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INVITED REVIEWS |
Department of Animal Science, Iowa State University, Ames 50011
1 Corresponding author: sjlamont{at}iastate.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONS AND FUTURE PROSPECTSNOTE IN PROOFREFERENCES |
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Key Words: chicken • quantitative trait loci • genetic architecture • high-resolution mapping • marker-assisted breeding
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONS AND FUTURE PROSPECTSNOTE IN PROOFREFERENCES |
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A similar review conducted by Hocking (2005) summarized chicken QTL results published through the end of 2004. There has been rapid progress in QTL studies, with 17 new papers reporting 370 QTL in chickens since the Hocking (2005) review. Hocking (2005) proposed use of much more stringent significance thresholds for inclusion of QTL than that used in the current paper, which is appropriate when evaluating single studies. Because the purpose of the present review was to discern biological patterns from independent studies, less stringent thresholds were used for reporting. Furthermore, the reports analyzed for the preparation of this review were used to establish the Chicken QTLdb (http://www.animalgenome.org/QTLdb/chicken.html), which allows for easy search and comparison of QTL results from different studies and complements other major public QTL databases for the chicken: ChickCmap (http://www.animalsciences.nl/Cmap) and ChickVD (http://chicken.genomics.org.cn; Wang et al., 2005).
Results of the present review will be useful for directing future genetic and genomic studies. It is particularly timely in that we are now at a transition point in analysis methods for quantitative traits in the chicken because of the recent availability of genomic sequence information (Soller et al., 2006).
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONS AND FUTURE PROSPECTSNOTE IN PROOFREFERENCES |
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The evaluated phenotypic traits were classified into 5 major trait categories (Table 1
): "growth" for traits related to BW, body composition, and feed intake; "egg" for traits related to egg production, egg quality and skeleton; "disease resistance" (DR) for traits related to DR, "metabolic" for traits related to metabolic parameters, and "behavior" for traits related to behavior. This working version of trait ontology to discuss these general trait categories may differ slightly from that found in some databases, because there is no standard trait ontology for poultry. For studies that evaluated QTL for carcass, organ, or tissue weights, only those QTL identified using adjustments for BW or carcass weight were summarized, as those are likely the most biologically relevant. From studies that reported QTL for both BW and weight gain, only QTL for BW were included.
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The QTL locations reported in the original publications were converted to consensus map (Cmap) locations based on marker positions in the current version of the chicken consensus linkage map (Schmid et al., 2005; http://www.animalsciences.nl/Cmap). For single-point analyses, the Cmap positions of markers with significant associations were presented as the Cmap QTL location. For QTL detected by multipoint QTL analyses, the QTL were positioned on the Cmap in the same marker intervals and at equal distances from the closest marker, as in the original publication. If the information provided by the original publication was not sufficient to calculate QTL distance from at least 1 of the flanking markers, the average Cmap location of the QTL flanking markers was given as the Cmap QTL location. Less than 1% of the data was rejected because of unresolved discrepancies regarding the locations of flanking markers between the original paper and the consensus map.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONS AND FUTURE PROSPECTSNOTE IN PROOFREFERENCES |
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and are designated as F2, backcross (BC), and F1 designs. In these designs, the first generation is produced by crossing 2 divergent populations. A second generation (G2) is produced by backcrossing to 1 of the parental lines (BC design) or by intercrossing the first generation individuals (F2 design). In the F1 design used by Kaiser et al. (2002), Kaiser and Lamont (2002), and Deeb and Lamont (2003), males from an outbred line were crossed to an inbred line to produce F1 half-sib families whose genotypes and phenotypes were used for QTL mapping, as in a half-sib design (Soller et al., 2006). In F2 and BC designs, phenotypic information of G2 is used for QTL mapping. In an F2-F3 design, a third generation is produced by intercrossing the G2 individuals, and mean phenotypes of the third generation progeny of G2 birds are used for analysis of QTL segregation in the G2. Among QTL mapping designs, the F2 design is the most frequently used in chicken QTL studies.
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). The advanced intercross line (AIL) approach proposed by Darvasi and Soller (1995) can also be used to improve resolution of QTL location (Jennen et al., 2005). To obtain AIL, the F2 generation is further intercrossed for several generations.
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summarizes the distribution of identified QTL across the 5 major trait categories. About 700 QTL were reported in the reviewed studies. There were more QTL for growth than the other traits, possibly because more studies investigated traits from this category. About 31% of the QTL were significant at the 5% genome-wise level. The percentage of genome-wise significant QTL was less for DR (6%), because most studies of DR traits used single-point analyses.
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) with MapChart 2.0 (Voorrips, 2002). There was no obvious pattern for QTL distribution across the genome. However, the distal end of some of the macrochromosomes tended to have a lower QTL density, which is unexpected, because a higher gene density in subtelomeric regions of the macrochromosomes has been reported (Hillier et al., 2004). Relatively poor coverage with genetic markers could have reduced the power of detecting QTL in these regions.
Consensus of QTL Location for Similar Traits from Independent Studies Provides Strong Evidence for QTL Presence.
Despite differences in experimental conditions and populations used for QTL mapping, independent studies found QTL for similar traits in similar locations in several instances (Figure 1). Considering that the confidence interval of most QTL location estimates covers over 20 cM (Soller et al., 2006) and sometimes the complete chromosome (Schreiweis et al., 2005), QTL for similar traits that are reported as separate in Figure 1 may actually represent 1 QTL. For example, QTL for antibody response to different antigens were detected in the same chromosomal regions in independent populations in GGA3 (25 to 85 cM), GGA5 (65 to 90 and 198 cM), GGA6 (55 to 85 cM), GGA8 (45 to 80 cM), GGAZ (65 to 115 cM), and GGA18 (0 to 40 cM; Figure 1). These results provide strong evidence of QTL for antibody response in these regions. It is not possible at the level of resolution of the current studies to differentiate between a single broad-function QTL in each region that controls antibody response to multiple antigens and multiple closely spaced QTL that exert separate influence on response to the different antigens. Future fine-mapping studies with higher marker saturation in populations that allow greater resolution (AIL, AB, and LD mapping in outbred lines) and tests of candidate genes in these QTL regions may help to resolve these questions.
Some QTL were reported only in 1 study and were not detected in other studies that analyzed the same trait in different populations. Population type and size, genetic background, segregation of specific QTL in specific populations, and differences in trait definition or measurement may have led to inconsistencies in QTL results among experiments. Furthermore, type I and II errors in QTL detection may also contribute to lack of agreement in QTL results.
Consensus of QTL Location for Different Traits Explains Genetic Basis of Correlation Among Traits.
Quantitative trait loci affecting different traits were mapped to similar chromosomal regions (Figure 1). Such results represent evidence for the basis of genetic correlations among traits and for correlated response to selection, if they are indeed controlled by the same pleiotropic QTL or by closely linked QTL that are in LD. Higher resolution analysis is required to distinguish LD from pleiotropy.
In the instance of closely linked QTL, the results of high-resolution mapping would help to avoid selecting for an undesirable QTL allele for 1 trait while selecting for the desirable allele for the other trait in MAS. However, constraints will exist if an undesirable correlation is caused by pleiotropy. In this case, selection for the desirable QTL allele effect for 1 trait would cause an undesirable (antagonistic) effect in the other traits, and the best approach would be to select for the allele that has the most beneficial net effect across traits.
Complicating Factors for Application of QTL Results
Parent-of-Origin Effect.
Tests to detect QTL with parent-of-origin effects have been conducted in only some of the summarized studies (Ikeobi et al., 2002; Sewalem et al., 2002; Buitenhuis et al., 2003a,b; Siwek et al., 2003b; Buitenhuis et al., 2004; Ikeobi et al., 2004; Tuiskula-Haavisto et al., 2004; Navarro et al., 2005; Abasht et al., 2006; McElroy et al., 2006; Nones et al., 2006). The number of QTL reported with parent-of-origin effect is, therefore, likely to be underestimated (Table 3). There was agreement among multiple independent studies for QTL with parent-of-origin effect on GGA1, GGA3, GGA9, and GGA11 (Figure 1). The regions on GGA1 and GGA3, for which 3 independent studies reported parent-of-origin QTL, correspond to where chicken orthologs of mammalian imprinted genes were in silico mapped by Dunzinger et al. (2005). Both paternally and maternally expressed QTL were detected in these regions (Figure 1). In mammals, imprinted genes are also mostly seen in pairs or clusters, and most imprinted domains contain both maternally and paternally expressed genes (Vu and Hoffman, 2000; Reik and Walter, 2001; Dunzinger et al., 2005). Most chicken orthologs of mammalian imprinted genes showed synteny conservation between mammals and birds and have been mapped to distinct chromosomal regions that exhibit asynchronous DNA replication (Dunzinger et al., 2005). However, the imprinting center and many of the local regulatory elements identified in mammals have not been identified in analysis of the chicken ortholog to the imprinted mammalian Ascl2-Igf2-H19 region (Yokomine et al., 2005). These findings collectively suggest that parent-of-origin-specific QTL that have been detected in chicken may result from genomic imprinting but may involve different mechanisms or genes than in mammals.
There are conflicting results on allelic expression analyses of some chicken orthologs of mammalian imprinted genes. Monoallelic expression of both paternal and maternal alleles of IGF2 was reported by Koski et al. (2000). However, biallelic expression of IGF2 and of several other chicken orthologs of mammalian imprinted genes (IGF2R, ASCL2, and INS) were reported by O’Neill et al. (2000), Nolan et al. (2001) and Yokomine et al. (2005). Such studies have, however, been limited to a few chicken orthologs of mammalian imprinted genes. In addition, genes that are subject to imprinting may differ between mammals and birds. Large-scale evaluation of allelic gene expression by simultaneous analysis of high-throughput single nucleotide polymorphisms (SNP) and microarrays would help to answer this biologically important question.
Hidden Genetic Variation.
In chickens, as in other species, QTL mapping studies enable empirical detection of transgressive (cryptic) genetic variation by identifying transgressive alleles (Frankel, 1995). Transgressive QTL alleles show trait effects that are in the opposite direction to what would be expected based on the mean phenotypic difference among the breeds that are crossed. Examples of transgressive QTL alleles detected in the chicken include a low-fat allele from a high-fat line by Abasht et al. (2006) and Zhou et al. (2006b), a disease-resistance allele from susceptible lines by McElroy et al. (2006), and a low-egg weight allele from a high-egg weight line by Tuiskula-Haavisto et al. (2002). Transgressive alleles may exist in a population because of no or limited selection for the trait, drift, pleiotropic effects of the QTL allele on other traits that are under selection, or close linkage and LD with QTL that are under selection.
Another possible mechanism for the appearance of transgressive alleles is based on a shift in the allele effect in the mapping population as a result of the change in the matrix of genetic interactions (Gibson and Dworkin, 2004). This change can occur when crossing populations with different genetic backgrounds or when transferring a QTL allele to another genetic background (QTL introgression). In these cases, the transgressive effect is caused by epistasis in synergetic or antagonistic ways (Gibson and Dworkin, 2004; Carlborg et al., 2006). For example, dramatic improvement in red fruit color was observed in a nearly isogenic line produced by transferring the transgressive QTL allele from the wild tomato (in which fruits remain green even when ripe) to a cultivated tomato (Tanksley and McCouch, 1997)—an example of a transgressive effect produced by a combination of alleles at different loci (epistasis) from the 2 types of tomato.
Results from chicken QTL studies clearly show that beneficial alleles can be found in lines with generally undesirable characteristics. However, it would be difficult to fine map such QTL or to use them in selection without understanding whether the transgressive alleles represent true single locus effects or appeared because of epistasis. Furthermore, possible negative pleiotropic effects of transgressive alleles should be evaluated before using them in a selection program.
Epistatic QTL.
The QTL results summarized in this review were detected using nonepistatic models that do not account for interactions among QTL. These models have been successful in detecting many QTL (Figure 1). However, Carlborg and Haley (2004) showed that additional QTL can be detected by simultaneous mapping of QTL using an epistatic model. Total phenotypic variance was better explained by considering individual and epistatic QTL effects (Carlborg and Haley, 2004). Carlborg et al. (2003, 2004, 2006) reanalyzed chicken populations that were initially analyzed using traditional QTL methods with epistatic models for growth. Results showed important epistatic interactions for early growth rate and enabled identification of epistatic patterns and networks among QTL. Some statistically detected epistatic QTL did, however, not have an epistatic pattern that was biologically meaningful (Carlborg and Haley, 2004).
Epistatic QTL mapping could help to better understand the genetic architecture of quantitative traits, which is so important in dissecting the underlying quantitative trait genes and for implementating QTL results in selection programs. However, it is difficult to detect epistatic interactions among closely linked QTL based on an analysis of F2 populations because of limited mapping resolution. Therefore, breakdown of LD among epistatic QTL as a result of recombination in a high-resolution QTL mapping program can lead to a change in QTL effect, appearance of new QTL, or disappearance of the targeted QTL.
QTL by Sex Interaction.
Several QTL that show interactions with sex have been identified in both autosomal (GGA1, GGA2, GGA5, GGA6, GGA13, and GGA17) and sex (GGAZ) chromosomes (Figure 1). A QTL by sex interaction could result if the QTL affects only 1 sex (sex-specific effect), affects both sexes but at different levels (sex-biased effect), or affects both sexes but in opposite directions (sex-antagonistic effect; Anholt and Mackay, 2004). More generally, a QTL by sex interaction can be considered as a genotype by environment interaction, considering sex as an organismal environment for gene expression (Abasht et al., 2006).
Not all chicken QTL studies that included both sexes have evaluated evidence for QTL by sex interactions, and some did not report the specific traits for which the sex interaction was detected. The number of QTL reported with sex interaction (~20) is, therefore, likely to be underestimated. Furthermore, in some studies, the QTL by sex interaction was tested only for locations that were significant in the initial analysis using models without sex interaction (Ikeobi et al., 2002; Sewalem et al., 2002; Ikeobi et al., 2004; Nones et al., 2006), which does not detect QTL with sex-antagonistic effects and has less power to detect QTL with sex-specific and sex-biased effects. Conducting a full genome scan with a QTL by sex interaction model or conducting the analysis separately for each sex could help to detect these kinds of interactions. However, the larger number of tests conducted could also lead to an increase in false positive results. Further experiments are needed to confirm QTL by sex interactions detected in an initial genome scan before application in selection. Using an AB generation (BC2), Abasht et al. (2006) confirmed a sex interaction for fatness QTL that was identified in an F2 population.
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CONCLUSIONS AND FUTURE PROSPECTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONS AND FUTURE PROSPECTSNOTE IN PROOFREFERENCES |
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To date, most of the chicken QTL analyses have been carried out using experimental crosses, which limits direct application of the QTL results in commercial lines. Revolutionary opportunities have now opened for analysis of quantitative traits in the chicken because of the availability of sequence information (Hillier et al., 2004) and a large number of SNP (Wong et al., 2004). The major changes that are occurring in quantitative trait analysis in the chicken include changes in genotyping strategies (SNP markers instead of microsatellite or RFLP markers) and statistical analysis methods (LD mapping; Soller et al., 2006). These new approaches allow the use of commercial breeding populations for QTL mapping, which enables direct application of QTL results in commercial breeding programs (de Koning et al., 2003, 2004; Soller et al., 2006).
About 700 curated QTL from the reports analyzed for this review paper have been used to establish the Chicken QTLdb (http://www.animalgenome.org/QTLdb/chicken.html) as a new member of the Animal QTLdb, which is expanded from the Pig QTLdb described in Hu et al. (2005). Similar to the Pig QTLdb, the Chicken QTLdb integrates available chicken QTL data in the public domain by a chicken consensus linkage map (Schmid et al., 2005), which facilitates the use of the QTL information in future studies. The Chicken QTLdb also introduces a chicken trait classification and ontology to describe traits. A notable feature of the Chicken QTLdb is that it allows publishers and authors to enter their own data directly into the database, and thus the database will be continually updated. The chicken QTLdb allows for easy search and comparison of QTL results from different studies. This facilitates a narrowing of possible chromosomal regions from overlapping QTL results of different studies, which will speed positional searches for underlying genes (Hu et al., 2005). Because the Chicken QTLdb is part of the Animal QTLdb, QTL comparisons among comparable traits can be conducted across species, which may facilitate additional narrowing of QTL-containing chromosomal regions and will help locate underlying genes and previously undiscovered QTL with inferences from different species.
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NOTE IN PROOF |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONS AND FUTURE PROSPECTSNOTE IN PROOFREFERENCES |
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ACKNOWLEDGMENTS |
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Received for publication August 23, 2006. Accepted for publication August 23, 2006.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONS AND FUTURE PROSPECTSNOTE IN PROOFREFERENCES |
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