Molecular Ecology (2010), 19 (Suppl. 1), 89–99 doi: 10.1111/j.1365-294X.2009.04486.x

Genome-wide SNP detection in the great tit Parus majorusing high throughput sequencing

NIKKIE E. M. VAN BERS,* KEES VAN OERS,† HINDRIK H. D. KERSTENS,* BERT W. DIBBITS , *

RICHARD P. M. A. CROOIJMANS,* MARCEL E. VISSER† and MARTIEN A. M. GROENEN*

*Animal Breeding and Genomics Centre, Wageningen University, Marijkeweg 40, Wageningen, 6709 PG, The Netherlands,

†Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), PO Box 40, 6666 ZG Heteren, The

Netherlands

Corresponde

k.vanoers@ni

� 2010 Black

Abstract

Identifying genes that underlie ecological traits will open exiting possibilities to study

gene–environment interactions in shaping phenotypes and in measuring natural

selection on genes. Evolutionary ecology has been pursuing these objectives for decades,

but they come into reach now that next generation sequencing technologies have

dramatically lowered the costs to obtain the genomic sequence information that is

currently lacking for most ecologically important species. Here we describe how we

generated over 2 billion basepairs of novel sequence information for an ecological model

species, the great tit Parus major. We used over 16 million short sequence reads for the denovo assembly of a reference sequence consisting of 550 000 contigs, covering 2.5% of the

genome of the great tit. This reference sequence was used as the scaffold for mapping of

the sequence reads, which allowed for the detection of over 20 000 novel single

nucleotide polymorphisms. Contigs harbouring 4272 of the single nucleotide polymor-

phisms could be mapped to a unique location on the recently sequenced zebra finch

genome. Of all the great tit contigs, significantly more were mapped to the microchro-

mosomes than to the intermediate and the macrochromosomes of the zebra finch,

indicating a higher overall level of sequence conservation on the microchromosomes

than on the other types of chromosomes. The large number of great tit contigs that can be

aligned to the zebra finch genome shows that this genome provides a valuable

framework for large scale genetics, e.g. QTL mapping or whole genome association

studies, in passerines.

Keywords: natural population, next generation sequencing, Parus major, reduced representation

libraries, short sequence reads, single nucleotide polymorphisms

Received 29 May 2009; revision received 30 September 2009; accepted 15 October 2009

Introduction

Genetic variation underlying phenotypic differences

between individuals, either of the same or of different

species, has been demonstrated in many, often long-

term, studies throughout the world (Garant & Kruuk

2005; Nussey et al. 2005; Postma & Van Noordwijk

2005; Charmantier et al. 2008). Understanding this

genetic variation is essential to estimate the rate at

which species can adapt to their changing environment,

nce: Kees van Oers, Fax: 31 26 4723227; E-mail:

oo.knaw.nl

well Publishing Ltd

due to e.g. global climate change (Visser 2008), and

whether this rate of adaptation is sufficient to prevent

species extinction (Both et al. 2006). Passeriformes are

likely to be the most widely studied vertebrate taxo-

nomic order in ecology and evolution (Lack 1968; Ben-

nett & Owens 2002). The ease with which passerines

can be studied in the wild, in particular by marking

individuals and following them through time, has

resulted in many, often long-term, research pro-

grammes on a wide diversity of passerine species.

Hence, extensive knowledge has been gathered by

researchers investigating natural selection, sexual selec-

tion, behavioural ecology and speciation. In addition,

90 N. E . M. VAN BERS ET AL.

reviews on quantitative genetic analysis in natural pop-

ulations make it clear that also many of the long-term

studies of marked individuals have been conducted in

passerines (Merila & Sheldon 2001; Kruuk 2004). Link-

ing quantitative genetic variation in life-history traits to

polymorphisms in the actual genes that code for this

variance is essential for our understanding of the causes

and consequences of trait diversity. Quantitative genetic

techniques such as the ‘animal model’ (Kruuk 2004)

and QTL analyses conducted in specially created map-

ping crosses (Slate 2005) have undoubtedly enhanced

our understanding of adaptation, reproductive isolation

and speciation.

In order to perform QTL-mapping studies in a natu-

ral population, several requirements need to be met: (i)

the population should be sufficiently large and pedigree

information needs to be available; (ii) the traits of inter-

est should have been determined quantitatively; and

(iii) the availability of a genetic map, consisting of poly-

morphic markers (Kruuk 2004; Slate 2005). For many

non-model wild species, advances have been hampered

by the lack of pedigree information as well as the lack

of sufficient numbers of markers to be able to construct

genetic maps. Over the past two decades, personality

traits and timing of reproduction have been determined

quantitatively for pedigreed populations of the great tit

(Parus major). These traits affect important ecological

processes such as reproduction, survival and dispersal

and as a result have important consequences for the fit-

ness of an individual (for a review see e.g. Van Oers

et al. 2005). However, a genetic map is not yet available

for this ecological model species and the number of

publicly available polymorphic markers, e.g. microsatel-

lites, amplified fragment length polymorphisms (AF-

LPs) and single nucleotide polymorphisms (SNPs), is

very limited. At the time of writing, the NCBI database

only contains 23 microsatellite sequences for P. major,

and no SNP (but see Fidler et al. 2007) or AFLP mark-

ers (http://www.ncbi.nlm.nih.gov). Microsatellites have

been the markers of choice for the construction of the

majority of linkage maps of natural populations of ver-

tebrates (Slate et al. 2002; Hansson et al. 2005; Beraldi

et al. 2006). However, SNPs have several advantages

that favour their use as markers for gene mapping

[reviewed by Vignal et al. (2002) and Slate et al. (2009)].

SNP genotyping is highly automated: over 10 000 SNPs

can be typed simultaneously using a single custom

made chip (Illumina). Additionally, SNPs are more

abundant in the genome, and their discovery is more

time efficient.

The development of novel sequencing platforms, like

Roche ⁄ 454 Life Sciences’ Genome Sequencer and Illu-

mina’s Genome Analyzer, have dramatically lowered

the costs for generating vast amounts of sequence data.

For example, Illumina’s Genome Analyzer produces in

a single sequence run of a couple of days several giga-

basepairs (Gbp) of sequence data, in short sequence

reads. These short sequences form an excellent resource

for the detection of SNPs (Hillier et al. 2008; Van Tassell

et al. 2008), however, for genotyping assays, sufficient

sequence flanking the SNP needs to be available to

allow for probe design. For species with a (partially)

sequenced genome, this information can relatively eas-

ily be retrieved by mapping the reads onto the (draft)

genome (Van Tassell et al. 2008; Matukumalli et al.

2009; Ramos et al. 2009). Although for many species the

lack of a sequenced reference genome presents a serious

drawback, the de novo assembly of the short sequence

reads into contigs providing the sequence context of a

SNP is an efficient approach to overcome this problem

(Kerstens et al. 2009). To allow for contig assembly and

reliable SNP detection, the number of sequences cover-

ing a genomic region needs to be sufficiently large.

Reducing the complexity of the dataset, which corre-

sponds to the portion of the genome covered, is a

straightforward strategy to reach sufficient sequence

depth at reasonable costs. Reduced representation

libraries (RRLs) generally represent 1–5% of the gen-

ome and are created by the size selection of fragments

in a limited size range, produced by enzymatic diges-

tion of the DNA (Altshuler et al. 2000). RRLs have suc-

cessfully been employed for the discovery of thousands

of SNPs in species for which a genome sequence is

available, such as humans (Altshuler et al. 2000),

bovines (Van Tassell et al. 2008) and pigs (Wiedmann

et al. 2008; Ramos et al. 2009). Recently, an RRL was

used for highly efficient SNP detection in turkey (Ker-

stens et al. 2009), a species for which a reference gen-

ome is currently still lacking.

Here, we describe the discovery of 20 000 novel SNPs

in the genome of an ecological model species currently

lacking a sequenced genome. By using the combination

of RRLs and next generation sequencing we generated

over 2 billion nucleotides of novel sequence information

for this species. For SNP detection, we assembled this

information into reference sequences for mapping of

the reads. The reference sequences and the SNPs were

mapped onto the recently sequenced zebra finch Taenio-

pygia guttata genome, thereby investigating the possibili-

ties of using this passerine genome in future genetic

studies of the great tit and other passerines.

Materials and methods

Library preparation and sequencing

Blood from ten wild caught hand-reared male great tits

(Parus major) from ten different broods was used as the

� 2010 Blackwell Publishing Ltd

GENOME-WIDE SNP DETECTION IN PARUS MAJOR 91

starting material for DNA isolation with the Puregene

system (Gentra, USA). The birds originated from two

different, but closely located (<10 km), populations in

the Netherlands, respectively ‘Westerheide’ (five birds)

and ‘de Hoge Veluwe’ (five birds). In order to reduce

complexity, we generated two RRLs. A pool of 80 lg of

DNA of these ten birds was digested with RsaI (160u,

NEB, o ⁄ n at 37 �C) and dephosphorylated using 37.5 u

CIAP (Fermentas) according to the manufacturers’ pro-

tocol. Dephosphorylation was performed because it

may reduce preferential adapter ligation during library

preparation which leads to an over-representation of

sequence reads derived from the 5¢ ends of the digested

DNA fragments (Kerstens et al. 2009). The sample was

size-fractionated on a 1% low melting point agarose gel

(SeaPlaque). The size fractions of 3000–3500 bp (Gt3000)

and 3500–4000 bp (Gt3500) were purified from the gel

by treatment with b-agarase (NEB), and were purified

by phenol ⁄ sevag treatment and precipitation. Gel Doc

XR (BioRad) was used to estimate the fraction of the

genome covered by the libraries. For library preparation

the Genomic DNA Sample Prep Kit (Illumina) was used

according to the manufacturers’ instructions, with the

exception of phosphorylation of the sample. Randomly

sheared, adapter ligated, fragments in the size-range of

170–250 bp were used as the starting material for

sequencing on the Illumina 1G Genome Analyzer.

Data filtering and assembly of the reference sequence

For each of the RRLs (Gt3000 and Gt3500) we generated

two datasets of sequence reads: dataset A was used for

the assembly of the reference sequence and dataset M

was used for mapping of the reads against the reference

sequence. All sequence reads have been submitted to the

Short Read Archive (SRA) with accession number

SRA009913. As input for filtering, we used the GERALD

files of the sequence reads. The filtering applied in order

to obtain the two datasets was the same, with exception

of the minimal quality score that we required for each

individual nucleotide of sequence reads that were repre-

sented only once in the dataset. This value was at least

20 (which corresponds to an error probability of <1%)

for a read in order to be retained in dataset A, and at

least 10 (which corresponds to an error probability of

<10%) for reads in order to be retained in dataset M.

Sequence reads that were likely to be derived from

repetitive sequences in the genome were removed.

These were reads containing either a stretch of more

than 17 times (‡0.5 · read length of 36 nucleotides) the

same base (poly-A, T, G or C), were overabundant

(observed more than five times the expected

sequence depth of 25) or were reads that were tagged

by the program RepeatMasker (default settings)

� 2010 Blackwell Publishing Ltd

(http://www.repeatmasker.org) based on known

repeats in the chicken genome. All the reads of dataset

A were used for assembly using the program SSAKE

(default parameters) (Warren et al. 2007). All the result-

ing sequences of 37 or more nucleotides are further

referred to as contigs.

Mapping of the reads and SNP detection

All the reads of dataset M were used for mapping onto

the reference sequence with the software package MAQ

version 0.6.6, using the default settings (Li et al. 2008).

In order to be classified as a SNP we required the fol-

lowing criteria to be met: (i) the minor allele needs to

be observed at least three times to limit false SNP iden-

tification due to sequencing errors; (ii) the best mapping

read has a mapping quality (Q) of at least 40; (iii) the

consensus quality (C) is at least 30; and (iv) the SNP

position is flanked at one side by at least 15 nucleo-

tides.

Alignment to the zebra finch genome

All the contigs assembled from the short sequence reads

were aligned against the zebra finch (Taeniopygia gutta-

ta) genome (version July 2008, assembly WUSTL

v.3.2.4). These data were produced by the Genome

Sequencing Center at Washington University School of

Medicine in St. Louis and can be obtained from http://

genome.ucsc.edu. Because of its time efficiency, initial

alignments were done using MegaBLAST (Zhang et al.

2000). We used the default parameters, except for:

wordsize W = 16 and an identity in the aligned region

(p) of at least 60%. To be considered as a hit, we

required the alignment to include >80% of the length of

the contig or of the sequence read. For the alignment of

the initial sequence reads, an identity of at least 90%

and a minimal bit score of 20 were required. Hits were

classified as unique if there was only one hit for the

corresponding sequence or if there was a hit on one

chromosome and a hit with nearly (96%) the same bit

score on chromosome unassigned. All contigs of at least

100 nucleotides that did not give a unique hit with

MegaBLAST were re-aligned to the zebrafinch genome

(version July 2008, assembly WUSTL v.3.2.4) using

BlastZ (Schwartz et al. 2003). BlastZ is specifically

designed for the alignment of sequences of dissimilar

species and BlastZ alignments can overspan gaps of

hundreds of nucleotides. However, this comes at a com-

putational cost, which is the reason why the initial

alignments were done with MegaBLAST. For the BlastZ

alignments the default settings were used except for the

option Y = 3400, which restricts the size of gaps to at

most 100 bp.

92 N. E . M. VAN BERS ET AL.

Single Nucleotide Polymorphism* (SNP*), which is

the number of SNPs corrected for the number of nucle-

otides mapping to each of the zebra finch chromo-

somes, is calculated as follows for each of the zebra

finch autosomes: SNP* =P

SNP ⁄ m, where m is the

number of mapped nucleotides per 1000 basepairs of

chromosome. SNP* and m were tested for significance

by performing t-test.

Validation

We selected 66 SNPs located on 40 different contigs for

validation by PCR amplification and sequencing (the

primer and contig sequences are available as supple-

mentary info online). Primers for contig amplification

were designed using the web-based software Primer 3

v 0.4.0 (Rozen & Skaletsky 2000). The amplification was

performed on DNA isolated from at least four of the

individual birds used for the library preparation.

Amplification products were used as the template for

sequencing on a ABI 3730 DNA analyzer (Applied Bio-

systems), and sequencing results were analysed with

the STADEN package. Confirmed SNPs have been sub-

mitted to dbSNP with accession numbers:

NCBI_ss161110015-NCBI_ss161110056.

Results

Building a reference sequence

For reliable SNP prediction, the putative SNP position

needs to be covered by a sufficient number of

sequence reads (Van Tassell et al. 2008). To reach a

sequence depth of about 25, we reduced the complex-

ity of our dataset by only sequencing a few percent of

the great tit’s genome. This was accomplished by gen-

erating RRLs (Van Tassell et al. 2008). DNA was

digested with the restriction enzyme RsaI and after

separation of the DNA fragments on an agarose gel,

the size fractions of 3000–3500 bp and of 3500–4000 bp

were isolated. These two fractions represent an esti-

mated �4.1% and �3.3% of the great tit genome

assuming a genome size of �1.2 · 109 bp, similar to

the genome size of the zebra finch Taeniopygia guttata.

The libraries are further referred to as Gt3000 and

Gt3500, respectively.

In total, 61 million short sequence reads (36 bp) were

generated, 32 million of Gt3000 and 29 million of

Gt3500 (Fig. 1). This corresponds to around 1 billion

nucleotides of data for each of the libraries. Sequencing

errors in the reads can lead to the abortion of contig

extension and as a result, shorter contigs. Therefore, we

only selected those reads for the assembly of which all

the bases were called with an error probability of <1%,

unless the exact sequence of the read was found more

than once. Additionally, we used the RepeatMasker

program to remove reads that are likely to be derived

from repetitive sequences in the genome, which corre-

sponded to about 70 000 reads for each of the libraries.

Repetitive sequences are more likely to match ambigu-

ously during the assembly. This will result in incor-

rectly assembled contigs or in shorter contigs due to

premature termination of the assembly (Warren et al.

2007). In addition to the RepeatMasker program we

used an abundancy filter to limit the number of repeti-

tive sequences in the dataset. After the filtering steps,

we retained 9.4 million (29%) of the reads of Gt3000

and 7.0 million (24%) of the reads of Gt3500 for contig

assembly (Fig. 1).

In the absence of a sequenced genome of the great tit

we build an in silico set of sequences that served as ref-

erence for the subsequent detection of SNPs. Addition-

ally, it provides the SNP sequence context necessary for

the design of probes for use in genotyping (Fig. 1). The

filtered reads were assembled using the assembly soft-

ware SSAKE, which is specifically designed for the

assembly of short sequence reads (Warren et al. 2007).

The resulting reference sequence consisted of over

250 000 contigs for each of the RRLs, with a total length

of 16.2 and 14.8 million nucleotides for Gt3000 and

Gt3500, respectively (Table 1). The assembly of the

Gt3000 reads has a N50 value of 53, and the assembly

of the Gt3500 has a N50 of 52. The N50 length of an

assembly is the length x such that 50% of the genome,

or, in this case, reference sequence, is contained in seg-

ments of length x or greater (Adams et al. 2003).

Validation of the reference sequence by alignment tothe zebra finch genome

For the validation of our assembly we used two differ-

ent strategies. The first was alignment of the assembled

contigs against the genome of a closely related species,

the zebra finch [divergence time great tit-zebra finch is

40–45 million years (Barker et al. 2004)], and the second

was the independent amplification of a subset of the

assembled contigs. For alignment against the recently

sequenced zebra finch genome we used the programs

MegaBLAST and BlastZ. Of the initial short sequence

reads, we could align 26% (Gt3000) and 32% (Gt3500)

against the zebra finch genome. Subsequent assembly

increased this percentage to 35% and 37%, respectively,

for contigs smaller than 100 nucleotides. Of the contigs

larger than 100 nucleotides, we could map in total 62%

(Gt3000) and 63% (Gt3500) to the zebra finch genome

(Table 2). A graphical representation of the distribution

of the contigs over the zebra finch genome is provided

as supplementary data. For validation by re-amplifica-

� 2010 Blackwell Publishing Ltd

Table 1 Assembly statistics

Contig size Gt3000 Gt3500

37–49 154 228 143 588

50–75 112 046 97 396

76–100 19 830 17 649

101–150 8655 6907

151–200 1761 1350

201–300 703 622

301–400 170 198

401–500 77 86

501–601 42 43

>601 51 88

Total number 297 563 267 927

Total length 16.2 · 106 14.8 · 106

N50 53 52

32 million reads (Gt3000) 29 million reads (Gt3500)

Quality (>20), polyA,G,T or C and repeat filtering 9.4 million reads (Gt3000) or

7.0 million reads (Gt3500)

Quality (>10), polyA,G,T or C filtering

21.4 million reads (Gt3000) or 15.1 million reads (Gt3500)

Assembly into contigs

Contigs function as reference sequence for mapping and SNP detection

>20.000 SNPs

2 RRLs: Gt3000 and Gt3500

*** *

**

Fig. 1 Schematic overview of the SNP

detection pipeline. Two RRLs, gt3000

and gt3500, were used for the genera-

tion of in total 61 million short sequence

reads. These reads were filtered with

two different filter settings: a base call

quality score of at least 20 was required

for all uniquely represented reads that

were used for the assembly of the con-

tigs. These contigs form the framework

for the mapping of all the reads with a

base call quality score of at least 10 (in

grey). Single nucleotide polymorphisms

(*) are detected between reads that map

to the same position on the reference

sequences.

GENOME-WIDE SNP DETECTION IN PARUS MAJOR 93

tion, we selected 40 contigs, ranging in size from 200 to

500 bp. Of the selected contigs 35 mapped uniquely to

locations distributed over the whole zebra finch gen-

ome, while five contigs could not be mapped. For 85%

of the contigs a product of the expected size was ampli-

fied (supplementary information is available online).

� 2010 Blackwell Publishing Ltd

Mapping distribution over the different chromosometypes

Avian chromosomes are highly variable in size, which

led to their classification into micro- (<20 Mb), inter-

mediate- (20–40 Mb) and macrochromosomes (�50–

200 Mb) (ICGSC 2004, Axelsson et al. 2005). Based on

the convention of the ICGSC (ICGSC 2004), the zebra

finch chromosomes covered by the genome assembly

can be classified into six macrochromosomes (Tgu1A

and Tgu1–5), eight intermediate chromosomes (Tgu4A

and Tgu6–12) and 17 microchromosomes (Tgu1B and

Tgu13–28). Because our dataset is expected to ran-

domly cover the whole great tit genome, it allows a

comparison of sequence conservation between the

great tit and the zebra finch on the different chromo-

some types (Table 3). The results show that signifi-

cantly more nucleotides map to microchromosomes

than to intermediate (P < 10)4) or macrochromosomes

(P < 10)6), and that the number of great tit nucleotides

mapping to intermediate chromosomes is significantly

higher than the number mapping to macrochromo-

somes (P < 10)3).

Table 2 Alignment against the zebra finch genome

Unique hit Two hits More than two hits Total

Gt3000 All sequence reads MegaBLAST 18.7% (1 759 890) 3.7% (344 609) 3.8% (354 478) 26.2%

BlastZ — — —

Contigs < 100 nucleotides MegaBLAST 27.9% (79 634) 4.8% (13 676) 2.6% (7356) 35.3%

BlastZ — — —

Contigs ‡ 100 nucleotides MegaBLAST 35.2% (4174) 4.6% (546) 2.9% (344) 42.7%

BlastZ 15.6% (1850) 2.4% (290) 1.0% (116) 19.0%

Total (MegaBLAST&BlastZ) 50.7% (6024) 7.0% (836) 3.9% (460) 61.7%

Gt3500 All sequence reads MegaBLAST 22.4% (1575856) 4.5% (313116) 5.8% (410 444) 32.7%

BlastZ – – –

Contigs < 100 nucleotides MegaBLAST 28.2% (72 879) 4.7% (12 227) 4.4% (11 467) 37.3%

BlastZ – – –

Contigs ‡ 100 nucleotidest MegaBLAST 28.0% (2695) 4.0% (389) 8.5% (818) 40.5%

BlastZ 17.4% (1676) 3.1% (300) 1.5% (140) 22.0%

Total (MegaBLAST&BlastZ) 45.4% (4371) 7.2% (689) 10.0% (958) 62.5%

15 30 450

10

20

30

40

50

60

70

80

90

100

Num

ber

of S

NP

s (%

)

Number of flanking nucleotides on side 1

>30 nt on side 2

>2 nt on side 2

>15 nt on side 2

Fig. 2 The distribution of the number of nucleotides flanking

the SNP positions on one side (side 1), which are flanked at the

other side (side 2) by at least two nucleotides (straight line), 15

nucleotides (dashed line) or 30 nucleotides (dot-dash line).

94 N. E . M. VAN BERS ET AL.

Large scale SNP identification

To identify SNPs within the DNA pool used for the

construction of the RRLs, we mapped 21 million

(Gt3000) and 15 million (Gt3500) reads, respectively

onto the reference sequences. These are all the reads

containing only bases with a probability of >90% of

being called correctly (Fig. 1). Nucleotide differences

were marked as SNPs if the difference at that position

in the reference sequence was observed at least three

times, with a minimal mapping quality of 10 for all of

the reads and a minimal mapping quality of 40 for the

best mapping read. Using these thresholds, we detected

13 153 SNPs in Gt3000 and 7556 SNPs in Gt3500. 89%

of the SNPs was flanked by at least 30 nucleotides on

one side and two nucleotides on the other (Fig. 2),

which is sufficient to allow probe design for an iSelect

(Illumina) genotyping assay. The allele frequencies of

the SNPs can be estimated based on the proportion of

sequence reads harbouring the minor allele. A plot of

the estimated allele frequencies of the SNPs in our data-

set (Fig. 3) shows that SNPs with a minor allele fre-

quency (MAF) of <0.2 are under-represented in our

dataset, as compared to the allele frequency distribution

Table 3 Mapping statistics

Mapped

number

nucleotides ⁄ kbp SNP*

Macrochromosomes

(Tgu1A&Tgu1–5)

4.62 ± 1.13 46.6 ± 17.4

Intermediate

chromosomes (Tgu6–12)

10.24 ± 2.31 11.3 ± 3.1

Microchromosomes

(Tgu13–28&Tgu1B)

21.00 ± 5.50 4.4 ± 3.0

reported for human SNPs (The International HapMap

Consortium 2005).

Sequencing errors are often found in the last nucleo-

tides of the sequence reads (Dohm et al. 2008). If a sub-

stantial amount of the SNPs in the dataset is the result

of sequencing errors, than an increase in the number of

SNPs towards the end of the reads is expected. As a

first indication for the validity of our SNP detection

approach, we plotted the distribution of the SNPs over

the 36 positions in the sequence reads (Fig. 4). Except

for an under-representation at the termini of the reads

(positions 1 and 36), the SNPs are equally distributed

over the reads. Additionally, we calculated the transi-

tion:transversion ratio of the SNPs in our dataset. If

polymorphisms would be introduced at random, a tran-

sition (AMG or CMT) to transversion (A or G M C or

T) rate of 1:2 is expected. The observed transition:trans-

version ratio for our dataset is 1.7:1.

� 2010 Blackwell Publishing Ltd

02468

101214161820

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5MAF

SN

Ps

(%)

Fig. 3 The allele frequency distribution (bin size 0.05) of the

total SNP dataset.

0 5 10 15 20 25 30 350

750

1500

2250

3000

3750

4500

5250

6000

6750

7500(a)

(b)

Gt3000

Read position

nr o

f SN

Ps

Read position0 5 10 15 20 25 30 35

0

500

1000

1500

2000

2500

3000

3500

4000Gt3500

nr o

f SN

Ps

Fig. 4 The number of SNPs detected on each of the 36 posi-

tions of the sequence reads of (a) gt3000 and (b) gt3500.

GENOME-WIDE SNP DETECTION IN PARUS MAJOR 95

In order to confirm the validity of our approach, we

sequenced 40 different contigs, containing in total 66

SNPs. Due to technical limitations (e.g. no amplification

� 2010 Blackwell Publishing Ltd

product), 16 SNPs could not be typed. Of the remaining

SNPs, the presence of 84% could be confirmed by

sequencing of amplification products of the individual

birds used for the construction of the dataset (supple-

mentary information is available online).

For future use in the build of a genetic map of the

great tit it is essential that the SNPs are widely distrib-

uted over the genome. In the absence of the sequence of

the great tit genome, we used the genome of the zebra

finch for the mapping of the contigs (see above, and

Table 2). We plotted the distribution of the SNPs found

in the two RRLs over the zebra finch chromosomes

(Fig. 5). A total of 4272 SNPs were located on contigs

that could be mapped to unique locations evenly distrib-

uted over the zebra finch genome. Of these, 2660 SNPs

were located on contigs smaller than 100 bp, and 1609

SNPs were located on contigs of at least 100 bp.

A comparison of the chicken and turkey genome

revealed that nucleotide divergence between these bird

species is higher on microchromosomes than on macro-

chromosomes (Axelsson et al. 2005). To investigate

whether the same holds true for the great tit, we calcu-

lated the mean number of SNP harbouring contigs that

mapped to each of the different chromosome types of the

zebra finch (SNP*, see ‘Methods’). To avoid a bias intro-

duced due to the significant difference in number of nu-

cleotides mapping to each of the chromosome types (see

above), we corrected for the number of nucleotides that

mapped to each chromosome. SNP* decreases with the

size of the chromosomes and is significantly higher for

macrochromosomes than for intermediate (P < 10)5) and

for microchromosomes (P < 10)8), and is also signifi-

cantly higher for intermediate chromosomes than for mi-

crochromosomes (P < 10)4).

Discussion

Here we report the discovery of over 20 000 novel SNPs

in the great tit genome by high throughput sequencing.

We assembled 16 million short sequence reads, derived

from two RRLs, into a total of over 550 000 contigs.

These contigs have a total length of more than 30 million

basepairs, which corresponds to about 2.5% of the great

tit genome. Linking SNPs to positions on the genome is

problematic for species for which a genome sequence is

currently lacking. This can partially be circumvented by

mapping the sequence reads onto the genome of a

related species, e.g. the sequenced chicken genome in

the case of turkey (Kerstens et al. 2009). Recently, the

first passerine genome sequence; that of the zebra finch,

was released. The great tit and the zebra finch diverged

from their common ancestor 40–45 million years ago

(Mya) (Barker et al. 2004). Furthermore, the avian karyo-

type is highly conserved (Shetty et al. 1999; Van Tuinen

ChrUn

LGE22

M

LG2

ChrZ

Chr1A

Chr1

Chr1B

Chr2

Chr3

Chr4A

Chr4

Chr5

Chr6

Chr7

Chr8

Chr9

Chr10

Chr11

Chr12

Chr13

Chr14

Chr15

Chr16

Chr17

Chr18

Chr19

Chr20

Chr21

Chr22

Chr23

Chr24

Chr25

Chr26

Chr27

Chr28

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

Fig. 5 Mapping of the SNPs onto the zebra finch genome. Each graph corresponds to an individual chromosome. The number of

SNPs is plotted over intervals of 50 000 basepairs.

96 N. E . M. VAN BERS ET AL.

& Hedges 2001; Derjusheva et al. 2004) and avian ge-

nomes appear to have undergone relatively few chromo-

somal rearrangements (Griffin et al. 2008; Stapley et al.

2008). Therefore, the availability of the zebra finch gen-

ome sequence is likely to boost molecular research on

ecologically relevant quantitative traits in other passe-

rines. We were able to map over 60% of the contigs lar-

ger than 100 nucleotides onto the genome of the zebra

finch. In a similar alignment of turkey contigs against

the chicken genome (divergence time 25–30 Mya), 67%

of the contigs could be mapped (Kerstens et al. 2009).

For the turkey, the number of SNPs was increased by

50% by comparative assembly using the chicken gen-

ome and by including publicly available BAC-end

sequences. In comparative assembly, contigs that are

overlapping or immediately adjacent to each other are

merged into larger contigs. In analogy to the turkey

study, the use of the zebra finch genome as a framework

for comparative assembly of the great tit contigs is likely

to multiply the number of SNPs detected, and to

increase the number of SNPs with sufficient flanking

sequence for use in a genotyping assay.

Sequencing errors, which are mainly found in the last

nucleotide positions of the sequence reads (Dohm et al.

� 2010 Blackwell Publishing Ltd

GENOME-WIDE SNP DETECTION IN PARUS MAJOR 97

2008), can falsely be identified as SNPs. The fact that

we did not observe a bias in the number of SNPs in the

last nucleotides of the reads supports our approach.

Furthermore, the SNPs in our dataset show a transi-

tion:transversion rate of 1.7:1, which is only slightly less

than the ratio of 2:1 observed in neutrally evolving

genes in humans (Zhang & Gerstein 2003) and the ratio

of 2.2:1 calculated for chicken based on more than 3

million chicken SNPs present in dbSNP.

Single nucleotide polymorphisms (SNPs) with an

MAF < 0.2 are under-represented in our dataset. This is

due to the combination of the stringent requirement of

a minor allele count of at least three times, which we

set to avoid false SNP discovery due to sequencing

errors and the average sequence coverage of our dataset

of 15 times (after quality filtering). Increasing the

sequence depth of the dataset will also allow the detec-

tion of SNPs with a lower allele frequency. We vali-

dated our assembly and SNP detection by PCR

amplification and sequencing of 40 contigs. Eighty-five

per cent of the selected contigs was amplified success-

fully and of the 50 SNPs that could be typed, the pres-

ence of 84% was confirmed by sequencing.

The majority of the contigs that we assembled are

small (<75 bp), which is reflected in the N50 values of

52 (Gt3000) and 53 (Gt3500), respectively. The total

length of the contigs assembled was 16.2 Mbp for

Gt3000 and 14.8Mbp for Gt3500. This means that our

contigs represent at most 1.3% and 1.2%, respectively

of the great tit genome, which is considerably less than

the 4.1% and 3.3% that we estimated to cover with

these two RRLs. This is mainly due to the fact that not

all sequence reads are assembled into contigs. However,

in a similar study Kerstens et al. (2009) assembled 3%

of the turkey genome, while the dataset was expected

to cover 5–6% of the genome. Even though this is only

60% of the expected target, it is still more than the

�35% of the target we retrieved in our assembly. An

explanation for this is the difference in the proportion

of larger sized contigs (>100 nucleotides) between the

datasets, e.g. 7.2% of the turkey contigs is larger than

100 nucleotides vs. 3.9% (Gt3000) and 3.5% (Gt3500),

respectively in our dataset. We attribute the lower pro-

portion of contigs > 100 nucleotides to the higher level

of diversity between the individuals used for the prepa-

ration of the datasets: six turkeys from two interbred

lines vs. ten wild great tits from two different popula-

tions in the Netherlands. This difference in diversity is

further reflected in the numbers of SNPs detected: using

the same methods Kerstens and coworkers detected 207

SNPs ⁄ million base pairs of reference sequence, while

we detected over 645 SNPs ⁄ million base pairs of refer-

ence sequence. Based on this, we expect that SNP detec-

tion can be optimized by using sequence reads derived

� 2010 Blackwell Publishing Ltd

from only one individual for the assembly of the refer-

ence sequence, and reads from a pool of highly diverse

individuals for subsequent SNP detection.

Chicken micro- and intermediate chromosomes have

a higher G + C content, a lower repeat density and a

higher gene density than macrochromosomes (ICGSC

2004). We find that significantly more nucleotides of the

great tit contigs map to zebra finch microchromosomes

than to intermediate and to macrochromosomes, indi-

cating that the overall level of sequence conservation

between the great tit and the zebra finch is higher on

microchromosomes than on the other types of chromo-

somes, which is probably the result of a higher gene

density on the zebra finch microchromosomes. On the

other hand, this observation could be due to a bias in

our dataset, e.g. over-representation of sequences from

smaller chromosomes or a better assembly of contigs

derived from microchromosomes. However, we do not

find a significant difference in the average length of

contigs mapping to the different chromosome types

(data not shown) and it is reported for Sanger sequenc-

ing that microchromosomal sequences tend to be

under-represented rather than over-represented (ICGSC

2004). Chicken microchromosomes are estimated to

account for 18% of the genome, while they harbour

31% of all chicken genes (ICGSC 2004). Macrochromo-

somes, on the other hand, generally have larger inter-

genic regions, which tend to be more variable. This is

reflected in both the higher overall level of sequence

conservation that we find for the microchromosomes

and also in the observation that we find significantly

less SNPs on microchromosomes than on intermediate-

and macrochromosomes. This may seem contradictory

to the higher rate of nucleotide divergence reported for

chicken and turkey microchromosomes (ICGSC 2004;

Axelsson et al. 2005), but these previous studies focused

on the intronic and coding regions of the chromosomes,

while our study also includes intergenic regions. Cod-

ing regions are under different evolutionary constraints

and for future analysis of the nucleotide divergence on

the different chromosome types of the great tit and the

zebra finch it will be beneficial to separately focus on

the coding regions as well.

Contigs harbouring 4272 (21%) of the SNPs could be

mapped to a unique location on the zebra finch gen-

ome. This number is lower than expected based on the

observation that 28–51% of the contigs result in a

unique hit on the zebra finch genome. This observation

that a relatively high number of SNPs are located on

contigs that do not align to the zebra finch genome can

partially be explained by regions that are highly con-

served between the great tit and the zebra finch, which

will result in a relatively high number of contigs that

map, but will, due to selective constraint, harbour rela-

98 N. E . M. VAN BERS ET AL.

tively less SNPs. Additionally, in regions that are not

highly conserved, the presence of SNPs will hamper the

alignment, further reducing the number of contigs with

SNPs that map to the zebra finch genome. A further

increase of the size of the reference genome sequence

will also improve the alignment of great tit sequences

to the zebra finch genome. This in turn will enhance the

number of great tit SNPs that can be uniquely mapped

onto the zebra finch genome, further facilitating the

analysis of the molecular evolution of bird genomes.

Recently, paired end sequencing was added to the pos-

sibilities of next generation sequencing. In this case, the

sequence template is sequenced from both the 5¢ and

the 3¢ end, resulting in two sequence reads with a spac-

ing of known size. This, together with the increase in

read length (currently 50–75 bp) will improve the

length of the assembled sequence. As a result, the effi-

ciency of SNP mapping and SNP detection will multi-

ply, and also the fraction of SNPs with sufficient

suitable sequence context to allow the design of a probe

for use in genotyping assays will increase.

In conclusion, we showed that combining next genera-

tion sequencing with RRLs is an efficient strategy for the

detection of thousands of SNPs in an ecological model

species for which a sequenced genome is currently lack-

ing. This approach can be further optimized by including

paired end data, longer sequence reads and by compara-

tive assembly to the zebra finch genome. We showed

that the zebra finch genome can provide the framework

to select several thousands of evenly distributed SNPs.

In the near future, these SNPs will be used for the geno-

typing of a panel of individual great tits and the con-

struction of a linkage map of the great tit. This map can

provide further insight into the evolution of (bird) ge-

nomes, but, above all, this map will be essential in identi-

fying genomic regions that explain phenotypic variation

between individuals in loci associated with quantitative

traits, e.g. behavioural and life history traits.

Acknowledgements

This project was financed by the Horizon program of the

Netherlands Genomics Initiative. Supercomputer facilities

were sponsored by the National Computing Facilities Founda-

tion (NCF), grant number SH-088-2-08, with financial support

from the Netherlands Organization for Scientific Research,

NWO. The authors would like to thank the Genome Sequenc-

ing Center at Washington University School of Medicine in

St. Louis for letting us use the zebra finch genome sequence

data.

Conflicts of interest

The authors have no conflict of interest to declare and note that

the sponsors of the issue had no role in the study design, data

collection and analysis, decision to publish, or preparation of

the manuscript.

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This paper is part of an ongoing project on SNP discovery to

map QTLs for timing of breeding and personality in great tits.

Nikkie van Bers is a postdoctoral fellow working on this pro-

ject. Knees van Oers is at the Netherlands Institute of Ecology

and its interested in the evolutionary genetics of animal per-

sonality. Hindrik Kerstens is a PhD at Animal Breeding and

Genomics Centre, Wageningen University and has a strong

interest in bioinformatics. Bert Dibbits is technical assistant in

molecular biology at Animal Breeding and Genomics Centre,

Wageningen University. Richard Crooijmans is assistant pro-

fessor at Animal Breeding and Genomics Centre, Wageningen

University, Dand is interested in genome research of farm ani-

mals. Marcel Visser is professor at the Department of Animal

Ecology at the Netherlands institute of Ecology and is inter-

ested in great tit lay date plasticity and its micro-evolution in

response to climate change. Martien Groenen is Professor in

Animal Genomics at Animal Breeding and Genomics Centre,

Wageningen University, Project and has a broad interest in

comparative and population genomics of animals.

Supporting Information

Additional supporting information may be found in the online

version of this article.

Supplementary information is available online containing the

primer and contig sequences, used for the validation of the

assembly and the SNP detection. It also contains the mapping

positions of these contigs, the confirmation status of the SNPs

used for the validation and the accession numbers of the con-

firmed SNPs. Furthermore, a graphical representation of the

distribution of the great tit contigs over the zebra finch genome

is provided.

Please note: Wiley-Blackwell are not responsible for the content

or functionality of any supporting information supplied by the

authors. Any queries (other than missing material) should be

directed to the corresponding author for the article.