Construction of a linkage map of the zebra finch genome using SNP markers

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UPTEC X09 012

Examensarbete 30 hp Mars 2009

Construction of a linkage map of the zebra finch genome using SNP markers

Harriet Mellenius

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Molecular Biotechnology Programme

Uppsala University School of Engineering

UPTEC X 09 012 Date of issue 2009-03

Author

Harriet Mellenius

Title (English)

Construction of a linkage map of the zebra finch genome using SNP markers

Title (Swedish)

Abstract

The zebra finch (Taeniopygia guttata) is one of the most used model organisms for studies of behaviour and neurology, especially pertaining to the learning process. Nevertheless, genetic information about the zebra finch has until recently been scarce. The aim of this project was to produce a linkage map over the zebra finch genome. A pedigree of 1,351 birds was investigated, and 1,080 were successfully genotyped for 1,424 single nucleotide polymorphisms (SNPs). The linkage analysis resulted in a framework map consisting of 423 markers, covering 32 chromosomes and 1,340.2 cM. The results reveal that physical and genetic distances show a non-linear relationship in the zebra finch. The map will be used to trace the genetic background of the phenotypic traits recorded in this zebra finch pedigree.

Keywords

Evolutionary genetics, genetic linkage analysis, linkage map, zebra finch, recombination, single nucleotide polymorphism

Supervisors

Niclas Backström

Uppsala universitet

Scientific reviewer

Mikael Thollesson

Uppsala universitet

Project name Sponsors

Language

English

Security

ISSN 1401-2138 Classification

Supplementary bibliographical information

Pages

68 Biology Education Centre Biomedical Center Husargatan 3 Uppsala

Box 592 S-75124 Uppsala Tel +46 (0)18 4710000 Fax +46 (0)18 555217

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Construction of a linkage map of the zebra finch genome using SNP markers

Harriet Mellenius

Sammanfattning

En genetisk karta informerar om både hur en arts arvsmassa är arrangerad och var man ska söka efter de gener som påverkar en viss egenskap. Kartan placerar de genetiska markörer man undersöker i grupper motsvarande artens kromosomer och i ordning längs kromosomerna. Avstånden mellan markörerna i en grupp mäts i förekomsten av överkorsning mellan dem; hur ofta två nästan likadana kromosomer utbytt information.

Av de två nästan likadana kromosomerna kommer en från mamman och en från pappan. Därför är en genetisk markör en bit av arvsmassan som ofta skiljer sig åt mellan individer så att den kan påvisa överkorsningarna. I denna studie användes variation av enstaka byggstenar i den genetiska koden som markörer.

Kartan byggs genom att man i ett släktträd observerar hur ofta markörerna tillsammans förs vidare till nästa generation. Om två markörer nästan alltid ärvs ihop betyder det att de tillhör samma kromosom, och när de ibland skiljs åt har överkorsning skett mellan dem.

I detta projekt användes ett släktträd med 1351 zebrafinkar vars arvsmassa undersöktes i 1424 punkter, och den karta som byggdes inkluderade 423 markörer. Hos dessa zebrafinkar hade också många beteenderelaterade egenskaper studerats, vars genetiska bakgrund kanske kommer att kunna utforskas med hjälp av kartan.

Examensarbete 30hp

Civilingenjörsprogrammet Molekylär bioteknik

Uppsala universitet mars 2009

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ABBREVIATIONS 3

INTRODUCTION 4

GENETIC MAPS, A HISTORY 4

GENETIC MAPPING AND SEQUENCING IN AVIAN GENOMES 4

RECOMBINATION IS NECESSARY FOR GENETIC MAPPING 5

GENETIC MARKERS 6

MAPPING METHODS 7

THE ZEBRA FINCH 8

THE POPULATION 9

AIMS AND STRATEGY 9

METHODS 10

SNP IDENTIFICATION 10

GENOTYPING 10

DATA PROCESSING 11

MAPPING 11

GRAPHICS 13

PHYSICAL DISTANCES 13

RESULTS 14

MARKER GENOTYPING 14

LINKAGE GROUPS 14

THE FRAMEWORK MAP 15

COMPARISONS 20

THE GENETIC MAPS OF THE ZEBRA FINCH 20

THE CHICKEN GENOME 21

GENETICAL VS. PHYSICAL DISTANCES 25

DISCUSSION 28

CONCLUSIONS 30

ACKNOWLEDGEMENTS 30

REFERENCES 32

LIST OF APPENDICES 34

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Abbreviations

SNP Single Nucleotide Polymorphism

cM centiMorgan

RFLP Restriction Fragment Length Polymorphism

QTL Quantitative Trait Loci

LOD Logarithm of Odds

BLAST Basic Local Alignment Search Tool

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Introduction

Genetic maps, a history

The aim of genetic mapping, unlike genetic sequencing, is not to determine every nucleotide in the genetic sequence, but to construct a general representation of a portion of the genome, consisting of the linear order of a set of selected markers.

The map, genetic or physical, can give information about the karyotype and reveal the approximate position of the included markers. While a physical map will give the distance between markers in physical units, that is, the number of base pairs, the distances in a genetic map are based on the frequency of recombination events over a distance.

Genetic mapping and sequencing in avian genomes

The species that scientists have been most eager to genetically map are the ones that have been used extensively in previous research, called model organisms, in order to be able to compare previously obtained knowledge with new genetical data. The very first genetic maps of chromosomes were developed for the Drosophila melanogaster genome; one of the most commonly used model

organisms (Sturtevant 1913). Other species of interest have been organisms of economical significance.

Those are the reasons why the chicken (Gallus gallus) was the first, and at

present the only, avian genome to be sequenced. Yet, the chicken is not

representative for all birds and yields only limited insight to wild avian

populations. The chicken belongs to the fowls, though the biggest of the avian

orders containing nearly half of all avian species is Passeriformes, the passerines

(Sibley and Ahlquist 1990, Barker et al. 2004), which have been the focus of many

ecological studies. At this point, the only passerine birds genetically mapped for

the whole genome are the zebra finch, for which a genetic map was published in

May 2008 (Stapley et al. 2008), and the collared flycatcher in July 2008

(Backström et al. 2008). Partial maps of passerine genomes worth mentioning

have been constructed for the great reed warbler (Hansson et al. 2005, Åkesson et

al. 2007) and the house sparrow (Hale et al. 2008).

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Previous studies on chicken and passerine genomes have revealed an almost one- to-one homology between chicken and passerine chromosomes. The exceptions are chicken chromosomes 1 and 4, which are represented by two chromosomes each in the passerine genomes (Derjusheva et al. 2004, Stapley et al. 2008).

However, even though the general chromosome organisation is preserved, there is evidence of intrachromosomal rearrangements (Stapley et al. 2008, Backström et al. 2008).

Recombination is necessary for genetic mapping

Recombination is a process that occurs uniquely in meiosis, after DNA replication. Homologous chromosomes, consisting of the two sister chromatids with a common centromere, pair up forming tetrads, where chiasmata can be formed along the chromosome arms. A chiasma is the complex where crossover, or recombination, occurs. Since the homologous chromosomes originate from the two parents, the recombined chromosome will be an assembly of genes from both parents. When the newly formed gamete results in offspring, each chromosome will be composed of genetic material from both grand-parents, either maternal or paternal.

The unit of the genetic map is the centiMorgan, cM. The distance of one cM

between two linked markers implicates a 1% incidence that they are separated by

recombination. The cM is related to the physical distance measured in base pairs

in the sense that recombination is generally more likely to occur somewhere

along a long stretch of the chromosome than over a shorter one, but they are not

proportional as additional factors affect the rate of crossing-over. For instance,

the recombination rate varies along the chromosome, with reduced rate of

recombination close to the centromere. It has been suggested from studies on

chicken that this tendency is weaker in birds than in other vertebrates (Jensen-

(11)

together onto the same chromosome, or, on the other hand, break up a collection of genetic variants, called a haplotype. When genes are linked, grouped together on the same chromosome, they are allowed to co-segregate through a pedigree. If the genes of a haplotype interact to give a combined effect, this effect may be observable in the pedigree, and may also be subject to selection. Thus, the mapping of genetic linkage can be of use for gene annotation, and regions where recombination seems to be underrepresented can be assumed to contain informative haplotypes.

Genetic markers

In order to distinguish the two chromosomes in a chromosome pair from each other, the genetic marker must be a site where variation is common, so that a large enough number of individuals in the investigated pedigree are heterozygous for that site. Only where it is possible to tell the alleles apart, recombination events can be detected. Throughout the history of genetic linkage analysis, different types of markers have been used.

Among the first genetic markers to be used were the RFLPs, or restriction fragment length polymorphisms (Grodzicker et al. 1974). Restriction enzymes are used to cleave DNA strands at sites of short, specific sequences. Where there is variation among the restriction sites between the chromosomes, the DNA fragments resulting from restriction enzyme cleavage will be of distinctly different lengths. The major benefit with RFLPs is the ease with which new markers can be developed; the analysis is, on the other hand, slow and inconvenient.

Sometimes, a nucleotide sequence can be repeated many times consecutively. The

number of times the sequence is repeated varies among individuals, and they can

thus be used as a genetic marker. If the sequence is very short, up to six base

pairs, the repeat is called a microsatellite (Ellegren 2004). Due to the presence of

multiple alleles, microsatellite polymorphisms are so variable in number of

repeats that they can even tell closely related individuals apart, and are

therefore the genetic marker used in DNA profiling. The variability is clearly a

(12)

big advantage when microsatellites are used as genetic markers, but also makes comparisons between species troublesome.

The genetic markers used in this study were single nucleotide polymorphism, or SNPs. A SNP is a substitution of a single nucleotide by mutation that makes a common variation in the population. Typically, the SNP nucleotide alternates between only two bases. The frequency of each of these in the population is called allele frequency. For a SNP to be suitable as a genetic marker, the allele frequency must be sufficiently high for it to be fairly common with biallelic individuals in the population. An advantage using SNPs as markers, that was a benefit to this study, is that the flanking sequences of the SNP is known, thus enabling searches for homologues in other sequenced species.

Mapping methods

Genetic linkage analysis is a statistical technique for building genetic maps from linkage data. Linkage data is typically obtained through genetic analysis of a pedigree where the segregation of markers can be observed, and recombination events traced. The collected data will tell how often a pair or a set of markers are inherited together. Allele combinations that are inherited together more often than expected are assumed to pertain to the same chromosome or in the same linkage group on a chromosome.

Subsequently, the markers within each linkage group can be ordered. The order

of the markers is based on how often recombination occurs between each pair of

markers, and to be able to determine where a recombination event has chanced,

the descent of the markers must be revealed. Only after deciding whether a

chromosome is paternal or maternal, and the parental markers are genotyped, it

is possible to detect recombination events.

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distance between them. However, there is always a risk that two or more recombination events occur between two adjacent markers. On these occasions, the total count of recombinations will be underestimated, as double recombinations re-change the alleles. This problem is dealt with by the recalculation of all recombination rates using a function that takes into account the chance of double crossovers between adjacent markers. Moreover, the probability of a recombination event is reduced close to other crossovers. This phenomenon is called crossover interference (Sturtevant 1915), and the mechanism behind it is not yet fully uncovered. The Kosambi mapping function take this as well into account, and this function is hence used to make observed recombination rates better represent the true recombination fractions along the chromosome (Kosambi 1944, Zhao & McPeek 1996).

Using these data, the most likely marker order can be computed by addition of the calculated genetic inter-distances between the markers. This information can be ambiguous, so that there is seldom a single best order that includes all markers, but more often many plausible marker orders of similar likelihood. The concept of framework maps refers to that the final marker order has significantly higher probability compared to the alternatives. This map unlikely includes all markers as some can be placed at more than one locus with equal probability.

The Zebra Finch

The zebra finch (Taeniopygia guttata) may not be one of the most well-known model organisms, but is in fact the most used model organism for the study of behaviour and neurology, especially pertaining to the learning process (Jin &

Clayton 1997, Bottjer et al. 1985). Every male bird learns to sing by imitating a tutor, much like human babies learn to speak by listening to grown-ups (Williams 2004). An interesting aspect of zebra finch singing is the social context, such as the mating behaviour where the males sing in courtesy to the female.

Consequently, the singing has been widely studied due to an interest in

examining the genetic basis of learning. As a model organism, the zebra finch has

the advantages of being easy to breed in captivity, and it is just as popular among

pet owners as in scientific contexts.

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A genetic map of the zebra finch genome will enable scientists to associate their behavioural and neurological data to the regions in the genome that affect the observed phenotype. Furthermore, the zebra finch genome is currently being sequenced (http://genome.ucsc.edu/cgi-bin/hgGateway?db=taeGut1), which gives the opportunity to compare physical and genetic distance to exhibit variations in recombination rate over the chromosomes.

The population

The population of zebra finches used in this study was bred at the Department of Behavioural Ecology and Evolutionary Genetics at the Max Planck Institute for Ornithology at Seewiesen by Dr Wolfgang Forstmeier. The original ancestors were 63 males and 84 females, taken from the same population used in the zebra finch linkage map constructed by Stapley et al. (2008). The ancestors, of unknown kinship, gave rise to a pedigree of 1,204 individuals, all interrelated. The pedigree consisted of 535 males, 524 females, and 145 offspring of unknown sex, the youngest separated from the ancestors by at most four generations. The birds were paired under controlled conditions. Maternity of offspring was determined by observation, and paternity, when uncertain, by DNA analysis of ten microsatellites. The entire pedigree contained 1,351 individuals.

The most interesting feature of the pedigree is how well-studied it is. Numerous

traits such as mass and tarsus length (relating to growth), beak colour and song

rate (relating to attractiveness of males), and aggressiveness and responsiveness

in mate choice situations (behavioural traits) have been monitored. A genetic

map covering this specific pedigree will hopefully enable the localisation of

approximate chromosome positions connected to these traits, which would be a

significant scientific progress.

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(quantitative trait loci) analysis and for comparative genomics of birds. This genetic map would be obtained by linkage analysis of recombination data from a pedigree of zebra finches, genotyped for some marker of preference. This project used SNPs that were previously identified.

Methods

The assignment of the degree project was the data analysis of the already genotyped SNPs, but to put it in its correct context, the previous steps of the mapping project need to be described.

SNP identification

The original SNP panel consisted of 1,920 SNPs in total; 617 SNPs from the SNP panel used by Stapley et al. (2008) in the first zebra finch map, 187 identified from the sequencing of a number of pooled individuals from the pedigree in Sheffield, 917 from the genome sequencing of a single zebra finch of American origin, and 199 that had been identified in both of the latter reads. The newly identified SNPs were chosen from sequences that had a single known chicken homologue, as well as some of the Stapley SNPs. Hence 1,775 of the markers were homologues to loci with known position in the chicken genome.

Genotyping

The genotyping of the 1,920 SNPs was performed for 1,080 individuals from the

pedigree, using the Golden Gate Assay (Fan et al. 2003) from Illumina (San

Diego) at the SNP Technology Platform in Uppsala, Uppsala University

(http://www.genotyping.se). Prior to the genotyping, the DNA sequences flanking

the SNPs were examined for nucleotide composition, other polymorphisms etc. to

determine that they were appropriate as primers in the genotyping process. The

quality of the genotype data and the SNPs as genetic markers was controlled by a

set of quality control tests, including call rate, minor allele frequency, duplicate

tests for reproducibility and detection of inheritance conflicts. The call rate of

each SNP was calculated as the fraction of genotyping analyses that were

successful, including duplicates, for all individuals. The minor allele frequency

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shows whether the SNP is polymorphic in this population; if the minor allele frequency is zero, there is no variation to use for linkage analysis. An inheritance conflict is the deviation from Mendelian inheritance of the genotype data of parents and offspring. Inheritance conflicts in the data hence represent either genotyping errors or errors in the pedigree of the population.

Data processing

Prior to the linkage analysis, both the genotyping data and the pedigree had to be revised to assure linkage data quality. Among the markers, 108 out of 1,920 SNPs failed completely in all genotyping attempts. In addition, 380 SNPs had a minor allele frequency equal to zero and had to be discarded. Many inheritance conflicts were reported, but most of them erroneously, as no respect were taken to the fact that all markers on the Z chromosome were reported as homozygous. One individual however had so many inheritance errors that its pedigree position was considered wrong, and all its genotype data had to be removed. Fortunately, this had only minor effect on the analysis as this individual had neither siblings nor offspring. Eight SNPs had to be rejected due to excessive inheritance failures that indicated genotyping difficulties.

Ultimately, 1,424 out of 1,920 markers persisted. The proportion of succeeded

markers for the SNPs of different origin was 587/617 from the Sheffield panel

(Stapley et al. 2008), 144/187 from the sequencing on pooled individuals, 506/917

from the sequencing of a single American zebra finch and 187/199 of the

combined. Obviously, the SNP identification in the reads from the single

American individual did not conform with the pedigree in question, perhaps due

to differences in allele frequencies between population as all other birds were

related to the Sheffield population, or due to individual variations being

mistaken for polymorphisms.

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program option was executed with LOD score > 3 (logarithm of odds, corresponding to a p-value of 0.001). TWOPOINT makes twopoint linkage analyses to establish linkage in between each pair of markers by calculating whether two markers co-segregate through the pedigree. Henceforth, markers were grouped by the command AUTOGROUP, an addition in the Monsanto version. AUTOGROUP places markers that display linkage to each other in the twopoint data output in linkage groups.

Twopoint analysis for 1,424 markers can make up in 1,013,176 different pairs in theory. In practice, with a significant LOD score of at least 3 and linkage presumably only within linkage groups, there were only 50,714 pairs with significant linkage in the TWOPOINT analysis. However, with such an amount of data points, a probability of one false positive in a thousand would make a considerable amount of false linkages. A LOD score of 8 was consequently used for the AUTOGROUP command as a threshold for inclusion in a linkage group, producing only 1 expected false positive in each 10

⁸

. This produced a set of linkage groups that corresponded well with the expected chromosomes.

To include all significant linkages in the linkage groups, the AUTOGROUP option was executed for each linkage group separately using LOD score 3 within groups. In those executions, all markers suggested in first AUTOGROUP analysis were included, as well as the markers suggested with reference to the chicken chromosome organisation that had displayed significant linkage (of LOD score > 3) in the original TWOPOINT execution. The linkage groups contained at maximum 177 markers, which substantially reduced the risk of false positives.

Finally, all markers that had not been included in the AUTOGROUP executions despite having significant linkage (but with unknown position in the chicken genome) were assigned to linkage groups by their twopoint linkage data output.

Besides the LOD score, the AUTOGROUP command requires three additional

parameters to be specified to sort the markers into linkage groups; the minimum

number of informative meioses relative to the average number of informative

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meioses, the maximum number of linkages to other groups, and minimum fraction of linkages for each marker that are to the assigned group. These parameters were set to 0, 20 and 0.3, respectively. Those constrains were all relaxed so that the LOD score was hence the only restraining parameter.

When the linkage groups were established, the BUILD command that computes the most likely marker order with LOD score > 3 was run in each linkage group to produce a framework map. The BUILD command iteratively adds markers to the set of ordered markers, if the maximum likelihood of the order is improved by more than the threshold LOD score. Throughout the linkage analysis, distances were sex-averaged and calculated in Kosambi cM, with exception for TgZ (Taeniopygia guttata chromosome Z).

When building TgZ, individuals of unknown sex were excluded, using only 1,206 individuals of which 935 were genotyped. All females appeared as homozygous for all loci due to females having only one Z chromosome, why one of the assumed alleles for each locus was dismissed in females. Since recombination on sex chromosomes only occurs in the homogametic sex, the distances calculated were sex-specific for males.

Graphics

Alignment of maps, as well as visualisation of maps, was performed using MapChart (Voorips 2002).

Physical distances

For the comparison of genetic and physical distances, the flanking regions of the

SNPs were used in BLAST searches in the zebra finch sequence assembly

(http://genome.ucsc.edu/cgi-bin/hgGateway?db=taeGut1) to obtain the physical

positions of the SNPs.

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Results

Marker Genotyping

The average success rate, i.e. sample call rate, among the residual markers was 95.04%. In this group, 18 out of 81,563 duplicates failed, giving a reproducibility of 99.98%.

Linkage Groups

The linkage group assignment resulted in 32 linkage groups, corresponding to chromosome 1-28 plus chromosome Z, with double linkage groups for chromosome 1, 24 and 25, in the chicken genome and named accordingly; where a chicken chromosome corresponded to more than one linkage group in the zebra finch linkage, those were simply numbered (Table 1). Using LOD score

> 3, 1,404 of 1,424 markers displayed linkage to some linkage group. All markers included in the linkage groups are listed in Appendix A.

For chicken chromosome 22, there was only a single marker with no linkage to any other group. It is included in the table of linkage groups anyhow, which thus contains 1,405

Linkage group

Number of markers

Number of markers in framework map

Distance covered in Kosambi cM

Tg1.1 150 36 118.3

Tg1.2 109 31 90.8

Tg2 177 28 75.7

Tg3 131 44 69.5

Tg4 125 17 44.4

Tg5 120 30 63.7

Tg6 76 22 60.2

Tg7 53 20 41.2

Tg8 55 23 47.1

Tg9 39 17 52.3

Tg10 31 13 55.0

Tg11 29 11 34.1

Tg12 38 16 34.8

Tg13 35 12 47.7

Tg14 20 7 46.1

Tg15 41 9 43.8

Tg16 3 2 27.5

Tg17 18 11 49.4

Tg18 10 5 30.7

Tg19 29 17 55.3

Tg20 24 14 52.4

Tg21 8 4 32.4

Tg22 1 1 0

Tg23 10 7 33.6

Tg24.1 3 2 4.2

Tg24.2 2 2 1.4

Tg25.1 4 2 14.9

Tg25.2 3 2 1.7

Tg26 7 5 64.7

Tg27 7 2 0.7

Tg28 8 2 0

TgZ 39 9 46.6

Total 1405 423 1340.2

Table 1. Number of markers by linkage group. LOD > 3.

Linkage groups named according to chicken counterparts.

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markers, owing to its presence in the former zebra finch map. Most of the markers were assigned to the expected chromosome, with only three exceptions;

one marker located at chromosome 2 in chicken was found on chromosome 6 in zebra finch, and another, located at chromosome 10 in chicken, appeared on chromosome 14 in zebra finch, and lastly, a marker from chicken chromosome 4 was found in zebra finch chromosome 2.

Linkage group sizes correspond roughly with chicken chromosome lengths. As expected, chicken chromosome 1 correspond to two big linkage groups. However, chromosome 4, which was previously divided into two linkage groups as well, is only represented by one linkage group in these results. This linkage group contains markers from both chromosome 4 linkage groups in the previous zebra finch map (Stapley et al. 2008).

The Framework Map

The framework map provided by linkage analysis with LOD score > 3 contained

423 of the 1,404 linked markers (30.1%) and spanned 1,340.2 Kosambi cM. The

average genetic distance between adjacent markers was 3.43 cM, with a standard

deviation of 5.64 cM. Chromosome 22 was also included in the framework map

(Table 1, Figure 1) for further comparisons to other maps. Table 1 provides a

summary of the number of markers included in the framework map for each

linkage group. To have only two markers ordered is obviously equal to have no

markers ordered at all (as reading the linkage group from one end or the other

makes no difference). Linkage groups with only two ordered markers have

nevertheless been included in the framework map if the original linkage group

contained only very few markers (eight or less), where comparisons to other maps

are still informative.

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TS1572

A16569 A12272 A13872 TS0674 A18344 C00145 A19836 F09931 A15078 TS0017 TS1455 TS0649 TS0262 TS0168 TS1188 TS0016 C00191 C00198 A14883 A18836 TS0832 TS0444 C00119 TS1451 A06985 TS0816 C00184 TS0446 A12605

TS0973

F10196 F11969 C00188 C00149

TS0939 0

20

40

60

80

100

Tg1.1

TS1475

TS1538

C00269 C00243 TS0802 TS0453 TS0959 C00281 C00384 TS0805 A07057 F27377 TS0571 TS1289 C00130 C00134 C00059 TS0924 TS1660 A04369 TS1308 TS1453 A42430 C00086 TS0171 TS1291 TS0898

A09491 TS0815 TS0958 TS1393 0

20

40

60

80

Tg1.2

TS0789

TS1458

TS0744 TS0403 TS1243 A33405 A41865 C00399 TS0257 TS0745 C00113 A42699 F02848 TS0724 A07523 TS0591 TS0992 A10115 A13751 F11064 TS0894 A13559 F09532 TS0025 F13693 TS0738 C00205 TS1304 0

20

40

60

Tg2

A19608 TS1441 A16738 F10496 TS0558 F05200 TS0844 F05987 TS0180 F06312 F28042 TS0796 TS1211 TS1069 TS0365 A41720 TS1414 TS0335 A40085 A41675 A39343 A40039 C00369 A34284 A35359 TS0422 A37893 A41302 A37111 A36468 A25769 A23077 A29198 A35582 A17389 A27742 A30006 A28961 TS0121 A35630 C00381 A35839 A30742 TS1178 0

20

40

60

Tg3

F15999

A29722 TS0648 C00364

TS0810 TS1209 A36052 TS1220 A33982 TS1272 TS0361 F17130 C00272 C00212 TS1505 F14679 TS0011 0

20

40

Tg4

(22)

TS0368

TS0289

TS0391

TS1312 TS0829 TS0041 TS1292 TS0459 C00041 A03158 A04695 A43331 A05544 C00070 TS0166 A42516 C00116 TS0573 C00456 F23839 TS1101 TS1147 TS1504 TS1420 A22924 F01517 TS0690 A26084 TS1091 F17808 0

20

40

60

Tg5

A00955

TS1321 C00012 TS0201 A01528 TS0822 A00201 TS0491 A25632 TS1629 A36826 TS0023 F21486 TS0431 A33993 TS0987 TS0786 A31359 TS0812 A31051 F18753

A28113 0

20

40

60

Tg6

TS0178 A05508 A43180 A42625 A05129 TS0358 TS0276 TS1224 A40829 TS0486 A28355 A31013 C00138 C00240 A27405 F05452 TS1066 TS1113 C00056

TS1463 0

20

40

Tg7

A27182 TS1494 A22212 A21974 A23550 A23631 A35525 A37197 TS0264 A39248 A38560 A40409 TS0191 TS1382 C00448 A39167 F25450 TS1153 A40627 C00440 TS0200 TS1464

TS0823 0

20

40

Tg8

TS1419

TS1105

F23770 C00338 A30141 TS0599 C00284 TS0903 A25561 A27076 A22253 A27226 A21012 C00214

TS0106

C00004

C00009 0

20

40

Tg9

TS0390

TS0095

F26842 A38288

A40455 A35438 C00376 A28077 F21558 A32032 A22173 TS0934

TS0463 0

20

40

Tg10

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TS0909 A27945

TS0797 C00331 TS0946 C00343 F16314 TS1467 TS1431 TS0971 TS1444 0

20

Tg11

TS1476

A19389 A19735 A17518 A15050 A12406 TS1422 TS1506 A14062 F11646 F10615 TS0911 TS1344 TS0635 TS1454 TS1383 0

20

Tg12

A00771

C00005 TS0570 C00228 TS0870 TS1346 TS0477 A20964 C00215 C00028 TS1006 C00256 0

20

40

Tg13

A32839

TS0842

A02940 TS0389 TS0256

A01451

TS1550 0

20

40

Tg14

C00414 F25014

TS0585

TS1060

TS0333 A28843 TS0341 C00350

A23726 0

20

40

Tg15

TS0725

TS1363 0

20

Tg16

TS0388

TS0732 A36774 A32346 TS0115

TS0369 TS0504 TS0350

A28575 TS0542 TS1071 0

20

40

Tg17

A34181

TS1125 F26008 F22993 F23695 0

20

Tg18

TS0864 F22829 A28744 A18395

A32569 TS0768 TS0235

TS0997 TS1253 A27101 C00222 TS1158

F18227 F16699 TS0561

TS0590

TS1446 0

20

40

Tg19

A02020

TS0800

A22881

C00241 F23558 TS1590 TS1119 TS0920 A31598

TS1325 TS1569 TS0912 TS1324

A37069 0

20

40

Tg20

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F15517

A02955

F16527

A23214 0

20

Tg21

TS0787 0

Tg22

TS0627

TS0655 A02187 TS1403 TS1256 TS0149

C00007 0

20

Tg23

A01734

TS1263 0

Tg24.1

TS1315 TS0948 0

Tg24.2

TS0675

TS0662 0

Tg25.1

C00218 TS1376 0

Tg25.2

TS1117

TS1402

TS1565

TS1260 0

20

40

Tg26

C00264 TS1385 0

Tg27

TS0758 TS1490 0

Tg28

A12246

A24644 A08205

A34858 A03084 A37593

A34046 A29010

A43515 0

20

40

TgZ

(25)

The framework map (Figure 1) shows an obvious condensation of markers somewhere in the middle of the chromosome for many linkage groups. This condensation is assumed to represent chromosome areas close to the centromere, where theory states that fewer recombination events occur and genetic distances are thereby shorter.

Comparisons

The genetic maps of the zebra finch

The genetic map of the zebra finch developed in this project had 587 markers in common with the previous genetic map by Stapley et al. (2008). Out of these, 191 are included in the framework map. An alignment of the two zebra finch maps exposes the good coherence between them, as is exemplified with linkage groups Tg1.1-2, Tg2 and Tg3 in Figure 2.

Tg1.1 Tgu1B Tg1.2 Tgu1A Tg2 Tgu2 Tg3 Tgu3

Figure 2. Linkage groups Tg1.1-2, Tg2, and Tg3; a comparison between the framework map (to the left) and the map by Stapley et al. (2008) (to the right). All markers are included and common markers are connected. Tg1.1 and Tg3 have more than one homologous linkage group in the other map.

Tgu1

Tgu1C

Tgu3A

(26)

The chromosome assignment of the markers corresponds perfectly between the two maps, and marker order is almost identical. Due to the higher power of the linkage analysis of the Uppsala map, linkage was established to 7 linkage groups (in Tg1.1, Tg3, Tg16, Tg20, Tg25 and Tg26) that were out-groups in the Sheffield map (see Appendix B). The homologues to chicken chromosome 4, which was represented by two linkage groups in the Sheffield map but only by one in this study, would have been interesting to align, but unfortunately, all markers in Tg4 in the framework map corresponded to the linkage group Tgu4A in the Sheffield map. The whole alignment between the two zebra finch maps can be found in Appendix B.

The chicken genome

As the zebra finch SNPs were chosen in genes that were known to have chicken orthologues, almost all of the markers in the framework map could be linked to its chicken homologue. The linkage groups were, as mentioned above, almost perfectly preserved between chicken and zebra finch with only a few exceptions.

There was, however, some disparity in gene order as is evinced in figure 3 that

aligns the framework map with a physical map over the chicken genome and

connects homologues.

(27)

Ggal1 Tg1.1 Ggal2 Tg2 Ggal3 Tg3 Ggal4 Tg4

Tg1.2

(28)

Ggal5 Tg5 Ggal6 Tg6 Ggal7 Tg7 Ggal8 Tg8 Ggal9 Tg9

Ggal10 Tg10 Ggal11 Tg11 Ggal12 Tg12 Ggal13 Tg13

Ggal14 Tg14 Ggal15 Tg15 Ggal16 Tg16 Ggal17 Tg17

(29)

Ggal18 Tg18 Ggal19 Tg19 Ggal20 Tg20 Ggal21 Tg21

Ggal22 Tg22 Ggal23 Tg23 Ggal24 Tg24.1 Ggal25 Tg25.1

Ggal26 Tg26 Ggal27 Tg27 Ggal28 Tg28 GgalZ TgZ

Figure 3. Alignment of zebra finch framework map (right) and the chicken homologues (left). The chicken markers are interspersed by physical distances. Chicken chromosome 1, 24 and 25 are represented by two linkage groups each in the zebra finch framework map. Homologue sequences are connected.

Tg25.2

Tg24.2

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Genetical vs. physical distances

In the BLAST search for physical positions of the SNPs, 1,408 out of the 1,424 markers (98.9%) were found. However, some of the sequences yielded duplicate hits. Hits on other chromosomes than the assigned were dismissed. Duplicates very close to each other, within ~20 kbp (much closer than any two markers), were considered as one. Duplications that were further apart were not included in the comparisons. The genetic distance was plotted against the physical distance for all linkage groups containing more than 15 markers in the framework map; linkage groups Tg1.1-2, Tg2-9, Tg12 and Tg19. Figure 4 displays the plots for linkage groups Tg1.1-2, Tg2, Tg4, Tg12 and Tg19. The rest of the plots can be found in Appendix C.

Tg1.1

0 20 40 60 80 100 120 140

cM

Mbp

(31)

Tg1.2

0 10 20 30 40 50 60 70 80

0 20 40 60 80 100

cM

Mbp

Tg2

0 20 40 60 80 100 120 140 160 180

0 10 20 30 40 50 60 70 80

cM

Mbp

(32)

Tg4

0 5 10 15 20 25

0 10 20 30 40 50

cM

Mbp

Tg12

0 5 10 15 20 25

0 5 10 15 20 25 30 35 40

cM

Mbp

(33)

Tg19

0 2 4 6 8 10 12

0 10 20 30 40 50 60

cM

Mbp

Figure 4. Physical distance plotted against genetic distance for linkage groups Tg1.1-2, Tg2, Tg4, Tg12 and Tg19.

Some linkage groups, such as Tg1.1-2 and Tg2-5, showed an apparent s-shaped curve as expected if recombination rate is negatively correlated with the distance to the centromere. For others, such as Tg19, the relation was a very evident straight line, indicating no such correlation. Some of the plots, like Tg12, were difficult to interpret, and, naturally, this concerned the plots with few data points in particular.

Markers that appear outside the general curve or line are the ones for which the genetical map and the zebra finch sequence are not in agreement. None of the displayed plots contained any obvious out-groups, but in most plots, there were a few data points that call for closer investigation in the future, such as the two trios of markers that seem to have changed places at around 60 cM in Tg2.

Discussion

The result of the linkage analysis was a linkage map containing 30% of the

markers, spanning 31 linkage groups. This map was compared with the previous

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zebra finch linkage map (Stapley et al. 2008) and with physical positions of the markers from the sequencing. The marker order was in general consentient. The framework map was also aligned with a physical map of the chicken genome.

The most significant disparity between the previous zebra finch map and the linkage groups from this study regarded the equivalent of chicken chromosome 4, which had been divided into two linkage groups. This was not only proposed for zebra finch by Stapley et al. (2008), but also by Derjusheva et al. (2004) for domestic pigeon and the two passerines chaffinch and redwing, by Backström et al. (2008) for the collared flycatcher, and others (Guttenbach et al. 2003). The fact

that chicken chromosome 4 was represented by only one linkage group in this study does not rule out the possibility that it is also homologous to another microchromosome. The only contradiction to previous results is the markers that were included in both this study and the map by Stapley et al. (2008), which are believed to belong to the same linkage group when a larger dataset provides more power to the calculations.

As mentioned above, the chicken does not belong to the group of passerines (species of the order Passeriformes), but to the fowls. This means that the chicken and the zebra finch are separated by at least ~100 million years of evolution (Sibley and Ahlquist 1990, Barker et al. 2004). The genetic map of the zebra finch yields new insight in how the two lineages have evolved since the split between them. Further studies might reveal how these changes are associated with the phenotypic differences between the fowls and the passerines.

It has been suggested that the chromosome organisation of markers in avian genomes are preserved, but not the synteny within chromosomes (Schmid et al.

2005). The first genetic map of the zebra finch was in support of this statement

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In some chromosomes, such as chromosome 2, synteny seems to be remarkably well-preserved despite the 100 million years that separates the two species. In other, such as chromosome 15, there are rearrangements; however, the rearrangements seem to be explainable by only a few major inversions, in this example three.

The hypothesis that the in other vertebrates observed correlation between recombination rate and distance to the centromere (Jensen-Seaman et al. 2004) is not so strong in avian genomes (Schmid et al. 2005), based on the chicken genome, was definitely contradicted by the physical-genetic distance plots of zebra finch linkage groups Tg1.1-2 and Tg2-5. However, the tendency was not as evident for all linkage groups, and not supported at all by Tg19. A possible explanation is that microchromosomes are too small to demonstrate a pronounced effect. It can nevertheless be concluded that the negative correlation between recombination rate and distance to the centromere is applicable to at least some avian genomes, if not chicken.

Conclusions

The new framework map over the zebra finch genome has already provided new insights in the passerine genome. The alignment against the chicken genome reveals chromosomal rearrangements that have occurred since the divergence of the orders of the fowls and the passerines. The physical-genetic distance plots in the zebra finch linkage groups shed light on the correlation between physical and genetic distances over the chromosome.

The natural progression of the project is to construct a best-order map including all markers that were sorted into the linkage groups. These results will hopefully be of use in exploring the genetic background to the phenotypic traits recorded in this zebra finch pedigree.

Acknowledgements

First, I want to thank my supervisor Niclas Backström for guidance and support,

and Professor Hans Ellegren, who initialised the project and provided valuable

(36)

advice. I thank Mikael Thollesson for accepting the task of the scientific reviewer,

and Maryam Montazerolghaem and Martin Dahlö for kindly accepting to be my

opponents. The whole research group of Professor Ellegren at the Department for

Evolutionary Biology was a support in creating a good scientific work

environment with many sensible ideas and opinions. I want to thank Axel

Künstner in particular for providing invaluable technical assistance. Mathieu

Authier’s help with the software was also appreciated. Dr Wolfgang Forstmeier

with colleagues at the Department of Behavioural Ecology and Evolutionary

Genetics at the Max Planck Institute for Ornithology at Seewiesen are gratefully

acknowledged for the breeding and the sampling of the pedigree. I thank Tomas

Axelsson and his colleagues at the SNP Technology Platform at Uppsala

University for the thorough SNP genotyping. Finally, Rickard Hedman is

thanked for all his considerate help during the writing of this report.

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List of appendices

Appendix A. List of all markers included in the linkage groups (Appendices, p.1) Appendix B. Complete comparison with Sheffield map (Appendices, p.28)

Appendix C. The remaining physical-genetic distance plots (Appendices, p.31)

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Appendices

Appendix A. List of all markers included in the linkage groups

SNP Chicken chromosome

Zebra finch linkage group

In framework map?

A03701 chr1 Tg1.1

A06985 chr1 Tg1.1 yes

A06992 chr1 Tg1.1

A07879 chr1 Tg1.1

A12263 chr1 Tg1.1

A12264 chr1 Tg1.1

A12274 chr1 Tg1.1

A12281 chr1 Tg1.1

A12390 chr1 Tg1.1

A12909 chr1 Tg1.1

A13002 chr1 Tg1.1

A13306 chr1 Tg1.1

A13603 chr1 Tg1.1

A13629 chr1 Tg1.1

A13669 chr1 Tg1.1

A14092 chr1 Tg1.1

A14504 chr1 Tg1.1

A14657 chr1 Tg1.1

A14891 chr1 Tg1.1

A14901 chr1 Tg1.1

A14941 chr1 Tg1.1

A15165 chr1 Tg1.1

A15394 chr1 Tg1.1

A15865 chr1 Tg1.1

A16150 chr1 Tg1.1

A16886 chr1 Tg1.1

A16982 chr1 Tg1.1

A17039 chr1 Tg1.1

A17590 chr1 Tg1.1

A17722 chr1 Tg1.1

A18161 chr1 Tg1.1

A18339 chr1 Tg1.1

A18719 chr1 Tg1.1

(41)

C00139 chr1 Tg1.1

C00142 chr1 Tg1.1

C00143 chr1 Tg1.1

C00145 chr1 Tg1.1 yes

C00147 chr1 Tg1.1

C00152 chr1 Tg1.1

C00154 chr1 Tg1.1

C00155 chr1 Tg1.1

C00158 chr1 Tg1.1

C00171 chr1 Tg1.1

C00172 chr1 Tg1.1

C00174 chr1 Tg1.1

C00178 chr1 Tg1.1

C00180 chr1 Tg1.1

C00181 chr1 Tg1.1

C00183 chr1 Tg1.1

C00199 chr1 Tg1.1

C00202 chr1 Tg1.1

C00207 chr1 Tg1.1

F04399 chr1 Tg1.1

F06703 chr1 Tg1.1

F08918 chr1 Tg1.1

F09243 chr1 Tg1.1

F09317 chr1 Tg1.1

F09931 chr1 Tg1.1 yes

F11425 chr1 Tg1.1

F11429 chr1 Tg1.1

F11984 chr1 Tg1.1

F12228 chr1 Tg1.1

F12778 chr1 Tg1.1

F13467 chr1 Tg1.1

TS0016 chr1 Tg1.1 yes

TS0092 chr1 Tg1.1

TS0154 chr1 Tg1.1

TS0196 chr1 Tg1.1

TS0197 chr1 Tg1.1

TS0202 chr1 Tg1.1

TS0254 chr1 Tg1.1

TS0259 chr1 Tg1.1

TS0384 chr1 Tg1.1

TS0400 chr1 Tg1.1

TS0408 chr1 Tg1.1

TS0429 UN Tg1.1

TS0433 chr1 Tg1.1

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TS0454 UN Tg1.1

TS0455 UN Tg1.1

TS0478 chr1 Tg1.1

TS0487 chr1 Tg1.1

TS0524 chr1 Tg1.1

TS0556 chr1 Tg1.1

TS0665 chr1 Tg1.1

TS0681 chr1 Tg1.1

TS0757 chr1 Tg1.1

TS0759 chr1 Tg1.1

TS0761 chr1 Tg1.1

TS0836 chr1 Tg1.1

TS0862 chr1 Tg1.1

TS0915 chr1 Tg1.1

TS0961 chr1 Tg1.1

TS0973 UN Tg1.1 yes

TS0974 UN Tg1.1

TS1002 chr1 Tg1.1

TS1008 chr1 Tg1.1

TS1030 chr1 Tg1.1

TS1103 chr1 Tg1.1

TS1108 chr1 Tg1.1

TS1115 chr1 Tg1.1

TS1126 chr1 Tg1.1

TS1225 chr1 Tg1.1

TS1236 chr1 Tg1.1

TS1246 chr1 Tg1.1

TS1301 chr1 Tg1.1

TS1310 chr1 Tg1.1

TS1366 UN Tg1.1

TS1433 UN Tg1.1

TS1450 UN Tg1.1

TS1451 UN Tg1.1 yes

TS1455 UN Tg1.1 yes

TS1479 UN Tg1.1

TS1515 UN Tg1.1

TS1536 UN Tg1.1

TS1572 UN Tg1.1 yes

TS1582 UN Tg1.1

(43)

A08195 chr1 Tg1.2

A09775 chr1 Tg1.2

A11045 chr1 Tg1.2

A11361 chr1 Tg1.2

A22128 chr1 Tg1.2

A27553 chr1 Tg1.2

A27798 chr1 Tg1.2

A28548 chr1 Tg1.2

A28702 chr1 Tg1.2

A29647 chr1 Tg1.2

A31676 chr1 Tg1.2

A32293 chr1 Tg1.2

A32650 chr1 Tg1.2

A35146 chr1 Tg1.2

A37946 chr1 Tg1.2

A39183 chr1 Tg1.2

A39858 chr1 Tg1.2

A41768 chr1 Tg1.2

A42364 chr1 Tg1.2

A42367 chr1 Tg1.2

A42413 chr1 Tg1.2

A43247 chr1 Tg1.2

C00037 chr1 Tg1.2

C00038 chr1 Tg1.2

C00048 chr1 Tg1.2

C00072 chr1 Tg1.2

C00083 chr1 Tg1.2

C00278 chr1 Tg1.2

C00279 chr1 Tg1.2

C00385 chr1 Tg1.2

C00393 chr1 Tg1.2

C00394 chr1 Tg1.2

C00436 chr1 Tg1.2

C00449 chr1 Tg1.2

F02396 chr1 Tg1.2

F04028 chr1 Tg1.2

F04095 chr1 Tg1.2

F05777 chr1 Tg1.2

F06395 chr1 Tg1.2

F07555 chr1 Tg1.2

F19832 chr1 Tg1.2

F20801 chr1 Tg1.2

F27252 chr1 Tg1.2

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TS0091 chr1 Tg1.2

TS0199 chr1 Tg1.2

TS0207 chr1 Tg1.2

TS0223 chr1 Tg1.2

TS0227 chr1 Tg1.2

TS0239 chr1 Tg1.2

TS0261 chr1 Tg1.2

TS0326 chr1 Tg1.2

TS0364 chr1 Tg1.2

TS0373 chr1 Tg1.2

TS0407 chr1 Tg1.2

TS0418 chr1 Tg1.2

TS0468 chr1 Tg1.2

TS0472 chr1 Tg1.2

TS0529 chr1 Tg1.2

TS0553 chr1 Tg1.2

TS0576 chr1 Tg1.2

TS0683 chr1 Tg1.2

TS0692 chr1 Tg1.2

TS0766 chr1 Tg1.2

TS0849 chr1 Tg1.2

TS0878 chr1 Tg1.2

TS0982 chr1 Tg1.2

TS1086 chr1 Tg1.2

TS1136 chr1 Tg1.2

TS1181 chr1 Tg1.2

TS1265 chr1 Tg1.2

TS1286 chr1 Tg1.2

TS1333 UN Tg1.2

TS1386 UN Tg1.2

TS1393 UN Tg1.2 yes

TS1418 UN Tg1.2

TS1453 UN Tg1.2 yes

TS1475 UN Tg1.2 yes

(45)

A05152 chr2 Tg2

A05449 chr2 Tg2

A05759 chr2 Tg2

A06221 chr2 Tg2

A06365 chr2 Tg2

A06418 chr2 Tg2

A07256 chr2 Tg2

A07391 chr2 Tg2

A07523 chr2 Tg2 yes

A07672 chr2 Tg2

A07779 chr2 Tg2

A08080 chr2 Tg2

A08187 chr2 Tg2

A08758 chr2 Tg2

A08971 chr2 Tg2

A08988 chr2 Tg2

A09151 chr2 Tg2

A09979 chr2 Tg2

A10115 chr2 Tg2 yes

A10348 chr2 Tg2

A10361 chr2 Tg2

A11299 chr2 Tg2

A11463 chr2 Tg2

A11861 chr2 Tg2

A11985 chr2 Tg2

A12481 chr2 Tg2

A13559 chr2 Tg2 yes

A13751 chr2 Tg2 yes

A14702 chr2 Tg2

A15098 chr2 Tg2

A15310 chr2 Tg2

A15936 chr2 Tg2

A15969 chr2 Tg2

A16924 chr2 Tg2

A17365 chr2 Tg2

A17555 chr2 Tg2

A18705 chr2 Tg2

A18942 chr2 Tg2

A19061 chr2 Tg2

A19258 chr2 Tg2

A19271 chr2 Tg2

A19295 chr2 Tg2

A19759 chr2 Tg2

A19954 chr2 Tg2

A20726 chr2 Tg2

A25817 chr4 Tg2

A29441 chr2 Tg2

A29465 chr2 Tg2

A33405 chr2 Tg2 yes

A36138 chr2 Tg2

A36166 chr2 Tg2

A37629 chr2 Tg2

A37782 chr2 Tg2

A39297 chr2 Tg2

(46)

A39631 chr2 Tg2

A40693 chr2 Tg2

A40713 chr2 Tg2

A40739 chr2 Tg2

A41837 chr2 Tg2

A41865 chr2 Tg2 yes

A41914 chr2 Tg2

A42300 chr2 Tg2

A42699 chr2 Tg2 yes

A42953 chr2 Tg2

A42964 chr2 Tg2

A42969 chr2 Tg2

A43011 chr2 Tg2

A43442 chr2 Tg2

A43475 chr2 Tg2

C00052 chr2 Tg2

C00053 chr2 Tg2

C00071 chr2 Tg2

C00074 chr2 Tg2

C00077 chr2 Tg2

C00103 chr2 Tg2

C00108 chr2 Tg2

C00110 chr2 Tg2

C00113 chr2 Tg2 yes

C00120 chr2 Tg2

C00153 chr2 Tg2

C00161 chr2 Tg2

C00167 chr2 Tg2

C00173 chr2 Tg2

C00179 chr2 Tg2

C00186 chr2 Tg2

C00187 chr2 Tg2

C00189 chr2 Tg2

C00195 chr2 Tg2

C00201 chr2 Tg2

C00205 chr2 Tg2 yes

C00399 chr2 Tg2 yes

C00451 chr2 Tg2

C00459 chr2 Tg2

C00471 chr2 Tg2

F02848 chr2 Tg2 yes

F06932 chr2 Tg2

F07545 chr2 Tg2

F07947 chr2 Tg2

F08517 chr2 Tg2

F08712 chr2 Tg2

F08999 chr2 Tg2