Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Medicine 1304
Mapping Genes Affecting
Phenotypic Traits in Chicken
BY
SUSANNE KERJE
ACTA UNIVERSITATIS UPSALIENSIS
UPPSALA 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 be
conducted in English.
Abstract
Kerje, S. 2003. Mapping Genes Affecting Phenotypic Traits in Chicken. Acta Universitatis
Upsaliensis. Comprehensive Summaries of Uppsala Dissertations from the Faculty of
Medicine 1304. 40 pp. Uppsala. ISBN 91-554-5805-X.
The purpose of gene mapping is to understand the underlying genetics of simple and complex
traits like plumage colour and growth. This thesis is based on a cross between the wild
ancestor of the modern chicken, the red junglefowl, and a White Leghorn line selected for high
egg mass. There are obvious phenotypic differences between these two breeds in several
aspects such as growth, egg production and behaviour. These complex traits are often
influenced by a number of genes or Quantitative Trait Loci (QTL) as well as environmental
factors.
Identification of QTL regions involves testing of association between genetic markers and
the phenotype of interest. The QTL identified in this study explain most of the difference in
adult body weight between the red junglefowl and the White Leghorn, but less of the
difference at earlier age. By applying a different method for detection of QTL, including gene
interactions, epistasis, we can understand more of the genetics behind early growth. The allele
coming from the red junglefowl is generally associated with lower weight, egg production and
food consumption.
In this study we have also identified two genes explaining the difference in plumage colour
in the cross. The Extension locus, encoded by the melanocortin receptor 1 (MC1R), controls
the amount of pigment produced has shown to be associated with plumage colour. A mutation
in the MC1R gene causes black pigmentation of the plumage.
We have also found association between the PMEL17 gene, known to be involved in normal
pigmentation, and the Dominant white phenotype present in the White Leghorn. After
comparison of sequences from different alleles at the Dominant white locus, amino acid
alteration caused by insertion and deletion in the transmembrane region of the PMEL17 protein
has been revealed. These mutations are associated with alleles representing different plumage
colour variants.
Keywords: chicken, Quantitative Trait Loci, growth, egg production, epistasis, plumage
colour, 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 0282-7476
ISBN 91-554-5805-X
URN: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.
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Contents
Introduction.....................................................................................................1
Chicken as a model organism ....................................................................1
The chicken genome ..................................................................................2
Linkage mapping........................................................................................3
Pedigrees................................................................................................3
Genetic markers.....................................................................................4
Linkage analysis ....................................................................................4
Genetic maps .........................................................................................6
QTL mapping .............................................................................................7
Mapping of single QTL .........................................................................7
QTL mapping including epistasis..........................................................8
Gene identification .....................................................................................8
The genetics of plumage colour .................................................................9
Aims of this thesis ........................................................................................12
Results and discussion ..................................................................................13
Linkage mapping (I).................................................................................13
The genetic map ..................................................................................13
QTL mapping (I, II) .................................................................................15
Mapping of single QTL (I) ..................................................................15
Mapping of interacting QTL (II) .........................................................17
Mapping of monogenic traits (III, IV) .....................................................18
Genetics of plumage colour.................................................................18
Conclusions...................................................................................................27
Future prospects............................................................................................28
Acknowledgements.......................................................................................30
References.....................................................................................................33
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
th
20 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 et
al. 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)).
1
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
2
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 20fold 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
Pedigrees
A 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.
3
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
4
Figure 1. Crossing over between parental chromosomes homozygous at the A, B and
C loci. In 1A, recombination between B and C loci occurs while the parental
haplotype A and B is maintained. 1B illustrates a double recombination where the
parental 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 between
two loci, in each meiosis, the recombination fraction can directly be used as
the map distance (cM) (1% of recombination = 1 cM). This may not always
be true if the distance between the markers is large more than one
5
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 (T  0.5), divided by the likelihood of obtaining the genotypic data
under the null hypothesis (H0) of independent assortment of both loci (T =
0.5). The likelihood estimations of linkage are carried out from a T of 0 to
0.5. The estimate of the recombination rate is the value of T 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).
6
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
7
(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
8
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
9
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 carboxyterminal 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 Barred
phenotypes.
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 D-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).
10
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 Dominant
white 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.
11
Aims of this thesis
The objectives of this study have been:
x to generate a genetic map including markers evenly dispersed in the
chicken genome
x to combine the marker information in the genetic map with
phenotypic data from the chicken intercross for mapping
Quantitative Trait Loci (QTL)
x to simultaneously search for epistatic QTL to understand the
growth process in chicken
x to map the melanocortin receptor 1 (MC1R) gene and evaluate it as
a candidate for variation in plumage colour
x to identify the gene causing the Dominant white plumage
phenotype in chicken
12
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 threegeneration 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
13
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).
Paper I
Groenen et al. 1998
Chromosomea
Male/
Male/
Male
Female
Male
Female
female
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
a
Chromosome 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 et
al. 1996, Binadel et al. 2001). Moreover, our results are not in line with the
14
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
15
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.
Trait
Approx.
Candidate
QTL Chr Location
(cM)
QTL found
genes
in
pres.
in other
stud.
study
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
2, 3a
G7a 4 122-150 B112, G46-112, total E
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.
16
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
17
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 Extension
locus encodes the melanocortin receptor 1, MC1R, a transmembrane protein
18
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 sombre
mutation 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 S
allele 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
19
Table 3. Plumage colour and MC1R genotype distributions in a red junglefowl/
White Leghorn intercross.
I, E, Ba
MC1R genotypeb
Phenotype class
Total F2 (df=2)c
genotype
J/J
J/W
W/W
Females
White
21
137
50
208 29.0***
I/40
1
0
41 115.1***
Cream
I/-, e+/e+
White with black spots
0
17
17
34 17.0***
I/-, E/I/61
155
67
283 2.8
Total white
Black with white spots
Grey
Black
Wild type, grey
Wild type
Barred
Total coloured
i/i, E/i/i, e+/e+
i/i, E/i/i, e+/e+
i/i
i/i, B/W
i/i
1
6
0
19
2
2
31
4
0
16
0
6
17
43
4
0
8
0
2
13
27
9
6
24
19
8
38
101
2.1
18.0***
8.0*
57.0***
0.4
7.7*
2.5
92
198
94
384
0.4
I/I/I/I/I/-
3
3
48
1
55
37
10
118
0
166
37
11
32
1
81
77
24
198
3
302
30.1***
6.0*
9.9**
7.5*
i/i
i/i, E/i/i
i/i, e+/i/i, B/b+
i/i
0
0
4
14
5
23
1
7
5
16
11
40
0
2
2
2
6
12
1
9
11
32
38
75
3.7
0.8
9.0*
0.1
3.6
78
206
93
377
4.4
Total for females
Males
White
Cream
White with red/brown
White with black spots
Total white
Black with white spots
Black
Wild type, grey
Wild type
Barred
Total coloured
Total for males
Total across sexes
170
404
187
761 3.7
a
Presumed 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.
b
J=allele inherited from red junglefowl;W=allele inherited from White Leghorn.
c
Chi-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
20
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 et
al. 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
21
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 White
Nucleo- jungle- Langs- Brolier Leghorn Leghorn
Exon/
tide
fowl
han
line
L13 ADOL Smoky
Dun
(i/i)
(i/i)
(I/I)
(I/I)
(IS/IS) (ID/ID)
Intron position (i/i)
Intron 1
141
C
T
145
A
G
G
197
T
C
Y
C
214
C
Y
220
T
C
C
234
G
A
236
G
T
T
330
A
G
G
336
C
A
A
381
G
C
S
C
413
T
C
479
C
T
Exon 2
701
T
C
Y
705
C
T
Intron 2
823
C
G
G
G
Exon 3
930
C
T
Y
967
G
A
Intron 3 1058
A
T
1060
A
G
1066
A
G
1068
G
A
1070
T
A
1072
G
A
1090
G
A
1104
D12
D12
D12
D12
D12
Intron 4 1316
A
G
G
1340
C
G
G
D22
D44c
D22
D22
D22
D44
Intron 5 1594
1600
G
A
A
A
A
1651
C
T
Exon 6
1709
C
T
1818
C
T
T
1836
G
A
A
A
1853
D12
1897
C
Y
2127
C
T
2209
A
G
G
G
G
G
Intron 6 2337
C
T
Exon 7
2489
C
T
2498
A
G
G
G
G
G
r4
r3
r3
r3
r3
r3
2514
22
2889
G
A
3007
A
G
3008
G
C
3018
G
A
Exon 8
3098
G
A
Intron 8 3262
G
A
3272
A
G
G
3296
I4
I4
3315
C
T
3318
A
C
C
3334
C
T
T
3356
*4
G+*2
G+*2
Exon 9
3371
G
C
C
3395
G
A
3398
C
T
Intron 9 3586
G
C
3589
T
A
3607
T
G
I9
Exon 10 3696
I9
I9
3721
D15
Intron 10 3762
G
A
3766
T
C
Y
Exon 11 3844
C
T
a
Heterozygous positions: M=A/C; R=A/G; S=G/C; Y=C/T
b
Length 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
repeats
c
This animal was heterozygote for this position
Intron 7
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
34 35 105 232 280-4 399 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
- - - - del D A-B-A
- WAP
Smoky (IS/IS)
D D
- V S del
C
Dun (I /I )
a
Repeat composition exon 7
23
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
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
I
D
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
I
S
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
Breed
Fayoumi
i
A
a
see Figure 3, paper IV
b
A = 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
24
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 et
al. 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 MD and a
carboxyterminal ME fragment.
25
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
Signal sequence
MRLHGAIVLL AALLALVTAQ
---------- ------------------- ------------------- ---------MDLVLKRC LLH--VIG-L
MV-VQRRS FLPVLVLS-L
MV-VQRRS FLPVLVLS-L
QRGGGRSRGG
---------------------------LAV-ATKVPR
LAV-ALEGSR
LAV-ALEGSR
VKGSAWGGRP
----------------------V----NQDWLGVS-Q
NQDWLGVP-Q
NQDWLGVP-Q
APFRSWDTAR
---------------------------LRTKA-NRQL
LVTKT-NRQL
LVTKT-NRQL
YRPWQEGTAR
----------------------------PE-T-..-Q
-PE-T-..VQ
-PE-T-..VQ
QNDCWRGGDV
---------------------------RL------QGSN-----QGSN-----Q-
TFDISNDAPT
---------------------------SLKV---G-SLRVI--G-SLRVI--G--
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
LVGARATFSI
----------------------------I--N-S------N-S------N-S---
ALRFPGTQTV
-----------------------S-----N---S-K--H---S-K--H---S-K-
LPDGRVVWSQ
-------------------------------Q-I-VN
----Q-I-AN
----Q-I-AN
NCTVNGTRML
----------------------------TII--SQVW
-TII--SQVW
-TII--SQVW
QGDPVYPEQL
---------------------------G-Q----QET
G-Q----QEP
G-Q----QEP
AEGSDGVFPD
---------------------------DDAC..I--DDAC..---DDAC..----
GQPFPRSAWG
----------------------------G-C-SGS-S
-G-C-SGPKP
-G-C-SGPKP
KRGRFVYVWW
---------------------------QKRS-----K
PKRS-----K
PKRS-----K
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
TWGRYWQVVD
------------------------------Q----LG
---K----LG
---K----LG
GATSQLTVGT
----------------------------PV-G-SI--PV-RSSIA-PV-R-SIA-
DGVALGSYTM
---------------------------GRAM--TH-RHAK--TH-GHAK--TH--
EVVVYHYRGR
-----------------------------T---R--S
--T---R--S
--T---R--S
QRFIPIGHAS
---------------------------RSYV-LA-S-SYV-LA---SYV-LA---
TQFSITDQVP
---------------------------SA-T-----ST-T-----ST-T------
IAVDVTQLEV
---------------------------FS-S-S--RA
FS-S-S--QA
FS-S-S--QA
AAGDGGSFVR
---------------------------LD-GNKH-LLD-ETKH-LLD-ETKH-L-
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
NRPVAFNVRL
----------------------------Q-LT-ALQ-H-LI-ALQ-H-LI-ALQ-
HDPSHYLRDA
-------------------------------G--AE----G--AE----G--AE-
DISYSWDFGD
----------------------------L--T-----L--T-----L--T-----
QSGTLISRSP
------------------.
---------S-------AL
GT------AL
GT------AL
TVTHTYLQAG
---------...---------------V------EPD------ESD------ES-
SFAARLVLQA
---------------------------PVT-QV----VT-QV----VT-QV----
AIPLSSCGTS
-------------------------------T---S----V---S----V---S-
APPVVDPTTG
---------------------------PV-.......
PV-.......
PV-.......
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
PVPSLGPTAT
---------------------------..........
..........
..........
QPVGPTGSGT
---------------------------...-T-DGHR
...-T-DGYM
...-T-DGYM
ATAPSNLTGS
---------------------------P--EAPN-TA
P--EAPG-TP--EAPG-T-
GTAAAPGTTA
----------------------------QVPTTEVVG
RQGTTTKVVG
RQGTTTKVVG
APRASGAPAE
---------------------------TTPGQAPT-TTPGQMPTTQ
TTPGQMPTTQ
PTGVSVAVLS
----------------------------S-TTSVQVP
-S-TT-VQMP
-S-TT-VQMP
PVLSTAVANA
--------D--------D---------QMPTAESTGM
QM-T......
QM-T......
AAGTDPTADP
---------------------------TPEKV-VSEV
.......SAV
.......SAV
Repeat
chicken_i
IADPTAGATD
chicken_I
---------chicken_I*S ---------chicken_I*D ---------human
GPLLDGT--.
mouse
SPLLDDTD-.
mouse_silv SPLLDDTD-.
LPPTSVSSGG
---------------------------MGT-LAEMST
IDT-LAEVST
IDT-LAEVST
2
GDAVGPTAAA
-----....-----....---------..........
..........
..........
TAEDVAAATP
---------------------------A-QVTTTEWV
V-QATTTE..
V-QATTTE..
TAAATAESIA
---------------------------..........
..........
..........
GATAADVAVD
---------------------------ET--RELPIP
..........
..........
Repeat 4
DPIVGATDGD
--........
--........
---------..........
..........
..........
AVGPTAAATA
..........
..........
---------..........
..........
..........
ESIADPTAGA
......---......------------..........
..........
..........
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
GATAEPLLLV
---------------------------......-R-......IM-......IM--
DAPGTVAPTA VEGSVAAGVG
---------- ------------------- ------------------- ---------PEATGMT-AE -SIV-LS-TT
TEGTGTT--R .....PS-TT
TEGTGTT--R .....PS-TT
Repeat 3
TAESIADPTA GATDGDAVGP
---------- ------------------- ------------------- ---------.......... ..........
.......... ..........
.......... ..........
Ð
KRQAPEAEPT GCVLYRYGTF
---------- ------------------- ------------------- ------------V-LD... .-------S---V-LD... .-------S---V-LD... .-------S-
DSAATEPLPD
---------------------------TTEVISTA-V
TTEV-ATTSE
TTEV-ATTSE
Repeat 1
TAGATDGDAV
---------------------------EPEGP-ASSI
...GP-ASPL
...GP-ASPL
STELNIVQGI
----------------------------VT-D-----LA-D-----LA-D-----
ESVAIVQVVP
-----------------------------AE-L-A---AE-L-A---AE-L-A--
AAPEGSGNSV
---------------------------S...-E-DAF
F...SE-DAF
F...SE-DAF
PEEVCTVVAD AECRTAQMQT CSAVAPAPGC QLVLRQDFNQ
---------- ---------- ---------- ------------------- ---------- ---------- ------------------- ---------- ---------- ----------K-A-MEISS PG-QPPAQRL -QP-L-S-A- ----H-ILKG
-K-A-MDISS PG-QPPAQRL -QS-P-S-D- ----H-VLKG
-K-A-MDISS PG-QPPAQRL -QS-P-S-D- ----H-VLKG
membrane region
Cytoplasmic region
chicken_i
GLL...LIAA ALGTAAYTYR RVKYSPLLPT APTAPRPHSW
chicken_I
---WAP---- ---------- ---------- ---------chicken_I*S ---WAP---- ---------- ---------- ---------chicken_I*D ---------- -.....---- C--------- ---------human
-I-...-VLM -VVL-SLI-- -RLMKQDFSV PQLPHSSSHmouse
-I-...-VLV -VVL-SLIH- HRLKKQGS.V SQMPHGSTHmouse_silv -I-...-VLV -VVL-SLIH- HRLKKQGS.V SQMPHGSTH*
.SGLYCLNVS
.--------.-----------------G--T-----G--T-----G--T------
LANGNGLAVA
-----------------------------DT-S---V
--DA-S-----DA-S----
STHVAVGGAS
-----------------------------QLIMP-QE
--QLV-P-QD
--QLV-P-QD
ELTVTCEGSL
-------------------------------S-Q-G----S-Q-G----S-Q-GTransPAASGTTLTV
---------------------------AGLGQVP-IGGLGQAP-LGGLGQAP-L-
LPPGATLRLL
----------------------------RLPRIFCSC
-RLPPVF-AR
LRQAFGGAPS
---------------------------........-I
........GL
GESSPLLRAN
-----------------------------N----SGQ
--N----SGQ
AV*
------Q-Q--
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
TAVSSGSATA
---------------------------..........
..........
..........
chicken_i
chicken_I
chicken_I*S
chicken_I*D
human
mouse
mouse_silv
26
GPTAAATAES
---------------------------MS-ESI-GSL
L--QSS-GSI
L--QSS-GSI
Conclusions
x 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.
x By applying a genome-wide search for epistasis we can explain
more of the QTL variation for early growth.
x 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.
x The melanocortin receptor 1 gene (MC1R) corresponds to the
classical Extended black (E) locus and affects plumage colour in
the chicken.
x The dominant E allele at the Extension locus carried by the White
Leghorn has a missense mutation corresponding to the sombre
mutation in mouse that causes a constitutively active receptor and
black pigment production.
x The Dominant white, Smoky and Dun alleles at the Dominant white
locus 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.
27
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
28
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 Extended
black 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.
29
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 “genotypingcollaboration” - 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!
30
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 thesiswriting-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…
31
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!
32
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38
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