on chromosome 1. A linkage map was constructed, and interval mapping was conducted to identify putative QTL. For BW, 10 QTL were identified at the 1% chromosome wide level, and these 10 QTL were grouped to 3 different regions; 2 QTL were identified at the 5% chromosome
CHICKEN QUANTITATIVE TRAIT LOCI ON CHROMOSOME ONE
objective of the present study was to identify and confirm QTL affecting BW and abdominal fat traits on chromosome 1 by using a unique F2 designed population from a broiler × layer cross.
MATERIALS AND METHODS Experimental Populations
The linkage map was constructed by using CRIMAP (Green et al., 1990). All options were used to order markers and to obtain distances between markers. The QTL express software under an F2 model at http:// qtl.cap.ed.ac.uk/ (Seaton et al., 2002) was used for QTL analyses. Date was subjected to a model containing additive and dominant effects of a putative QTL, with sex, hatch, and family as fixed effects in the model. When BW of 1 to 12 wk of age and CW at 12 wk of age were analyzed, BW at hatch (BW0) was used as a covariate trait, and when the AFW and AFP were analyzed, the CW was used as a covariate trait. The percentage difference in the residual sums of squares between the full and reduced model was calculated as the phenotypic variance, which that QTL could explain. Significance thresholds for analyses were calculated using a permutation test (Churchill and Doerge, 1994). A total of 1,000 permutations were computed to determine the empirical distribution of the statistical test under the null hypothesis of no QTL associated with the part of the genome under study. Identification of 2 QTL was declared for a trait when peak F-ratios were ≥ 40 cM apart. Three significance levels were used: suggestive, 5%, and 1% chromosome-wide (Lander and Kruglyak, 1995).
Phenotyping The BW was measured at hatch and weekly up to 12 wk of age. Carcass weight (CW) and abdominal fat weight (AFW) were recorded at 12 wk of age. The AFW was also expressed as a percentage of BW at 12 wk of age (AFP).
Genotyping The 23 fluorescent microsatellite markers on chromosome 1 were selected from the Web site (http:// www.ncbi.nlm.nih.gov/ and http://www.thearkdb. org/arkdb/do/getChromosomeDetails?accession=AR KSPC00000004) in the current study. Genomic DNA was isolated from venous blood samples using a phenol-chloroform method (Wang et al., 2006). Polymerase chain reactions for each marker were carried out separately in a reaction volume of 25 L included 100 ng of template DNA, 1 × PCR reaction buffer (10 mM of Tris-HCI, 50 mM of KCI, and 1.5 mM of MgCI2, pH 8.3), 0.25 M of each primer, 200 M of each deoxynucleotide triphosphate, and 1 U of Taq polymerase (Takara Biotechnology Co., Ltd., Dalian, China). The PCR products were electrophoresed in 6% denaturing polyacrylamide gels using an ABI377 sequencer (Applied Biosystems, Foster City, CA). A total of 369 F2 individuals produced from 4 F1 families, their parents, and F0 birds were genotyped. Genotype data was collected using GeneScan 3.1 and Genotyper 2.1 (Applied Biosystems).
Statistical Analyses Phenotypic data were analyzed by using JMP software (SAS Institute, Cary, NC). Means, standard deviation, and coefficient of variation of traits were calculated.
RESULTS Phenotypic Data The mean values and standard deviations (in parentheses) of the traits were 2,070.75 (418.48) g for BW at 12 wk (BW12), 1,832.97 (379.15) g for CW, 77.80 (30.72) g for AFW, and 0.038 (0.015) for AFP (Table 1). The CV of BW were from 9.46% (for BW0) to 20.21% (for BW12), and the CV of abdominal fat traits were higher (39.49% for AFW and 39.47% for AFP) than the ones for BW.
Linkage Map In the current study, multilocus linkage analysis resulted in a sex average chromosome 1 map of 23 ordered markers. The linkage map covered 637.9 cM from the total length of chromosome 1 (Figure 1). Estimated distance between the first and the last marker on chromosome 1 was longer than the consensus map distance. Locus orders were in general agreement with other published linkage maps, except for 1 discrepancy: marker ROS0025 and marker MCW0115 were reversed in this map relative to the East Lansing map; however, there was agreement with the Wageningen linkage map.
QTL Analysis for Body Weight and Abdominal Fat Traits The QTL with suggestive and significant linkages for each trait, details of the additive and dominance effects of the QTL, and the phenotypic variance that the QTL explained are summarized in Table 2, and details of the markers flanking each QTL and the estimated location
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The Northeast Agricultural University Resource Population (NEAURP) was used in the current study. The NEAURP was established by crossing broiler sires, derived from a line at Northeast Agricultural University selected for increased abdominal fat, with Baier layer dams, a Chinese local breed. The F1 birds were intercrossed to produce the F2 population. All F2 birds had free access to feed and water. Commercial corn-soybeanbased diets that met all NRC requirements (National Research Council, 1994) were provided in the study. From hatch to 3 wk of age, birds received a starter feed (3,000 kcal of ME/kg and 210 g/kg of CP); and from 3 to 12 wk of age, birds were fed a grower diet (3,100 kcal of ME/kg and 190 g/kg of CP; Wang et al., 2006).
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LIU ET AL. Table 1. Statistics of the various traits Mean (g)
SD (g)
CV (%)
Minimum (g)
Maximum (g)
BW01 BW1 BW2 BW3 BW4 BW5 BW6 BW7 BW8 BW9 BW10 BW11 BW12 CW2 AFW2 AFP2
38.79 73.99 160.40 286.53 446.89 621.66 819.41 1,037.31 1,250.10 1,490.69 1,682.60 1,887.55 2,070.75 1,832.97 77.80 0.038
3.67 10.09 22.60 44.02 72.09 97.38 137.09 185.32 227.73 284.19 324.51 370.31 418.48 379.15 30.72 0.015
9.46 13.64 14.09 15.36 16.13 15.66 16.73 17.87 18.23 19.06 19.29 19.62 20.21 20.69 39.49 39.47
31.2 39 74.2 150 235 335 480 605 735 895 1,005 1,105 1,225 1,065 4 0.0024
47.4 99.3 218.8 410 650 960 1,250 1,620 1,970 2,440 2,710 3,135 3,550 3,250 184 0.081
1
Numbers following BW indicating age in weeks. CW = carcass weight; AFW = abdominal fat weight; AFP = AFW expressed as percentage of BW12.
2
relative to the first marker of the NEAURP linkage map (Figure 1) are presented. For BW, 10 QTL were identified at the 1% chromosome wide level, 2 QTL were identified at the 5% chromosome wide level, and 5 QTL were identified at the suggestive level. For abdominal fat traits, 1 QTL was identified at the 1% chromosome wide level, 1 QTL was identified at the 5% chromosome wide level, and 2 QTL were identified at the suggestive level. A positive additive effect indicated that the allele conferring the higher trait value was inherited from the broiler line. The test statistics for BW of 4 to 12 wk of age and CW peaked in the region 523 to 555 cM. The F-ratios of QTL mapping for BW11, BW12, and CW are shown in Figure 2. Two QTL affecting AFW and AFP were detected in the present study, respectively. Test statistics for AFW peaked at 550 cM and AFP peaked at 548 cM. They were close to the QTL region associated with BW. Furthermore, test statistics for AFW and AFP also peaked at 183 and 69 cM, respectively. However, the test statistics only reached suggestive linkage level. The F-ratios of QTL mapping for AFW and AFP are shown in Figure 3.
DISCUSSION Body weight is under complex genetic control. Uncovering the molecular mechanism of growth will contribute to more efficient selection for growth in broiler chickens (Deeb and Lamont, 2002). From the results of present study, the QTL affecting BW at 4 to 12 wk of age were located in the region 523 to 555 cM on the linkage map of NEAURP; the markers associated with this region were LEI0079, ADL328, and ROS0025. Kerje et al. (2003) indicated that when the 2 estimated QTL positions differed by a recombination distance of <30 cM in a chromosome region, a single QTL for the given trait was assumed on that chromosome. Because BW at 4 to 12 wk of age were highly correlated (unpublished data) and the QTL positions were close, it was reasonable to assume that the same QTL affected these
traits. The phenotypic variances explained by this QTL were from 3.32% (for BW5) to 13.51% (for BW12). The additive effects of this QTL affecting BW at 4 to 12 wk were all significant and positive, which indicated the allele resulting in larger BW was derived from the broiler sire. This QTL was consistent with the QTL (flanked by ADL0183 and ROS0025) for BW6 reported by Sewalem et al. (2002). Van Kaam et al. (1999b) identified this QTL for carcass percentage at the same position. Kerje et al. (2003) reported a QTL affecting BW at 46 d at nearly the same region as the QTL identified in the present study. Atzmon et al. (2006) reported that a microsatellite marker MCW0102 was significantly associated with BW at 7 wk in a commercial broiler line. The marker MCW0102 was in the QTL region reported in the present study. The previous studies reported that there were other QTL affecting BW on chromosome 1. Sewalem et al. (2002) detected a QTL affecting BW at 3 and 6 wk of age, and the markers related to this QTL were LEI0068, LEI0146, and MCW0018. Nones et al. (2006) reported a QTL affecting BW at 35 and 42 d at ∼150 cM on the consensus map, and the flanking markers were LEI0068 and MCW0097. Atzmon et al. (2006) found a microsatellite marker ADL0037 significantly associated with BW at 7 wk. These studies suggested that a different set of genes may be involved at different life stages of chicken growth and development, and the QTL found may vary with the population used. The QTL affecting BW traits detected in the current study had a significant effect at 4 to 12 wk of age, and this result was in agreement with previous studies (Van Kaam et al., 1999b; Sewalem et al., 2002; Kerje et al., 2003). Body fat should be limited to enhance production efficiency and product quality. Owing to difficulty of measuring of these kinds of traits such as abdominal fat, the identification and use of QTL in selection programs, therefore, will offer the potential for more rapid genetic improvement. In the present study, 2 QTL associated with AFW and AFP were detected, respectively. All of them
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Trait
CHICKEN QUANTITATIVE TRAIT LOCI ON CHROMOSOME ONE
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Figure 1. The linkage map of chromosome 1 of the Northeast Agricultural University resource population.
had significant additive effects, but the dominant effects were not significant. The QTL affecting AFW located at 183 cM reached suggestive linkage level. The flanking markers of this QTL were LEI0068 and MCW0297. This QTL explained 2.53% of the phenotypic variance. In this region, Ikeobi et al. (2002) mapped a QTL affecting abdominal fatness that could explain 3.0% of the phenotypic variance in a broiler × layer cross population. Jennen et al. (2005) had also confirmed this QTL in an improved intercross line. The flanking markers of this QTL were MCW0289 and MCW0297. Nones et al. (2006) identified a QTL affecting AFW, and the flanking markers were LEI0146 and LEI0174. In the present study, a new QTL
affecting AFP was detected at 69 cM, and the flanking markers were MCW0010 and MCW0106. This QTL only reached suggestive linkage level. In this region, Kerje et al. (2003) and Gao et al. (2006) reported a QTL affecting BW, but no QTL affecting AFP was reported. Significant QTL affecting AFW and AFP was detected at 550 and 548 cM, respectively. Because they were in the QTL region affecting BW, maybe the same QTL affects BW and abdominal fat. This QTL explained 3.97% of the phenotypic variance for AFW and 6.24% of the phenotypic variance for AFP. The flanking markers related to this QTL were ADL0328 and ROS0025. This QTL was identified in a cross of chicken lines divergently selected for high and low BW, and the flanking markers were LEI0162 and LEI0134 (Park et al., 2006). This QTL also was confirmed by Lagarrigue et al. (2006) in a cross of lines divergently selected for abdominal fatness, and the flanking markers were ADL0328 and LEI0061. Atzmon et al. (2006) found 2 microsatellite markers ADL0150 and MCW0109 significantly associated with AFW, and the 2 markers were in the QTL region found in the current study. Nones et al. (2006) reported 2 QTL affected AFW at 196 and 251 cm on chromosome 1. The additive effects of them were positive. The QTL they described were not detected in the current study. In the present study, the QTL affecting abdominal fat traits (AFW and AFP) were cryptic because the additive effects of the QTL were negative. This indicated that the allele responsible for high AFW and AFP was derived from the dam line, which had lower AFW and AFP. The reason that the cryptic QTL exist in a population is complicated. Cryptic QTL can be detected in QTL mapping studies because of no or limited selection for the trait, drift, pleiotropic effects of the QTL allele on other traits that are under selection, or close linkage and linkage disequilibrium with QTL that are under selection (Abasht et al., 2006). Cryptic QTL would be difficult to be applied in breeding programs because it is unknown whether it
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Figure 2. The F-ratio of QTL mapping for BW at 11 wk (BW11), BW at 12 wk (BW12), and carcass weight (CW). Triangles above the X-axis indicate the marker positions as shown in Figure 1.
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LIU ET AL. Table 2. The QTL locations and effects on BW and abdominal fat traits Position1 (cM)
F-ratio
Flanking markers
AFP AFW BW5 BW0 BW3 BW2 BW4 BW1 BW8 BW8 BW9 BW11 BW12 CW BW6 AFP BW10 AFW BW7 BW4 BW5
5.08† 4.67† 11.54** 3.92† 4.01† 4.51† 11.43** 3.22† 8.72* 6.94† 14.29** 18.72** 28.12** 28.06** 11.61** 11.91** 9.14** 7.39* 19.23** 9.31** 6.19*
MCW0010-MCW0106 LEI0068-MCW0297 MCW0297-LEI0146 LEI0146-MCW0018 LEI0146-MCW0018 MCW0018-MCW0058 ADL251-MCW0061 MCW0061-LEI0088 MCW0061-LEI0088 LEI0079-ADL328 LEI0079-ADL328 LEI0079-ADL328 LEI0079-ADL328 LEI0079-ADL328 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025
Dominant effect (SE)
−0.0015 (−0.001093) −5.15 (1.94) 52.13 (12.29) −0.57 (0.72) 14.79 (6.02) 13.49 (5.81) 37.83 (8.63) 3.99 (1.59) 91.63 (25.08) 102.87 (29.89) 144.12 (28.68) 155.71 (25.56) 195.39 (26.33) 171.04 (23.15) 53.98 (16.01) −0.005 (−0.001035) 136.21 (32.48) −8.14 (2.19) 104.68 (17.01) 35.72 (8.30) 42.53 (12.82)
−0.0056 (−0.001953) 3.88 (2.93) 47.99 (19.07) 3.43 (1.33) 17.31 (11.03) −21.36 (13.81) 18.20 (15.26) −2.33 (2.61) 53.08 (41.23) −55.53 (55.83) −63.32 (52.46) 2.12 (43.66) −14.95 (44.98) −21.91 (38.29) −82.11 (25.41) 0.0012 (−0.001535) −29.19 (52.50) 2.84 (3.32) −9.75 (27.64) 8.71 (13.59) 29.43 (20.76)
Phenotypic variance2 (%) 2.7 2.53 6.02 2.1 2.18 2.43 6 1.9 4.62 3.71 7.35 9.42 13.51 13.49 6.06 6.24 4.83 3.97 9.65 4.92 3.32
1
QTL positions relative to the genetic map of Northeast Agricultural University resource population in Figure
1. 2 Phenotypic variance = percentage difference in the residual sums of squares between the full and reduced model. †Suggestive linkage; *Chromosome wide significant, P < 0.05; **chromosome wide significant, P < 0.01.
represents true single locus effects or appeared because of epistasis (Abasht et al., 2006). The present study identified several QTL in a broiler × layer resource population. Although it is encouraging that the identified QTL were consistent with previous studies, there were many inconsistent results across studies. Inconsistent results can occur for many reasons, including markers used, choice of statistical models, and most importantly the use of a different experiment population (McElroy et al., 2006). Therefore, it is essential to define marker-QTL phase and effect in the specific populations of interest for application.
In summary, commercial breeding programs of broiler chickens have become more complex and challenging because so many objectives need to be simultaneously considered to reduce production costs, maintain health, and improve product quality. Breeding goals must include increased growth rate, decreased abdominal fat, maintenance of good development and growth of the skeletal system, and overall fitness. The relationships of these traits are complex, and some of the traits are very difficult to measure. Therefore, molecular MAS may be required to improve genetic selection programs. The current study found that there exist highly significant QTL affecting BW and abdominal fat traits on chromosome 1. This is the first step toward the fine mapping QTL affecting BW and abdominal fat. With focus on the QTL identified on chromosome 1, valuable candidate genes may be found by combining results of fine mapping and the chicken genome sequence, and further function study of these genes will benefit understanding of the genetic background of growth and fat deposition of chickens.
ACKNOWLEDGMENTS
Figure 3. The F-ratio of QTL mapping for abdominal fat weight (AFW) and abdominal fat percentage (AFP). Triangles above the X-axis indicate the marker positions as shown in Figure 1.
The authors gratefully acknowledge the members of the Poultry Farm of Northeast Agricultural University for managing the birds. The authors thank Qin Zhang for the help with data analyses. This research was supported by Program for New Century Excellent Talents in University (No. NCET-04-0343), National Natural Science Foundation Key Project (No. 30430510), and the National Basic Research Program (No. 2006CB102105).
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69 183 195 219 231 271 339 343 351 523 528 534 534 536 548 548 550 550 551 553 555
Trait
Additive effect (SE)
CHICKEN QUANTITATIVE TRAIT LOCI ON CHROMOSOME ONE
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Key words: chicken, quantitative trait loci, body weight, abdominal fat weight, microsatellite marker 2007 Poultry Science 86:1084–1089
INTRODUCTION Genetic improvement programs for livestock and crop species can be enhanced by the use of molecular genetic information in introgression, genotype building, and recurrent selection programs. The prospects for molecular MAS are great for traits difficult to improve through conventional means because of low heritability and difficulty and expense for recording phenotypes (Dekkers and Hospital, 2002). Knowledge on position and effects of QTL would be useful for MAS as well as understanding of genetic background of traits. The chicken is not only a widely raised farm animal but also an excellent model organism, and studies on the chicken genome have an important significance in agriculture and medicine. Growth rate in broiler chickens has been intensely selected for more than half a century
©2007 Poultry Science Association Inc. Received September 15, 2006. Accepted February 10, 2007. 1 This research was supported by Program for New Century Excellent Talents in University (No. NCET-04-0343), National Natural Science Foundation Key Project (No. 30430510), and the National Basic Research Program (No. 2006CB102105). 2 Corresponding author: [email protected] or [email protected]
and will continue to be one of the most important economic traits in broiler breeding programs. Progress in rapid growth has been accompanied by an increase in fat deposition in the broiler. Fat is considered a by-product with very low commercial value, and large amounts of fat deposition can decrease feed efficiency. Measuring body fat content is expensive, and the availability of QTL for use in molecular MAS would therefore be of great value (Ikeobi et al., 2002). The combination of traditional genetic selection and modern molecular methods may be preferred for breeding chickens (Li et al., 2003). Chromosome 1 is the largest in the chicken genome. Quantitative trait loci affecting growth and fat traits were identified on chicken chromosome 1 by Van Kaam et al. (1999a,b), Tatsuda and Fujinaka (2001), Sewalem et al. (2002), Ikeobi et al. (2002, 2004), Burt and Hocking (2002), De Koning et al. (2004), Jennen et al. (2004, 2005), Nones et al. (2006), Lagarrigue et al. (2006), Gao et al. (2006), and Park et al. (2006). These studies made it possible to further study QTL and potential genes that are associated with BW and fatness. However, before attempting to identify potential genes and exploiting them in animal breeding programs by MAS, confirmation is necessary to verify the existence of QTL observed in an initial genome scan, preferably by using independent populations (Spelman and Bovenhuis, 1998; Marklund et al., 1999). The
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ABSTRACT Body weight and abdominal fat traits are complex and important economic traits that may benefit from the implementation of MAS. The objective of the current study was to identify QTL associated with BW and abdominal fat traits. The Northeast Agricultural University resource population was used in the current study. Body weight and abdominal fat weight were measured in the F2 population. A total of 369 F2 individuals produced from 4 F1 families, their parents, and F0 birds were genotyped by 23 fluorescent microsatellite markers on chromosome 1. A linkage map was constructed, and interval mapping was conducted to identify putative QTL. For BW, 10 QTL were identified at the 1% chromosome wide level, and these 10 QTL were grouped to 3 different regions; 2 QTL were identified at the 5% chromosome
CHICKEN QUANTITATIVE TRAIT LOCI ON CHROMOSOME ONE
objective of the present study was to identify and confirm QTL affecting BW and abdominal fat traits on chromosome 1 by using a unique F2 designed population from a broiler × layer cross.
MATERIALS AND METHODS Experimental Populations
The linkage map was constructed by using CRIMAP (Green et al., 1990). All options were used to order markers and to obtain distances between markers. The QTL express software under an F2 model at http:// qtl.cap.ed.ac.uk/ (Seaton et al., 2002) was used for QTL analyses. Date was subjected to a model containing additive and dominant effects of a putative QTL, with sex, hatch, and family as fixed effects in the model. When BW of 1 to 12 wk of age and CW at 12 wk of age were analyzed, BW at hatch (BW0) was used as a covariate trait, and when the AFW and AFP were analyzed, the CW was used as a covariate trait. The percentage difference in the residual sums of squares between the full and reduced model was calculated as the phenotypic variance, which that QTL could explain. Significance thresholds for analyses were calculated using a permutation test (Churchill and Doerge, 1994). A total of 1,000 permutations were computed to determine the empirical distribution of the statistical test under the null hypothesis of no QTL associated with the part of the genome under study. Identification of 2 QTL was declared for a trait when peak F-ratios were ≥ 40 cM apart. Three significance levels were used: suggestive, 5%, and 1% chromosome-wide (Lander and Kruglyak, 1995).
Phenotyping The BW was measured at hatch and weekly up to 12 wk of age. Carcass weight (CW) and abdominal fat weight (AFW) were recorded at 12 wk of age. The AFW was also expressed as a percentage of BW at 12 wk of age (AFP).
Genotyping The 23 fluorescent microsatellite markers on chromosome 1 were selected from the Web site (http:// www.ncbi.nlm.nih.gov/ and http://www.thearkdb. org/arkdb/do/getChromosomeDetails?accession=AR KSPC00000004) in the current study. Genomic DNA was isolated from venous blood samples using a phenol-chloroform method (Wang et al., 2006). Polymerase chain reactions for each marker were carried out separately in a reaction volume of 25 L included 100 ng of template DNA, 1 × PCR reaction buffer (10 mM of Tris-HCI, 50 mM of KCI, and 1.5 mM of MgCI2, pH 8.3), 0.25 M of each primer, 200 M of each deoxynucleotide triphosphate, and 1 U of Taq polymerase (Takara Biotechnology Co., Ltd., Dalian, China). The PCR products were electrophoresed in 6% denaturing polyacrylamide gels using an ABI377 sequencer (Applied Biosystems, Foster City, CA). A total of 369 F2 individuals produced from 4 F1 families, their parents, and F0 birds were genotyped. Genotype data was collected using GeneScan 3.1 and Genotyper 2.1 (Applied Biosystems).
Statistical Analyses Phenotypic data were analyzed by using JMP software (SAS Institute, Cary, NC). Means, standard deviation, and coefficient of variation of traits were calculated.
RESULTS Phenotypic Data The mean values and standard deviations (in parentheses) of the traits were 2,070.75 (418.48) g for BW at 12 wk (BW12), 1,832.97 (379.15) g for CW, 77.80 (30.72) g for AFW, and 0.038 (0.015) for AFP (Table 1). The CV of BW were from 9.46% (for BW0) to 20.21% (for BW12), and the CV of abdominal fat traits were higher (39.49% for AFW and 39.47% for AFP) than the ones for BW.
Linkage Map In the current study, multilocus linkage analysis resulted in a sex average chromosome 1 map of 23 ordered markers. The linkage map covered 637.9 cM from the total length of chromosome 1 (Figure 1). Estimated distance between the first and the last marker on chromosome 1 was longer than the consensus map distance. Locus orders were in general agreement with other published linkage maps, except for 1 discrepancy: marker ROS0025 and marker MCW0115 were reversed in this map relative to the East Lansing map; however, there was agreement with the Wageningen linkage map.
QTL Analysis for Body Weight and Abdominal Fat Traits The QTL with suggestive and significant linkages for each trait, details of the additive and dominance effects of the QTL, and the phenotypic variance that the QTL explained are summarized in Table 2, and details of the markers flanking each QTL and the estimated location
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The Northeast Agricultural University Resource Population (NEAURP) was used in the current study. The NEAURP was established by crossing broiler sires, derived from a line at Northeast Agricultural University selected for increased abdominal fat, with Baier layer dams, a Chinese local breed. The F1 birds were intercrossed to produce the F2 population. All F2 birds had free access to feed and water. Commercial corn-soybeanbased diets that met all NRC requirements (National Research Council, 1994) were provided in the study. From hatch to 3 wk of age, birds received a starter feed (3,000 kcal of ME/kg and 210 g/kg of CP); and from 3 to 12 wk of age, birds were fed a grower diet (3,100 kcal of ME/kg and 190 g/kg of CP; Wang et al., 2006).
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LIU ET AL. Table 1. Statistics of the various traits Mean (g)
SD (g)
CV (%)
Minimum (g)
Maximum (g)
BW01 BW1 BW2 BW3 BW4 BW5 BW6 BW7 BW8 BW9 BW10 BW11 BW12 CW2 AFW2 AFP2
38.79 73.99 160.40 286.53 446.89 621.66 819.41 1,037.31 1,250.10 1,490.69 1,682.60 1,887.55 2,070.75 1,832.97 77.80 0.038
3.67 10.09 22.60 44.02 72.09 97.38 137.09 185.32 227.73 284.19 324.51 370.31 418.48 379.15 30.72 0.015
9.46 13.64 14.09 15.36 16.13 15.66 16.73 17.87 18.23 19.06 19.29 19.62 20.21 20.69 39.49 39.47
31.2 39 74.2 150 235 335 480 605 735 895 1,005 1,105 1,225 1,065 4 0.0024
47.4 99.3 218.8 410 650 960 1,250 1,620 1,970 2,440 2,710 3,135 3,550 3,250 184 0.081
1
Numbers following BW indicating age in weeks. CW = carcass weight; AFW = abdominal fat weight; AFP = AFW expressed as percentage of BW12.
2
relative to the first marker of the NEAURP linkage map (Figure 1) are presented. For BW, 10 QTL were identified at the 1% chromosome wide level, 2 QTL were identified at the 5% chromosome wide level, and 5 QTL were identified at the suggestive level. For abdominal fat traits, 1 QTL was identified at the 1% chromosome wide level, 1 QTL was identified at the 5% chromosome wide level, and 2 QTL were identified at the suggestive level. A positive additive effect indicated that the allele conferring the higher trait value was inherited from the broiler line. The test statistics for BW of 4 to 12 wk of age and CW peaked in the region 523 to 555 cM. The F-ratios of QTL mapping for BW11, BW12, and CW are shown in Figure 2. Two QTL affecting AFW and AFP were detected in the present study, respectively. Test statistics for AFW peaked at 550 cM and AFP peaked at 548 cM. They were close to the QTL region associated with BW. Furthermore, test statistics for AFW and AFP also peaked at 183 and 69 cM, respectively. However, the test statistics only reached suggestive linkage level. The F-ratios of QTL mapping for AFW and AFP are shown in Figure 3.
DISCUSSION Body weight is under complex genetic control. Uncovering the molecular mechanism of growth will contribute to more efficient selection for growth in broiler chickens (Deeb and Lamont, 2002). From the results of present study, the QTL affecting BW at 4 to 12 wk of age were located in the region 523 to 555 cM on the linkage map of NEAURP; the markers associated with this region were LEI0079, ADL328, and ROS0025. Kerje et al. (2003) indicated that when the 2 estimated QTL positions differed by a recombination distance of <30 cM in a chromosome region, a single QTL for the given trait was assumed on that chromosome. Because BW at 4 to 12 wk of age were highly correlated (unpublished data) and the QTL positions were close, it was reasonable to assume that the same QTL affected these
traits. The phenotypic variances explained by this QTL were from 3.32% (for BW5) to 13.51% (for BW12). The additive effects of this QTL affecting BW at 4 to 12 wk were all significant and positive, which indicated the allele resulting in larger BW was derived from the broiler sire. This QTL was consistent with the QTL (flanked by ADL0183 and ROS0025) for BW6 reported by Sewalem et al. (2002). Van Kaam et al. (1999b) identified this QTL for carcass percentage at the same position. Kerje et al. (2003) reported a QTL affecting BW at 46 d at nearly the same region as the QTL identified in the present study. Atzmon et al. (2006) reported that a microsatellite marker MCW0102 was significantly associated with BW at 7 wk in a commercial broiler line. The marker MCW0102 was in the QTL region reported in the present study. The previous studies reported that there were other QTL affecting BW on chromosome 1. Sewalem et al. (2002) detected a QTL affecting BW at 3 and 6 wk of age, and the markers related to this QTL were LEI0068, LEI0146, and MCW0018. Nones et al. (2006) reported a QTL affecting BW at 35 and 42 d at ∼150 cM on the consensus map, and the flanking markers were LEI0068 and MCW0097. Atzmon et al. (2006) found a microsatellite marker ADL0037 significantly associated with BW at 7 wk. These studies suggested that a different set of genes may be involved at different life stages of chicken growth and development, and the QTL found may vary with the population used. The QTL affecting BW traits detected in the current study had a significant effect at 4 to 12 wk of age, and this result was in agreement with previous studies (Van Kaam et al., 1999b; Sewalem et al., 2002; Kerje et al., 2003). Body fat should be limited to enhance production efficiency and product quality. Owing to difficulty of measuring of these kinds of traits such as abdominal fat, the identification and use of QTL in selection programs, therefore, will offer the potential for more rapid genetic improvement. In the present study, 2 QTL associated with AFW and AFP were detected, respectively. All of them
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Trait
CHICKEN QUANTITATIVE TRAIT LOCI ON CHROMOSOME ONE
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Figure 1. The linkage map of chromosome 1 of the Northeast Agricultural University resource population.
had significant additive effects, but the dominant effects were not significant. The QTL affecting AFW located at 183 cM reached suggestive linkage level. The flanking markers of this QTL were LEI0068 and MCW0297. This QTL explained 2.53% of the phenotypic variance. In this region, Ikeobi et al. (2002) mapped a QTL affecting abdominal fatness that could explain 3.0% of the phenotypic variance in a broiler × layer cross population. Jennen et al. (2005) had also confirmed this QTL in an improved intercross line. The flanking markers of this QTL were MCW0289 and MCW0297. Nones et al. (2006) identified a QTL affecting AFW, and the flanking markers were LEI0146 and LEI0174. In the present study, a new QTL
affecting AFP was detected at 69 cM, and the flanking markers were MCW0010 and MCW0106. This QTL only reached suggestive linkage level. In this region, Kerje et al. (2003) and Gao et al. (2006) reported a QTL affecting BW, but no QTL affecting AFP was reported. Significant QTL affecting AFW and AFP was detected at 550 and 548 cM, respectively. Because they were in the QTL region affecting BW, maybe the same QTL affects BW and abdominal fat. This QTL explained 3.97% of the phenotypic variance for AFW and 6.24% of the phenotypic variance for AFP. The flanking markers related to this QTL were ADL0328 and ROS0025. This QTL was identified in a cross of chicken lines divergently selected for high and low BW, and the flanking markers were LEI0162 and LEI0134 (Park et al., 2006). This QTL also was confirmed by Lagarrigue et al. (2006) in a cross of lines divergently selected for abdominal fatness, and the flanking markers were ADL0328 and LEI0061. Atzmon et al. (2006) found 2 microsatellite markers ADL0150 and MCW0109 significantly associated with AFW, and the 2 markers were in the QTL region found in the current study. Nones et al. (2006) reported 2 QTL affected AFW at 196 and 251 cm on chromosome 1. The additive effects of them were positive. The QTL they described were not detected in the current study. In the present study, the QTL affecting abdominal fat traits (AFW and AFP) were cryptic because the additive effects of the QTL were negative. This indicated that the allele responsible for high AFW and AFP was derived from the dam line, which had lower AFW and AFP. The reason that the cryptic QTL exist in a population is complicated. Cryptic QTL can be detected in QTL mapping studies because of no or limited selection for the trait, drift, pleiotropic effects of the QTL allele on other traits that are under selection, or close linkage and linkage disequilibrium with QTL that are under selection (Abasht et al., 2006). Cryptic QTL would be difficult to be applied in breeding programs because it is unknown whether it
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Figure 2. The F-ratio of QTL mapping for BW at 11 wk (BW11), BW at 12 wk (BW12), and carcass weight (CW). Triangles above the X-axis indicate the marker positions as shown in Figure 1.
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LIU ET AL. Table 2. The QTL locations and effects on BW and abdominal fat traits Position1 (cM)
F-ratio
Flanking markers
AFP AFW BW5 BW0 BW3 BW2 BW4 BW1 BW8 BW8 BW9 BW11 BW12 CW BW6 AFP BW10 AFW BW7 BW4 BW5
5.08† 4.67† 11.54** 3.92† 4.01† 4.51† 11.43** 3.22† 8.72* 6.94† 14.29** 18.72** 28.12** 28.06** 11.61** 11.91** 9.14** 7.39* 19.23** 9.31** 6.19*
MCW0010-MCW0106 LEI0068-MCW0297 MCW0297-LEI0146 LEI0146-MCW0018 LEI0146-MCW0018 MCW0018-MCW0058 ADL251-MCW0061 MCW0061-LEI0088 MCW0061-LEI0088 LEI0079-ADL328 LEI0079-ADL328 LEI0079-ADL328 LEI0079-ADL328 LEI0079-ADL328 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025 ADL0328-ROS0025
Dominant effect (SE)
−0.0015 (−0.001093) −5.15 (1.94) 52.13 (12.29) −0.57 (0.72) 14.79 (6.02) 13.49 (5.81) 37.83 (8.63) 3.99 (1.59) 91.63 (25.08) 102.87 (29.89) 144.12 (28.68) 155.71 (25.56) 195.39 (26.33) 171.04 (23.15) 53.98 (16.01) −0.005 (−0.001035) 136.21 (32.48) −8.14 (2.19) 104.68 (17.01) 35.72 (8.30) 42.53 (12.82)
−0.0056 (−0.001953) 3.88 (2.93) 47.99 (19.07) 3.43 (1.33) 17.31 (11.03) −21.36 (13.81) 18.20 (15.26) −2.33 (2.61) 53.08 (41.23) −55.53 (55.83) −63.32 (52.46) 2.12 (43.66) −14.95 (44.98) −21.91 (38.29) −82.11 (25.41) 0.0012 (−0.001535) −29.19 (52.50) 2.84 (3.32) −9.75 (27.64) 8.71 (13.59) 29.43 (20.76)
Phenotypic variance2 (%) 2.7 2.53 6.02 2.1 2.18 2.43 6 1.9 4.62 3.71 7.35 9.42 13.51 13.49 6.06 6.24 4.83 3.97 9.65 4.92 3.32
1
QTL positions relative to the genetic map of Northeast Agricultural University resource population in Figure
1. 2 Phenotypic variance = percentage difference in the residual sums of squares between the full and reduced model. †Suggestive linkage; *Chromosome wide significant, P < 0.05; **chromosome wide significant, P < 0.01.
represents true single locus effects or appeared because of epistasis (Abasht et al., 2006). The present study identified several QTL in a broiler × layer resource population. Although it is encouraging that the identified QTL were consistent with previous studies, there were many inconsistent results across studies. Inconsistent results can occur for many reasons, including markers used, choice of statistical models, and most importantly the use of a different experiment population (McElroy et al., 2006). Therefore, it is essential to define marker-QTL phase and effect in the specific populations of interest for application.
In summary, commercial breeding programs of broiler chickens have become more complex and challenging because so many objectives need to be simultaneously considered to reduce production costs, maintain health, and improve product quality. Breeding goals must include increased growth rate, decreased abdominal fat, maintenance of good development and growth of the skeletal system, and overall fitness. The relationships of these traits are complex, and some of the traits are very difficult to measure. Therefore, molecular MAS may be required to improve genetic selection programs. The current study found that there exist highly significant QTL affecting BW and abdominal fat traits on chromosome 1. This is the first step toward the fine mapping QTL affecting BW and abdominal fat. With focus on the QTL identified on chromosome 1, valuable candidate genes may be found by combining results of fine mapping and the chicken genome sequence, and further function study of these genes will benefit understanding of the genetic background of growth and fat deposition of chickens.
ACKNOWLEDGMENTS
Figure 3. The F-ratio of QTL mapping for abdominal fat weight (AFW) and abdominal fat percentage (AFP). Triangles above the X-axis indicate the marker positions as shown in Figure 1.
The authors gratefully acknowledge the members of the Poultry Farm of Northeast Agricultural University for managing the birds. The authors thank Qin Zhang for the help with data analyses. This research was supported by Program for New Century Excellent Talents in University (No. NCET-04-0343), National Natural Science Foundation Key Project (No. 30430510), and the National Basic Research Program (No. 2006CB102105).
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69 183 195 219 231 271 339 343 351 523 528 534 534 536 548 548 550 550 551 553 555
Trait
Additive effect (SE)
CHICKEN QUANTITATIVE TRAIT LOCI ON CHROMOSOME ONE
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