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Poult Sci 2007. 86:267-276
© 2007 Poultry Science Association
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GENETICS

Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. IV. Metabolic Traits1

H. Zhou*,{ddagger},2, C. M. Evock-Clover{dagger}, J. P. McMurtry{dagger}, C. M. Ashwell{dagger},3 and S. J. Lamont*,4

* Department of Animal Science, Iowa State University, Ames 50011; {dagger} Growth Biology Laboratory, Livestock and Poultry Sciences Institute, Agricultural Research Service, USDA, Beltsville, MD 20705; and {ddagger} Department of Poultry Science, Texas A&M University, College Station 77843

4 Corresponding author: sjlamont{at}iastate.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The current study is a comprehensive genome analysis to detect QTL affecting metabolic traits in chickens. Two unique F2 crosses generated from a commercial broiler male line and 2 genetically distinct inbred lines (Leghorn and Fayoumi) were used in the present study. The plasma glucagon, insulin, lactate, glucose, tri-iodothyronine, thyroxine, insulin-like growth factor I, and insulin-like growth factor II concentrations at 8 wk were measured in the 2 F2 crosses. Birds were genotyped for 269 microsatellite markers across the entire genome. The program QTL Express was used for QTL detection. Significance levels were obtained using the permutation test. For the 10 traits, a total of 6 and 9 significant QTL were detected at a 1% chromosome-wise significance level, of which 1 and 6 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Most QTL for metabolic traits in the present study were detected in Gga 2, 6, 8, 9, 13, and Z for the broiler-Leghorn cross and Gga 1, 2, 4, 7, 8, 13, 17, and E47 for the broiler-Fayoumi cross. Phenotypic variation for each trait explained by all QTL across genome ranged from 2.73 to 14.08% in the broiler-Leghorn cross and from 6.93 to 21.15% in the broiler-Fayoumi cross. Several positional candidate genes within the QTL region for metabolic traits at the 1% chromosome-wise significance level are biologically associated with the regulation of metabolic pathways of insulin, triiodothyronine, and thyroxine.

Key Words: genome scan • quantitative trait loci • metabolic trait • broiler • inbred line


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The study of circulating protein hormones and metabolites has long been of interest to both the biomedical and animal agricultural communities. Since the discovery of insulin (INS) in 1921 and its subsequent production in swine and cattle to treat diabetes, the study of hormones and their regulation of metabolism has been the focus of vast research efforts. Initially, these efforts focused on determining the underlying hormonal basis of disease and eventually defined the functional pathways for how many hormones affect metabolic homeostasis. It is this information that sparked the interest in hormonal regulation of metabolism in domestic species whose growth, feed efficiency, and health are of significant economic importance to animal agriculture.

The inactivation of specific hormone genes leads to dramatic shifts in hormone levels and their related metabolites (O’Shea and Williams, 2002; Robson et al., 2002), but little is known about the natural genetic variation present and its contribution to the variation in normal circulating hormone concentrations. Previous genome-wide studies in rodents (Rosen et al., 2000; Suto and Sekikawa, 2002; Almind et al., 2003; Anunciado et al., 2003; Harper et al., 2003), swine (Desautes et al., 2002), and humans (Santos et al., 2004; Sonnenberg et al., 2004) have identified QTL for specific hormone concentrations specifically related to disease states. Park et al. (2006) detected several QTL affecting metabolic traits including Glc, INS, and INS-like growth factor- (IGF) I in an intercross generated from chicken lines divergently selected for growth. The current study investigated the natural variation present in the genomes of a novel inbred-outbred cross resource population to determine if QTL exist for circulating INS, glucagon (GLG), glucose (Glc), lactate (LCT), IGF-I, IGF-II, and thyroid hormones triiodothyronine (T3) and thyroxine (T4).

Insulin and GLG are produced by the pancreas in response to circulating Glc concentrations and act hormonally to regulate Glc metabolism and storage in the form of GLG (Hazelwood, 1984). These hormones act antagonisticly; INS activates the storage of Glc in the form of glycogen, and GLG activates the breakdown of glycogen to Glc. The 2 hormones therefore act in concert to maintain Glc homeostasis.

In periods of activity, circulating plasma LCT levels are elevated due to the metabolism of Glc and glycogen in the muscle and incomplete oxidation to pyruvate. At steady state, circulating LCT is indicative of protein turnover or lack in uptake in peripheral tissues (Ashwell and McMurtry, 2003).

Insulin-like growth factor-I is produced primarily in the liver in response to pituitary-derived growth hormone. Insulin-like growth factor-I has been shown to regulate growth, reproduction, energy balance, cell proliferation, and cell death (McMurtry et al., 1997). Animal agriculture has focused much research on IGF-I as a potential enhancer of lean muscle growth (Duclos et al., 1999). Conversely, IGF-II is primarily expressed in the developing embryo but is activated in mature animals during periods of stress. The T3 and T4 hormones are produced by the thyroid gland and are key regulators of basal metabolism via effects on the mitochondrial respiratory pathway. Effects on the mitochondrial uncoupling protein directly influence heat production and thus metabolic activity (Dridi et al., 2004).

Previous analysis of the parental lines and the resulting F2 phenotypic data of the populations investigated in the present study indicated significant genetic variation is present in the chicken-affecting hormone and metabolite concentrations and that the effect is polygenic (Ashwell et al., 2002). The major objective of the present study was to detect and localize QTL affecting metabolic traits in the 2 unique F2 populations.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Resource Populations
The Iowa Growth and Composition Resource Population was established by crossing sires from a broiler breeder male line with dams from genetically distinct, highly inbred ( > 99%) chicken lines, the Leghorn G-B2 and Fayoumi M15.2 (Zhou and Lamont, 1999; Deeb and Lamont, 2002). The F1 birds were intercrossed, within dam line, to produce 2 related F2 populations. Birds (n = 417 in broiler-Leghorn cross; n = 325 in broiler-Fayoumi cross) of the 2 F2 populations were analyzed, with each population representing progeny from 1 broiler grandsire and 1 F1 sire of each cross.

Phenotypic Measurements
Blood samples were collected in EDTA-treated tubes from 8-wk-old birds before euthanizing, and plasma was transferred into tubes containing 1,000 IU of trasylol as a preservative. Plasma concentrations of INS (McMurtry et al., 1983), T3, and T4 (McMurtry et al., 1988) were measured using a double antibody RIA. Plasma GLG was measured using an RIA kit purchased from Linco Research Inc. (St. Charles, MO) as described previously (Ashwell et al., 2002). A double antibody RIA was used to measure plasma concentrations of IGF-I, with an intraassay CV of 2.6% (McMurtry et al., 1994), and chicken IGF-II, with an intraassay CV of 3.6% (McMurtry et al., 1998). Plasma Glc and LCT were measured by specific electrode analysis (YSI 2700 SELECT Biochemistry Analyzer, YSI Inc., Yellow Springs, OH).

Marker Selection, Genotyping, Linkage Analysis, and QTL Mapping
All birds were genotyped for 269 markers as described by Zhou et al. (2006). The marker linkage analysis and QTL mapping used were as described in Zhou et al. (2006). Significance levels at the 5 and 1% chromosome-wise and the 5 and 1% genome-wise levels were determined by permutation as described by Zhou et al. (2006).

Partial Correlation Analysis
The phenotypic correlations between metabolic traits were obtained using the JMP program (Sall and Lehman, 1996). Each partial correlation was simultaneously adjusted for all other variables than the 2 being compared.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Phenotypic Correlations Between Metabolic Traits
The phenotypic correlations between metabolic traits in the combined 2 F2 populations are presented in Table 1Go. There were general high correlations between each 2 traits, except that there were relative low correlations between T4, Glc, and IGF-I with most of the other traits. The T4 had relative high correlations with T3 and T3:T4, as expected, and relative low correlations with all others except LCT. There were relative high correlations between Glc and LCT, IGF-I, and IGF-II.


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  		Table 1. The partial correlations between metabolic traits in the F2 population (P < 0.05)
 
Significance Thresholds
Individual chromosome significance levels at the 5 and 1% level, as determined by the permutation test, differed slightly by trait within chromosome (Table 2Go). Average 5% chromosome-wise thresholds ranged from 4.19 to 7.65 in the broiler-Leghorn cross and from 4.49 to 7.48 in the broiler-Fayoumi cross. Average 1% chromosome-wise thresholds ranged from 6.04 to 10.38 in the broiler-Leghorn cross and from 6.65 to 10.00 in the broiler-Fayoumi cross. Average 5 and 1% genome-wise thresholds were 10.26 and 13.66, respectively, in the broiler-Leghorn cross and were 10.71 and 14.45, respectively, in the broiler-Fayoumi cross.


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  		Table 2. The significant 5 and 1% chromosome-wise level, as determined by permutation test, for metabolic traits by chromosome in the broiler-Leghorn cross and the broiler-Fayoumi cross
 
General QTL Mapping Results
Estimates for QTL significant at the 5% chromosome-wise level are presented in Tables 3Go and 4Go. The QTL graphs, representing plots of the F-statistic across chromosomes, are presented in Figures 1Go and 2Go. Although some graphs suggest evidence for multiple QTL in adjacent intervals for the same trait, only results for the most significant position are presented in Tables 3Go and 4Go, because only single QTL models were tested.


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  		Table 3. Evidence for QTL significant at the 5% chromosome-wise level for metabolic traits by chromosome in the broiler-Leghorn cross1
 

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  		Table 4. Evidence for QTL significant at the 5% chromosome-wise level for metabolic traits by chromosome in the broiler-Fayoumi cross1
 

Figure 1
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  		Figure 1. The F-value curves for evidence of QTL for glucagon (GLG), insulin (INS), INS:GLG (IGR), thyroxine (T4), and triiodothyronine (T3) traits. The x-axis indicates the relative position on the linkage group. The y-axis represents the F-value. Arrows on the x-axis indicate the positions where a marker was present. Two lines are provided for 1% chromosome-wise (—) and 1% genome-wise (- - - -) significance.

 

Figure 2
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  		Figure 2. The F-value curves for evidence of QTL for triiodothyronine:thyroxine (T3:T4), lactate (LCT), glucose (Glc), insulin-like growth factor-(IGF) I and IGF-II traits. The x-axis indicates the relative position on the linkage group. The y-axis represents the F-value. Arrows on the x-axis indicate the positions where a marker was present. Two lines are provided for 1% chromosome-wise (—) and 1% genome-wise (- - - -) significance.

 
In total, 28 and 23 QTL were detected at the 5% chromosome-wise level for the 10 traits examined in the broiler-Leghorn cross and the broiler-Fayoumi cross, respectively, not counting potential multiple QTL in adjacent intervals. Ten QTL would be expected to be significant at suggestive threshold by chance alone, given the 10 traits examined. Therefore, about 3 and 2 times as many QTL were detected at this level than were expected by chance in the broiler-Leghorn cross and the broiler-Fayoumi cross, respectively. Of the 28 suggestive QTL in the broiler-Leghorn cross, 1 QTL was significant at the 5% genome-wise level (Table 5Go). Of the 23 suggestive QTL in the broiler-Fayoumi cross, 6 QTL were significant at the 5% genome-wise level (Table 5Go). Over the 10 traits examined, 0.5 QTL would be expected to be significant at this level by chance alone. Thus, 10-fold more QTL were identified at this level than were expected. The QTL affecting metabolic-related traits were only identified on Gga 1, 2, 3, 5, 6, 8, 9, 11, 13, 15, 17, and Z in the broiler-Leghorn cross and on Gga 1, 2, 3, 4, 6, 7, 13, 17, 18, E47, and Z in the broiler-Fayoumi cross (Table 2Go).


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  		Table 5. Number of QTL significant at the 5 and 1% chromosome-wise level (CHR) and genome-wise (GEN) level, respectively, by trait in F2 broiler-Leghorn and broiler-Fayoumi crosses
 
The phenotypic trait variances explained by individual QTL ranged from 2.37 to 6.30 in the broiler-Leghorn cross and from 3.30 to 9.91 in the broiler-Fayoumi cross (Tables 3Go and 4Go).

GLG.
For the broiler-Leghorn cross, 1 QTL for GLG was detected on Gga 13 (Table 3Go). The additive effect suggested that broiler alleles were superior to the Leghorn alleles. The QTL showed overdominance, and heterozygotes concerning breed origin of microsatellite had lower GLG than either of the homozygotes (Table 3Go). For the broiler-Fayoumi cross, 3 QTL for GLG were identified on Gga 7, 13, and 17 (Table 4Go). Broiler alleles tended to be associated with higher GLG than the Fayoumi alleles, except for the QTL on Gga 7 (Table 4Go). One of the 3 QTL showed a high degree of overdominance, and heterozygotes had higher Glc than either of the homozygotes (Gga 17; Table 4Go). The total trait variances explained by QTL for GLG were 4.01 and 13.78% in the broiler-Leghorn and broiler-Fayoumi crosses, respectively (Table 5Go).

INS.
For the broiler-Leghorn cross, 5 QTL effects on INS were detected on Gga 2, 6, 8, 9, and 17 (Table 3Go). Broiler alleles were superior to Leghorn alleles for 3 of 5 QTL (Table 3Go). Heterozygotes had the greatest INS:GLG (IGR) for 1 of 5 QTL. For the broiler-Fayoumi cross, 2 QTL for INS were identified on Gga 6 and 17, with Fayoumi alleles resulting in greater INS (Table 4Go). Heterozygotes showed lower INS than either of the homozygotes (Table 4Go). The total trait variances explained by QTL were 19.53 and 8.37% in the broiler-Leghorn and broiler-Fayoumi crosses, respectively (Table 5Go).

IGR.
For the broiler-Leghorn cross, 3 QTL for IGR were identified on Gga 6, 9, and Z (Table 3Go). Leghorn alleles tended to be associated with higher IGR than the broiler alleles, except for the QTL on Gga 9 (Table 3Go). One of the 3 QTL showed overdominance (Gga 6), and 1 showed complete dominance (Gga 9). Heterozygotes had lower IGR than either of the homozygotes in the QTL with overdominance effect (Table 3Go). For the broiler-Fayoumi cross, 3 QTL for IGR were identified on Gga 4, 6, and 17 (Table 4Go). The additive effect suggested that Fayoumi alleles were superior to the broiler alleles, except for the QTL on Gga 4. Two of 3 QTL had overdominance effect, and heterozygotes had lower IGR than either of the homozygotes (Table 4Go). The total trait variances explained by QTL were 9.08 and 13.38% in the broiler-Leghorn and broiler-Fayoumi crosses, respectively (Table 5Go).

T4, T3, and T3:T4.
In total, 10 QTL affecting T4, T3, and T3:T4 were found on Gga 1, 3, 6, 8, 11, and 15 (Table 3Go). The additive effect suggested that broiler alleles were superior to the Leghorn alleles, except for the QTL for T4 on Gga 3 (Table 3Go). Heterozygotes showed the greatest T4 at QTL on Gga 1. The results revealed 4 suggestive QTL on Gga 1, 3, and 4 in the broiler-Fayoumi cross (Table 4Go). Broiler alleles were superior to the Fayoumi alleles for 2 out of the 4 QTL. One of the 4 QTL showed overdominance effect, and the heterozygote showed greater T4 than either of the homozygotes (Gga 4). The total trait variances explained by QTL for T4, T3, and T3:T4 were 11.27, 9.42, and 13.59% in the broiler-Leghorn and 6.73, 8.66, and 14.32% in the broiler-Fayoumi crosses, respectively (Table 5Go).

LCT.
Two QTL were detected for LCT on Gga 8 and 13 in the broiler-Leghorn cross (Table 3Go). Leghorn alleles showed associations with greater LCT than the broiler alleles. One of 2 QTL showed overdominance (Table 3Go). Three QTL were identified on Gga 18, E47, and Z in the broiler-Fayoumi crosses (Table 5Go). Broiler alleles showed higher LCT than the Fayoumi alleles, except for the QTL on Gga 18. The results indicated overdominance in 2 out of 3 QTL. The total trait variances explained by QTL were 7.19 and 11.80% in the broiler-Leghorn and broiler-Fayoumi crosses, respectively (Table 5Go).

Glc.
One QTL was found on Gga 8 for Glc in the broiler-Leghorn cross, whereas 4 QTL were identified on Gga 2, 4, 7, and Z (Tables 3Go and 4Go). The additive effect indicated that broiler alleles were superior to the Leghorn alleles or Fayoumi alleles for 2 of the 5 QTL. Two of the 5 QTL showed overdominance, and heterozygotes had greater Glc than either of the homozygotes (Gga Z) and lower Glc (Gga 4; Table 4Go). These effects accounted for nearly 2.73 and 21.35% of the variation in the broiler-Leghorn and broiler-Fayoumi crosses, respectively (Table 5Go).

IGF-I and IGF-II.
Six QTL were detected for IGF-I and IGF-II on Gga 1, 3, 6, 8, and 17 in the broiler-Leghorn cross (Table 3Go). Leghorn alleles showed associations with greater IGF-I or IGF-II than the broiler alleles for 4 of the 6 QTL. One of 6 QTL showed strong overdominance for IGF-I (Table 3Go). Heterozygotes had lower IGF-I than either of the homozygotes. Four QTL were identified on Gga 1, 4, and 5 in the broiler-Fayoumi cross (Table 4Go). Broiler alleles showed lower IGF-I or IGF-II than the Fayoumi alleles, except for the QTL for IGF-I on Gga 4. The current results indicated overdominance in all 4 QTL. Heterozygotes had the lowest IGF-I or IGF-II for all 4 QTL. The total trait variances explained by QTL for IGF-I and IGF-II were 11.10 and 9.13% in the broiler-Leghorn and 12.15 and 7.51% in the broiler-Fayoumi crosses, respectively (Table 5Go).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In the present study, 9 metabolic factor traits were analyzed for QTL using a whole genome scan. A total of 51 QTL were detected in the 2 related F2 crosses at the 5% chromosome-wise significance level. Of these QTL, 14 were significant at the 1% chromosome-wise level. Those QTL (Table 6Go) with the highest significance levels will be discussed further.


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  		Table 6. Positional candidate genes for metabolic QTL identified in both the Leghorn and Fayoumi crosses
 
The regulation of metabolism in higher animals is a complex series of checks and balances that must respond to environmental stimuli including diet (nutrition) and temperature. Metabolic regulation follows patterns of response to nutrient availability that results from either feeding or some other stimulus such as physical activity. In general, metabolic response to feeding follows the schematic outlined in Figure 3Go. As dietary energy increases, primarily supplied as carbohydrate following a meal, blood Glc concentrations increase. Five QTL (Gga 2, 4, 7, 8, and Z) affecting plasma Glc concentrations were identified in the present study, but none of them appeared in the QTL positions (Gga 20 and 27) identified in a previous study (Park et al., 2006).


Figure 3
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  		Figure 3. Schematic of the relationships between circulating metabolic factors and metabolism in endocrine and energy-producing and utilizing tissues. As glucose (Glc) levels rise (from bottom to top), insulin levels rise and glycolysis and lipogenesis predominate. When blood Glc levels fall, glucagon increases and metabolism shifts to gluconeogenesis and lipolysis. Triiodothyronine (T3) and insulin-like growth factor-1 (IGF-I) both stimulate the shift of metabolism toward glycolysis and induce insulin production.

 
The increase in blood Glc following a meal stimulates the release of INS by the B-islet cells of the pancreas. Insulin stimulates the uptake and storage of this energy by the liver in the form of glycogen and lipid by also stimulating lipogenic enzymes (Hazelwood, 1984). Upon the reduction of blood Glc concentrations either by fasting or increased energy demand, to maintain homeostasis, the pancreas produces GLG, which stimulates the mobilization of energy from both glycogen and lipid stores to supply the peripheral tissues. This negative energy balance often follows physical activity, whereby the muscle tissues utilize LCT as an energy source for gluconeo-genesis (Juel, 1997). These processes rely on feedback mechanisms to maintain body homeostasis, whereby the IGR is maintained (Maruyama et al., 1984).

A family of significant QTL for INS, GLG, and IGR were also detected (Table 6Go). The first of these QTL was located on Gga 2 in the marker interval of MCW264 to GCT2. Although 2 QTL affecting INS (Gga 1 and 2) have been reported in chickens (Park et al., 2006), the QTL for INS in the present study has not been described previously. There are several positional candidate genes in this region, including high-Glc regulated protein-8 and corticotrophin-releasing hormone (CRH). The high-Glc regulated protein-8 is a gene that responds to hyperglycemia and is of interest clinically in diabetic nephropathy and may represent a therapeutic target for the complex disease (Lappin et al., 2002). It is logical that this gene may elicit some effects on INS concentrations, but there is no current evidence for their direct interaction.

The location of CRH in an interval containing a QTL for circulating INS is also logical, because energy homeostasis is regulated by neuropeptides including CRH. The response to CRH is anorexigenic, limiting feeding behavior (Hillebrand et al., 2002). The CRH itself is regulated by feeding behavior and is therefore cycling in a similar manner to INS after a meal is consumed. It has been shown that CRH inhibits INS secretion and increases GLG secretion in rodents (Karlsson and Ahren, 1988).

Additional QTL for INS concentrations were detected on Gga 6 and 9 in marker regions for ADL377 and MCW135 to MCW329, respectively. These QTL have not been previously reported in these regions for chickens, but there are positional candidate genes present that may contribute to INS concentrations. The pancreatic lipase-related protein I precursor gene is located on Gga 6, and the adiponectin gene is located on Gga 9, both near the QTL for INS. The pancreatic lipase-related protein I is an important mediator of INS action in peripheral tissues (Kintscher and Law, 2005). It has also been associated with gestational diabetes, where INS resistance occurs (Thyfault et al., 2005). Insulin resistance can be described as the general case in broiler-type chickens (Seki et al., 2003). The QTL on Gga 9 has a positive effect from the broiler allele, further supporting adiponectin, an adipocytokine, or adipose tissue signaling cytokine, as a positional candidate gene, possibly for affecting INS resistance as observed in broilers.

In addition to blood Glc, the production of INS is also stimulated by the hormones IGF-I and the thyroid hormone T3 (Decuypere et al., 2005). Thyroid hormones also regulate other metabolic processes in response to cold temperature or other stress (Wentworth and Ringer, 1986). The production of T4 and T3 in the thyroid is diagramed in Figure 4Go. The main hormone that is produced by the thyroid is T4, although some T3 is made. The bioactive thyroid hormone is T3, which is produced from deiodination of T4 by deiodinase in the periphery (Freeman and McMabb, 1991). Therefore, T3:T4 is relevant to the metabolic status of the animal. Increased T3 upregulates basal metabolism (O2 consumption) with a corresponding decrease in growth rate, due to allocation of metabolic resources (energy; Wentworth and Ringer, 1986). The T3 also affects carbohydrate metabolism by stimulating INS production and energy storage as glycogen (Decuypere et al., 2005).


Figure 4
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  		Figure 4. Schematic for the factors that influence the production of thyroid hormones and their effects on metabolism and development. The primary hormone produced by the thyroid is thyroxine (T4), whereas some triiodothyronine (T3) is also produced, as indicated by the arrow sizes.

 
Gga 1 contains a QTL for T3 and T3:T4 in the interval of markers MCW10 to MCW106. Other groups have previously observed QTL in this region for early growth traits (Carlborg et al., 2003) as well as for the behavior trait tonic immobility (Schütz et al., 2004) in Leghorn-Jungle Fowl crosses. Both of these traits may be related to the thyroid status of the bird due to T3 and T4 effects on basal metabolism (de Lange et al., 2001) and their general effects on growth and development (O’Shea and Williams, 2002). Upon querying the chicken whole genome draft sequence in this region for genes that may possibly be related to T3 or T4 concentrations, several positional candidates were found. The IGF-I gene is in this region. It is known that IGF-I can be regulated by T3 concentrations in birds and other vertebrates (Vasilatos-Younken et al., 1999; Schmid et al., 2003). An additional QTL for T3 concentrations was found on Gga 8 in the broiler-Leghorn cross. This QTL was located where a previous group identified QTL for egg production traits (Tuiskula-Haavisto et al., 2002). This chromosomal region also contains the gene for the type I iodothyronine deiodinase enzyme. This enzyme is responsible for conversion of T3 to T4 and is involved in regulating T3:T4. The expression of this gene linked to growth hormone production possibly might explain the positive contribution of the broiler alleles at this locus (Cogburn et al., 1997). The deiodinase is an ideal positional candidate gene whose circulating substrate levels appear to be associated with T3:T4 as detected by QTL.

A QTL for T4 levels was found on Gga 4 in the interval from LEI73 to ADL203. This region has been described by many groups as having effects on growth and egg production traits (Tuiskula-Haavisto et al., 2002; de Koning et al., 2003). There are a few positional candidate genes in the region including the SH3 domain-binding protein gene, which interacts with SH3 domains in cell-signaling protein kinases.

The QTL identified in this resource population represent a comprehensive study of association of metabolic factors and hormones with genetic loci in chickens. Associations between these circulating factors and performance traits have been previously shown, specifically for IGF-I and the thyroid hormones T3 and T4 (Tona et al., 2004). By identifying specific allele variants associated with circulating levels of these hormones, the cost of selecting for specific trends in the concentrations of these hormones (higher or lower) may be accomplished by genotyping at a significantly lower cost than performing the hormone assays. These QTL loci may also be useful in other species as determinants associated with metabolic status or hormone levels.


   FOOTNOTES
 
1 This is journal paper of the Iowa Agriculture and Home Economics Experiment Station, Ames, project number 6680, was supported by Hatch and State of Iowa funds. Back

2 At Texas A&M University since March 2006. Back

3 Present address: Department of Poultry Science, North Carolina State University, Raleigh 27695. Back

Received for publication June 30, 2006. Accepted for publication October 17, 2006.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Almind, K., R. N. Kulkarni, S. M. Lannon, and C. R. Kahn. 2003. Identification of interactive loci linked to insulin and leptin in mice with genetic insulin resistance. Diabetes 52:1535–1543.[Abstract/Free Full Text]

Anunciado, R. V., M. Nishimura, M. Mori, A. Ishikawa, S. Tanaka, F. Horio, T. Ohno, and T. Namikawa. 2003. Quantitative trait locus analysis of serum insulin, triglyceride, total cholesterol and phospholipid levels in the (SM/J x A/J)F2 mice. Exp. Anim. 52:37–42.[Web of Science][Medline]

Ashwell, C. M., and J. P. McMurtry. 2003. Hypoglycemia and reduced feed intake in broiler chickens treated with metformin. Poult. Sci. 82:106–110.[Abstract/Free Full Text]

Ashwell, C. M., J. P. McMurtry, N. Deeb, and S. J. Lamont. 2002. Endocrine and metabolic factors in unique inbred x outbred chicken crosses. 7th World Congr. Genet. Appl. Livest Prod., Montpellier, France. INRA and CIRAD, Paris, France.

Carlborg, Ö., S. Kerje, K. Schütz, L. Jacobsson, P. Jensen, and L. Andersson. 2003. A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Res. 13:413–421.[Abstract/Free Full Text]

Cogburn, L. A., L. Sofer, and J. Burnside. 1997. Molecular cloning and sequence analysis of chicken type I deiodinase cDNA: Expression in normal and dwarf broiler chickens. Biochem. Biophys. Res. Commun. 241:459–464.[Web of Science][Medline]

Decuypere, E., P. Van As, S. Van der Gesten, and V. M. Darras. 2005. Thyroid hormone availability and activity in avian species: A review. Domest. Anim. Endocrinol. 9:63–77.

Deeb, N., and S. J. Lamont. 2002. Genetic architecture of growth and body composition in unique chicken populations. J. Hered. 93:107–118.[Abstract/Free Full Text]

de Koning, D. J., D. Windsor, P. M. Hocking, D. W. Burt, A. Law, C. S. Haley, A. Morris, J. Vincent, and H. Griffin. 2003. Quantitative trait locus detection in commercial broiler lines using candidate regions. J. Anim. Sci. 81:1158–1165.[Abstract/Free Full Text]

de Lange, P., A. Lanni, L. Beneduce, M. Moreno, A. Lombardi, E. Silvestre, and F. Goglia. 2001. Uncoupling protein-3 is a molecular determinant for the regulation of resting metabolic rate by thyroid hormone. Endocrinology 142:3414–3420.[Abstract/Free Full Text]

Desautes, C., J. P. Bidanelt, D. Milant, N. Iannuccelli, Y. Amigues, F. Bourgeois, J. C. Caritez, C. Renard, C. Chevalet, and P. Mormede. 2002. Genetic linkage mapping of quantitative trait loci for behavioral and neuroendocrine stress response traits in pigs. J. Anim. Sci. 80:2276–2285.[Abstract/Free Full Text]

Dridi, S., O. Onagbesan, Q. Swennen, J. Buyse, E. Decuypere, and M. Taouis. 2004. Gene expression, tissue distribution and potential physiological role of uncoupling protein in avian species. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 139:273–283.[Medline]

Duclos, M. J., C. Beccavin, and J. Simon. 1999. Genetic models for the study of insulin-like growth factors (IGF) and muscle development in birds compared to mammals. Domest. Anim. Endocrinol. 17:231–243.[Web of Science][Medline]

Freeman, T. B., and F. M. A. McMabb. 1991. Hepatic 5'-deiodinase activity of Japanese quail using reverse-T3 as substrate: Assay validation, characterization, and developmental studies. J. Exp. Zool. 258:212–220.[Web of Science][Medline]

Harper, J. M., A. T. Galecki, D. T. Burke, S. L. Pinkosky, and R. A. Miller. 2003. Quantitative trait loci for insulin-like growth factor I, leptin, thyroxine, and corticosterone in genetically heterogeneous mice. Physiol. Genomics 15:44–51.[Abstract/Free Full Text]

Hazelwood, R. L. 1984. Pancreatic hormones, insulin/glucagon molar ratios, and somatostatin as determinants of avian carbohydrate metabolism. J. Exp. Zool. 232:647–652.[Web of Science][Medline]

Hillebrand, J. J., D. de Wied, and R. A. Adan. 2002. Neuropeptides, food intake and body weight regulation: A hypothalamic focus. Peptides 23:2283–2306.[Web of Science][Medline]

Juel, C. 1997. Lactate-proton cotransport in skeletal muscle. Physiol. Rev. 77:321–358.[Abstract/Free Full Text]

Karlsson, S., and B. Ahren. 1988. Effects of corticotropin-releasing hormone on insulin and glucagon secretion in mice. Acta Endocrinol. (Copenh.) 117:87–92.[Abstract/Free Full Text]

Kintscher, U., and R. E. Law. 2005. PPAR{gamma}-mediated insulin sensitization: The importance of fat versus muscle. Am. J. Physiol. Endocrinol. Metab. 288:E287–E291.[Abstract/Free Full Text]

Lappin, D. W., P. Doran, C. Godson, and H. R. Brady. 2002. Gene responses to hyperglycaemia. Exp. Nephrol. 10:120–129.[Web of Science][Medline]

Maruyama, H. A., A. Hisatomi, L. Orci, G. M. Grodsky, and R. H. Unger. 1984. Insulin within islets is a physiologic glucagon release inhibitor. J. Clin. Invest. 74:2296–2299.[Web of Science][Medline]

McMurtry, J. P., G. L. Francis, and Z. Upton. 1997. Insulin-like growth factors in poultry. Domest. Anim. Endocrinol. 14:199–229.[Web of Science][Medline]

McMurtry, J. P., G. L. Francis, F. Z. Upton, G. Rosselot, and D. M. Brocht. 1994. Developmental changes in chicken and turkey insulin-like growth factor-I (IGF-I) studied with a homologous radioimmunoassay for chicken IGF-I. J. Endocrinol. 142:225–234.[Abstract/Free Full Text]

McMurtry, J. P., I. Plavnik, R. W. Rosebrough, N. C. Steele, and J. A. Proudman. 1988. Effect of early feed restriction in male broiler chicks on plasma metabolic hormones during feed restriction and accelerated growth. Comp. Biochem. Physiol. A 91:67–70.

McMurtry, J. P., R. W. Rosebrough, D. M. Brocht, G. L. Francis, Z. Upton, and P. Phelps. 1998. Assessment of developmental changes in chicken and turkey insulin-like growth factor-II (cIGF-II) by homologous radioimmunoassay. J. Endocrinol. 157:463–473.[Abstract]

McMurtry, J. P., R. W. Rosebrough, and N. C. Steele. 1983. A homologous radioimmunoassay for chicken insulin. Poult. Sci. 62:697–701.[Web of Science][Medline]

O’Shea, P. J., and G. R. Williams. 2002. Insight into the physiological actions of thyroid hormone receptors from genetically modified mice. J. Endocrinol. 175:553–570.[Abstract]

Park, H. B., 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 25:216–223.[Abstract/Free Full Text]

Robson, H., T. Siebler, S. M. Shalet, and G. M. Williams. 2002. Interactions between GH, IGF-I, glucocordicoids, and thyroid hormones during skeletal growth. Pediatr. Res. 52:137–147.[Web of Science][Medline]

Rosen, C. J., G. A. Churchill, L. R. Donahue, K. L. Shultz, J. K. Burgess, D. R. Powell, and W. G. Beamer. 2000. Mapping quantitative trait loci for serum insulin-like growth factor-1 levels in mice. Bone 27:521–528.[Medline]

Sall, J., and A. Lehman. 1996. JMP Start Statistics: A Guide to Statistical and Data Analysis Using JMP and JMP IN Software. Duxbury Press, Eadsworth Publ. Co., Belmont, CA.

Santos, C. D., D. Fallin, C. Le Stunff, S. LeFur, and P. Bougneres. 2004. INS VNTR is a QTL for the insulin response to oral glucose in obese children. Physiol. Genomics 16:309–313.[Abstract/Free Full Text]

Schmid, A. C., I. Lutz, W. Kloas, and M. Reinecke. 2003. Thyroid hormone stimulates hepatic IGR-I mRNA expression in a bony fish, the tilapia Oreochromis mossambicus, in vitro and in vivo. Gen. Comp. Endocrinol. 130:129–134.[Web of Science][Medline]

Schütz, K. E., S. Kerje, L. Jacobsson, B. Forkman, Ö. Carlborg, L. Andersson, and P. Jensen. 2004. Major growth QTLs in fowl are related to fearful behavior: Possible genetic links between fear responses and production traits in a Red Junglefowl x White Leghorn intercross. Behav. Genet. 34:121–130.[Web of Science][Medline]

Seki, Y., K. Sato, T. Kono, H. Abe, and Y. Akiba. 2003. Broiler chickens (Ross strain) lack insulin-responsive glucose transporter GLUT4 and have GLUT8 cDNA. Gen. Comp. Endocrinol. 133:80–87.[Web of Science][Medline]

Sonnenberg, G. E., G. R. Krakower, L. J. Martin, M. Olivier, A. E. Kwitek, A. G. Comuzzie, J. Blangero, and A. H. Kissebah. 2004. Genetic determinants of obesity-related lipid traits. J. Lipid Res. 45:610–615.[Abstract/Free Full Text]

Suto, J., and K. Sekikawa. 2002. A quantitative trait locus that accounts for glucose intolerance maps to chromosome 8 in hereditary obese KK-A(y) mice. Int. J. Obes. Relat. Metab. Disord. 26:1517–1519.[Web of Science][Medline]

Thyfault, J. P., E. M. Hedberg, R. M. Anchan, O. P. Thorne, C. M. Isler, E. R. Newton, G. L. Dohn, and J. E. Devente. 2005. Gestational diabetes is associated with depressed adiponectin levels. J. Soc. Gynecol. Investig. 12:41–45.[Web of Science][Medline]

Tona, K., O. M. Onagbesan, Y. Jego, B. Kamers, E. Decuypere, and V. Bruggeman. 2004. Comparison of embryo physiological parameters during incubation, chick quality, and growth performance of three lines of broiler breeders differing in genetic composition and growth rate. Poult. Sci. 83:507–513.[Abstract/Free Full Text]

Tuiskula-Haavisto, M., M. Honkatukia, J. Vilkki, D. J. de Koning, N. F. Schulman, and A. Mäki-Tanila. 2002. Mapping of quantitative trait loci affecting quality and production traits in egg layers. Poult. Sci. 81:919–927.[Abstract/Free Full Text]

Vasilatos-Younken, R., X. H. Wang, Y. Zhou, J. R. Day, J. P. McMurtry, R. W. Rosebrough, E. Decuypere, N. Buys, V. Darras, J. L. Beard, and F. Tomas. 1999. New insights into the mechanism and actions of growth hormone (GH) in poultry. Domest. Anim. Endocrinol. 17:181–190.[Web of Science][Medline]

Wentworth, B. C., and R. K. Ringer. 1986. Thyroids. Pages 452–465 in Avian Physiology. P. D. Sturkie, ed. Springer-Verlag, New York, NY.

Zhou, H. J., N. Deeb, C. M. Ashwell, and S. J. Lamont. 2006. Genome-wide linkage analysis to identify chromosomal regions affecting phenotypic traits in the chicken. I. Growth and average daily gain. Poult. Sci. 85:1700–1711.[Abstract/Free Full Text]

Zhou, H. J., and S. J. Lamont. 1999. Genetic characterization of biodiversity in highly inbred chicken lines by microsatellite markers. Anim. Genet. 30:256–264.[Web of Science][Medline]





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