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. 2008 Jan 22;9:8. doi: 10.1186/1471-2156-9-8

Insertion-deletion polymorphisms (indels) as genetic markers in natural populations

Ülo Väli 1,2, Mikael Brandström 1, Malin Johansson 1, Hans Ellegren 1,✉
PMCID: PMC2266919  PMID: 18211670

Abstract

Background

We introduce the use of short insertion-deletion polymorphisms (indels) for genetic analysis of natural populations.

Results

Sequence reads from light shot-gun sequencing efforts of different dog breeds were aligned to the dog genome reference sequence and gaps corresponding to indels were identified. One hundred candidate markers (4-bp indels) were selected and genotyped in unrelated dogs (n = 7) and wolves (n = 18). Eighty-one and 76 out of 94 could be validated as polymorphic loci in the respective sample. Mean indel heterozygosity in a diverse set of wolves was 19%, and 74% of the loci had a minor allele frequency of >10%. Indels found to be polymorphic in wolves were subsequently genotyped in a highly bottlenecked Scandinavian wolf population. Fifty-one loci turned out to be polymorphic, showing their utility even in a population with low genetic diversity. In this population, individual heterozygosity measured at indel and microsatellite loci were highly correlated.

Conclusion

With an increasing amount of sequence information gathered from non-model organisms, we suggest that indels will come to form an important source of genetic markers, easy and cheap to genotype, for studies of natural populations.

Background

Advancement in population and evolutionary genetic research has been accompanied by – or perhaps better phrased – been a consequence of continuous improvement in the way genetic similarity or dissimilarity between genomes is assessed. Seen in long time perspective, genetic marker methodology has evolved from focusing on phenotypes, via immunological parameters and proteins, to genotypes. Following their introduction to the study of natural populations about 15 years ago [1-3], microsatellites or short simple tandem repeats have been the genotype-based marker approach of choice for many applications where the relatedness between individuals, populations or species is sought. Preceding and subsequently in parallel to this, non-repetitive DNA sequence variation has been assessed through various approaches, including DNA sequencing, restriction fragment length polymorphism (RFLP) analysis, single strand conformation polymorphism (SSCP) analysis, random amplified polymorphism detection (RAPD) and amplified fragment length polymorphism (AFLP) analysis [4]. More recently, single nucleotide polymorphisms (SNPs) are increasingly finding their application in studies of natural populations [5,6].

The benefits of microsatellites are several and well-known. They are multi-allelic, show high heterozygosity and are relatively easy to analyse at moderate cost. Because of the high polymorphism information content, a rather limited number of markers suffice for many applications in molecular ecology and population genetics. It is usually not too difficult to isolate the required markers from DNA libraries [7] or to employ markers originally developed for related species [8]. SNPs merit as genetic markers for other reasons. They are very common, with genomic densities outnumbering that of microsatellites by orders of magnitudes. Large numbers of individuals may be genotyped at large number of loci by simple and fast automatic methods, and data interpretation is usually straightforward [5,9]. Moreover, SNP variation at protein-coding genes and in other functionally constrained regions of the genome is likely to form the main genetic background to phenotypic variation. Furthermore biallelic SNPs evolve in a manner well described by simple mutation models. There are good reasons to believe that they in many cases will gradually come to replace the use microsatellites in molecular ecology and population genetics/genomics research [6].

Unfortunately, however useful, both microsatellites and SNPs suffer from some shortcomings. The complex and heterogenous mutation pattern of microsatellites [10] introduces ambiguities to further data analysis. Genotyping errors may occur because of stutter bands and technical artefacts (allelic dropouts, null alleles, false alleles, size homoplasy) [11]. As for SNPs, many more markers are needed to get the same amount of information [6,9]. Moreover, despite the many elegant genotyping methods available [9], most of them are relatively costly at small or medium scales, and requires special equipment for high-throughput genotyping.

With a few years' lag phase, the introduction of new genetic markers to the study of natural populations has generally followed methodological developments made in the genetic analysis of model organisms [4]. Currently, there is an increasing focus on polymorphisms of the type short insertions and deletions (indels) in genomic research of humans [12,13] and model species such as Drosophila melanogaster [14] and chicken G. gallus [15]. Indels have been recognised as an abundant source of genetic markers that are widely spread across the genome, though not as common as SNPs. For instance, Mills et al. [13] used data from re-sequencing surveys to identify 415,436 indels segregating in human populations and they estimated that among the total number of >10 million polymorphisms known in humans, some 1.5 million represent indels. Clearly, this indicates that indels could form a very common class of genetic markers also in non-model species and this is particularly so given that genetic diversity in many natural populations typically seems to be higher than in humans [5,6,16]. Most importantly, indels can be genotyped with simple procedures based on size separation. Another advantage is the minuscule chance of two indel mutations of exactly the same length happening at the same genomic position, meaning that shared indels can confidently been seen as representing identity-by-descent [cf. [17]].

In this study we present a test of the usefulness of indel markers in natural populations. We use a bioinformatics approach to survey dog shot-gun reads [18] for the presence of indels and based on this we design a pipeline for development of PCR-based indel markers. We subsequently genotype 100 indels in natural wolf populations and compare the results with data on microsatellite variability obtained from the same animals.

Results

There are ≈100,000 shot-gun reads available from each of 9 different dog breeds, sequences data that come in addition to data obtained for the partial [19] or full genome sequencing [18] of two dogs. We surveyed 200,000 of these trace reads for the occurrence of short insertion and deletion polymorphisms, as detected by alignment against the reference sequence of one female boxer [18]. Note that there is essentially no sequence overlap among trace reads so the alignments were consistently in the form of only two alleles drawn from the population of dogs. In total, this yielded 30,116 length polymorphisms, corresponding to about one length variant every 2400 bp. Consistent with what has been found in other organisms cf. [12,13,15,20], the great majority of indels were very short with a dominance of 1-bp events (Table 1). From these polymorphism data we chose 4-bp indels for further analysis since they are easily scored by size separation and relatively abundant in the genome. We selected 100 4-bp non-repetitive indels located within unique sequence. They were spread across the canine genome and consistently represented autosomal loci; the great majority of them likely to reside in non-protein coding sequence. Of the 100, 94 could be readily amplified and scored and were selected for further analysis (Table 2).

Table 1.

Number and density of indels found.

Indel size (bp) Count Density (indels per million bp)
1 20558 284.6
2 3352 46.4
3 1942 26.9
4 2185 30.2
5 678 9.4
6 436 6.0
7 297 4.1
8 297 4.1
9 219 3.0
10 152 2.1
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Table 2.

Location of selected indel-markers (position on the respective chromosome in the dog genome assembly), primer sequences, amplicon length and expected heterozygosities from the genotyping of 18 wolves from worldwide.

No. Chromosome Position Primer F Primer R Fragment length (bp) He
1 23 35377868 ccaggcttgtgtgaagctct gccttgttggtttcagtggt 126 0.43
2 21 44954960 tgtcatttggccagatctctaa ttggattaaccctaccacacg 117 0.26
3 5 56432744 catgctgcttgaagtgcaata tctctgtgtgcctctcatgaat 123 0.00
4 6 31824728 cacaatgaccacttattaaagattaca ctaggatgagagcccagctt 139 0.00
5 29 17604384 tgtcaggtttcatatccttttgtg gaactatccttaaatagaaccaatgc 111 0.29
6 15 52623379 ttcacatccatctgtcttgga acccgggagtttgcctatac 125 0.20
7 12 4364137 ctcctgttccctccagca ggaccatgctgtggatctg 115 0.17
8 30 32665884 agaccagggtctgaatttgc tttccaaggtcccaccacta 116 0.24
9 7 52754426 ttcacaaattgctatacctaaaaatg ttcctgtgggcataataatca 136 0.48
10 26 32666193 tccaagaacaaagaagtaatgtaaaa ggaattgatttactgatagtgagatg 130 0.39
11 37 18098142 gaaaggtccctctgaattgaa tctgtgctcttcactggaaaaa 122 0.18
12 14 38060282 gtgtgctctaggggccatt gaatgaaatcatggaagagcaa 115 0.30
13 17 46184901 gaagggacaaaaccttggaa tgaactaccctcgtgatcca 139 0.37
14 28 38785954 aaaggagggcttgcagtttt ttctccttttagaccctttgtca 90 0.32
15 25 8846761 tgccttagcgttggcatt tggtgctctttcttgttgga 169 0.36
16 34 7543229 caggagcaaagtaagggtaatca gctttggtattgttgattctattgtaa 90 0.44
17 20 21505420 aatggggacaccagtcactt tgggagttctggctccac 139 0.00
18 1 32746664 tcctgcggcagtttgg ccaagattgtgcatgtcagg 104 0.36
19 23 42171234 caaaggcaagaaggcagatg tacatggtccctgtgttcca 81 0.51
20 35 25523850 ttagcgatgttgagcgttttt ttcccttaagaaataggcagagg 92 Excluded
21 22 41896467 tgcaaaggagtgggaattatc tggatgttaaaaacctggtatattgt 164 0.37
22 30 32665884 ccaagccccttccaatacta tttccaaggtcccaccacta 92 0.29
23 10 55417044 tgctttgcatgttacattcttca catgtcatagtcacatgctgtacg 151 0.32
24 22 20849044 tattgctgccctgtttcaga gctgaaggaaatatctgttgaatg 102 0.16
25 24 40849741 ctgcggtctcacatccttag tgagggggatttgatctctt 69 0.14
26 31 8148287 tctgctcaggtttagccttg ggatgcaagaaaatctgctg 80 0.43
27 3 67261495 ttactcccagctctgtgcat gacccaggtggggatatcta 88 0.06
28 26 37097342 tgcccccactactcttgc aaagggtgatggtcctttga 98 0.09
29 1 109880549 tgttgagcccttgaaatgag agcgaaaagtggcagtgg 68 0.51
30 13 20047698 tggctgccccatcttatg cacaatggcagaacacgag 78 0.06
31 10 13999933 gccttcttcctctgcctct ctcaggcaggcaaataaaaa 90 0.13
32 5 6092689 gcttgggaaatcatggtca gctaaggaaagcaagctgga 100 0.50
33 3 52499600 tctgactggcctccttcg attcaagtgtgcccgagag 67 0.51
34 2 87005110 gccgccgtgtcttgtc cgaatgcgtgcttaccg 80 0.07
35 5 16794632 cgatgctggtgaggaagc ccatccctgagccacct 91 Excluded
36 1 40637985 aagggccgatgccagt caggttcttgtttccccaaa 100 0.42
37 14 16593885 cccaggtgccccttattt ggctcatgctgctctgg 110 0.00
38 38 3128143 gcttcccttgtttctttcca tgcccatgtaccaaatgaa 121 0.51
39 6 78974466 gtagggcaagcggcaag tgcttcctggacatttgga 110 0.14
40 4 68490768 ttgcttgggaacatggag gcccttgtcatccactagga 121 0.00
41 1 41518626 cctggtgcaggttgcag ccttcggagcccatgc 106 0.42
42 19 29399024 caggacacttgcaccagatt gagcagaggtgaggctgaa 120 0.00
43 12 27463695 cagtagccaaattgtggaagc accacgtagtcttgacccattc 68 0.30
44 16 10834862 gttcccttctcagaggacca caatgagtgaagggggtcag 69 0.00
45 14 38539141 gagtggcacacgagcactt gcaggactgtctggaggttg 68 0.31
46 8 60840667 tgcctgagggagctgtatatg tctcattgtggagcaaagacat 75 0.51
47 5 36083663 gctttgttgtaagcagcgata tgtgagaaactccattgcctta 74 0.21
48 7 53313567 agaaggggcagacttgagg tccctcatttcacaagctga 75 0.19
49 1 88201810 tggctcattgatttgtgattct ggccagctcttcttgttgag 78 0.00
50 14 61925547 gggttctctagggagatgacaa aggacccaagtggattctga 83 Excluded
51 7 17212546 gtcatggtgacatcgcagtt gcctcatgccaatgagagac 85 0.12
52 2 67041786 gatggccgattgtacatcaa tggttgcagggaagattagg 95 0.34
53 5 3965764 ccgtctagttgtcgggtgtt tgcagtatttagggtggagga 94 0.43
54 34 24388648 cccttgtaaaggggaggaga tggctctgaatttaggcattt 94 0.48
55 12 23053279 agctctcctgctgtgattttt tgcagacaaatggactgaaga 98 0.44
56 20 29687857 tgagcacgaagaggtagagaag tcaagtgcaagtcaccaaact 100 0.43
57 30 37964459 cgtgaatggtccaaaatgat catcagcatttccagagttctt 100 0.26
58 13 42973844 tttctgggcaaaaacagtga tttagatgggagggaatggtt 108 Excluded
59 5 91665974 accttctgtttcccctttgg agagcgcagcagagatgact 103 0.00
60 4 43098715 gtttacaccacccagcctga aggcagttacaggcattaatca 109 0.40
61 20 40585796 cccatccctgaggaagagag ggacacccgcatttctgtc 114 0.12
62 7 8798933 cccagaaaacaagagaaggaaa tgttggcagatctcatggtc 114 0.00
63 14 23314695 acccaccagattggctaaaa tcgaacaggtccagtttacatt 116 0.26
64 26 35769409 cctggcttaaccccttacct aaccgctatcccacattctg 96 0.00
65 4 56495029 tcccactgtagcttgaaaacg ttcaggtattgctgtccaaaaa 95 0.51
66 17 19537687 atgctttgcagagatgcttg tgctcatgtcagaacagagagg 94 0.31
67 31 26928202 atgaacaagcaccccaaaac cagtccacttcaatgcacca 68 0.27
68 10 66801300 ccccaaattaagggaagttca tccatgaccaaatctgcatc 70 0.12
69 10 63275151 tccagaacttggagagaatcaa tccaggcaagactatgagca 70 0.30
70 5 58727403 ctggaccacatatggcttga tgccggagagatacgtgtaa 72 0.00
71 5 29583832 ccacacagtgttgcctgtagt ggaaagcacagaaagttgtgaa 73 0.49
72 11 25068367 cccctgcttgtgctctctc ctctcagcaacccacagagat 75 0.49
73 19 54304531 aagccttgcacttgagcttg ccattggaaaggcacgtact 77 0.27
74 30 39573069 gcttttgtcatgaaacaccaa ccaccagatgctcaagtctg 79 0.48
75 19 55236292 gcccacagggtctttattttt gcttgggtctcttcctctctc 78 0.33
76 3 53761240 ctgtggtgcagcggtttag ggagcctgacatgggactt 78 0.00
77 4 90768017 gttctccctgtgtctgactgtt aaagaccaaggggtgaaaga 84 0.13
78 11 57430384 agggacagacccactaagtgtc tccttaggcgacatggagac 86 Excluded
79 36 32128918 tgatatagccgaagtcaggaag aatggaaagatcatccagacag 89 0.40
80 14 49479392 tttcaaattgctgaatgttgg ccctggtctccaggatcac 89 0.47
81 27 13108059 cagccaattggacacaaaaa aaaatcaagatgtcagcagagg 88 0.00
82 6 61627505 gtcatgatcccatggtccta gtagaggggcagagggagag 88 0.00
83 36 14699428 gtgttttcttttgggcaagg tgcaaccaacacacagatga 92 0.49
84 32 36108071 cccagtgttggtcacatataca gcgatgaaaattgggaaaga 96 Excluded
85 17 57586353 gtgattagggttgaggggaga ttttccatcaaggtttgtcca 97 0.42
86 5 16750608 aattttccaggaggctttgg ttccctcctgctgatctagg 98 0.06
87 19 43306776 tggggtaaatcagtgagtgaag ggcattaagagaagcctgctg 99 0.07
88 29 43945653 aacccgaataacattaggagga tgggtttaagctggttacgg 104 0.51
89 5 31951980 cccagaaatccacttaatgacc tgtgttaccagggctaggttc 104 0.44
90 7 50783779 ggttagcttagctcctctccaa agccacatgctgaaaggaag 104 0.00
91 31 10957463 ttggcaactgccttacaataaa ttgaatgtggacatgaaacaaa 106 0.47
92 16 6629437 gaaatgggaaggttttattcca ggtgctgacaacagaaaacct 109 0.46
93 5 75136583 aggacacagacagatgtgagga ccgaagaggaatctgcactc 111 0.00
94 3 89213750 gagaacttcgatgtgagggaat ctctcccaccaaaaatctcct 113 0.29
95 21 51457175 aggcattcagggtgttaaaaa tggtgaactggaaagtagctga 108 0.51
96 12 53379600 ttcccaaggagttggagaga gctgagggcagctgtgttat 68 0.00
97 14 58723304 ccctgtggtaaccatcaacc caaagtgaacaagcaaagcaa 67 0.51
98 3 11002853 ccactggccctaagtgactg ttagggttttaaaggctgtgc 70 0.06
99 30 30015918 tcaggttttggatttgaagga taagcacaaccattagctcca 72 0.06
100 7 65831405 gaggttcaaatttcccatatcc gggatccatgcaaaatagttc 73 0.37
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Using conventional genotyping based on fragment length separation in a DNA sequencing instrument, 81 out of the 94 putative markers were found to be polymorphic in a screening of 7 dogs and 76 of them were polymorphic in a global sample of 18 wolves (Figure 1A). As PCR primers were designed to generate amplicons of varying size within the 70-120-bp interval, combinations of multiplex reactions (three markers per PCR) were readily formed. This allowed simultaneous amplification, and consequently simultaneous genotyping within a single capillary, of several markers even using the same fluorofore (Figure 1B).

Figure 1.

Figure 1

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(a) Genotyping of a 4-bp indel locus in wolves showing (upper panel) a homozygote for the longer allele, (mid panel) a heterozygote and (lower panel) a homozygote for the shorter allele. (b) Multiplex amplification and simultaneous genotyping in a single capillary of five indel markers in one individual heterozygous for all these markers. The long and short alleles of marker 1–5 are labelled. All markers show some form of extra fragments that likely represent PCR artefacts. These may either be shorter (marker 2) or longer (marker 3–5) than the amplified allele, alternatively both shorter and longer (marker 1).

In wolves, 74% of the polymorphic loci had a minor allele frequency of >10% and 49% of >20%. The average observed and expected heterozygosities were respectively 19.4% and 26.1% in wolves, while they were 26.8% and 35.5% in dogs. The distribution of wolf heterozygosities is shown in Figure 2.

Figure 2.

Figure 2

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Distribution of observed heterosygosities at 94 indel loci genotyped in 18 wolves from five populations worldwide.

The 76 indels found to be polymorphic in the global sample of wolves were subsequently genotyped in 27 wolves from a Swedish population. Fifty-one loci were polymorphic and showed an observed mean heterozygosity of 25.3%, or 17.0% if including all 76 markers. The same wolves were also genotyped for a set of 20 microsatellites known to be informative in this population [e.g. [21]]. Expected heterozygosities for these loci ranged between 28–75%. There was a positive correlation between mean heterozygosity at indel and microsatellite loci in individual wolves (r2 = 0.41, P < 0.001; Figure 3).

Figure 3.

Figure 3

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Correlation between average observed individual indel (51) and microsatellite (20) heterozygosities in 22 Swedish wolves.

Discussion

Our study shows the feasibility of using large-scale genomic sequence data for extracting putative insertion and deletion polymorphisms, marker loci subsequently can be validated to represent informative genetic markers at a population level. It also demonstrates the feasibility of transfer of genomic data from a model species to a natural population of a close relative. Dogs were domesticated from wolves 10,000–100,000 years ago [22-24], and their divergence has since then been accentuated by strong artificial selection during domestication. Finally, by genotyping of indels and microsatellites in the same wolves it also shows that polymorphism levels of the two marker types are highly correlated.

A lack of large-scale genome sequence information has up till now hampered the introduction of indels as genetic markers in non-model species. It can be anticipated that this will come to change in the near future. There is a rapid increase in the number of genome sequencing initiatives and new sequencing technology, like "454-sequencing" [25], offers immense possibilities for generating massive amount of sequence data from hitherto uncharacterised genomes. Importantly, the depth of sequence coverage provided by new technology means that it is well suited for sequence analysis of pools of individuals, from which a wealth of polymorphism data can be obtained [26]. For example, if 100 Mb of sequence is generated from each of two individuals (with a 1 Gb-genome) in two mega-sequencing runs, and with an indel density of 1 every 2 kb in pairwise comparisons, several hundred indels are expected to be detected.

Indel density has not been as well characterized in natural populations as nucleotide diversity. In domestic chicken, the pairwise heterozygosity for indels is 2 × 10-4 per bp [15]. In a natural population of collared flycatchers, Backström et al. [27] found a similar occurrence of indels, 1–2 × 10-4 per bp. In this study we found about 30,000 indels in 7.2 Mbp of dog sequence, which translates into a heterozygosity of 4 × 10-4 per bp. This includes length variants in unique sequence as well as in repetititve DNA, like microsatellites. Using a similar search algorithm and a similar type of shot-gun vs. genomic reference data set for chicken, we recently found that about half of all length variants detected in this way represent tandem repeats [15]. This would suggest that in dogs, the heterozygosity for short non-repetitive indels is about 2 × 10-4 per bp, similar to chicken. Moreover, the length distribution of dog indels (Table 1) show congruence with such data from chicken.

The Swedish wolf population was functionally extinct by the 1960s–1970s but has subsequently recovered to a current size of well over 100 individuals [28]. All contemporary Scandinavian wolves are thought to originate from only three founders, that were eastern immigrants arriving to Sweden around 1980 and 1990, respectively [21]. The strong bottleneck, subsequent inbreeding and the associated loss of genetic diversity experienced by this population [21,29], give the opportunity to test the utility of indel markers in a small and endangered natural population. The finding that about 50% of in silico predicted indels from pairwise sequence comparisons of dog alleles is informative in this wolf population confirms the usefulness of indel markers even in a population with limited genetic diversity.

The mean heterozygosity of the 51 polymorphic indels within the Scandinavian wolf population (25%) is somewhat lower than what was been observed for 21 SNPs (34%) in the same population [29]. However, those SNPs were initially identified from a screening of a limited number of Scandinavian wolves so there was an ascertainment bias in favour of markers with high polymorphism information content. Generally, for those indels and SNPs that represent neutral markers, there should be no reason to believe that heterozygosity for polymorphic loci differs between the two marker categories. Indels in coding sequence are likely to more often be deleterious than point mutations, at least indels that cause frame shift mutations, which should act as to reduce their diversity due to negative selection. On the other hand, point mutations in coding sequence may potentially more often than indels be subject to positive selection, which also reduces diversity. In any case, although probably comparable to SNPs, indels do show less variation than microsatellites. Thus, to obtain the same resolution power in relatedness analyses, a higher number of biallelic markers are needed compared to multiallelic microsatellites [30-32]. However, the rich abundance of indels in genome sequence surveys and the ease by which they are genotyped (Figure 1a) and multiplexed (Figure 1b) add to their benefit. Moreover, it is possible to design microarrays specifically for short indels, by which genotyping costs become very low [32].

Conclusion

With an increasing amount of sequence information gathered from non-model organisms, we suggest that indels will come to form an important source of genetic markers, easy and cheap to genotype, for studies of natural populations.

Methods

Samples

Genomic DNA was extracted from wolf tissue samples using standard phenol-chloroform extraction protocols or the DNEasy Tissue Kit (Qiagen). Altogether 18 samples, from Sweden (5), Finland (3), Spain (3), Russia (2) and Canada (5), were used to test the amplification ability and to get a first idea of polymorphism of indels. Seven domestic dog samples from different breeds (Dachshund, Dalmatian, Gordon Setter, Greenland Dog, Lakeland Terrier, Pyrenean Mountain Dog, Welsh Corgi) were also added. Subsequently, we tested the ability of indel markers to analyse the genetic diversity at the intra-populational level using tissue samples of 27 wolves collected between 1985 and 2005 from roadkills or shot animals from Sweden [21,29].

Selection of markers

A total of about 200,000 dog trace read sequences were obtained from GenBank. These sequence tags were almost exclusively derived from light shot-gun sequencing of unrelated dogs that was done in conjunction to the sequencing of the dog genome [18]. An automated pipeline was set up to survey the sequences for potential indels and for design of primers. The initial step in the pipeline was to place all STS sequences onto the dog genome. This was done using local NCBI BLAST [33], with a conservative setting to require an E value of less than 10-70. To avoid possible duplicated loci all cases where there was more than one BLAST hit were discarded. Next, the BLAST results were surveyed for 4 bp indels, recognised as 4 bp gaps in alignments of shot-gun reads and the genome reference sequence. To avoid selection of microsatellites only those 4 bp indels where none of the flanks were identical to the indel were used for further processing. For each indel with at least 70 bp flanking sequence on both sides, Primer3 [34] was used for primer design. Primers were requested from the program for fragment lengths between 70 and 120 bp. The primers were constrained by a required melting temperature between 58 and 62°C, as well as a primer length between 19 and 22 bp. Finally the primers were evaluated with regard to self complementarity, as well as for the possibility of the resulting product to form a hair-pin. This was done through a simple complementarity testing procedure where possible self-complementarity at the sharp end of the hairpin was scored higher, and decreasing score inwards. The top 100 loci passing through all steps were picked for screening. Primers were fluorescently labelled with either FAM, HEX, or TET.

The same animals were also genotyped for a set of 20 autosomal microsatellites, as described in ref. 20: c2001, c2006, c2010, c2017, c2054, c2079, c2088 and c2096, vWF, u109, u173, u225, u250 and u253 and PEZ01, PEZ03, PEZ05, PEZ06, PEZ08 and PEZ12.

Genotyping and data analysis

Amplification by polymerase chain reaction (PCR) was performed in 10 μl solution containing 20 ng DNA, 0.25 U AmpliTaq Gold polymerase with 1× Amplitaq Gold PCR buffer (Applied Biosystems), 2.5 mM MgCl2, 0.3 μM of each primer and 0.4 mM dNTP. The PCR profile for the indel markers included initial heating at 95°C for 5 min, followed by 35 cycles of 95°C for 30 s, 58°C for 30 s and 72°C for 1 min, and a final extension at 72°C for 10 min. The profile for microsatellites included an initial denaturation step of 95°C for 10 min, 11 touch-down cycles with 94°C for 30 s, 58°C for 30 s, decreasing by 0.5°C in each cycle, and 72°C for 1 min, then 28 cycles of 94°C for 30 s, 52°C for 30 s and 72°C for 1 min and a final extension of 72°C for 10 min. PCR products were run on a MegaBACE 1000 capillary sequencer (Amersham Biosciences) and analyzed using the accompanied software Genetic Profiler 2.2. Observed and expected heterosygosities calculated using Microsatellite Toolkit for MS Excel [35], and correlation between the observed individual heterozygosities according to indel and microsatellite data was estimated.

Authors' contributions

ÜV carried out the molecular studies and performed the data analysis. MB participated in the design of the study, selected markers and designed primers. MJ participated in the molecular analyses. HE conceived of and coordinated the study, and wrote the paper together with ÜV. All authors read and approved the final manuscript.

Acknowledgments

Acknowledgements

We thank Annika Einarsson for technical assistance, Jennifer Leonard and Carles Vilà for wolf samples, and two anonymous reviewers for useful comments on the manuscript. Financial support was obtained from the Norwegian and Swedish Natural Environmental Protection Agencies. ÜV was supported by the fellowship of the Visby programme from the Swedish Institute.

Contributor Information

Ülo Väli, Email: ulo.vali@ebc.uu.se.

Mikael Brandström, Email: mikael.brandstrom@ebc.uu.se.

Malin Johansson, Email: malin.johansson@ebc.uu.se.

Hans Ellegren, Email: hans.ellegren@ebc.uu.se.

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