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GENETICS |
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* Department of Animal Science, Iowa State University, Ames 50011; and Growth Biology Laboratory, Livestock and Poultry Sciences Institute, USDA-ARS, Beltsville, MD 20705
4 Corresponding author: sjlamont{at}iastate.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Key Words: genome scan • quantitative trait loci • growth trait • broiler • inbred line
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Quantitative genetic variation is caused by intrapopulation and interpopulation differences for many traits of biological, medical, and agricultural importance (Sewalem et al., 2002). Dissection of the genetic architecture of complex traits in livestock could greatly advance our general understanding of biology and physiology of quantitative phenotypic variation observed in the population (Rocha et al., 2004). The identification and utilization of QTL provide the potential for more rapid genetic improvement in selection programs, especially for traits that are difficult to improve with traditional selection (Ikeobi et al., 2002). Van der Beek and van Arendonk (1996) indicated additional selection responses of 6 to 13% using MAS by incorporating a marker-linked QTL in a simulation study after 5 generations of selection. With detection of large effects on phenotypic traits and the origin of potentially beneficial alleles, the introgression of favorable alleles into commercial lines, or increasing the frequency of desirable alleles, would become feasible in the poultry industry.
Based on chicken linkage maps and data from a variety of populations, several studies have reported the discovery of QTL for BW in chickens. A whole genome scan for QTL affecting BW and growth in a 3-generation population generated from 2 broiler lines was conducted (van Kaam et al., 1998, 1999). Tatsuda and Fujinaka (2001) reported QTL for growth in a F2 population based on crosses between fast- and slow-growing lines. The QTL affecting early growth in chickens were discovered in a F2 cross between Red Jungle Fowl and layers (Carlborg et al., 2003), between 2 commercial broiler lines (de Koning et al., 2003; Zhu et al., 2003), between 2 egg-layer lines (Tuiskula-Haavisto et al., 2002), between the White Leghorn and Rhode Island Red breeds (Sasaki et al., 2004), between 2 outcrossed White Plymouth Rock broiler dam lines (Jennen et al., 2004), between 2 layer lines divergently selected for primary antibody response to sheep red blood cell (Siwek et al., 2004), and a F2 population between Cobb-Cobb broilers and Hy-Line White Leghorn lines (Schreiweis et al., 2005).
Different QTL effects and positions affecting chicken BW and growth were detected in different populations, due to the different lines analyzed and the nature of the quantitative traits. The resource populations used in the current study were generated by crossing 1 modern broiler sire line with 2 unrelated highly inbred lines (Leghorn and Fayoumi lines; Zhou and Lamont, 1999). The unique population design not only maximizes the power to detect linkage disequilibrium, because of the diverse crosses, but also offers the opportunity to identify modulation of QTL effects caused by different genetic backgrounds. Results for BW and growth are presented in this paper; results for body composition traits are presented in companion papers (Zhou et al., 2006). The primary objective of the present study was to detect and localize QTL affecting BW and growth traits in the F2 population.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The Iowa Growth and Composition Resource Population (IGCRP) 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 by Leghorn cross, n = 325 in broiler by 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
Body weight was measured at hatch and in 2-wk intervals up to 8 wk of age. Average daily weight gain (ADG) was calculated as the average daily change in BW between 2 consecutive BW measurements.
Marker Selection and Genotyping
Microsatellite markers from primer kits 1 to 4 were supplied by the Poultry Subcommittee of the National Animal Genome Research Program (USDA, Washington, DC). Using the available marker intervals from the chicken genetic consensus map (http://www.thearkdb.org), markers were selected to test in the parental inbred lines and the broiler (grandsire #360) of the IGCRP. The PCR amplification of 50 ng of genomic DNA from these parental samples with fluorescently labeled primers (either with 6FAM-, HEX-, or TET-) was used to identify markers that would be informative (polymorphic) in the IGCRP. Markers were selected to test based on their position on the consensus map. A target for marker spacing of 10 cM was used to test markers across the genome. If a given marker was informative in the IGCRP, then the next adjacent marker present in the primer kits had to be at least 10 cM away for it to be evaluated for polymorphic content. Any gaps in marker coverage larger than 20 cM were filled by markers identified using the consensus map and synthesized denovo with either a 6FAM- or HEX- fluorescent label. Marker screening and any pilot genotyping were conducted using an ABI310 genetic analyzer (Applied Biosystems, Foster City, CA). The PCR was conducted in 384-well format using an MJ Research-Tetrad thermal cycler (Global Medical Instrumentation, Ramsey, MN). Reactions were setup using a Multiprobe II robotic liquid handler (Packard Instruments Co. Inc., Meriden, CT) in 384-well format from genomic DNA stocks stored in 96-well format. The PCR reaction conditions varied, depending on the marker being genotyped, from 1.5 to 4 mM Mg Cl2, 0.1 to 0.25 µM primers, with annealing temperatures from 48 to 64°C. All reactions contained 50 ng of genomic DNA in a total reaction volume of 12 µL.
All IGCRP F2 genotyping was conducted using an ABI3700 genetic analyzer (Applied Biosystems) by pooling PCR products from 2 markers labeled with different fluorescent dyes. All genotyping with Applied Biosystems instruments utilized the ROX-400HD internal size standard for accurate microsatellite allele sizing. Genotypes were obtained using Genotyper software (Applied Biosystems), and the data were exported to Microsoft Excel (Microsoft Corp., Redmond, WA) for archiving.
Linkage Analysis and QTL Mapping
Marker linkage analysis was performed using CRIMAP program package Version 2.4 (Green et al., 1990). All markers were preprocessed in terms of polymorphism between broiler sire and Leghorn and Fayoumi dam lines and genotype errors. Markers that did not meet these criteria were deleted from analysis. Option FLIPS and ALL were used to get the best order of the markers, and the FIXED option was used to obtain the map distance among markers. The maps were then used for QTL detection on the 18 autosomes, 2 linkage groups, and the Z chromosome by using QTL Express software (Seaton et al., 2002). The least square regression model was used for QTL analysis including the fixed effects of sex and hatch, along with additive and dominance coefficients for the putative QTL. Detection of QTL was based on an F statistic that was computed from sums of squares explained by the additive and dominance coefficients for the QTL. Significance thresholds of the F statistic were derived at the chromosome and genome-wise levels on a single-trait basis by the permutation test. Average thresholds across the 8 traits in each F2 cross were used for significance testing. A total of 5,000 random permutations of the data were performed. The 5% chromosome-wise level threshold was used as suggestive QTL, and the 5% genome-wise level threshold was used as significant QTL. Percentage of F2 variance explained by model was calculated as
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where RMS = the residual mean square from the reduced model, omitting QTL but including all fixed effects, and FMS = the residual mean square from the full model, including QTL and all fixed effects.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The phenotypic correlations between growth traits in the combined 2 F2 populations are presented in Table 1
. In general, there were high positive correlations between each 2 traits, as expected. Phenotypic correlations between any BW traits and neighbor BW traits or average daily gain traits in terms of measuring time were relatively higher than other correlations, such as correlations among BW6 and BW4 (0.86), BW8 (0.95), ADG2-4 (0.84), and ADG4-6 (0.89).
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The 269 microsatellite markers genotyped in the 2 F2 crosses cover 23 autosomes, 3 linkage groups, the Z chromosome, and an unknown linkage group. The chromosomes or linkage groups were eliminated from the linkage analysis if there were less than 3 markers for each chromosome after preprocessing of all the markers. The number of microsatellite markers, chromosome map length, and average marker interval by chromosome in 2 F2 crosses are presented in Table 2. Ultimately, 19 autosomes, 1 linkage group, and the Z chromosome containing 195 microsatellite markers in the broiler-Leghorn cross were used for linkage analysis. The total map length was 42.77 M, with average spacing of markers of 21.93 cM ranging from 8.71 to 31.33 cM. Nineteen autosomes, 2 linkage groups, and the Z chromosome containing 191 microsatellite markers were used for linkage analysis in the broiler-Fayoumi cross, with total map length of 38.35 morgan. The average marker interval ranges from 6.03 to 28.86 cM with average spacing of markers of 20.08 cM across chromosomes in the broiler-Fayoumi cross. In general, map order of the markers in both F2 crosses was similar to the chicken consensus map. Map lengths for these chromosomes were considerably longer compared with the chicken consensus map.
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Individual chromosome significance levels at the 5 and 1% levels, as determined by the permutation test, differed slightly by trait within chromosome (Table 3). Average 5% chromosome-wise thresholds ranged from 3.99 to 7.03 in the broiler-Leghorn cross and from 4.29 to 7.05 in the broiler-Fayoumi cross. Average 1% chromosome-wise thresholds ranged from 5.75 to 8.97 in the broiler-Leghorn cross and from 6.14 to 8.90 in the broiler-Fayoumi cross. Average 5 and 1% genome-wise thresholds were 9.12 and 11.28, respectively, in the broiler-Leghorn cross and were 9.08 and 11.14, respectively, in the broiler-Fayoumi cross.
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Estimates for QTL significant at the 5% chromosome-wise level are presented in Tables 4 and 5. For the QTL graphs, representing plots of the F statistic across chromosomes, only QTL significant at the 1% chromosome-wise level are presented in Figure 1, panels A and B, (BW traits) and Figure 2, panels A and B (average daily gain). For comparison purposes, if 1 QTL in a group of traits in 1 cross was significant at the 1% chromosome-wise level, QTL for all traits in the both F2 crosses were presented. Although some graphs suggest evidence for multiple QTL in adjacent intervals for the same trait, only results for the most significant position were presented in Tables 4 and 5, because only single QTL models were tested.
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). Of the 38 suggestive QTL in the broiler-Fayoumi cross, 10 QTL were significant at the 5% genome-wise level (Table 5). Over the 8 traits examined, 1 QTL would be expected to be significant at this level by chance alone. Thus, clearly, more QTL were identified at this level than were expected. In general, the additive effect suggested that broiler alleles were superior (greater weight and faster growth) to both Leghorn and Fayoumi alleles, except for the QTL affecting BW2 and ADG0-2 on Gga 14, QTL affecting BW8 and ADG4-6 on Gga 18 in the broiler-Leghorn cross, and for the QTL affecting BW2 and ADG0-2 on Gga 4 in the broiler-Fayoumi cross (Tables 4 and 5). There were no QTL affecting growth-related traits detected on chromosomes 11, 12, 13, 15, 17, 27, and Z in the broiler-Leghorn cross, whereas there were no QTL detected on chromosomes 10, 11, 12, 15, 17, 18, 24, 27, E46, E47, and Z in the broiler-Fayoumi cross. The phenotypic trait variances explained by QTL ranged from 2.24 to 10.12% in the broiler-Leghorn cross and from 2.94 to 9.14% in the broiler-Fayoumi cross.
BW2.
For the broiler-Leghorn cross, QTL effects on BW at 2 wk were detected on chromosomes 1, 2, 4, 5, 7, 10, 14, and E46 (Table 4). Two of 8 QTL were significant at the 5% genome-wise level (Table 6). Four of the 8 QTL showed overdominance (3 with a high degree of over-dominance). For 2 of the 4 QTL, heterozygotes concerning QTL breed origin had lower BW than either of the homozygotes, and the opposite effect was observed for another 2 QTL (Table 4). For the broiler-Fayoumi cross, QTL were identified on chromosomes 1, 2, 4, 5, 6, and 13 (Table 5). Two of 6 QTL were significant at the 5% genome-wise level (Table 6). Two of the 6 QTL showed high degrees of overdominance, and 1 QTL had near complete dominance. Heterozygotes had higher BW at 2 wk than either of the homozygotes in 2 QTL with overdominance effects (Table 5). The total trait variances explained by QTL were 32.12% in broiler-Leghorn and 29.33% in broiler-Fayoumi crosses, respectively (Table 6).
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). Three of the 6 QTL showed high degrees of overdominance. Heterozygotes had higher BW at 4 wk than either of the homozygotes in 2 of the 3 QTL with overdominance effects (Table 4). For the broiler-Fayoumi cross, QTL were identified on Gga 1, 2, 5, 7, 8, and 13 (Table 5). Three of 6 QTL had overdominance effects. For 2 of the 3 QTL, heterozygotes had lower BW at 4 wk than either of the homozygotes (Table 5). The total trait variances explained by QTL were 28.67% in broiler-Leghorn and 29.12% in broiler-Fayoumi crosses, respectively (Table 6).
BW6.
For the broiler-Leghorn cross, QTL effects on BW at 6 wk were detected on Gga 1, 2, 3, 4, 7, and 9 (Table 4). One of the 6 QTL showed overdominance with heterozygotes showing the lowest BW at 6 wk. For the broiler-Fayoumi cross, QTL were identified on Gga 1, 2, 3, 4, 8, and 13 (Table 5). Three of the 6 QTL showed strong overdominance. Heterozygotes at QTL on Gga 2 and 13 had the lowest BW at 6 wk. The total trait variances explained by QTL were 31.46% in broiler-Leghorn and 31.56% in broiler-Fayoumi crosses, respectively (Table 6).
BW8.
For the broiler-Leghorn cross, QTL affecting BW at 8 wk were found on chromosome 1, 2, 4, 7, 9, and 18 (Table 4). One of the 6 QTL showed strong overdominance. Heterozygotes showed the lowest BW at 8 wk at QTL on Gga 1. For the broiler-Fayoumi cross, QTL were identified on chromosomes 1, 2, and 8. Two of the 3 QTL showed overdominance effect with opposite effects observed each other. The total trait variances explained by QTL were 31.56% in broiler-Leghorn and 15.78% in broiler-Fayoumi crosses, respectively (Table 6).
ADG0-2.
The QTL effects on ADG from 0 to 2 wk were detected on Gga 1, 2, 4, 6, 7, 10, 14, and E46 in the broiler-Leghorn cross (Table 4). Four of the 8 QTL showed over-dominance. Heterozygotes had higher ADG0-2 than either of the homozygotes in 2 QTL with overdominance effects (Gga 10 and 14) and lower ADG0-2 on Gga 2 and 7 (Table 4).
Five QTL were identified on chromosomes 1, 2, 4, 5, and 13 in the broiler-Fayoumi cross (Table 5). Two of the 5 QTL showed high degrees of overdominance, and heterozygotes had higher ADG0-2 than either of the homozygotes (Gga 4 and 13, Table 5). The total trait variances explained by QTL were 32.32% in broiler-Leghorn and 26.28% in broiler-Fayoumi crosses, respectively (Table 6).
ADG2-4.
Five QTL affecting ADG from 2 to 4 wk were found on Gga 1, 2, 4, 7, and 24 in the broiler-Leghorn cross (Table 4). One of the 5 QTL showed overdominance, and heterozygotes had lower ADG2-4 than either of the homozygotes. Five QTL were identified on chromosomes 1, 6, 7, and 8 in the broiler-Fayoumi cross (Table 5). Two of the 5 QTL showed overdominance, and heterozygotes for the Gga 6 QTL had lower ADG2-4 than either of the homozygotes, with the opposite situation for QTL on Gga 8. The total trait variances explained by QTL were 24.24% in broiler-Leghorn and 20.14% in broiler-Fayoumi crosses, respectively (Table 6).
ADG4-6.
The QTL effects on ADG from 4 to 6 wk were detected on Gga 1, 2, 3, 4, 7, 9, and 18 in the broiler-Leghorn cross (Table 4). One of the 7 QTL showed over-dominance, and heterozygotes had lower ADG4-6 than either of the homozygotes (Gga 1). Eight QTL were identified on Gga 1, 2, 3, 4, 5, 8, 9, and 14 in the broiler-Fayoumi cross (Table 5). Two of the 8 QTL showed overdominance, and heterozygotes had higher ADG4-6 than either of the homozygotes (Gga 8 and 14). The total trait variances explained by QTL were 33.55% in broiler-Leghorn and 36.92% in broiler-Fayoumi crosses, respectively (Table 6).
ADG6-8.
Six QTL, with effects on ADG from 6 to 8 wk, were detected on Gga 1, 2, 4, 7, 8, and 14 in the broiler-Leghorn cross. One of the 6 QTL showed overdominance, and heterozygotes had lower ADG6-8 than either of the homozygotes (Gga 1, Table 4). No QTL were identified in the broiler-Fayoumi cross. The total trait variance explained by QTL was 27.57 in the broiler-Leghorn cross (Table 6).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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There were similar QTL profiles among the 8 traits within each F2 cross because of general high correlations among these traits, especially in the traits with very high correlations, such as BW2 and ADG0-2. This suggested that these QTL might have pleiotropic effects on growth at different ages. In contrast, different QTL profiles were observed for the traits with lower correlations, (e.g., BW2 vs. ADG4-6, ADG0-2 vs. ADG4-6).
There were significant differences for all 8 growth traits between the broiler male line and 2 inbred dam lines. The founder line that was initially crossed to generate the F2 crosses differed extremely in BW at 8 wk, the Leghorn and Fayoumi lines had mean BW of 515 and 492 g, respectively, whereas the broilers had a 3,214-g mean BW (Deeb and Lamont, 2002). The mean BW at 8 wk of the F2 crosses was 1,575 g for the broiler-Leghorn cross and 1,545 g for the broiler-Fayoumi cross. The BW range was 998 to 2,311 g for the broiler-Leghorn cross and 1,013 to 2,316 g in the broiler-Fayoumi cross. Most of the additive effects detected in the study showed positive value, as expected. However, both inbred lines in the study had a cryptic allele for growth (2-wk BW and ADG0-2 on Gga 14 and 8-wk BW on Gga 18 in the broiler-Leghorn cross; 2-wk BW on Gga 4 in the broiler-Fayoumi cross), whereas the broiler allele was associated with considerably lower growth in these QTL. The cryptic alleles for growth in the current study have been not reported in other studies, although these QTL locations for growth traits were reported in other studies (Sewalem et al., 2002; Carlborg et al., 2003, 2004; Kerje et al., 2003; Jennen et al., 2004). Two reasons might explain these phenomenas: 1) different crosses used in various studies and 2) different ages of measurement of growth among studies. If these cryptic alleles are confirmed in future studies, inbred-line alleles for these QTL can be introgressed as a unique source of alleles into breeding program for improving growth in chickens in the future by MAS.
Many studies have been conducted to detect QTL affecting growth traits in chickens. The F2 population used in the present study is similar to the QTL study by Sewalem et al. (2002), in which a F2 population was generated from a commercial broiler line and White Leghorn line. Even though the ages of growth traits in their study were slightly different than the current study, all QTL for growth traits found in their study were detected in the present study, except for QTL on Gga 27 and Gga Z. This independent confirmation of QTL location helps verify the true nature of these QTL. Several QTL detected in the current study were not shown in the study of Sewalem et al. (2002; Gga 3, 10, 14, and 18), and this might be because they did not use these markers in Gga 10, 14, and 18. For QTL on Gga 3, there were few QTL detected for growth, compared with other large chromosomes such as Gga 1, 2, and 4. In this study, 3 out of 4 QTL on Gga 3 were suggestive QTL. McElroy et al. (2006), Carlborg et al. (2003), and Jennen et al. (2004) reported QTL for growth on Gga 3. The QTL for growth on Gga 10, 14, and 18 in the present study were also found in a F3 population crossed by 2 White Plymouth Rock broilers (Jennen et al. 2004) and in a F2 population generated by Red Jungle Fowl and White Leghorn line (Carlborg et al., 2003). Several QTL for growth traits on Gga 11, 12, and 15 were reported in other studies (Carlborg et al., 2003; Kerje et al., 2003), which were not identified in our study. Carlborg et al. (2003) and McElroy et al. (2006) found QTL for growth on Gga 20 and 26, whereas the current study did not evaluate these 2 chromosomes. The present study identified new QTL for growth traits on Gga 24 and E46 that have not been reported in any other studies.
A strength of the current study was that the conditions for trait recording and the majority of markers were identical across the 2 F2 crosses. Therefore, line cross-specific effects of QTL could be confidently compared. Despite the similar BW and ADG between 2 inbred parent lines (Leghorn and Fayoumi) and between the 2 F2 crosses (Deeb and Lamont, 2002), different QTL positions or effects between the 2 different crosses were observed. For example, for BW and ADG traits, both line crosses shared 1 QTL position on Gga 1 (687 cM on the broiler-Leghorn cross and 610 cM on the broiler-Fayoumi cross); however, the 2 line crosses also had unique QTL on Gga 1 for these traits (218 cM on the broiler-Leghorn cross and 439 cM on the broiler-Fayoumi cross). On Gga 4 for 2-wk BW and ADG0-2 traits, similar QTL positions were detected for both line crosses; however, distinct QTL effects were found between the 2 line crosses. The broiler-Leghorn cross had a positive additive effect, as expected for this QTL, whereas the broiler-Fayoumi cross had a negative additive effect, which means that the Fayoumi QTL allele had greater 2-wk BW and ADG0-2 than the broiler QTL allele. Majority QTL detected for growth traits in this study were similar between the 2 line crosses; however, a few QTL showed considerable differences for QTL position and effects between the 2 line crosses. These results suggested that similar phenotypic values for growth traits between the 2 inbred dam lines were not necessarily contributed by same genes with similar effects. The diversity of QTL detected in the 2 different F2 crosses provides more opportunities to identify genomic regions bearing QTL and, eventually, the causative genes.
The QTL found in the current study generally covered a 20 to 30 cM chromosome region, given the linkage disequilibrium in a F2 population. This size region will contain many candidate genes. Studies on growth and other growth-related traits in human, mouse, and livestock species provide useful information for identifying potential positional candidate genes. Based on the comparative maps among humans, mice, and chickens, potential candidate genes can be selected for the regions of interest.
In the present study, QTL that had large influences on growth traits were located on several major chromosomal regions (Gga 1, 2, and 4 for the broiler-Leghorn cross and Gga 1, 2, 3, 4, and 9 for the broiler-Fayoumi cross). For Gga 1, potential positional candidate genes are growth hormone 1, lysosomal associated membrane protein 1, and uncoupling protein 2 (Table 7). The potential candidate genes mapped in the region on Gga 2 are transforming growth factor-ß (TGFB) type I receptor and pituitary adenylate cyclase-activating polypeptide 1. The TGFB type II receptor is mapped on Gga 4 nearby QTL affecting growth traits. A potential candidate gene on Gga 10 is insulin-like growth factor type 1 receptor. Growth hormone gene has been associated with growth in chickens (Kuhn et al., 2002). The insulin-like growth factor and TGFB family genes have previously shown associations with growth-related traits in chickens (Amills et al., 2003; Li et al., 2003; Zhou et al., 2005). In the human, pituitary adenylate cyclase-activating polypeptide 1 has been indicated to increase growth hormone release (Yunker and Chang, 2004). So far, no association has been found for the genes above with growth-related traits in chickens.
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FOOTNOTES |
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2 Present address: Department of Poultry Science, Texas A&M University, College Station, TX 77843.
3 Present address: Department of Poultry Science, North Carolina State University, Raleigh 27695.
Received for publication February 20, 2006. Accepted for publication May 1, 2006.
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