A HIGH-DENSITY SCAN OF THE Z CHROMOSOME IN FICEDULA FLYCATCHERS REVEALS CANDIDATE LOCI FOR DIVERSIFYING SELECTION
2010; Oxford University Press; Volume: 64; Issue: 12 Linguagem: Inglês
10.1111/j.1558-5646.2010.01082.x
ISSN1558-5646
AutoresNiclas Backström, Johan Lindell, Yu Zhang, Eleftheria Palkopoulou, Anna Qvarnström, Glenn‐Peter Sætre, Hans Ellegren,
Tópico(s)Genetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
ResumoEvolutionVolume 64, Issue 12 p. 3461-3475 Free Access A HIGH-DENSITY SCAN OF THE Z CHROMOSOME IN FICEDULA FLYCATCHERS REVEALS CANDIDATE LOCI FOR DIVERSIFYING SELECTION Niclas Backström, Niclas Backström Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorJohan Lindell, Johan Lindell Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorYu Zhang, Yu Zhang Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, Sweden College of Animal Science and Technology, China Agricultural University, No. 2 Yuanmingyuan Xi Lu, Haidian, Beijing 100094, ChinaSearch for more papers by this authorEleftheria Palkopoulou, Eleftheria Palkopoulou Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorAnna Qvarnström, Anna Qvarnström Department of Animal Ecology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorGlenn-Peter Sætre, Glenn-Peter Sætre Center for Ecological and Evolutionary Synthesis (CEES), Department of Biology, University of Oslo, P. O. Box 1066 Blindern, N-0316 Oslo, NorwaySearch for more papers by this authorHans Ellegren, Hans Ellegren Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, Sweden E-mail: Hans.Ellegren@ebc.uu.seSearch for more papers by this author Niclas Backström, Niclas Backström Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorJohan Lindell, Johan Lindell Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorYu Zhang, Yu Zhang Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, Sweden College of Animal Science and Technology, China Agricultural University, No. 2 Yuanmingyuan Xi Lu, Haidian, Beijing 100094, ChinaSearch for more papers by this authorEleftheria Palkopoulou, Eleftheria Palkopoulou Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorAnna Qvarnström, Anna Qvarnström Department of Animal Ecology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, SwedenSearch for more papers by this authorGlenn-Peter Sætre, Glenn-Peter Sætre Center for Ecological and Evolutionary Synthesis (CEES), Department of Biology, University of Oslo, P. O. Box 1066 Blindern, N-0316 Oslo, NorwaySearch for more papers by this authorHans Ellegren, Hans Ellegren Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, Sweden E-mail: Hans.Ellegren@ebc.uu.seSearch for more papers by this author First published: 06 December 2010 https://doi.org/10.1111/j.1558-5646.2010.01082.xCitations: 32AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Theoretical and empirical data suggest that genes located on sex chromosomes may play an important role both for sexually selected traits and for traits involved in the build-up of hybrid incompatibilities. We investigated patterns of genetic variation in 73 genes located on the Z chromosomes of two species of the flycatcher genus Ficedula, the pied flycatcher and the collared flycatcher. Sequence data were evaluated for signs of selection potentially related to genomic differentiation in these young sister species, which hybridize despite reduced fitness of hybrids. Seven loci were significantly more divergent between the two species than expected under neutrality and they also displayed reduced nucleotide diversity, consistent with having been influenced by directional selection. Two of the detected candidate regions contain genes that are associated with plumage coloration in birds. Plumage characteristics play an important role in species recognition in these flycatchers suggesting that the detected genes may have been involved in the evolution of sexual isolation between the species. A major goal in evolutionary biology is to reveal the genetic basis of traits that affect the fitness of individuals in natural populations (Ellegren and Sheldon 2008). Having that knowledge is essential to be able to address a number of general problems in ecology and evolution, including the fitness distribution of new mutations (Eyre-Walker and Keightley 2007), the relative effects of mutation, selection, and drift on genetic and phenotypic diversity (Mitchell-Olds et al. 2007), and the relative importance of changes in gene expression and protein structure on phenotypic evolution (Hoekstra and Coyne 2007; Carroll 2008). Given that directional selection is an influential force in driving speciation (Schluter 2009; Schluter and Conte 2009), understanding which genes that cause differences in reproductive success among individuals within specific populations should also provide insight into the genetics of differentiation and the build up of reproductive isolation between populations (Coyne and Orr 2004; Gavrilets 2004; Price 2007). Recently, several examples of identification of gene or regulatory sequences, or genomic regions, that underlie critical phenotypes in "ecological model species" have started to appear (e.g., Abzhanov et al. 2004; Colosimo et al. 2005; Hoekstra et al. 2006; Prud'homme et al. 2006; Linnen et al. 2009). Additionally, there are a few cases in which the genes governing hybrid inviability and sterility have been successfully identified (Coyne and Orr 2004; Noor and Feder 2006; Orr et al. 2007; Phadnis and Orr 2009). However, this progress is mainly restricted to the identification of genes involved in intrinsic postzygotic isolation in model species. Finding causative loci associated with adaptation and/or speciation remains a grand challenge for studies of natural populations. For practical reasons, candidate gene approaches or other means by which the search for causative loci can be limited to particular regions of the genome are desirable, if possible. A traditional view holds that many genes with small additive effects are important in generating differentiation between populations (Fisher 1930). According to this way of reasoning, speciation should proceed through a gradual build-up of genetic incompatibilities between the genomes of diverging populations (e.g., Dobzhansky 1936; Muller 1942). Somewhat contrary to this view, recent empirical data suggest that few genes of large and epistatic effects can play an important role in the diversification process (Coyne and Orr 2004; Orr et al. 2004). Yet another possibility is that incompatibilities are built up at different rates in different regions of the genome. The rate of gene flow for loci that evolve under influence of divergent selection, for example, genes involved in local adaptation, is likely to be reduced compared to the rate at neutrally evolving regions (Smadja et al. 2008). Hence, if divergent populations develop isolating mechanisms that reduce levels of introgression (Qvarnström and Bailey 2009), interspecific recombination around loci experiencing divergent selection will be strongly reduced. This can result in large genomic blocks of elevated between-population divergence, a phenomenon termed "divergence hitchhiking" (Payseur et al. 2004; Harr 2006; Rogers and Bernatchez 2007; Via and West 2008). The size of regions affected by hitchhiking will be dependent on the rate of migration and the effective population size of each of the incipient species and can be extensive, especially if multiple loci are involved (Feder and Nosil 2010). Studies on the genetic basis of reproductive isolation suggest that sex chromosomes play a large role in causing low fitness in hybrids (Qvarnström and Bailey 2009). The Drosophila X chromosome, for example, shows a disproportionately large effect in genetic analyses of hybrid sterility, an observation known as the "large X-effect" (Coyne 1992; Presgraves 2008). On a similar note, the avian Z chromosome (in birds, males are ZZ and females are ZW) has been shown to contain an overrepresentation of loci subject to adaptive evolution (Ellegren 2009). In addition, recent work in Ficedula flycatchers has established that species-specific male plumage traits, female mating preferences, and genes causing low hybrid fitness are all linked to the Z chromosome, (Sætre et al. 2003; Sæther et al. 2007). Tight genetic coupling of loci affecting both prezygotic isolation and postzygotic isolation would allow differentiation to proceed despite some gene flow and could facilitate genetic differentiation via reinforcement (Servedio and Sætre 2003). Together, these data suggest that a focus on sex chromosomes in search of genomic regions involved with divergent selection and speciation is warranted. We have previously shown that avian sex-linked (Z-chromosomal) genes display an accelerated rate of sequence evolution compared to autosomal genes (Mank et al. 2007), a "fast-Z effect" analogous to the well-known "fast-X" phenomenon described for XY systems (Charlesworth et al. 1987). The tendency for fast evolution of sex-linked genes thus seems independent of form of heterogamety. Although it has been suggested that "fast-X" is owing to the exposure of recessive beneficial mutation in the heterogametic sex, and thereby a consequence of selection, we recently showed that the pattern of polymorphism and divergence in avian sequence data is also consistent with a neutral explanation for the "fast-Z" effect (Mank et al. 2010). Birds are important model organisms for understanding the speciation process (Price 2007). Yet, it has been difficult to study the genetics of adaptation and population divergence in birds based on large-scale genetic marker analysis due to a shortage of genomic resources. There is now a draft genome sequence available for the chicken Gallus gallus (ICGSC 2004) and this has recently also become the case for the zebra finch Taeniopygia guttata (Warren et al. 2010). Moreover, we have developed a platform for genomic analyses of nonmodel bird species by the establishment of a conserved gene-based marker set evenly spread across the avian genome (Backström et al. 2008a). We have now extended this approach by developing a high-density, gene-based marker resource for the Z chromosome and apply these markers in a "genome scan" of two closely related sister species of the Old World flycatcher genus Ficedula, the pied flycatcher (F. hypoleuca) and the collared flycatcher (F. albicollis). A major advantage of this study system is that questions concerning all three major sources of reproductive isolation (i.e., ecological divergence, sexual isolation, and genetic incompatibilities) can be addressed (Qvarnström et al. 2010; Sætre and Sæther 2010). The main objective of our study was to investigate patterns of diversification along the Z chromosome of pied flycatcher and collared flycatcher, and to search for loci evolving under directional selection. We find evidence for several regions of elevated between-species divergence and reduced within-species nucleotide diversity, and discuss their relevance to adaptive population divergence. Methods STUDY SPECIES The pied flycatcher and the collared flycatcher have a mitochondrial DNA divergence of approximately 3% and are estimated to have diverged from each other during the last one-and-a-half to two million years (Sætre et al. 2001). The distribution range of the pied flycatcher encompasses large areas of western and northern Eurasia, whereas the collared flycatcher occupies regions of central and eastern Europe. Because their common ancestor should have been affected repeatedly by the cyclic climate conditions of the Pleistocene, this suggests that the pied flycatcher was largely restricted to refugia on the Iberian Peninsula during glacial periods, whereas the collared flycatcher was restricted to a refugium on the Appenine peninsula (Sætre et al. 2001). The two species currently live sympatrically in central and eastern Europe, and on the Baltic Sea islands of Öland and Gotland. Hybridization occurs in these regions (Sætre et al. 1999, 2001; Veen et al. 2001), but females prefer males of their own species as mates (Sætre et al. 1997; Sæther et al. 2007), and both song (Qvarnström et al. 2006) and plumage (Wiley et al. 2005) play important roles in species recognition. Female hybrids are sterile whereas male hybrids are fertile but have severely reduced fitness (Svedin et al. 2008). The reduction in fitness remains for several backcrossed generations (Wiley et al. 2009). SAMPLING AND DNA EXTRACTION Blood samples from male pied and collared flycatchers were collected from four different populations representing both allopatric and sympatric locations. Allopatric individuals were sampled in Lingen, Germany (pied flycatcher, n= 10) and near Budapest, Hungary (collared flycatcher, n= 10). Sympatric individuals were collected on Öland, Sweden (pied flycatcher, n= 12 and collared flycatcher, n= 9). One red-breasted flycatcher (F. parva), collected in the Jeseník Mts, Czech Republic was included as outgroup. DNA was extracted by incubation with proteinase K (0.05 mg/mL final concentration) in Laird's buffer at 37°C over night, after which DNA was purified by two rounds of phenol-chloroform extraction and one round of pure chloroform treatment followed by precipitation with cold 96% ethanol and NaAc. DNA was rinsed once with cold 70% EtOH, dried and dissolved in ddH2O. MARKER DEVELOPMENT We have previously shown that chicken and collared flycatcher Z chromosomes are essentially syntenic and for large parts colinear (Backström et al. 2006). The Z chromosome is 75 Mb in the most recent version of the chicken genome assembly (http://www.ncbi.nlm.nih.gov/projects/genome/guide/chicken/) and our aim was to have Z-linked flycatcher markers corresponding to a density of one marker per MB along the homologous chicken Z chromosome. We used 23 previously published Z-linked markers (Backström et al. 2006) and developed additional markers by aligning chicken exon sequences from target locations (i.e., every Mb) to the draft assembly of the zebra finch genome (http://www.ncbi.nlm.nih.gov/genome/seq/BlastGen.cgi?taxid=59729). PCR primers were designed to anneal to conserved regions of exons (i.e., showing high degree of sequence identity between chicken and zebra finch), flanking introns 500–1000 bp in size. Following optimization of PCR and sequencing conditions using flycatcher DNA, the final set of markers consisted of 74 introns from 73 different genes, which in chicken are evenly spread along the entire Z chromosome (Fig. 1, Table 1). Figure 1Open in figure viewerPowerPoint Position of loci along the Z chromosome according to the chicken physical map. Gene names are given to the left and physical positions (Mb) to the right. The bracketed interval has been affected by chromosomal rearrangements so the gene order is different in flycatchers (Backström et al. 2006). Gene names starting with UNKN are uncharacterized genes that lack a name. Table 1. Summary of diversity and divergence estimates. Pos = position on the chicken Z chromosome (Mb), Gene = gene name, Length = length of aligned intronic sequences, Fix = number of fixed differences between species, Share = number of shared polymorphisms between species, Fhpol = number of polymorphism segregating in the pied flycatcher only, Fapol = number of polymorphisms segregating in the collared flycatcher only, S = number of segregating sites, π= nucleotide diversity, FST= level of differentiation (Weir and Cockerhams FST), Fh = pied flycatcher and Fa = collared flycatcher, *= loci included in the subsequent zoom-in analysis. UNKN= uncharacterized gene with no known name. Pos Gene Length Fix Share Fhpol Fapol Fh S Fh π Fa S Fa π FST 0.25 NARS 227 0 0 3 1 3 0.0010 1 0.0003 0.0087 0.70 MALT 390 0 5 5 15 9 0.0053 18 0.0116 0.0509 01.32 MADH2 403 1 0 5 4 5 0.0016 4 0.0028 0.5163 01.76 LOXHD1 748 0 0 10 9 10 0.0032 9 0.0045 0.0911 03.79 PI3K 870 0 0 7 8 6 0.0011 8 0.0020 0.2971 03.82 PI3K 435 3 1 2 6 3 0.0007 7 0.0048 0.4886 07.07 KIF24 460 0 4 1 0 5 0.0016 4 0.0035 0.5098 07.41 UNKN1 206 0 0 3 5 3 0.0034 5 0.0090 0.1308 07.93 FANCG 360 0 2 1 3 3 0.0023 5 0.0014 0.0320 08.46 KIAA0258 372 0 1 1 4 2 0.0012 5 0.0025 0.4859 09.08 MTMR12* 606 1 0 3 5 5 0.0003 7 0.0031 0.5263 09.47 TARS 737 0 1 4 7 5 0.0015 8 0.0023 0.3056 09.72 SLC45A2* 1009 0 2 12 6 14 0.0028 8 0.0017 0.6153 09.75 UNKN9* 671 1 0 7 1 7 0.0018 1 0.0001 0.6529 09.88 RAI14* 180 0 0 0 2 0 0 2 0.0011 0.0360 09.92 BRIX* 95 0 0 2 2 2 0.0037 2 0.0031 0.6567 09.93 DNAJA5* 340 0 0 0 2 0 0 2 0.0005 0.0360 09.95 AGT2 798 0 0 5 4 5 0.0006 4 0.0015 0.6652 10.29 LMBR1* 200 0 1 0 1 1 0.0008 2 0.0021 0.6991 10.35 UPF0465* 791 2 0 2 6 2 0.0013 6 0.0011 0.4392 10.36 UNKN10* 387 0 0 1 1 1 0.0014 1 0.0003 0.4406 10.39 RANBP3L* 332 0 1 0 2 1 0.0001 1 0.0018 0.2361 10.78 NIPBL* 653 0 0 5 5 5 0.0024 5 0.0022 0.4121 10.86 UNKN11* 552 1 0 5 2 5 0.0017 2 0.0011 0.5250 10.88 UNKN12* 295 0 0 1 4 1 0.0002 4 0.0021 0.1431 10.91 NUP155* 634 0 1 0 11 1 0.0001 12 0.0042 0.4952 10.93 NUP155 327 4 0 3 2 3 0.0013 2 0.0015 0.6514 11.36 EGF 574 0 1 0 0 1 0.0002 1 0.0026 0.6017 11.38 EGFLAM* 374 0 0 8 3 8 0.0049 3 0.0012 0.2891 11.40 LIFR* 632 0 0 2 4 2 0.0002 4 0.0021 0.5715 11.75 DOC2 627 0 0 1 2 1 0.0001 2 0.0009 0.3111 12.31 PRKAA1 838 0 0 0 2 0 0.0002 2 0.0005 0.7227 13.16 NNT 755 1 0 3 11 3 0.0018 11 0.0042 0.2427 14.32 PARP8 879 0 0 0 0 1 0.0033 7 0.0117 0.3982 15.12 VLA1 693 0 0 5 5 5 0.0018 5 0.0021 0.2957 16.88 GPBP1 477 0 4 7 12 11 0.0050 16 0.0116 0.0917 18.88 IPO11 784 0 3 7 11 10 0.0027 14 0.0055 0.1364 19.91 ADAMTS6 418 0 0 1 3 1 0.0006 3 0.0039 0.4945 19.98 PPWD1 578 0 1 3 12 4 0.0003 13 0.0074 0.5472 21.31 ZTL1 361 0 1 5 2 6 0.0036 3 0.0044 0.1552 22.93 IQGAP2 342 0 0 3 5 3 0.0003 5 0.0059 0.5623 23.07 SV2C 354 0 0 3 4 3 0.0006 4 0.0026 0.1012 23.68 LNR42 360 1 1 7 4 8 0.0035 5 0.0033 0.2735 25.91 MIP 652 0 2 0 2 2 0.0003 4 0.0036 0.2795 26.59 DOCK8 678 0 3 2 4 5 0.0030 7 0.0025 0.2883 27.48 RFX3 575 2 0 5 9 5 0.0016 9 0.0057 0.2836 28.57 GLDC 415 2 1 1 4 2 0.0017 5 0.0060 0.3598 29.41 PTPRD 705 0 2 7 5 9 0.0029 7 0.0029 0.2432 30.56 TYRP1 531 1 1 3 0 3 0.0027 0 0.0000 0.6704 31.46 FRAS1 569 0 0 7 7 7 0.0038 7 0.0039 0.3066 31.79 UNKN2 596 0 0 2 4 2 0.0004 4 0.0030 0.2379 33.21 ADAMTSL3 624 0 1 3 3 3 0.0008 3 0.0006 0.6858 33.54 ASAH3L 576 0 1 7 4 8 0.0041 5 0.0017 0.1890 34.37 UNKN3 632 0 0 6 4 6 0.0019 4 0.0024 0.4121 34.77 MLSN2 519 0 0 3 6 3 0.0023 6 0.0036 0.0965 35.44 ZFAND5 383 0 0 2 3 2 0.0003 3 0.0036 0.5948 36.32 TRPM6 734 2 0 3 14 3 0.0006 14 0.0051 0.3634 37.20 VPS13A 599 0 1 5 4 6 0.0015 5 0.0035 0.3886 37.54 PSAT 991 0 0 1 5 1 0.0008 5 0.0065 0.4517 38.13 TLE4 644 1 0 4 6 4 0.0010 6 0.0019 0.4877 39.20 RASEF 898 0 2 12 9 14 0.0043 11 0.0018 0.2385 40.25 MAK10 967 1 0 5 8 5 0.0007 8 0.0026 0.4226 42.64 SPINZ 959 1 2 0 3 2 0.0024 5 0.0018 0.2887 43.33 UNKN4 429 0 0 2 6 2 0.0010 6 0.0045 0.2981 45.20 APC 565 0 0 6 3 6 0.0015 3 0.0011 0.0813 47.43 EF5 407 0 0 3 1 3 0.0022 1 0.0014 0.4326 48.90 HISPPD1 374 0 1 0 4 1 0.0023 5 0.0025 0.0443 50.19 CHD1Z 557 1 0 3 0 3 0.0009 0 0.0000 0.7636 50.99 UDP 452 0 0 3 10 3 0.0003 10 0.0039 0.4958 52.37 GAF 611 0 0 8 4 7 0.0021 4 0.0019 0.1812 52.84 NRG1 506 0 0 4 2 4 0.0014 2 0.0013 0.5358 53.60 ABCA1 231 0 1 5 2 6 0.0113 3 0.0039 0.1800 56.59 MCTP1 356 0 0 2 0 2 0.0017 1 0.0002 0.2142 58.48 GPR98 419 2 1 3 2 4 0.0006 3 0.0012 0.7359 59.65 CCNH 354 0 0 2 1 2 0.0026 1 0.0010 0.0897 59.70 RASGAP 731 3 0 7 0 7 0.0008 0 0.0000 0.7851 61.31 UNKN5 897 0 0 6 13 6 0.0014 13 0.0037 0.1439 62.03 RPS23 262 0 1 4 0 5 0.0039 1 0.0007 0.3089 62.74 UNKN6 377 0 0 5 5 5 0.0021 5 0.0055 0.2921 64.41 SNX30 651 0 0 7 14 7 0.0030 14 0.0075 0.2294 65.03 SMC2 433 0 0 1 3 1 0.0002 3 0.0011 0.1230 66.80 PLAA 433 1 0 1 2 1 0.0010 2 0.0004 0.6994 67.01 UNKN7 684 0 0 2 7 2 0.0003 7 0.0012 −0.0144 68.68 SMU1 629 2 0 0 11 2 0.0009 11 0.0024 0.2993 69.74 DMXL1 395 1 0 3 4 3 0.0005 4 0.0018 0.6323 71.23 MCCC2 536 3 0 5 2 5 0.0025 2 0.0005 0.5185 72.31 ALDH7A1 613 0 0 5 4 5 0.0035 4 0.0025 0.3386 73.07 ZNF608 525 0 1 5 2 6 0.0014 3 0.0013 0.3847 73.86 UNKN8 227 0 0 0 1 0 0.0000 1 0.0020 0.6571 74.59 MELK 274 1 0 0 4 0 0.0000 4 0.0032 0.8738 In the absence of a flycatcher genome assembly, or a detailed physical or genetic map, we use the physical location of markers on the chicken Z chromosome when graphically showing the chromosomal distribution of estimates of flycatcher population genetic parameters. It should be noted, however, that the gene order differs between collared flycatcher and chicken Z chromosomes in a region corresponding to positions 33.5 Mb and 53.6 Mb in the most recent assembly of the chicken genome (http://www.ncbi.nlm.nih.gov/projects/genome/guide/chicken/) (Backström et al. 2006). Several inversions appear to have occurred in this region and it is difficult based on sparse marker maps to unveil the precise chromosomal homologies (Backström et al. 2006). MOLECULAR METHODS All PCRs were conducted in 15–20 μl reactions with approximately 50 ng of template DNA, 0.20 μM of each primer, 50 μM dNTP, 0.025 U TaqGold polymerase (Applied Biosystems, Carlsbad, CA), and 2.5 mM MgCl2. The general temperature profile was an initial 5-min activation step at 95°C followed by 40 cycles of 30 s denaturation at 95°C, 40 s annealing at an optimized locus-specific annealing temperature and 45–60 s elongation at 72°C. PCR-products were cleaned with ExoSAP (USB Corp., Cleveland, OH) and 2 μl of purified PCR-product were used in sequencing reactions using BigDye cycle sequencing terminator chemistry and temperature profile settings according to the manufacturer's recommendations (Applied Biosystems, Carlsbad, CA). Sequencing reactions were cleaned with the XTerminator System following the manufacturer's recommendations and sequencing was performed on an ABI3730xl DNA Analyzer (Applied Biosystems, Carlsbad, CA). All sequences included in the analyses have been deposited in GenBank under accession numbers HQ207834-HQ211489 (Table S1). DATA ANALYSIS Raw sequence data were edited in Sequencher 4.7 (Gene Codes Corp., Ann Arbor, MI) and locus-specific contigs were created and used for polymorphism screening. Sequences from individual birds were exported and realigned using ClustalW as implemented in MEGA version 4.0 (Tamura et al. 2007), and MEGA was also used to estimate the nucleotide diversity (π). Single nucleotide polymorphisms in individual bird sequences (diploid) were randomly assigned to single chromosome sequences (haploid) using DAMBE version 5.0.23 (Xia and Xie 2001) and intraintronic haplotypes were estimated for each individual by applying the Bayesian ELB algorithm as implemented in Arlequin version 3.11 (Excoffier et al. 2005). Point estimates of FST-values were calculated between species for both allopatric, sympatric, and combined populations and between allopatric and sympatric populations within species using the method of Weir and Cockerham (1984) as implemented in fdist2 (Beaumont and Nichols 1996). Intraspecific estimates of FST were low (mean = 0.027 and 0.047 for the pied and the collared flycatcher, respectively) and therefore we restrict the figures and tables to mainly include the interspecific FST-values for allopatric and sympatric populations combined. The number of alleles and the expected heterozygosity for different loci were estimated in FSTAT version 2.9.3.2 (Goudet 1995). We used the Bayesian method implemented in BAYESFST (Beaumont and Balding 2004) to identify loci in which FST-estimates were indicative of positive selection. The method uses a Markov-Chain-Monte-Carlo approach to estimate locus, population, and locus by population effects. The posterior distribution of the locus effect is used to identify if any of the empirical FST-values show signs of positive or balancing selection. The method appears to be relatively robust to variation in mutation rates and less sensitive to different demographic histories of included populations (Beaumont and Balding 2004) than the fdist2 method (Beaumont and Nichols 1996). BAYESFST was run with default settings for all individuals within each species, and also for allopatric and sympatric populations separately. A 5% significance level was applied to the statistical tests. Because we were mainly interested in loci putatively evolving under the influence of diversifying selection, we did not consider outlier loci with a lower than expected degree of differentiation (a negative value of the 97.5% quantile indicative of balancing or stabilizing selection). All runs were conducted twice to verify that the same outlier loci were detected in independent runs. Applying corrections for multiple testing is not straightforward in Bayesian posterior analyses (Beaumont 2008). However, simulations indicate that the false positive rate of neutral loci appearing as positively selected is low when applying the BAYESFST method. Only 25 of 6800 neutral loci were erroneously detected as positively selected by Beaumont and Balding (2004), giving a false positive rate of 0.0037. As the number of loci included in the analysis is 74 we expected to find approximately 0.27 outliers (0.0037 × 74 = 0.27) per analysis by pure chance even if all loci evolve neutrally. It should be noted, however, that certain demographic scenarios potentially could affect the rate whereby false positives are discovered. Hence, it is possible that our estimate of expected false positives is naïve because it is likely that both species in this study have been subjected to population size changes and perhaps also population subdivision during the time since their divergence. No matter the significance of these factors on the patterns of genetic differentiation, our main aim was to identify loci putatively evolving under influence of selection and to avoid the risk of discarding possible candidates we include all significant outliers from the Bayesian analysis in the subsequent analyses and in the discussion. We assessed the variance within populations as well as among populations of partitions of loci by analysis of molecular variance (AMOVA) of haplotype data as implemented in Arlequin version 3.11 (Excoffier et al. 2005). Recent analyses have indicated that false positive results can be obtained when applying a model that does not take hierarchical population structure into account and we therefore also ran the analysis using a hierarchical structure model (Excoffier et al. 2009). With the exception of KIF24, all genes detected to be subject to diversifying selection in any of the Bayesian analyses were also significant or nearly significant outliers in the hierarchical analysis (data not shown). Therefore we restrict the presentation of results to only include the results from the Bayesian analysis. Avian substitution rates at presumably neutral sites vary on a regional scale, suggesting variation in underlying mutation rate (Berlin et al. 2006). The neutral theory predicts that mutation rate and level of genetic diversity should be positively correlated. Low nucleotide diversity of a mutation cold-spot region could therefore mimic the low diversity seen after a selective sweep in a region with higher mutation rate. To investigate the effect of variation in mutation rate on nucleotide diversity levels, we estimated locus-specific average pairwise divergence between the pied flycatche
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