Supplementary Material online
Supplementary file S2. Pooled PCR protocol.
Description
Validation
Raw data
Description
DNA extractions were performed from one hind-leg of each one-day-old offspring using the
DNeasy 96 tissue kit (QIAGEN). DNA quantities were assessed with a Nanodrop ND-1000
spectrophotometer (Nanodrop Technologies). We pooled DNA samples of 4 siblings (for a
total of 437 pools) systematically adjusting their respective proportion in the pool above 0.125
(i.e., 1/8th) to ensure detectability of all alleles (see below). Microsatellite loci were then
genotyped in PCR multiplex assays, using a multilocus amplification kit (QIAGEN),
fluorescently labelled PCR primers and an ABI 3130 DNA sequencer (Applied Biosystems),
as described in Blondin et al. (2013). Electrophoresis profiles were read independently by
three of the authors. If one or more reader suspected a mutation event in a pool, it was
confirmed or invalidated by amplifying each of the 4 DNA samples of the pool in four
separate PCRs.
A PCR pool design is sensitive to allelic drop-outs, i.e. the non-scoring of a genuine
allele or mutation, and may therefore reduce the detection of true positives. Possible causes of
microsatellite allelic drop-outs include (i) the preferential amplification of short alleles
associated with inconsistent DNA template quantity or (ii) polymerase slippage during PCR
amplification leading to reproducible artifact peaks preceding the true allele peak called
stutter peaks (Gagneux et al. 1997; Shinde et al. 2003). In a heterozygous sample, stutter
peaks from one allele may overlap other adjacent alleles and lead to the scoring of the longer
allele only. Consequently, we investigated for false negatives due to PCR amplification in a
pool in our focal pedigree data. To this aim, we performed separate PCR amplifications on
single DNA templates for 6% of the electrophoresis profiles in which no mutation events had
been suspected. All these independent PCR amplifications were confirmed to be negative.
More importantly, we constructed, in a preliminary study, artificial mutant tests to assess the
mutation detection ability in a pool of 4 DNA samples for 27 microsatellite loci.
Validation
We pooled DNA extracts of siblings whose genotype was known and mimicked the presence
of a mutant allele by selecting an unrelated individual which introduced in the mixture of
alleles present a single copy of a foreign allele. We maximized difficult pooling cases with
artificial mutant alleles that were long or in the stutter of other alleles. We analysed 328
mutant situations in total, and each was repeated twice. Three of the authors, with a thorough
knowledge of patterns typical of each study microsatellite marker, blind-read the
electrophoresis profiles (Blondin et al. 2013; Chapuis et al. 2014). False additions of an extra
allele in the correct genotypes (i.e. false positives) were of little concern because they would
have been rebutted by the further separate amplification of each of the 4 DNA samples in
independent PCRs. We therefore assessed the mutation detection failures by counting the
number of artificial mutant alleles that were undetected by any one of the three readers.
This preliminary offspring-parent segregation data revealed the existence of a null
allele for the marker DL06 only that was undetected in Mauritanian natural populations
(Blondin et al. 2013). We also showed that two loci (DL01 and DL09) exhibited
electrophoresis profiles from which all three readers failed to detect the artificial mutant allele
in 1/5th of cases. Since this threatened to seriously underestimate the mutation rate for these
three markers, we excluded them from our study, which thus consisted in analyses of 24
microsatellite loci.
Out of the 288 tests of validity of the pooled PCR protocol, we failed to detect 16
artificial mutant alleles, all at an observed initial proportion that was in the lower 10% of the
distribution (supplementary figure S2-1). There were 27 successful tests in this first decile,
and the total rate of allelic drop-outs was 16/(27+16)=37%. The decile value of the initial
proportion of the artificial mutant allele in the DNA pools was 0.0626, which corresponded to
half the value expected if DNA yield had been consistent across the 4 pooled DNA extraction
reactions.
Figure S2-1. Distributions of the proportion of a single copy of gene in DNA pools in our
validation and pedigree studies.
Black curve: density distribution of the proportion of the artificial mutant allele in DNA pools constructed for the
validation study (and in purple the 16 undetected mutants and first percentile). Histograms: proportion of a single
copy of gene in DNA pools constructed for the pedigree study after (filled) and before (outlined) adjustment of
quantities of single DNA extracts (see text for more details).
We also tried to identify the factors responsible for most mutation detection failures in
our assay. The factors considered were 1) the risk of poor amplification of alleles from DNA
template in low relative quantity (due to inconsistent DNA extraction efficiency), 2) the
preferential amplification of short alleles, and 3) the risk of misinterpretation of
electrophoresis pattern due to stutter peaks (i.e. scoring the longer allele only in samples
heterozygous for adjacent alleles). To this effect, we ran a linear mixed model analysis on the
relative peak intensity, measured as the peak area of the artificial mutant allele divided by the
sum of areas of all alleles. Relative peak intensity proved to be highly repeatable between
PCR runs (R2 = 0.95; supplementary figure S2-2). The square-transformation of relative peak
intensities yields the best fit to the normality hypothesis among the Box-Cox families of
transformations (P=0.08 for Shapiro-Wilk test for normal distributions of residuals).
Figure S2-2. Regression of the relative peak intensity of the artificial mutant allele between
replicate PCR runs.
In addition to the random factors 'PCR run' and 'Locus', we considered three fixed
factors: 'Initial proportion of the artificial mutant allele', 'Number of shorter alleles' and
'Number of bases from the first longer allele'. Initial proportion of the artificial mutant allele
was computed as the ratio of half the quantity of the DNA sample from which the allele was
derived to the sum of quantities of the four DNA samples of its pool (pipetting errors were
considered negligible). We selected factors affecting relative peak intensity by applying a
backward stepwise variable selection starting by fitting the full model (and using ANOVA
tests on the candidate linear mixed models). We used R system v. 3.1.0 (R Core Team 2013)
and the package lme4 to conduct analyses.
The linear mixed model showed a significant effect of the initial proportion of the
artificial mutant allele on its relative peak intensity (P < 0.001; supplementary figure S2-3).
Since the mutation detection ability dropped when the initial proportion in the DNA pool was
below 0.0626, we systematically measured and adjusted the quantity of DNA samples so that
all eight allelic copies of loci were above this 10% threshold in initial proportion in the
subsequent pools.
Figure S2-3. Relative peak intensity of the artificial mutant allele as a function of its initial
proportion in pooled PCR tests.
The 16 undetected mutants are shown in purple.
Relative peak intensity was also affected by the number of shorter alleles (P < 0.001)
without significant interaction with the initial proportion of the foreign allele (P = 0.31).
Moreover, the 16 allele drop-outs were randomly distributed across numbers of shorter
alleles, which suggests that the effect of a large number of shorter alleles alone is insufficient
to decrease our mutation detection ability (supplementary figure S2-4). In addition, despite
the potential difficulty in identifying mutant alleles in stutter peaks, we did not detect an
effect of the number of bases from the first longer allele (P = 1), i.e. a bias towards not
detecting deletions.
Figure S2-4. Box plot of the relative peak intensity of the foreign allele as a function of the
number of shorter alleles.
The whiskers extend 1.5 times the interquartile range out from the box. The 16 undetected mutants are shown in
purple.
References
Blondin L, Badisco L, Pagès C, Foucart A, Risterucci A-M, Bazelet CS, Vanden
Broeck J, Song H, Ould Ely S, Chapuis M-P. 2013. Characterization and comparison of
microsatellite markers derived from genomic and expressed libraries for the desert locust. J
Appl Entomol. 137:673-683.
Chapuis M-P, Plantamp C, Blondin L, Pagès C, Vassal J-M, Lecoq M. 2014.
Demographic processes shaping genetic variation of the solitarious phase of the desert locust.
Mol Ecol. 23:1749-1763.
Gagneux P, Boesch C, Woodruff DS. 1997. Microsatellite scoring errors associated
with noninvasive genotyping based on nuclear DNA amplified from shed hair. Mol Ecol.
6:861–868.
R Core Team. 2013. R: A language and environment for statistical computing. Vienna:
R Foundation for Statistical Computing.
Shinde D, Lai YL, Sun FZ, Arnheim N. 2003. Taq DNA polymerase slippage mutation
rates measured by PCR and quasilikelihood analysis: (CA/GT)(n) and (A/T)(n)
microsatellites. Nucleic Acids Res. 31:974–980.
Raw data: numbers of typed offspring and detected mutation events per
family and electrophoresis profiles of parents and offspring for each
mutation.
Table S2-1. Numbers of offspring and mutations per family.
Family
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
# pools of offspring
3
19
36
26
15
26
11
28
26
22
2
14
31
14
3
2
8
4
1
14
29
12
27
26
9
31
# offspring
12
76
144
104
60
104
44
112
104
88
8
56
124
56
12
8
32
16
4
56
116
48
108
104
36
124
# mutations [locus name]
0
0
1 [Sgr36]
1 [SgM96]
0
1 [diEST-5]
0
0
2 [SgM74; Sgr53]
0
0
0
2 [SgM51;SgM74]
1 [diEST-5]
0
0
1 [SgM96]
0
0
0
1 [diEST-5]
0
0
0
0
0
In the following pages, mutation events are identified using locus name and mutant size (in
square brackets). The mutant allele is shown by a yellow arrow both in the pool of 4 offspring
and in the single mutant offspring.