R E S E A R C H A R T I C L E
Open Access
An empirical comparison of population
genetic analyses using microsatellite and
SNP data for a species of conservation
concern
Shawna J. Zimmerman
1,2*
, Cameron L. Aldridge
1,2
and Sara J. Oyler-McCance
1
Abstract
Background: Use of genomic tools to characterize wildlife populations has increased in recent years. In the past,
genetic characterization has been accomplished with more traditional genetic tools (e.g., microsatellites). The
explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of
increased precision in population genetic parameter estimates and identification of demographically and
evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic
tools. At present, few empirical comparisons of population genetic parameters and clustering analyses performed
with microsatellites and SNPs have been conducted.
Results: Here we used microsatellite and SNP data generated from Gunnison sage-grouse (Centrocercus minimus)
samples to evaluate concordance of the results obtained from each dataset for common metrics of genetic
diversity (H
O
, H
E
, F
IS
, A
R
) and differentiation (F
ST
, G
ST
, D
Jost
). Additionally, we evaluated clustering of individuals using
putatively neutral (SNPs and microsatellites), putatively adaptive, and a combined dataset of putatively neutral and
adaptive loci. We took particular interest in the conservation implications of any differences. Generally, we found
high concordance between microsatellites and SNPs for H
E
, F
IS
, A
R
, and all differentiation estimates. Although there
was strong correlation between metrics from SNPs and microsatellites, the magnitude of the diversity and
differentiation metrics were quite different in some cases. Clustering analyses also showed similar patterns, though
SNP data was able to cluster individuals into more distinct groups. Importantly, clustering analyses with SNP data
suggest strong demographic independence among the six distinct populations of Gunnison sage-grouse with
some indication of evolutionary independence in two or three populations; a finding that was not revealed by
microsatellite data.
(Continued on next page)
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* Correspondence:
szimmerman@usgs.gov
1
U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Avenue,
Bldg. C, Fort Collins, CO 80526, USA
2
Department of Ecosystem Science and Sustainability and Natural Resource
Ecology Laboratory, Colorado State University, Fort Collins, CO 80526, USA
Zimmerman
et al. BMC Genomics (2020) 21:382
https://doi.org/10.1186/s12864-020-06783-9
(Continued from previous page)
Conclusion: We demonstrate that SNPs have three main advantages over microsatellites: more precise estimates of
population-level diversity, higher power to identify groups in clustering methods, and the ability to consider local
adaptation. This study adds to a growing body of work comparing the use of SNPs and microsatellites to evaluate
genetic diversity and differentiation for a species of conservation concern with relatively high population structure
and using the most common method of obtaining SNP genotypes for non-model organisms.
Keywords: Population structure, Population genetics, Evolutionarily significant units, Conservation, Genomics,
Microsatellites, Single nucleotide polymorphisms
Background
Accurate estimation of population genetic parameters
has become an important part of wildlife conservation
[
1
]. Genetic characterization can be used to identify pop-
ulations and understand gene flow [
2
–
5
]. More recently,
genetic data have been used to begin to understand local
adaptation [
6
–
8
] and to identify groups with distinct
evolutionary or demographic characteristics [
9
–
12
].
Most past genetic studies of wildlife species have been
accomplished with relatively few (< 20) highly variable
microsatellite loci. Microsatellites, also called simple se-
quence repeats, were discovered in the 1980s and were
quickly adopted as one of the most commonly used gen-
etic markers [
13
,
14
] because they tend to be highly
polymorphic, are evenly distributed throughout the gen-
ome [
15
,
16
], and are located in non-coding regions
allowing the general assumption that neutral processes
were being meausured. Unlike many other types of
markers, microsatellites have a high mutation rate that is
quite variable across different loci. This mutation rate is
the result of slippage during DNA replication, a process
that is not well understood [
17
]. The high mutation rate
of microsatellites that results in highly informative
markers may also lead to an underestimate of heterozy-
gosity through homoplasy, or when two individuals have
the same allelic state through independent mutation and
not from a common ancestor [
17
]. Additionally, repeat-
ability of genotyping across laboratories can be challen-
ging [
18
–
21
] largely because allele size calls are
somewhat subjective and size determination methods
can impact inferred fragment size [
22
], even with use of
automated software [
23
].
A single nucleotide polymorphism (SNP) is a location
in the DNA sequence where individuals vary at a single
nucleotide. Technological advancements have allowed
creation of much larger SNP genotype datasets, greatly
increasing the number of loci sampled with less effort
and lower cost in comparison to microsatellite develop-
ment and genotyping [
16
]. Because of their high preva-
lence in the genome and the potential to target
functional regions, SNPs are predicted to replace micro-
satellites for genetic characterization [
24
]. SNPs are
more abundant and uniformly distributed across the
genome than microsatellites, and have a well-understood
mutational mechanism with low levels of homoplasy
[
25
], but they have lower allelic diversity [
26
]. Lower
allelic diversity in comparison to microsatellites is
expected, because a nucleotide base at a SNP can only
be one of four possible states: A, T, C, or G. In reality,
the natural pairing of certain bases in DNA structure
and the low likelihood of multiple mutations at one
location results in the majority of SNPs being biallelic.
Because of the relatively low allelic diversity, equal distri-
bution throughout the genome, ascertainment bias of
highly polymorphic microsatellite regions, and relatively
constant mutation rate of SNPs, some have argued that
SNPs provide a more accurate representation of
genome-wide variation [
27
,
28
]. Until recently, SNP
datasets were only available for species with reference
genomes, such as model organisms or important agricul-
tural species. The development of reduced representa-
tion methods to obtain SNP genotypes without a
reference genome has broadened the application of SNP
markers to numerous species [
29
,
30
]. One of the main
appeals of SNP loci is the ease with which high through-
put/automatic analyses can be used in comparison with
development and genotyping of microsatellites [
24
,
31
,
32
] resulting in the generation of large numbers of geno-
types in a relatively short period of time and for minimal
cost. Further, increasing the number of loci sampled is
expected to increase precision of population genetic
estimates [
33
,
34
].
In addition to the potential improvement in precision
of population parameter estimates from the increased
number of loci, the explosion of genomic techniques
and their application to non-model organisms has also
led to the ability to ask new questions about conserva-
tion [
35
,
36
]. SNPs are found in coding and non-coding
regions of the genome and they can represent both
demographic (i.e., drift) and functional (i.e., selection)
processes. Many authors have suggested that conserva-
tion units identified below the species level should
incorporate an evaluation of demographic and evolution-
ary distinctness [
37
–
41
]. Defining genetically similar
Zimmerman
et al. BMC Genomics (2020) 21:382
Page 2 of 16
units for conservation can inform management actions
(e.g., habitat restoration, translocation) or potentially
impact legal protection status under the Endangered
Species Act (ESA), which allows for the separate protec-
tion of geographically and ecologically distinct popula-
tions [
42
]. The predicted advantages to using SNP data
as opposed to microsatellite data for conservation have
lead us to question if microsatellites will be a useful tool
in the future or will be completely replaced by SNP data.
Technological advancements in genomic approaches
for non-model organisms has resulted in use of reduced
representation sequencing methods to generate large
SNP datasets for many wildlife species; datasets that are
often archived and available for potential future use. Un-
derstanding how SNP data compare to inferences made
from the more traditional microsatellite data is import-
ant for long-term genetic monitoring given the increas-
ing trend of using SNP data for conservation objectives.
Previous studies have compared the relative abilities of
SNP and microsatellite loci to evaluate levels of related-
ness [
43
–
49
], probability of identity and parentage [
50
–
54
], create linkage maps [
55
,
56
], evaluate genetic diver-
sity [
43
,
45
,
46
,
51
,
57
–
61
], and detect low to mid levels
of differentiation [
45
,
57
–
62
]. Some studies have even
used genome-wide SNP data to identify distinct popula-
tion units [
11
,
12
,
63
,
64
]. Here we used SNP and micro-
satellite datasets from the same group of genetic samples
from a species of conservation concern to empirically
evaluate agreement across marker types for population
genetic analyses and consider the potential consequences
in conservation decision making. The samples we used
are typical of many conservation studies: opportunistic-
ally collected, variable source, variable quality, and from
multiple populations of variable size that are represented
by variable numbers of samples. Additionally, we used
previously identified candidate adaptive loci [
65
] to
evaluate identification of distinct units using datasets
composed of genetic markers reflecting different evolu-
tionary processes.
The Gunnison sage-grouse (
Centrocercus minimus) is
a sagebrush obligate avian species listed as threatened
under the Endangered Species Act in 2014. The species
exists as a network of seven populations predominantly
occurring in Colorado and a small portion of the range
extending into Utah (Fig.
1
) [
66
,
67
]. The majority of in-
dividuals in the species (~ 85
–90%) are located in the
Gunnison Basin population, which is largest in land area
and highest in genetic diversity [
69
]. The six remaining
satellite populations support much smaller numbers of
birds; in descending order San Miguel Basin, Piñon
Mesa, Crawford, Dove Creek-Monticello (Dove Creek
from here on), Cerro Summit-Cimarron-Sims Mesa
(Cimarron from here on), and Poncha Pass (Table
1
)
[
70
].
Genetic
differentiation
is
high
between
all
populations [
69
], local environmental conditions are
variable [
68
], and there is some evidence of adaptive di-
vergence among populations [
65
]. The Poncha Pass
population is thought to have been extirpated in the
1970s, re-established with individuals translocated from
Gunnison Basin, and currently persists as the result of
on-going translocations [
71
]. Consequently, the Poncha
Pass population was not included in the analyses
presented here.
The double digestion RAD-Seq approach to reduced
representation sequencing is one of the most commonly
used genomic library preparations to generate SNP
genotypes. With the increasing use of RAD-Seq gener-
ated SNPs instead of microsatellite data for conservation
questions and monitoring, here we aim to compare
population genetic parameters specifically from RAD-
Seq generated SNPs and microsatellites. To date, few
studies have compared the consequences of marker
types in conservation objectives when a typical RAD-Seq
protocol is used (though see [
34
,
45
,
50
,
51
,
64
]). Given
the impact of decisions made during RAD-Seq protocols
on downstream analysis [
72
], the prevalence of RAD-Seq
generated SNP datasets, and the limited empirical exam-
ples of comparisons to more traditional microsatellite
analyses, more comparisons can provide insight into the
limitations or benefits of RAD-Seq generated SNP data
and the future utility of microsatellite loci. Through pre-
vious studies on this species of conservation concern, we
had access to two range-wide Gunnison sage-grouse
genetic datasets, one of microsatellite [
73
] and one of
SNP loci [
65
]. We had three specific objectives in this
study: (1) compare genetic diversity metrics across data-
sets, (2) compare genetic differentiation metrics across
datasets, and (3) compare clustering methods across
datasets and investigate evidence of evolutionary inde-
pendence among populations.
Results
Genetic diversity
For all diversity metrics, 95% confidence intervals calcu-
lated from SNPs were narrower than confidence inter-
vals from microsatellites (Fig.
2
; Additional file
1
: Table
S1). Microsatellite estimates had large confidence inter-
vals in all cases, which resulted in no significant differ-
ences among population estimates. In contrast, the
narrower confidence intervals with SNPs resulted in
significant differences between populations. Of the four
metrics,
H
O
had the lowest correlation across marker
type (Spearman
ρ = 0.257, Pearson r = 0.345) (Fig.
2
b).
As theoretically expected, values of
H
O
from microsatel-
lites in all populations were ~ 0.500 (range: 0.464
–0.548)
while values from SNPs were lower, ~ 0.200 (range:
0.183
–0.197; Fig.
2
a). However, both marker types re-
sulted in relatively consistent population ranks based on
Zimmerman
et al. BMC Genomics (2020) 21:382
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mean
H
O
(
p = 0.031, Wilcoxon paired signed-rank).
Values of
H
E
showed high correlation (Spearman
ρ =
0.886, Pearson
r = 0.925), and relative consistency in
ranking populations across marker types (
p = 0.031,
Wilcoxon paired signed-rank). The values for
H
E
were
within similar ranges as
H
O
, microsatellite estimates
at ~ 0.500 (range: 0.413
–0.578) and SNP estimates at
~ 0.200 (range: 0.154
–0.194; Fig.
2
c and d). Similarly,
allelic richness showed high levels of correlation
(Spearman
ρ = 0.943, Pearson r = 0.925), and consist-
ent ranking of populations by levels of genetic diver-
sity (
p = 0.031, Wilcoxon paired signed-rank) across
marker type (Fig.
2
e and f). Estimates of
F
IS
also
showed relatively high correlation (Spearman
ρ =
0.600,
Pearson
r = 0.978), however, ranking of
Table 1 Sample size for each population of Gunnison sage-
grouse and each marker type
Population
# Samples
2004 Pop. Est.
MSAT
SNP
Cimarron
4
4
74
Crawford
21
12
157
Dove Creek
43
12
98
Gunnison Basin
116
12
3978
Piñon Mesa
19
10
182
Poncha Pass
0
0
10
San Miguel
51
10
206
MSAT microsatellites, SNP single nucleotide polymorphisms. Population
estimates of the 2004 population size = 2004 Pop. Est. [
70
]
Fig. 1 Gunnison sage-grouse distribution. Historical (gray) and current (yellow) distribution of Gunnison sage-grouse in the southwestern United
States. Populations are labeled with respective names. Black rectangle designates the study area. Spatial data files were originally developed by
Schroeder et al. [
66
], and the present map created by SJZ in ArcMap 10.1. The historical range map is as described by Braun et al. [
67
]; the two
northernmost portions of the historical range correspond to an unknown species of sage-grouse and are not verified by Colorado Parks and
Wildlife [
68
]
Zimmerman
et al. BMC Genomics (2020) 21:382
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populations was not as consistent (
p = 0.563, Wil-
coxon paired signed-rank) across marker types and
the magnitude of the values for each marker type re-
sulted in different inferences in some cases (Fig.
2
g
and h); microsatellites indicated outbred (minimum
value:
− 0.279) to slightly inbred (maximum value
0.071) populations while SNPs indicated slightly to
moderately outbred populations (
− 0.194 – − 0.004).
Genetic differentiation
Generally, genetic differentiation estimates from SNP
datasets had narrower confidence intervals in compari-
son to estimates from microsatellites (Fig.
3
; Additional
file
1
: Table S2) which were significantly correlated in all
pair-wise comparisons (Mantel
r > 0.9, p < 0.001). All dif-
ferentiation metrics had a high correlation across marker
types and datasets (Fig.
4
). For
F
ST
and
G
ST
, confidence
Fig. 2 Comparison of genetic diversity values for Gunnison sage-grouse populations. Confidence intervals (95%) around mean values for
microsatellite (
●) and putatively neutral SNP (▲) loci were constructed. Estimates for observed heterozygosity (H
O
; a), expected heterozygosity
(H
E
; c), allelic richness (A
R
; e), and inbreeding coefficient (F
IS
; g) are shown in the left-hand column. Populations are abbreviated along the x-axis:
CM = Cimarron, CR = Crawford, DC = Dove Creek, GB = Gunnison Basin, PM = Piñon Mesa, SM = San Miguel. Relationships between estimates from
microsatellites and SNPs for H
O
(b), H
E
(d), A
R
(f) and F
IS
(h) are shown in the right-hand column. Spearman rank and Pearson
’s correlation
coefficient are also included in the plots in the right-hand column. Dashed line corresponds to a 1:1 relationship
Zimmerman
et al. BMC Genomics (2020) 21:382
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intervals for population estimates from microsatellites
and both SNP datasets typically overlapped (Fig.
3
a and
b). Estimates of D
Jost
from microsatellites and both SNP
datasets did not overlap and the magnitude of microsat-
ellite estimates were consistently much higher in com-
parison to SNP estimates (Fig.
3
c), though the same
general pattern remained (Fig.
4
c and i). Similarly, values
of
G
ST
estimated with microsatellites were also larger in
magnitude than with SNPs, though to a lesser degree
than observed with
D
Jost
(Fig.
3
b).
Clustering
The lowest BIC for hypothetical genetic clusters in
DAPC corresponded to 6 groups with microsatellites
(BIC = 484.603), 5 groups with all SNPs (BIC = 454.768),
and putatively adaptive SNPs (BIC = 298.376), and 4 with
putatively neutral SNPs (BIC = 449.803). The optimal
number of PCs to include in the DAPC analysis as deter-
mined by the a-score method was 22 for microsatellites,
6 for all SNPs, 5 for putatively neutral SNPs, and 6 for
putatively adaptive SNPs. Clustering of individuals in
Fig. 3 Comparison of genetic differentiation values for pair-wise comparisons of Gunnison sage-grouse populations. Confidence intervals (95%)
around mean values for microsatellite (
●), putatively neutral SNP (▲), and all SNP (■) loci. Pair-wise estimates are for F
ST
(a), G
ST
(b), and D
Jost
(c).
Populations in pair-wise comparisons are abbreviated along the x-axis: CM = Cimarron, CR = Crawford, DC = Dove Creek, GB = Gunnison Basin,
PM = Piñon Mesa, SM = San Miguel; CM.CR = F
ST
between Cimarron and Crawford
Zimmerman
et al. BMC Genomics (2020) 21:382
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DAPC with microsatellites identified Piñon Mesa as the
only population that clearly separates from the other
populations along discriminant function 1 (Fig.
5
a),
while discriminant function 2 pulls populations into
identifiable groups though still with overlap (Fig.
5
a).
With all and putatively neutral SNPs, discriminant func-
tion 1 separates Gunnison Basin and Piñon Mesa from
the other populations (Fig.
5
b and c), and discriminant
function 2 separates Dove Creek (Fig.
5
b and c). The
candidate adaptive loci dataset shows Piñon Mesa and
Dove
Creek
clearly
separated
along
discriminant
function 1, while San Miguel, Cimarron, Crawford, and
Gunnison Basin cluster together (Fig.
5
d).
The dendrogram created from microsatellite data
generally grouped individuals into known populations
where
Cimarron,
Crawford,
and
Gunnison
Basin
grouped closest together with Piñon Mesa, Dove Creek,
and San Miguel grouping closer together but away from
Fig. 4 Correlation of differentiation metrics for Gunnison sage-grouse populations. Relationships between estimates from different datasets:
microsatellites, putatively neutral SNPs, and all SNPs for F
ST
(a,d,g), G
ST
(b,e,h), and D
Jost
(c,f,i) are shown in respective panels. Axes are labeled by
dataset. Spearman rank and Pearson
’s correlation coefficient are included in the upper left-hand corner of each panel. Dashed line corresponds to
a 1:1 relationship
Zimmerman
et al. BMC Genomics (2020) 21:382
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the Cimarron, Crawford, Gunnison Basin individuals
(Fig.
6
a). Cimarron and Crawford individuals were
grouped together on a single branch, along with two
Gunnison Basin individuals. Additionally, two individ-
uals from Gunnison Basin and an individual from San
Miguel cluster with the Dove Creek individuals. Similar
to the clustering pattern observed in DAPC, all SNPs
and putatively neutral SNPs resulted in nearly indistin-
guishable grouping patterns where all populations are
identifiable on individual branches (Fig.
6
b and c, re-
spectively). With both the all SNP and putatively neutral
SNP datasets Cimarron, Crawford, and Gunnison Basin
group most closely, Piñon Mesa is the most distant from
the center, and a single individual sampled in Crawford
grouped with the San Miguel individuals. With neutral
SNPs a single San Miguel individual grouped with
Cimarron (Fig.
6
c). Though similar to the other SNP
dendrograms in that samples clustered into distinct
Fig. 5 Star-plots of DF1 (x-axis) and DF2 (y-axis) from discriminant analysis of principle components (DAPC) for Gunnison sage-grouse. Panels
correspond to different datasets: a microsatellite, b all SNPs, c putatively neutral SNPs, d and candidate adaptive SNPs. Each point represents an
individual color coded by sampling origin
Zimmerman
et al. BMC Genomics (2020) 21:382
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populations, branch lengths appear somewhat longer in
the candidate adaptive loci dataset (Fig.
6
d). When con-
sidering
hierarchical
clustering
using
methods
in
addition to
“ward. D2”, the patterns are generally similar
though some differences are notable, particularly when
comparing the results of microsatellites to any of the
SNP datasets. The
“single” method, which bases branch
length between groups on the closest individual in each
group, does not result in distinct populations using
microsatellite data (Additional file
1
: Fig. S1A), but re-
sults in the same clustering pattern as the
“ward. D2”
method for all SNPs (Additional file
1
: Fig. S1B), puta-
tively neutral SNPs (Additional file
1
: Fig; S1C), and can-
didate loci (Additional file
1
: Fig. S1D). The
“complete”
method, which bases branch length between groups on
the most distant individuals, shows Cimarron, Crawford,
and San Miguel individuals nested between groups of
Gunnison Basin individuals while Dove Creek and Piñon
Mesa are distinct when using microsatellites (Additional
file
1
: Fig. S2A), but results in nearly the same clustering
pattern as with
“ward. D2” when using all SNPs (Add-
itional file
1
: Fig. S2B), putatively neutral SNPs (Add-
itional file
1
: Fig. S2C) and candidate adaptive loci
(Additional file
1
: Fig. S2D), though a single San Miguel
individual clusters with Cimarron using all SNPs and
putatively neutral SNPs (Additional file
1
: Fig. S2B and
S2C).
Discussion
In general, we found that measures of diversity and dif-
ferentiation generated from microsatellite and SNP data
were typically in agreement in ranking of population
Fig. 6 Comparison of dendrograms of individual Gunnison sage-grouse using the hierarchical clustering method
“ward. D2”. Panels correspond
to different datasets: microsatellites (a), all SNPs (b), putatively neutral SNPs (c), and candidate adaptive loci (d). Colors indicate sampling origin
Zimmerman
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estimates, although magnitudes of estimates were quite
different. Metrics of differentiation, however, had con-
sistently higher correlation than most metrics of diver-
sity. Our results also confirmed that increased numbers
of SNP loci can dramatically reduce the confidence
intervals for mean estimates, increasing precision, al-
though this was not true for all differentiation measures.
We also demonstrated that clustering of individuals for
the purpose of identifying evolutionarily or demograph-
ically distinct units can be variable depending on cluster-
ing method used and marker type.
Genetic diversity
Of the four diversity metrics evaluated here,
H
E,
F
IS,
and
A
R
were the metrics with the highest correlation be-
tween microsatellites and SNPs (see Fig.
2
).
H
O
, how-
ever, showed relatively low correlation across marker
types. Several previous studies with variable numbers of
markers, sample sizes, and SNP discovery approaches
generally agree with our correlation of diversity metrics
across marker types, though positive relationships were
sometimes moderate [
43
,
45
,
51
,
58
,
59
,
64
]. None of
these studies report
H
O
, and so there was no comparison
for our relatively low correlation across marker types for
this metric. Still, some argue higher correlation may be
influenced by the number of SNPs [
43
], or whether loci
represent a high proportion of the genome-wide poly-
morphism [
74
,
75
]; two aspects of SNP datasets that will
vary by study and may play a role in our observed corre-
lations. Importantly, similar to findings by Fischer et al.
[
59
], the high variance in microsatellite data for all diver-
sity metrics resulted in almost no significant difference
between populations; differences that were detected with
SNPs. Though we, like others, show high correlation
among marker types, the increased precision in esti-
mates allow distinction of populations when using SNP
data. For general monitoring of changes in diversity for
conservation or management of a species, either marker
type would prove useful. This was not necessarily true
for
F
IS
, where generally high correlation was observed,
though ranking was not as consistent, and SNPs failed to
detect the indication of inbreeding that was apparent
with microsatellites (i.e.,
F
IS
> 0; Fig.
1
& Table S
1
). It is
worth restating that samples were originally selected for
the SNP data based on relatedness values estimated with
the microsatellite data. Logically, selecting minimally re-
lated individuals could result in the SNP data producing
F
IS
estimates consistent with more outbreeding than the
original full microsatellite dataset. However, when we
compared diversity metrics from a reduced microsatellite
dataset including only the individuals used in the SNP
dataset, we find no significant differences for any diver-
sity metric according to the confidence intervals (Table
S
1
). However, the mean F
IS
estimates for the reduced
microsatellite dataset would suggest a change in the sign
of the estimate (i.e., either from inbreeding to outbreed-
ing or outbreeding to inbreeding) for three populations.
SNPs, however, would have an obvious advantage if con-
servation actions required an understanding of the rela-
tive levels or ranking of most measures of genetic
diversity.
Genetic differentiation
In general agreement with multiple studies [
21
,
45
,
58
–
60
,
64
] all metrics of differentiation showed high correl-
ation between microsatellites and SNP datasets, with
correlation coefficients greater than 0.90 in all tests (Fig.
4
) and significant Mantel correlations (Mantel
r > 0.9,
p ≤ 0.05 for all comparisons). Some argue reliance on a
single measure of differentiation for conservation pur-
poses risks inaccurate characterization of populations
[
76
–
78
]. Our findings, however, echo other empirical
examples where different metrics result in the same in-
ference [
11
,
60
], but only when ranking populations
(Wilcoxon paired signed-rank test
p ≤ 0.05 in all com-
parisons). Different metrics of population differentiation
showed a consistent pattern of which populations were
most similar, though the magnitude of a metric was
sometimes very different. If the magnitude of the differ-
entiation metric is of conservation relevance, then the
marker types are not equivalent.
The appropriateness of a differentiation metric in con-
servation can be further impacted by additional differ-
ences in marker types. The different metrics measure
different things.
D
Jost
is considered a relative degree of
allelic differentiation, while
F
ST
and
G
ST
are fixation in-
dices [
78
]. Similar to some previous studies, we found
D
Jost
tended to produce values higher in magnitude with
microsatellites than with SNPs [
64
]. Each microsatellite
locus will always have higher per locus allelic diversity
than biallelic SNP loci, and therefore result in higher
magnitude estimates of
D
Jost
[
78
,
79
]. We also found
higher
G
ST
estimates with microsatellites (Fig.
3
b &
Table S
2
), although the magnitude of difference between
the values calculated from different marker types was
not as dramatic as that with
D
Jost
(Fig.
3
c & Table S
2
).
G
ST
depends on heterozygosity, so the higher theoretical
maximum heterozygosity for microsatellite loci (
H
E
= 1),
versus the theoretical maximum heterozygosity for SNP
loci (
H
E
= 0.5), will also result in higher magnitudes of
G
ST
when calculated from microsatellite data. Import-
antly,
F
ST
has proven to be more robust to per locus
allelic diversity and heterozygosity (though see [
59
,
60
]).
From a conservation perspective,
F
ST
may prove most
useful in the transition from microsatellite loci to SNPs
for
genetic
monitoring
because
of
the
observed
consistency across marker types. However,
D
Jost
may ac-
tually be the more relevant conservation metric if
Zimmerman
et al. BMC Genomics (2020) 21:382
Page 10 of 16
comparing relative degrees of differentiation or identify-
ing isolated groups because the level of differentiation
takes into account allelic identity and not just population
level fixation [
78
]. Fixation indices will indicate two pop-
ulations fixed for the same allele are distinct because
both populations lack diversity at a locus while allelic
differentiation metrics will not because the identity of
the fixed allele is the same and therefore populations are
not different at that locus.
The three differentiation metrics evaluated here also
have different sensitivities to the underlying mutation
rate generating each type of marker with
F
ST
proving
more robust [
78
,
80
]. In addition to impacts from muta-
tion rate on different metrics, migration rate and popu-
lation size are also important to consider. Whitlock [
80
]
demonstrated that for low mutation rates, approximately
that of SNPs (10E-9), and low migration rates among a
small number of populations, measures of differentiation
as measured by
D
Jost
will be much smaller in magnitude,
and to a lesser degree, so will
G
ST
. Relatively low levels
of mutation, migration, and small populations typically
correspond to lower allelic diversity. Gunnison sage-
grouse is composed of seven isolated populations, with
very low migration rates among populations, though not
as low as SNP mutation rates. Our comparison of
F
ST
values across marker types demonstrates relatively con-
sistent agreement between both magnitude and ranking
of pair-wise comparisons (Fig.
3
a). As predicted,
D
Jost
and
G
ST
consistently rank comparisons across marker
type, though the magnitude of metrics were lower for
SNPs than microsatellites; much lower in the case of
D
Jost
(Fig.
3
b and c). Overall, our results empirically
demonstrate the predicted impact population configura-
tions can have on measures of differentiation.
Both marker types suffer from additional characteris-
tics that can influence estimates of differentiation. In
addition to the influence of heterozygosity and allelic di-
versity on measures of differentiation, there is a trade-off
between the number of loci and the per locus informa-
tion content. More SNP loci will be required to obtain
the same resolution in differentiation metrics from fewer
microsatellite loci, because the number of alleles per
locus can impact the ability to detect reproductively iso-
lated groups. If a locus only has two alleles, as is typical
with SNP loci, the chances of populations differing in al-
lele frequencies at high enough levels to detect isolation
is lower. Conversely, if a locus has multiple alleles shared
among populations, the differences in allele frequencies
are more likely to be detected, therefore showing the
level of reproductive isolation. Many studies have pro-
vided suggestions on the number of SNPs required to
obtain resolution in differentiation comparable to that
obtained with microsatellites, ranging from two to 11
times more SNP loci [
25
,
57
,
81
]. However, more recent
work has indicated fewer SNPs than previously sug-
gested can be sufficient [
47
,
58
,
60
,
82
]. We did not ex-
plicitly evaluate the number of SNP loci required to
obtain estimates with the precision of microsatellites,
though we do demonstrate that 14,091 biallelic puta-
tively neutral SNPs results in comparable estimates to
22 microsatellites with three to 18 alleles per locus. Our
study likely reflects a typical number of SNPs which
would be obtained with a RAD-Seq protocol, the most
commonly used approach for wildlife species. We there-
fore, demonstrate RAD-Seq generated SNP genotypes
can produce comparable differentiation estimates to
those obtained with microsatellites.
We do not, however, demonstrate a dramatic reduc-
tion in confidence intervals around those measures of
differentiation (Fig.
3
), as has been predicted. However,
in pair-wise comparisons of differentiation, the small
sample size of one of the populations is known to im-
pact the confidence intervals [
83
]. In our data, we see
this trend particularly for
F
ST
and with comparisons in-
volving our smallest population represented by the
fewest samples, Cimarron (Fig.
2
). In species of conser-
vation concern variable population sizes are often
unavoidable, and by increasing the number of loci sam-
pled (> 1000 SNPs) robust estimates of differentiation
can still be obtained [
83
].
Clustering
Contrary to our findings for differentiation, the cluster-
ing analyses showed an increase in precision with SNP
data that is consistent with previous studies [
45
,
61
,
64
].
We used multiple methods to cluster individuals (den-
drograms and DAPC) all of which showed general agree-
ment of clustering by population of origin (Figs.
5
b, c,
and d,
6
b, and c). The SNP data, however, resulted in
tighter groups of individuals (Figs.
5
c and
6
c) relative to
the somewhat loose clusters of individuals with micro-
satellite data (Figs.
5
a and
6
a). The number of individ-
uals sampled varied by marker type in our study (256 in
the microsatellite dataset versus 60 in the SNP datasets),
which could potentially contribute to the lower reso-
lution in clustering analyses when compared to the SNP
data. However, when we looked at clustering of micro-
satellite data using only the 60 individuals included in
the SNP data, the patterns of clustering remain the same
(Additional file
1
: Fig. S3 and S4), similar to what Lemo-
poulos et al. [
45
] found.
The potential impact of conservation actions on a
species local fitness and how that relates to adaptive
divergence is important to consider [
84
], especially for a
species with geographically distinct and declining popu-
lations. Identifying candidate adaptive loci can provide
insight into the potential adaptive divergence among
populations and the potential for local adaption. We also
Zimmerman
et al. BMC Genomics (2020) 21:382
Page 11 of 16
compared clustering of individuals by previously identi-
fied candidate adaptive loci [
65
], an objective that cannot
be accomplished with microsatellite loci (i.e., neutral
loci) alone. We found evidence of adaptive divergence in
two or three populations (Figs.
5
d,
6
d, S
1
D, and S
2
D),
depending on the method used for clustering. Though
the small populations and small sample sizes could be
causing fixation of alleles due to strong drift, the ap-
proaches used to identify candidate adaptive loci gener-
ally control for demography (e.g., BayPass and partial
RDA; see Zimmerman et al. [
65
]).
Comparing the clustering of individuals with all SNP
loci (Figs.
5
b and
6
b) to clustering including only puta-
tively neutral (Figs.
5
c and
6
c) or candidate adaptive
(Figs.
5
d and
6
d) loci, we see that neutral genetic pro-
cesses in Gunnison sage-grouse may be stronger than
adaptive divergence. Evidence of adaptive divergence
corresponded to approximately 6% (942 SNPs) of the
sampled genome. Neutral and adaptive variation are
both important to consider for designation as an ESU or
conservation unit [
37
,
40
]. However, the ratio of neutral
versus adaptive loci undoubtedly influences identifica-
tion of distinct units. In addition to considering the
marker type, it could be important to identify what pro-
portion of the genome must hold the signal for adaptive
divergence for formal designation. The term functionally
significant unit (FSU) was recently suggested to describe
conservation units based on ecologically important genes
[
85
]. More recently, a single ecologically important gene
was used to propose conservation units for salmon [
12
].
Most genes underlying phenotypes are quantitative in
nature with only preliminary ecological links, and so sin-
gle gene definitions of conservation units will be rare at
best [
86
]. Further, the focus on identifying conservation
units based on potential adaptive divergence may result
in unintended consequences such as reduced effort to
conserve or restore habitat [
86
], overlooking the role vic-
ariance events may play in adaptation [
87
], or a failure
to acknowledge traits that are adaptive in a given envir-
onment presently may not be locally adapted in future
environments. Importantly, questions of local adaptation
and evolutionary independence cannot be considered
with microsatellite loci, or any neutral loci alone. At-
tempts to identify distinct units with genetic data should
focus on using SNP data, or a combination of neutral
(microsatellite or SNP) in combination with known eco-
logically important functional regions.
Conclusions
We demonstrated that RAD-Seq generated SNPs from a
non-model organism are generally comparable to micro-
satellites for measuring population genetic parameters,
in agreement with some previous studies [
45
,
51
,
64
].
The rapid progression away from use of microsatellites
and toward use of SNP data in conservation and man-
agement applications highlights the importance of these
types of comparisons and calls into question the future
usefulness of microsatellite data. As we, and others, have
shown, the same general inference can typically be
drawn about population-level genetic differentiation and
diversity, irrespective of marker type. However, we
showed that SNPs had three main advantages over
microsatellites. First, the much smaller confidence inter-
vals around diversity measures allowed distinctions be-
tween populations to be made with SNP data. From a
conservation perspective, all populations of Gunnison
sage-grouse would have been considered equally diverse
using microsatellite loci, while there were clear differ-
ences in relative diversity with SNP data. Second, clus-
tering methods showed a dramatic increase in the power
to separate individuals into distinct groups. Microsatel-
lite data failed to clearly separate individuals into popu-
lations in nearly all instances; populations that were
clearly differentiated with SNP data. Third, SNP data al-
lows consideration of local adaptation.
We also further demonstrated the impact of marker
choice on differentiation metrics
—different marker types
resulted in very different magnitudes. This finding exem-
plifies the dangers of using thresholds for differentiation
and diversity metrics for conservation objectives. If the
magnitude of the value is not of importance, all metrics
except
H
O
and
F
IS
were able to consistently rank popula-
tions or population pairs across marker types in our study.
While we found clear advantages for use of SNPs in popu-
lation genetics, there remain some limitations at present.
Primarily, generation of SNP datasets requires relatively
large quantities of high quality DNA, which is often diffi-
cult to obtain from species of conservation concern. How-
ever, investing in the development of a SNP panel or
using a target capture approach can facilitate use of low
quality samples [
88
]. On the other hand, microsatellites
are extremely useful with low quality samples, are becom-
ing less costly and time consuming to develop (e.g., Castoe
et al. [
89
]), and have already been widely used in conserva-
tion and management programs for long-term monitoring
of many species. Although general usefulness of microsa-
tellites in the future is uncertain, microsatellite loci will
likely remain useful for relatedness, parentage analysis,
and genetic mark-recapture due to their highly poly-
morphic nature and mixed performance with SNP data
[
46
,
47
,
49
,
53
,
54
,
88
].
Methods
Data
Microsatellite genotypes
Blood samples were collected near breeding grounds
within six of the populations as part of a 2005 study
[
69
]. The dataset we use here is composed of 254
Zimmerman
et al. BMC Genomics (2020) 21:382
Page 12 of 16
individuals from these previously collected samples that
were genotyped at a larger set of microsatellite loci for a
2019 study [
73
]. Sample size varied by population: Cim-
arron = 4, Crawford = 21, Dove Creek = 43, Gunnison
Basin = 116, Piñon Mesa = 19, San Miguel = 51. Popula-
tions are named after nearby Colorado towns with 2 ex-
ceptions, Piñon Mesa is located west of Grand Junction
and San Miguel is south of Norwood. We amplified 22
grouse-specific microsatellite loci using the Polymerase
Chain Reaction (PCR) and with the components and
concentrations described in Oyler-McCance and Fike
[
90
] with thermal profiles and annealing temperatures as
originally published. The microsatellite primers used in-
cluded: MSP11, MSP18, reSGCA5, reSGCA11, SG21,
SG23, SG24, SG28, SG29, SG30, SG31, SG33, SG36,
SG38, SG39, SGCTAT1, SGMS06.4, SGMS06.8, TTT3,
TUT3, TUT4, and WYBG6 [
91
–
96
]. See Zimmerman
et al. [
73
] for details on DNA extraction and genotyping.
The final microsatellite dataset was composed of 22
relatively polymorphic sampled loci, for a total of 254
individuals, with variable representation by geographic
population.
Single nucleotide polymorphism (SNP) genotypes
From the same 254 previously collected blood samples
that were genotyped at microsatellite loci, a subset were
previously chosen for RAD-Seq [
65
] based on two
criteria: population of origin and relatedness. The goal
was to obtain an equal number of minimally related in-
dividuals from each population. The exception to these
requirements was the Cimarron population, which only
had four samples; consequently all Cimarron samples
were included. These criteria for sample selection were
necessary because of limited available funding and high
enough quality samples. See Zimmerman et al. [
65
] for
details on RAD-Seq library preparation and bioinformat-
ics. The complete SNP dataset was composed of 15,033
loci across 35
“pseudo-chromosomes” (chromosome
scaffolds inferred from synteny with chicken) for 60
individuals (Cimarron = 4, Crawford = 12, Dove Creek =
12, Gunnison Basin = 12, Pinon Mesa = 10, San Miguel =
10). A putatively adaptive SNP dataset composed of all
942 loci that were previously identified as potentially
under selection in outlier locus analyses and genotype-
environment association analyses was also created.
Methods used to identify putatively adaptive loci in-
cluded BayPass [
97
], pcadapt [
98
], and a redundancy
analysis as described in [
99
]. Environmental covariates
used in the genotype-environment association included
average spring precipitation, average fall precipitation,
spring maximum temperature, winter maximum vapor
pressure deficit, compound topographic index (a proxy
for soil moisture), green-up rate (a measure of the pro-
gression of the growing season), big sagebrush cover,
and a dryness index (see Zimmerman et al. [
65
] for de-
tails on loci under selection). A putatively neutral SNP
dataset was created by excluding all putatively adaptive
loci. The final putatively neutral SNP dataset included
14,091 biallelic loci across 34 pseudo-chromosomes, for
60 individuals with relatively equal representation from
each geographic population.
Analysis of genetic diversity
For each putatively neutral dataset, we estimated ob-
served heterozygosity (
H
O
), expected heterozygosity
(
H
E
), allelic richness per locus (
A
R
), and inbreeding coef-
ficient (
F
IS
) using the
‘diveRsity’ [
100
] package in R
[
101
]. Diversity metrics were estimated for each locus
based on 1000 bootstraps and reported as a mean and
95% confidence intervals constructed from the standard
deviation across all loci. Mean allelic richness per locus
was also estimated with rarefaction for comparison (re-
sults included in Additional file
1
: Table S1). Diversity
metrics were calculated for both datasets and used to
compare estimates from microsatellite and putatively
neutral SNPs. Pearson and Spearman rank correlation
coefficients were estimated to evaluate congruence for
all paired metrics. Wilcoxon paired signed-rank test in
the R package
‘MASS’ [
102
] was used to evaluate the
consistency of ranked values among datasets.
Analysis of genetic differentiation
For genetic differentiation we compared analysis results
from microsatellites, all SNPs, and putatively neutral
SNPs. We used the
‘diveRsity’ package in R to calculate
F
ST
[
103
] with confidence intervals based on 1000 boot-
straps. Because there is concern about comparing pair-
wise
F
ST
values when using loci with variable levels of
heterozygosity, we also calculated pair-wise
G
ST
[
104
]
and
D
Jost
[
79
] with confidence intervals based on 1000
bootstraps.
D
Jost
differs from both
F
ST
and
G
ST
in that it
is a measure of the fraction of allelic variation among
populations and is not constrained by the expected level
of heterozygosity within the subpopulation [
79
]. Signifi-
cance of correlation between pair-wise differentiation
measures for each dataset was evaluated with the Mantel
p-value as calculated with the ‘vegan’ R package [
105
].
Analysis of clustering
We compared the identification of distinct units using
microsatellites, all SNPs, putatively neutral SNPs, and
putatively adaptive SNPs. First, we performed discrimin-
nant analysis of principal components (DAPC) with
microsatellites, putatively neutral SNPs, all SNPs, and
candidate adaptive loci with the
‘adegenet’ package in R
[
106
]. DAPC summarizes genotypes in principal compo-
nents (PC) that are then used to construct linear func-
tions
that
simultaneously
maximize
among-cluster
Zimmerman
et al. BMC Genomics (2020) 21:382
Page 13 of 16
variation and minimize within cluster variation. We used
the
K-means clustering algorithm and identified the
number of genetic clusters based on the Bayesian Infor-
mation Criterion (BIC). We retained all of the PCs, ran
the algorithm for 100,000 iterations, and used 10 starting
centroids per run. The number of genetic clusters (
K)
with the lowest BIC was selected, as recommended by
Jombart et al. [
107
]. After we identified optimal
K for
each dataset, we used the a-score method to identify the
optimal number of PCs to retain in DAPC while con-
structing linear functions to describe genetic differenti-
ation among
K groups. Second, we created dendrograms
from an individual-based genetic distance matrix calcu-
lated as the proportion of differing nucleotide sites
[
108
], excluding missing data in pair-wise estimations,
with 1000 bootstraps for each dataset. We used the hier-
archical clustering algorithm
hclust in R and the “ward.
D2
” method [
109
]. The
“ward. D2” method minimizes
the total within cluster variance and minimizes informa-
tion loss associated with each cluster. For comparison of
hierarchical clustering methods we also included den-
drograms created with a more conservative method
tending to form loose groups, sometimes prematurely
(
“single” method; Additional file
1
: Fig. S1) and a more
relaxed method tending to form tighter and smaller
groups (
“complete” method; Additional file
1
: Fig. S2).
For comparison, results for clustering analyses with a re-
duced microsatellite dataset using only individuals in the
SNP dataset are included in the supplemental materials
(Additional file
1
: Fig. S3 and Fig. S4).
Supplementary information
Supplementary information accompanies this paper at
https://doi.org/10.
1186/s12864-020-06783-9
.
Additional file 1: Table S1. Diversity statistics. Table S2. Differentiation
statistics. Fig. S1. Dendrograms created using the
“single” (based on
closest pair) method. Fig. S2. Dendrograms created using the
“complete” (based on furthest pair) method. Fig. S3. Dendrograms
created using microsatellite loci from the 60 individuals included in the
SNP dataset. Fig. S4. Discriminant analysis of principle components
(DAPC) for microsatellite from the 60 individuals sampled for SNPs.
Abbreviations
BIC:
Bayesian information criterion; CM: Cimarron; CR: Crawford;
DAPC: Discriminant analysis of principal components; DC: Dove Creek;
DNA: Deoxyribonucleic acid; ESA: Endangered species act; ESU: Evolutionarily
significant unit; FSU: Functionally significant unit; GB: Gunnison Basin;
MSAT: Microsatellite; PC: Principal component; PCR: Polymerase chain
reaction; PM: Piñon Mesa; SM: San Miguel Basin; SNP: Single nucleotide
polymorphism
Acknowledgements
The data used in this manuscript were collected by many entities for
previous projects. We would like to acknowledge the groups that provided
the original samples explicitly: Colorado Parks and Wildlife (especially Dr.
Anthony D. Apa), the National Park Service, the Bureau of Land Management,
the U.S. Forest Service, volunteers from Western Colorado University, the U.S.
Geological Survey, and Colorado State University. We would also like to
thank those who helped in the review process: Drs. M.P. Miller, W.C. Funk,
M.B. Hooten, and 3 anonymous reviwers. Any use of trade, firm, or product
names is for descriptive purposes only and does not imply en- dorsement
by the U.S. Government.
Authors
’ contributions
SJZ, SJOM and CLA designed the study. SJZ performed analyses. All authors
contributed to the manuscript. All authors have read and approved the
manuscript.
Funding
Not applicable.
Availability of data and materials
The microsatellite dataset is available in the U.S. Geological Survey Science
Base repository,
https://doi.org/10.5066/P920WO0Q
. Genomic sequencing
data for this study were deposited in GenBank (biosample accession
numbers: SAMN10844489-SAMN10844548) and SNP genotypes for this study
were deposited in the U.S. Geological Survey ScienceBase,
https://doi.org/10.
5066/P94ET592
.
Ethics approval and consent to participate
Not applicable. Samples used here were obtained for previously published
studies from birds that had been either killed by hunters or trapped under
Colorado Division of Wildlife IACUC protocols [
69
,
73
].
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 27 November 2019 Accepted: 14 May 2020
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