Department of Physics, Chemistry and Biology Master Thesis
The genetic basis of a domestication trait in
the chicken: mapping quantitative trait loci
for plumage colour
Md. Nazmul Huq
LiTH-IFM- Ex--12/2626--SE
Supervisor: Dominic Wright, Linköping University Examiner: Jordi Altimiras, Linköping University
Department of Physics, Chemistry and Biology Linköpings universitet
Rapporttyp Report category Examensarbete D-uppsats Språk/Language Engelska/English Titel/Title:
The genetic basis of a domestication trait in the chicken: mapping quantitative trait loci for plumage colour
Författare/Author:
Md. Nazmul Huq Sammanfattning/Abstract:
Domestication is the process by which animals become adapted to the environment provided by humans. The process of domestication has let to a number of correlated behavioural, morphological and physiological changes among many domesticated animal species. An example is the changes of plumage colour in the chicken. Plumage colour is one of the most readily observable traits that make distinction between breeds as well as between strains within a breed. Understanding the genetic architecture of pigmentation traits or indeed any trait is always a great challenge in evolutionary biology. The main aim of this study was to map quantitative trait loci (QTLs) affecting the red and metallic green coloration in the chicken plumage. In this study, a total of 572 F8 intercross chickens between Red
Junglefowl and White Leghorn were used. Phenotypic measurements were done using a combination of digital photography and photography manipulating software. Moreover, all birds were genotyped with 657 molecular markers, covering 30 autosomes. The total map distance covered was 11228 cM and the average interval distance was 17 cM. In this analysis, a total of six QTLs (4 for red and 2 for metallic green colour) were detected on four different chromosomes: 2, 3 11 and 14. For red colour, the most significant QTL was detected on chromosome 2 at 165 cM. An additional QTL was also detected on the same chromosome at 540 cM. Two more QTLs were detected on chromosomes 11 and 14 at 24 and 203 cM respectively. Additionally, two epistatic pairs of QTLs were also detected. The identified four QTLs together can explain approximately 36% of the phenotypic variance in this trait. In addition, for metallic green colour, one significant and one suggestive QTLs were detected on chromosomes 2 and 3 at 399 and 247 cM respectively. Moreover, significant epistatic interactions between these two QTLs were
detected. Furthermore, these two QTLs together can explain approximately 24% of the phenotypic variance in this trait. These findings suggest that the expression of pigmentation in the chicken plumage is highly influenced by both the epistatic actions and pleiotropic effects of different QTLs located on different chromosomes.
ISBN
LITH-IFM-A-EX—12/2626—SE
__________________________________________________ ISRN
__________________________________________________ Serietitel och serienummer ISSN
Title of series, numbering
Handledare/Supervisor Dominic Wright
Ort/Location: Linköping
Nyckelord/Keyword:
qtl, quantitative trait loci, genome scan, qtl analysis, plumage colour, domestication trait, chicken, pigmentation trait
Datum/Date
2012-05-30
URL för elektronisk version
Institutionen för fysik, kemi och biologi
Department of Physics, Chemistry and Biology
Avdelningen för biologi
Content
1 Abstract ... 3
2 Introduction ... 4
2.1 Domestication and domestic traits ... 4
2.2 Domestication of chicken and plumage colour... 5
2.3 Chicken plumage colour genomics ... 6
2.4 Quantitative trait locus (QTL)... 6
2.4.1 Mapping population ... 7
2.4.2 Genetic markers ... 9
2.4.3 Phenotypic and genotypic data ... 9
2.4.4 Statistical analysis ... 9
2.5 Aim of the study ... 11
3 Material & methods ... 12
3.1 Study population ... 12
3.2 Recording of phenotypic data ... 12
3.3 Genotyping ... 14
3.4 QTL analysis ... 14
4 Results ... 15
4.1 QTLs for red colour ... 15
4.2 QTLs for metallic green colour ... 17
5 Discussion ... 19
5.1 Red colour ... 19
5.3 Future research ... 24
5.4 Conclusions ... 24
6 Acknowledgement ... 24
1 Abstract
Domestication is the process by which animals become adapted to the environment provided by humans. The process of domestication has let to a number of correlated behavioural, morphological and physiological changes among many domesticated animal species. An example is the changes of plumage colour in the chicken. Plumage colour is one of the most readily observable traits that make distinction between breeds as well as between strains within a breed. Understanding the genetic architecture of pigmentation traits or indeed any trait is always a great challenge in evolutionary biology. The main aim of this study was to map quantitative trait loci (QTLs) affecting the red and metallic green
coloration in the chicken plumage. In this study, a total of 572 F8
intercross chickens between Red Junglefowl and White Leghorn were used. Phenotypic measurements were done using a combination of digital photography and photography manipulating software. Moreover, all birds were genotyped with 657 molecular markers, covering 30 autosomes. The total map distance covered was 11228 cM and the average interval
distance was 17 cM. In this analysis, a total of six QTLs (4 for red and 2 for metallic green colour) were detected on four different chromosomes: 2, 3 11 and 14. For red colour, the most significant QTL was detected on chromosome 2 at 165 cM. An additional QTL was also detected on the same chromosome at 540 cM. Two more QTLs were detected on
chromosomes 11 and 14 at 24 and 203 cM respectively. Additionally, two epistatic pairs of QTLs were also detected. The identified four QTLs together can explain approximately 36% of the phenotypic variance in this trait. In addition, for metallic green colour, one significant and one suggestive QTLs were detected on chromosomes 2 and 3 at 399 and 247 cM respectively. Moreover, significant epistatic interactions between these two QTLs were detected. Furthermore, these two QTLs together can explain approximately 24% of the phenotypic variance in this trait. These findings suggest that the expression of pigmentation in the chicken plumage is highly influenced by both the epistatic actions and pleiotropic effects of different QTLs located on different chromosomes.
2 Introduction
2.1 Domestication and domestic traits
Domestication is the process by which animals become adapted to the environment provided by humans (Price 1984) and is defined in different ways. Darwin (1859, 1868) considered domestication as more than taming including the captive breeding of animals, that is goal oriented, may take place without conscious effort of human, increases fecundity, may produce the atrophy of some body parts, fit animals to attain larger plasticity, and is close supervision of human. Likewise, Hale (1969) and Clutton-Brock (1977) considered domestication as a condition in which man controlled the breeding, feeding and care of animals. Up to now, no one of the discussion considered genetic mechanisms as a factor of
domestication. However, Price (1984) first included genetic factors in the definition of domestication and defined domestication as “that process by which a population of animals becomes adapted to man and to the captive environment by some combination of genetic changes occurring over generations and environmentally induced developmental events reoccurring during each generation”.
In general, the process of domestication involves a number of changes in the living environment of animals, for instance very limited living space, higher population density, easy availability of food and water, and low predator pressure compared to their wild counterparts (Price 1999). In addition to the environmental factors, Price and King (1968) suggested three key selective factors of domestication: relaxation from natural selection in captivity, artificial selection by man for preferable traits and natural selection in captivity. Thus humans have induced an altered
selection pressure. During domestication animals were selected for only a few traits, for instance sheep for wool production (Diamond 2002). The process of domestication and more specifically the altered selection
pressure has produced a number of correlated behavioural, morphological and physiological changes among different animal species. For example, earlier sexual maturation (Belyaev et al. 1984), changes in the size of the whole body and different parts of the body (Clutton-Brock 1999) and alteration of pigmentation (Clutton-Brock 1999). The phenotype that altered during domestication, differs from its wild progenitors and is seen among many domesticated breeds is referred as “domestic phenotype” (Price 1984). Jensen (2006) identifies some of the distinctive changes that appear in domesticated animals during the process of domestication and broadly categorized them into five different groups. These include external morphological changes, internal morphological changes
2.2 Domestication of chicken and plumage colour
In general, it is believed that chickens were domesticated in South East Asia around 8000 years ago. It is also commonly believed that Red Junglefowl (Gallus gallus) is the common ancestor for all domestic chicken breeds (Gallus gallus domesticus) (Fumihito et al. 1988, Fumihito et al. 1996). Already in 1868, Darwin noticed the greater phenotypic diversity among different chicken breeds and concluded that domesticated chicken originated exclusively from Red Junglefowl. Following Darwin, many studies have supported a single-origin of
domesticated chicken (Frisby et al. 1979, Fumihito et al. 1988, Fumihito et al. 1996). On the contrary, Hutt (1949) and West & Zhou (1988)
challenged the view of single-origin and considered four wild species of genus Gallus as possible contributor to the domesticated chickens. They are the Red Junglefowl (Gallus gallus), the Grey Junglefowl (Gallus
sonnerati), the Green Junglefowl (Gallus varius ) and the Ceylon
Junglefowl (Gallus lafayettei) (Hutt 1949, Tixier-Boichard 2011). Moreover, Nishibori et al. (2005) found evidence that indicate the hybridization between Grey and Ceylon Junglefowls. Furthermore, a recent study by Eriksson et al. (2008) provided evidence that the yellow skin pigmentation locus of domestic chicken has inherited from Grey Junglefowl.
The initial purpose of chicken domestication is still a debate.However, Crawford (1990) suggests that chicken may have initially been kept for cultural purposes such as, decoration, cock fighting, and sacrificing for religious purpose. The selective breeding of chicken for production purposes may have been started by the Romans (Crawford 1990). In the 20th century, commercial breeding companies have started intensive selection and breeding for either meat or egg production (Elferink et al. 2012). These intense selection pressures have changed certain behvioural, morphological and physiological traits of chicken that differ from their wild progenitors and seen among hundreds of different domestic breeds. According to Jensen (2006) the most prominent morphological trait in chicken that has been changed in the course of domestication is the plumage colour. During the domestication of the chicken, selection for numerous different colour phenotypes has occurred, giving rise to a wealth of different coloured domestic. For example, the plumage colour of the Red Junglefowl is wild type that is comprised of white, red, brown, gold, orange, metallic green and blue colours. In contrast, the white
leghorn is completely white in colour (Collias & Collias 1967, Collias & Saichuae 1967).
By utilizing the domestication process and the modern genomic techniques it is possible to map the genetic architecture of plumage colour pigmentation (i.e. a domestication trait) (Wright et al. 2010). This can be performed using Quantitative Trait Locus (QTL) analysis.
2.3 Chicken plumage colour genomics
Pigmentation in birds and mammals iss due to the variation of synthesis of two different kinds of melanin, i.e. eumelanin which is responsible for brown or black colour and pheomelanin which is responsible for yellow or red colour (Gunnarsson et al. 2007). Moreover, the visual appearance of the colour of chicken’s plumage is usually associated with the amount, type and packaging of both of these two types of melanins and is due to the combined effect of many loci located in different chromosomes. The loci involved in coat colour pigmentation (colour loci) are highly
pleiotropic (Lauvergne 2010). Besides this, loci involved in the expression of coat colour pigmentation are also involved in the
expression of different other traits, such as body weight (Lauvergne 2010, Kerje et al. 2003a). Additionally, a large number of loci are involved in the expression of coat colour. For example in the case of mice over 280 colour loci have already been identified (Lauvergne 2010). Using linkage analysis and sequence analysis, many genes at the typical loci that are associated with chicken plumage colour variations have already been identified (Sato et al. 2007). Many studies showed that feather
pigmentation of chicken is linked to solute carrier family 45, member 2, protein (SLC45A2) (Gunnarsson et al. 2007), Melanocortin 1 receptor (MC1R) (Kerje et al. 2003b) and melanocyte protein 17 precursor (PMEL17) (Kerje et al. 2004). Linkage analysis and sequence analysis done by Kerje et al. (2004) revealed that PMEL17 gene (premelanosomal protein gene) is responsible for the dominant white colour of chickens. The same study also revealed that PMEL17 is responsible for the Dun and Smokey phenotypes. Dorshorst & Ashwell (2009) showed that the alternative prototype of white and black bars of the adult chicken plumage is a product of a sex-linked barring gene. Mutations of these genes lead to the total loss or gain of pigmentation across all feathers in the chicken (Dorshorst & Ashwell 2009).
2.4 Quantitative trait locus (QTL)
A Quantitative Trait Locus (QTL) is a genomic region that in general harbours more than one gene and affects a quantitative trait (Gelderman 1975). According to The Oxford Dictionary of Biochemistry and
Molecular Biology a QTL is “a region of a chromosome containing genes that are believed to make a significant contribution to the expression of a complex phenotypic trait.” A quantitative trait is a measurable trait that
shows continuous variation and that cannot be classified into a few
discrete classes. Examples of quantitative trait include, plant height, body weight and so on (Andersson 2001). The phenotypic expressions of quantitative traits are typically influenced by several genes along with environmental factors. Therefore, they are generally considered as
polygenic traits (Andersson 2001). The recent advancement in molecular marker technologies and statistical techniques has opened up the
opportunity to identify the QTLs quickly and precisely (Andersson 2001). The process of identification of QTL by linking phenotypic and
genotypic data is known as ‘QTL analysis’ (also known as ‘QTL
mapping’ or ‘Genome mapping’) (McCough & Doerge 1995, Mohan et al. 1997). In recent years, QTL analysis has been widely used and appears as a very beneficial tool for genome mapping. The goal of QTL analysis is to identify different loci, their actions and interactions, and exact location of each locus affecting a trait or traits of interest (Borevitz & Chory 2004). In order to start a QTL analysis experiment, scientists need to consider several factors. These factors include, obtaining a mapping population, selection of genetic markers, collection of phenotypic and genotypic data and statistical analysis (Miles & Wayne 2008, Weller 2001). A brief explanation of each step is given below.
2.4.1 Mapping population
Establishing an appropriate mapping population among which QTLs underlying a trait or traits of interest are segregating is one of the foremost steps of starting a QTL analysis experiment. The mapping
population could be obtained by crossing of two parental breeds or strains that differ both phenotypically and genotypically for the trait of interest. For example, two chicken breeds that produce either a small or large egg masses and fixed for alleles that influence the size of the egg (Miles & Wayne 2008, Lynch & Walsh 1998). To produce a mapping population two different breeding schemes could be used; one is backcross and another is intercross. Again, intercross breeding scheme can be grouped into two groups: F2-intercross and advanced intercross line (AIL)
(Darvasi & Soller 1995, Broman & Sen 2009). The use of an AIL offers couples of advantages over backcross or F2-intercross. It ensures, high
recombination frequency, increase mapping resolution, reduce confidence interval and reduce linkage disequilibrium (Darvasi & Soller 1995,
Falconer & Mackay 1996). The production of an AIL is initiated with the production of the F2-intercross, where two parental lines are mated to
produce F1 population and then F1 siblings are crossed to obtain the F2
Figure 1. Production of an F8 Fadvanced intercross line (AIL) from
Finally, the AIL is produced by mating F2 siblings and each generation of
AIL, i.e. F3, F4, F5……Fn is produced by sequentially and randomly
mating of the siblings of the previous generation (Darvasi & Soller 1995, Broman & Sen 2009, Rockman & Kruglyak 2008) (Figure 1). The later generations are usually phenotyped and genotyped for QTL analysis, whereas the previous generations are reared for breeding purpose only (Darvasi & Soller 1995).
2.4.2 Genetic markers
The second requirement of a QTL analysis experiment is a defined set of genetic markers that make a distinction between the parental lines of the mapping population (Miles & Wayne 2008). Different types of molecular markers are available and can be used depending on the experimental design. These include, restriction fragment length polymorphisms
(RFLPs), random amplification of polymorphic DNA (RAPDs), amplied fragment length polymorphism (AFLPs), single stranded conformation polymorphism (SSCPs), single nucleotide polymorphisms (SNPs), and simple sequence repeats (SSRs, or microsatellites) (Mohan et al. 1997, Vignal et al. 2002, Neale 2010). Among these, microsatellite and SNP markers are the most popular and widely used markers for genotyping (Vignal et al. 2002).
2.4.3 Phenotypic and genotypic data
The next step is collection of phenotypic data for the desirable trait. Derived population also need to be genotyped simultaneously. In order to avoid the Behavis-Effect and accurately identify the QTLs, size and effect of each QTL, especially QTL with smaller effect at least 100 to 300 individuals need to be scored (Lynch & Walsh 1998, Beavis 1998)
2.4.4 Statistical analysis
As soon as both the phenotypic and genotypic data are available, QTL regions underlying the traits of interest are identified by studying the associations between molecular markers and phenotypic data (Collard et al. 2005). Therefore, to test the association and to identify the QTLs a suitable statistical framework (both method and statistical software) is needed. Scientists have suggested different statistical methods for QTL analysis. Such methods include, single marker analysis and interval mapping (simple, composite or multiple interval mapping) (Lander & Botstein 1989, Haley & Knott 1992, Jansen & Stam 1994).
‘Interval Mapping’ first proposed by Lander & Botstein (1989) is one of the most widely used statistical method for QTL analysis. This method determines the interval between two adjacent markers and estimates a QTL’s position within those two markers (Kearsey 1998, Touré et al. 2000). Interval mapping is principally performed based on maximum likelihood approach. In maximum likelihood approach, it is considered that a single QTL is located between two markers (AABB, AAbb, aaBB and aabb are the marker haplotypes for two loci). Moreover, a number of positions across the genome are considered as the probable location for that QTL. Then, LOD scores (LOD stands for likelihood of odds, i.e. the likelihood ratio of the effect occurs by linkage vs. chance) are estimated at each position and for the whole genome as well. When a LOD peak on certain position exceeds the significance threshold level, the presence of a QTL on that position affecting a trait has been confirmed (Figure 2).
Figure 2. Typical plotting of QTL analysis results. For a detail explanation see the main text.
Interval mapping using the maximum likelihood approach is complex, especially during whole genome mapping. A search at every genomic position is required. In addition, this method is computationally
expensive (Haley & Knott 1992, Kao 2000, Xu 1995). To overcome the problems, Haley & Knott (1992) developed a simplified regression based approach of interval mapping, which is commonly known as ‘Haley-Knott regression’. Regression based approach of interval estimation is simple and offer great computational speed (Haley & Knott 1992, Kao
2000, Xu 1995). In addition, this method can make more accurate estimation than likelihood method, especially if there are many missing genotype or if there are larger gaps between markers (Touré et al. 2000). The basic principles of both approaches are same. Only exception is that Haley-Knott regression approach regressed phenotypes on marker
genotype instead of marker haplotypes (Haley & Knott 1992, Kao 2000, Xu 1995)
To determine the significance threshold level a permutation test is usually performed. In this test, marker-trait associations are broken by randomly shuffling the phenotypic value and keeping constant the maker genotypic value. Simultaneously, QTL analyses are performed based on the shuffled data to determine the level of false positive marker-trait associations. Finally, the entire process (shuffling and QTL analysis) is repeated 100 to 1,000 times and even 10,000 times depending on experimental design (Churchill & Doerge1994, Doerge & Churchill 1996, Doerge & Rebaï 1996).
A number of computer programmes for QTL analysis are available. For example, Mapmaker/QTL (Lander et al. 1987), Map Manager QTX (Manly et al. 2001) and R/qtl (Broman et al. 2003). R/qtl is a freely available add-on for the open source statistical platform ‘R statistical and scripting language’ (Broman et al. 2003, Ihaka & Gentleman 1996). It provides functions for genetic map and genetic error estimation and both single and two-dimensional QTL scan for whole genome using multiple statistical approaches (Broman et al. 2003).
2.5 Aim of the study
Although several genes have been identified that affect plumage colour in chickens (as presented in section 2.3), many more remain to be identified. Therefore, the aim of the study was to identify additional loci affecting chickens’ plumage colour using 8th generation advanced intercross chickens between Red Junglefowl and White Leghorn Layer. This study focuses on the identification of QTLs for red colour pigmentation and metallic green colour pigmentation.
3 Material & methods 3.1 Study population
In this study a total of 572 chickens (572 eighth generation advanced intercross chickens, although F7 chickens were measured, only the F8
ones were used in the study) showing diversified plumage colour were used (Figure 1). Advanced intercross lines (AIL) were generated by crossing between the Red Junglefowl (RJF) and White Leghorn (WL) (Figure 1). Maintaining both the phenotypic and genotypic diversity was the main focus during the breeding of each generation, which in turn ensures higher allelic recombination and facilitate QTL detection. Only four chickens were used as founder (P0) animals; a single male Red
Junglefowl and three White Leghorn Layer females (SLU13). The main reason of using so few founders is to maximize the power of QTL
detection (Weller 2001, Rönnegård et al. 2008).
The Red Junglefowl originated from a Swedish zoo, but originally wild caught in Thailand and have been kept in both zoo and at Tovetorp Research station of Stockholm University for approximately 10 generations (Schütz et al. 2001). The White Leghorn layer strain (SLU13) was developed through the Scandinavian Selection and
Crossbreeding Experiment (Liljedahl et al. 1979) and maintained by the Swedish University of Agricultural Sciences located at Uppsala in Sweden (Schütz et al. 2001). SLU13 was developed for high feed
conversion efficiency more specifically for high egg masses. The F1 birds
were hatched at the Tovetorp research station of Stockhom University and the F2 to F5 birds were hatched and reared at the research station of
Swedish University of Agricultural Sciences at Skara (Schütz et al. 2001, Kerje et al. 2003b). The F6 to F8 birds were hatched and reared under
standard indoor rearing conditions at the research facility of Linköping University (LiU) located outside of Linköping in Sweden.
3.2 Recording of phenotypic data
At the age of 230 days all birds were sacrificed, and the whole wing of each birds was separated by surgically removing from the shoulder joint of the body. All the wings were tagged and stored in freezers immediately at around -200 C temperature.
Photographs were taken maintaining the same conditions for all photos (Figure 3.a): camera to subject distance was 1.4 m, camera was set to maximum zoom (0.5 f), without flash, with same blue background and with same lighting conditions. Photographs were taken by a Canon D300 camera. A small white board was placed in front of the photography
board to write down the ID number for ease of identification. A colour chart (X-Rite ColorChecker Passport) (Figure 3.b) was also placed in front of the board to calibrate and adjust the Hue, Saturation and Lightness (HSL) values.
Prior to photographing, wings were thawed at room temperature for two hours. Then all the wings were cleaned properly using brushes to remove dust and any other foreign material over the wing. Then a photograph of each wing was taken by placing the wing on photography board and attaching by three pins (Figure 3.c). After taking the wing photograph, each wing was removed from the board and a photograph of the blank board was taken (Figure 3.d).
Figure 3. Different photographic parameters: (a) standard
photographing condition, (b) X-Rite ColorChecker Passport, (c) wing on board to be photographed and (d) blank board
Colour measurements were done in Adobe Photoshop CS4. A
representative region of red and white colour of wing was selected using the Rectangular Marquee Tool (Figure 4). Colour density was then measured using the histogram function. Values were taken for two channels only; luminosity and red channel. In all of the cases, mean, standard deviation and median value were taken for further calculations. In addition, by following the same procedures, measurements were taken from white, black and red part of the colour chart. Finally, the red colour density of each wing was calculated by adjusting the HSL values.
Furthermore, presence or absence of metallic green colour was recorded as an additional phenotype.
Figure 4. Colour measurement through Adobe Photoshop.
3.3 Genotyping
DNA preparation and genotyping for all birds were done in the laboratory at Linkoping University. In this study a total of 657 molecular markers were used covering 30 autosomes and Z chromosome. In the current study, the total map distance covered was 11228 centimorgan (cM) and the average interval distance was 17 cM
3.4 QTL analysis
QTL analyses for red and metallic green pigmentations were performed using standard interval mapping through R/qtl available at
http://www.rqtl.org/ (Broman et al. 2003). To test and to identify the single locus effect standard interval mapping (through scanone function in R/qtl) was performed. As in the standard qtl analysis and in all qtl models, sex and batch were included as fixed factors. Moreover, all QTLs were also tested as sex interaction term. As soon as, a QTL at any
position was detected, the significance of that QTL was tested (through fitqtl function in R/qtl).
Significance thresholds values were calculated for each tratit separately. These were calculated using random permutation tests (Churchill & Doerge 1994), using n.perm option available in R/qtl (Broman et al. 2003). A 5% genome-wide significance levels for different colour were 4.68 and 6.44 LOD for red colour and metallic green colour respectively. Lander & Kruglyak (1995) first formulated suggestive thresholds for QTL detection with levels of around one false positive per genome scan. In the present analysis, to minimize such false positives, a more stringent suggestive threshold of 20% genome wide significance was used. In case of 20% genome-wide significance, the thresholds values were 3.76 and 4.11 LOD for red colour and metallic green colour respectively.
4 Results
4.1 QTLs for red colour
For red colour, four were detected (Figure 5, Figure 6, Table 1, Table 2, Table 3). Out of these four QTLs, one highly significant major QTL was identified on the chromosome 2 at 165 cM position. This QTL had an interval size of 25 cM and alone can explain more than 12% of the variation in this trait. An additional QTL was also detected on the same chromosome (chromosome 2) at 540 cM position. This QTL had a fairly large interval size of 342 cM and can explain more than 4% of the
variance in this trait. These two QTLs acted in opposite directions; QTL at the locus 165 cM acted in the direction of Red Junglefowl line,
whereas QTL at the locus 540 cM acted in the direction of White
Leghorn line. Two more QTLs were identified on chromosome 14, and 11 at 203 and 24 cM position respectively. QTL at the locus 203 cM (on chromosome 14) had an interval size of 43 cM, with the additive effect greater from the White Leghorn allele. This QTL alone can explain over 10% of the phenotypic diversity of this trait. Additionally, QTL at the locus 24 (on chromosome 11) had an interval size of 30 cM, with the additive effect greater from the Red Junglefowl allele. This QTL can explain more than 11% of the phenotypic variance in this trait.
Figure 5. LOD scores for selected chromosomes showing QTLs affecting red colour variation. The upper grey dashed line
represents 5% genome-wide significant threshold and the lower grey line represents 20% genome-wide suggestive threshold.
In addition, significant effect of sex on red colour variation was detected (P= 0.000368). Further, significant interaction between the locus 24 at chromosome 11 and sex was also detected (P=0.009311) and had a greater additive effect from the White Leghorn allele. In addition, significant interactions were identified between the locus at 165 cM on chromosome 2 and the locus at 203 cM on chromosome 14 (P=017399). A combination of dominant effect from both loci showed the highest effect (38.37±37.81) and acted in the Red Junglefowl line. Additionally, interactions between the locus at 165cM on chromosome 2 and the locus at 24 cM on chromosome 11 were also significant (P=0.006862). A combination of dominant effect from locus 165 cM (on chromosome 2) and additive effect from locus 24 cM (on chromosome 11) showed the highest effect (19.16±6.36) and acted in the Red Junglefowl line.
Figure 6. Locations of the identified QTLs affecting red colour pigmentation on the genetic map
4.2 QTLs for metallic green colour
For metallic green colour, one significant and one putative QTLs were identified (Figure 7, Figure 8, Table 1, Table 2, Table 3).
Figure 7. LOD scores for selected chromosomes showing QTLs affecting metallic green colour variation. The upper grey dashed line represents 5% genome-wide significant threshold and the lower grey line represents 20% genome-wide suggestive threshold.
The significant QTL affecting this trait was identified on the chromosome 2 at 399 cM position, which had an interval size of 21 cM. This QTL had a greater additive effect from the Red Junglefowl allele and alone can explain more than 14% of the phenotypic variance in this trait.
The suggestive QTL was identified on the chromosome 3 at 247 cM position, which had a large interval size of 272 cM. This QTL also had effect in the same direction like the first one, i.e. additive effect higher from the Red Junglefowl allele. This QTL can explain only 9.9% of the phenotypic diversity of this trait.
Figure 8. Locations of the identified QTLs with metallic green colour as covariance on the genetic map
Significant effect of sex on metallic green colour variation was detected (p=2.34e-08). In addition, both of these two QTLs had significant
interactions (P=5.46e-05 and P=0.0024) with sex, but the effects were in opposite directions. The interactions between the locus 399 cM on
chromosome 2 and sex had a greater additive effect from the White Leghorn allele, whereas the interactions between the locus 247 cM on chromosome 3 and sex had a greater additive effect from the Red
Junglefowl allele. In addition, significant interactions between these two QTLs were also identified (P=6.75e-08). A combination of dominant effect from both loci showed the highest effect and acted in the Red Junglefowl line, the effect was too low though (0.29±0.07).
5 Discussion 5.1 Red colour
Four QTLs were identified affecting red colour pigmentation in the chicken plumage. The most significant QTL was identified on the chromosome 2 at 165 cM position. Previous studies have revealed a significant QTL close to this region (at 150.8 cM) that affects breast muscle weight (Tercic et al. 2009). In addition, a significant QTL
affecting antibody response to KLH antigen was mapped within 1cM of this locus (at 164 cM, Siwek et al. 2003). In this study, another significant QTL was identified on the chromosome 11 at 24 cM position. Ankra-Badu et al. (2010) detected a growth QTL that perfectly coincide with this QTL (Pectoralis minor percent at 23.7 cM). Moreover, researchers have found a number of growth related QTLs in chromosome 11, which are close to the locus 24 cM. These include, breast muscle weight (at 22 cM, Atzmon et al. 2008), abdominal fat weight (at 22 cM, Atzmon et al. 2007), growth at 112-200 days (at 21.9 cM, Le Rouzic et al. 2008) and back percentage (at 20 cM, Baron et al. 2011). From the above
observations it can be inferred that closely linked QTLs or pleiotropic QTLs played an important role in the expression of red colour
pigmentation. No previous studies have mapped QTLs close to the locus 540 cM on chromosome 2 and the locus 203 cM on chromosome 14.
Table 1: Identified QTLs for red and metallic green colour pigmentation in the chicken plumage. LOD scores, % variation explained by each QTL, estimated effect and p-values of each QTL.
Trait
(Feather Colour)
Chromosome Position (cM)
LOD p-value % variation Estimated Effect
Additive ± SE Dominant ± SE
Red 2 165 13.84+ 3.50e-09*** 12.72 10.44±11.49 3.6±12.19
Red 2 540 4.50+ 6.40e-05*** 3.89 -8.48±2.5 9.43±3.64
Red 11 24 11.27+ 7.01e-08*** 10.19 12.64±7.66 -45.31±12.07
Red 14 203 10.31+ 5.86e-08*** 9.26 -26.28±14.21 -13.59±18.21
Metallic Green 2 399 18.03+ 4.14e-14*** 14.23 0.2±0.03 -0.1±0.05
Metallic Green 3 247 12.85++ 1.57e-09*** 9.90 0.01±0.04 -0.39±0.08
+
Significant at 5% genome-wide threshold level
++
Significant at 20% genome-wide suggestive threshold level Significant codes: 0.0001 ‘***’
Additionally, QTL at the locus 24 cM (on chromosoeme 11) is about 5.14 Mb long (starts 1.3 Mb and ends 6.4 Mb). Rubin et al. (2010) have
identified two selective sweeps within this region. The first selective sweep is present in commercial broiler lines only (chr11:3.48–3.52 Mbp), and the second one is present in all domestic lines (chr11:3.56–3.60 Mb). A good candidate gene is located in this region, namely SLC6A2.
Additionally, QTL at locus 203 cM (on chromosome 14) is about 3.8 Mbp long (starts 10.17 and ends 13.99 Mbp). Rubin et al. 2010 have identified a selective sweep within this region (chr14:11.36–11.40) that presents in layer lines. A good candidate gene is located in this region, namely A2BP1. Moreover, Elferink et al. 2012 have identified two more selective sweeps (chr14:11.26-11.32 and chr14:11.42-11.55) within this region.
Out of the identified four QTLs, two QTLs located in the same
chromosome (on chromosome 2 at 165 cM and 540 cM), but they acted in the opposite directions. QTL at the locus 165 cM homozygous for Red Junglefowl allele, while QTL at the locus 540 cM homozygous for White Leghorn allele. Two other QTLs on chromosomes 11 and 14 at 24 and 203 cM positions respectively were detected. These two loci also acted in opposite directions. The first one acted in the Red Junglefowl allele and the second one acted in the White Leghorn allele.
Table 2: Confidence interval of each QTL, flanking markers associated with the QTL and candidate genes identified in each region. Trait (Feather Colour) Chromosome Position (cM)
Confidence Interval (cM) Flanking Markers Candidate gene Lower Upper Size Left Right
Red 2 165 151 176 25 Gg_rs15070042 snp-2-314-169117-S-3
Red 2 540 523 865 342 Gg_rs15112090 Gg_rs14200463
Red 11 24 3 33 30 Gg_rs14019836 11_6450003 SLC6A2
Red 14 203 173 216 43 rbl1499 Gg_rs15002638 A2BP1
Metallic Green 2 399 388 409 21 Gg_rs15091040 Gg_rs15094455
Metallic Green 3 247 239 511 272 Gg_rs15282380 3_21992770 ESR1,
ESR2, ESRRA, ESRRB, ESRRG, NR4A2, NR4A3
In this study, significant effect of sex on red colour variation was detected. In addition, significant effect of sex on locus 165 cM
(chromosome 2) was detected. The results are not surprising, because it is generally perceived that male individual (chicken) has deeper red feather than female. These observations suggest that expression of the red colour in the chicken plumage is highly influenced by sex.
In addition, two pairs of epistatic QTLs were detected. Both of them acted in the Red Junglefowl line. In both of the cases, the interactive effects were lower than the sum of the additive effect of those two loci. Moreover, Each QTL identified in this study explained only a smaller proportion of phenotypic variance (3 to 12% by each) and all the
identified QTLs (i.e. 4 QTLs) together can explain approximately 36% of the variation. Moreover, out of the four QTLs, three QTLs showed
significant epistatic interaction in different combinations. These observations suggest that expression of the red colour controlled by epistatic QTLs present in different chromosomes.
Previous QTL analyses for this trait have identified many significant loci such as SLC45A2 (Gunnarsson et al. 2007), MC1R (Kerje et al. 2003b), PMEL17 (Kerje et al. 2004), SOX10 (Gunnarsson et al. 2011), MLPH (Vaez et al. 2008) and TYR (Sato et al. 2007). Unfortunately, none of them neither closely located nor coincide with the loci identified in this study.
5.2 Metallic green colour
One significant and one putative QTLs were identified for metallic green colour. They are located at 399 and 247 cM positions on chromosomes 2 and 3 respectively. In the case of chicken, 3162 QTLs on 29
chromosomes representing 270 different traits have already been identified (AnimalQTLdb 2012, Hu et al. 2007). Surprisingly, no attempts were made to identify chromosomal regions affect metallic green pigmentation in the chicken plumage. Therefore no previous QTL analysis result for this trait is available. However, some of the previously identified QTLs affecting other traits, for instance body weight,
abdominal fat percentage etc. are located closely to the loci identified in this study. A QTL affecting abdominal fat percentage has been identified at 396.5 cM (Ankra-Badu et al. 2010), which lies 2.5 cM upstream of the first QTL detected in this study, i.e. QTL on chromosome 2 at 399 cM. A few other QTLs detected in different studies and are close to this QTL includes, at 392 cM for abdominal fat weight (Atzmon et al. 2008), at 403
second QTL detected in this study (on chromosome 3 at 247 cM) also lies close to QTLs affecting abdominal fat weight (at 246 cM) (Atzmon et al. 2007) and abdominal fat percentage (at 249.1 cM) (McElroy et al. 2006). Therefore it can be inferred that closely linked QTLs or pleiotropic QTLs played an important role in the expression of metallic green colour
pigmentation in the chicken.
Table 3: Interactions among identified QTLs, QTL Vs. Sex and Sex as a whole.
Trait (Feather Colour) Interaction Estimated Effect ± SE p-value
Red sex 9.14±3.25 0.000368*** Red 11@24.0:sex 0.009311** 11@24.0a:sex -4.87±5.27 11@24.0d:sex 23.77±8.02 Red 2@165.0:14@203.0 0.017399* 2@165.0a:14@203.0a 11.31±28.35 2@165.0d:14@203.0a 2.99±29.34 2@165.0a:14@203.0d 8.43±36.03 2@165.0d:14@203.0d 38.37±37.81 Red 2@165.0:11@24.0 0.006862** 2@165.0a:11@24.0a 13.5±4.37 2@165.0d:11@24.0a 19.16±6.36 2@165.0a:11@24.0d -7.14±6.85 2@165.0d:11@24.0d 0.35±9.85
Metallic Green sex -0.07±0.01 2.34e-08***
Metallic Green 2@399.0:sex 5.46e-05***
2@399.0a:sex -0.09±0.02
2@399.0d:sex 0.02±0.03
Metallic Green 3@247.0:sex 0.00246**
3@247.0a:sex 0.01±0.02
3@247.0d:sex 0.16±0.05
Metallic Green 2@399.0:3@247.0 6.75e-08***
2@399.0a:3@247.0a 0.08±0.02 2@399.0d:3@247.0a -0.16±0.04 2@399.0a:3@247.0d -0.3±0.05 2@399.0d:3@247.0d 0.29±0.07
QTL at the locus 247 cM (on chromosoeme 3) is about 2.9 Mb long (starts 19.0 Mb and ends 21.99 Mb), and perfectly coincide with two selective sweeps indentified in different studies. The size of the first selective sweep is about 159 kb (20.08 to 20.23 Mb) and present in different domestic breeds (Elferink et al. 2012). The size of the second selective sweep is about 40 kb (21.12 to 21.16 Mb) and present in all domestic chicken (Rubin et al. 2010). Therefore some of the candidate genes located in this region that likely to have effect on domestication traits. The candidate gene include, ESR1, ESR2, ESRRA, ESRRB, ESRRG, NR4A2, NR4A3 (Rubin et al. 2010).
Both of these two QTLs had significant interaction with sex, but the effects were in opposite directions. In addition, there was significant sex interaction. Moreover, in this study a total number of 572 samples
(chickens’ wings) were phenotyped (n=572). Surprisingly, the occurrence of metallic green colour in feather was limited to only 9 birds. Moreover, all birds have metallic green colour on their feather were male. There was no occurance of metallic green colour among females. These findings indicate that occurrence of metallic green colour mostly depends on sex and may be controlled by sex linked gene.
As expected, both of the QTLs acted in the same direction and are homozygote for the Red Junglefowl allele. This is probably because White Leghorns were selected exclusively for white colour and are homozygous for white colour allele. Moreover, these two QTLs had significant epistatic interactions. The interactive effects was lower (0.08±0.02) than the sum of the additive effect of the two loci
(0.21±0.07), and acted in the Red Junglefowl line. Furthermore, each QTL identified in this study explained only a smaller proportion of phenotypic variance (9 to 14% by each) and both of the two QTLs together can explain approximately 24% of the variation. These results indicate that epistatic QTLs, located in different chromosomes played an important role in the expression of metallic green colour in the chicken plumage. These results also indicate that more QTLs are involved in the expression of metallic green colour but remain undetected.
As discussed earlier, the occurrence of metallic green colour was limited to only 9 chickens. Therefore, QTLs detected for metallic green colour and the effect size of those QTLs may be over estimated (Beavis 1998). Moreover, there are clear indications that expression of metallic green colour is highly influenced by sex. So, to identify the full significance of different QTLs, sex chromosome needed to be included in the analysis, but was not included in this study.
5.3 Future research
In this study only one significant QTL was identified for metallic green colour. Although the population size used in this study was large enough to detect QTLs for metallic green colour, but probably because of very low occurrence of this colour among population more QTLs affecting this trait remain undetected. Therefore, further studies, involving higher
number of individuals appearing metallic green colour would reveal more QTLs and thus it would be possible to generate fine genome map. This study also identifies few vital candidate genes for both red and metallic green colour pigmentation. Therefore, further study may help to identify the specific roles and expression patterns of those genes.
5.4 Conclusions
In this study several QTLs were identified for feather pigmentation traits in the chicken. The results suggest that sex may play an important role in the expression of pigmentation in the chicken plumage. The results of this study also suggest that not only the pleiotropic QTLs but also the
epistatic QTLs have significant roles in the expression of pigmentation in the chicken plumage.
6 Acknowledgement
I would like to thank my supervisor Dr Dominic Wright for his guidance and support throughout this study. I would also like to thank Professor Per Jensen for his support during this study. Special thanks to all members of AVIAN research group. Many thanks to my fellow classmates for their kind help. Finally, I would like to thank everyone inside or outside of the campus for helping me over the study period.
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