Open Access

Depauperate genetic variability detected in the American and European bison using genomic techniques

  • Cino Pertoldi1, 2Email author,
  • Małgorzata Tokarska1,
  • Jan M Wójcik1,
  • Ditte Demontis2,
  • Volker Loeschcke2,
  • Vivi R Gregersen3,
  • David Coltman4,
  • Gregory A Wilson4, 5,
  • Ettore Randi6,
  • Michael M Hansen2 and
  • Christian Bendixen3
Biology Direct20094:48

DOI: 10.1186/1745-6150-4-48

Received: 18 November 2009

Accepted: 8 December 2009

Published: 8 December 2009


A total of 929 polymorphic SNPs in EB (out of 54, 000 SNPs screened using a BovineSNP50 Illumina Genotyping BeadChip), and 1, 524 and 1, 403 polymorphic SNPs in WB and PB, respectively, were analysed. EB, WB and PB have all undergone recent drastic reductions in population size. Accordingly, they exhibited extremely depauperate genomes, deviations from genetic equilibrium and a genome organization consisting of a mosaic of haplotype blocks: regions with low haplotype diversity and high levels of linkage disequilibrium. No evidence for positive or stabilizing selection was found in EB, WB and PB, likely reflecting drift overwhelming selection. We suggest that utilization of genome-wide screening technologies, followed by utilization of less expensive techniques (e.g. VeraCode and Fluidigm EP1), holds large potential for genetic monitoring of populations. Additionally, these techniques will allow radical improvements of breeding practices in captive or managed populations, otherwise hampered by the limited availability of polymorphic markers. This result in improved possibilities for 1) estimating genetic relationships among individuals and 2) designing breeding strategies which attempt to preserve or reduce polymorphism in ecologically relevant genes and/or entire blocks.


This article was reviewed by: Fyodor Kondrashov and Shamil Sunyaev


Fifty-four EB (Bison bonasus bonasus), 30 WB (Bison bison athabascae) and 26 PB (Bison bison bison) were used for genome screening. The PB samples derive from Elk Island National Park while WB samples come from Wood Buffalo National Park (Canada). The relationships (mother, father and offspring) of three EB families were used in the SNP screening to verify Mendelian segregation. Genome-wide screening encompassed 54, 000 single nucleotide polymorphisms (SNPs) across the entire bovine genome. More than half of the SNPs were discovered using the sequencing system Genome Analyzer (Illumina®) [1]. The additional SNP content was derived from publicly available sources such as Btau (, the bovine reference genome, and the Bovine HapMap Consortium data set (). The BeadChip has an MAF of 0.25 across all loci and has been validated in both dairy and beef cattle. SNPs were genotyped on the BovineSNP50 BeadChip according to the Infinium II Multi-Sample assay protocol provided by Illumina® (Manual Experienced User Card, 11208000 Rev. A., Illumina Inc.).

Despite the fact that the Bos and Bison lineages split about 1 million years ago [2], a total of 2, 209 polymorphic SNPs were found when EB, PB and WB were pooled. The average call rate for the bison samples was relatively high (96.60%) compared to the call rate found for the cattle samples (99.5%) (Illumina, Inc. Pub No. 370-2007-029) [1]. The small difference in call rate (2.9%) confirms the reliability of our comparisons and also reflects differences in the genomic DNA between the two groups such as the possibility of different alleles or deletions in SNPs.

The P% for each bison species was calculated relative to the total number of loci that were polymorphic when EB, WB and PB samples were pooled. The pairwise FST used as a measure of differentiation between EB, PB and WB was estimated using FSTAT version 2.9.3 [3] (). Genetic differentiation between PB and WB was significant (FST = 0.114), albeit much lower compared to EB-PB (FST = 0.466) or EB-WB (FST = 0.474). Genetic differentiation estimated between EB, WB and PB might provide a relative estimate of the time elapsed since divergence in isolation.

The number of mapped, polymorphic SNPs in EB was 929 (P% = 42%). The distribution of these markers along the autosomal chromosomes (on the corresponding known position on the 29 autosomal bovine chromosomes) is shown in Figure 1. More polymorphic SNPs were found in WB (P% = 69%) and PB (P% = 63%) (1, 524 and 1, 403 SNPs, respectively, see Figures 2 and 3).
Figure 1

The distribution of the 929 polymorphic SNPs in EB and haplotype block partitioning. Hatch marks of all SNPs in a certain block are connected by a line. Each hatch represents a SNP.

Figure 2

The distribution of the 1, 524 polymorphic SNPs in WB and haplotype block partitioning. Hatch marks of all SNPs in a certain block are connected by a line. Each hatch represents a SNP.

Figure 3

The distribution of the 1, 403 polymorphic SNPs in PB and haplotype block partitioning. Hatch marks of all SNPs in a certain block are connected by a line. Each hatch represents a SNP.

After considering the distribution of the distance between the polymorphic loci the haplotypes were calculated according to the four gamete rule implemented in the Haploview 4.1 software [4].

Inspection of the polymorphic SNPs (see Figure 1, 2 and 3) reveals long chromosomal regions fixed for one allele. Our findings have some similarities with a study of the cattle genome [5] which consists mainly of a mosaic of haplotype blocks, i.e. regions with low haplotype diversity and high levels of linkage disequilibrium [5]. These haplotype blocks may reflect several factors, including genetic hitchhiking, variable mutation rates and recombination, gene flow, drift, inbreeding and lack of genetic equilibrium [6]. Therefore, the processes underlying these haplotype blocks should be interpreted with caution. However, it is likely that many of the shared blocks and, in turn, highly polymorphic regions are ancestral. Hence, more detailed screening could answer important evolutionary questions.

Very few segregating SNPs were shared between EB and PB (1.44%) and EB and WB (1.89%) when compared to the overlap between PB and WB (41.91%). The number of shared polymorphisms is inversely related to the estimated FST values, confirming that they are related to the time elapsed since divergence in isolation of EB, PB and WB.

Simulations made with the software BOTTLENECK 1.2.02 [7] assuming an infinite alleles mutation model, showed decreased abundance of low frequency alleles (< 0.1) relative to intermediate allele frequency classes (0.1-0.2, 0.2-0.3, 0.3-0.4) in WB and PB, whereas for EB the distribution was L-shaped. This mode-shift in the distribution of allele frequencies in WB and PB signifies population bottlenecks. The tests indicate therefore that WB and PB are not in genetic equilibrium (as expected after recent demographic declines). However, the EB population is in genetic equilibrium, despite the documented recent, strong bottleneck. This unexpected result could be due to the fact that the bottleneck in EB may have reduced Ne, which is negatively correlated to the speed at which the population re-establishes mutation-drift equilibrium and show therefore an L-shaped distribution. Given the distortion of the L-shaped distribution is transient, and likely to be detectable until genetic drift begin to re-establish mutation-drift equilibrium [7] we hypothesize that the extremely small Ne produced by the drastic bottleneck in EB, has allowed a rapid return of the population to genetic equilibrium.

As a consequence of the finding that WB and PB are not at genetic equilibrium we must expect that the relation between FST and divergence time between EB, WB and PB can be strongly biased. Additionally, the presence of blocks in the genome can probably partially be explained by this lacks of genetic equilibrium. Even if the EB population is now at equilibrium, we cannot exclude that previous bottlenecks have contributed to the observed pattern of a mosaic of haplotype blocks.

The presence of a mosaic blocks pattern and evidences for a mode-shift in the distribution of allele frequencies found in WB and PB agrees with theoretical expectations for populations which have undergone a severe bottleneck [8]. The finding that P% in EB is relatively smaller than P% PB and WB, is not surprising as the European bison underwent the most extreme bottleneck in population size of the three groups at the beginning of the last century; EB stems from only 7 animals [8]. Consequently, due to the founder effect, the current free-living Białowieża population of the European bison (about 400 individuals in 2007) is expected to have lower genetic diversity than the historic population. PB and WB have also experienced bottlenecks [9, 10], of about 150 individuals in PB [11] and 250 in WB [12]. During the last 100 years the population size has increased and census population sizes are now estimated to be large both for PB and WB, although most of the bison in North America occur in commercial herds [10]. Despite rapid population growth in the last century, Ne has therefore increased slowly in bison. This is because the long term Ne is a function of the harmonic mean which is strongly influenced by the minimum population size reached [13, 14].

In order to detect regions of bison chromosomes which show signs of positive selection we checked for the existence of "outlier SNPs" by creating distributions of HE for every locus in EB, PB and WB. The SNPs were considered outliers if their HE was > mean HE + 3 SD or < mean HE - 3SD. These tests were performed pooling all loci and testing every single chromosome singularly. Additionally, we tested (with a t-test) for EB, WB and PB if the HE estimated from the polymorphic SNPs shared among EB, WB and PB were significantly different from the HE estimated from the unshared polymorphic SNPs. These tests were conducted because we suspected that the reasons for this shared polymorphism could be due to stabilizing selection. However, no significant differences were found. The lack of evidence for positive selection in EB, WB and PB is not surprising as the selection intensity has to be larger than for selection to be efficient [15, 16]. Given that Ne is low for bison in general, all SNPs, even if not selectively neutral, behave neutrally until Ne reaches a certain threshold.

By screening a large number of SNPs we considerably reduce the sampling error of the estimated overall genomic variability compared to traditional investigations that use a much more limited number of markers (SNPs or microsatellites). The SNPs can also be used in more limited numbers for genetic monitoring of populations. For many years, microsatellites have been the markers of choice for this purpose, using non-invasive techniques like extraction of DNA from scat or other organic material, and also using historical samples in order to add a temporal dimension to the survey. However, non-invasive DNA samples are often of poor quality and prone to genotyping errors due to DNA degradation, making long genetic markers like microsatellites difficult to use. Conversely, SNPs have the advantage that they are able to amplify short DNA sequences, decreasing therefore artifacts occurring due to degraded DNA. Additionally, the information from the BeadChip might allow genome-wide based breeding schemes designed to preserve rare alleles and minimize inbreeding (by estimating the true relationships between individuals). Traditional methods for making breeding decisions to reduce the level of inbreeding (by increasing Ne) utilize only pedigree information, which describes the expected relationship among individuals. With the same pedigree, however, individuals still vary in the realized genetic relationship among them [17]. Therefore, information obtained from genetic markers can be useful in this respect as they will provide the realized genetic relationships.

The fact that the genome of EB, WB and PB is organised in blocks allows for designing breeding strategies which can use genotype data instead of pedigree data and which have the scope to increase or reduce the polymorphism of the blocks. The fact that there are several polymorphic SNPs embedded in the blocks (see fig. 1, 2 and 3), allow us to reduce considerably the number of SNPs which need to be screened in order to apply a breeding strategy. In fact, our results suggest that one single SNP can detect the level of polymorphism of an entire block. Hence, choosing a subset of the polymorphic SNPs (48 or 96 SNPs) we can apply relatively cheap technologies such as VeraCode or Fluidigm EP1 system (). With the information obtained from the BeadChip it will be possible to create a SNP panel on the polymorphisms described here and use them in a MAS strategy [18].

Usage of a SNP panel as proposed here would allow not only reliable parentage and identity analysis but would also provide a standardized panel for exchange of genotype data between laboratories. As the range of off-the-shelf SNP genotyping systems grows, they may find increased use for a number of endangered species (and rare domestic breeds). Further research is needed to determine if existing SNP genotyping systems can be applied across whole taxonomic orders or families, or if they are only transferable at the species or genus level, and to what extent screening success depends on taxonomic distance from the source taxon or population history of the study taxon. A problem often associated with SNPs application in population studies is ascertainment bias [19, 20]. It is generated by heterogeneity in the SNP discovery process, variable sample size and composition, and may cause underestimation or overestimation of globally distributed SNP frequencies [20]. Since the chip markers used in our survey were selected for cattle, the possible ascertainment bias could appear when comparing the SNP based genetic variability of cattle and bison. Thus, a bison - cattle comparisons should be interpreted with caution. However the random selection of markers should not constitute a problem when the European bison is the only subject of an inquiry thus it was not considered in our study.

Reviewers' comments

Reviewers' report 1

Fyodor Kondrashov (Centre for Genomic Regulation Barcelona Spain).

Reviewers' comments

This reviewer provided no comments for publication

Reviewers' report 2

Shamil Sunyaev (Harvard Medical School, Boston MA, United States).

Reviewers' comments

This is an interesting study of genetic variation in bison. The authors characterized variation in the European bison and American wood and plains bisons using the BovineSNP50 Illumina Genotyping BeadChip. I have three comments listed below: 1)The manuscript seems to suggest that the observed small number of SNPs from the BovineSNP50 chip segregating in the bison population reflects a population bottleneck. However, the authors measured the level of shared polymorphism between bison and cow rather than the overall levels of variation in bison. The level of shared polymorphism has a more complex relationship with Ne and also depends on other parameters (for the theoretical treatment see Clark AG. Neutral behaviour of shared polymorphism. Proc Natl Acad Sci U S A. 1997 94:7730-4.) Thus, a more sophisticated discussion of the observations would be appropriate instead of simply stating the low level of variation and, subsequently, Ne.

Author's response

We agree with the Reviewer that it would have been interesting to estimate the N e following the method presented in Clark (1997). This method is based on the fact that the loss of a shared polymorphism of a neutral allele having an initial allele frequency of 1/2 occurs sooner than loss of polymorphism in a single species and has an expected time of 1.7 N e generations, compared to 2.77N e , which is the mean time necessary for one neutral allele to undergo fixation within a single population. Therefore, given the fact that in our study we have found several shared polymorphisms, we would have been able to estimate the historical N e of EB, WB and PB. However, the method presented by Clark requires that the loci utilised for the estimation of N e are unlinked. Hence, we performed tests for linkage disequilibrium and ran the program Haploview 4.1 which showed that the genome of the bison is mainly a mosaic of haplotype blocks where strong linkage disequilibrium between SNPs is observed (see please the last paragraph on page 5 and please see fig. 1, 2and 3where the blocks are shown graphically). Therefore, we have not been able to use Clark's method, but we decided to utilise the shared polymorphism in order to test if H E within a subspecies (pooling all the SNPs with shared polymorphism) was significantly different from H E within a subspecies (pooling all the SNPs which are not shared among EB, WB and PB). The differences of mean H E was not significant. Therefore, we did not obtain evidence for the fact that the shared polymorphism could be due to stabilizing selection acting on these SNPs (please see the first paragraph on page 8).

2) The manuscript would benefit from the analysis of allele frequency distribution (excess of high frequency variants may be suggestive of a bottleneck) and FST between the analyzed populations.

Author's response

Population bottlenecks cause a characteristic mode-shift distortion in the distribution of allele frequencies at selectively neutral loci. In a stable population the distribution is expected to be L-shaped. Bottlenecks cause alleles at low frequency (< 0.1) to become less abundant than alleles in more intermediate allele frequency classes (e.g., 0.1-0.2). We performed simulations with the software BOTTLENECK 1.2.02, assuming an infinite allele mutation model searching for mode-shift distortion in the distribution of allele frequencies. We found evidence for lack of genetic equilibrium in WB and PB (see please the third paragraph on page 5). Furthermore, the pairwise F ST values as a measure of differentiation between EB, PB and WB have been added (see please the first paragraph on page 5).

3) Is it possible to quantify the improvement of breeding strategies using genotyping instead of pedigree data?

Author's response

An exact quantification of the improvement of breeding strategy can only be made using empirical data comparing the improvement of a certain trait in mating individuals chosen on the basis of their pedigree or on the basis of their genetic similarity at the molecular level. Traditional methods for making breeding decisions to reduce the level of inbreeding (by increasing N e ) utilize only pedigree information, which describes the expected relationship among individuals. With the same pedigree, however, individuals still vary in the realized genetic relationship between them. Therefore, information obtained from genetic markers can be useful in this respect as they will provide the realized genetic relationships. Additionally, information from genome-wide screening also enables detection of genes associated with hereditary genetic diseases and inbreeding depression (these issues have been discusses in the last paragraph on page 8).

List of abbreviations


European bison


expected heterozygosity


Minor allele frequency


Marker assisted selection


Effective population size


Percent of polymorphic loci


Plain bison


Selection gradient


Wood bison



This study has been partly supported by a Marie Curie Transfer of Knowledge Fellowship BIORESC of European Community's Sixth Framework Programme (contract number MTKD-CT-2005-029957), by the Frankfurt Zoological Society - Help for Threatened Wildlife and by the LIFE financial instrument of the European Community (project "Bison Land - European bison conservation in Białowieża Forest", LIFE06 NAT/PL/000105 BISON-LAND). Furthermore we wish to thank the ConGen program (funded by the European Science Foundation) and the Danish Natural Science Research Council for financial support to CP (grant number: #21-01-0526, #21-03-0125 and 95095995) and Torsten N Kristensen for helpful comments on the ms.

Authors’ Affiliations

Mammal Research Institute, Polish Academy of Sciences
Department of Biological Sciences, Ecology and Genetics, Aarhus University
Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University
Department of Biological Science, Ctr Biol Sci, University of Alberta
Canadian Wildlife Service
Istituto Superiore per la Protezione e la Ricerca Ambientale


  1. Tokarska M, Marshall T, Kowalczyk R, Wojcik JM, Pertoldi C, Kristensen TN, Loeschcke V, Gregersen VR, Bendixen C: Effectiveness of microsatellite and SNP markers for parentage and identity analysis in species with low genetic diversity: the case of European bison. Heredity. 103: 326-332. 10.1038/hdy.2009.73.
  2. Loftus RT, MacHugh DE, Bradley DG, Sharp PM, Cunningham EP: Evidence for two independent domestications of cattle. Proc Natl Acad Sci USA. 1994, 91: 2757-2761. 10.1073/pnas.91.7.2757.PubMedPubMed CentralView ArticleGoogle Scholar
  3. Goudet J: FSTAT Version 1.2: a computer program to calculate F-statistics. J Heredity. 1995, 86: 485-486.Google Scholar
  4. Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.PubMedView ArticleGoogle Scholar
  5. Khatkar MS, Zenger KR, Hobbs M, Hawken R, Cavanagh J, Barris W, McClintock A, McClintock S, Thomson P, Tier B, Nicholas F, Raadsma H: A primary assembly of a bovine haplotype block map based on a 15k SNP panel genotyped in Holstein-Friesian cattle. Genetics. 2007, 174: 79-85. 10.1534/genetics.106.060418.View ArticleGoogle Scholar
  6. Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, Visscher PM: Recent human effective population size estimated from linkage disequilibrium. Genome Res. 2007, 17: 520-526. 10.1101/gr.6023607.PubMedPubMed CentralView ArticleGoogle Scholar
  7. Cornuet JM, Luikart G: Description and power analysis for two tests for detecting recent population bottlenecks from allele frequency data. Genetics. 1996, 144: 2001-2014.PubMedPubMed CentralGoogle Scholar
  8. Nei M, Maruyama T, Chakraborty R: The bottleneck effect and genetic variability in populations. Evolution. 1975, 29: 1-10. 10.2307/2407137.View ArticleGoogle Scholar
  9. Pucek Z, Belousova IP, Krasińska M, Krasiński Z, Olech W: European Bison Status Survey and Conservation Action Plan. 2004, IUCN/SSC Bison Specialist Group. Gland and Cambridge: IUCNGoogle Scholar
  10. Halbert ND, Raudsepp T, Chowdhary BP, Derr JN: Conservation genetic analysis of the Texas state bison herd. J Mamm. 2004, 85: 924-931. 10.1644/BER-029.View ArticleGoogle Scholar
  11. Freese CH, Aune KE, Boyd DP, Derr NJ, Forrest SC, Gates CC, Gogan PJP, Grassel SM, Halbert ND, Kunkel K, Redford KH: Second chance for the plains bison. Biol Conserv. 2007, 136: 175-184. 10.1016/j.biocon.2006.11.019.View ArticleGoogle Scholar
  12. Wilson GA, Strobeck C: Genetic variation within and relatedness among wood and plains bison populations. Genome. 1999, 483-496. 10.1139/gen-42-3-483. 42:
  13. Pertoldi C, Bach LA, Loeschcke V: On the brink between extinction and persistence. Biology Direct. 2008, 3: 47-10.1186/1745-6150-3-47.PubMedPubMed CentralView ArticleGoogle Scholar
  14. Pertoldi C, Bach LA, Barker JSF, Lundberg P, Loeschcke V: The consequences of the variance-mean rescaling effect on effective population size. OIKOS. 2007, 5: 769-774. 10.1111/j.0030-1299.2007.15672.x.View ArticleGoogle Scholar
  15. Pertoldi C, Bijlsma R, Loeschcke V: Conservation genetics in a globally changing environment: present problems, paradoxes and future challenges. Biodivers Conserv. 2007, 16: 4147-4163. 10.1007/s10531-007-9212-4.View ArticleGoogle Scholar
  16. Pertoldi C, Bach L: Evolutionary aspects of climate induced changes and the need for multidisciplinarity. J Therm Biol. 2007, 32: 118-124. 10.1016/j.jtherbio.2007.01.011.View ArticleGoogle Scholar
  17. Nielsen RK, Pertoldi C, Loeschcke V: Genetic evaluation of the captive breeding program of the Asiatic wild ass, onager (Equus hemionus onager). J Zool. 2007, 272: 349-357. 10.1111/j.1469-7998.2007.00294.x.View ArticleGoogle Scholar
  18. Wang J, Hill WG: Marker assisted selection to increase effective population size by reducing Mendelian segregation variance. Genetics. 2000, 154: 475-489.PubMedPubMed CentralGoogle Scholar
  19. Nielsen R, Signorovitch J: Correcting for ascertainment biases when analyzing SNP data: applications to the estimation of linkage disequilibrium. Theoretical Population Biology. 2003, 63: 245-255. 10.1016/S0040-5809(03)00005-4.PubMedView ArticleGoogle Scholar
  20. Clark AG, Hubisz MJ, Bustamante CD, Williamson SH, Nielsen R: Ascertainment bias in studies of human genome-wide polymorphism. Genome Research. 2005, 15: 1496-1502. 10.1101/gr.4107905.PubMedPubMed CentralView ArticleGoogle Scholar


© Pertoldi et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.