Bombus RAD tag Diversity Supplemental Material
J. Lozier
Supplementary Material
Table S1 Sample locations for individuals used for RAD tag sequencing
Species
County
State
Latitude
Longitude
Sequence Reads (106)
B. impatiens
Bibb
Alabama
33.05
-87.01
2.57
Washington
Arkansas
35.82
-94.16
5.11
Hartford
Connecticut
41.77
-72.90
1.74
Crawford
Iowa
41.99
-95.39
4.00
Ogle
Illinois
41.98
-89.36
4.10
Peoria
Illinois
40.83
-89.80
7.42
Montgomery
Indiana
39.96
-87.07
3.00
Ripley
Indiana
39.07
-85.44
6.63
Ossage
Kansas
38.64
-95.60
3.31
McCracken
Kentucky
37.03
-88.76
3.08
Franklin
Kentucky
38.16
-84.94
3.24
Winona
Minnesota
43.98
-91.43
6.13
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Bombus RAD tag Diversity Supplemental Material
B. pensylvanicus
J. Lozier
Franklin
Missouri
38.48
-90.82
16.6
Stokes
North Carolina
36.47
-80.39
1.58
Seneca
New York
42.68
-76.85
4.31
Belmont
Ohio
40.00
-81.14
4.73
Cameron
Pennsylvania
41.40
-78.03
3.80
Kershaw
South Carolina
34.16
-80.57
1.33
Cocke
Tennessee
35.92
-82.98
5.09
Appomattox
Virginia
37.26
-78.68
1.13
Windsor
Vermont
43.41
-72.71
3.62
Dane
Wisconsin
43.04
-89.43
3.17
Bibb
Alabama
33.05
-87.01
2.40
Prowers
Colorado
38.11
-102.31
7.65
Columbia
Florida
29.89
-82.67
7.02
Crawford
Iowa
41.99
-95.39
2.95
Union
Illinois
37.45
-89.12
8.27
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Piatt
Illinois
40.01
-88.64
7.88
Cherokee
Kansas
37.34
-94.83
5.23
Saline
Kansas
38.70
-97.43
6.80
Bossier
Louisiana
32.53
-93.68
3.32
Lawrence
Missouri
36.96
-93.68
7.24
Boone
Missouri
38.99
-92.52
4.17
Franklin
Missouri
38.48
-90.82
7.27
Clay
Mississippi
33.55
-88.64
8.02
Lenoir
North Carolina
35.29
-77.79
6.90
Howard
Nebraska
41.13
-98.55
1.72
Wayne
Ohio
40.91
-81.98
5.61
Cleveland
Oklahoma
35.25
-97.27
6.72
Marion
South Carolina
34.23
-79.15
4.47
Jasper
South Carolina
32.59
-81.21
5.88
Stanley
South Dakota
44.29
-100.33
2.41
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Bastrop
Texas
30.35
-97.38
7.69
Eastland
Texas
32.10
-98.96
5.08
Greensville
Virginia
36.64
-77.56
3.83
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Impacts of read depth variation
B. pensylvanicus RAD tags received, on average, higher sequencing coverage than B. impatiens
RAD tags (in part due to the greater number of RAD tags in B. impatiens). Although sequencing
depth in both species is very high (average >100x reads per site per bee; Fig. S1), which should
facilitate accurate calling of heterozygous sites, I aimed to examine how this difference might
bias diversity estimates. I filtered the SNP set to only consider those SNPs that received 125200X read depth, a region of substantial coverage overlap for the two species (Fig S1). I focused
on comparing data sets for Filter Set 4 (20 individuals per species with the highest sequencing
coverage; maximum of 5% missing data per SNP) to avoid sample size differences. Overall
diversity estimates were slightly reduced in both species by this standardization, but SNP
(heterozygosity per SNP) was similar for the two species, and RAD (average nucleotide diversity
per entire RAD tag) was slightly higher in B. pensylvanicus, as in the full data set (Table S1).
The site frequency spectra are also highly similar for the two data sets, with a slight increase in
the proportions of low-frequency alleles in both species (Fig S2), explaining the slightly lower
nucleotide diversity estimates. Thus, the relative comparison is not altered by limiting the
analysis to SNPs with similar coverage.
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Fig. S1 Average sequencing coverage per single nucleotide polymorphism per individual (a) and number
of single nucleotide polymorphisms per RAD tag locus (b) following initial filtering (filter set 1).
Invariant RAD tag loci not shown.
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Table S2 Parameters estimated by filtering Filter Set 4 to mean SNP coverage of
125-200X per SNP per individual
Mean
read
depth
per SNP
SNP
No.
No SNPs
per Ind
SNP
SE
RADtags
RAD
RAD SE
B imp
3029
158.508
0.133
0.002
1932
0.0023
0.000055
B pen
2106
165.093
0.136
0.003
1211
0.0026
0.000078
0.7
impfil4
Proportion of SNPs
0.6
penfil4
0.5
impfil4_125-200x
0.4
penfil4_125-200x
0.3
0.2
0.1
0.
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Minor Allele Frequency
Figure S2 SNP minor allele frequency histograms with the “125-200X per site per individual read depth”
filter [shows both the original ‘filter set 4’ data (solid colors), and for filter set 4 using the depth cutoff of
125-200x (solid+hatch)].
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Simulations of Diversity Under Population Declines
Coalescent simulations were conducted in SIMCOAL 2.1.2. A historical population size of
50,000 haplodiploid individuals (= 75,000 chromosomes) was assumed, which provides a per
site  = 0.0015 with  = 1 x 10-8 per site per generation in a stable population, similar to that seen
in the present sequence data (see Results). A microsatellite  = 5 x 10-5 per generation was
applied, selected to produce a mean gene diversity of ~0.75 for a stable population. A series of
instantaneous bottleneck sizes was considered starting 75 generations before present. 100
replicates of either ten microsatellite loci or 10,000 90 bp DNA sequence loci (no intralocus
recombination, 3:1 Ts/Tv ratio) were simulated for each scenario and mean microsatellite
heterozygosity or per-site nucleotide diversity for each replicate were analyzed in ARLEQUIN
(references in main text).
Contemporary eastern Bombus populations are essentially unstructured, with near-zero
FST, D, no clustering in Bayesian approaches like STRUCTURE, and essentially no isolation by
distance. For an unstructured population with starting diversity similar to B. impatiens (stable),
results show that massive bottlenecks to 50-500 individuals would be needed to reduce
microsatellite heterozygosity by ~20%, the proportional difference currently observed between
B. pensylvanicus and B. impatiens whether individual populations are examined, or a pool of
single individuals from separate populations (see main text). Per site nucleotide diversities are
smaller than multilocus heterozygosity estimates and thus the absolute difference in diversity is
smaller, but the proportional loss in diversity is similar for RAD tag-like data (Fig. S3). Also
note that the RAD-tag like data had less variability among replicates, with no overlap in
confidence regions among scenarios, as expected given the far greater number of loci (Fig. S3).
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Thus RAD-tags should be capable of resolving differences in diversity due to demographic
effects of sufficient impact to alter microsatellite gene diversity.
Table S3 Coalescent simulation parameter summary
Locus Type
Demography
Ancestral Ne
(haplodiploids)
Current
Ne
Msat
Msat
Msat
Msat
90bp sequence
(RAD-like)
90bp sequence
(RAD-like)
90bp sequence
(RAD-like)
Stable
Bneck-75 gen
bp
Bneck-75 gen
bp
Bneck-75 gen
bp
Stable
50,000
50,000
50,000
5,000
Sample
Size
(diploids)
20
20
Independent
Loci
Mutation
Rate
10
10
5.00E-05
5.00E-05
50,000
500
20
10
5.00E-05
50,000
50
20
10
5.00E-05
50,000
50,000
20
10,000
1.00E-08
Bneck-75 gen
bp
Bneck-75 gen
bp
50,000
500
20
10,000
1.00E-08
50,000
50
20
10,000
1.00E-08
Figure S3 Box plots of mean diversity from 100 simulation replicates per scenario for microsatellite
(left) and RADtag-like (right) data under a range of bottlenecks from an ancestral Ne = 50,000
haplodiploids. See Table S3 for simulation parameters. Note that based on microsatellite simulation
results, the 5,000 Ne bottleneck was not performed for the more computationally intensive RADtag-like
data as differences from the stable population simulation should be small.
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Quality checks to ensure integrity of sample data
One concern with RAD tag and other barcoded NGS library approaches is the potential for
laboratory or computational errors to result in erroneous data sets. Such errors can be difficult to
detect because of the massive amounts of data produced by these methods prohibit visual
screening of samples for inconsistencies. Of particular interest in the present study is the
potential for barcode-swaps during library preparation or demultiplexing to result in mixed data
sets, which would corrupt any between species comparisons. I took advantage of the
evolutionary distance between B. impatiens and B. pensylvanicus to check the integrity of
sequence data within each species. For this analysis, I took the two data sets reported in the study
(B. imp aligned against the B. imp RAD reference and B. pen aligned against the B. pen RAD
reference) and made use of alignments for both B. impatiens and B. pensylvanicus against a
common B. impatiens reference (not otherwise discussed in the main paper) using Bowtie and
filtered as for Filter Set 1 (see Methods). The latter analysis produces data of a characteristic
form: a large and nearly identical fraction (~45%) of the called SNPs are fixed for the alternate
allele state across all B. pensylvanicus individuals, as one might expect for such deeply divergent
lineages (Figure S4), while no B. impatiens individuals show this pattern.
Propor on of Sites Fixed for
non B. impa ens reference
allele (alt allele)
0.5
0.45
0.4
0.35
0.3
0.25
0.2
impa ens
pensylvanicus
0.15
0.1
0.05
0
Figure S4 Proportion of fixed non-B. impatiens-reference alleles when reads from individuals in both
species are simultaneously aligned against a B. impatiens reference. Each point = 1 individual.
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A PERL script by C. Bergey (https://code.google.com/p/vcf-tab-to-fasta/) was then used
to convert the vcf files to fasta format (missing data = -, heterozygotes = IUPAC ambiguities) as
a second approach to detect outlier individuals. MEGA 5.2.2 (Tamura et al., 2011) was used to
construct neighbor-joining trees, deleting missing data and calculating branch lengths as the
number of differences. Figure S5 shows the resulting trees. As expected for very-low FST species,
the data sets used in the main paper show unstructured star-like trees with no geographical
patterns apparent and small numbers of differences separating individuals. The lower tree shows
an analysis with both species aligned to the B. impatiens reference and shows a large divergence
of several thousand fixed base differences between the two species, and no individuals sorting
incorrectly among species.
Such patterns suggest no major errors in read identity across samples consistent with
barcode swaps. Subsequent errors in post-alignment processing of BAM files could be detected
by comparing VCF SNP call files with those produced by independent analysis of the original
alignment files with ANGSD. Overall, similar sites were called as variable in both sets, and
comparison of allele frequencies in the two analyses show comparable nucleotide diversities
(main text). Differences appear mostly due to the larger number of regions including in the
ANGSD analysis, and a minor differences for sites that were filtered out of the VCF-formatted
genotyping data (e.g., due to low coverage, which ANGSD allows and takes into account), or
due to the fact that ANGSD is estimating nucleotide diversities by taking into account multiple
factors (sequence depth, quality, etc), and not calculating them solely from allele counts. These
checks suggest that nothing erroneous occurred during post-alignment processing of bam files.
The vcf files for the two species, the per site ANGSD diversity output, and original alignments,
are located on DRYAD. Based on these data checks, incorrect pooling of data among samples
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and species would also presumably result in larger numbers of SNPs per RAD tag, as well as
elevated intermediate allele frequencies in the site frequency spectra, rather than the typical “Lshaped” distributions observed here. Together, these analyses suggest that the present RAD tag
data contain no major contamination errors that would affect inferences.
Figure S5 NJ trees for RAD tag reads aligned to within species (top) and for both species together
aligned against the B. impatiens reference (bottom). Scale provided as number of differences.
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Tamura K, Peterson D, Peterson N et al. (2011) MEGA5: molecular evolutionary genetics analysis using maximum
likelihood, evolutionary distance, and maximum parsimony methods. Molecular Biology and Evolution, 28,
2731–9.
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