Genome-wide analyses suggest parallel selection for
universal traits may eclipse local environmental selection in
a highly mobile carnivore
Astrid Vik Stronen1,2, Bogumiła Jezdrzejewska2, Cino Pertoldi1,3, Ditte Demontis4, Ettore Randi1,5,
Magdalena Niedziałkowska2, Tomasz Borowik2, Vadim E. Sidorovich6, Josip Kusak7, Ilpo Kojola8,
Alexandros A. Karamanlidis9,10, Janis Ozolins11, Vitalii Dumenko12 & Sylwia D. Czarnomska2
1Section of Biology and Environmental Science, Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, DK-9220
Aalborg Øst, Denmark
2Mammal Research Institute, Polish Academy of Sciences, ul. Waszkiewicza 1, PL 17-230 Bialowieza, Poland
3Aalborg Zoo, Mølleparkvej 63, DK-9000 Aalborg, Denmark
4Department of Human Genetics, University of Aarhus, Wilhelm Meyers All
e, DK-8000 Aarhus, Denmark
5Laboratorio di Genetica, ISPRA, via Ca Fornacetta 9, I-40064 Ozzano Emilia (BO), Italy
6Institute of Zoology, Scientific and Practical Centre for Biological Resources, National Academy of Science of Belarus, Akademicheskaya Str 27,
220072 Minsk, Belarus
7Department of Biology, Faculty of Veterinary Medicine, University of Zagreb, Zagreb, Croatia
8Natural Resources Institute Finland, Box 16, FI-96500 Rovaniemi, Finland
9ARCTUROS, Civil Society for the Protection and Management of Wildlife and the Natural Environment, GR-53075 Aetos, Greece
10Department of Ecology and Natural Resources Management, Norwegian University of Life Sciences, NO-1432 As, Norway
11Latvian State Forest Research Institute “Silava”, Rıgas 111, LV-2169 Salaspils, Latvia
12Biosphere Reserve Askania Nova, Frunze Str. 13, Askania-Nova, Chaplynka District, Kherson Region 75230, Ukraine
Keywords
CanineHD BeadChip microarray, Canis lupus,
environmental selection, genome-wide
association study, single nucleotide
polymorphism, wolf.
Correspondence
Astrid Vik Stronen, Section of Biology and
Environmental Science, Department of
Chemistry and Bioscience, Aalborg University,
Fredrik Bajers Vej 7H, DK-9220 Aalborg Øst,
Denmark.
Tel: +45 99403616;
Fax: +45 96350558;
E-mail: avs@bio.aau.dk
Funding Information
We gratefully acknowledge funding from
BIOCONSUS – Research Potential in
Conservation and Sustainable Management
of Biodiversity (contract no. 245737, FP7/
2009-2014), the Mammal Research Institute
of the Polish Academy of Sciences, the Polish
Ministry of Science and Higher Education
(grant no. NN 303 418437) and BIOGEAST –
Biodiversity of East-European and Siberian
large mammals on the level of genetic
variation of populations, 7th Framework
Programme (contract no. 247652). AVS
received funding from the Danish Natural
Science Research Council (postdoctoral grant
1337-00007). CP was supported by the
Abstract
Ecological and environmental heterogeneity can produce genetic differentiation
in highly mobile species. Accordingly, local adaptation may be expected across
comparatively short distances in the presence of marked environmental gradi-
ents. Within the European continent, wolves (Canis lupus) exhibit distinct
north–south population differentiation. We investigated more than 67-K single
nucleotide polymorphism (SNP) loci for signatures of local adaptation in 59
unrelated wolves from four previously identified population clusters (northcen-
tral Europe n = 32, Carpathian Mountains n = 7, Dinaric-Balkan n = 9, Ukrai-
nian Steppe n = 11). Our analyses combined identification of outlier loci with
findings from genome-wide association study of individual genomic profiles
and 12 environmental variables. We identified 353 candidate SNP loci. We
examined the SNP position and neighboring megabase (1 Mb, one million
bases) regions in the dog (C. lupus familiaris) genome for genes potentially
under selection, including homologue genes in other vertebrates. These regions
included functional genes for, for example, temperature regulation that may
indicate local adaptation and genes controlling for functions universally impor-
tant for wolves, including olfaction, hearing, vision, and cognitive functions.
We also observed strong outliers not associated with any of the investigated
variables, which could suggest selective pressures associated with other unmea-
sured environmental variables and/or demographic factors. These patterns are
further supported by the examination of spatial distributions of the SNPs asso-
ciated with universally important traits, which typically show marked differ-
ences in allele frequencies among population clusters. Accordingly, parallel
selection for features important to all wolves may eclipse local environmental
selection and implies long-term separation among population clusters.
4410 ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.
Aalborg Zoo Conservation Foundation
(AZCF), the Danish Natural Science Research
Council (grant nos: 11-103926, 09-065999,
95095995), and the Carlsberg Foundation
(grant no. 2011-01-0059).
Received: 12 August 2015; Accepted: 18
August 2015
Ecology and Evolution 2015 5(19):
4410–4425
doi: 10.1002/ece3.1695
Introduction
Local adaptation may be predicted in areas with limited
influx of novel genes, which can interrupt selection for
local environmental conditions, or in regions of high gene
flow countered by strong selective pressures (Slatkin 1987
and references therein). An alternate explanation is selec-
tive dispersal with genotypes preadapted to the local envi-
ronment – or natal habitat-biased dispersal (Davis and
Stamps 2004; Nosil et al. 2005; Edelaar et al. 2008) – a
process that may help explain local adaptation in highly
mobile organisms with broad geographic distributions.
Ecological and environmental differentiation can cause
population genetic structure in highly mobile species
(Davis and Stamps 2004; Nosil et al. 2005), whereby dis-
persers select habitat conditions for which they have natal
experience and are better able to survive and reproduce.
Accordingly, genetic divergence may be expected across
comparatively short geographic distances in the presence of
marked environmental gradients if genetic drift is not over-
whelming the selective forces. Long-term responses to
selection in a finite population are also influenced by fac-
tors dependent on the effective population size and popula-
tion structure (De Souza et al. 2000; Pertoldi et al. 2007).
Although long-distance gene flow occurs sufficiently
often to produce genetic homogeneity over a wide geo-
graphic range (Slatkin 1985), new findings imply that eco-
logical and environmental variation can result in genetic
differentiation across taxa including wide-ranging terres-
trial and marine species. Examples include fish such as her-
ring (Clupea harengus, Andr
e et al. 2011), hake (Merluccius
merluccius, Milano et al. 2014), and Baltic Sea stickleback
(Gasterosteus aculeatus, DeFaveri et al. 2013); sea turtles
(reviewed in Bowen and Karl 2007); and mammals includ-
ing orca (Orcinus orca, Hoelzel et al. 2007), cougar (Puma
concolor, McRae et al. 2005), lynx (Lynx canadensis, Rue-
ness et al. 2003), and coyote (Canis latrans, Sacks et al.
2004, 2005). The understanding of local adaptation there-
fore has implications across the taxonomic range including
wild species and domestic animals (e.g., Pariset et al. 2009).
Whereas carnivores are highly mobile, they can exhibit
marked population genetic structure that may have
important evolutionary implications. Preference for natal
habitats is proposed to explain population structure in
one of the most mobile and widely distributed species of
large carnivores, the gray wolf (Canis lupus, Carmichael
et al. 2001; Weckworth et al. 2005, 2010, 2011; Pilot et al.
2006, 2012; Musiani et al. 2007; Mu~noz-Fuentes et al.
2009; Stronen et al. 2014). European wolves have been
affected by human-induced landscape changes that
resulted in small and often isolated populations (Linnell
et al. 2008) in part due to overharvesting (Randi 2011).
Populations such as those of the Italian and Iberian
peninsulas have been subject to a substantial amount of
genetic drift due to low effective population size and
demographic stochasticity (Lucchini et al. 2004; Fabbri
et al. 2007; Stronen et al. 2013; Pilot et al. 2014a).
The genetic divergence between populations of wide-
ranging species has been influenced by biogeographic pro-
cesses such as glaciations, and recolonization from glacial
refugia may help explain differentiation between neigh-
boring populations of wide-ranging carnivores (e.g.,
Manel et al. 2004 and references therein). Wolves appear
to have been common in the Eurasian Late Pleistocene
faunal complex and may have been distributed through-
out Europe during this time (Kahlke 1999). The occur-
rence of cold-adapted prey species such as reindeer
(Rangifer tarandus) and mammoth (Mammuthus primige-
nius) (Kahlke 1999; Sommer and Nadachowski 2006) in
southern and central Europe during the last glacial maxi-
mum suggests that a wolf ecotype adapted to arctic con-
ditions might have been widely distributed. Wolves may
have been present in central Europe during the Pleni-Gla-
cial epoch (circa 75–15,000 BC) with dynamic range
changes during the Holocene for different ecotypes
adapted to conditions such as arctic tundra, forest, and
humid climates (Sommer and Benecke 2005). The spatio-
temporal extent of selection in wolves may be highly
complex, and the relative influence of local environmental
selection since the last glacial maximum versus indepen-
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 4411
A. V. Stronen et al. Genome-wide Analyses of Selection in Wolves
dent selection in previously separated populations is not
well understood. For simplicity, we henceforth refer to
“ancient” selection as that having occurred prior to the
last glacial maximum and “recent” as having taken place
afterward.
The European continent encompasses important envi-
ronmental variation. The diverse geography with (par-
tially) east–west-oriented mountain chains (Alps,
Carpathians) and the Mediterranean and Baltic Seas
might exert more complex spatial influence on population
structure and gene flow than that observed in North
America with well-separated coastal and continental cli-
mates (e.g., Geffen et al. 2004). European wolves showed
clear population genetic structure when evaluated over
67,000 (henceforth 67 K) single nucleotide polymorphism
(SNP) markers (Stronen et al. 2013), but it remains
unclear whether adaptation to various environmental con-
ditions might help explain the observed population clus-
ters. Although some level of genetic structure seems to
have been established prior to the last glacial maximum
(Pilot et al. 2010), wolves likely had a continuous range
through the Holocene with population fragmentation and
habitat loss primarily occurring in the past few centuries
(Pilot et al. 2014a). Whereas genetic drift has affected
European wolves over the past hundred years, this process
seems to have been less pronounced in east–central Eur-
ope where populations have remained relatively well con-
nected (Stronen et al. 2013; Pilot et al. 2014a). Genetic
drift, population demographic history and other neutral
processes could be major influences on allele frequencies
and distributions where selection is weak (Coop et al.
2009). We nonetheless expect genetic drift to have an
overall influence across the entire genome whereas selec-
tion is predicted to act only on certain genes. Addition-
ally, we expect the correlation between neutral molecular
diversity and non-neutral genetic variation to be weak in
stable populations, and to decrease further when popula-
tions expand or decline in size (Pertoldi et al. 2007). Our
study aimed to determine whether population structure
associated with functional genetic variation in European
wolves 1) is consistent with previously observed (and
assumed predominantly “neutral”) genetic structure and
2) appears better explained by ancient selection for com-
mon traits occurring in parallel in separate populations,
or by recent selection based on local environmental con-
ditions.
Materials and Methods
Samples DNA extraction and genotyping
We examined wolf profiles from 10 countries across Eur-
ope, genotyped with the CanineHD BeadChip microarray
with 170,000 SNP loci from Illumina (Illumina, Inc., San
Diego, CA) as described in Stronen et al. (2013). The ear-
lier study included Italian wolves, but owing to their
highly divergent status (Stronen et al. 2013; Pilot et al.
2014a) and the possibility that strong genetic drift
between Italian and other European wolves might con-
found signals of selection, we excluded all Italian individ-
uals from the analyses. Moreover, we removed outlier
profiles from other countries including putative wolf–dog
hybrids, which resulted in a sample of n = 113 wolves.
Subsequently, we used PLINK (Purcell et al. 2007) to
identify pairs of wolves with an identity-by-descent (IBD,
or PI_HAT) score of ≥0.1 and removed one individual
per pair (some wolves had values above the threshold for
multiple pairwise comparisons) to limit the potentially
confounding effect of cryptic relatedness (see, e.g., Smith
et al. 2010) on possible signals of selection. The screening
resulted in a sample of n = 59 European wolves from
four population clusters (northcentral Europe n = 32,
Carpathian Mountains n = 7, Dinaric-Balkan n = 9,
Ukrainian Steppe n = 11, Fig. 1) previously identified by
Stronen et al. (2013).
Statistical analyses of genetic structure
We performed analyses in two stages. We first combined
genome-wide association study (GWAS, e.g., Smith et al.
2010) of genotype–environment associations in PLINK
with a complimentary approach using BayeScan (Foll and
Gaggiotti 2008) for detecting outlier loci without consid-
ering environmental data. We performed GWAS with
99,551 SNPs quality-controlled and filtered for minor
allele frequency and genotyping call rate (PLINK settings:
maf 0.01, geno 0.02) as described in Stronen et al. (2013).
For the GWAS, we included all data to retain as much
information as possible for individuals and SNP loci asso-
ciated with environmental factors. Subsequently, we used
a 67-K version of the data pruned for linkage disequilib-
rium as described in Stronen et al. (2013) to perform the
BayeScan analyses carried out per population (cluster).
The resulting candidate SNPs were evaluated with the spa-
tial analysis method (SAM) implemented in the program
MatSAM v2 Beta (Joost et al. 2007, 2008) because analysis
involving thousands of loci was not practically feasible in
MatSAM v2 Beta. However, the SAM approach is devel-
oped for analyses of genotype–environment associations in
wild or domestic species (see, e.g., Pariset et al. 2009) and
therefore well-suited to the purpose of our study.
We performed GWAS in PLINK using the linear
regression option, whereby each individual was assessed
based on 12 environmental variables (Table 1). Environ-
mental variables were tested for deviations from normal
distribution in PAST (Hammer et al. 2001), and we log-
4412 ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Genome-wide Analyses of Selection in Wolves A. V. Stronen et al.
transformed values for which the probability plot correla-
tion coefficient (PPCC) was <0.8. As a result, PPCC for
all variables except two (log altitude = 0.83, log
biome = 0.86) was >0.93. We examined correlation
among environmental variables in PAST using the test
option Kendall’s tau for nonparametric data, which is a
recommended option for data sets with many tied ranks
(Legendre and Legendre 1998). We adjusted for multiple
testing using the Bonferroni correction and categorized
relationships between pairs of variables as highly (>0.6),
moderately (0.3–0.6), or not correlated (<0.3). A priori
exclusion of correlated variables (e.g., July, January, and
annual temperature) might miss important information,
and we chose to retain all variables and report their
extent of correlation (Table S1). We evaluated the inclu-
sion of 2–15 covariates obtained from multidimensional
scaling of the data in PLINK to account for population
stratification (Freedman et al. 2004 and references therein;
Stronen et al. 2013) and performed GWAS with six
covariates, the lowest number of covariates for which the
genome-inflation factor was <1.05 for all variables. GWAS
tests were performed for the minor allele for each locus,
and we implemented Bonferroni corrections for multiple
testing (P < 0.05). We included all environmental vari-
ables for the final analysis in MatSAM, as this approach is
based on logistic regression and does not require normal
distributions.
Subsequently, we performed simulations in BayeScan
(Foll and Gaggiotti 2008) for the 67-K SNPs to identify
outlier loci. We tested various levels of prior (10, 100,
Ukrainian SteppeDinaric-Balkan
CarpathianNorthcentral
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
5 Groups
Sample locations
1
2
3
4
5
6
7
0 500 km
Figure 1. Study area and locations for 59
wolves used in analyses of single nucleotide
polymorphisms (SNPs). Spatial interpolation for
four SNPs with genotypes specific for different
population clusters is shown as examples. The
large northcentral European cluster was
divided into groups 1–4 for investigation of
possible regional patterns (genotype 223AA),
group 5 is the Carpathian Mountains (342GA),
group 6 is the Ukrainian Steppe (236AG), and
group 7 is Dinaric-Balkan (214AA). SNP allele
frequencies among samples in each cluster
were classified as <25% (white), 25–49%
(light gray), 50–75% (medium gray), and
>75% (dark gray). SNP identifications are
provided in Table S2.
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 4413
A. V. Stronen et al. Genome-wide Analyses of Selection in Wolves
and 1000) as the chosen value represents a trade-off
between false positives and the ability to detect possible
outliers (Foll 2012). Because the loci identified as outliers
were highly consistent among runs, we retained the results
for prior of 10 and report loci for which the log10(PO) val-
ues were >0.5 as recommended in the program guidelines
(Foll 2012). We ran analyses including all four population
clusters (labeled 4P) and then performed comparisons
between each pair of clusters (northcentral Europe = N,
Carpathian Mountains = C, Dinaric-Balkan = B, Ukrai-
nian Steppe = U). Positive values for the parameter alpha
(alpha > 0) indicate divergent selection, whereas negative
values (alpha < 0) suggest balancing selection.
For the second stage of analysis, we evaluated GWAS
and BayeScan candidate loci with MatSAM. The program
performs tests of logistic regression for each SNP geno-
type (for which there are normally three: AA, AB, BB)
and the environmental variable in question. The program
implements two separate tests, a likelihood ratio (hence-
forth G) test and a Wald-Beta test (Joost et al. 2007), to
determine whether a particular genotype is associated
with a given environmental variable. The program reports
both test results, as well as a cumulative test. The cumula-
tive test is significant when both Wald and G-tests reject
the null hypothesis that the model with the observed vari-
able does not explain the observed genotype distribution
better than a model with a constant only (Joost et al.
2007). The program implements the Bonferroni correc-
tion for multiple tests, and we chose a P-level of 0.05.
Values for two categorical variables, ecozone and biome,
were entered as numbers using the “independent” design
(Joost and Kalbermatten 2010).
We evaluated spatial patterns throughout the study
area by plotting allele frequency distributions for all
candidate loci. The large northcentral cluster was
divided into four groups based on geographic proximity
of sample locations to evaluate the possibility of local
patterns. Interpolation maps for results that displayed
geographic patterns were prepared with ArcGIS 10.2
(ESRI 2013). Samples were interpolated into continuous
surfaces with the inverse distance weighted (IDW)
method. The interpolation was conducted for four SNPs
with genotypes specific for different population clusters.
Genotype frequencies were stored in binary format (1 –
present, 0 – not present), and the mean value was cal-
culated for each cluster. For the northcentral cluster, we
used the four above-mentioned groups to assess the possi-
ble presence of local patterns. Inverse distance weighted
interpolation was performed with default parameters. Sin-
gle nucleotide polymorphism allele probability was classi-
fied into four groups (low – less than 25%, moderate – 25–
50%, high – 50–75%, and very high – more than 75%).
Subsequently, we used the 67-K SNPs in Genepop
(Rousset 2008) to calculate FST values for each locus. We
then employed HierFstat (Goudet 2005) to obtain pair-
wise FST values with 95% confidence intervals between
population clusters for the 353 candidate loci identified
in GWAS and BayeScan. We examined these population
clusters by principal component analyses (PCA) with the
adegenet package (Jombart 2008) in R 2.14.2 (R Develop-
ment Core Team 2012).
Results
We detected 178 outlier SNPs in BayeScan and 175 SNPs
with putative association with environmental variables by
Table 1. Environmental variables for genome-wide association study
of European wolves (n = 59) with 67-K single nucleotide polymor-
phism (SNP) loci.
Variable Label Unit Data source
Longitude long Decimal degrees Sample
coordinates
Latitude lat Decimal degrees Sample
coordinates
Human population
density
popd Number of people/km2 1)
Mean annual
temperature
annt Degrees Celsius 2)
Mean January
temperature
jant Degrees Celsius 2)
Mean July
temperature
jult Degrees Celsius 2)
Annual
precipitation
pred mm 2)
Road density road km road/100 km2 3)
Altitude alt Meters above sea level 4)
Snow cover depth snow cm 5)
Ecosystem code ecoc Number (ordinal) 6)
Biome code bioc Number (ordinal) 6)
1) http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/,
March 2012.
2) Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis
(2005). Very high resolution interpolated climate surfaces for global
land areas. International Journal of Climatology 25: 1965–1978.
(WorldClim project data).
3) ESRI Data & Maps (2008). Redlands, CA: Environmental Systems
Research Institute [CD-ROM].
4) U.S. Geological Survey (2004), EROS Data Center Distributed Active
Archive Center (EDC DAAC), Global Digital Elevation Model
(GTOPO30), Redlands, California, USA. (GTOPO30 database).
5) Afonin, A.N., S.L. Greene, N.I. Dzyubenko, A.N. Frolov (2008) Inter-
active Agricultural Ecological Atlas of Russia and Neighboring Coun-
tries. Economic Plants and their Diseases, Pests and Weeds. Available
at: http://www.agroatlas.ru.
6) Olson, D. M., E. Dinerstein (2002). The Global 200: Priority ecore-
gions for global conservation. (PDF file) Annals of the Missouri Botani-
cal Garden 89:125–126. Available at: http://www.worldwildlife.
org/science/data/terreco.cfm. (WWF database).
4414 ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Genome-wide Analyses of Selection in Wolves A. V. Stronen et al.
GWAS. There was no overlap between the loci reported
by each method. One hundred and seventy-five of 178
SNPs (98%) identified by BayeScan had FST values ≥ 0.15
(as estimated by Genepop across the 59 wolves and 353
loci), which may be considered as a high (Balloux and
Lugon-Moulin 2002), whereas for GWAS the number of
SNPs with FST values ≥ 0.15 was 21 of 175 (12%). Mean
FST value for BayeScan loci was 0.305 (range 0.118–
0.571), and for GWAS, it was 0.085 (range 0.000–0.432).
All BayeScan results had a positive alpha value, suggesting
directional rather than balancing selection. Over 66% of
the BayeScan loci had a high loading (here defined
as ≥ [0.01]) on one or two of the three PC axes in a PCA
of the European wolf population with 67-K loci (Stronen
et al. 2013 Fig. 2B and C) and thus made an obvious
contribution to population structure. GWAS loci showed
no such pattern.
Of the 353 SNPs, genotypes in 117 (46 from GWAS
and 71 from BayeScan) were identified as associated with
environmental variables by SAM. All cases in which geno-
types were significantly associated with the variable
“biome code” (bioc) were identified by the Wald test. No
other genotype–environment association was found by
the Wald test, and results for all other variables were
identified by the G-test. GWAS results affected by linkage
(n = 99) are marked in Table S2. With the exception of
five SNPs (identified in Table S4), the following results
include only loci unaffected by linkage.
We examined each SNP and one megabase (Mb; one
million bases) on either side (hereafter flanking regions)
in the UCSC dog genome browser (http://genome.ucs-
c.edu/cgi-bin/hgTracks) and the NCBI Map Viewer
(http://www.ncbi.nlm.nih.gov/projects/mapview/) to iden-
tify genes or genomic regions known or assumed to be
3 4 7
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5 6 7 1 2 3 4 5
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5 1 2 3 4 5
1 2 5 66 7
6 7 6 7
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5 6 7
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5
6 7
6 7
Northcentral
Carpathian
0
0.4
0.6
1.0
0.8
0.2
Ukrainian Steppe
0
0.4
0.6
1.0
0.8
0.2
Dinaric-Balkan
0
0.4
0.6
1.0
0.8
0.2
Region
Fr
eq
ue
nc
y 
of
 g
en
ot
yp
es
Fr
eq
ue
nc
y 
of
 g
en
ot
yp
es
Figure 2. Spatial distributions of European
wolf single nucleotide polymorphism (SNP) loci/
genotypes typical for single population clusters.
The graphs show frequencies of loci/genotypes
differentiating among wolves in northcentral
(groups 1–4), Carpathian (5), Ukrainian Steppe
(6), and Dinaric-Balkan clusters (group 7).
Numbers on x-axis are wolf groups 1–7 (see
Fig. 1). Left panels: loci/genotypes with high
frequencies in a given cluster. Right panels:
loci/genotypes with low frequencies in a given
cluster. SNP loci and genotypes are listed in
Table S4.
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 4415
A. V. Stronen et al. Genome-wide Analyses of Selection in Wolves
Table 2. Functional genes near single nucleotide polymorphisms (SNP) identified as outlier loci and/or associated with environmental variables
based on a study of 59 wolves in four European population clusters. Environmental variables are given in Table 1. Full locus identification from
the Illumina CanineHD BeadChip is provided in Table S2. Function summary is based on references from the NCBI database (http://
www.ncbi.nlm.nih.gov/gene).
Chr and SNP
number1
BayeScan
log10(PO)2 BayeScan FDR3
SAM
result4 FST
5 Gene(s) Function summary
TEMPERATURE
Chr9_143 0.904 (4P)
1.134 (BU)
0.058 (4P)
0.034 (BU)
– 0.327 RPTOR Thermogenesis
Chr9_148 1.217 (4P) 0.027 (4P) jult (AA) 0.332 TRPV1/TRPV3 Thermoregulation
Chr25_269 0.771 (BC) 0.069 (BC) bioc (AA) 0.197 TRPM8 Thermosensation (cold sensor)
METABOLISM
Chr5_85 – – bioc (GA) 0.022 SGIP1 Fat mass, food intake
Chr5_85 – – bioc (GA) 0.022 LEPR Fat metabolism
Chr5_100 0.860 (CU) 0.079 (CU) bioc (AC) 0.260 TK2 mtDNA synthesis
Chr9_151 – – bioc (AA,CC) 0.236 CRAT Energy homeostasis, fat metabolism
Chr9_151 – – bioc (AA,CC) 0.236 DNM1 Exercise-induced collapse
Chr15_188 1.089 (4P) 0.034 (4P) bioc (GG) 0.230 NPYR1 Vasoconstriction in exercising skeletal muscle
Chr18_208 0.725 (4P)
1.118 (NC)
0.080 (4P)
0.064 (NC)
bioc (AG,GG) 0.202 CPT1A mtDNA membrane, lipid metabolism
Chr26_280 0.813 (NU) 0.068 (NU) bioc (AA), jult (GG) 0.255 SLC5A1 Carbohydrate digestion/absorption.
Chr32_326 – – bioc (CG) 0.033 SCD5 Energy metabolism
PHYSICAL DEVELOPMENT
Chr3_23 0.841 (4P)
1.341 (BC)
0.067 (4P)
0.028 (BC)
– 0.342 IGFI1R Reduced size (dogs)
Chr4_46 0.889 (4P)
1.423 (NB)
0.059 (4P)
0.017 (NB)
lat, alt (AA) 0.497 ZFR RNA regulation
Chr13_169 1.207 (4P)
1.214 (CU)
0.028 (4P)
0.042 (CU)
– 0.260 RSPO2 Dog coat color
Chr13_175 1.329 (CU) 0.038 (CU) – 0.283 KIT Dog coat patterns (spotted Weimaraner)
Chr15_183 0.511 (NB) 0.098 (NB) – 0.231 ATP2B1 Intracellular calcium homeostasis; vascular
smooth muscle cells; possibly Chagas disease
(American trypanosomiasis)
Chr15_187 1.168 (4P)
1.648 (NU)
0.030 (4P)
0.015 (NU)
– 0.332 FNIP2 Hypomyelination in the brain; spinal cord
defects (Weimaraner dogs)
Chr18_208 0.725 (4P)
1.118 (NC)
0.080 (4P)
0.064 (NC)
bioc (AG,GG) 0.202 FGF4 Bone morphogenesis
Chr19_210 1.206 (NU) 0.033 (NU) – 0.303 DARS Hypomyelination (brain, spinal cord)
Chr21_217 0.591 (4P)
1.475 (NB)
0.672 (BU)
0.096 (4P)
0.014 (NB)
0.096 (BU)
– 0.288 PPFIBP2 Neural synapse development
Chr21_222 0.629 (NU) 0.111 (NU) bioc (AG,GG) 0.231 HPS5 Hermansky–Pudlak syndrome (oculocutaneous
albinism, platelet abnormality)
Chr21_223 0.818 (NU) 0.061 (NU) lat, annt (AA)6 0.395 NAV2 Neuron growth and regeneration
Chr21_225 0.804 (NU) 0.074 (NU) – 0.226 ANO3 Dominant craniocervical dystonia (sustained
muscle contractions – repetitive movements
or abnormal postures); eczema, asthma
Chr23_236 0.908 (4P)
0.794 (NU)
0.052 (4P)
0.085 (NU)
prec (AG,GG) 0.361 AGTR1 Angiotensin II (blood pressure and volume)
Chr23_236 0.908 (4P)
0.794 (NU)
0.052 (4P)
0.085 (NU)
prec (AG,GG) 0.361 HPS3 Hermansky–Pudlak syndrome (oculocutaneous
albinism, platelet abnormality)
Chr23_236 0.908 (4P)
0.794 (NU)
0.052 (4P)
0.085 (NU)
prec (AG,GG) 0.361 CP Aceruloplasminemia (iron accumulation and
tissue damage)
Chr24_245 0.537 (4P) 0.106 (4P) long (AA) 0.340 BMP7 Bone growth
Chr24_246 0.579 (NB) 0.079 (NB) bioc (GG, GA, AA) 0.338 COL9A3 Collagen (dwarfism, ocular defects)
4416 ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Genome-wide Analyses of Selection in Wolves A. V. Stronen et al.
of functional importance (henceforth referred to as
functional genes). Thirty-two key functional genes (or
groups of genes) near SNP loci identified as outliers
(n = 27) and/or associated with environmental variables
(n = 22) are listed in Table 2 and divided into groups
based on function: temperature (n = 3), metabolism
(n = 9), and physical development (n = 20). One SNP
was associated with variables (latitude, annual tempera-
ture) found to be correlated (Table 2; Table S1).
Complete nomenclature and identification for SNP loci
are provided in Table S2. Furthermore, we observed
SNPs near key functional genes associated with features
for which we do not have environmental data or that
appear important to all wolves across their range. We
have highlighted n = 12 SNPs associated with disease
and parasites, n = 16 for sensory functions, and n = 9
for brain and cognition (Table S3). Four of these SNPs
were associated with correlated variables (Tables S1
and S3).
Our results exhibited clear spatial patterns in one
(Figs. 1, 2) or – less frequently – two population clus-
ters (Fig. 3), including SNPs near genes for functions
believed to be important for wolves across their range.
Certain of these SNPs showed distinct geographic distri-
butions of genotypes (Table S4). For example, genotypes
varied between the Carpathian Mountains and the
Ukrainian Steppe/Dinaric-Balkan clusters for SNP
Chr13_175 located near the gene KIT (dog coat pat-
tern). We selected one representative genotype for each
cluster for interpolation into continuous surface (Fig. 1).
We then noted SNPs near genes for important functions
that showed no obvious spatial patterns (Table S5). Sev-
eral SNPs were also identified as strong outliers in
BayeScan but not associated with any of the 12 environ-
mental variables, nor were there any functional genes
reported in the 1-Mb flanking regions. These results
might nevertheless be of interest for future investigation
(Table S6).
Pairwise FST values between the four population clus-
ters, with the full sample of 113 individuals and all 353
loci, showed the highest value for Carpathian Mountains –
Ukrainian Steppe – and the lowest value for Carpathian
Mountains – northcentral Europe (Table 3). Pairwise FST
values for the sample of 59 individuals were similarly high
and generally consistent with the larger sample, although
the highest value was between northcentral Europe and
Dinaric-Balkan and the lowest was for northcentral Eur-
ope – Ukrainian Steppe (Table S7). Principal component
analyses of all 113 wolves showed differentiation among
all population clusters (Fig. 4). Although northcentral
Europe and Carpathian Mountain individuals overlapped
on the 1st axis, they were clearly distinct on the 3rd axis.
The 1st axis reflects north–south differentiation in Euro-
pean wolves, whereas the 2nd axis generally (although
there is some spatial overlap between northcentral Europe
and Ukrainian Steppe) indicates east–west structure.
When compared to the other three clusters, the individual
profiles from northcentral Europe appear highly concen-
trated relative to their spatial distribution (Figs. 1, 4).
Discussion
Our results identified genes potentially influencing local
adaptation for temperature, metabolism, physical develop-
ment, and disease/immune system functions in European
wolves. However, the importance of SNPs associated with
genes for putative local adaptations appears overshadowed
by findings linked to traits of universal importance,
including hearing, vision, olfaction, and cognitive func-
tions. This suggests that ancient, concurrent, and possibly
parallel selection may have played a more prominent role
than recent local adaptation in structuring functional
Table 2. Continued.
Chr and SNP
number1
BayeScan
log10(PO)2 BayeScan FDR3
SAM
result4 FST
5 Gene(s) Function summary
Chr26_281 1.547 (4P)
0.779 (NU)
0.012 (4P)
0.089 (NU)
bioc (GG) 0.352 ADORA2A Cardiac rhythm and circulation, blood flow,
immune function, pain regulation, sleep
Chr28_299 1.1880 (NB) 0.008 (NB) lat (GG) 0.290 SPRCS3 Central nervous system development
Chr31_317 1.062 (BC) 0.043 (BC) bioc (GG) 0.315 ADAMTS1 Organ morphology and function
1Full SNP identification given in Table S2.
2Pairwise comparisons for: B – Balkan-Dinaric; C – Carpathian Mountains.; U – Ukrainian Steppe; N – northcentral Europe. 4P: across all four clus-
ters.
3False discovery rate threshold (q-value).
4Environmental variables identified by the spatial analysis method (SAM) as significantly associated with one or more genotypes. SAM incorporates
two separate tests: the Wald and the likelihood ratio (G) test (Joost et al. 2007). The variable “bioc” was identified by the Wald test; all other
variables by the G-test. No result was identified in both.
5FST calculated across all 353 loci for all population clusters.
6Correlations between (some) variables. See Table S1 with results for all variable combinations.
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 4417
A. V. Stronen et al. Genome-wide Analyses of Selection in Wolves
genetic variation in wolves throughout our study area.
Concurrent selection for ubiquitous traits in separate
populations may have been divergent or parallel. How-
ever, for traits such as hearing and vision a trajectory of
parallel selection appears most likely.
Our results nonetheless suggest local adaptation may
play a role. Although the wolf is a highly mobile species,
it has been reported to exhibit population structure corre-
sponding with environmental heterogeneity in Europe
(Pilot et al. 2006, 2012) and North America (Geffen et al.
2004; Musiani et al. 2007; Mu~noz-Fuentes et al. 2009;
Stronen et al. 2014). Our findings indicate that variables
such as temperature and habitat may influence local
adaptation, which appears consistent with earlier results
from the study area (Pilot et al. 2006). A SNP flanking
two genes reported to influence temperature regulation
(TRPV1/TRPV3) was associated with July temperature.
Because wolves are long-distance pursuing (as opposed to
ambush) predators, physiological mechanisms to prevent
overheating could represent important selective factors.
The possibility of local environmental selection for tem-
perature regulation merits further investigation, particu-
larly in light of warming earth surface temperatures and
changes in the degree of variability for temperature and
other climatic factors.
Local adaptation can occur if individuals are more likely
to survive and reproduce within their natal habitats (Davis
and Stamps 2004; Nosil et al. 2005; Edelaar et al. 2008),
which subsequently affects population genetic structure.
The wolf population clusters examined in this study (Stro-
nen et al. 2013) are exposed to markedly different climatic
factors such as temperature and precipitation. Northcentral
European and Carpathian wolves are not usually subject to
very hot weather but experience cold (including subzero)
temperatures extensive parts of the year, whereas the oppo-
site is typically true for the Balkan-Dinaric wolves of south-
ern Europe. Ukrainian Steppe wolves, in contrast, may
experience both hot summers and cold winters. Although
speculative, the capacity for temperature regulation might
play a particularly important role for wolves in the steppe.
The northcentral and Carpathian environments have much
in common with regard to climate. The differences in day
length between the two areas likely influence other pro-
cesses of ecological importance such as plant photoperiods,
and differences in day length have been reported to affect
the behavior of Arctic mammals such as Svalbard reindeer
(R. t. platyrhynchus) (van Oort et al. 2005). The clear struc-
turing seen between Carpathian and northcentral European
wolves, which reflects the division into two major phyloge-
netic clades of wolves (Pilot et al. 2010; Czarnomska et al.
2013), could, at least in part, also be caused by habitat frag-
mentation and human landscape development (Huck et al.
2011).
The divergent profiles of Ukrainian Steppe wolves may
to some extent be a result of immigration from outside
the study area. The Ukrainian part of our study area
could be receiving immigrants from the steppe or forest-
steppe regions farther east and north, and similar immi-
gration from eastern and northern regions may occur in
the western Russian part of our study area (Pilot 2005).
FST values for 67-K loci were lowest between northcentral
Europe and Ukrainian Steppe wolves (Stronen et al.
2013). Drift is expected to influence the entire genome
and selection to act only on certain loci, and the FST val-
ues for the 353 loci between northcentral Europe and the
Ukrainian Steppe suggest diversifying selection might play
a role in increasing divergence between wolves from these
regions. Spatio-temporal resolution of the selective forces
that may have produced the current patterns is challeng-
ing because of limited available data from the eastern part
of our study area and beyond – for our study and in gen-
eral.
Prey and habitat have been reported as important vari-
ables in earlier investigations with (presumed) neutral
markers (Geffen et al. 2004; Musiani et al. 2007; Mu~noz-
Fuentes et al. 2009; Stronen et al. 2014). This includes
findings from our study area (Pilot et al. 2006, 2012).
Importantly, neither ecosystem nor biome may be the
appropriate scale at which to examine the local patterns
of selection in species such as wolves; ecosystem may be
too narrow, whereas biome could be too broad. Other
features of the local environment, such as the size and
behavior of available prey, may be more informative for
elucidating the patterns of selection (Benson et al. 2012;
Monzon et al. 2014). We did not have prey data for our
study area, but earlier investigations within Europe
(Jezdrzejewski et al. 2012; Pilot et al. 2012, 2014a) accord
with new data from North America (Benson et al. 2012;
Monzon et al. 2014) in suggesting that the influence of
diet merits further attention.
None of the BayeScan and GWAS candidate loci over-
lapped, although a number of SAM results were supported
by outlier detection as well as gene–environment associa-
tions. Earlier studies have reported similar lack of overlap
between BayeScan and other tests (e.g., Narum and Hess
2011). Several potentially important drivers of selection are
not included in our study (e.g., diet, disease, and parasites),
which might help explain why a number of outlier loci in
BayeScan were not identified in gene–environment tests.
However, we would expect loci detected by environmental
selection to be identified by BayeScan, and it is uncertain
why this did not occur. Possibly, methodical differences
may play a role. For example, Bayesian methods imple-
mented in BayeScan differ from that of significance testing
in classical statistics (Foll 2012). Another factor could be
our small sample sizes from some population clusters. We
4418 ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Genome-wide Analyses of Selection in Wolves A. V. Stronen et al.
included a large number of loci per individual, but certain
tests might require more samples to identify important
genotypes and alleles. The possible presence of polygenic
effects – small changes in allele frequencies at a large num-
ber of loci (Pritchard and Di Rienzo 2010) – combined
with relatively strict correction for multiple testing (Joost
et al. 2007) could also help explain the discrepancies
between the test results. Finally, GWAS in PLINK and SAM
are individual-based whereas BayeScan examines popula-
tions, which could mask important heterogeneity.
All our BayeScan results indicated directional selection,
yet balancing selection has been reported for MHC loci in
wolves from Finland (Niskanen et al. 2014). Whereas out-
lier methods appear prone to reporting false positives for
1 2 3 4 5
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5
1 2 3 4 5
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5
0
0.4
0.6
1.0
0.8
0.2
1 4 5 1 2 3 4 5
Carpathian and Dinaric-Balkan
0
0.4
0.6
1.0
0.8
0.2
Ukrainian Steppe and Dinaric-Balkan
0
0.4
0.6
1.0
0.8
0.2
Carpathian and Ukrainian Steppe
0
0.4
0.6
1.0
0.8
0.2
Fr
eq
ue
nc
y 
of
 g
en
ot
yp
es
Fr
eq
ue
nc
y 
of
 g
en
ot
yp
es
1 2 3 4 5
0
0.4
0.6
1.0
0.8
0.2
1 2 3 4 5
6 76 7
6 76 7
2 3 6 7 6 7
6 76 7
Ukrainian Steppe and group 4 in Northcentral
0
0.4
0.6
1.0
0.8
0.2
Region
Figure 3. Spatial distributions of European
wolf single nucleotide polymorphism (SNP) loci/
genotypes typical for two neighboring clusters.
The graphs show frequencies of loci/genotypes
differentiating among wolves in northcentral
(groups 1–4), Carpathian (5), Ukrainian Steppe
(6), and Dinaric-Balkan clusters (group 7).
Numbers on x-axis are wolf groups 1–7 (see
Fig. 1). Left panels: loci/genotypes with high
frequencies in the two clusters. Right panels:
loci/genotypes with low frequencies in the two
clusters. SNP loci and genotypes are listed in
Table S4.
Table 3. Pairwise FST values with 95% confidence intervals for n = 113 wolves in four population cluster, across n = 353 SNP loci reported as
outliers (BayeScan) or associated with environmental variables (GWAS in PLINK), calculated in HierFstat with bootstrap resampling (n = 1000). All
were significant at P < 0.001 except#.
Cluster (n) Northcentral Europe (n = 60) Ukrainian Steppe (n = 12) Dinaric-Balkan (n = 29)
Ukrainian Steppe (n = 12) 0.220 [0.191–0.246] – –
Dinaric-Balkan (n = 29) 0.227 [0.196–0.257] 0.240 [0.213–0.269] –
Carpathian Mountains (n = 12) 0.190 [0.155–0.226] 0.243# [0.212–0.274] 0.223 [0.187–0.256]
#P = 0.013.
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 4419
A. V. Stronen et al. Genome-wide Analyses of Selection in Wolves
balancing selection (Narum and Hess 2011), we cannot
exclude the possibility of balancing selection in our data set
that we were unable to detect. We identified certain loci
associated with the MHC complex, but our study did not
include any data on diseases or parasites. The absence of
signals for balancing selection in our study might reflect
our choice of variables and limitations of BayeScan com-
bined with strict criteria for avoiding false positives.
The spatio-temporal presence of glacial refugia for
wolves is not resolved, and contributions from possible
glacial refugia in eastern Europe and Asia may be under-
valued (Taberlet et al. 1998). Furthermore, the range of
large northern wolves could have been restricted during
interglacial periods during which these wolves appear to
have been replaced by smaller forms (Kahlke 1999). Such
a scenario might produce a complex spatio-temporal
genetic admixture that confounds the effects of repeated
recolonization events with the presence of locally adapted
ecotypes. Besides, the strength and direction of selective
pressures may have varied with time (Coop et al. 2009).
A recent study including profiles from Caucasian wolves
suggested gene flow between Caucasus and eastern Europe
(Pilot et al. 2014b), and additional genomic investigation
of samples from far eastern Europe and Asia is an impor-
tant priority.
A historic wolf ecomorph specializing in megafaunal
prey appears to have gone extinct in North America
(Leonard et al. 2007). This mtDNA haplogroup (hence-
forth haplogroup 2) was common in ancient European
wolves, but seems to have been replaced over much of
the continent and is now largely confined to southern
Europe (Pilot et al. 2010). The reason(s) for the appar-
ent replacement is uncertain, and larger prey species
such as moose and reindeer are more common in
northern and central Europe (Sidorovich et al. 2003;
Kojola et al. 2004; Jezdrzejewski et al. 2010), whereas prey
species in southern areas are typically small to mid-size
(Papageorgiou et al. 1994; Kusak et al. 2005). The rela-
tionship between the “megafaunal” mtDNA haplogroup
2 and the current wolf of southern Europe therefore
merits additional attention. Possibly, mtDNA haplogroup
2 persists in wolves now adapted to the warmer climate
and smaller prey species of the contemporary environ-
ment.
We observed a number of SNPs near genes coding for
traits that seem important for wolves across their range,
including memory and olfaction. These results exhibited
spatial patterns consistent with previously observed popu-
lation clusters. In humans, recent reports imply that selec-
tion on different genes involved in, for example,
immunity can occur in separate populations (Laurent
et al. 2012). We hypothesize that the high prevalence of
SNPs flanking genes associated with universal traits may,
at least in part, reflect parallel evolution. We recommend
additional research, with numerically and geographically
larger sample sizes, to explore whether differentiation
PC1 (15.4%)
P
C
2 
(9
%
)
Eigenvalues
PC1 (15.4%)
P
C
3 
(5
.5
%
)
Eigenvalues
PC2 (9%)
P
C
3 
(5
.5
%
)
Eigenvalues
Ukrainian
Steppe
Ukrainian
Steppe
Ukrainian
Steppe
Dinaric-Balkan
Dinaric-BalkanNorthcentral
Northcentral
Northcentral
Carpathian
Carpathian
Carpathian
Dinaric-Balkan
Figure 4. Principal component analyses results for n = 113 European
wolves in 4 population clusters with 353 loci. Population clusters:
northcentral Europe (n = 60), Carpathian Mountains (n = 12),
Ukrainian Steppe (n = 12), Dinaric-Balkan (n = 29). Upper panel: PC
axes 1 and 2. Middle panel: PC axes 1 and 3. Lower panel: PC axes 2
and 3. Percentages of variation explained by PC1–PC3 shown on axes.
4420 ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Genome-wide Analyses of Selection in Wolves A. V. Stronen et al.
linked to such ubiquitous traits might indicate deep evo-
lutionary divergence among population clusters.
Main limitations of the study
No SAM result was detected in the cumulative test that
requires significance for both Wald and G-tests, which
might suggest that our results are not robust. However,
the Wald test shows a lack of power for smaller sample
sizes (Quinn and Keough 2002). The Bonferroni correc-
tion implies a very conservative test level (in this case, an
adjusted P-level of 3.93e-06) that should yield only the
most robust associations (Joost et al. 2007). Pilot et al.
(2006) found temperature to be important in explaining
neutral genetic structure within our study area. As the
area extends from arctic tundra to the Mediterranean Sea,
with large variations in summer and winter temperatures,
our findings appear to offer a reasonable explanation.
Although we accepted levels of relatedness up to 0.1,
which might have included distantly related individuals,
the PLINK calculation of relatedness assumes a relatively
homogeneous sample. Because of population structure in
European wolves (Stronen et al. 2013), our results for
pairwise relatedness are expected to be conservative as
samples within one cluster will appear relatively more
similar to each other than to wolves from other clusters.
Yet, European wolves have been found to disperse
>800 km (Wabakken et al. 2007; Andersen et al. 2015),
and because of uncertainties associated with gaps in our
sampling (e.g., whether wolf profiles from Romania might
represent a cline between Carpathian and Dinaric-Balkan
clusters, Stronen et al. 2013 Fig. 1), we preferred to
screen for relatedness across the entire sample. We are
therefore confident that relatedness will not have con-
founded our results.
Our study is descriptive (nonexperimental), and we
cannot exclude the possible influence of other evolution-
ary factors such as genetic drift. Wolves are apex preda-
tors with large home ranges (Jezdrzejewski et al. 2007) and
occur at low density on the landscape. Despite their high
dispersal capability, a history of small and fragmented
wolf populations in large parts of Europe (Linnell et al.
2008) has probably caused substantial amounts of genetic
drift because of population fluctuations and bottlenecks,
as well as family structure and low effective population
size (Mech and Boitani 2003).
Another possible confounding factor may be the pres-
ence of endogenous incompatibilities that exhibit spatial
structuring in accordance with environmental transition
zones and therefore (incorrectly) suggest that selection is
driven by environmental factors (Bierne et al. 2011).
Although we examined over 67-K SNPs in a well-studied
species with a wide global distribution, it remains chal-
lenging unequivocally to separate influences of selection,
endogenous incompatibilities, and genetic drift, and the
results must be interpreted with caution. Furthermore, it
can be difficult to determine whether genes are under
selection or rather extreme outliers in the neutral distri-
bution (Coop et al. 2009). Our findings for, for example,
metabolism and temperature regulation might also
involve pleiotropic effects whereby one gene exerts influ-
ence on multiple apparently unrelated traits (Lynch and
Hayden 1995). Further attention is also warranted on the
importance of polygenic traits, which can be difficult to
identify with common tools for detecting selection
because of small changes at multiple loci (Pritchard and
Di Rienzo 2010 and references therein). We could have
implemented alternate multiple-test procedures such as
the false discovery rate correction instead of the more
stringent Bonferroni adjustment to augment the chance
of detecting such loci. However, we opted to focus on the
more robust SNP results and flanking genes under (possi-
ble) selection in our study area.
Conclusion
Our findings suggest that ancient, concurrent, and possi-
bly parallel selection could have played a more prominent
role than recent local adaptation in structuring functional
genetic variation in wolves throughout our study area.
However, local adaptation may also have influenced the
population structure and evolution of European wolves.
The results indicate differences between northern and
southern Europe, the Carpathian Mountain and Ukrai-
nian Steppe population clusters for a number of SNP loci
and neighboring genes with known or assumed functions.
Although several genes appear to be associated with envi-
ronmental features, others, such as genes implicated in
olfaction, are presumably important for all wolves and
may reflect parallel selection. Several strong outlier loci,
many of which contributed clearly to population struc-
ture, were not associated with any of 12 environmental
factors under study. Although genetic drift and small
population size complicate the ability to evaluate local
adaptation in wolves, the species is a well-suited study
organism because of its adaptability (as evidenced by its
broad global distribution) and capacity for long-distance
movement. Future research could moreover illuminate
how wild species such as wolves might adapt to their local
environments by means of hybridization with related spe-
cies, which represents a rapid means of acquiring genetic
variation that has already been filtered through natural
selection. We examined contemporary individuals, but
results will indicate selective pressures as they occurred in
the past. Additional attention is required to understand
how wild organisms will respond to ongoing or future
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 4421
A. V. Stronen et al. Genome-wide Analyses of Selection in Wolves
influences such as climate change and shifts in the distri-
bution and abundance of the species that constitute their
main diet.
Acknowledgments
We thank I. Dykyy, W. Jezdrzejewski, V.A. Lobkov, M.
Pilot, E. Tsingarska, and other persons and organizations
across many countries that contributed samples and
information to make this study possible. M. G
orny
assisted with GIS analyses and map production. We grate-
fully acknowledge funding from BIOCONSUS – Research
Potential in Conservation and Sustainable Management of
Biodiversity (contract no. 245737, FP7/2009-2014), the
Mammal Research Institute of the Polish Academy of
Sciences, the Polish Ministry of Science and Higher Edu-
cation (grant no. NN 303 418437) and BIOGEAST – Bio-
diversity of East-European and Siberian large mammals
on the level of genetic variation of populations, 7th Fra-
mework Programme (contract no. 247652). AVS received
funding from the Danish Natural Science Research Coun-
cil (postdoctoral grant 1337-00007). CP was supported by
the Aalborg Zoo Conservation Foundation (AZCF), the
Danish Natural Science Research Council (grant nos:
11-103926, 09-065999, 95095995), and the Carlsberg
Foundation (grant no. 2011-01-0059).
Data accessibility
Sampling locations and single nucleotide polymorphism
genotypes: Dryad doi: 10.5061/dryad.p6598.
Conflict of Interest
None declared.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Table S1. Correlation between environmental variables
(detailed in Table 1).
Table S2. Complete identification for single nucleotide
polymorphism (SNP) loci on the Illumina CanineHD
BeadChip (170K SNPs) with information from the MAP-
file in PLINK.
Table S3. Summary of major functional genes near single
nucleotide polymorphism (SNP) loci identified as outlier
loci and/or associated with environmental variables based
on a study of 59 wolves in four European population
clusters.
Table S4. Functional genes where genotype frequencies
show spatial patterns between population clusters (e.g.,
Northcentral against the other three, or Northcentral and
Ukrainian Steppe against others).
Table S5. Functional genes without obvious spatial pat-
terns.
Table S6. SNP loci identified as outliers by BayeScan but
not associated with environmental variables included in
this study.
Table S7. Pairwise FST-values with 95% confidence inter-
vals for n = 59 wolves in four population cluster, across
n = 353 SNP loci reported as outliers (BayeScan) or asso-
ciated with environmental variables (GWAS in PLINK),
calculated in HierFstat with bootstrap resampling
(n = 1000).
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 4425
A. V. Stronen et al. Genome-wide Analyses of Selection in Wolves