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High-Density Linkage Mapping of the Z-chromosome in the Ficedula species Eleftheria Palkopoulou Degree project in biology, Master of science (2 years), 2009 Examensarbete i biologi 45 hp till masterexamen, 2009 Biology Education Centre and Department of Evolutionary Biology, Uppsala University Supervisor: Hans Ellegren

High-Density Linkage Mapping of the Z-chromosome in the ...1 Abstract . In this study, the first linkage map of the Z-chromosome of the pied flycatcher (Ficedula hypoleuca) is reported

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Page 1: High-Density Linkage Mapping of the Z-chromosome in the ...1 Abstract . In this study, the first linkage map of the Z-chromosome of the pied flycatcher (Ficedula hypoleuca) is reported

High-Density Linkage Mapping of theZ-chromosome in the Ficedula species

Eleftheria Palkopoulou

Degree project in biology, Master of science (2 years), 2009Examensarbete i biologi 45 hp till masterexamen, 2009Biology Education Centre and Department of Evolutionary Biology, Uppsala UniversitySupervisor: Hans Ellegren

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Abstract

In this study, the first linkage map of the Z-chromosome of the pied flycatcher (Ficedula

hypoleuca) is reported together with a linkage map of the collared flycatcher (Ficedula

albicollis) of higher coverage than the previously published map. Pedigrees of 183 and 189 individuals, collected from the wild populations of the pied and the collared flycatcher respectively, were genotyped for a large SNP set (single nucleotide polymorphisms) derived from 74 intronic sequences. Linkage analysis led to the construction of the maps including 9 markers (haplotypes) in the case of the pied flycatcher and 26 markers (haplotypes) in the case of the collared flycatchers. The total length of the maps was 45.2cM and 181.5cM respectively. In a comparative context, rearrangements were observed both between pied and collared flycatcher, as well as between flycatchers and chicken. Understanding in more detail the genetic structure and organization of the Z-chromosome of the two closely related flycatcher species will advance our knowledge in their speciation. In addition to that, the linkage maps constitute essential tools for the identification of QTLs (quantitative trait loci).

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Introduction

Genetic linkage mapping is a method that makes use of linkage disequilibrium estimates, a measure of non-random association of alleles at different loci along a chromosome, to infer their relative position. It follows the segregation/co-inheritance of genetic markers in pedigrees over a number of generations and hence deduces the order of the markers along the chromosome, while fixing the distances separating them such that they are proportional to the frequency of recombination between them (Hartl and Jones 2004). In this way the arrangement of genes on a chromosome is known and the distance between them is measured in centiMorgans (cM). This map unit was named after Thomas Hunt Morgan, by his student Alfred Henry Sturtevant and stands for the probability of recombination between two alleles in two homologous loci, with one cM equal to one crossing-over event (Figure 1) or 1% recombination frequency (Griffiths et al. 1999). The linkage map unit does not correspond to a fixed physical map distance since recombination rates might differ within and between chromosomes as well as between sexes and different organisms. Linkage mapping has been used widely for the detection of alleles associated with particular traits/phenotypes in different organisms. It has been broadly applied in human genetics studies and was proven successful in detecting genes responsible for simple monogenic diseases such as diastrophic dysplasia (Slatkin M. 2008), heritable cancers, degenerative neurological disorders, adult polycystic kidney disease, respiratory and gastrointestinal tract disorder and cystic fibrosis (Farall M. 1991). In species other than human, genetic maps have been more commonly constructed for model organisms because large families and a large number of molecular markers are required for the model. Likewise, genetic maps from non-model organisms whose populations can be manipulated are added to the former ones; for example from domesticated populations such as sheep (Maddox et al. 2001), red deer (Slate et al. 2002), resourse pig populations (Zhang et al. 2007) and atlantic salmon (Moen et al. 2008), and from natural populations such as three-spined sticklebacks (Peichel et al. 2001), potato beetle (Hawthorn J. 2001), Bombina toads (Nürnberger et al. 2003), Colias butterflies (Wang B. and Porter A. 2004), European sea bass (Christiankov et al. 2005) and Coregonus whitefish (Rogers et al. 2006). Due to the constraints mentioned above, linkage mapping is difficult to apply on wild species whose populations are not prone to manipulations and other methods are used instead, such as genetic association mapping. Still, Beraldi et al. (2006) published a linkage map for a free-living Soay sheep population from the islands of Soay and Hirta and Slate et al. (2002b) performed linkage and QTL mapping on a wild red deer population from the island of Rum. Even in these two cases though, the populations are restricted to islands and intensely monitored. Regarding bird species, linkage maps have been assembled for the genomes of both model organisms, chicken (Groenen et al. 2000) and more recently zebra finch (Stapley J. et al. 2008). Furthermore the domesticated and agriculturally important galliforms, quail (Kayang et al. 2004) and turkey (Reed et al. 2005), have been mapped. Due to the limits of the method and the difficulty of breeding in captivity most of the bird species, little effort has been put on wild species. However, it has been observed that attempts for linkage mapping on wild, unmanipulated bird populations are growing. These include the AFLP/microsatellite genetic map of the great reed warbler (Acrocephalus arundinaceus) (Hansson et al. 2005, Åkeson et al. 2007), the gene-based linkage map of the collared flycatcher (Ficedula albicollis) (Backström et al. 2008) and the microsatellite-based linkage map of the Siberian jay (Perisoreus infaustus) (Jaari et al. 2009). These three birds represent species of one of the most diverse bird orders, the passeriformes (family Sylviidae, Muscicapidae and Corviidae respectively), which contains almost half of the bird species and has been extensively studied

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in evolutionary research. Still, more studies need to be done concerning the genetic architecture of these species in order to reveal the processes that occurred during their evolutionary history.

Figure 1: Crossing-over event between homologous non-sister chromatids during meiosis (Los Alamos Science 1992). In this study linkage mapping of the Z-chromosome of the pied (F. hypoleuca) and the collared (F. albicollis) flycatcher was performed. These two European black and white flycatchers of the genus Muscicapiidae have been subject to many studies, providing insight into their phylogeography (Saetre et al 2001), life-history related ecological traits (Qvarnström et al. 2000, Qvarnström et al. 2005, Adamik and Burek 2007), fitness traits (Pärt and Qvarnström 1997, Morales et al. 2007), reproductive isolation, hybridization and reinforcement (Saetre et al. 1997, Veen et al. 2001, Haavie et al. 2004, Borge et al 2005, Wiley et al. 2007, Svedin et al. 2008). Concisely, flycatchers occupied refugia around the Mediterranean during the Pleistocene and spread to the north when the glaciations receded after the last ice age. Pied flycatchers from the Iberian Peninsula dispersed to Germany, England and across Scandinavia up to Russia whereas the collared flycatchers from Italy colonized the Czech Republic, Hungary and the Baltic isles (Figure 2). Their recolonization pattern led to their co-existence in the islands of Öland and Gotland and in Central-Eastern Europe, where they hybridize at low to moderate frequency (Saetre et al.2001). In these sympatric regions, it has been shown that character displacement on male secondary sexual characteristics has occurred (Saetre et al. 1997), especially in Central Europe (Borge et al. 2005), indicative of reinforcement. Accordingly hybridization rates and gene flow were found to be higher in the islands of Gotland and Öland in contrast to Central Europe, perhaps due to more recent secondary contact in these overlapping regions (Borge et al. 2005). Female hybrids have reduced fitness as a result of complete sterility while male hybrids exhibit reduced fertility (Veen et al. 2001), in agreement with Haldane’s rule (Turelli and Orr 1995).

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Figure 2: Geographical distribution and morphology of allopatric and sympatric pied and collared flycatchers (Buggioti L. 2007).

When introgression rates were compared between autosomes and sex chromosomes, it was found to be totally absent in the Z-chromosome, implying a role of sex-linked genes in post-zygotic isolation and possibly reinforcement (Saetre et al. 2003). The same study concluded that species-recognition traits such as male plumage characteristics are located on the sex chromosome, facilitating the build-up of genetic barriers. In addition to that Borge et al (2005b) found non-neutral patterns of polymorphism and divergence on the Z-chromosome compared to autosomes. They attributed these findings to recurrent selective sweeps of Z-linked genes, based on the assumption that genes associated with reproduction, sexual characteristics and sex-antagonistic genes are more likely carried by sex chromosomes, enabling the action of sexual selection on them. Later on, it was shown that species-specific traits (male plumage characteristics) and species-recognition traits (female mate preferences) were physically linked on the Z-chromosome, maintaining assortative mating in the presence of gene flow (Saether et al. 2007). Empirical data demonstrate that sex-linked genes in birds exhibit an accelerated rate of protein evolution, suggesting higher rates of adaptive evolution of the sex chromosomes and a large Z-effect (Ellegren H. 2009). However, standing genetic variation on autosomes is expected to promote faster adaptive evolution, even though selection is considered to be more efficient on sex chromosomes, owing to advantageous recessive mutations being exposed in the hemizygous sex. Still, sex-linked genes that are coding for sex-specific fitness traits facilitate divergent adaptation through their effects on prezygotic isolation (Qvarnström and Bailey. 2009). In line with that, it has been shown that sexually antagonistic genes (beneficial to one sex and detrimental to the other) are favorably positioned on the Z-chromosome due to its mode of inheritance and the time it spends in the different sexes (Mank and Ellegren 2008). Moreover, according to the Bateson-Dobzhansky-Muller model, alleles with genetic incompatibilities are considered to be responsible for reduced fitness in hybrids. Incompatibilities between alleles that are sex-linked and other loci (e.g. autosomal) are expected to have more severe effects than when linked only on autosomes (Qvarnström and Bailey 2009). In addition to that, sex chromosomes are more likely to lock up combinations of alleles involved in reproductive isolation because of the reduced recombination rate (Hoffman and Rieseberg 2008) observed in sex chromosomes. From the evidence described above, sex-linked genes are implicated to play an essential role in reproductive isolation and adaptive speciation, creating pre- and post zygotic barriers,

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mediating reinforcement and enabling local adaptation. Thus, a detailed genetic map of the Z-chromosome is crucial for the localization of such “speciation“ loci. A genetic map would also be a valuable tool for QTL (quantitative trait loci) mapping, the identification of genomic regions underlying the phenotypic variability observed between individuals. A genetic map, trait measurements and a pedigree to monitor the co-inheritance of genetic markers and phenotypic traits, are the three major prerequisites to map QTLs (Slate 2005). Phenotypic and life history data for the sympatric populations of collared and pied flycatchers on the Baltic island of Öland have been collected for more than 25 years. Discovering the links between genes and phenotype will allow us to understand the genetic basis of variable fitness-related ecologically important traits responsible for differential adaptation and speciation between these two closely related species. Single nucleotide polymorphisms (SNPs) were the marker of choice for linkage analysis because of the reasons described below. Although polymorphic markers such as microsatellites are considered more informative for linkage analysis studies in natural populations (Slate 2005), bi-allelic SNPs confer more advantages since they are more abundant, more stable and co-dominant. Moreover, the lack of homoplasy (very rare in comparison to microsatellites) facilitates linkage analysis and their association with particular genomic regions (genes) assists comparative mapping studies, in contrast to anonymous markers such as AFLPs and microsatellites. In addition to that, large-scale sequencing technologies yield a surprisingly growing number of genome-wide sequences from various organisms, making the identification of SNPs easier and more popular. Besides, the development of SNP platforms allows for highly parallel, high-quality SNP genotyping with relatively low costs, in greatly reduced time. Backström et al. (2006) have already published a first genetic linkage map of the Z-chromosome of the collared flycatcher, including 23 markers in the best-order linkage map spanning 62,7cM. Conserved gene synteny (gene content) but gene order rearrangements were established from the comparison between this first Z-linkage map of the collared flycatcher and the chicken physical map. Another comparison between chicken and zebra finch Z-chromosomes using FISH (fluorescent in situ hybridization) revealed a remarkably different order of Z-genes between the two bird species (Itoh et al. 2006), confirmed by the linkage map of the zebra finch that was subsequently published by Stapley et al (2008). The availability of the draft assembly of the zebra finch genome provided the opportunity to develop a large number of Z-linked markers, which could then be tested for applicability in both species of flycatchers, due to the closer relatedness between flycatchers and zebra finch than between the distantly related flycatchers and chicken. The large marker set and the collection of large pedigrees offers the required resources for the development of a dense linkage map of the Z-chromosome, of use to unveil in detail the evolutionary history of the organization of avian Z-chromosomes. Two genetic linkage maps were constructed for the Z-chromosome of the two flycatcher species, which were subsequently studied in a comparative context to survey the organization of the sex chromosomes in the avian lineage.

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Materials and methods

Pedigrees structure The pied (Ficedula hypoleuca) and the collared (F. albicollis) flycatcher have been monitored for more than 25 years by placing nest boxes on the Baltic islands. Their prominent philopatry (Pärt 1995) (returning to the same breeding site every year) makes surveillance and continuous gathering of data a feasible task. Thus pedigrees have been established for both species. For this study 24 half-sib families (each male mating with 2-5 females) of pied flycatchers and 30 interconnected families (containing F2 and F3 offspring) of collared flycatchers were used for SNP genotyping. The pied pedigree consisted of 153 offspring with a range of 4-11 offspring per family, whereas the total number of offspring in the collared pedigree was 153 with a range of 2-8 offspring per family. The structures of the two pedigrees are shown in tables 1 and 2. No known hybrids were included in this study.

Table 1: Structure of the pied flycatcher pedigree.

Father ID Mother

ID

Offspring

nunber

PF1 PF2 6

PF9 5

PF15 6

PF22 6

PF29 5

PF35 PF36 7

PF44 11

PF56 7

Unknown PF65 6

PF72 11

PF91 PF92 6

PF101 5

PF107 6

PF115 PF116 5

PF123 6

PF131 7

PF139 PF140 6

PF147 5

PF29 5

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PF160 PF161 5

PF167 4

PF172 8

PF200 PF201 9

PF212 6

Sum 153

Table 2: Structure of the collared flycatcher pedigree. F1 and F2 offspring that are the parents of F2 and F3 offspring respectively, are indicated with one and two asterisks (*) respectively. Extra-pair offspring that were removed from the subsequent linkage analysis are indicated as EPOs.

Father ID Mother

ID

Offspring

number

CF1 CF2 6

CF6* Unknown 3

CF18 7 (7 EPOs)

CF26 CF27 5 (1 EPO)

CF48 CF30* 4

CF54 CF55 4 (1 EPO)

CF59* CF60 5

CF67 CF68 8

CF77 CF74* 3 (3 EPOs)

CF81** CF82 6

CF91 Unknown 6

CF93* CF98 3

CF95* CF102 7

Unknown CF94* 4 (4 EPOs)

CF119 CF122 5

CF121 CF130 5

CF137 CF138 5

CF153 CF143* 2

CF184 CF162 5

CF178 CF179 4

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CF199 CF188* 4

CF205 CF206 7

CF214 CF213* 7

CF222 CF223 5

CF243 CF228* 6 (2 EPOs)

Unknown CF248** 5

CF314 CF315 6

CF320* CF323 7

CF331 7

Unknown CF322* 2

Sum 153

Collection of samples and DNA extraction Blood samples were collected from the breeding families of the pied and the collared flycatcher in Öland during the years 2002-2007 and were stored frozen in buffer for subsequent usage. DNA extraction was performed by proteinase K digestion, followed by three cycles of phenol/chloroform purification and NaAc, EtOH precipitation. DNA concentration was then measured and samples were diluted with ddH2O in 50ng/μl. Microsatellite genotyping and removal of EPOs Cases of extra-pair offspring (EPOs) have been frequently observed, especially in the collared flycatcher. Females sometimes copulate with more than one male; they first mate with one male and then form an extra-pair with another male to raise their own offspring together with the offspring from the previous copulation (Sheldon and Ellegren 1999). Thus hatchlings found in nests are not necessarily progeny of the apparent pair, but could instead represent extra-pair offspring. Consequently it was important to remove such individuals from the subsequent linkage analysis. Even though EPOs would have been detected by checking for non-mendelian inheritance in the SNP dataset, it was preferred to exclude them from the beginning, in order to keep their number as low as possible in the families to be SNP-typed. Microsatellite profiling was performed for this purpose. All families from both species were genotyped for six microsatellite markers, F403, F407, F401, PhTr1, Fhu2 and Pdou5 (Table 3) (Ellegren 1992, Primer 1996, Leder et al 2008), through multiplex PCR and fragment length analysis. Multiplex PCR was performed with primers tagged with fluorescent dyes on the PTC225 instrument and fragment length analysis with a mixture of Hi-DiTM Formamide and GeneScanTM 600 LIZ (size standard) on the ABI 3730xl 96-capillary instrument. Multiplex PCR (Box 1) yielded PCR products for all markers but the Ph1, so only the rest of the five markers were further analysed. The software GeneMapper version 4.0 (Applied Biosystems) was used to score genotypes and EPOs were identified if they carried non-parental alleles in their genotypes in more than two markers. This threshold was used in order to avoid typing errors or the possible occurrence of a microsatellite mutation.

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Table 3: Primer sequences and allele sizes for the microsatellite markers

Marker

Name

Primer sequence (forward, reverse) Allele size

Fhy403 ACAAGCTCTCCTTCTTACTTAT 168-202

GTTTCAGTAAAGCTTGTTAGAACCTA

Fhy407 AAAGTTAGCCTATGTCTACCAGA 221-243

GTTTAGCTCTTCCCAGATTCTAAG

Fhy401 TCAAATATTAATTGGTTACACTT 278-312

GTTTCTCTTAAACTAACAACTTGCTAA

PhTrl CTGGGAGAAGACTCTAAGCCTT 103-119

CTACTTTTTAATGTGAGATCCAAACT

FhU2 GTGTTCTTAAAACATGCCTGGAGG 135-181

GCACAGGTAAATATTTGCTGGGCC

Pdou5 GATGTTGCAGTGACCTCTCTTG 227-245

GCTGTGTTAATGCTATGAAAATGG

Box 1: Multiplex PCR protocol

Two multiplex PCR reactions were set by combining primer pairs for the markers F403, F407, F401 and PhTr1, Fhu2, Pdou5 respectively. The first multiplex PCR reaction consisted of 0.8μM of each primer, 50μM dNTP, 0.025U Taq polymerase (Applied Biosystems) and approximately 50ng of template DNA with a final volume of 18.6μl. The second multiplex PCR reaction consisted of different concentrations for each primer pair, (1μM for PhTr1, 0.68μM for Fhu2 and 0.4μM for Pdou5), 50μMdNTP, 0.025U Taq polymerase (Applied Biosystems) and approximately 50ng of template DNA with a final volume of 18.6μl. The general temperature profile for the first multiplex PCR reaction was: an activation step of 2 min at 94 o, 10 cycles of a denaturation step for 30sec at 94 o, an annealing step for 45sec at 55 o decreasing 1 o in every cycle (touchdown) and an elongation step for 60sec at 68 o. Then another 15 cycles of a denaturation step for 20sec at 94 o, an annealing step for 45sec at 45 o and an elongation step for 60sec at 68 o followed, with a final extension step for 4min at 4

o. The general temperature profile for the second multiplex PCR reaction differed only in the annealing temperatures and the number of cycles. So the optimized annealing temperature was initially 63 o with 1 o decrease in every cycle for the first 10 cycles, followed by 20 cycles with an annealing temperature of 51 o. Therefore, 8 EPOs from the pied families (213 offspring tested) and 30 EPOs from the collared families (205 offspring tested) were removed from the initial pedigrees. The high incidence of EPOs (14.6%) in the collareds as opposed to the pieds (3.8%) is in agreement with other paternity-testing studies (Lifjeld J. 1995, Sheldon and Ellegren 1999) and a previous microsatellite fingerprinting in a collared pedigree from the study of Backström et al. (2006), exhibiting a frequency of 12.8% EPOs. Thus in total 183 pied individuals and 189 collared individuals were SNP genotyped. The SNP genotyping data were examined with the programs CheckFormat and CheckErrors for the identification of wrongly assigned offspring or parents. These programs are included

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in the software Genepi.jar developed by Alun Thomas (2006) and check the linkage format of the input files and calculate the posterior probability of genotype errors in pedigrees. Thus another 20 EPOs were detected and excluded from the collared pedigree (24.4% total frequency) by examining the SNP genotypes (table 2). No more inheritance errors were detected in the pied families. Therefore the final pedigrees included in the linkage analysis consisted of 183 pied and 169 collared individuals. SNP selection and genotyping Species-specific SNPs were identified and selected by scanning 74 previously sequenced intronic markers in two populations, each consisting of 10 individuals from each species (Backström et al. manuscript). From these 74 markers, 23 have been already mapped on the Z-chromosome of the collared flycatcher and have been developed from orthologous Z-linked chicken genes (Backström et al. 2006). The rest of the markers were developed from orthologous chicken Z-linked exons (flanking introns), which were blasted against the draft genome assembly of the zebra finch. The resulting loci were then amplified and sequenced in the two populations of flycatchers (for protocols/methods see Backström et al. manuscript) and comprised the final marker set of 74 loci corresponding to 73 genes. These introns are evenly distributed along the Z-chromosome with an average of one locus per Mb (Figure 3), according to their position on the physical map of the chicken Z-chromosome.

Figure 3: Position of the 74 markers on the Z- chromosome according to the chicken physical map (Backström et al. unpublished). In the total 40.5kb of sequence data, 265 and 352 species-specific SNPs were detected in the pied and collared sequenced populations respectively, while 46 SNPs were common in both species. From these SNPs, 176 pied-specific SNPs, 182 collared-specific SNPs and 12 shared SNPs were selected for genotyping, according to certain criteria; SNPs should not be located at the very beginning or the very end of the sequence (more than 100bp from the start and the end of the sequence), and the distance between adjacent SNPs should be larger than 100bp, so that primers could be designed and would not interfere with each other. In addition to that SNPs were selected with a preference for those exhibiting a MAF (minor allele frequency) of 20%, to minimize the possibility of monomorphic SNPs in the studied pedigrees. Furthermore, SNPs were selected to be evenly spread along the Z-chromosome, even though in most cases the data set consisted of more than one SNP from each locus and a few loci were unrepresented since they lacked SNPs that could be used in the genotyping process. On average the SNP data set contained 1-7 SNPs per locus from 67 genes. Whole-genome sequencing from brain and testis/ovary tissue of both flycatchers has been performed, using the 454 large-scale sequencing technology. Since the 454 sequence data from brain tissue were available at that time, single nucleotide polymorphisms could be

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detected by comparing overlapping reads of the Z-chromosome. The data from the collared flycatcher for the Z-chromosome were of low quality, impeding SNP detection, whereas the data from the pied flycatcher provided a few SNPs. Thus another 14 pied-specific SNPs were added to the final SNP set, with a total of 384 SNPs from both species. Highly multiplexed genotyping was performed, using Illumina’s GoldenGate Assay at the SNP technology platform, Uppsala University (http://www.medsci.uu.se/molmed/ snpgenotyping/index.htm). The method is described in box 2. In total 372 individuals were scored for 384 SNPs. Box 2: GoldenGate Assay method (see also figure 4)

The Golden gate assay achieves highly parallel genotyping by annealing locus-specific (LSOs) and allele-specific oligonucleotides (ASOs) downstream and upstream of each SNP site of the genomic DNA (that has been bound on a solid support), followed by an extension step and wash-steps to remove excess and mis-hybridized oligonucleotides. Afterwards amplification takes place using two types of universal primers, one primer complementary to the LSOs with a locus-specific tag, and two primers complementary to the ASOs, which are dye-labeled. Thus each PCR amplicon contains a unique tag specific for each locus, through which it hybridizes on its complementary address sequence on the universal array carried by a single bead. Each SNP locus is then scored by reading the fluorescent signal emitted from the dye-labeled universal primers, with their unique address known. (Fan et al. 2006, Illumina 2006) Data analysis and map construction Before the linkage analysis, missing parental genotypes were inferred from the genotypes of their offspring so that no information was lost. In addition, genotypes were assembled into haplotypes whenever SNPs were located in the same intron, based on the assumption that they are tightly enough for recombination not to break up associations. The use of haplotypes instead of simple genotypes increases the informativeness of those markers. The software CRIMAP (Green et al. 1990) was used to perform maximum-likelihood multipoint linkage analysis for quick, highly automated construction of multilocus linkage maps. A modified version (Liu and Grosz 2006) of the original CRIMAP software was preferred over the original version, because new algorithms of larger capacity and efficiency, and additional useful options were implemented in this new version, without any changes in the linkage analysis. This linkage mapping software calculates recombination fractions and LOD scores between pairs of loci, and uses the Kosambi mapping function (Kosambi 1944) to infer map distances in CM from recombination fraction estimates. It can also infer genotypes from untyped individuals from the parents/offspring data whenever possible. The building process performed by CRIMAP starts with the most informative loci and continues with the gradual addition of more loci in order of decreasing informativeness to the map. A LOD score (logarithm of the likelihood ratio) threshold is used as a significant statistical estimate of the likelihood that two loci are linked rather than randomly associated. According to the LOD score method developed by Newton E. Morton in 1955, the highest LOD score, obtained by a series of LOD scores estimated from different linkage distances in a pedigree, is considered significant (McClean 1998). Hence a certain order of loci is retained if the LOD score between them is above the threshold value, otherwise it gets rejected and a different order is tried out. The order of the loci that can be mapped with significant LOD score gives the “framework” map. The building procedure can further proceed for the rest of the loci by trying out different orders, until no “better” order can be found (with higher LOD score). This final order of loci, including loci that have been mapped with insignificant LOD scores, gives the “best-order map”.

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Figure 4: Illustration of the GoldenGate Assay. P1, P2 in black color: allele-specific oligonucleotides (ASOs), P3 in black color: locus-specific oligonucleotides (LSOs), P1, P2 in purple color: universal dye-labeled PCR primers, P3 in purple colour: universal PCR primer with unique address sequence (Modified from: Illumina 2006).

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Linkage between each marker pair was tested with the two-point option of CRIMAP, which estimates pairwise recombination fractions and LOD scores. For the present data set a LOD score of 3.0 was used as a threshold for significance, which corresponds to 1000:1 likelihood ratio for linkage. The same LOD score has been used in most human linkage studies as a significant value. Markers that showed linkage to at least another marker were ordered with the option build to construct a framework map. For the collared flycatcher map, the order of the markers from the previously published framework map (Backström et al. 2006) was used as a scaffold to build on the rest of the markers from the collared data set. For the pied flycatcher map, markers were positioned one at a time according to their degree of informativeness. Both sex average and sex-specific map distances were calculated. Next the LOD threshold was lowered to zero to link the rest of the markers that were not included in the framework maps and to get an approximate order for them. The flips option was used to iteratively evaluate different orders of the non-significant map, until no better order would be found. Finally the fixed option was used to construct the best-order map with the order of loci given by flips. The maps were visualized with the software MapChart (Voorips R.E. 2002) that produces charts of genetic linkage maps. Comparative mapping The linkage maps of the collared and the pied flycatcher were plotted next to each other with connections between homologous loci to visualize rearrangements. Gene order rearrangements between the Z-chromosome of flycatchers and chicken were revealed by plotting the genetic position of each locus against its predicted position on the physical map of chicken. In addition to these plots, an artificial map was constructed for the Z-chromosome of chicken to compare with the two framework maps. The artificial linkage map of the chicken Z-chromosome was created based on a combination of data from the chicken genome assembly (map viewer build 2.1) and the linkage map by Wageningen University.

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Results

SNP Genotyping From the total of 384 SNPs, 313 (81.5%) were approved for the collared flycatchers and 324 (84.4%) for the pied flycatchers depending on their call rate (percentage of the number of samples that received a genotype call per SNP). Only SNPs with call rate > 75% were approved and included in the analysis. On average the sample-call rate per approved SNP was 96.1% for the collared flycatchers and 97.4% for the pied flycatchers. The accuracy of the method was found to be 100% based on the reproducibility of duplicate genotype calls. A total of 56827 genotypes were delivered for the collared flycatchers and 57756 genotypes for the pied flycatchers. Since almost half of the markers were specific to the pied flycatchers and the other half specific to the collared flycatchers, 153 SNPs (49%) were monomorphic for the collared flycatchers and 176 (54%) for the pied flycatchers. Surprisingly enough, 14 markers specific to the pied flycatchers were also found polymorphic in the collared flycatchers and 16 markers specific to the collared flycatchers were also found polymorphic in pied flycatchers. From these polymorphic markers 48.8% and 41.9% had MAF > 20% in the collared flycatchers and the pied flycatchers respectively. 6 SNPs in the case of collared flycatchers and 5 SNPs in the case of pied flycatchers showed heterozygote females and were consequently removed from subsequent analysis. Three possible explanations exist for this observation; these markers could have been erroneously genotyped, they could have been wrongly assigned to the Z-chromosome and instead be located on autosomes, or they could be part of the PAR (pseudoautosomal region) in which crossing-over occurs between the Z and the W chromosome in females. All the other markers were hemizygous in females confirming their location on the Z-chromosome. After the elimination of non-approved, monomorphic and erroneously genotyped SNPs, the remaining 153 SNPs were coming from 59 introns in the collared flycatchers and the remaining 143 SNPs were coming from 67 introns in the pied flycatchers. From this point haplotypes were used instead of genotypes to increase the informativeness of the markers, presuming no recombination occurred within introns in one to three generation-pedigrees. Thus intra-intronic SNPs were assembled into haplotypes with an average of 1-7 SNPs per haplotype. Linkage mapping

The Z-chromosome of the collared flycatcher: In the pairwise linkage analysis all markers showed significant linkage to at least another marker with LOD score > 3. The number of informative meioses varied from 49 to 83 with an average of 68 informative meioses per haplotype. The order of 11 markers (1316, 1688, 1888, 2131, 2293, 2857, 3354, 4333, 5237, 3437, 3632) was used as a scaffold to increase the information required for the construction of the framework map. These markers were taken from the previously published framework map of the Z-chromosome of the collared flycatcher (Backström et al. 2006). Hence another 15 markers (in total 26) were ordered with significant LOD score, spanning 181.5cM on the sex-average map (figure 5a). The average marker interval is 7.3cM (with 4.2 SD). The length of the male-specific map is 210cM, longer than the 143.2cM long female-specific map (Figure 5b, 5c) with a male to female map ratio of 1.47. The distance between adjacent markers is 8.4cM and 5.7cM on average in the male-specific and the female-specific map respectively. No recombination should be observed in females across the Z-chromosome, apart from the pseudoautosomal region, however many of the marker pairs exhibited

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recombination rates > 0 in females. 12 marker pairs showed higher recombination rates in males, 10 marker pairs showed higher recombination rates in females and the remaining 3 marker pairs had equal recombination rates. The heterogeneity of recombination rates in females across the Z linkage map made impossible the formation of marker-blocks with 0% or > 0% recombination rate, consequently no assumptions can be made about a candidate pseudoautosomal region. The PAR region should be located on one of the two ends of the linkage map and the markers included in it should be heterozygous and recombine in both males and females. The markers that were not included in the framework map were ordered on the best-order map with insignificant linkage (Figure 6). Almost half of these markers could be assigned to two alternative positions on the framework map with significance (figure 5a). The length of the best-order map is 312.5cM, 383.4cM and 228.7cM for the sex-average, male-specific and female specific map.

11750,010934,313169,0

199925,7168833,3113638,7188849,0213158,0259161,2229369,3294176,828573354

79,3

489083,0317991,2230795,0433398,25237104,01512110,75099121,86274136,73437144,15848150,85360154,8947169,33632181,5

12

31

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36

8

26

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11750,010933,9131613,1

199936,1168838,5113643,0188851,021312591

67,7

229383,72941285733544890

91,8

3179103,72307106,14333111,65237122,71512128,65099141,9

6274159,2

3437174,058485360

185,2

947198,6

3632210,0

b

11750,010931316

4,0

199915,7168828,0113632,8188842,5213145,525912293

50,7

294156,628573354

62,9

48903179

72,5

230743335237

77,9

151286,1509994,462743437

104,9

5848107,65360114,3947130,23632143,2

c

Figure 5: Sex-average and sex-specific framework linkage maps of the Z-chromosome of the collared flycatcher. Next to the sex-average map (a) markers with two significant alternative positions and their positions are shown. Male-specific (b) and female specific (c) map. Positions are given on the left side of the map in cM and locus names on the right side of the map.

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1320.011751.510934.913169.984611.6199916.3274817.974122.7474330.1644139.1168861.9123168.699572.0113675.117679.5143285.3188890.52659 2131104.92591110.52293118.22941122.53754126.22857127.23354130.86503 4890136.16701137.77386138.13920148.73179157.3382164.66974166.77231170.65284174.74333 38136680

178.3

2368182.32307 3720184.14025190.95237194.21512204.44264208.93321212.25099215.46131222.46868232.0379238.43437240.86274252.75848265.05360268.6947282.03146292.13544 3632304.93477312.5

a

1320.011752.910936.3131615.584618.51999 274822.374130.7474344.9644153.7168873.9123181.499586.21136 17690.0143297.11888101.82659116.22131 2591123.92293141.82941 3754142.42857147.23354147.86503 4890153.36701 7386156.83920175.83179187.9382194.26974 72315284

197.6

4333 3813201.46680202.02368 23073720

206.2

4025218.25237224.71512239.24264 3321244.55099254.06131262.26868273.9379284.23437288.06274305.65848 5360330.6947344.53146353.03544 3632374.43477383.4

b

132 11750.01093 1316846

2.9

19999.02748 7414743

12.3

644121.4168846.0123153.2995 113653.3176 143267.4188874.72659 213186.32591 229389.6294193.43754 2857102.53354 6503108.74890 67017386 3920

113.9

3179117.3382 6974124.47231136.55284145.94333 38136680 2368

149.9

2307 37204025 5237

156.7

1512161.54264165.93321 5099168.76131174.26868180.3379 3437180.46274 5848186.25360192.6947206.43146 35443632

220.2

3477228.7

c

Figure 6: Best-order linkage map of the Z-chromosome of the collared flycatcher (a) sex-average, (b) male-specific, (c) female-specific. Positions are given on the left side of the map in cM and locus names on the right side of the map. The Z-chromosome of the pied flycatcher: All markers except 6 (1093, 15345188, 3179, 6203, 741 6274) were significantly linked to at least another marker in the two-point linkage analysis. Locus 6274 insistently showed recombination fractions close to 50% with most of the other markers and the remaining 5 loci had 0% recombination fractions with all other markers and low number of informative meioses. Since these loci would not provide any information in the linkage analysis they were removed. The number of informative meioses varied from 107 to 209 with an average of 144 informative meioses per haplotype. Linkage mapping has not been tried before for the Z-chromosome of the pied flycatcher, thus no order of markers was available to use as a scaffold. The loci were added one by one in decreasing

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order of informativeness to build the framework map. 9 markers were ordered with significant LOD score, covering a genetic distance of 45.2cM on the sex-average map (Figure 7a). The mean marker interval is 5.7cM (with 5.0 SD). Again both sex-average and sex-specific analysis was performed. The length of the male-specific and the female-specific map is 139.2cM and 15.1cM with 17.4cM and 1.9cM average marker distance respectively (Figure 7b, 7c). The male to female map ratio is 9.2. Once more females unexpectedly showed recombination rates > 0 (as in the case of the collared flycatchers) for half of the marker pairs. 5 marker pairs had higher recombination rates in males and three marker pairs had higher recombination rates in females. Loci located on both edges as well as in the middle of the Z-linkage map seemed to recombine, so no PAR region could be recognized in the pied flycatchers either. Most of the markers that could not be ordered on the framework map with a significant LOD score were linked on the best-order map (Figure 8) with a lower LOD score. Less than half of these markers could be assigned to two alternative positions with significance on the framework map (Figure 7a). The best-order maps, both sex-average (188.8cM) and sex-specific (596.6cM for the male-specific and 93.5cM for the female-specific) are much more extensive than the respective framework maps.

23680,023076,1335410,1402513,2

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23680,02307 33545,84025 36328,27231 11361231

12,7

143215,1

c

Figure 7: Framework linkage map of the Z-chromosome of the pied flycatcher. Sex-average (a), male-specific (b) and female specific (c) map. Positions are given on the left side of the map in cM and locus names on the right side of the map.

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71230.052376.821317.8236816.0639681 26592857

24.5

2748 2307624103 2293

26.2

294128.8335431.63146 305634.1372035.63321 381338.84025 392040.9426443.8433344.2686846.8668049.1644149.7565952.8730756.8363260.4697464.3604263 347767.0343772.9723175.8258484.21999 489096.74520107.94743 50195965

119.4

6131122.5793 1136135.8337817141.01688 706911150.3132 176160.6379162.8382163.5873190165.3846 947168.11316171.91231174.91888 307453175.01432177.15970187.0380487188.8

a

71230.052375.9213129.1236834.3639681 26592857

45.4

2748 2307624103

48.4

2293 294154.8335456.63146 305660.53720 332162.5381367.9402570.7392070.8426475.2433376.9686882.86680 644188.4565995.77307107.33632116.06974119.4604263 34773437

121.2

7231126.32584152.61999252.64890352.64520 4743358.25019 5965371.16131403.3793 1136503.3337817517.01688 706911530.0132 176549.8379552.6382 873190555.8846 947562.81316570.41231 1888307453 1432

574.2

5970 380487596.6

b

71230.05237 21315.2236812.2639681 26592857 27482307 6241032293

19.3

2941 33543146 3056

20.1

372024.83321 38134025 3920

28.1

4264 43336868 6680

29.3

6441 56597307 36326974 604263

30.5

347738.53437 72312584 19994890

46.2

452052.64743 50195965

61.1

613162.279362.91136 33781766.61688 70691172.613272.7176 379382

77.4

873190 846947

80.7

1316 123182.21888 30745384.6143288.5597091.738048793.5

c

Figure 8: Best-order linkage map of the Z-chromosome of the pied flycatcher (a) sex-average, (b) male-specific, (c) female-specific. Positions are given on the left side of the map in cM and locus names on the right side of the map.

Comparative mapping The use of SNPs makes the identification of homologous loci between the two flycatcher species possible, hence the two framework maps can be compared. In Figure 9, one rearrangement is reported from the comparison of the linkage maps of the collared and the pied flycatcher (the framework map of the collared flycatcher is completely inverted to be able to show the rearrangement more clearly). Locus 3632 is involved in this rearrangement,

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which is located at the end of the linkage map of the collared flycatcher and in the middle of the linkage map of the pied flycatcher. Two inversion events could explain this rearrangement, concerning the markers 3354, 2307 and 3632. Since only 4 loci were homologous between the two species, locus 1136 was the only one found to be collinear in respect to the remaining loci. This could be an effect of the “poor” framework map of the pied flycatcher, which inhibits the clarification of gene order changes in the two species.

117510931316

1999

16881136

188821312591229329412857 3354489031792307433352371512

5099

6274

343758485360

947

3632

Fal

14321231 1136

723136324025335423072368

Fhy

Figure 9: A comparison of the framework maps of the Z-chromosome from the collared (Fal) and the pied flycatcher (Fhy). Figures 10 and 11 present the genetic position of each locus plotted against its predicted position on the physical map of chicken. If the order of loci was collinear between chicken and the two flycatcher species, the data points should follow a diagonal trendline. In these graphs it is clear that the trendline is interrupted by many loci that are probably involved in rearrangements. These changes are not easy to explain by single inversion events. More complicated rearrangements (such as complex inversions and translocations) must have occurred since the split of the chicken and the flycatcher lineages. A few short collinear regions between flycatchers and chicken appear in the plots. These include the region between genetic positions ~ 49-58cM in the collared linkage map (Figure 10) and the region between genetic positions ~ 24-32cM in the pied linkage map (Figure 11). More rearrangements seem to have taken place on the collared linkage map than on the pied linkage map, but this is probably an artifact due to the low number of loci that were ordered on the framework map. Figures 12 and 13 present the rearrangements between the three linkage maps (collared flycatcher-chicken and pied flycatcher-chicken). Again it is difficult to explain the complex structural changes that have occurred in the gene order of the collared flycatcher compared to chicken (figure 12). On the other hand, one rearrangement is apparent at the top of the linkage maps of the pied flycatcher and chicken. This can be explained by an inversion of the loci

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1432, 1231, 1136, covering a genetic distance of 2.6cM. Numerous rearrangements appear in the region between markers 7231-2368 (25.9cM), which require several changes in the order of the markers.

0

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chicken physical position (Mb)

colla

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Figure 10: Comparative plot of the position of each locus on the genetic map of the collared flycatcher (cM) against its predicted position on the chicken physical map (Mb).

0

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Figure 11: Comparative plot of the position of each locus on the genetic map of the pied flycatcher (cM) against its predicted position on the chicken physical map (Mb).

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117510931316

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3632

Fa

947109311361175131615121688199918882131229323072591285729413179335434373632433348905099523753605848

6274

Gga

Figure 13: Comparison between the collared linkage map (Fa) and the chicken artificial linkage map (Gga). The chicken linkage map was created based on combined data from the chicken genome assembly (build 2.1) and the linkage map from the Wageningen University (WUSTL 2006,WUR 2000).

143212311136

723136324025335423072368

Fhy

113612311432

23682307

3354

3632

4025

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Gga

Figure 12: Comparison between the pied linkage map (Fhy) and the chicken artificial linkage map (Gga). The chicken linkage map was created based on combined data from the chicken genome

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assembly (build 2.1) and the linkage map from the Wageningen University (WUSTL 2006, WUR 2000).

Recombination rates

Because rearrangements were discovered at both ends of the linkage map of the pied flycatcher, recombination rates cannot be estimated by a comparison between the lengths of the two framework maps from the two flycatcher species. Instead another method was used; assuming a similar length between chicken and flycatchers (74Mb), the average recombination rate can be estimated from the total genetic distance of each linkage map. Thus the average recombination rate is 2.6cM/Mb for the collared flycatchers (181.5cM total length) and 0.6cM/Mb for the pieds (45.2cM total length). The recombination rate ratio between the two species (collareds to pieds) is 4.3. When the best order map of the pied flycatcher is concerned, the recombination rate is estimated to be 2.5cM/Mb.

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Discussion

The aim of this project was the construction of high-density linkage maps of the Z-chromosome of two wild bird species, the collared and the pied flycatcher, of use in comparative approaches and QTL mapping. Genetic mapping of the whole collared flycatcher genome was accomplished recently (Backström et al. 2008), but this is the first trial of mapping the Z-chromosome of the pied flycatcher. This task was challenging since no established SNP database exists for these species and the required number of the genotyped individuals for high resolution was unknown. The density of the framework maps of the two species differed significantly, with the collared linkage map including 26 loci covering a distance of 181.5cM and the pied linkage map including only 9 markers spanning 45.2cM. This great difference in the coverage of the two framework maps is probably an effect of the use of a known loci order for the collared flycatchers which was used as a scaffold. This order provided essential information for more loci to become incorporated on the map, resulting in a richer framework map for the collared flycatcher. Since no available known order of loci exists for the Z-chromosome of the pied flycatcher, a potential solution would be to manually establish linkage between a few informative markers from the examination of the two point linkage analysis data. Re-building the map provided significantly ordered loci, would possibly facilitate the incorporation of more markers. The number of informative meioses played an important role in the efficiency of loci to become linked on the framework map in the case of the pied flycatchers while that was not the case for the collared flycatchers. The inability to recognize the phase of the chromosomes in a large proportion of the meiosis events was probably an inhibiting factor during the building procedure. Other factors that must have been involved include the number of genotyped individuals, the heterozygosity of the markers, the number of generations in the pedigrees and possible genotyping errors. Higher numbers of genotyped individuals and increased heterozygosity have been previously shown to enhance the probability of significant mapping (Åkeson et al. 2007). Since SNPs are not as polymorphic as other markers, elevated heterozygosity is required to increase the informativeness of the markers. Shallow pedigrees (one generation) disable chromosome phase recognition, which in turn reduces the number of informative meioses, whereas genotyping errors are known to inflate the total map length. The greatly extended length of the best-order maps in both flycatcher species reflects these obstructive factors. The total number of loci included in the framework maps does not reflect their density. The resolution of the genetic map of the collared flycatcher is approximately 7cM marker per marker, whereas the resolution for the pied genetic map is about 5cM per marker. This is lower compared to the previous Z-framework map of the collared flycatcher (4cM per marker) (Backström et al. 2006) and the highly dense Z-linkage map of chicken (~1cM per marker) (Walberg et al. 2007). Still the present density is higher than the density of the zebra finch Z-linkage map (9.5cM per marker) (Stapley et al. 2008), the density of the siberian-jay Z-linage map (~9cM per marker) and the density of the great reed warbler Z-linkage map (20.4cM per marker) (Åkeson at al.2007) if only the framework maps are taken into account. The great extension of the best-order maps by the inclusion of all markers makes those maps spurious. This means that loci were added on the tips of the map instead of being incorporated between other loci through linkage. This is almost always the case and indicates genotyping

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errors. The sex-average and male-specific maps of the collared flycatchers became approximately 2 times longer, while the female-specific map became ~ 1.5 times longer than the respective framework maps. The sex-average and male-specific maps of the pied flycatchers became more than 4 times longer, while the female-specific map became more than 6 times longer than the respective framework maps. Because of these wide expansions the best-order maps were considered unreliable and most of the discussion is based on the framework maps. Since the Z-chromosome does not recombine in females (except from the PAR region) and males are known to have 22% higher recombination than females (Backström et al.2008), sex-specific analysis was performed along with the sex-average analysis. In that sense male-specific maps are expected to be longer than female-specific maps. Both collared and pied sex-specific maps validate this expectation. However the occurrence of recombination events in females of both species indicates either that they are positioned in the pseudoautosomal region or that an error occurred during the sex-specific analysis. The first hypothesis can be excluded because all markers that were heterozygous in females were removed before the analysis and only hemizygous markers were kept. In addition to that there is no clear pattern in the distribution of recombining and non-recombining loci across the Z-linkage group. Consequently these markers cannot be suggested to be part of the PAR region, an error seems more plausible. A closer examination of the sex-specific analysis is required to avoid this problem in subsequent analyses. Compared to the linkage map of the chicken Z-chromosome (Walberg et al. 2007), the length of the collared linkage map (181.5cM) is more similar to chicken (200.9cM), while the length of the pied linkage map (45.2cM) is much shorter. In agreement to this, the average recombination rate between collared flycatcher and chicken (Walberg et al. 2007) is more similar (2.6cM/Mb and 2.7cM/Mb respectively) than is the average recombination rate of the pied flycatcher (0.6cM/Mb). This comes in contrast with the previous finding from Backström et al. (2006) that collared flycatchers recombine 0.5 times less than chicken. Another calculation of the recombination rate was made for the pied flycatcher, based on the best-order map (2.5cM/Mb). However none of the two estimations is reliable enough due to the bias induced by the number of loci included on the significant and insignificant map. In general passerines are known to have lower recombination rates than chicken, with zebra finch having approximately ¼ the total length of the chicken linkage map (Slate et al. 2008), Siberian jays having slightly lower linkage maps than zebra finch (Jaari et al 2009) and great reed warblers having extremely lower recombination rates (Hansson et al. 2005). Thus the present estimated recombination rates in the flycatchers could be partly influenced by the inflation of the maps when more loci are added. It should be mentioned at this point, that even though differences in the map size of different species are usually attributed to different recombination rates, other factors could be responsible too. The telomeric effect (higher recombination in telomeres) should not be neglected and the density of the map should be considered when inferring recombination rates from map lengths.

Two rearrangements are evident from the comparison between the linkage maps of the collared and pied Z-chromosome (Figure 9). Two inversion events between markers 3354, 2307 and 3632 can explain this rearrangement. No collinear regions are apparent but this probably results from the fact that only four loci were found to be homologous between the two linkage maps. A higher density is needed to elucidate the changes that occurred in the gene organization of the Z-chromosome of the pied and the collared flycatcher. A comparison of the two best-order maps would be more fruitful in this case since they include a higher

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number of markers, but their order cannot be trusted due to the fact that they were greatly extended. Following this no inference can be made about conserved gene synteny (conserved gene content) between the Z-chromosomes of the two flycatcher species, due to the sparsity of the linkage maps. Numerous complex rearrangements are also evident from the comparison between collared flycatcher and chicken (Figure 12), occupying the whole length of the linkage maps. One inversion event can explain the different gene order of markers 1432, 1231, 1136 at the top of the linkage maps of the pied flycatcher and chicken. The remaining gene order changes are too complex to be owing to single inversion events, thus other types of rearrangements must be implicated too. The extremely altered gene order in the collared flycatchers compared to chicken is striking. Backström et al. (2006) found only two rearrangements, which can be explained by at least 4 inversion events, concerning the two distal ends of the framework map in the same comparison. However when the best-order map was taken into account, more complex structural changes were involved, covering a larger interval. Conserved gene synteny is more pronounced between the collared flycatcher and chicken than between the pied flycatcher and chicken. 26 Z-derived chicken markers are also linked to the Z-chromosome of the collared flycatcher while only 9 Z-derived chicken markers are linked to the Z-chromosome of the pied flycatcher. Once more, this is probably a consequence of the poor pied map. Moreover as already mentioned above (see introduction), a comparison between the zebra finch linkage map and the chicken physical map revealed many intra-chromosomal rearrangements, with simple inversions of large chromosomal segments being more common on the Z-linkage group compared to other autosomal linkage groups (Stapley et al. 2008). From this evidence we can conclude that intra-chromosomal rearrangements in the Z-chromosomes of different bird lineages are frequent. These observations can be related to the proposed chromosomal speciation models. According to them, chromosomal rearrangements such as inversions and some types of translocations (such as chromosomal fusions and fissions) create the reproductive barriers between diversifying populations. Two different classes of these models have been developed, namely the “underdominance” or “hybrid dysfunction” model and the “suppression of recombination” model. The first one concerns the underdominance of hybrids heterozygous for chromosomal rearrangements, thought to facilitate genomic divergence and eventually speciation (Hoffman A and Rieseberg L.2008). Recombination between inverted and non-inverted chromosomes in heterokaryotypic hybrids generates unbalanced gametes, which may cause inviability or sterility. This theory has been evidenced on the wingless Morabinae grasshoppers in southeastern Australia (White 1968) but has been heavily criticized, since chromosomal rearrangements that confer inviability or sterility in hybrids are unlikely to get established in the population. Only special conditions, such as genetic drift in populations of small size, meiotic drive and strong selection promoting locally adapted alleles in inter-crossing population have been reported to facilitate the fixation of chromosomal rearrangements (White 1968, Hoffman A. and Rieseberg L. 2008). The second class of chromosomal speciation models is more applicable. According to this model, reduced recombination between rearranged regions of chromosomes protects these parts of the genome from gene flow, due to reduced pairing and crossing over between inverted and non-inverted segments and selection against recombinant gametes (Hoffman A. and Rieseberg L. 2008). Hence it is favourable for reproductive isolating genes to be linked to a rearranged region in order to ensure their establishment, in populations with migration. Moreover, locally adapted alleles, assortative mating alleles and pre- and/or post-zygotic isolation alleles are more prone to be linked to each other when located within inversions,

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accelerating the process of speciation. Evidence for this model comes from several organisms such as the wild sunflowers Helianthus petiolaris and Helianthus annus (Rieseberg L. 2001, Strasburg et al. 2009), the fruitflies Drosophila pseudoobsura and Drosophila persimilis

(Noor et al. 2001), mosquitoes of the A. gambiae complex (Coluzzi et al. 2002), human and chimpanzee (Navarro and Barton 2003) and the shrews of the Sorex araneus group (Basset et al. 2006). In most of these studies chromosomal rearrangements are mostly found on the sex chromosomes compared to autosomes, implicated to promote population divergence. Therefore it would be of great interest to explore in more detail the rearrangements that have occurred in the Z-chromosomes of the two flycatcher species in order to connect them to processes that led to the split of their populations. Following this, a comparison between the Z-chromosome gene order of flycatchers, zebra finch and chicken would reveal in which of these lineages major rearrangements have arisen, to provide more insight into their divergence. Nevertheless a prerequisite for these comparisons is the availability of higher-density maps for the flycatchers, to acquire a better view of gene order rearrangements. In conclusion, the first linkage map of the Z-chromosome of the pied flycatcher was generated along with a linkage map of higher coverage for the Z-chromosome of the collared flycatcher. Rearrangements were revealed from the comparison of the two closely related species of flycatchers as well as from the comparison of each species with chicken. Further analysis needs to be done to improve these linkage maps. Better scrutinization of the data and a closer monitoring of all steps in the linkage analysis will perhaps allow for the construction of maps with higher density and quality.

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Acknowledgments

First of all, I would like to thank my supervisor Hans Ellegren for giving me the opportunity to do my degree project at the Department of Evolutionary Biology. His supervision and support have been exceptional during the last year. I also wish to thank Niclas Backström for his valuable ideas and help throughout the project and his advice during the writing of the report. I would like to thank Harriet Mellenius for sharing with me her experience on the software CRIMAP. Furthermore, I would like to thank all the members of the Molecular Evolution group for the stimulating and fruitful discussions we had at group meetings and journal clubs. Finally, many thanks to all the people from the Evolutionary Biology department for providing a very friendly and cooperative environment and for the fun times we spent together. Special thanks to Tomas Axelsson and the unit of the SNP technology platform at Uppsala university for performing the genotyping and Uppsala University, Uppsala University Hospital and the Knut and Alice Wallenberg foundation for their support to the SNP genotyping platform.

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