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Poult Sci 2008. 87:1314-1319. doi:10.3382/ps.2007-00512
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GENETICS

Fine-Mapping Quantitative Trait Loci for Body Weight and Abdominal Fat Traits: Effects of Marker Density and Sample Size1

X. Liu*,2, H. Zhang*,2, H. Li*,3, N. Li{dagger}, Y. Zhang{ddagger}, Q. Zhang§, S. Wang*, Q. Wang* and H. Wang*

* College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China; {dagger} National Laboratories for Agribiotechnology, China Agricultural University, Beijing 100094, P. R. China; {ddagger} Animal Genetics and Breeding Unit, University of New England Armidale, New South Wales, 2351, Australia; and § College of Animal Science and Technology, China Agricultural University, Beijing 100094, P. R. China

3 Corresponding author: lihui{at}neau.edu.cn


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Highly significant QTL for BW and abdominal fat traits on chicken chromosome 1 were reported previously in a unique F2 population. The objective of this study was to confirm and refine the QTL locations. Compared with the previous experiment, this study added 8 new families, including all the animals in the pedigree, and genotyped 9 more microsatellite markers, including 6 novel ones. Linkage analyses were performed. The results of the linkage analyses showed that the confidence intervals for BW and abdominal fat percentage were narrowed sharply to a small interval spanning 5.5 and 3.7 Mb, respectively. The results of the present study showed that using more markers and individuals could decrease the confidence interval of QTL effectively. In the current QTL region, by combining the biological knowledge of genes and the results of a microarray analysis that was performed in divergently selected lean and fat lines, several genes stood out as potential candidate genes.

Key Words: chicken • quantitative trait loci • fine mapping • body weight • abdominal fat weight


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Body weight has been intensively selected for more than half a century 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 a large amount of fat deposition can decrease feed efficiency. Knowledge of the position and effects of QTL affecting BW and abdominal fat traits would be useful for MAS and for understanding the genetic background of traits.

Over the past few years, several experimental crosses of chickens have been used to detect QTL for BW and abdominal fat traits. Chromosome 1 is the largest of these in the chicken genome. Quantitative trait loci for growth and fat traits have been reported on chicken chromosome 1 (Abasht et al., 2006). These studies have facilitated further fine-mapping of QTL and the identification of causal genes. However, identification of the underlying genes still remains one of the major challenging tasks because the confidence interval (CI) of most reported QTL covers more than 20 cM (Soller et al., 2006) or sometimes a whole chromosome (Schreiweis et al., 2005), which is insufficient for positional cloning of the underlying genes. Two factors limiting the achievable mapping resolution are marker density and sample size. Although increasing the marker density is time-consuming in many organisms, it is conceptually the simplest bottleneck to resolve (Nezer et al., 2003). Major steps toward fine-mapping of QTL can be assisted simply by increasing the sample size because of the inverse relationship between resolving power and increasing the sample size (Darvasi and Soller, 1997).

We reported the initial genome scan that mapped QTL for BW and abdominal fat traits on chicken chromosome 1 by using a unique F2 design of a broiler x layer cross (Liu et al., 2007). The QTL interval was flanked by markers LEI0079 and ROS0025, spanning 50.8 cM in genetic distance or 24 Mbp of the chicken genome sequence (http://www.genome.ucsc.edu/). This interval was too large to identify causal genes or markers for improving chicken production. The objectives of this study were to refine the previously reported QTL interval by increasing the population size and linkage map density, and to investigate the benefit of increasing the marker density and sample size.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Animal and Experimental Design
The Northeast Agricultural University Resource Population was used in the current study and was described previously by Liu et al. (2007). Briefly, this population was a uniquely designed F2 population, and was established by crossing broiler sires derived from a high abdominal fat line divergently selected for abdominal fat with Baier layer dams (a Chinese native breed). The F1 birds were intercrossed to produce an F2 population. All F2 birds had free access to feed and water. Commercial corn- and soybean-based diets that met all NRC (1994) requirements 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). Body weights of F2 individuals were 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 fat measure was also expressed as a percentage of BW at 12 wk of age, denoted as abdominal fat percentage (AFP). Trait means and variations (SD) are presented in Table 1Go. The 1,011 F2 individuals used in this study were derived from 12 half-sib families.


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  		Table 1. Trait means and variation
 
Marker Density and Genotype Data
Genomic DNA was isolated from venous blood samples by using a phenol-chloroform method (Wang et al., 2006). Genotypes on 3 microsatellite markers (LEI0079, ADL328, and ROS0025) for 369 individuals of 4 families were available from the previous study (Liu et al., 2007). Within the reported QTL region (Liu et al., 2007), 9 additional microsatellite markers were added in this study. Of these, 3 were selected from a public database (http://www.thearkdb.org/) and the rest were novel microsatellite markers isolated based on the chicken genome sequences (http://www.genome.ucsc.edu/). The size and primers of markers, with an expected average marker interval of 2 Mb, are listed in Table 2Go. Polymerase chain reactions for all the markers were carried out separately. The PCR products were electrophoresed by using an ABI377 sequencer (Applied Biosystems, Foster City, CA). Genotypes of 12 microsatellite markers were assessed for 1,011 F2 individuals, their parents, and the F0 birds by using GeneScan3.1 and Genotyper2.1 (Applied Biosystems) as described by Liu et al. (2007).


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  		Table 2. Primer and size of microsatellite markers used in the present study
 
Linkage Map Construction
Marker order and map distances were estimated by using Crimap 2.4 software (Green et al., 1990). The CHROM-PIC option was used to identify unlikely double crossover. The FLIPS option with a 5-marker window was used to obtain the most likely order given the present data set. A sex average linkage map was built by using the BUILD option. All markers used in this study were anchored into the chicken genome database (http://www.genome.ucsc.edu/) to obtain the genome positions.

QTL Linkage Mapping
The F2 regression analysis was carried out and implemented by using QTLexpress software (Seaton et al., 2002). The QTL effects (additive and dominant effects) were fitted in a model with sex, hatch, and family as fixed effects (all tests for sex x QTL or family x QTL were not significant). In addition, the BW at hatch (BW0) was fitted as covariate for BW of 4 to 12 wk of age (QTL for BW4 to BW12 reached chromosome-wide significance in a previous chromosome scan), and CW at 12 wk of age was fitted as a covariate for AFW and AFP. The phenotypic variance explained by QTL was calculated as the difference in the residual sums of squares between the full model (including QTL) and reduced model (without QTL). Significance thresholds were calculated by using permutation tests (Churchill and Doerge, 1994). For each test point, a total of 10,000 permutations were computed to determine the empirical distribution of the statistical test under the null hypothesis, and the 95% CI for each QTL was calculated from 10,000 bootstrap samples (Visscher et al. 1996).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Construction of an Integrated Genetic Linkage and Physical Map
Genotypes for 12 microsatellite markers were used to build a new linkage map. The linkage map covered 181.8 cM from the first marker (LEI0079) to the last one (ROS0025; Table 3Go). The map order was in line with map Consensus 2000 (Groenen et al., 2000). However, adding 9 additional microsatellite markers in the QTL region reported previously (Liu et al., 2007) and detecting more individuals extended the length of the chromosome by 131 cM. Sequences of the markers were compared with the chicken genome database (http://www.genome.ucsc.edu/) and their physical positions were confirmed, as shown in Table 3Go.


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  		Table 3. Linkage map of microsatellite markers and their chromosomal positions
 
QTL for BW and Fat Composition
As shown in Table 4Go, QTL for BW and AFP were identified in this study, confirming the previous findings. Their F-ratios were higher than those from the previous study (Liu et al., 2007). Nevertheless, the F-ratio for QTL for AFW reached suggestive significance (single-position significance, P < 0.01) in the present study, whereas it was at 5% chromosome-wide significance in the previous study. Very strong evidence pointing to QTL for BW12 was suggested between markers NEAU0006 and ADL0328, and the most likely position was at 590 cM or at marker ADL0328. The QTL effects explained 7.00 (for BW4) to 10.60% (for BW8 and BW12) of phenotypic variance. A significant QTL for CW was also identified at 590 cM. The QTL for AFP peaked between markers ADL0328 and NEAU0010 and explained 3.96% of the phenotypic variance.


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  		Table 4. Locations and effects of QTL for BW and abdominal fat traits
 
The QTL CI estimated from bootstrap samples are listed in Table 4Go. The intervals for CW and BW at 9 to 12 wk of age ranged from 40 to 70 cM, locating between 169.8 and 175.3 Mb of genomic sequence. The interval for AFP was small (25 cM) and partially overlapped with those for BW and CW, whereas this value was very large for AFW (181 cM).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
QTL for BW and AFP
The highly significant QTL affecting BW and AFP on chromosome 1 reported previouly (Liu et al., 2007) has been identified in several studies (Van Kaam et al., 1999a,b; Tatsuda and Fujinaka, 2001; Burt and Hocking, 2002; Ikeobi et al., 2002, 2004; Sewalem et al., 2002; De Koning et al., 2004; Jennen et al., 2004, 2005; Gao et al., 2006; Lagarrigue et al., 2006; Nones et al., 2006; Park et al., 2006). However, the location of QTL identified in the previous study remained very imprecise. In an attempt to refine its location, the 2 factors affecting mapping resolution were considered simultaneously: more markers (12 markers, compared with 3 in the previous study) and a larger sample size (12 F1 families and 1,011 F2 animals, compared with 4 F1 families and 369 F2 animals in the previous study) were used in the current study. In the present study, QTL for BW and AFP reported previously were confirmed and positioned more precisely. However, the QTL for AFW, which reached 5% chromosome-wide significance in the previous study, was suggestive of linkage in this study. This may suggest a false positive finding owing to the relatively small sample size in the previous study. In addition, the marker density of the previous study was low, and this may have been a possible reason for the false positive QTL finding. Therefore, additional genotyping was beneficial to select QTL for fine-mapping and to avoid searching for a gene that was not present (Rattink et al., 2000). The additive effects of QTL for BW were positive, indicating that the alleles increasing BW derived from the high-weight broiler lines, although the QTL affecting AFP was still cryptic because the additive effect of the QTL was negative. This indicated that the allele increasing fat composition may derive from the dam line (the AFW and AFP of which were lower than those of the sire line). The reason that the cryptic QTL might 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 apply in breeding programs because we do not know whether they represent true single-locus effects or whether they appeared because of epistasis (Abasht et al., 2006).

Using the new linkage map, we carried out bootstrap analyses to calculate the QTL CI. In the previous study, the CI for BW and AFP was approximately 50 cM or 24 Mb, which covered the region of markers LEI0079 to ROS0025 (our unpublished data). The results of this study showed that the CI for BW and AFP were narrowed sharply to a small interval containing 5.5 and 3.7 Mb of genome sequences, respectively.

From the genomic biology database (http://www.ncbi.nlm.nih.gov/Genomes/), approximately 300 identified genes were found in this region, 8 of which were identified based on their known biological mechanisms (Table 5Go). The functions of still many other genes are as yet unknown. Conservation of synteny between the human and chicken makes it possible to identify regions on the human map that are homologous to the QTL region in the chicken. For the QTL region of GGA1, the homologous human region is HSA13. However, comparative mapping is complicated because the QTL region for GGA1 is large and partly crossing the breakpoint of 2 conserved synteny groups, although the 2 groups belong to one chromosome. Another important tool for candidate gene selection is microarray analysis, especially if there are detectable differences in steady-state mRNA levels at the temporal and spatial coordinates selected for study (Jerez-Timaure et al., 2005). Because de novo fatty acid synthesis in birds takes place mainly in the liver, most studies have been performed on hepatic tissue. Few studies have been conducted to analyze the adipose tissue expression of genes involved in pathways and mechanisms leading to adiposity in chicken. Some studies have combined research on gene expression and QTL mapping; however, these results have shown few potential candidate genes (Douaire et al., 1992; Daval et al., 2000; Assaf et al., 2004). Our group used chicken genome arrays to investigate genes involved in fat deposition (Wang et al., 2007). In that study, genome arrays were used to construct an adipose tissue gene expression profile of 7-wk-old broilers in divergently selected lean and fat lines. Gene expression profiling identified approximately 50 genes expressed in chicken adipose tissue at 7 wk of age in the QTL region in this study. According to the method used by Wang et al. (2007), 2 of these expressed genes were differentially expressed, and 4 of them were highly expressed (Table 5Go).


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  		Table 5. Potential candidate genes in the QTL region for BW and abdominal fat percentage
 
Effects of Using More Markers and Individuals
Many studies have shown that increasing marker density and population size can improve QTL resolution effectively. To fine-map QTL for pig fatness traits, Rattink et al. (2000) genotyped 25 additional markers, and the location of backfat thickness QTL was reduced to a 33-cM CI. Gautier et al. (2006) reported that 2 linked QTL affecting milk fat yield in dairy cattle were fine-mapped by using more markers and animals. However, the power of detecting a QTL was actually the same for a marker spacing of 10 cM as for an infinite number of markers, and was only slightly decreased for a marker spacing of 20 or even 50 cM in linkage analysis (Darvasi et al., 1993). Darvasi and Soller (1997) indicated that resolving power was inversely related to sample size and to the square of the QTL gene effect. When the effect of QTL was fixed, sample size played a key role in narrowing the CI of QTL mapping. A simulation study (Da et al., 2000) indicated that a population of 1,000 individuals could map a QTL to a narrow chromosome region of 1.5 cM if no linked QTL were present. As the sample size is increased, the CI would be further narrowed; however, for a sample size beyond 2,000, the decrease in width of the CI would become lower (Da et al., 2000). This suggests that using a sample size 1,000 to 2,000 may be appropriate for the purpose of fine QTL mapping. In the current study, more markers and individuals were used to fine-map QTL for BW and AFP, and CI of the QTL of interest was narrowed from 24 Mb to 5.5 and 3.7 Mb, respectively, justifying the benefits of using more markers and individuals.

In summary, this study confirmed that further addition of more markers and more animals increased the precision of mapping. Previously reported QTL for BW and fat deposition were confirmed. The QTL location and CI were narrowed. Additional significant markers were identified and will have significant benefits for improvement of MAS. Fourteen potential candidate genes were selected for further study of the genetic architecture of QTL for BW and abdominal fat traits.


   ACKNOWLEDGMENTS
 
The authors gratefully acknowledge the members of the Poultry Farm of Northeast Agricultural University for managing the birds.


   FOOTNOTES
 
1 This research was supported by the National 973 Project of China (No. 2006CB102105), the National Natural Science Foundation Key Project of China (No. 30430510), and the National 863 project of China (No. 2006AA10A120). Back

2 These authors contributed the same to this work. Back

Received for publication December 18, 2007. Accepted for publication March 14, 2008.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Abasht, B., J. C. M. Dekkers, and S. J. Lamont. 2006. Review of quantitative trait loci identified in the chicken. Poult. Sci. 85:2079–2096.[Abstract/Free Full Text]

Assaf, S., S. Lagarrigue, S. Daval, M. Sansom, B. Leclercq, J. Michel, F. Pitel, M. Alizadeh, A. Vignal, and M. Douaire. 2004. Genetic linkage and expression analysis of SREBP and lipogenic genes in fat and lean chicken. Comp. Biochem. Physiol. Part B Biochem. Mol. Biol. 137:433–441.[CrossRef]

Burt, D. W., and P. M. Hocking. 2002. Mapping quantitative trait loci and identification of genes that control fatness in poultry. Proc. Nutr. Soc. 61:441–446.[CrossRef][Web of Science][Medline]

Churchill, G. A., and R. W. Doerge. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963–971.[Abstract]

Da, Y., P. M. van Raden, and L. B. Schook. 2000. Detection and parameter estimation for quantitative trait loci using regression models and multiple markers. Genet. Sel. Evol. 32:357–381.[CrossRef][Web of Science][Medline]

Darvasi, A., and M. Soller. 1997. A simple method to calculate resolving power and confidence interval of QTL map location. Behav. Genet. 27:125–132.[CrossRef][Web of Science][Medline]

Darvasi, A., A. Weinreb, V. Minke, J. I. Wellert, and M. Soller. 1993. Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134:943–951.[Abstract]

Daval, S., F. Pitel, C. Le Nigen, M. Douaire, A. Vignal, and S. Lagarrigue. 2000. Cloning and mapping of the ACLY gene to a chicken microchromosome. Anim. Genet. 31:412–413.[CrossRef][Web of Science][Medline]

De Koning, D. J., C. S. Haley, D. Windsor, P. M. Hocking, H. Griffin, A. Morris, J. Vincent, and D. W. Burt. 2004. Segregation of QTL for production traits in commercial meat-type chickens. Genet. Res. 83:211–220.[CrossRef][Web of Science][Medline]

Douaire, M., N. Le Fur, C. el Khadir-Mounier, P. Langlois, F. Flamant, and J. Mallard. 1992. Identifying genes involved in the variability of genetic fatness in the growing chicken. Poult. Sci. 71:1911–1920.[Web of Science][Medline]

Gao, Y., X. X. Hu, Z. Q. Du, X. M. Deng, Y. H. Huang, J. Fei, J. D. Feng, Z. L. Liu, Y. Da, and N. Li. 2006. A genome scan for quantitative trait loci associated with body weight at different developmental stages in chickens. Anim. Genet. 37:276–278.[CrossRef][Web of Science][Medline]

Gautier, M., R. R. Barcelona, S. Fritz, C. Grohs, T. Druet, D. Boichard, A. Eggen, and T. H. E. Meuwissen. 2006. Fine mapping and physical characterization of two linked quantitative trait loci affecting milk fat yield in dairy cattle on BTA26. Genetics 172:425–436.[Abstract/Free Full Text]

Green, P., K. Falls, and S. Crooks. 1990. Program CRI-MAP, Version 2.4. Washington University School of Medicine, St. Louis, MO.

Groenen, M. A. M., H. H. Cheng, N. Bumstead, B. F. Benkel, W. E. Briles, T. Burke, D. W. Burt, L. B. Crittenden, J. Dodgson, J. Hillel, S. Lamont, A. P. de Leon, M. Soller, H. Takahashi, and A. Vignal. 2000. A consensus linkage map of the chicken genome. Genome Res. 10:137–147.[Abstract/Free Full Text]

Ikeobi, C. O. N., J. A. Woolliams, D. R. Morrice, A. Law, D. Windsor, D. W. Burt, and P. M. Hocking. 2002. Quantitative trait loci affecting fatness in the chicken. Anim. Genet. 33:428–435.[CrossRef][Web of Science][Medline]

Ikeobi, C. O. N., J. A. Woolliams, D. R. Morrice, A. Law, D. Windsor, D. W. Burt, and P. M. Hocking. 2004. Quantitative trait loci for meat yield and muscle distribution in a broiler layer cross. Livest. Prod. Sci. 87:143–151.[CrossRef]

Jennen, D. G. J., A. L. J. Vereijken, H. Bovenhuis, R. P. M. A. Crooijmans, J. J. der Poel, and M. A. M. Groenen. 2005. Confirmation of quantitative trait loci affecting fatness in chickens. Genet. Sel. Evol. 37:215–228.[CrossRef][Web of Science][Medline]

Jennen, D. G. J., A. L. J. Vereijken, H. Bovenhuis, R. P. M. A. Crooijmans, A. Veenendaal, J. J. der Poel, and M. A. M. Groenen. 2004. Detection and localization of quantitative trait loci affecting fatness in broilers. Poult. Sci. 83:295–301.[Abstract/Free Full Text]

Jerez-Timaure, N. C., E. J. Eisen, and D. Pomp. 2005. Fine mapping of a QTL region with large effects on growth and fatness on mouse chromosome 2. Physiol. Genomics 21:411–422.[Abstract/Free Full Text]

Lagarrigue, S., F. Petel, W. Carre, B. Abstht, P. Le Roy, A. Neau, Y. Amigues, M. Soundioux, J. Simon, L. Cogburn, S. Aggrey, B. Leclercq, A. Vignal, and M. Douaire. 2006. Mapping quantitative trait loci affecting fatness and breast muscle weight in meat-type chicken lines divergently selected on abdominal fatness. Genet. Sel. Evol. 38:85–97.[CrossRef][Web of Science][Medline]

Liu, X., H. Li, S. Wang, X. Hu, Y. Gao, Q. Wang, N. Li, Y. Wang, and H. Zhang. 2007. Mapping quantitative trait loci affecting body weight and abdominal fat weight on chicken chromosome one. Poult. Sci. 86:1084–1089.[Abstract/Free Full Text]

NRC. 1994. Nutrient Requirements of Poultry. Natl. Acad. Press, Washington, DC.

Nezer, C., C. Collette, L. Moreau, B. Brouwers, J. Kim, E. Giuffra, N. Buys, L. Andersson, and M. Georges. 2003. Haplotype sharing refines the location of an imprinted QTL with major effect on muscle mass to a 250 Kb chromosome segment containing the porcine IGF2 gene. Genetics 165:277–285.[Abstract/Free Full Text]

Nones, K., M. C. Ledur, D. C. Ruy, E. E. Baron, C. M. R. Melo, A. S. A. M. T. Moura, E. L. Zanella, D. W. Burt, and L. L. Coutinho. 2006. Mapping QTLs on chicken chromosome 1 for performance and carcass traits in a broiler x layer cross. Anim. Genet. 37:95–100.[CrossRef][Web of Science][Medline]

Park, H., L. Jacobsson, P. Wahlberg, P. B. Siegel, and L. Andersson. 2006. QTL analysis of body composition and metabolic traits in an intercross between chicken lines divergently selected for growth. Physiol. Genomics 3:1–21.[CrossRef]

Rattink, A. P., D. J. De Koning, M. Faivre, B. Harlizius, and J. A. M. van Arendonk. 2000. Fine mapping and imprinting analysis for fatness trait QTLs in pigs. Mamm. Genome 11:656–661.[CrossRef][Web of Science][Medline]

Schreiweis, M. A., P. Y. Hester, and D. E. Moody. 2005. Identification of quantitative trait loci associated with bone traits and body weight in an F resource population of chickens. Genet. Sel. Evol. 37:677–698.[CrossRef][Web of Science][Medline]

Seaton, G., C. S. Haley, S. A. Knott, M. Kearsey, and P. M. Visscher. 2002. QTL express: Mapping quantitative trait loci in simple and complex pedigrees. Bioinformatics 18:339–340.[Abstract/Free Full Text]

Sewalem, A., D. M. Morrice, A. Law, D. Windsor, C. S. Haley, C. O. N. Ikeobi, D. W. Burt, and P. M. Hocking. 2002. Mapping of quantitative trait loci for body weight at three, six, and nine weeks of age in a broiler layer cross. Poult. Sci. 81:1775–1781.[Abstract/Free Full Text]

Soller, M., S. Weigend, M. N. Romanov, J. C. Dekkers, and S. J. Lamont. 2006. Strategies to assess structural variation in the chicken genome and its associations with biodiversity and biological performance. Poult. Sci. 85:2061–2078.[Abstract/Free Full Text]

Tatsuda, K., and K. Fujinaka. 2001. Genetic mapping of the QTL affecting body weight in chickens using an F2 family. Br. Poult. Sci. 42:333–337.[CrossRef][Web of Science][Medline]

Van Kaam, J. B. C. H. M., M. A. M. Groenen, H. Bovenhuis, A. Veenendaal, A. L. J. Vereijken, and J. A. M. van Arendonk. 1999a. Whole genome scan in chickens for quantitative trait loci affecting carcass traits. Poult. Sci. 78:1091–1099.[Abstract/Free Full Text]

Van Kaam, J. B. C. H. M., M. A. M. Groenen, H. Bovenhuis, A. Veenendaal, A. L. J. Vereijken, and J. A. M. Van Arendonk. 1999b. Whole genome scan in chickens for quantitative trait loci affecting growth and feed efficiency. Poult. Sci. 78:15–23.[Abstract/Free Full Text]

Visscher, P. M., R. Thomopson, and C. S. Haley. 1996. Confidence intervals in QTL mapping by bootstrapping. Genetics 143:1013–1020.[Abstract]

Wang, H. B., H. Li, Q. G. Wang, X. Y. Zhang, S. Z. Wang, Y. X. Wang, and X. P. Wang. 2007. Profiling of chicken adipose tissue gene expression by genome array. BMC Genomics 8:193.[CrossRef][Medline]

Wang, Q., H. Li, N. Li, L. Leng, Y. Wang, and Z. Tang. 2006. Identification of single nucleotide polymorphism of adipocyte fatty acid-binding protein gene and its association with fatness traits in the chicken. Poult. Sci. 85:429–434.[Abstract/Free Full Text]





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