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Physiol. Genomics 25: 216-223, 2006. First published January 3, 2006; doi:10.1152/physiolgenomics.00113.2005
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Received 11 May 2005; accepted in final form 20 December 2005.
Physiological Genomics 25:216-223 (2006)
1094-8341/06 $8.00 © 2006 American Physiological Society

QTL analysis of body composition and metabolic traits in an intercross between chicken lines divergently selected for growth

Hee-Bok Park1, Lina Jacobsson2, Per Wahlberg1, Paul B. Siegel3 and Leif Andersson1,2

1 Department of Medical Biochemistry and Microbiology, Uppsala University, Biomedical Center (BMC), Uppsala
2 Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, BMC, Uppsala, Sweden
3 Virginia Polytechnic Institute and State University, Department of Animal and Poultry Sciences, Blacksburg, Virginia


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
The high- and low-growth lines of chickens have been developed from a single founder population by divergent selection for body weight at 56 days of age for more than 40 generations. The two lines show a ninefold difference in body weight at selection age and several interesting correlated selection responses such as altered body composition and metabolic differences. We have generated a reciprocal intercross comprising >800 F2 birds. In a previous study, we reported the detection of 13 quantitative trait loci (QTLs) affecting growth. Here we report QTLs for body composition (fat deposition, muscle development), weight of internal organs, and metabolic traits (plasma concentrations of glucose, insulin, cholesterol, glucagon, triglycerides, and IGF-I). Most of the QTLs with convincing statistical support mapped in the vicinity of growth QTLs. One of the most interesting observations was that the type of reciprocal cross had highly significant effects on body weight at hatch and on plasma concentrations of glucose, cholesterol, insulin, and IGF-I, but it had no significant effect on body weight at 56 days of age. The reciprocal cross explained between 15 and 35% of the phenotypic variance for weight at hatch and for plasma concentrations of glucose and insulin. The observed pattern indicated that these effects were caused by maternal effects or by genetic differences in mitochondrial DNA.

quantitative trait locus; correlated selection response


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
THE CHICKEN IS BECOMING a prime vertebrate model for the genetic dissection of complex phenotypic traits because of the release of a high-quality draft genome sequence at 6.6x coverage (9) and a genetic map comprising 2.8 million single-nucleotide polymorphisms (SNPs; Ref. 10). Other merits with the chicken include a fairly small genome size (1.06 Gbp) and a high recombination rate (9). There also exist a number of chicken lines that carry mutations causing a monogenic phenotype or that have been selected for different purposes (3). One example is the high- (HW) and low-weight (LW) lines developed at the Virginia Polytechnic Institute and State University (Blacksburg, VA) from a base population of the White Plymouth Rock breed (5, 17). The selection experiment was initiated in 1957, and, after more than 40 generations of divergent selection solely on body weight at 56 days of age, the two lines differ ninefold in weight at this age, corresponding to approximately eight standard deviations. A number of correlated responses for body composition and metabolic traits have been obtained. The HW birds become obese and must be feed restricted to avoid severe metabolic disorders, whereas the LW birds tend to be anorectic and are very lean. The HW birds have elevated plasma concentrations of glucose, insulin, lipids, and glucagon and show impaired glucose tolerance (4, 5). Thus these two lines are novel models for metabolic disorders in humans. We have generated an intercross between the HW and LW lines comprising >800 F2 birds. In a previous study, we reported the identification of 13 quantitative trait loci (QTLs) affecting growth (11). However, each of them explained only a small portion of the residual variance for body weight at 56 days in the F2 generation (1.3–3.1%), and combined they explain at most ~50% of the difference between the two lines.

In this study we report the QTL analysis of body composition and metabolic traits. In addition, we analyzed phenotypic differences between reciprocal crosses that may be caused by maternal effects, QTLs on sex chromosomes, or genetic variation in mitochondrial DNA.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Experimental Animals
The HW and LW selection lines have been developed and maintained at the Virginia Polytechnic Institute and State University in Blacksburg, VA (5, 17). The base population was formed by crossing seven partially inbred lines of White Plymouth Rock chickens. The selection lines have been maintained at the same location as closed populations selected for either high or low body weight at 56 days of age. Birds representing generation 41 of this long-term selection experiment were used to generate a reciprocal F2 intercross. Ten HW males were mated to 22 LW females, and 8 LW males were mated to 19 HW females to produce each reciprocal half of the cross, i.e., H x L and L x H F1 progeny (Fig. 1). From the F1 generation, 4 HL males were intercrossed to 37 LH females, and 4 LH males were intercrossed to 38 HL females. A total of 874 F2 offspring comprising 75 full-sib families were used for QTL analysis. All F2 birds were from a single hatch and were killed on the same day at 70 days of age. The intercross was raised using the same dietary formulation and feeding program as was used for the founder lines. The experimental procedure was approved by the Animal Care Committee at the Virginia Polytechnic Institute and State University, which adheres to United States Department of Agriculture and National Institutes of Health guidelines.


Figure 1
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  		Fig. 1. Pedigree structure of the F2 intercross between 2 chicken lines divergently selected for growth. The F1 sires are marked with their ID nos. The nos. of dams mated to each sire as well as the nos. of F2 offspring in each half-sib family are indicated.

 
Phenotype Analysis
All phenotypes were recorded in the facilities where the selection experiment was conducted. The phenotypic measures included body weight at 56 days of age and weight of abdominal fat, breast muscle, lung, shank, bursa, and spleen at 70 days of age. Mesenteric fat and gizzard fat were not included in the measurement of abdominal fat weight. The weight of pectoralis major was collected for breast muscle weight. The weight of shank (metatarsus) plus toes and lung was also recorded separately.

In addition to the body composition traits, plasma concentrations of glucose, cholesterol, triglycerides, insulin, glucagon, and IGF-I were measured. For practical reasons, it was not possible to collect the blood samples from fasted birds, although this would have been preferred. At 63 days of age, blood samples were collected via the brachial vein, and plasma was separated from whole blood within 1 h of collection. Plasma samples were frozen immediately and stored at –70°C. At the time of assay, samples had been thawed and refrozen for other assays one time. The concentrations of glucose, cholesterol, and triglycerides were recorded using the Beckman Synchron CX system. Glucagon levels were determined using a glucagon radioimmunoassay kit (ICN Pharmaceuticals). Insulin was measured by the ImmuChem Coated Tube Insulin 125I Radioimmunoassay kit (ICN Pharmaceuticals). IGF-I was also assayed by a radioimmunoassay (ALPCO Diagnostics).

Several of the physiological traits showed significant deviations from normality and were transformed using the natural logarithm (i.e., glucose) or the square root (i.e., triglycerides, insulin, glucagon, and IGF-I) to remove skewness. Extreme outlying values were excluded based on an ascertainment of normality (see Table 1) to reduce the risk of statistical artifacts in the QTL analysis.


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  		Table 1. Summary of phenotypic data from the F2 generation of an intercross between the high and low selection lines

 
Genetic Marker Data
Genotype data on 145 DNA markers representing 26 linkage groups have been generated for this intercross (12). The total map length, summarizing the intervals flanked by markers, was 2,469.8 cM. The average distance between adjacent markers assigned to linkage groups was 17.0 cM, but there were seven gaps >40 cM. Average information content was 0.72 when information on flanking markers was taken into account. The map fits well with previously published linkage maps (24) and with the genome assembly of February 2004, which is available at the ENSEMBL (http://www.ensembl.org) and the UCSC (http://genome.ucsc.edu) genome browsers.

Statistical Analysis
Analysis of variance (ANOVA) was performed using Minitab (20) to identify factors affecting phenotypic variation. Significant effects of family (for all traits) and sex (in the case of abdominal fat, breast muscle, lung, shank, cholesterol, triglycerides, and body weight) were observed, and these were therefore included in the model for QTL analysis. The 70-day body weight was included as a covariate in the QTL analysis of body composition traits. Thus all results concerning body composition traits were compared at an adjusted equal body weight. Pearson correlation coefficients and the significance of each pairwise comparison of traits were estimated with the correlation procedure of Minitab, and the effect of reciprocal cross (i.e., HLLH and LHHL) was analyzed using the ANOVA and regression procedures (20).

A least-squares method for QTL analysis of outbred populations was used for autosomes (8). Marker genotypes were used to estimate probabilities of the parental origin of each gamete at 1-cM intervals through the genome. These conditional probabilities were used to calculate coefficients of additive and dominance components for a putative QTL at each position under the assumption that the QTL was fixed for alternative alleles in the parental lines. Residuals derived from the ANOVA were used as the dependent variable and regressed onto the additive and dominance coefficients in intervals of 1 cM. At each position, an F-value comparing a full model with a model without a QTL was calculated. A two-QTL model was also evaluated. The web-based QTL express program (http://qtl.cap.ed.ac.uk) was used for single- and two-QTL analyses (25).

Inclusion of previously detected QTLs should decrease the residual variance and thereby increase the statistical power to detect QTLs with smaller effects (13, 26). Therefore, the additive and dominance regression indicator variables for the most significant single QTL in the initial analysis were added as covariates, and a new genome scan was carried out using the updated model. Coefficients of additive and dominance components for the putative QTLs at each position through the genome, computed by QTL express, were transferred to the QTL Fast program (1, 18) for these analyses. QTL mapping for the Z chromosome was performed using the Qxpak package based on the dosage compensation model (23).

To address the multiple testing issue in QTL scans, genome-wide and chromosome-specific empirical significance levels of the test statistic were established by randomization using 1,000 permutations of data (2). Genome-wide thresholds for highly significant ({alpha} = 0.01) and significant linkage ({alpha} = 0.05) were employed as proposed by Lander and Kruglyak (15). The chromosome-wide 5% significance levels obtained for chromosome 4 were used as suggestive evidence for the presence of QTL because the genetic map length for this chromosome constitutes ~5% of the total map length for chicken. By using this suggestive significance threshold, we expected to obtain one false positive QTL per genome scan and trait. We employed the 1-LOD (logarithm of odds) drop method to estimate confidence intervals for identified QTLs at the suggestive and significant level of significance (22).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Descriptive Statistics
The overall means and standard deviations of body weight, body composition traits, and metabolic parameters for the F2 generation derived from a reciprocal intercross between the HW and LW lines are presented in Table 1. The body weight at 56 days of age can be compared with the corresponding means and standard deviations for the HW line (1,522 ± 212 g), the LW line (181 ± 33 g), and the F1 birds (768 ± 143 g). Data on body composition and metabolic traits have not been recorded on the F1 generation, but previous studies have documented that HW chickens exhibit markedly higher mean weights of abdominal fat, altered body composition, and elevated plasma levels of glucose, lipid, insulin, and IGF-I (5).

A statistical analysis of the phenotypic data from the F2 population revealed that a number of the traits were significantly correlated (Table 2). Body weight was strongly correlated with body composition traits (r = 0.64 or higher). Positive correlations were also found between abdominal fat and both muscle weight and weight of internal organs (r = 0.39 to 0.56). However, some traits did not show significant associations, suggesting that loci influencing these phenotypes may segregate independently. For example, there was no significant correlation between glucagon and insulin levels.


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  		Table 2. Pearson correlation coefficients among body weight, body composition, and metabolic traits in a chicken F2 intercross population

 
Significant Phenotypic Differences Between Reciprocal Crosses
The evaluation of possible phenotypic differences between reciprocal crosses (i.e., HLLH, LHHL) was performed for each sex separately to allow us to conclude the basis for any observed effect (Tables 3 and 4). Consistent and highly significant effects of reciprocal crossing were found in both males and females for weight at hatch and for several metabolic traits (plasma concentrations of glucose, cholesterol, insulin, and IGF-I). The effect of reciprocal crosses explained an astonishing 15–35% of the residual phenotypic variance for weight at hatch, glucose, and insulin concentrations. If the maternal grand-dam originated from the HW line, the F2 chickens had higher weight at hatch, higher glucose, and lower insulin concentrations (Table 3). A minor reciprocal cross effect on triglyceride concentration was only significant in females. No significant effect on 56-day body weight was found.


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  		Table 3. Phenotypic effects of reciprocal crosses (i.e., HLLH vs. LHHL) in the F2 generation of an intercross between 2 selection lines in chicken

 

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  		Table 4. Genetic constitution as regards sex chromosomes and mtDNA in a reciprocal intercross between the high and low growth selection lines in chicken

 
QTL Analysis of Body Composition and Weight of Internal Organs
The results of the QTL analysis are summarized in Table 5, and the chromosomal locations of detected QTLs are depicted in Fig. 2 compared with the previously reported growth QTLs detected in this intercross (11). QTL graphs for loci detected on chromosomes 1, 3, and 7 are given in Fig. 3.


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  		Table 5. Summary of QTLs affecting correlated responses to selection for growth in chicken

 

Figure 2
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  		Fig. 2. Schematic overview of quantitative trait loci (QTLs) detected in an intercross between the high- and low-growth chicken lines. Blue bars represent QTLs affecting body composition: AF, abdominal fat; BM, breast muscle; LU, lung; SP, spleen; BU, bursa; and SH, shank. Green bars represent QTLs affecting metabolic parameters: GL, glucose; CH, cholesterol; INS, insulin; TG, triglycerides; and IGFI, insulin-like growth factor I. Red bars represent growth QTLs (11). Confidence intervals for the QTLs were estimated with the 1-LOD drop method (22). The closest flanking markers of each QTL and some obvious candidate genes are indicated. GGA1, Gallus gallus chromosome 1, etc.; G1–G13, Growth1–Growth13 QTLs.

 

Figure 3
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  		Fig. 3. Test statistic curves for chicken QTLs detected on chromosomes 1, 3, and 7 using an intercross between the high- and low-growth selection lines. Marker map with distances between markers in Kosambi cM is given on the x-axis. The y-axis represents the F-ratio testing the hypothesis of a single QTL in a given position on the chromosome. Horizontal lines represent the 1% genome-wide and 5% suggestive significance thresholds.

 
There was a highly significant correlation between body weight at slaughter (i.e., 70 days) and body composition traits (Table 2). Therefore, body weight at 70 days was included as covariate in the QTL analysis to allow us to detect differences in body composition at a fixed weight. Family and sex were included in the model for those traits where a significant effect of family or sex was detected by the ANOVA.

Abdominal fat deposition.
We detected three suggestive QTLs for abdominal fat content (Table 5). This is marginally higher than the single suggestive QTL expected to occur as a type I error in a full genome scan. However, we believe that all three reflect true QTL effects because they are all colocalized with QTLs affecting other body composition traits and/or growth (Figs. 2 and 3). For two of the QTLs, the allele from the high line was associated with higher fat deposition.

Muscle mass.
Two suggestive and two highly significant QTLs for breast muscle weight were detected (Table 5). The latter two were located on chromosomes 1 (around position 467 cM) and 3 (around position 107), and they explained 9–10% of the residual phenotypic variance when body weight at 70 days was used as covariate. The two QTLs were located in the regions harboring the Growth1 and Growth4 QTLs as well as QTLs for abdominal fat and shank weight. At both these loci, the allele from the high-growth line was associated with higher body weight but less breast muscle mass.

Shank weight.
One suggestive and two highly significant QTLs for shank weight were identified (Table 5). The two highly significant QTLs on chromosomes 1 and 27 showed largely additive effects and explained 13.1 and 6.6%, respectively, of the residual phenotypic variance for this trait. At both loci, the allele from the high line was associated with heavier shanks. The QTL on chromosome 1 was colocalized with Growth1 and the QTLs for breast muscle weight and fat deposition described above (Fig. 2). However, the QTL on chromosome 27 was not detected in our QTL analysis of growth.

The suggestive QTL on chromosome 26 showed overdominance, and it may or may not reflect a true QTL effect.

Weight of internal organs.
We did not detect any convincing QTL for the weight of lung, bursa, or spleen (Table 5). We observed five suggestive QTLs for these traits compared with three expected to occur by chance only, when carrying out three genome scans. Only the one for lung weight on chromosome 3 was located in the vicinity of convincing QTLs for other traits (Fig. 2).

QTL Analysis of Metabolic Traits
The concentrations of glucose, insulin, glucagon, IGF-I, cholesterol, and triglycerides in blood plasma were measured when the birds were 63 days of age. QTL analysis of these six traits revealed one significant and seven suggestive QTLs, which are only marginally higher than what we expect by chance (Table 5). Six of these QTLs showed a location that overlapped with QTLs for growth and/or body composition (Fig. 2). The significant QTL for glucose on chromosome 27 showed overdominance; i.e., there was no significant difference between the two homozygotes, whereas the heterozygote had significantly higher glucose values than the mean of the two homozygotes.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Forty-one generations of bidirectional selection for body weight at 56 days of age from a common base population resulted in dramatic differences in body weight and a number of correlated responses for body composition and metabolic traits between the high- and low-growth lines. Because the criterion of selection in these two lines across all generations has been solely for high or for low body weight at 56 days of age, we expected that the great majority of QTLs detected in our intercross between these two lines should influence body weight. The results are, in fact, in good agreement with this expectation. We detected 13 QTLs affecting growth that segregated in our reciprocal intercross (11). Most of the convincing QTLs for body composition and/or metabolic traits detected in the present study were located in the vicinity of growth QTLs (Fig. 2). The only exception was chromosome 27, for which no growth QTL was detected in our previous study (11) but which harbored one highly significant QTL for shank weight and a significant QTL for plasma glucose concentrations. It is an open question whether these results represent one or two QTLs on chromosome 27, but a single QTL is less likely because the shank QTL showed perfect codominance, whereas the glucose QTL showed overdominance. The clear trend for colocalization between QTLs for growth and QTLs for correlated traits may be caused by pleiotropy or "linkage drag;" the latter means that a selection pressure on a QTL will influence the allele frequencies at closely linked QTLs affecting other traits. High-resolution mapping is required to resolve whether any colocalization reflects pleiotropy or linkage (21).

How can we explain the observation of a highly significant QTL for shank weight at chromosome 27 with no significant effect on growth, despite the fact that the selection scheme was focused entirely on growth? First, the QTL difference on this chromosome may have developed by genetic drift during the course of the selection experiment. This appears less likely, since the observed effect of this QTL makes sense in relation to the phenotypic differences between lines. The QTL allele inherited from the high line was associated with heavier shanks that should be able to carry a heavier bird. Second, it could be a matter of statistical power. The main conclusion from our previous study (11) was that the difference in body weight between the high and low lines was determined by many QTLs, each with a small effect. Many of the QTLs were on the border to reach the stringent statistical significance threshold that is required in a genome scan. Thus the QTL on chromosome 27 may influence growth as well, but it did not reach the significance threshold in the analysis of growth. However, the previous QTL analysis did not indicate the presence of a growth QTL on chromosome 27, not even with the use of a nominal significance threshold. Third, the effect on growth of this locus may have a threshold effect, which means that it is only observed when the birds have reached a certain weight, and most F2 birds did not pass a putative threshold at which more robust shanks were required for high growth.

Another interesting observation was that the 13 growth QTLs detected in this intercross explained only 1.3–3.1% of the residual phenotypic variance for growth or body weight (11), whereas most of the significant QTL tests for body composition gave estimates of the explained residual variance higher than this, and three were >9%. Growth is a highly complex trait affected by many loci influencing appetite, feed uptake, nutrient allocation, body composition, metabolic rate, physical activity, and so forth. This means that any individual locus affecting growth in this cross explains only a rather small fraction of the genetic variance. In contrast, we expect that a more limited number of QTLs affect body composition, and thus each one of them will explain a larger fraction of the variance for the correlated trait in the F2 generation.

One of the more interesting observations in this study was the highly significant effects of reciprocal crosses on body weight at hatch and on plasma concentrations of glucose, cholesterol, insulin, and IGF-I but with no significant effect on body weight at 56 days of age, the age at which selection took place. The reciprocal cross explains an astonishing 15–35% of the phenotypic variance for body weight at hatch, glucose, and insulin. F2 chickens having a maternal grandmother from the high line were heavier at hatch and had higher glucose, cholesterol, and IGF-I concentrations but lower insulin levels. To interpret the cause of these effects, one needs to consider the genetic constitution of the F2 birds as regards mitochondrial DNA (mtDNA) and the sex chromosomes as outlined in Table 4. Thus, if the same effect is observed in both males and females, it is likely to reflect a maternal effect or genetic differences in mtDNA. An effect only observed in females is likely to reflect differences in the W chromosome, since the F2 females are "balanced" as regards the Z chromosome. An effect only seen in males would most likely reflect the segregation of QTLs located on the Z chromosome. All the effects of reciprocal crosses showed essentially the same pattern in both males and females. Thus they are most likely caused by either maternal effects or differences in mtDNA. A maternal effect appears to be a plausible explanation for weight at hatch, since F1 females that are offspring to a high-line female rather than a low-line female are slightly larger, and it is well known that larger females produce larger eggs, which in turn cause a larger hatch weight (7). A maternal effect appears less likely for the effects on metabolic traits, which thus may be caused by genetic differences in mtDNA. This is a possible explanation because of the key role of the mitochondria in energy metabolism. Interestingly, ~0.5–2.8% of all patients with Type 2 diabetes have mtDNA mutations (6). The question of whether the observed reciprocal cross effects are caused by maternal influence or mtDNA differences can be resolved using data from our forthcoming F8 intercross generation. Six generations of intercrossing should have randomized any association between maternal effect and an effect caused by differences in mtDNA.

Although the confidence intervals for the observed QTLs are large, we would like to point out some obvious positional candidate genes. The QTL regions on chromosomes 3 and 7 harbor the genes for the IGF-II receptor (IGF2R) and IGF-binding proteins-2 and -5 (IGFBP2, IGFBP5), respectively (Fig. 2). The suggestive QTL for breast muscle weight on chromosome 4 maps to a region containing the gene for peroxisome proliferative-activated receptor, gamma, coactivator 1 alpha (PGC-1). Previous studies have shown that the nuclear PGC-1 protein is involved in the regulation of genes affecting energy metabolism as well as muscle physiology (14, 19). Lin et al. (16) showed that expression of PGC-1 is involved in control of fiber type composition in mouse skeletal muscle.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
The work was funded by Wallenberg Consortium North, The Foundation for Strategic Research, the AgriFunGen program at the Swedish University of Agricultural Sciences, and Arexis.


   ACKNOWLEDGMENTS
 
Sincere thanks are due to Lena Andersson-Eklund for valuable comments on the manuscript; to Miguel Pérez-Enciso for help with the Qxpak software; to Jenny Jonsson, Inger Jonasson, and Ann-Sofi Strand at the Genome Centre at the Rudbeck laboratory for genotyping; and to Siw Johansson, Ulla Gustafsson, and Sara Price for technical assistance.

Present address of H.-B. Park: Lund Univ., Dept. of Clinical Sciences, Malmö University Hospital, Wallenberg Laboratory, SE-205 02 Malmö, Sweden.


   FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: L. Andersson, Dept. of Medical Biochemistry and Microbiology, Uppsala Univ., BMC, Box 597, SE-75124 Uppsala, Sweden (e-mail: Leif.Andersson{at}imbim.uu.se).


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Carlborg O and Andersson L. Use of randomization testing to detect multiple epistatic QTLs. Genet Res 79: 175–184, 2002.[CrossRef][Medline]
  2. Churchill GA and Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics 138: 963–971, 1994.[Abstract]
  3. Crawford RA. Poultry Breeding and Genetics. New York: Elsevier, 1990.
  4. Dunnington EA and Siegel PB. Long-term selection for 8-week body weight in chickens—direct and correlated responses. Theor Appl Genet 71: 305–313, 1985.[CrossRef]
  5. Dunnington EA and Siegel PB. Long-term divergent selection for eight-week body weight in white Plymouth rock chickens. Poult Sci 75: 1168–1179, 1996.[ISI][Medline]
  6. Guillausseau PJ, Massin P, Dubois-LaForgue D, Timsit J, Virally M, Gin H, Bertin E, Blickle JF, Bouhanick B, Cahen J, Caillat-Zucman S, Charpentier G, Chedin P, Derrien C, Ducluzeau PH, Grimaldi A, Guerci B, Kaloustian E, Murat A, Olivier F, Paques M, Paquis-Flucklinger V, Porokhov B, Samuel-Lajeunesse J, and Vialettes B. Maternally inherited diabetes and deafness: a multicenter study. Ann Intern Med 134: 721–728, 2001.[Abstract/Free Full Text]
  7. Halbersleben DL and Mussekl FE. Relationship of egg weight to chick weight at hatching. Poult Sci 1: 143–144, 1922.
  8. Haley CS, Knott SA, and Elsen JM. Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics 136: 1195–1207, 1994.[Abstract]
  9. International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432: 695–716, 2004.[CrossRef][Medline]
  10. International Chicken Polymorphism Map Consortium. A genetic variation map for chicken with 28 million single-nucleotide polymorphisms. Nature 432: 717–722, 2004.[CrossRef][Medline]
  11. Jacobsson L, Park HB, Wahlberg P, Fredriksson R, Pérez-Enciso M, Siegel PB, and Andersson L. Many QTLs with minor additive effects are associated with a large difference in growth between two selection lines in chickens. Genet Res 86: 115–125, 2005.[CrossRef][ISI][Medline]
  12. Jacobsson L, Park HB, Wahlberg P, Jiang S, Siegel PB, and Andersson L. Assignment of 14 microsatellite markers to the chicken linkage map. Poult Sci 83: 1825–1831, 2004.[Abstract/Free Full Text]
  13. Jansen RC. Interval mapping of multiple quantitative trait loci. Genetics 135: 205–211, 1993.[Abstract]
  14. Knutti D and Kralli A. PGC-1, a versatile coactivator. Trends Endocrinol Metab 12: 360–365, 2001.[CrossRef][ISI][Medline]
  15. Lander E and Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11: 241–247, 1995.[CrossRef][ISI][Medline]
  16. Lin J, Wu H, Tarr PT, Zhang CY, Wu Z, Boss O, Michael LF, Puigserver P, Isotani E, Olson EN, Lowell BB, Bassel-Duby R, and Spiegelman BM. Transcriptional co-activator PGC-1 alpha drives the formation of slow-twitch muscle fibres. Nature 418: 797–801, 2002.[CrossRef][Medline]
  17. Liu G, Dunnington EA, and Siegel PB. Responses to long-term divergent selection for eight-week body weight in chickens. Poult Sci 73: 1642–1650, 1994.[Medline]
  18. Ljungberg K, Holmgren S and Carlborg Ö. Efficient algorithms for quantitative trait loci mapping problems. J Comput Biol 9: 793–804, 2002.[Medline]
  19. Michael LF, Wu Z, Cheatham RB, Puigserver P, Adelmant G, Lehman JJ, Kelly DP, and Spiegelman BM. Restoration of insulin-sensitive glucose transporter (GLUT4) gene expression in muscle cells by the transcriptional coactivator PGC-1. Proc Natl Acad Sci USA 98: 3820–3825, 2001.[Abstract/Free Full Text]
  20. Minitab. Minitab User's Guide (Release 13). State College, PA: Minitab, 2000.
  21. Nuzhdin SV, Dilda CL, and Mackay TF. The genetic architecture of selection response: inferences from fine-scale mapping of bristle number quantitative trait loci in Drosophila melanogaster. Genetics 153: 1317–1331, 1999.[Abstract/Free Full Text]
  22. Paterson AH, Damon S, Hewitt JD, Zamir D, Rabinowitch HD, Lincoln SE, Lander ES, and Tanksley SD. Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments. Genetics 127: 181–197, 1991.[Abstract]
  23. Pérez-Enciso M and Misztal I. Qxpak: a versatile mixed model application for genetical genomics and QTL analyses. Bioinformatics 20: 2792–2798, 2004.[Abstract/Free Full Text]
  24. Schmid M, Nanda I, Guttenbach M, Steinlein C, Hoehn H, Schartl M, Haaf T, Weigend S, Fries R, Buerstedde JM, Wimmers K, Burt DM, Smith J, A'Hara S, Law A, Griffin DK, Bumstead N, Kaufman J, Thomson PA, Burke T, Groenen MA, Crooijmans RP, Vignal A, Fillon V, Morisson M, Pitel F, Tixier-Boichard M, Ladjali-Mohammedi K, Hillel J, Maki-Tanila A, Cheng HH, Delany ME, Burnside J, and Mizuno S. First report on chicken genes and chromosomes. Cytogenet Cell Genet 90: 171–218, 2000.
  25. Seaton G, Haley CS, Knott SA, Kearsey M, and Visscher PM. QTL Express: mapping quantitative trait loci in simple and complex pedigrees. Bioinformatics 18: 339–340, 2002.[Abstract/Free Full Text]
  26. Zeng ZB. Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci USA 90: 10972–10976, 1993.[Abstract/Free Full Text]



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