Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Medicine 1304
Mapping Genes AffectingPhenotypic Traits in Chicken
BY
SUSANNE KERJE
ACTA UNIVERSITATIS UPSALIENSISUPPSALA 2003
Dissertation to be publicly examined in B42, BMC, Uppsala University, on Tuesday,December, 16, 2003 at 09:15, for the degree of Doctor of Philosophy. The examination will beconducted in English.
AbstractKerje, S. 2003. Mapping Genes Affecting Phenotypic Traits in Chicken. Acta UniversitatisUpsaliensis. Comprehensive Summaries of Uppsala Dissertations from the Faculty ofMedicine 1304. 40 pp. Uppsala. ISBN 91-554-5805-X.
The purpose of gene mapping is to understand the underlying genetics of simple and complextraits like plumage colour and growth. This thesis is based on a cross between the wildancestor of the modern chicken, the red junglefowl, and a White Leghorn line selected for highegg mass. There are obvious phenotypic differences between these two breeds in severalaspects such as growth, egg production and behaviour. These complex traits are ofteninfluenced by a number of genes or Quantitative Trait Loci (QTL) as well as environmentalfactors.
Identification of QTL regions involves testing of association between genetic markers andthe phenotype of interest. The QTL identified in this study explain most of the difference inadult body weight between the red junglefowl and the White Leghorn, but less of thedifference at earlier age. By applying a different method for detection of QTL, including geneinteractions, epistasis, we can understand more of the genetics behind early growth. The allelecoming from the red junglefowl is generally associated with lower weight, egg production andfood consumption.
In this study we have also identified two genes explaining the difference in plumage colourin the cross. The Extension locus, encoded by the melanocortin receptor 1 (MC1R), controlsthe amount of pigment produced has shown to be associated with plumage colour. A mutationin the MC1R gene causes black pigmentation of the plumage.
We have also found association between the PMEL17 gene, known to be involved in normalpigmentation, and the Dominant white phenotype present in the White Leghorn. Aftercomparison of sequences from different alleles at the Dominant white locus, amino acidalteration caused by insertion and deletion in the transmembrane region of the PMEL17 proteinhas been revealed. These mutations are associated with alleles representing different plumagecolour variants.
Keywords: chicken, Quantitative Trait Loci, growth, egg production, epistasis, plumagecolour, Extended black, MC1R, Dominant white, PMEL17
Susanne Kerje, Department of Medical Biochemistry and Microbiology, Uppsala University,Box 582, SE-751 23 Uppsala, Sweden.
© Susanne Kerje 2003
ISSN 0282-7476ISBN 91-554-5805-XURN:NBN:se:uu:diva-3776(http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3776)
... for sure, I know there is something genetical going on... Lingaas, 2002
Populärvetenskaplig sammanfattning
Med hjälp av genetisk kartläggning kan geners position i arvsmassan bestämmas. I denna studie har vi bland annat sökt efter gener som påverkar tillväxt, äggproduktion och fjäderfärg. För att kunna avgöra vilka gener som påverkar dessa egenskaper har vi korsat den röda djungelhönan, som är ursprunget till våra vanliga tamhöns, med den vita Leghorn hönan som är selekterad för hög äggproduktion (se Figure 3). Det finns tydliga skillnader mellan dessa höns och detta kan vi utnyttja för att förklara det som gör dem olika. Med hjälp av en genetisk karta baserad på genetiska markörer och med statistiska metoder kan vi hitta kopplingar mellan de olika egenskaperna och markörerna i kartan. Vi har kunnat identifiera flera platser i arvsmassan där det finns gener som påverkar olika delar av tillväxten. Under tidig tillväxt byggs de inre organen med matsmältningssystemet upp och när det är klart går energin till att bygga upp muskler och att lägga upp fettreserver. Vi har lokaliserat ett antal gener som är inblandade i den senare delen av tillväxten och några som påverkar produktionen av ägg. Under den tidiga tillväxtperioden kan vi däremot inte hitta så många gener som påverkar tillväxten och därför har vi använt en metod för att söka efter gener genom att ta hänsyn till deras samspel med varandra. Med denna nya metod har vi kunnat hitta fler gener som förklarar större del av skillnaden i tidig tillväxt mellan djungelhöns och tamhöns i vår korsning. Detta visar att samspel mellan gener är viktigt när ett mer komplicerat biologiskt system byggs upp.
Vi har också identifierat gener som påverkar pigmenteringen hos höns. Pigment produceras av celler som finns i huden. Det finns två olika typer av pigment, svart/brunt och röd/gult. Variation i en gen som kodar för melanokortin receptor 1 i pigmentproducerande celler kan kopplas till olika färgvarianter i vår korsning. Genen påverkar också pälsfärg hos andra arter som kor, hästar, får, hundar, katter och möss men även hårfärg hos människa beror till viss del på denna gen. Med hjälp av kunskapen från musen kunde vi identifiera en kandidatgen för dominant vit färg hos höns. I det fallet är det en variation i en gen (PMEL17) som bygger upp den del av den pigmentproducerande cellen som förvarar pigmentet (melanosomen). Vi har upptäckt att vita höns har en insertion av tre aminosyror i den del av proteinet som ska förankra det i melanosomens membran. Denna förändring leder uppenbart till en allvarlig defekt i de pigmentproducerande cellerna och de vita hönsen saknar därför pigment i fjäderdräkten.
MC1R
Contents
Introduction.....................................................................................................1Chicken as a model organism ....................................................................1The chicken genome ..................................................................................2Linkage mapping........................................................................................3
Pedigrees................................................................................................3Genetic markers.....................................................................................4Linkage analysis ....................................................................................4Genetic maps .........................................................................................6
QTL mapping .............................................................................................7Mapping of single QTL .........................................................................7QTL mapping including epistasis..........................................................8
Gene identification .....................................................................................8The genetics of plumage colour .................................................................9
Aims of this thesis ........................................................................................12
Results and discussion ..................................................................................13Linkage mapping (I).................................................................................13
The genetic map ..................................................................................13QTL mapping (I, II) .................................................................................15
Mapping of single QTL (I) ..................................................................15Mapping of interacting QTL (II) .........................................................17
Mapping of monogenic traits (III, IV) .....................................................18Genetics of plumage colour.................................................................18
Conclusions...................................................................................................27
Future prospects............................................................................................28
Acknowledgements.......................................................................................30
References.....................................................................................................33
1
Introduction
There are indications that domestication of chicken occurred more than 8000 years ago in Southeast Asia. It has been shown that the red junglefowl (Gallus gallus) is the main or only ancestor of all domestic breeds (West and Zhou 1989, Fumihito et al. 1994) and has therefore been proposed as the wild-type standard for mutations found in domestic fowl (Jaap and Hollander 1954).
The main reason for domestication of the chicken was not the need for another food source, as for some other domesticated animals. Instead, the chicken was used for cultural reasons: in religion, for decorative arts and in cock fighting. It was not until the Romans started selective breeding that the chicken was more widely used as a source of food (Crawford 1996).
Natural and artificial selection have been the main force for the genetic modification of the domestic chicken. The first archaeological evidence for domestication is an increase in skeletal size of the chicken. Variation in plumage colour and morphology were also among the first traits to change and changes in behaviour, muscling, fat deposition and brain size are assumed to have occurred more recent (Sossinka 1982).
The worlds poultry industry that emerged during the late 19th and early 20th centuries has had a profound effect on the animal material that dominates today’s industry. Specialised breeds such as White Plymouth Rock for meat production and Leghorn for egg production were developed during that era (Crawford 1996).
Chicken as a model organism During thousands of years of selection an immense amount of genetic variation have been accumulated in chicken breeds of the world. This variation is an important resource, which can be utilised to understand basic biology and gene function. Chicken is in fact an important animal model in developmental biology, due to the easily accessible embryo, (Capdevila etal. 2001) and in immunology (the fundamental discoveries of the B-cells and extensive studies on evolution of vertebrate immune system have been done in chicken (Kaufman 1999)).
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Chicken is a vertebrate that shares most of its biology with mammals, but is evolutionary distinct. Birds and mammals shared a common ancestor about 300 million years ago and by investigating conserved syntenic regions the genome evolution invertebrates can be studied. Comparisons of human, mouse and chicken genomes have revealed fewer major chromosomal rearrangements between human and chicken than between human and mouse even though chicken and human are more distantly related (Burt et al. 1999, Gregory et al. 2002, Waterston et al. 2002). Chicken can therefore also be a good model to study the evolution of the vertebrate genome.
The chicken genome has a high frequency of recombination, the same number of genes as human but only 40% of the genome size. Chicken has therefore some advantages for gene mapping studies. The fact that 1 cM in chicken correspond to about 300 kb facilitates high resolution mapping compared with the mouse where 1 cM correspond to, on average, about 2 Mb.
The chicken has several advantages as a model organism compared to other domestic animals. It is smaller and thus easier and cheaper to hold. A large number of progeny can be obtained from each female and thereby very large full sib families can be generated. Inbred lines are also available in chicken but not in other domestic animals. Large pedigrees including several generations can easily be generated due to a short generation interval.
The chicken genome
The chicken genome is relatively small, the smallest among domestic animals. It has the same genetic length, about 4000 cM, as human but only about one third of the physical length, 1.2 x 109 bp (Olofsson and Bernardi 1983). The number of genes in chicken is estimated to about 35,000 and it has less repetitive sequences, 15% compared to about 50% in human (Burt and Pourquie 2003), making the genome gene dense. The chicken karyotype comprises 39 chromosome pairs, which are divided into eight large, cytogenetically distinct chromosomes, two sex chromosomes (Z and W) and 30 pairs of small cytogenetically indistinguishable microchromosomes. The female is the heterogametic sex carrying the Z and W chromosomes and the male is homogametic carrying two Z copies. The microchromosomes are estimated to comprise about 30% of the genome. They have a high G+C content and are suggested to have a higher gene density than the larger macrochromosomes (Smith et al. 2000). Physical mapping using fluorescent in situ hybridisation (FISH) indicates that microchromosomes include about 25 to 40% of the genes (Smith and Burt 1998, Brown et al. 2003) and have a
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higher frequency of recombination than the larger chromosomes (Rodionov et al. 1992a, 1992b, Schmid et al. 2000).
The chicken genome is the first avian genome to be sequenced. A six-fold genome coverage will be done at the Washington University and the draft sequence is expected to be completed during the spring 2004 (http://genome.wustl.edu/projects/chicken/). A physical map based on 20-fold genome coverage of overlapping BAC clones is being assembled, owing to collaboration between several groups in Europe and USA, and a number of BAC libraries are available (Burt and Porquie 2003, Brown et al. 2003, http://www.thearkdb.edu). Other publicly accessible mapping resources are high-density linkage maps, the consensus map (Groenen et al. 2000, Schmid et al. 2000), and a radiation hybrid panel assembled at INRA in France (Morrison et al. 2002). During the last year there has been a dramatic progress in the development of expressed sequence tags (EST) resources. In the EST databases there are in total about 600,000 chicken EST sequences present (Burt and Porquie 2003). Spotted cDNA microarrays are also available for gene expression studies. SNP detection will be facilitated by one-fold genome sequencing from White Leghorn (SLU line 13, also used in our red junglefowl and White Leghorn intercross) and a broiler chicken to compare with the red junglefowl sequence generated at Washington University. The creation of a marker dense genetic map using microsatellite and SNP markers is of importance for gene mapping.
Linkage mapping
PedigreesA good strategy when setting up a pedigree with the purpose of mapping genes is that the initial cross should be between lines as genetically divergent as possible. Two outbred parental populations may be fixed or nearly fixed for different alleles at trait loci due to selection for different purposes. The crossing of these divergent lines generates F1 offspring with a high degree of heterozygosity, and these F1 animals can be used for backcrossing, to any of the parental lines, or for intercrossing. It is crucial that the parental lines differ for the trait of interest and that the number of individuals in the pedigree is sufficiently large for the type of statistical analysis the study is designed for.
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Genetic markers The definition of a genetic marker is a trait, easy to observe, that shows simple genetic inheritance. The first markers to be used were phenotypic traits like coat colour variants. Later on the protein polymorphisms were found but the number of markers was too low to create a genetic map with good genome coverage in non-experimental organisms. There was thus a need for other types of markers. The first DNA markers were restriction fragment length polymorphisms (RFLP) based on sequence differences at restriction enzyme target sites (Botstein et al. 1980). Later on mini- and microsatellites were discovered as length polymorphisms in tandem repeated sequences (Jefferys et al. 1985; Weber and May 1989; Tautz 1989). The repeated unit in a minisatellite can be 10-60 bp and in a microsatellite 1-5 bp. Advantages with microsatellites are that they are highly abundant in many eukaryotic genomes and evenly distributed throughout the genome compared to minisatellites that can be too large to be detected by PCR and show a non-random distribution, clustering in the telomeric regions of the chromosomes in several species. Microsatellites are highly polymorphic and can easily be detected with PCR facilitating high-throughput genotyping in large pedigrees and are the most commonly used marker for linkage studies.
The next generation of markers will be single nucleotide polymorphisms (SNP), the most common class of polymorphism in genomes. SNP markers are highly abundant with an approximate frequency of one SNP per 200-300 bp in chicken (Smith et al. 2000) compared to one in 1300 bp in human (Lander et al. 2001). However, when building linkage maps for mapping studies a larger number of bi-allelic SNP markers are needed to generate the same level of information content as obtained with multi-allelic microsatellites (Kruglyak 1997). The soon available genome sequences from different breeds and the rich EST resource will provide a huge number of SNP markers all over the chicken genome (Emara and Kim 2003).
Linkage analysis Genetic linkage causes non-independent segregation between loci located on the same chromosome. Loci on non-homologous chromosomes show independent assortment and there is a probability of 0.5 that alleles at two unlinked loci will be inherited together. The frequency of recombination between two loci is lower the more closely located they are. A requirement for linkage analysis is genotype information from individuals in a pedigree where at least one of the parents is heterozygous at both of the loci analysed. The allele combination of loci located on the same chromosome is referred to as the haplotype (Figure 1). When a parent is heterozygous at two loci, A and B, the transmission of haplotypes (AB, Ab, aB, ab) to the offspring can
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Figure 1. Crossing over between parental chromosomes homozygous at the A, B andC loci. In 1A, recombination between B and C loci occurs while the parentalhaplotype A and B is maintained. 1B illustrates a double recombination where theparental combination of the A and C loci is regenerated.
be scored. Two linkage phases are possible: AB/ab and Ab/aB. In the case of genetic linkage the two haplotypes with the lowest number generally represent the recombinant haplotypes. The recombination fraction, ( ), can be calculated by dividing the number of observed recombinants (Ab + aB, if the linkage phase is AB/ab), with the total number of informative meioses.There is evidence for genetic linkage if the recombination fraction is significantly less than 0.5.
If we assume that only one recombination event can take place betweentwo loci, in each meiosis, the recombination fraction can directly be used asthe map distance (cM) (1% of recombination = 1 cM). This may not alwaysbe true if the distance between the markers is large more than one
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recombination event may occur. If there are two recombination events, the haplotype will appear as non-recombinant and the recombination frequency underestimates the actual map distance. The relationship between the recombination rate observed for a pair of loci and the estimated genetic distance is referred to as a mapping function. Haldane and Kosambi have described different mapping functions designed to compensate for double recombinants. The Kosambi mapping function (Kosambi 1944) was used for construction of the linkage map used in this thesis (paper I).
A variety of statistical tests can be applied to test for significant deviations from independent assortment for a pair of loci. The most common test is based on maximum likelihood calculations and referred to as a lod score test (Morton 1955). The lod score (Z) is the logarithm of a likelihood ratio between the likelihood of the genotypic data under the alternative hypothesis (H1) of linkage between the two loci at a given recombination rate ( 0.5), divided by the likelihood of obtaining the genotypic data under the null hypothesis (H0) of independent assortment of both loci ( = 0.5). The likelihood estimations of linkage are carried out from a of 0 to 0.5. The estimate of the recombination rate is the value of at which the lod score is the highest. A generally accepted threshold for linkage is a lod score (Z) above 3 and the H0 hypothesis is rejected in favour of linkage at the corresponding recombination fraction. Z lower than –2 is evidence against linkage at a specific recombination fraction.
Large-scale mapping projects require linkage analysis software like the CRI-MAP package (Green et al. 1990) to be able to perform the extensive calculations needed to construct a genome-wide linkage map.
Genetic maps The first “classical” genetic map of the chicken was established in 1936 by Hutt and included only a few loci. Today there are three reference maps available in chicken, East Lansing, Compton and Wageningen (Bumstead and Palyga 1992, Crittenden et al. 1993, Groenen et al. 1998). A joint effort by collaborators in the international chicken research community have made it possible to combine the three reference maps to a consensus map including 1965 loci covering 50 linkage groups (Groenen et al. 2000, Schmid et al.2000). For most genes identified in chicken, the location of the human orthologue is known and the information about most of the markers identified in chicken is publicly available (http://www.thearkdb.org).
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QTL mapping
Mapping of single QTL A quantitative trait has a continuous distribution and examples of traits that belong to this group are body weight and milk yield. These traits are also referred to as complex, multifactorial or polygenic traits because they are influenced by several genes as well as environmental factors. Advances in molecular genetics have made it possible to identify regions in the genome, Quantitative Trait Loci or QTL, that contain one or several genes affecting quantitative traits. The QTL may contribute to different extent to the phenotypic trait.
A line-cross approach to QTL mapping involves a cross between lines divergent for the quantitative trait. Segregating progeny are produced by either an intercross or a backcross and are scored for the trait as well as for a large number of genetic markers. The simplest test for QTL is based on detecting statistically significant associations between the trait and the genotype at a marker locus assuming that a QTL is located at or near the marker. The major disadvantage with this method is that it is not possible to distinguish whether the detected effect is due to tight linkage between the marker and a QTL with a small effect or loose linkage to a QTL with a large effect. To separate the effects of QTL effect and distance from the marker, Lander and Botstein (1989) introduced the concept of interval mapping. This was first developed for inbred lines and has later also been implemented for crossed between outbred lines (Haley et al. 1994). In interval mapping a statistical test for a QTL, such as the ratio of the likelihood that a QTL exists to the likelihood that there is no QTL, is performed at each position along the chromosome. For each location tested, the additive and dominance effects are estimated and the highest value of the test statistics along the chromosome indicates the most likely position of the QTL influencing the trait. The significance level is determined by randomisation testing and test statistics exceeding the threshold indicate locations of a significant QTL (Churchill and Doerge 1994).
QTL mapping is an important first step in the process to increase our understanding of the genetic basis of quantitative traits. The information about the co-segregation between traits and marker loci can be used for marker assisted selection (MAS), but also for characterisation of the genes influencing the trait.
QTL mapping has been applied to both plants and animals. For instance, QTL have been mapped for growth, back fat and other carcass traits in pigs (Andersson et al. 1994) as well as for blooming behaviour in apple
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(Liebhard et al. 2003). The genes underlying a QTL have also been characterised in a few instances, like a QTL on chromosome 2 in pigs affecting lean meat in ham. The phenotypic effect is caused by a mutation in the non-coding part of the IGF2 gene (Van Laere et al. 2003).
QTL mapping including epistasis As quantitative traits are influenced by genes at multiple loci it is likely that the gene products interact to some extent to give the phenotype. Genetic interactions, epistasis, are generally ignored in QTL mapping, but several studies of different traits in various species have shown that ignoring interactions in the mapping process might decrease the power to detect QTL (Fijneman et al. 1996, Long et al. 1996, Li et al. 1997, Shook and Johnson 1999, Carlborg 2002). By searching for multiple QTL and at the same time take into account both the effects the QTL have individually and the effect of their interactions, the power to detect QTL can be increased. A simultaneous search for epistatic QTL can be performed using a genetic algorithm and model including both marginal effects and effects from interaction (Carlborg 2002). When a pair of interacting QTL is found, it is possible to estimate the effects of different genotype combinations, which can be of importance when searching for candidate genes.
Gene identification Once a QTL has been identified for the trait of interest, the next step is to find the gene causing the phenotype. The region harbouring the QTL can be large and span tens of cM. Narrowing down the region as much as possible is an important step towards the identification of the causative gene or genes. Additional crosses can be set up, either by intercrossing individuals from the same generation or backcrossing to either of the parental lines to generate new generations and recombination between the QTL and surrounding markers. The QTL genotype of parental animals can be determined using progeny testing in such crosses. The results of such experiments make it possible to exclude the QTL from some parts of the region. When the region is small enough there may be only a few genes to investigate.
Comparative mapping can be applied to locate genes in the QTL region to isolate new genetic markers and identify possible candidate genes for the trait. In chicken, 342 of the known genes that show homology with known human and mouse genes have been used to construct a comparative gene
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map. The map is sparse but regions with conserved synteny can be found for most of the linkage groups (Schmid et al. 2000).
It is important with correct assignment of the chromosomes when identifying homologous regions between species. The assignment can sometimes be difficult in chicken due to the many small and cytogenetically indistinguishable microchromosomes. As there are also a limited number of genes positioned in chicken gene map, to anchor the maps originating from different species it can sometimes be difficult to locate the corresponding chicken sequence in human or mouse. The order of genes in syntenic groups can be rearranged between species, but with the chicken draft sequence available, this will not be a problem. The function of the gene in mammals can also be utilised when candidate genes are selected in chicken.
The genetics of plumage colour The richest resource to study coat colour variation is the mouse, where about 150 colour loci have been described (Jackson 1994). Many factors have been found to be involved in the complex expression of coat and plumage colours. In mammals and in the chicken, the pigment in skin, fur and feathers is produced by the melanocyte located in the skin, hair and feather follicles. During embryonic development, the melanocytes migrates from the neural crest into the skin where two types of melanin pigments are produced. The eumelanin gives black/brown pigmentation and the phaeomelanin a yellow/red. The melanin is produced and stored in the melanosome within the melanocyte. The melanogenesis, the process of pigment production involves many phases and many colour loci are known to be involved in this process. Colour loci have been categorised depending on which part of the system they affect, the structure and morphology of the melanocyte and melanosome, the amount and kind of melanin produced or the migration and proliferation of the melanocyte.
Unless otherwise stated, the mouse nomenclature will be used in the following section. Three loci involved in the structure of the melanocyte and melanosome are the dilute (d), pink-eye (p) and silver (s) loci. Individuals, homozygous for the mutant allele at the dilute locus get a general lightning of the coat colour. The pigment made by mutant melanocytes is normal but the cells are supposedly lacking dendrites and thereby the pigment is not effectively transported to the skin (Jenkins et al. 1981). The diluted phenotype is also present in chicken. The Dilution locus appears to be a partial restrictor of eumelanin but the affect seems to differ depending on the genetic background at other colour loci. Birds with wild type background in other loci appear paler than normal wild type coloured birds (Brumbaugh
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and Hollander 1966, Carefoot 1985). It is not clear whether this locus is the same in chicken and mouse.
Alleles at the pink-eye locus causes reduction of pigmentation in both coat and eye due to the morphology of the melanosome. The pink-eye phenotype is described in chicken as well and is documented to have a dilution effect on eumelanin (Warren 1940). The silver mutation affects the melanosome structure. The silver phenotype is caused by a truncation of the carboxy-terminal end of the PMEL17 protein. This mutation causes a premature graying of the hair (Kwon et al. 1994).
Mutations affecting the amount of pigment produced are represented by the well-known albino (c), brown (b) and slaty (slt) loci. Individuals homozygous for the albino mutation lack all kind of pigmentation. They still have melanocytes but are unable to produce pigment. The tyrosinase gene, the key enzyme in melanogenesis (Yokoyama et al. 1990), is mutated in albino individuals. The classical mutation is a single amino acid change (Jackson and Bennett 1990, Yokoyama et al. 1990, Kim and Wistow 1992). In chicken several alleles are present at the albino locus. The alleles give different phenotypes from the classically non-pigmented albino to phenotypes including slight pigmentation in both feather and eyes (Brumbaugh et al. 1983, Smyth et al. 1986).
The brown and slaty loci are encoded by the tyrosinase related proteins, TRP1 and TRP2. These proteins are involved in later stages of melanogenesis. Loss of function mutations in TRP1 produces a brown rather than black phenotype and mutations in TRP2 give a grey-brown phenotype instead of a black (Jackson 1988, Jackson et al. 1992). The corresponding mutations in chicken are to my knowledge not known. However the proposed location of TRP1 on the Z chromosome in chicken suggests that the gene might be involved in either the sex linked Silver or Barredphenotypes.
The relative amount of black/brown and yellow/red pigment produced is controlled by the ligand binding to the melanocortin receptor 1 (MC1R) encoded by the Extension locus. The relative amounts of pigment produced depend on the type of ligand binding and the MC1R allele (Robbins et al.1993). Dominant MC1R mutations give constant signalling and black pigmentation regardless of ligand binding. When the -melanocyte stimulating hormone binds the MC1R, black eumelanin pigment is produced but if the antagonist produced by the agouti allele binds, phaeomelanin is produced. Thus, loss of function mutations at the agouti locus cause recessive black colour. Dominant alleles at the agouti locus in mouse, leading to ectopic expression of agouti, have shown to exhibit pleoitropic effects including obesity, insulin resistant hyperglycaemia and an increased tumour susceptibility (Heston and Deringer 1947, Wolff 2003).
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In chicken, the Extended black locus corresponds to the Extension locus in mouse. The Extended black locus is known to be involved in pigmentation of the feathers and assumed to encode the MC1R gene (Takeuchi et al. 1996)
Mutations affecting the melanocyte migration and proliferation may cause white colour. In this case the melanocyte never reaches the skin or is unable to produce pigment. In mouse, the Dominant white spotting (W) and Steel(Sl) loci affect the development of melanocytes. Some alleles at these loci are lethal when homozygous, and sub lethal alleles can cause severe anaemia or sterility. The W and Sl loci are known to encode the KIT receptor and its ligand. The KIT gene is important for the survival and migration of melanocytes (MGI:96677, http://www.informatics.jax.org). The Dominantwhite phenotype in pig is caused by two mutations, one gene duplication of the KIT gene and a splice mutation in one of the copies leading to skipping of exon 17 (Johansson et al. 1992, Johansson Moller et al. 1996, Marklund et al. 1996, Pielberg et al. 2003, Giuffra et al. 2003). This mutation is widely spread and obviously not lethal.
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Aims of this thesis
The objectives of this study have been:
to generate a genetic map including markers evenly dispersed in the chicken genome to combine the marker information in the genetic map with phenotypic data from the chicken intercross for mapping Quantitative Trait Loci (QTL) to simultaneously search for epistatic QTL to understand the growth process in chicken to map the melanocortin receptor 1 (MC1R) gene and evaluate it as a candidate for variation in plumage colour to identify the gene causing the Dominant white plumage phenotype in chicken
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Results and discussion
This project was started in 1998 with the aim to study the genetics behind domestication of production and behavioural traits in chicken. A large three-generation pedigree based on an intercross between the red junglefowl and a White Leghorn line was set up. The White Leghorn has been selected for high egg mass and high food conversion i.e. minimum amount of food for maximum output and the red junglefowl is the wild ancestor of the domestic chicken. There is a marked difference between the lines in several aspects. The White Leghorn is about twice as heavy as the red junglefowl. The two types of birds also differ in sexual maturity, growth pattern, egg production, body composition, behaviour and plumage colour (Figure 3). A number of phenotypic traits were collected from the pedigree, and the F2 generation, comprising more than 800 individuals. The data have been used for linkage analysis and QTL mapping for a number of the collected traits, including behaviour (Schütz et al. 2002, Schütz et al. 2004), production traits (paper I, II), body composition and bone density (A. Kindmark et al. in prep), and meat and egg quality (J. Babul et al. in prep). Plumage colour has been recorded and used for identification of colour loci (paper III, IV).
Linkage mapping (I)
The genetic map Already available reference maps were used as a guide for selection of evenly distributed microsatellite markers to be typed in the pedigree. The genotype information was used to construct a genetic map including 105 markers distributed over 25 linkage groups representing the same number of chromosomes. 14 of the microchromosomes were left out due to the sparse marker coverage, but still about 80-90% of the genome is included in the study as the microchromosomes represent about 25-40% of the genome (Smith and Burt 1998, Brown et al. 2003).
The sex specific genetic maps differed by 8% in favour of the female (2561 cM) compared to the male (2372 cM) map. There is a significant sex
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difference in recombination rate but the direction varies from region to region (Table 1). The overall genome trend with a small difference between the sexes has also been observed in a study by Groenen et al. (1998). In this study the male map was found to be 1% longer than the female. Table 1 shows the difference in recombination rate between males and females between the two studies. In chromosomes 4, 5, 8, 10, 13 and 18 the trend with a longer female map is the same in both studies. In chromosomes 1, 3, 6, 7 and 9 the male map is longer and in 2, 11, 12, 14, 15, 19 and 13 the ratio between the male and female maps are in different directions in the compared studies.
Table 1. Comparison of map lengths between males and females in our pedigree based on the red junglefowl and White Leghorn intercross compared with results from a cross between two broiler lines (Groenen et al. 1998). Chromosomea Paper I Groenen et al. 1998
Male Female Male/female Male Female Male/
female 1 489.4 471.5 3.8 555.5 543.0 2.3 2 434.5 520.4 -16.5 451.2 423.7 6.5 3 277.7 269.9 2.9 331.8 309.6 7.2 4 125.6 154.2 -18.5 241.5 261.4 -7.6 5 90.5 103.9 -12.9 167.7 173.2 -3.2 6 121.9 112.9 8.0 102.1 92.2 10.7 7 168.0 163.9 2.5 166.7 144.7 15.2 8 63.1 98.1 -35.7 95.4 101.7 -6.2 9 45.9 43.1 6.5 77.7 77.4 0.4
10 89.5 105.2 -15.0 73.5 82.7 -11.1 11 104.8 80.3 30.5 80.6 85.8 -6.1 12 52.8 100.0 -47.2 27.9 2.3 1113.0 13 21.3 31.3 -32.0 55.4 63.2 -12.3 14 39.6 36.7 8.0 64.3 84.3 -23.7 15 48.8 55.2 -11.6 44.1 40.6 8.6 18 27.8 28.8 -3.5 48.3 48.7 -0.8 19 19.5 25.6 -23.8 53.5 42.3 26.5 23 79.7 82.7 -3.6 35.8 30.9 15.9 28 34.8 34.8 0 58.4 61.3 -4.7
% difference is calculated as (male size – female size) / (female size) x 100 aChromosome numbers are according to the consensus map.
These results show that the map difference between the sexes in chicken is not as pronounced as in other species like human and pig, for which the difference is about 70% and 40%, respectively (Morton 1991; Marklund etal. 1996, Binadel et al. 2001). Moreover, our results are not in line with the
15
postulation by Haldane (1922), that the homogametic sex, females in mammals and males in birds, has a higher degree of recombination and thereby also a longer genetic map.
QTL mapping (I, II)
Mapping of single QTL (I) The marker information in the genetic map was combined with phenotypic measurements in the F2 generation and QTL analysis by interval mapping was performed (Haley et al. 1994). For the nine measurements of body weight and growth (body weight was measured at 1, 8, 46, 112 and 200 days of age and growth data was calculated from these measurements), 14 QTL were identified. 13 of them were significant at the 5% genome wide significance level derived by randomisation testing. One additional QTL was significant at the 20% genome wide suggestive level (Table 2). Most of the identified QTL had an additive effect on the phenotype. This implies that the heterozygote is intermediate between the two homozygotes. The alleles coming from the junglefowl was generally associated with lower bodyweight. Variation in food consumption was associated with the two major QTL identified for growth and body weight (Growth1 and Growth2),indicating an association between high food consumption, fast growth and high body weight. The same trend was observed for the egg production traits where low egg production was associated with the junglefowl allele at the QTL. Two measurements, total and average egg weight, were recorded and 3 additional QTL were identified in addition to the previously mapped QTL for growth and body weight. QTL for total and average egg weight coincide with Growth1. This indicates that the Growth1 can have pleiotropic effects.
The single QTL analysis explains more of the variance for growth at later age than for growth and body weight at earlier ages. This indicates that there might be a limited number of genes controlling adult body weight.
An additional 47 markers have recently been genotyped in the pedigree, and this marker information has been included in the genetic map. The genetic map for both sexes is only marginally longer with the male map of 2585 cM and female 2778 cM. This gives a 7.5% difference in length in favour of the female. The new markers were selected to cover regions with low marker information or previously uncovered microchromosomes. The QTL analysis for the same growth and weight measurements was repeated using the extended map and another major QTL located on chromosome 24 explaining the same amount of variation as the second largest QTL on
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Table 2. Quantitative trait loci for growth (G), body weight (B), egg production traits (E) and food consumption (F) by chromosome location according to our linkage map. Approximate QTL found in other studies in the same region as the QTL detected in our study are given and possible candidate genes in QTL regions are noted. QTL
in pres. stud.
Chr Location (cM)
Trait Approx. QTL found
in other study
Candidate genes
G1 1 58-82 B8-200, G1-200, total & aver. E, F 2a
G2 1 399-431 B8-200, G1-200, F 1, 2, 3a, 4 G3 2 411 B200 G4 3 50 B8 3 G5 3 112-117 B46, G1-8 E1 3 162 Aver. E 3 G6 3 201-208 B112, 200 3 G7a 4 122-150 B112, G46-112, total E 2, 3a
G8 5 21 B200 G9 7 145 B112 2, 3 G10 8 64-69 B8, G1-8 2, 4 G11 11 60-92 B8, 46, G8-46 G12 12 59-65 B46, 112 E2 14 14 Aver. E E3 19 72 Aver. E G13 27 7-20 B112, 200, G46-112 2 GH G14 Z 22 B200, G112-200 GHR, PRLR 1, carcass percentage (Van Kaam et al. 1999a), 2, body weight at 9 weeks (Sewalem et al. 2002), 3, fat traits (Ikeobi et al. 2002), 4, body weigth at 37 days (Zhu et al.2002), asuggestive QTL
chromosome 1 (Growth2), was found. Including this new QTL about 80% of the variation for adult body weight can be explained by the QTL we have identified so far. It is still possible that there are QTL undetected for one or several of the traits analysed due to the fact that several of the microchromosomes still are uncovered.
In chicken there are several QTL studies carried out for a number of growth (Li et al. 2003, Zhu et al. 2003), body weight (Van Kaam et al. 1999, Tatsuda and Fujinaka 2001, Sewalem et al. 2002, de Koning et al. 2003) and fat (Ikeobi et al. 2002) traits. Some of the QTL found in these studies are estimated to coincide with QTL from our study (Table 2). However, only one suggestive QTL in the study of Sewalem et al. (2002) is located at the same position as Growth1, the QTL with the largest effect found in our study. It is possible that the allele for high growth at this QTL was selected early during domestication before modern breeds were separated.
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In some of the other QTL regions identified there are a few obvious candidate genes such as the gene for growth hormone (GH) located in the same region as Growth 13 on chromosome 27. Growth 14 is located in the same region as the gene for growth hormone receptor (GHR) and the prolactin receptor (PRLR) on chromosome Z. However, there are no obvious candidates in the same region as Growth1 on chromosome 1.
Mapping of interacting QTL (II) One explanation why we found fewer QTL for early growth and none for weight at hatch when the interval mapping approach was used, is that there seems to be more interaction, epistasis, between genes affecting early growth. When applying the method for detection of epistatic QTL for the same weight and growth traits as in the previous QTL mapping effort, several new regions were found with an effect on early growth and weight at hatch. In chickens most of the internal organs such as the digestive system are developed early in the growth cycle (Björnhag et al. 1994). When more complex biological systems are developed, such as the internal organs, interactions between genes seem more important. When the internal organs are set the increase in body mass is due to deposition of muscles and fat.
Figure 2. Weight at hatch (least squares means) depending on the genotype at the interacting QTL loci. Alleles segregating from the red junglefowl are called J and alleles inherited from White Leghorn are called L.
The search for interacting QTL indicates that genetic interactions are much more important when a more complicated biological system is set up
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than when muscles and fat are deposited during adult growth. Muscle growth is also more extension and expansion of the cell instead of multiplication of cells. In the single QTL analysis there was no QTL found for weight at hatch but when using the simultaneous QTL search a pair of interacting QTL was detected (Figure 2). Individuals that were homozygous junglefowl at one locus and homozygous Leghorn at the other locus had lower hatch weight than all other genotype combinations. This interesting result indicates some incompatibility between the gene products from the two loci. Perhaps these two gene products represent receptor and a ligand. A mild form of dysgenesis between genotypes originating from different breeds can perhaps result in a slightly poorer affinity between a receptor and ligand. The reduced fitness that was detected in the F2 generations of crosses between divergent lines (Falconer 1981) can thus possibly be explained by the type of epistatic interactions detected in this cross.
There are no other studies in chicken were QTL for weight at hatch has been reported and that is possibly mainly due to the strong maternal influence on hatch weight and that no other studies have included epistasis in their mapping efforts.
Mapping of monogenic traits (III, IV)
Genetics of plumage colour The phenotypic difference between the colourful red junglefowl and the White Leghorn is very striking (Figure 3). The variation of plumage colour in the cross is mainly determined by four loci. The most obvious one is the Dominant white locus, involving segregation of the dominant I allele carried by the White Leghorn. Alleles at the Extended black, or Extension locus, and the sex linked Silver and Barred loci are also segregating in the cross. The dominant S allele inhibits the expression of the yellow/red phaeomelanin. At the fourth locus, the dominant B allele gives the feather a striped pattern with alternated pigmented and white stripes. The type of pigment produced in the stripes depends on the genotype at the other colour loci.
Segregation at the Extension locus was scored in our intercross. As the Dominant white locus is epistatic to the Extension locus only 25% of the individuals in the cross are completely coloured. However, due to the incomplete dominance of Dominant white the effect of the Extension locus can be seen on a white background. In several other species the Extensionlocus encodes the melanocortin receptor 1, MC1R, a transmembrane protein
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Figure 3. Red junglefowl male (left) and White Leghorn female (right).
of the melanocyte. Dominant mutations cause a constitutively active receptor producing only black pigments independent of ligand binding. Several MC1R mutations affecting colour have been characterised in mouse (Robbins et al. 1993), cattle (Klungland et al. 1995), pig (Kijas et al. 1998), horse (Marklund et al. 1996), dog (Everts et al. 2000, Newton et al. 2000) and sheep (Våge et al. 1999).
The Glu92Lys mutation in chicken MC1R corresponds to the sombremutation in mouse giving a dominant black phenotype (Robbins et al. 1993). This mutation was also used for genotyping the individuals in the pedigree to test the effect of MC1R on the phenotype. If MC1R has no effect, the Mendelian 1:2:1 ratio is expected to be found among all phenotype classes. According to results in Table 3, MC1R has a clear effect on the plumage phenotype. There is also a more pronounced influence of MC1R in females suggesting a sex influenced effect. MC1R has contributed to the expression of the completely white phenotype as 54% of the females but only 20% of the males are in that class. Another contribution is from the dominant Sallele at the Silver locus that is incompletely dominant in males and the full expression should only be seen in S/W females. This explains the high proportion of pure white females but also the higher frequency of completely black females (6%) compared to males (2%).
The Leghorn allele at MC1R is also associated with black spots among the females and was overrepresented among males with white or cream colour. In the wild type colour class there are no clear MC1R association but in the grey wild type the junglefowl allele is associated with the colour.
The Barred locus has been mapped to the Z chromosome using the pedigree. Unfortunately there is not enough information in the cross to map the Silver locus. However, the TYRP1 gene known to be involved in pigment
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Table 3. Plumage colour and MC1R genotype distributions in a red junglefowl/ White Leghorn intercross.
MC1R genotypebPhenotype class I, E, Ba
genotype J/J J/W W/W Total 2 (df=2)c
Females White I/- 21 137 50 208 29.0*** Cream I/-, e+/e+ 40 1 0 41 115.1*** White with black spots I/-, E/- 0 17 17 34 17.0*** Total white I/- 61 155 67 283 2.8
Black with white spots i/i, E/- 1 4 4 9 2.1 Grey i/i, e+/e+ 6 0 0 6 18.0*** Black i/i, E/- 0 16 8 24 8.0* Wild type, grey i/i, e+/e+ 19 0 0 19 57.0*** Wild type i/i 2 6 2 8 0.4 Barred i/i, B/W 2 17 13 38 7.7* Total coloured i/i 31 43 27 101 2.5
Total for females 92 198 94 384 0.4
Males White I/- 3 37 37 77 30.1*** Cream I/- 3 10 11 24 6.0* White with red/brown I/- 48 118 32 198 9.9** White with black spots I/- 1 0 1 3 - Total white I/- 55 166 81 302 7.5*
Black with white spots i/i 0 1 0 1 - Black i/i, E/- 0 7 2 9 3.7 Wild type, grey i/i 4 5 2 11 0.8 Wild type i/i, e+/- 14 16 2 32 9.0* Barred i/i, B/b+ 5 11 6 38 0.1 Total coloured i/i 23 40 12 75 3.6
Total for males 78 206 93 377 4.4
Total across sexes 170 404 187 761 3.7aPresumed genotype at the Dominant white (I), Extended black (E) and Barred (B)loci based on plumage colour and MC1R genotype; Barred is sex-linked and barred females are therefore B/W.bJ=allele inherited from red junglefowl;W=allele inherited from White Leghorn. cChi-square test of the expected 1:2:1 segregation in the absence of any association with plumage colour; *= P< 0.05, ** = P< 0.01, *** = P< 0.001
production is located on the Z chromosome and can be a candidate for any of these two loci.
In the F2 generation of the cross there was no significant deviation from the expected 3:1 ratio of white and coloured birds based on the dominant
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inheritance of the Dominant white allele. The Dominant white locus was previously mapped to linkage group E22C19W28 in the chicken consensus map (Ruyter-Spira et al. 1996, paper I). Three genes, ERBB3, TUBAL1 and GLI, all located on the same linkage group were used for comparative mapping with human and mouse. The homologues were located on chromosome 12 in human and chromosome 10 in mouse (Schmid et al.2000). These chromosome regions in human and mouse harbour the PMEL17 gene known to have a role in pigmentation. PMEL17 is a protein present in the membrane of the melanosome and is involved in fibrous striations upon which melanins are polymerised (Berson et al. 2003, Du etal. 2003). The PMEL17 gene was previously sequenced in chicken and denoted MMP115 (Mochii et al. 1991). However, no association between the PMEL17 (MMP115) and the Dominant white phenotype in chicken has previously been reported.
Figure 4. Chicken with the Smoky plumage colour variant.
Two colour variants considered to have other alleles at the I locus has been identified. In 1987 Ziehl and Hollander described the Dun, ID allele. This allele seems to have the similar inhibiting effect on eumelanin as the I allele and an individual homozygous for the ID allele is described as whitish. The results are based on test crossing the individual carrying ID with a breed carrying the dominant I allele.
In a White Leghorn line (ADOL) another colour variant appeared as a grey or smoky coloured bird (Figure 4) among the white birds. Test crossing
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Table 4. DNA sequence polymorphism in PMEL17 among chicken breeds (the Dominant white genotypes are given in parenthesis). A blank indicates identity to the master sequence (red junglefowl)a,b.
Red Black White WhiteNucleo- jungle- Langs- Brolier Leghorn Leghorn
Exon/ tide fowl han line L13 ADOL Smoky Dun Intron position (i/i) (i/i) (i/i) (I/I) (I/I) (IS/IS) (ID/ID)
141 C T145 A G G197 T C Y C 214 C Y220 T C C 234 G A236 G T T 330 A G G336 C A A 381 G C S C 413 T C
Intron 1
479 C T701 T C YExon 2 705 C T
Intron 2 823 C G G G930 C T YExon 3 967 G A1058 A T1060 A G1066 A G1068 G A1070 T A 1072 G A1090 G A
Intron 3
1104 D12 D12 D12 D12 D121316 A G GIntron 4 1340 C G G 1594 D22 D44
c D22 D22 D22 D441600 G A A A A
Intron 5
1651 C T1709 C T1818 C T T1836 G A A A 1853 D121897 C Y2127 C T
Exon 6
2209 A G G G G G Intron 6 2337 C T
2489 C T2498 A G G G G G
Exon 7
2514 r4 r3 r3 r3 r3 r3
23
2889 G A3007 A G3008 G C
Intron 7
3018 G AExon 8 3098 G A
3262 G A3272 A G G3296 I4 I43315 C T3318 A C C3334 C T T
Intron 8
3356 *4 G+*2 G+*23371 G C C3395 G A
Exon 9
3398 C T3586 G C3589 T A
Intron 9
3607 T G3696 I9 I9 I9Exon 10 3721 D153762 G AIntron 10 3766 T C Y
Exon 11 3844 C TaHeterozygous positions: M=A/C; R=A/G; S=G/C; Y=C/T bLength polymorphisms: Dx = deletion, x = no. of deleted bases; Ix = insertion, x = no. of inserted bases; rx = 72 bp repeat, x = no. of repeats; *x = CA repeat, x = no. of repeatscThis animal was heterozygote for this position
Table 5. Amino acid polymorphisms among five chicken breeds (the Dominant white genotypes are given in parenthesis). A dash indicates identity to the master sequence. Codon Breed 3435 105232 280-4 399 Repeata 586 723-5 731-5 740 Red junglefowl (i/i) S A G A PTVT N A-A-A-C E LGTAA R Black Langshan (i/i) P - - - - D A-B-A - - - - Broiler line (i/i) - - - V - D A-A-C K - - - White Leghorn L13 (I/I) - - - - - D A-B-A - WAP - - White Leghorn ADOL (I/I) - - - - - D A-B-A - WAP - - Smoky (IS/IS) - - - - del D A-B-A - WAP - - Dun (ID/ID) - V S - - - - - - del C aRepeat composition exon 7
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of the Smoky individual with the White Leghorn indicated that Smoky was most likely carrying another allele (IS) at the I locus.
Sequencing of the complete PMEL17 gene in chicken from individuals carrying the dominant I, recessive i, Dun (ID) and Smoky (IS) alleles revealed a number of polymorphisms (Table 4). Most of the sequence differences were found in introns or were silent. All sequence polymorphisms affecting the protein sequence are summarised in Table 5.
Table 6. Polymorphic sequence motifs in the PMEL17 coding sequence among chicken breeds with known Dominant white alleles
Breed Allele Exon 7 repeata Exon 10b
Red junglefowl i A-A-A-C A Smyth Brown Line i A-A-A-C Buff Minorca i A-A-A-C Dun ID A-A-A-C C White Leghorn L13 I A-B-A B White Leghorn ADOL I A-B-A B Polish Buff Laced I A-B-A B Commercial broiler, line A I A-B-A B Smoky IS A-B-A B Rhode Island Red i A-B-A Black Langshan i A-B-A A Broiler line, White Plymouth Rock i A-A-C A Japanese Phoenix i A-A-C Light Brahma i A-A-C White Crested Black Polish i A-A-C New Hampshire Red i A-A-C Commercial broiler, line B i A-A-C Red junglefowl, line UCD001 i A-A Black Australop i A-A Commercial broiler, line C i A-A Fayoumi i Aasee Figure 3, paper IV bA = wild type; B = WAP insertion; C = LGTAA deletion; blank = not tested
The I, ID and IS all have mutations in exon 10. The I and IS alleles have an insertion and ID a deletion. This region is the transmembrane region of the
25
protein and these changes in amino acid sequence of the protein can have a major effect on its function. None of the breeds carrying the recessive i allele in this study has any mutation in this region. Beside a mutation in exon 10, the IS allele has a four amino acid deletion (PTVT) in exon 6. These findings show that Dominant white and Dun represents two independent mutations altering the same domain of the PMEL17 protein and that Smoky is a revertant mutation partly restoring PMEL17 function.
In the PMEL17 gene there is also a polymorphic repeat region in exon 7. In this region, the tested breeds show a considerable variation from one to four repeats (Table 6). However, there is no repeat-combination that is exclusively associated with the dominant, I or the recessive, i alleles at the Dominant white locus and this region is apparently not causing the phenotype.
There is only one known mutation in the PMEL17 gene in mouse. A single base insertion at the 3’-end of silver in mouse alter the last 24 amino acids and results in a premature stop truncating the last 12 amino acids (Kwon et al. 1994, 1995, Solano et al. 2000). This mutation is located in the transmembrane region of the PMEL17 gene (Figure 5). silver homozygotes show graying of the hair due to loss of follicular melanocytes (Quevedo etal. 1981).
Figure 5. (next page) Alignment of the PMEL17 amino acid sequence associated with the wild type (i), Dominant white (I), Smoky (IS) and Dun (ID) alleles in chicken in comparison with human and mouse sequences including the mouse silver allele. Sequence identities are indicated by dashes insertion/deletion differences are indicated with dots. The signal sequence, the four copies of the 24 amino acid repeat in chicken, the transmembrane and the cytoplasmic region are indicated. The arrow indicates the proteolytic cleavage site that generates an aminoterminal M and a carboxyterminal M fragment.
26
Signal sequencechicken_i MRLHGAIVLL AALLALVTAQ QRGGGRSRGG VKGSAWGGRP APFRSWDTAR YRPWQEGTAR QNDCWRGGDV TFDISNDAPT chicken_I ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*S ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*D ---------- ---------- ---------- ----V----- ---------- ---------- ---------- ---------- human MDLVLKRC LLH--VIG-L LAV-ATKVPR NQDWLGVS-Q LRTKA-NRQL -PE-T-..-Q RL------Q- SLKV---G-- mouse MV-VQRRS FLPVLVLS-L LAV-ALEGSR NQDWLGVP-Q LVTKT-NRQL -PE-T-..VQ GSN-----Q- SLRVI--G-- mouse_silv MV-VQRRS FLPVLVLS-L LAV-ALEGSR NQDWLGVP-Q LVTKT-NRQL -PE-T-..VQ GSN-----Q- SLRVI--G--
chicken_i LVGARATFSI ALRFPGTQTV LPDGRVVWSQ NCTVNGTRML QGDPVYPEQL AEGSDGVFPD GQPFPRSAWG KRGRFVYVWW chicken_I ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*S ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*D ---------- -----S---- ---------- ---------- ---------- ---------- ---------- ---------- human -I--N-S--- --N---S-K- ----Q-I-VN -TII--SQVW G-Q----QET DDAC..I--- -G-C-SGS-S QKRS-----K mouse ----N-S--- --H---S-K- ----Q-I-AN -TII--SQVW G-Q----QEP DDAC..---- -G-C-SGPKP PKRS-----K mouse_silv ----N-S--- --H---S-K- ----Q-I-AN -TII--SQVW G-Q----QEP DDAC..---- -G-C-SGPKP PKRS-----K
chicken_i TWGRYWQVVD GATSQLTVGT DGVALGSYTM EVVVYHYRGR QRFIPIGHAS TQFSITDQVP IAVDVTQLEV AAGDGGSFVR chicken_I ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*S ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*D ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- human ---Q----LG -PV-G-SI-- GRAM--TH-- --T---R--S RSYV-LA-S- SA-T------ FS-S-S--RA LD-GNKH-L- mouse ---K----LG -PV-RSSIA- RHAK--TH-- --T---R--S -SYV-LA--- ST-T------ FS-S-S--QA LD-ETKH-L- mouse_silv ---K----LG -PV-R-SIA- GHAK--TH-- --T---R--S -SYV-LA--- ST-T------ FS-S-S--QA LD-ETKH-L-
chicken_i NRPVAFNVRL HDPSHYLRDA DISYSWDFGD QSGTLISRSP TVTHTYLQAG SFAARLVLQA AIPLSSCGTS APPVVDPTTG chicken_I ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*S ---------- ---------- ---------- ---------. ...------- ---------- ---------- ---------- chicken_I*D ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- human -Q-LT-ALQ- ----G--AE- -L--T----- S-------AL V------EP- PVT-QV---- ----T---S- PV-....... mouse -H-LI-ALQ- ----G--AE- -L--T----- GT------AL D------ES- -VT-QV---- ----V---S- PV-....... mouse_silv -H-LI-ALQ- ----G--AE- -L--T----- GT------AL D------ES- -VT-QV---- ----V---S- PV-.......
chicken_i PVPSLGPTAT QPVGPTGSGT ATAPSNLTGS GTAAAPGTTA APRASGAPAE PTGVSVAVLS DSAATEPLPD PVLSTAVANA chicken_I ---------- ---------- ---------- ---------- ---------- ---------- ---------- --------D- chicken_I*S ---------- ---------- ---------- ---------- ---------- ---------- ---------- --------D- chicken_I*D ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- human .......... ...-T-DGHR P--EAPN-TA -QVPTTEVVG TTPGQAPT-- -S-TTSVQVP TTEVISTA-V QMPTAESTGM mouse .......... ...-T-DGYM P--EAPG-T- RQGTTTKVVG TTPGQMPTTQ -S-TT-VQMP TTEV-ATTSE QM-T...... mouse_silv .......... ...-T-DGYM P--EAPG-T- RQGTTTKVVG TTPGQMPTTQ -S-TT-VQMP TTEV-ATTSE QM-T...... Repeat 1chicken_i AAGTDPTADP LPPTSVSSGG DAPGTVAPTA VEGSVAAGVG TAEDVAAATP GATAADVAVD TAGATDGDAV GPTAAATAES chicken_I ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*S ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*D ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- human TPEKV-VSEV MGT-LAEMST PEATGMT-AE -SIV-LS-TT A-QVTTTEWV ET--RELPIP EPEGP-ASSI MS-ESI-GSL mouse .......SAV IDT-LAEVST TEGTGTT--R .....PS-TT V-QATTTE.. .......... ...GP-ASPL L--QSS-GSI mouse_silv .......SAV IDT-LAEVST TEGTGTT--R .....PS-TT V-QATTTE.. .......... ...GP-ASPL L--QSS-GSI Repeat 2 Repeat 3 Repeat 4chicken_i IADPTAGATD GDAVGPTAAA TAESIADPTA GATDGDAVGP TAAATAESIA DPIVGATDGD AVGPTAAATA ESIADPTAGA chicken_I ---------- -----....- ---------- ---------- ---------- --........ .......... ......---- chicken_I*S ---------- -----....- ---------- ---------- ---------- --........ .......... ......---- chicken_I*D ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- human GPLLDGT--. .......... .......... .......... .......... .......... .......... .......... mouse SPLLDDTD-. .......... .......... .......... .......... .......... .......... .......... mouse_silv SPLLDDTD-. .......... .......... .......... .......... .......... .......... ..........
chicken_i TAVSSGSATA GATAEPLLLV KRQAPEAEPT GCVLYRYGTF STELNIVQGI ESVAIVQVVP AAPEGSGNSV ELTVTCEGSL chicken_I ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*S ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- chicken_I*D ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- human .......... ......-R-- ---V-LD... .-------S- -VT-D----- --AE-L-A-- S...-E-DAF ----S-Q-G- mouse .......... ......IM-- ---V-LD... .-------S- -LA-D----- --AE-L-A-- F...SE-DAF ----S-Q-G- mouse_silv .......... ......IM-- ---V-LD... .-------S- -LA-D----- --AE-L-A-- F...SE-DAF ----S-Q-G- Trans-chicken_i PEEVCTVVAD AECRTAQMQT CSAVAPAPGC QLVLRQDFNQ .SGLYCLNVS LANGNGLAVA STHVAVGGAS PAASGTTLTV chicken_I ---------- ---------- ---------- ---------- .--------- ---------- ---------- ---------- chicken_I*S ---------- ---------- ---------- ---------- .--------- ---------- ---------- ---------- chicken_I*D ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- human -K-A-MEISS PG-QPPAQRL -QP-L-S-A- ----H-ILKG G--T------ --DT-S---V --QLIMP-QE AGLGQVP-I- mouse -K-A-MDISS PG-QPPAQRL -QS-P-S-D- ----H-VLKG G--T------ --DA-S---- --QLV-P-QD GGLGQAP-L- mouse_silv -K-A-MDISS PG-QPPAQRL -QS-P-S-D- ----H-VLKG G--T------ --DA-S---- --QLV-P-QD GGLGQAP-L- membrane region Cytoplasmic regionchicken_i GLL...LIAA ALGTAAYTYR RVKYSPLLPT APTAPRPHSW LPPGATLRLL LRQAFGGAPS GESSPLLRAN AV* chicken_I ---WAP---- ---------- ---------- ---------- ---------- ---------- ---------- --- chicken_I*S ---WAP---- ---------- ---------- ---------- ---------- ---------- ---------- --- chicken_I*D ---------- -.....---- C--------- ---------- ---------- ---------- ---------- --- human -I-...-VLM -VVL-SLI-- -RLMKQDFSV PQLPHSSSH- -RLPRIFCSC ........-I --N----SGQ Q-- mouse -I-...-VLV -VVL-SLIH- HRLKKQGS.V SQMPHGSTH- -RLPPVF-AR ........GL --N----SGQ Q-- mouse_silv -I-...-VLV -VVL-SLIH- HRLKKQGS.V SQMPHGSTH*
27
Conclusions
Mapping genes for production traits using the red junglefowl and White Leghorn intercross has been powerful and the main part of the variation in adult growth in the cross can be explained by the QTL identified. By applying a genome-wide search for epistasis we can explain more of the QTL variation for early growth. If we could cover all the microchromosomes in the mapping study it is very likely that we could identify more QTL involved in the traits of interest due to the gene density in the microchromosomes. The melanocortin receptor 1 gene (MC1R) corresponds to the classical Extended black (E) locus and affects plumage colour in the chicken. The dominant E allele at the Extension locus carried by the White Leghorn has a missense mutation corresponding to the sombremutation in mouse that causes a constitutively active receptor and black pigment production. The Dominant white, Smoky and Dun alleles at the Dominant whitelocus are all associated with insertion/deletion polymorphisms in the coding sequence of the PMEL17 gene. They all carry insertion/deletions in the part of exon 10 encoding the transmembrane region. The Smoky allele has a second deletion of 16 bp in exon 6.
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Future prospects
The aim of genome analysis in domestic animals is to better understand the genetics behind monogenic and multifactorial traits. Even more marker dense linkage maps will greatly facilitate the search for disease and trait loci. A whole genome shot gun sequence with five-fold coverage of the chicken genome has recently been released but no assembly or gene annotations are yet available. The final draft sequence will be released during the spring 2004 including a six-fold coverage of the red junglefowl and a one-fold coverage each of a broiler and a layer line. This resource will clearly facilitate the detection of SNP and other DNA markers that can be used for construction of dense genetic maps and for fine mapping of interesting QTL regions. New and cheaper technologies for genotyping large amounts of markers will be needed when the aim is to cover the entire genome. Perhaps the technology will allow us to genotype whole populations for QTL analysis. As the amount of genotype data is emerging the demand for better and more efficient computer software increases.
The chicken draft sequence will also reveal the gene content in QTL regions and make it easier to design chicken specific primers. This will facilitate QTL detection in two ways. Firstly, it will be much easier to identify candidate genes compared with the current situations where we can only guess the gene content based on possible conserved synteny to human. Secondly, a huge number of SNP markers and insertion/deletion polymorphisms will be revealed by an in silico analysis of the sequences from the red junglefowl, white Leghorn and a broiler line.
The genetic map used for QTL analysis in the red junglefowl and White Leghorn cross is based on 105 markers dispersed over 25 linkage groups. Increasing the marker density and including the rest of the microchromosomes in a new genetic map will allow the detection of even more of the variation between the red junglefowl and White Leghorn chickens. Many genes are located on the microchromosomes and in most of the QTL mapping projects they are not well covered.
The interest in EST sequences has grown as large cDNA libraries from various tissues have been generated. For about 40% of the available EST sequences the orthologous mammalian sequence can be found in databases (Brown et al. 2003). Some of the sequences appear to be avian specific and
29
no mammalian homologue was detected. The large amount of EST sequences provides a tool for identifying expressed genes and annotating a physical map of the genome. The EST databases are an important resource for genome research and the extensive EST collection in chicken will greatly facilitate the assembly of the genome sequence. Genes with unknown function can be spotted on chips and comparing their expression pattern is a first characterisation of their function. Differential gene expression can be detected using cDNA array analysis and this strategy can complement the genome mapping approach to find genes underlying a QTL. For instance, in a recent study, Van Laere et al. (2003) showed that a major QTL in pigs was associated with a three-fold increase of IGF2 mRNA expression in muscle.
Further characterisation of the PMEL17 gene associated with the Dominant white phenotype in chicken is of interest. The pigment system is very complex and understanding the function of the PMEL17 gene will shed light on the melanosome development and function. It would also be interesting to make a knock out of the PMEL17 gene in the mouse. The gene seems to be expressed in many different tissues in human and the gene may be important for the function of other cells besides its crucial role in the melanocyte. Further characterisation is also required to reveal why the observed insertion and deletion in the transmembrane region leads to a pigment defect. A knock out or transgenic mouse could answer that question.
The Dominant white mutation is also of commercial interest due to the fact that it removes any kind of tissue pigmentation that otherwise can be caused by the genetic background in other colour loci such as the Extendedblack locus. In some broiler lines the white plumage colour is caused by a recessive locus and there is an interest in introducing the dominant white mutation to minimize the pigment remaining in the hair follicle after removing the feather making the chicken more attractive to consume.
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Acknowledgements
The work presented in this thesis has mainly been carried out at the Department of Animal Breeding and Genetics at the Swedish University of Agricultural Sciences but were later ended at the Department of Microbiology and Biochemistry at Uppsala University. The work has been funded by the MISTRA project (FOOD21), SJFR (FORMAS), Swedish University of Agricultural Sciences and Uppsala University.
I would like to thank all the people that I have worked with and met in all kind of work related situations during my years as a PhD student, all support you have given me and for all interesting discussions about various matters. It has been a pleasure!
In addition to this I would like to express my sincere gratitude to
Leif Andersson, my main supervisor, first of all for giving me “dispens” to take the Genome Analysis course in 1996, it was then I realised that genetics is really interesting and how fun it can be to work in a “real” lab. Thank you for sharing your scientific knowledge and for being an excellent supervisor! Örjan Carlborg, for our great collaboration, all interesting discussions and for you always being supportive and helpful. You have a never ending energy and… is there anything that is not possible to do?!? Lina Jacobsson, my last office-mate and an outstanding member of the chicken team! Thank you for interesting discussions about all aspects of science and life, for your support and for the great “genotyping-collaboration” - you are a true friend! Karin Schütz, always giving me a place to sleep during all my trips to Skara. It has been really nice to know you and to get to know the ”behaviour” aspect of the project.Anette Wichman, for all help when handling the animals, assembling people for various chicken related activities, sending blood samples and phenotype files. Pelle Jensen, I am impressed by your bagpipe-playing-skills!
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Elisabetta Giuffra, the genotyping guru! Thank you for our collaboration during the PQT project and for all kinds of discussions regarding microsatellite typing and other things. Siw Johansson, we had a really good collaboration when running all the microsatellites! Robert Fredriksson, it has been nice working with you! Ulle Gunnarsson, taking over when it is getting really interesting and when all new resources will be available. Lucky you! It will be like “glida på en räkmacka”! Keep up the good work and thanks for all help during the thesis-writing-period, there are just a few more PCRs… Sonchita Bagchi, for all good lab work you have done for the last project. Jakub Babul, for nice talks during our trips to Skara. Andreas Kindmark, for inspiring discussions and encouragement. Maria Moller, you have been around from time to time and it is always interesting to talk to you. Valerie Amarger, it was nice when you were in Uppsala, but it is also nice to visit you in France! It has been great travelling with you and I think we have had many funny moments! Anna Törnsten, my first office-mate in the lab. You were also my supervisor during the FISH lab and I will never forget the chromosomes, wow, they were really impressive... Ulla Gustafsson, it is (almost) never impossible to squeeze in another sample for sequencing… Gudrun Wieslander, for being nice and always helpful. Hee-Bok Park, or was it Reebok?!?! You are always a smiling guy in the lab.Gerli Pielberg, the pyro-expert! For interesting discussions about science and life. Anne-Sophie van Laere, I just love your rrrrrr… more Kirrruna and köttbullaaaarrrr!! Frida Berg, keep on climbing! I hope we can continue our talks! Erik Bongcam Rudloff, for all help with my computer-frustrations and for taking the nice chicken pictures. One of the nicest ones is on the front page! Stefan Marklund, you are a resource in the lab. Carolyn Fitzimmons, for your singing! You give the lab a special touch. Göran Andersson, always helpful with a lot of good ideas! Meiying Fang, for nice chinese cooking! Calle Thulin, for having both feet on the ground and for being a nice person!Ana, Nikki, Per and Ellen, nice to have you around! Carles Vila, for interesting Saturday-lunch-down-town-discussions and for being a good friend. When your knee is better I will make you run…
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Ariane Charmichael, for telling about the available PhD position and for being a good friend and making me feel welcome in the lab.
Mina föräldrar Siv-Britt och Allan, min syster Affi, mormor Karin och morfen Gösta ni har alla verkligen varit ett stöd och hjälpt mig i alla möjliga och omöjliga situationer. Tack för att ni stått ut med mina projekt... inklusive de folkilskna tupparna... Vad skulle jag ha gjort utan er?!?
Carlos, my love, my friend, my driver in the white limo, my support, my proof-reader, my squash partner, my… everything... and yes, now perhaps there will be some time to learn Spanish… Jeffo, te amo inmensamente!
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References
Andersson, L., Haley, C.S., Ellegren, H., Knott, S., Johansson, M. et al. 1994. Genetic mapping of quantitative trait loci for growth and fatness in pig. Science263:1771-4.
Bidanel, J.P., Milan, D., Iannuccelli, N., Amigues, Y., Boscher, M.Y. et al. 2001. Detection of quantitative trait loci for growth and fatness in pigs. Genet Sel Evol33:289-309.
Berson, J.F., Theos, A.C., Harper, D.C., Tenza, D., Raposo, G. et al. 2003. Proprotein convertase cleavage liberates a fibrillogenic fragment of a resident glycoprotein to initiate melanosome biogenesis. J Cell Biol 161:521-33.
Björnhag, G., Knutson, P-G. and Sperber, I. 1994. Growth- Compendium on thephysiology of growth, 3rd ed. Department of Animal Physiology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Botstein, D., White, R.L., Skolnick, M. and Davis, R.W. 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphism. AmJ Hum Genet 32:314-31.
Brown, W.R.A., Hubbard, S.J., Tickle, C. and Wilson, S.A. 2003. The chicken as a model for large-scale analysis of vertebrate gene function. Nat Rev 4:87-98.
Brumbaugh, J.A. and Hollander, W.F. 1966. Genetics of buff and related colour patterns in the fowl. Poult Sci 45:451-7.
Brumbaugh, J.A., Bargar, T.W., Oetting, W.S. 1983. A “new” allele at the Cpigment locus in the fowl. J Hered 74:331-6.
Bumstead, N. and Palyga, J. 1992. A preliminary linkage map of the chicken genome. Genomics 13:690-7.
Burt, W.B., Bruley, C., Dunn, I.C., Jones, C.T., Ramage, A. et al. 1999. Thedynamics of chromosome evolution in birds and mammals. Nature 402:411-3.
Burt, D.W. and Hocking, P.M. 2002. Mapping quantitative trait loci and identification of genes that control fatness in poultry. Proc Nutr Soc 61:441-6.
Burt, D. and Pourquie, O. 2003. Chicken genome – Science nuggets to come soon. Science 300:1669.
Capdevila, J. and Izpisua-Belmonte, J.C. 2001. Patterning mechanisms controllingvertebrate limb development. Annu Rev Cell Dev Biol 17:87-132.
Carefoot, W.C. 1985. Effect of the eumelanin restrictor Db on plumage pattern phenotypes of the domestic fowl. Br Poult Sci 26:409-12.
Carlborg, Ö. 2002. New methods for mapping Quantitative Trait Loci. Doctorate thesis. Acta Universitatis Agriculturae Sueciae, Veterinaria 121, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Churchill, G.A. and Doerge, R.W. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963-71.
Crawford, R.D. 1996. In: Poultry breeding and genetics (Ed. R.D. Crawford). Elsevier Science, New York.
34
Crittenden, L.B., Provencer, L., Santagello, L., Levin. I., Abplanalp, H. et al. 1993. Characterisation of a red junglefowl by White Leghorn backcross reference population for molecular mapping of the chicken genome. Poult Sci 72:334-48.
de Koning, D.J., Windsor, D., Hocking, P.M., Burt, D.W., Law, A. et al. 2003. Quantitative trait locus detection in commercial broiler lines using candidate regions. J Anim Sci 81:1158-65.
Du, J., Miller, A., Widlund, H.R., Horstmann, M.A., Ramaswamy, S. et al. 2003. MLANA/MART1 and SILV/PMEL17/GP100 are transcriptionally regulated by MITF in melanocytes and melanoma. Am J Pat 163: 333-43.
Emara, M.G. and Kim, H. 2003. Genetic markers and their application in poultry breeding. Poult Sci 82:952-7.
Everts, R.E., Rothuizen, J. and van Oost, B.A. 2000. Identification of a premature stop codon in the melanocyte-stimulating hormone receptor gene (MC1R) in Labrador and Golden retrievers with yellow coat colour. Anim Genet 31:194-9.
Falconer, D.S. 1981. Introduction to Quantitative genetics 2nd ed., pp. 237. Longman Inc., New York.
Fijneman, R.J., De Vries, S.S., Jansen, R.C. and Dermant, P 1996. Complex interactions of new quantitative trait loci, Sluc1, Sluc2, Sluc3 and Sluc4, that influence the susceptibility to lung cancer in the mouse. Nat Genet 14:465-7.
Fumihito, A., Miyake, T., Sumi, S., Takada, M., Ohno, S. et al. 1994. One subspecies of the red junglefowl (Gallus gallus gallus) suffices as the matriarchic ancestor of all domestic breeds. Proc Natl Acad Sci 91:12505-9.
Giuffra, E., Törnsten, A., Marklund, S., Bongcam-Rudloff, E., Chardon, P. et al.2002. A large duplication associated with Dominant white color in pigs originated by homologous recombination between LINE elements flanking KIT.Mamm Genome 13:569-77.
Green, P., Falls, K. and Crook, S. 1990. Documentation for CRIMAP, ver. 2.4. Washington University School of Medicine, St. Louis.
Gregory, S.G., Sekhon, M., Schein, J., Zhao, S., Osoegawa, K. et al. 2002. A physical map of the mouse genome. Nature 418:743-50.
Groenen, M.A.M., Crooijmans, R.P.M.A., Veenendaal, A., Cheng, H.H. and Siwek, M. 1998. A comprehensive linkage map of the chicken genome. Genomics 49:
265-74.Groenen, M.A.M., Cheng, H.H., Bumstead, N., Benkel, B.F., Briles, W.E. et al.
2000. A consensus linkage map of the chicken genome. Gen Res 10:137-47. Haldane, J.B. 1922. Sex ratio and unisexual sterility in hybrid animals. J Genet
12:101-9. Haley, C.S., Knott, S.A. and Elsen, J.M. 1994. Mapping Quantitative Trait loci in
crosses between outbred lines using least squares. Genetics 136:1195-207. Heston, W.E. and Deringer, M.K. 1947. Relationship between lethal yellow (Ay)
gene of the mouse and susceptibility to spontaneous pulmonary tumours. J Natl Cancer Inst pp 463-5.
Hutt, F.B. 1936. Genetics of the fowl. VI. A tentative chromosome map. Neue Forsch Tierzucht Abstammungsl (Durest Festschrift), pp 105-12, Verbandsdruckerei AG, Bern.
Ikeobi, C.O., Woolliams, J.A., Morrice, D.R., Law, A., Windsor, D. et al. 2002. Quantitative trait loci affecting fatness in the chicken. Anim Genet 33:428-35.
Jaap, R.G. and Hollander, W.F. 1954. Wild type as a standard in poultry genetics. Poult Sci 33:94-100.
35
Jackson, I.J., Budd, P., Horn, J.M., Johnson, R., Raymond, S. et al. 1994. Genetics and molecular biology of mouse pigmentation. Pigment Cell Res 7:73-80.
Jackson, I.J. and Bennett, D.C. 1990. Characterization of the mouse albino mutation of tyrosinase and in vivo revertant. Proc Natl Acad Sci USA 87:7010-4.
Jackson, I.J. 1988. A cDNA encoding tyrosinase-related protein maps to the mouse brown locus. Proc Natl Acad Sci USA 85:4392-6.
Jackson, I.J., Chambers, D.M, Tsukamoto, K., Copeland, N.G., Gilbert, D.J. et al.1992. A second tyrosinase-related protein, TRP-2, maps to and is mutated at the slaty locus. EMBO J 11:527-35.
Jefferys, A.J., Wilson, V. and Thein, S.L. 1985. Hypervariable ‘minsatellite’ regions in human DNA. Nature 314:67-73.
Jenkins, N.A., Copeland, N.G., Taylor, B.A. and Lee, B.K. 1981. Dilute (D) coat colour mutation of DBA/2J mice is associated with the site of intergration of an ectopic MuLV genome. Nature 293:370-4.
Johansson, M., Ellegren, H., Marklund, L., Gustavsson, U., Ringmar-Cederberg, E. et al. 1992. The gene for Dominant white color in the pig is closely linked to ALB and PDGRFRA on chromosome 8. Genomics 14:965-9.
Johansson Moller, M., Chaudhary, R., Hellmen, E., Hoyheim, B., Chowdhary, B. etal. 1996. Pigs with the Dominant white coat color phenotype carry a duplication of the KIT gene encoding the mast/stem cell growth factor receptor. MammGenome 7:822-30.
Kaufman, J. 1999. Co-evolving genes in MHC haplotypes: the “rule” fornonmammalian vertebrates? Immunogen 50:228-36.
Kijas, J.M., Wales, R., Törnsten, A., Chardon, P., Moller, M. et al. 1998. Melanocortin receptor 1 (MC1R) mutations and coat color in pigs. Genetics150:1177-85.
Kim, R.Y. and Wistow, G.J. 1992. The cDNA RPE1 and monoclonal antibody HMB-50 define gene products differentially expressed in retinal pigment epithelium. Exp Eye Res 55:657-62.
Klungland, H., Våge, D.I., Gomez-Raya, L., Adalsteinsson, S. and Lien, S. 1995. The role of melanocyte-stimulating hormone (MSH) receptor in bovine coat color determination. Mamm Genome 6:636-9.
Kruglyak, L. 1997. The use of genetic map of biallelic markers in linkage studies. Nat Genet 17:21-4.
Kosambi, D.D. 1944. The estimation of map distances from recombination values. Annal Eugen 12:172-5.
Kwon, B., Kim, K.K., Halban, R. and Pickard, R. 1994. Characterization of mouse Pmel17 gene and Silver locus. Pigm Cell Res 7:394-7.
Kwon, B.S., Halaban, R., Ponnazhagan, S., Kim, K., Chintamaneni, C., et al. 1995. Mouse silver mutation is caused by a single base insertion in the putative cytoplasmic domain of Pmel 17. Nucleic Acids Res 23:154-8.
Lander, E.S., Linton, L., Birren, B., Nusbaum, C., Zody, M. et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:860-921.
Lander, E.S. and Botstein, D. 1989. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 56:1212-23.
Li, Z., Pinson, S.R., Park, W.D., Paterson, A.H. and Stansel, J.W. 1997. Epistasis for three grain yield components in rice (Oryza sativa L.). Genetics 145:453-65.
Li, H., Deeb, N., Zhou, H., Mitchell, A.D., Ashwell, C.M. et al. 2003. Chicken quantitative trait loci for growth and body composition associated with transforming growth factor-beta genes. Poult Sci 82:347-56.
36
Liebhard, R., Kellerhals, M., Pfammatter, W., Jertmini, M. and Gessler, C. 2003. Mapping quantitative physiological traits in apple (Malus x domestica Borkh.). Plant Mol Biol 52:511-26.
Long, A.D., Mullaney, S.L., Mackay, T.F. and Langley, C.H. 1996. Genetic interaction between naturally occurring alleles at quantitative trait loci and mutant alleles at candidate loci affecting bristle number in Drosophila melanogaster. Genetics 144:1497-510.
Marklund, L., Johansson Moller, M., Hoyheim, B., Davies, W., Fredholm, M. et al.1996. A comprehensive linkage map of the pig based on a wild pig-Large White intercross. Anim Genet 27:255-69.
Marklund, L., Moller, M.J., Sandberg, K. and Andersson, L. 1996. A missense mutation in the gene for melanocyte-stimulating hormone receptor (MC1R) is associated with the chestnut coat color in horses. Mamm Genome 7:895-9.
Mochii, M., Agata, K. and Eguchi, G. 1991. Complete sequence and expression of a cDNA encoding a chicken 115-kDa melanosomal matrix protein. Pigment Cell Res 4:41-7.
Morisson, M., Lemiere, A., Bosc, S., Galan, M., Plisson-Petit, F. et al. 2002. ChickRH6: a chicken whole-genome radiation hybrid panel. Genet Sel Evol34:521-33.
Morton, N.E. 1955. Sequential test for the detection of linkage. Am J Hum Genet7:277-318.
Morton, N.E. 1991. Parameters of the human genome. Proc Natl Acad Sci USA.88:7474-6.
Newton, J.M., Wilkie, A.L., He, L., Jordan, S.A., Metallinos, D.L. et al. 2000. Melanocortin 1 receptor variation in the domestic dog. Mamm Genome 11:24-30.
Olofsson, B. and Bernardi, G. 1983. Organisation of nucleotide sequences in the chicken genome. Eur J Biochem 130:241-5.
Pielberg, G., Olsson, C., Syvänen, A.C. and Andersson, L. 2002. Unexpectedly high allelic diversity at the KIT locus causing Dominant white color in the domestic pig. Genetics 160:305-11.
Quevedo, W.C., Fleischmann, R.D. and Dyckman, J. 1981. Premature loss of melanocytes from hair follicles (Blt) of light and silver (si) mice. In: Seiji M. Pigment cell. Phenotypic Expression in Pigment. Tokyo: Tokyo University Press; pp. 177-183.
Robbins, L.S., Nadeau, J.H., Johnson, K.R., Kelly, M.A. and Roselli-Rehfuss, L.1993. Pigmentation phenotypes of variant Extension locus alleles result frompoint mutations that alter MSH receptor function. Cell 72:827-34.
Rodionov, A.V., Myakoshina, Y., Chelysheva, L.A., Solovei, I.V. and Gaginskaya, E.P. 1992a. Chiasmata on lampbrush chromosomes of Gallus gallus domesticus:cytogenetic investigations of recombination frequency and linkage group length. Genetika 28:53-63.
Rodionov, A.V., Chelysheva, L.A., Solovei, I.V. and Myakoshina, Y. 1992b. Chiasmata distribution in the lampbrush chromosomes of the chicken Gallus gallus domesticus: hot spots of recombination and their role in proper disjunction of the homologous chromosomes at the first meiotic division. Genetika 28:151-60.
Ruyter-Spira, C. P., Gu, Z. L., van der Poel, J. J. and Groenen, M.A.M. 1996. Bulksegregant analysis using microsatellite: Mapping the Dominant white locus in the chicken. Poult Sci 76:386-91.
37
Sewalem, A., Morrice, D.M., Law, A., Windsor, D., Haley, C.S. et al. 2002. Mapping of quantitative trait loci for body weight at three, six, and nine weeks of age in a broiler layer cross. Poult Sci 81:1775-81.
Schmid, M., Nanda, I., Guttenbach, M., Steinlein, C., Hoehn, H. et al. 2000. First report on chicken genes and chromosomes 2000. Cytoget Cell Genet 90:169-218.
Schütz, K., Kerje, S., Carlborg, Ö., Jacobsson, L., Andersson, L. et al. 2002. QTL analysis of a red junglefowl x White Leghorn intercross reveals trade-off in resource allocation between behavior and production traits. Behav Genet32:423-33.
Schütz, K.E., Kerje, S., Jacobsson, L., Forkman, B., Carlborg, Ö. et al. 2004. Major growth QTLs in fowl are related to fearful behaviour: Possible genetic links between fear responses and production traits in a red junglefowl x White Leghorn intercross. Behav Genet 34:109-18.
Shook, D.R. and Johnson, T.E. 1999. Quantitative trait loci affecting survival and fertility- related traits in Caernorhabditis elegans show genotype- environment interaction, pleitropy and epistasis. Genetics 153:1233-43.
Smith, J. and Burt, D. 1998. Parameters of the chicken genome (Gallus gallus). Anim Genet 29:290-4.
Smith, J., Bruley, C.K., Paton, I.R., Dunn, I., Jones, C.T. et al. 2000. Difference in gene density on chicken macrochromosomes and microchromosomes. Anim Genet 31:96-103.
Smyth, J.R., Ring, N.M. and Brumbaugh, J.A. 1986. A fourth allele at the C locus of the chicken. Pout Sci 65:suppl1 pp 129.
Solano, F., Martinez-Esperanza, M., Jimenez-Cervantes, C., Hill, S.P., Lozano J.A., et al. 2000. New insight on the structure of the mouse silver locus and on the function of the silver protein. Pigment Cell Res 13:118-24.
Sossinka, R. 1982. Domestication in birds. Avian Biol 6:373-403. Takeuchi, S., Suzuki, H., Yabuuchi, M. and Takahashi, S. 1996. A possible
involvement of melanocortin 1-receptor in regulating feather color pigmentation in the chicken. Biochim Biophys Acta 1308:164-8.
Tatsuda, K. and Fujinaka, K. 2001. Genetic mapping of the QTL affecting body weight in chickens using a F2 family. Br Poult Sci 42:333-7.
Tautz, D. 1989. Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Res 16:6464-71.
Van Laere, A-S., Nguyen, M., Braunschweig, M., Nezer, C., Collette, C. et al. 2003. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425:832-6.
Van Kaam, J.B., Groenen, M.A., Bovenhuis, H., Veenendaal, A., Vereijken, A.L. et al. 1999a. Whole genome scan in chickens for quantitative trait loci affecting carcass traits. Poult Sci 78:1091-9.
Van Kaam, J.B., Groenen, M.A., Bovenhuis, H., Veenendaal, A., Vereijken, A.L. etal. 1999b. Whole genome scan in chickens for quantitative trait loci affecting growth and feed efficiency. Poult Sci 78:15-23.
Warren, D.C. 1940. Inheritance of Pinkeye in the Fowl. J Hered 31:291-2. Waterston, R., Lindblad-Toh, K., Birney, E., Rogers, J., Abril, F. et al. 2002. Initial
sequencing and comparative analysis of the mouse genome. Nature 420:520-62. Weber, J.L. and May, P.E. 1989. Abundant class of human polymorphism which can
be typed using the polymerase chain reaction. Am J Hum Genet 44:388-96.
38
West, B. and Zhou, B-X. 1989. Did chickens go north? New evidence for domestication. World’s Poult Sci J 45:205-18.
Wolf, G.L. 2003. Regulation of yellow pigment formation in mouse: A historical perspective. Pigment Cell Res 16:2-15.
Våge, D.I., Klungland, H., Lu, D. and Cone, R.D. 1999. Molecular and pharmacological characterization of dominant black coat color in sheep. MammGenome 10:39-43.
Yokoyama, T., Silversides, D.W., Waymire, K.G., Kwon, B.S., Takeuchi, T. et al.1990. Conserved cysteine to serine mutation in tyrosinase is responsible for the classical albino mutation in laboratory mice. Nucleic Acids Res 18:7293-8.
Zhu, J.J., Lillehoj, H.S., Allen, P.C., Van Tassell, C.P., Sonstegard, T.S. et al. 2003. Mapping quantitative trait loci associated with resistance to coccidiosis and growth. Poult Sci 82:9-16.
Ziehl, M.A. and Hollander, W.F. 1987. Dun, a new plumage-colour mutant at the I-locus in the fowl (Gallus gallus). Iowa State J Res 62:337-42.
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