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GENETICS |
* Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, Ames 50011; and Aviagen Limited, Newbridge, Midlothian, EH28 8SZ, Scotland, UK
1 Corresponding author: sjlamont{at}iastate.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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, transforming growth factor-ß 3, inducible nitric oxide synthase, macrophage migration inhibitory factor, interleukin-2, caspase-1, inhibitor of apoptosis protein-1, tumor necrosis factor-related apoptosis-inducing ligand, chicken B-cell marker, and bone morphogenetic protein-7. From a total of 56 identified single-nucleotide polymorphisms (SNP) in 12 genes, 14 SNP that had moderate allelic frequencies in at least 2 of the 3 lines were typed in about 100 progeny-tested sires from each of 3 elite commercial broiler chicken lines using restriction fragment length polymorphism techniques and then used in association analysis. The traits measured on the progeny (total progeny = 145,467) were: mortality from hatching to 14 d and from 14 to 40 d of age, BW at 7 and 40 d of age, feed conversion, ultrasound breast depth, percentage of breast, eviscerated carcass weight, twisted legs or evident tibial dyschondroplasia, x-ray-inspection–based subclinical or incipient development of tibial dyschondroplasia, curly or crooked toes or bowed legs, oxygen content of blood, and female’s antibody titer to infectious bursal disease virus at 27 wk. Association analyses were conducted with allele and haplotype substitution effect models using progeny mean data adjusted for fixed and mate effects as sire trait records. Ten of the 12 genes had SNP associations with at least 1 trait. Most detected effects were with mortality and growth traits. Most gene–SNP trait associations varied by genetic line or with environment. These results indicate that associations of candidate genes with important broiler traits can be identified in multiple environments, and they offer a potential for the implementation of marker-assisted selection for traits expressed in the environment in which the commercial broiler needs to perform. The effects of these immune-related candidate genes, however, are complex and affected by genetic background and environment.
Key Words: immune-related gene • single nucleotide polymorphism • environment • trait • broiler chicken
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Genetic selection generally takes place in high-hygiene environments (HH), but commercial production may be in contrasting hygiene environments. Differences between selection environments and production environments can affect phenotypic expression of traits. Kolmodin and Bijma (2004) showed that the optimum selection environment for a trait depends not only on the environment in which selection response is realized, but also on the degree of G x E, the correlation among the population average level in different environments, sensitivity to environmental change, and heritability of the trait. Mulder and Bijma (2005) showed that genetic gain in commercial conditions is reduced due to the G x E interaction between the selection environment and the production environment. Leenstra and Cahaner (1991) showed that when broilers were raised in different temperatures, broilers derived from Israeli strains (selected in hot environments) had higher weight gain and protein deposition at moderate to warm temperatures than those derived from Dutch strains (selected in temperate environments), whereas the Dutch chicks performed better at lower temperatures. Therefore, identification of gene-trait associations in different environments, including environments similar to that of the commercial production setting, is very important.
Previous studies have reported associations of immune-related genes with immune response, bacterial burden, and growth performance in experimental crosses and commercial chicken lines (Kramer et al., 2003; Lamont et al., 2002; Liu et al., 2003; Malek and Lamont, 2003; Malek et al., 2004; Zhou et al., 2001; Zhou and Lamont, 2003a,b). However, immune-related genes may have pleiotropic effects, and their expression is affected by environment and genetic background. Immune responsiveness is hypothesized to be important in maintaining performance under challenging environmental conditions. In this context, the objective of the current experiment was to study associations of single-nucleotide polymorphisms (SNP) in 12 immunity-related genes with growth, mortality, yield, and support traits in 3 elite commercial broiler chicken lines raised in HH and low-hygiene (LH) environments.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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), including breast depth measured by ultrasound (US), breast percentage of BW (BR), feed conversion ratio (FCR), eviscerated carcass weight (EV), 3 leg-defect related traits [twisted legs or evident tibial dyschondroplasia (LEG), x-ray inspection-based subclinical or incipient development of tibial dyschondroplasia detected by Lixiscope examination (LIXI), and curly or crooked toes or bowed legs (TOBO)]. Two further traits related to cardiovascular function and antibody response, the oxygen content of blood measured by oximeter (OXI) and the antibody titer of hens to infectious bursal disease virus at 27 wk (IBD) were also recorded. The traits with numbers of sires and mean numbers of progeny per sire for each trait are given in Table 1.
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(IFN-; Zhou et al., 2001), inhibitor of apoptosis protein-1 (IAP1; Lamont et al., 2002; Liu and Lamont, 2003; Zhou and Lamont, 2003b), and chicken B-cell marker (CHB6; Zhou and Lamont, 2003b) on chromosome 1; MD-2 (accessory protein of the toll-like receptor 4; Malek et al., 2004) on chromosome 2; interleukin-2 (IL-2; Kramer et al., 2003) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL; Malek and Lamont, 2003) on chromosome 4; transforming growth factor-ß3 (TGF-ß3; Zhou and Lamont, 2003a) on chromosome 5; macrophage migration inhibitory factor (MIF; Malek et al., 2004) on chromosome 15; toll-like receptor 4 (TLR4; Malek et al., 2004) on chromosome 17; inducible nitric oxide synthase (iNOS; Kramer et al., 2003; Malek and Lamont, 2003) and caspase-1 (CASP1; Liu and Lamont, 2003; Zhou and Lamont, 2003b) on chromosome 19; and bone morphogenetic protein-7 (BMP7) on chromosome 20.
Primer Design and Identification of SNP.
The SNP were identified for each studied gene by designing primers to amplify specific regions, performing PCR, sequencing the PCR products to verify their identity, and aligning the PCR product sequences. Primers were designed based on the known DNA and cDNA sequences of the genes using Primer3 public software (http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi). The PCR products were sequenced at the Iowa State University DNA Sequence and Synthesis Facility using the ABI model 377 (Applied Biosystems, Foster City, CA). The PCR product sequences were aligned, and SNP were identified using the software Sequencher 4.2 (demo version, Gene Codes Corporation, Ann Arbor, MI).
Most of the primers used in this study were designed and used in previous research (Kramer et al., 2003; Lamont et al., 2002; Liu et al., 2003; Malek and Lamont, 2003; Malek et al., 2004; Zhou et al., 2001; Zhou and Lamont, 2003a,b). New primers were designed for genes BMP7, TLR4, TGF-ß3, and iNOS (Table 2). The PCR conditions for the primers were basically the same as those used in previous studies, except the annealing temperature was 62 to 65°C for CASP1, 56°C for IL-2, and 53 to 56°C for MIF, and the magnesium concentration was 1.825 mM for BMP7, IL-2, iNOS, MD-2, MIF, and TLR4, and 2.25 mM for TGF-ß3.
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). Individual sires were genotyped for those SNP using PCR-RFLP. Restriction sites caused by the SNP were identified using Webcutter 2.0 (http://rna.lundberg.gu.se/cutter2/index.html). Digestion reactions for the PCR products were performed for 4 h or overnight in 10 µL of reaction solution, including 5 µL of PCR product solution and 2 or 3 units of restriction enzymes. Other conditions for restriction enzyme digestion (e.g., temperature and buffer solution) followed manufacturer’s recommendations (New England BioLabs Inc. Ipswich, MA).
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Association analyses were conducted using weighted least squares of SAS PROC GLM (SAS Inst. Inc., 2004), with weights equal to the number of progeny included in the mean to account for differences in variance of residuals. Only additive associations were evaluated because progeny means primarily reflect the additive effects of genes, being related to one-half of the sire’s breeding value. Association analyses were performed separately for single SNP and for haplotypes of SNP within a gene.
Analysis of Single SNP
Three allele substitution effect models were used to determine associations between an SNP and a trait. Model 1 was for within-line data analysis, and models 2 and 3 were for analyses across lines. In each model, the effect of allele 1 was estimated relative to allele 2 for each SNP. Allele 1 was defined as the allele with the restriction site
i ji ji
where yij = the adjusted progeny mean of the ith sire of line j; µ = a general mean; Lj = effect of the jth line (j = 1, 2, 3); fji = the number copies of allele 1 of the SNP in the ith sire; bj = the substitution effect for line j; and ji = the residual for sire i with 
(Ni = the number of progeny for sire i).
Analysis of Haplotypes
Two genes (MIF and TGF-ß3) had 2 SNP each. Because SNP within a gene may be in linkage disequilibrium, the haplotypes formed by these SNP were also used for analysis. For these 2 genes, haplotype frequencies were estimated by maximum likelihood using the software Arlequin (version 2.000; Schneider et al., 2000), and the presence of linkage disequilibrium between the SNP was quantified by r2 (Hill and Robertson, 1968) and tested using a 
2 test. Haplotype frequencies were then used to assign haplotype probabilities for sires whose haplotypes could not be inferred with certainty. Models to analyze associations of haplotypes with the traits were the same as described above, but replacing SNP allele effects by
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where fijh = the number of copies of haplotypes h in sire i (or the sum of the probabilities that the first and second haplotype of the sire are h) and bjh = the substitution effect for haplotype h. In these models, the most frequent haplotype (n) was set equal to zero, such that the models are of full rank. As a result, effects bjh represent the effect of substituting a copy of haplotype n by a copy of haplotype h.
In all analyses, residuals were assumed uncorrelated. Genetic relationships among sires were limited and were ignored in the analyses; the 102, 103, and 114 sires that were analyzed for lines X, Y, and Z originated from a total of 51, 50, and 66 sires and 71, 75, and 93 dams, respectively. Thus, half- and full-sib relationships among the evaluated sires were small, and ignoring them was not expected to bias results.
In addition, an outlier analysis was performed. A total of 21 possible outlier data points were identified in the 14 traits by visual inspection of distributions of progeny means against numbers of progeny. Outliers generally represented instances of small progeny groups. Results from analyses with all data were compared with those with removal of the outliers. In general, the identified outliers had minor effects on SNP trait association results. The results reported in this paper are, therefore, based on all data.
Significance Level for Claiming Trait Associations
Significance of SNP trait associations for a given line was tested by an F-test of the model sum of squares for model 1 against a model without SNP or haplotype effects. To test whether effects were consistent across lines, an F-test of model sums of squares of model 2 against model 3 was used. To account for the large number of tests conducted, (994 tests in single-SNP trait association analyses), methods developed by Mosig et al. (2001) and Fernando et al. (2004) were used to control the rate of false positive results across all tests. The number of positive tests that is required for these methods was estimated following the procedure described by Nettleton and Hwang (2003) by comparing the frequency distribution of observed P-values of individual tests to the expected uniform distribution of P-values for tests under the null hypothesis of no association.
For SNP trait associations in the complex experimental design that was used here, including repeated measures or measures on related groups of phenotypes, besides the P-values for the individual trait associations, the consistency of effects across genetic or environmental groups and across related traits can give important insights into the biological impact of the genes. Thus, results are presented and discussed for associations that showed consistency across genetic lines, across environments, or across traits, although some of the individual P-values may not be significant.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Six of the 14 SNP were in Hardy-Weinberg equilibrium in all 3 lines and the remaining 8 SNP (iNOS-Alu I, MIF-Hinf I, MIF-Bcg I, IL-2-Mnl I, IFN--Tsp509 I, IAP1-BstX I, TRAIL-Fok I, and BMP7-Cac8 I) were in Hardy-Weinberg disequilibrium (P < 0.05) in 1 or 2 lines, for a total of 11 out of 42 SNP by line combinations. For those cases, heterozygotes were more frequent than expected for iNOS-Alu I, MIF-Bcg I, IL-2-Mnl I, and IFN--Tsp509 I and less frequent than expected for MIF-Hinf I and TRAIL-Fok I. For MIF and TGF-ß3, which had 2 SNP each, haplotype frequencies, amount of linkage disequilibrium, and P-values for tests of linkage disequilibrium are given in Table 4. The 2 SNP in TGF-ß3 were in significant linkage disequilibrium in all 3 lines (P < 0.001). The 2 SNP in MIF were in significant linkage disequilibrium only in line X (P < 0.001).
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and 2. If the null hypothesis of no association was true for all SNP trait associations, P-values of significance tests would be expected to follow a uniform distribution between 0 and 1, 5% of tests conducted would have a P-value less than 0.05, and the cumulative frequency distribution of P-values would follow the 45° straight line shown in Figure 1. The actual distribution of P-values showed an excess of low P-values, demonstrating that true associations were present in these data. The estimated number of tests that were true among the 994 tests conducted was 95. Using this number of true tests, P-values that met different levels of false discovery rates were calculated. Based on this, on average, 20% of tests with a comparison-wise P-value less than 0.0029 were expected to be false positives, and 50% of tests with a comparison-wise P-value less than 0.031 were expected to be false positives. The significance level used to claim a significant association was set to be at P < 0.031.
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. Each graph in Figure 2 displays estimates of the effects of allele 1 for all traits for each SNP in the lines, regardless of the effects being significant, such that allele effects for a trait can be compared across environments, across lines, and with those for related traits. To facilitate comparisons across traits, estimates are given in units of genetic standard deviation, which were estimated using routine genetic evaluation procedures, by dividing estimates by 0.5 of the genetic standard deviation, noting that differences in progeny means represent half of the difference in breeding values of the sires. In addition, the direction of allele effect estimates was set such that the positive direction (bars above the horizontal axis) represents a desirable effect for all traits. Thus, for traits such as FCR, for which a negative effect is desired, the sign of the effect estimates was reversed. In Figure 2, significance is expressed as –log(P), such that higher values indicate greater significance. On this scale, significance at proportion of false positives levels of 50 and 20% are given by –log(P) equal to 0.031 and 0.0029. In the remainder of this section, results will be described by gene.
Significant interactions between SNP and lines were detected for some traits using model 2. This suggested that allele effects for those SNP were line-specific, and, thus, results shown in Figure 2 from the single-line analyses with model 1 were relevant and appropriate. For SNP that showed no significant interactions with lines, allele effects were reestimated using model 3, and significant results are presented in Table 5. The significant associations detected by model 3 were primarily due to the increase of sample size when analyzing all 3 lines jointly.
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). Associations were significant (P < 0.019) for BW40-LH2 and BW7-HH, and EMORT-HH. It must be noted, however, that allele 1 already had a high frequency in line Z (0.96), which is consistent with the direction of the associations but also required caution when interpreting the results. No associations were detected for the other 2 lines and, as a result, the across-line analysis showed a significant (P < 0.009) interaction between the effect of MD-2 and line for BW40-LH2 (results not shown).
iNOS-Alu I.
In line X, there was a significant positive effect of allele 1 on BW40 in HH (Figure 2). There was a consistent, but not significant, negative effect of allele 1 on growth in LH. Sires with allele 1 had consistently lower EMORT across environments LH and HH but had higher LMORT in the LH and lower LMORT in HH. In line Y, sires with allele 1 had significantly lower LIXI scores. In line Z, sires with allele 1 had significantly higher FCR. There was a consistent, but not significant, negative effect of allele 1 on growth across ages and environments LH and HH. Sires with allele 1 had consistently, but not significantly, higher EMORT across LH and HH environments. Significant interactions with line were detected for BW40-HH, FCR-HH, LMORT-LH, and LMORT-LH2 in the across-line analysis.
MIF.
Associations with traits were studied for SNP cut by Hinf I and Bcg I in MIF. For Hinf I, allele 1 had consistent positive effects on growth in HH but no consistent effect on growth in LH in line Y (Figure 2). Sires with allele 1 had consistently lower EMORT across LH and HH environments for line Z. Sires with allele 1 had consistently lower LEG scores across lines in HH. For Bcg I, there were negative effects of allele 1 on BW40 and BR in HH in line Y. In addition, allele 1 was consistently, but not significantly, associated with slower growth and lower EMORT across environments LH and HH in line X.
Haplotypes formed by Hinf I and Bcg I SNP showed significant associations with several traits. These associations were similar to those detected in the single-SNP analyses and are therefore not shown. Thus, haplotype analysis did not provide additional information compared with single-SNP analysis for MIF.
IL-2-Mnl I.
Allele 1 had consistent negative effects on growth across lines and ages in HH (Figure 2). It had consistent negative effects on early growth (BW7) across LH and HH environments in line Y. Allele 1 had consistent negative effects on FCR across lines but was only significant in line Y. Allele 1 had consistent negative effects on EMORT across LH and HH environments in line Z.
TLR4-Xba I.
In line X, there were consistent positive effects of allele 1 on growth across LH and HH environments and ages (Figure 2). Allele 1 had consistent associations with higher EMORT and lower LMORT across LH and HH environments in line X. Allele 1 also had a significant effect on BW7 in LH1 in line Z. The interaction of allele 1 with line was significant for EMORT in LH2 and HH.
IFN--Tsp509 I.
Allele 1 was associated with consistent negative effects on FCR in HH across lines and was significant in lines X and Z (Figure 2). Sires with allele 1 had significantly higher BW40 and lower LMORT in HH in line Y. Allele 1 was suggested to result in lower IBD in line Z and allele 1 showed a significant interaction with line.
CASP1-Bcg I.
In line X, allele 1 tended to have a positive effect on BW40 in LH, a negative effect in HH, and consistently lower LMORT across LH and HH environments, although they were not significant (Figure 2). In line Y, there were significant associations of allele 1 with higher BR and EV in HH and lower EMORT in LH1. In line Z, allele 1 was associated with higher EMORT in LH but lower EMORT in HH. Also, there were consistent, but not significant, positive effects of allele 1 on growth across LH and HH environments in line Z. There was a significant interaction of allele 1 with line for EMORT in LH1.
IAP1-BstX I.
In line X, allele 1 had consistent negative effects on BW7 across environments LH and HH (Figure 2). Allele 1 had consistent positive, but not significant, effects on BW40 in LH in Line X and a negative effect in HH.
TRAIL-Fok I.
In line Y, the effect of allele 1 on BW40 was negative in LH but positive in HH (Figure 2). There were positive effects of allele 1 on BR and EV. The effect of allele 1 on BR approached significance in all lines and was significant in the across-line analysis. Sires with allele 1 had consistently lower EMORT across LH and HH environments in line Y. In line Z, there was a consistent negative effect of allele 1 on BW40 across LH and HH environments; sires with allele 1 had significantly higher IBD, consistently lower EMORT, but higher LMORT across LH and HH environments. The interaction of allele 1 with line was significant for BW40, BR, and IBD in the HH environment.
CHB6-BstN I.
Allele 1 had consistent negative effects on growth and mortality across ages and LH and HH environments in line X, although most were not significant (Figure 2). Allele 1 had a significant negative effect on FCR in line Y and a consistent positive effect on mortality in line Z, although only 1 age approached significance.
BMP7-Cac8 I.
Allele 1 had a negative, but not significant, effect on IBD in line X and Z (Figure 2).
TGF-ß3.
Two SNP, cut by Bsr I and Msp I, in TGF-ß3 were evaluated. For Bsr I, allele 1 had consistent positive effects on BW40 in LH but negative effects on BW40 in HH in line X (Figure 2). Allele 1 was significantly associated with higher FCR and blood OXI and was consistently associated with mortality across environments in line X. In line Y, allele 1 was consistently associated with faster growth across LH and HH environments and with higher US, BR, EV, and lower mortality in HH. In line Z, negative effects of allele 1 on IBD and TOBO approached significance. Significant interactions of allele 1 with line were detected for BR, EV, and EMORT in HH. For Msp I, allele 1 was consistently associated with lower BW in LH, higher BW in HH, and higher LMORT across LH and HH environments in line X. In line Y, allele 1 was significantly associated with high FCR, lower US, lower EV, and higher mortality in HH. In line Z, allele 1 was consistently associated with slower late growth across LH and HH environments and with lower mortality in HH.
Haplotypes formed by Bsr I and Msp I had significant associations with several traits, but these were also identified by the single SNP results. Thus, haplotype analysis for this gene did not provide information above that determined from the single-SNP analysis and will not be discussed further.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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-Tsp509 I) had significantly more heterozygotes than expected in 1 or 2 lines, and 2 SNP (MIF-Hinf I and TRAIL-Fok I) had fewer heterozygotes than expected. Given that the birds genotyped were sires that had been stringently selected for combinations of broiler traits, the reason for this departure from Hardy-Weinberg equilibrium may be genetic selection. And indeed, the SNP with departure from Hardy-Weinberg equilibrium were associated with at least 1 broiler trait. In addition, these SNP may be associated with other traits under natural or artificial selection that were not analyzed. The test of Hardy-Weinberg equilibrium using case-only individuals was suggested for fine mapping of the gene responsible for the human disease hereditary hemochromatosis (Feder et al., 1996). This method later proved to be powerful for QTL gene detection (Nielsen et al., 1999; Lee, 2003), implying that departures from Hardy-Weinberg equilibrium are not random. Therefore, the observed deviations of marker genotype frequencies from Hardy-Weinberg equilibrium in the distribution of marker genotype frequencies may not be surprising and could indicate marker-trait associations.
Associations of the Studied Genes with the Traits of Interest
Ten of the 12 studied genes had a significant association with at least 1 trait in 1 or more environment and line. Nine genes (TGF-ß3, TLR4, IFN-, CASP1, TRAIL, MD-2, MIF, IL-2, and CHB6) had associations with general mortality. The SNP in TGF-ß3-Bsr I had especially consistent effects on mortality across ages and environments in line X. Allele 1 of TLR4-Xba I had consistent negative effects on EMORT across environments in line X. Allele 1 of MIF-Hinf I resulted in consistent lower EMORT across environments in line X. Associations of these SNP with mortality may be because of the relationship of these immune-related genes with general immunity and fitness of broiler chickens. Previous research demonstrated associations of SNP in those genes with immune response and bacterial burdens, which supports their role in immunity response and fitness. For example, MD-2-Ase I, iNOS-Alu I, CASP1-Hsp92 II, TGF- 3-Bsr I, and TRAIL-Sty I were associated with Salmonella enteritidis (SE) load after inoculation with the bacteria (Liu and Lamont, 2003; Malek and Lamont, 2003; Malek et al., 2004). The CASP1-Hsp92 II was significantly associated with SE load and antibody level to the SE vaccine in F1 chickens from a cross of outbred broiler breeder male line sires and highly inbred female line dams of Fayoumi and Leghorn (Liu and Lamont, 2003), and IFN- promoter Tsp509 I (Zhou et al., 2001) and CHB6-Pvu II (Zhou and Lamont, 2003b) were associated with primary antibody response to Brucella abortus in F2 chickens of a Fayoumi-Leghorn cross. In addition, Kramer et al. (2003) and Liu and Lamont (2003) found that SNP in TGF-ß3, CASP1, iNOS, and IL-2 had associations with SE load in chickens. These 9 genes that were associated with general mortality were also found to have associations with growth in the current study. Associations with additional traits were also detected in the current study. The TGF-ß3 SNP was significantly associated with FCR, BR, EV, and OXI in 1 or 2 lines. The SNP evaluated for iNOS was significantly associated with FCR, IBD, and LIXI in 1 or 2 lines. The TRAIL SNP was significantly associated with IBD and EV in 1 line. These results suggest that the studied immune-related genes have broad pleiotropic effects. Caution, therefore, should be exercised to determine associations with a variety of important traits before applying marker-assisted selection with these genes.
Effects of Environment on Detection of the Gene-SNP Trait Associations
In the present study, associations of SNP with traits were studied under HH and LH conditions. Four general patterns of SNP trait associations across environments were detected. One pattern was that SNP alleles had consistent effects on traits across environments (e.g., in line X, allele 1 of TLR4-Xba I was consistently associated with high EMORT and low LMORT across environments, and in line Z, allele 1 of MD-2-Ase I was consistently associated with high BW across environments). The second pattern was that a SNP allele had positive effects on traits in one environment but negative effects in another environment. For example, progeny of sires with allele 1 of iNOS-Alu I had very significantly higher BW at 40 d of age in HH but lower BW at 40 d in LH. The third pattern was that SNP allelic effects were not consistent between the 2 LH environments. For example, in line Y, allele 1 of CASP1-Bcg I was associated with significantly lower EMORT in LH1 but higher EMORT in LH2. The fourth pattern was that SNP effects were detected in 1 environment but not in the other environments. For example, in line Y, TGF-ß3-Msp I was detected to have significant associations with mortality in HH but not in LH. Apart from being the result of random chance and false positives, these different patterns imply that interactions between genetic and environmental factors are complex for those traits. Different patterns reflect different degrees of sensitivity to the environment. The first pattern of SNP trait association (consistency among all environments) implied that the effects of environmental factors on gene expression are low, and these traits are less sensitive to environmental effects. The second pattern of SNP trait association implied that the interaction of genetic and environmental factors strongly varies with environment, and the trait phenotypes are very sensitive to environmental effects. Qu et al. (1998) found that expression levels of some genes varied among alternative nutritional status, fasting, and refeeding in rats.
Differences in the detected SNP trait associations between HH and LH may be partially due to resource allocation (Dunnington and Siegel, 1996; Klasing, 1998). Animals tend to adapt to the environment in which they are selected and may develop a G x E and environmental sensitivity during selection (Kolmodin and Bijma, 2004; van der Waaij, 2004). Animals tend to allocate more resources to support the traits for which they are selected (Rauw et al., 2003). Van der Waaij (2004) found that the correlation between observed and potential production changed considerably when animals went from one environment to the other. When moved from optimal to suboptimal environments, animals may reallocate available resources to adapt to new environments, especially to use more resources for priority functions for the new environment, such as immunocompetence and maintenance. When the broiler chickens from 3 lines were raised in the HH and LH environments in the current study, the chickens displayed different phenotypes. This may have been a result of different resource allocation in LH conditions being reflected in different phenotypic values. Furthermore, these differences affected the ability to detect SNP trait associations, and sometimes the magnitude or direction of the effect, of the studied alleles and traits.
For application in marker-assisted selection in breeding programs, SNP showing consistent associations with traits across environments are most broadly useful. When such SNP are selected to favor a trait in the HH environment typical of nucleus populations, they can be expected to display similar effects in the LH environment which may be encountered in commercial production. This increases the efficiency and reduces the cost of selection and breeding, because separate tests need not be conducted in LH environments. For SNP that show strong G x E, however, a choice must be made regarding which allele should be selected for, which will depend on the relative economic importance of performance in the alternate environments and the ability to select for the trait in each environment. In extreme cases of G x E, development of alternate lines for different environments can be considered. With a better understanding of the interaction between genetic and environmental factors, more meaningful approaches or strategies can be developed for breeding, nutrition, and management of stocks distributed throughout the world. Marker-assisted selection using SNP that are less sensitive to environmental changes will help chickens be more adaptable to different environments and express more of their true genetic potential. However, under certain circumstances, such as specific, consistent, and well-defined environments, the selection of environmentally sensitive alleles might be advantageous.
Effects of Genetic Background (Line) on Gene-Trait Associations
Interactions of genetic factors always exist in living organisms. Such epistatic interactions can exist among genes that are involved in similar functions, such as those identified in the present study. Also, such interactions can occur among genes and the genetic background (in the present study, represented by the 3 different genetic lines). The SNP trait associations detected in the current study were often present in only 1 or 2 of the 3 lines. Besides random chance resulting from limited power, one reason for this may be the differences of background genome among lines. The elite commercial chicken lines used in the current study were genetically distinct. Interactions between a given candidate gene and the genetic background can vary with line, and the expression of the same gene may differ among lines. Although the molecular basis for interactions of genes and background genome is not well understood, evidence from transgenic mice has shown that phenotypes associated with the same transgene can vary with the recipient genome (Doetschman, 1999; Muller, 1999). One important aspect of the genetic background is the gene network involved with any specific gene’s function. Gene expression is coordinated by complex gene networks, and phenotypes are, therefore, determined by gene networks (Boldogkoi, 2004).
In addition, genes that are linked to the studied genes could affect detection and the direction of gene SNP trait associations. In the current study, some SNP had consistent effects on traits across lines, but the directions of allelic effects on the same traits differed among lines. A likely explanation is that these SNP are not causative but linked to the causal mutation in the studied or a nearby gene. Different linkage phases and degrees of linkage disequilibrium could result in different associations among lines. In the current study, CHB6-BstN I was found to be associated with BW, but directions of effects differed between lines X and Y. Therefore, the CHB6-BstN I is likely linked to, but not identical to, a QTL for BW.
With the completion of the first draft of chicken genome sequence (Hillier et al., 2004) and the chicken genetic variation map with about 2.8 million SNP (Wong et al., 2004), future studies can conduct massively parallel SNP typing and fine-mapping of causative mutations. With these genomic advances, the ability to study genetic interactions for specific traits in depth and to study functional genomics of the response of chickens to different environments is available.
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ACKNOWLEDGMENTS |
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Received for publication January 23, 2006. Accepted for publication March 13, 2006.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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