In vitro vs in silico detected SNPs for the development of a genotyping array: what can we learn from a non-model species?

Abstract

There is considerable interest in the high-throughput discovery and genotyping of single nucleotide polymorphisms (SNPs) to accelerate genetic mapping and enable association studies. This study provides an assessment of EST-derived and resequencing-derived SNP quality in maritime pine (Pinus pinaster Ait.), a conifer characterized by a huge genome size ( approximately 23.8 Gb/C).
A 384-SNPs GoldenGate genotyping array was built from i/ 184 SNPs originally detected in a set of 40 re-sequenced candidate genes (in vitro SNPs), chosen on the basis of functionality scores, presence of neighboring polymorphisms, minor allele frequencies and linkage disequilibrium and ii/ 200 SNPs screened from ESTs (in silico SNPs) selected based on the number of ESTs used for SNP detection, the SNP minor allele frequency and the quality of SNP flanking sequences. The global success rate of the assay was 66.9%, and a conversion rate (considering only polymorphic SNPs) of 51% was achieved. In vitro SNPs showed significantly higher genotyping-success and conversion rates than in silico SNPs (+11.5% and +18.5%, respectively). The reproducibility was 100%, and the genotyping error rate very low (0.54%, dropping down to 0.06% when removing four SNPs showing elevated error rates).
This study demonstrates that ESTs provide a resource for SNP identification in non-model species, which do not require any additional bench work and little bio-informatics analysis. However, the time and cost benefits of in silico SNPs are counterbalanced by a lower conversion rate than in vitro SNPs. This drawback is acceptable for population-based experiments, but could be dramatic in experiments involving samples from narrow genetic backgrounds. In addition, we showed that both the visual inspection of genotyping clusters and the estimation of a per SNP error rate should help identify markers that are not suitable to the GoldenGate technology in species characterized by a large and complex genome.

Full text

Page 1

In Vitro vs In Silico Detected SNPs for the Development
of a Genotyping Array: What Can We Learn from a Non-
Model Species?
Camille Lepoittevin1,2,3*, Jean-Marc Frigerio1,2, Pauline Garnier-Ge ´re ´1,2, Franck Salin1,2, Marı ´a-Teresa
Cervera4, Barbara Vornam5, Luc Harvengt3, Christophe Plomion1,2
1INRA, UMR1202 BIOGECO, Cestas, France, 2Universite ´ de Bordeaux, UMR1202 BIOGECO, Talence, France, 3FCBA, Laboratoire de Biotechnologies, Nangis, France, 4INIA,
Departamento de Ecologı ´a y Gene ´tica Forestal, Madrid, Spain, 5University of Goettingen, Goettingen, Germany
Abstract
Background: There is considerable interest in the high-throughput discovery and genotyping of single nucleotide
polymorphisms (SNPs) to accelerate genetic mapping and enable association studies. This study provides an assessment of
EST-derived and resequencing-derived SNP quality in maritime pine (Pinus pinaster Ait.), a conifer characterized by a huge
genome size (,23.8 Gb/C).
Methodology/Principal Findings: A 384-SNPs GoldenGate genotyping array was built from i/ 184 SNPs originally detected
in a set of 40 re-sequenced candidate genes (in vitro SNPs), chosen on the basis of functionality scores, presence of
neighboring polymorphisms, minor allele frequencies and linkage disequilibrium and ii/ 200 SNPs screened from ESTs (in
silico SNPs) selected based on the number of ESTs used for SNP detection, the SNP minor allele frequency and the quality of
SNP flanking sequences. The global success rate of the assay was 66.9%, and a conversion rate (considering only
polymorphic SNPs) of 51% was achieved. In vitro SNPs showed significantly higher genotyping-success and conversion rates
than in silico SNPs (+11.5% and +18.5%, respectively). The reproducibility was 100%, and the genotyping error rate very low
(0.54%, dropping down to 0.06% when removing four SNPs showing elevated error rates).
Conclusions/Significance: This study demonstrates that ESTs provide a resource for SNP identification in non-model
species, which do not require any additional bench work and little bio-informatics analysis. However, the time and cost
benefits of in silico SNPs are counterbalanced by a lower conversion rate than in vitro SNPs. This drawback is acceptable for
population-based experiments, but could be dramatic in experiments involving samples from narrow genetic backgrounds.
In addition, we showed that both the visual inspection of genotyping clusters and the estimation of a per SNP error rate
should help identify markers that are not suitable to the GoldenGate technology in species characterized by a large and
complex genome.
Citation: Lepoittevin C, Frigerio J-M, Garnier-Ge ´re ´ P, Salin F, Cervera M-T, et al. (2010) In Vitro vs In Silico Detected SNPs for the Development of a Genotyping
Array: What Can We Learn from a Non-Model Species? PLoS ONE 5(6): e11034. doi:10.1371/journal.pone.0011034
Editor: Pa ¨r K. Ingvarsson, University of Umea ˚, Sweden
Received February 15, 2010; Accepted May 19, 2010; Published June 9, 2010
Copyright: ? 2010 Lepoittevin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by grants from Agence Nationale de la Recherche Ge ´noplante (GenoQB project, GNP05013C) and Agence Nationale de la
Recherche Plates-Formes Technologiques du Vivant (BOOST-SNP project, 07PFTV002), the Aquitaine Region (20061201004PFM) and the European Union
(NOVELTREE project, FP7-211868). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: camille.lepoittevin@pierroton.inra.fr
Introduction
In the last few years, the development of high-throughput
methods for the detection and genotyping of single nucleotide
polymorphisms (SNPs) has led to a revolution in their use as
molecular markers [1]. Their abundance in animal and plant
genomes, the reduction in cost and the increased throughput of
SNP assays have made these markers attractive for high-resolution
genetic mapping, fine mapping of QTLs, linkage-disequilibrium
based association mapping, genetic diversity analyses, genotype
identification, marker-assisted selection and characterization of
genetic resources [2,3,4,5,6,7].
In non-model species, large scale SNP genotyping involves two
main steps: first the discovery of polymorphisms, and second the
genotyping of a set of specimens. SNP identification can proceed
either from in vitro or in silico approaches. In vitro methods, such as
the re-sequencing of targeted amplicons, are generally more
appropriate when sequence data is limited or when one is
interested in polymorphisms in specific genotypes or candidate
genes. This approach is generally costly and time consuming, but
has been proven successful to detect SNPs in many organisms
(reviewed by [8]). In contrast, in silico discovery is the most obvious
method for de novo SNP identification. Although this approach
mainly provides markers located in transcribed regions (mostly
coding and 39UTR), it offers a low cost source of abundant SNPs
and has been validated by large scale genotyping for a number of
plant species including Arabidopsis [9], maize [10], grapevine [11],
melon [12], tomato [13], spruce [14] or pine [15]. However, the
usefulness of EST resources for detecting in silico SNPs varies
depending on the assembly depth, the range of tissues considered,
PLoS ONE | www.plosone.org1 June 2010 | Volume 5 | Issue 6 | e11034

Page 2

the diversity of the target species, but also on how well this
diversity is represented within the database [2,16,17]. The number
of in silico SNPs available will thus differ considerably between
species, although a global trend towards more SNPs for more
ESTs from different tissues is expected for species with similar
diversity. For example, about 9,000 high quality SNPs were
detected in a first catfish assembly comprising 54,960 ESTs [18];
this number extended to 48,000 when using a second assembly of
nearly 500,000 ESTs [19]. EST resources can also be very useful
for closely related species when the assembly is performed with all
ESTs together, since detection of interspecific in silico SNPs is then
possible, as shown by WANG et al. [19] for blue and channel catfish
species.
There is no one ideal method for SNP genotyping and the
selection of an appropriate technique largely depends on many
factors including cost, accuracy, multiplexing capacity and
throughput, equipment and difficulty of assay development
[20,21]. A range of high-throughput methods are currently
developed for model species such as humans, but their use in
non-model species with large genome size, high level of ploidy
or redundancy is often a challenge [22]. Recently, PAVY et al.
[23] and ECKERT et al. [24] achieved the multiplexed genotyping
of hundreds of SNPs in conifers, a group of plants that is
characterized by a large genome size [25]. They used the
Illumina bead array platform combined with GoldenGate assay
[26,27]. This genotyping platform was also successfully used for
genomes containing a high number of paralogous genes such as
barley [28], soybean [29] or tetraploid and hexaploid wheat
[30].
Maritime pine (Pinus pinaster Ait.) genome is extremely large (up
to 23.8 Gb/C, which is 150 times larger than that of Arabidopsis
thaliana) [25]. Despite the economical and ecological importance of
this species in south-western Europe, where it covers over 4M ha,
it will be many years before its full genome sequence is available.
However, about 30,000 P. pinaster expressed sequence tags (ESTs)
were produced in the past decade, followed by the re-sequencing
of more than 40 wood-quality and drought-stress related candidate
genes [31,32]. We report here the valorization of these resources
to the first highly multiplexed SNP genotyping array in P. pinaster.
Our objectives were three-fold: i/ validate a number of SNPs for
future linkage mapping and candidate-gene-based association
studies, and ii/ compare the conversion rate of SNPs derived from
in vitro versus in silico datasets, as to our knowledge no other study in
conifers has attempted to genotype a large number of in silico SNPs
without preliminary re-sequencing, and iii/ estimate the genotyp-
ing error rate of the GoldenGate technology for a conifer genome,
which has not been reported so far. The SNPs validated in this
study have been made available through the NCBI database
(http://www.ncbi.nlm.nih.gov/SNP, see Table S1 for accession
numbers).
Materials and Methods
Plant material
Plant material consisted of 456 individuals, including: 212
unrelated trees resulting from mass selection in the natural forest of
south-western France (first-generation breeding population, re-
ferred as the ‘‘G0’’ Aquitaine population), 210 offspring resulting
from open-pollinated or controlled crosses among the G0 trees
(second-generation breeding population, referred as the ‘‘G1’’
Aquitaine population), 29 trees randomly sampled in the same
geographical area as the G0 trees, and 5 trees involved in two- and
three-generation outbreed pedigrees, used for linkage and QTL
mapping. DNA was extracted from needles using InvisorbH Spin
Plant Mini Kit (Invitek, Berlin, Germany), and quantified with a
Nanodrop ND-1000 spectrophotometer (NanoDrop Technolo-
gies, LLC, Wilmington, DEL, USA).
SNP discovery
For SNP discovery, two sets of sequences were considered. The
first dataset comprised maritime pine sequences for 41 different
genes involved in plant cell wall formation (candidate genes for
wood quality) or drought stress resistance (Table S2). For each
fragment, an average of 50 megagametophytes (haploid tissue
surrounding the embryo) from different populations were
sequenced. The chromatograms were visually checked (nucleotides
with phred scores below 20 were considered as missing data) and
the SNPs were considered as true. Indeed, the use of megagame-
tophytes lowered the risk of confusing polymorphism at a unique
locus with differences between paralogous loci, as amplification of
two or even more members of a gene family would have been
easily detected by the visualization of double peaks in the
chromatograms. This first set of SNPs will be referred to as in
vitro SNPs. The second sequence dataset consisted in a collection of
26,476 maritime pine ESTs assembled in 3,995 non-singleton
contigs and 7003 singletons (unigene available online at http://
cbi.labri.fr/outils/SAM2/COMPLETE/ under the project name
‘‘Pinus pinaster 14_02_2007’’). These ESTs were derived from six
different libraries constructed using different tissues, and a number
of segregating haploid genomes from 3 up to 300 from different
provenances (Table 1). We used the Polybayes software [33] to
detect SNPs with a high probability with the parameters described
for maritime pine in LE DANTEC et al. [15]. This second set of SNPs
will be referred to as in silico SNPs.
Table 1. cDNA library information.
Library Tissue Nb of haploid genomesMaritime pine provenanceNb of ESTs
GEMINI Xylem4 Corsica8,129
Normal aerial parts (AN)Needles 300Aquitaine240
Stressed aerial parts (AS) Needles300Aquitaine475
Normal roots (RN)Roots300Aquitaine4,592
Stressed roots (RS) Roots 300Aquitaine4,274
Buds (LG0ACA) BudsUnknownSpain8,766
TOTAL 26,476
doi:10.1371/journal.pone.0011034.t001
Genotyping Non-Model Species
PLoS ONE | www.plosone.org2June 2010 | Volume 5 | Issue 6 | e11034

Page 3

SNP selection for array construction
We developed a Perl script, snp2illumina, for automatically
extracting SNPs from multifasta sequence files and output them
as a SequenceList file compatible with the Illumina Assay
Design Tool software (available online at http://www.illumina.
com). This file contains the SNP names and surrounding
sequences with polymorphic loci indicated by IUPAC codes for
degenerated bases. The Perl script snp2illumina can work in
batch mode and is available upon request from the correspond-
ing author.
The functionality score provided by Assay Design Tool software
is similar to a predicted probability of genotyping success, taking
into account the sequence conformation around the SNP, the lack
of repetitive elements in the surrounding sequence, and in the case
of model species the sequence redundancy against the available
sequence database [34]. In the case of maritime pine, no sequence
database was available to test for sequence redundancy. All the
SNPs presenting a functionality score below 0.4, which is
considered as a lower limit for genotyping success by the
manufacturer, were discarded.
Two contrasted strategies ‘‘depth vs. breath of SNP coverage’’
were adopted to select informative SNPs. In respect to in vitro
SNPs, our objective was to include as many polymorphisms as
possible for each gene fragment so depth of coverage was
preferred. For in silico SNPs, our goal was to include a low
number of markers per unigene in a large number of unigenes,
thus giving more emphasis to breath of coverage. The main
technical constraint for selecting in vitro SNPs was that the selected
polymorphisms should not be less than 60 nucleotides away from
each other. When several SNPs stood within this limit it was
decided to filter out lowest frequency variants and polymorphisms
showing high level of linkage disequilibrium with other selected
SNPs of the same fragment. Rare variants (minor allele frequency
,5%) were also discarded. To select in silico SNPs we used the log-
file of the snp2illumina script that records for each SNP the number
of ESTs considered for the detection, the minor allele frequency
(MAF) and the PolyBayes score. To minimize the number of false
positives we included in the assay only SNPs with a PolyBayes
score above 99%, with either a minor allele appearing at least
twice within four to ten ESTs, or a MAF above 20% when more
than ten ESTs were available. Indeed, it is highly unlikely that
sequencing errors of two independently sequenced ESTs occur at
the same base location. We also excluded SNPs that were
surrounded by other polymorphisms in the immediate 60 bases to
avoid technical problems due to neighboring polymorphisms. In
both cases, chromatograms were visually checked to ensure the
quality of the flanking sequences, and we used BLASTN analysis
[35] to ensure that in vitro and in silico SNPs belonged to different
genes.
SNP genotyping array
The Illumina GoldenGate technology (Illumina Inc., San
Diego, CA, USA) was used to carry out the genotyping reactions
in accordance with the manufacturer’s protocol [36]. To assess
the reproducibility of the genotyping assay, 19 DNA samples
were duplicated across the different plates. Negative controls
were also added to each 96-well plate. Highly multiplexed
extension reactions were conducted using 250ng of template
DNA per sample. The clustering was realized with the
BeadStudio software (Illumina Inc.), and a quality score for each
genotype was generated. A GenCall score cutoff of 0.25 was used
to determine valid genotypes at each SNP and the SNPs retained
had to get a minimum GenTrain score of 0.25, which represents
a stringent criterion that is used in human genetic studies [27].
GenCall and GenTrain scores measure the reliability of SNP
detection based on the distribution of genotypic classes (AA, AB
and BB). Clusters were visually inspected to ensure high quality
data (Figure 1). When we observed cluster compression (i.e. when
the homozygous clusters normalized theta values were not in the
[0, 0.1] or [0.9, 1] ranges, as illustrated in Figure 1 B, C and D),
we considered that the genotyping failed, as this is likely due to
genome redundancy [29]. Indeed, the compression of the BB
homozygous cluster towards the AA cluster could result from a
paralog gene matching the A allele, increasing the signal for the A
dye for both BB and AB genotypes. We also considered as
genotyping failures monomorphic SNPs for which clusters could
be divided in two or more subgroups such as illustrated in
Figure 1E.
Measuring error rate using pedigree data
We used the breeding population pedigree information
(relationships between first and second generation) to detect
possible Mendelian Inconsistencies (MIs) between parents and
offspring using the PedCheck software [37]. Then, we used the
method described in SAUNDERS et al. [38] to estimate the
genotyping error rate P from MIs. Genotyping errors (GEs) are
not all detectable as MIs, but there is a linear relationship between
the GE and the MI counts has shown by HAO et al. [39]. The
expected number of MIs at a marker (P.PMI) in a family in which
one or both parents and m children have been genotyped can be
derived from the marker allele frequency p in the studied
population, m and P as follows [38]. If only one parent has been
genotyped:
PPMI~Pp p{1
ðÞ 2{ 1{1
2p{1
ðÞ
??m
{ 1{1
2p
??m
z1
2m
??
and if both parents have been genotyped
PPMI~ mz2
ðÞP{2P p2z 1{p
? ?m
{mPp 1 p
ð
ðÞ2z
?
ð
1
2
? ?m{1
p 1{p
ðÞ
(
z4
3
4
{
1
2
? ?m
Þ 3p2z4p 1 p
??
p21{p
ðÞ2
ðÞz3 1{p
Þ2
no
These relationships can be easily generalized to large non-inbred
pedigrees and many SNPs, by summation of P.PMI over all
families and averaging P over all SNPs. This procedure allows to
estimate a per SNP as well as a global genotyping error rate [38].
We performed this analysis on 17 unrelated families from the
breeding population, using for each marker the allele frequency
(p) estimated on the Aquitaine G0 genotyping dataset (212
samples).
Results
SNP detection and construction of the SNP array
A total of 448 in vitro SNPs were detected in the dataset of re-
sequenced fragments. Overall 155, 81 and 28 SNPs were
discarded because of low functionality scores, neighboring
polymorphisms, or because they corresponded to rare variants,
respectively. The 184 remaining SNPs included in the assay
represented 40 different gene fragments (Table S3).
Similarly, 9,364 in silico SNPs were detected in the unigene set,
and we selected 200 of them satisfying our very stringent criteria,
Genotyping Non-Model Species
PLoS ONE | www.plosone.org3 June 2010 | Volume 5 | Issue 6 | e11034

Page 4

i.e. PolyBayes and functionality scores, polymorphism proximity,
minimum number of ESTs for the detection, MAF and visual
validation of the chromatograms. They represented 146 different
unigene elements. Figure 2 shows the number of ESTs considered
for the detection of the 200 in silico SNPs.
Reproducibility and overall success rate of the SNP assay
No discordance was detected between the 19 replicated
samples, i.e. the same genotype was observed over the replicates,
yielding a reproducibility rate of 100%. For nine polymorphic
SNPs we observed cluster compression (as in Figure 1B), and for
Figure 1. Examples of clustering observed for the P. pinaster SNP array. Each dot represents the mean intensity derived from a population of
beads for a single sample. The normalized R (y axis) is the normalized sum of intensities of the two dyes (Cy3 and Cy5), and the normalized Theta (x
axis) is ((2/ )Tan21(Cy5/Cy3)), where a normalized Theta value nearest 0 is a homozygous for allele A and a Theta value nearest 1 is homozygous for
allele B. A/ classical pattern with three clusters for a SNP considered as successful and polymorphic. B and C/ ‘‘cluster compression’’ when both
homozygous clusters are closer to each other than expected. In panel B, the clustering algorithm is able to distinguish the three clusters and gives a
GenTrain score of 0.58, however this kind of pattern was considered as a genotyping failure in our analysis because one of the homozygous cluster
normalized Theta value does not fall in the [0, 0.1] or [0.9, 1] ranges. In panel C the clustering algorithm was not able to distinguish the three clusters
because of low separation scores, and the SNP was automatically considered as a genotyping failure because of its low GenTrain score. D and E/ SNPs
interpreted as genotyping failures either because of abnormal Theta values (D) or because of the presence of subgroups in a cluster (E).
doi:10.1371/journal.pone.0011034.g001
Genotyping Non-Model Species
PLoS ONE | www.plosone.org4June 2010 | Volume 5 | Issue 6 | e11034

Page 5

nine monomorphic SNPs we found either unexpected normalized
theta values, or subgroups in a homozygous cluster (as in Figure 1D
and 1E, respectively). In those cases we considered that the
genotyping failed despite acceptable GenTrain scores.
To measure the global success of the genotyping assay we first
estimated the success-rate, which corresponds to the number of
SNPs that are successfully genotyped (considering both mono-
morphic and polymorphic SNPs) divided by the total number of
SNPs in the assay, and second the conversion rate, which is the
number of polymorphic SNPs divided by the total number of
SNPs in the assay, as defined in FAN et al. [27]. Among the 384
SNPs analyzed, 257 were successfully genotyped (Table 2), leading
to a global success-rate of 66.9%. The minimum GenTrain score
observed for these SNPs was 0.53. A total of 60 SNPs were found
to be monomorphic in the tested samples, yielding a conversion
rate of 51% (Table 2).
The mean call rate, which is 1 minus the rate of missing data,
exceeded 98% at the SNP level and ranged from 73.4% to 93.5%
at the sample level for four of the five plates analyzed. It dropped
to 77.5% at the SNP level and ranged from 13.8% to 87.5% at the
sample level for the fifth plate where we noticed evaporation
problems during the genotyping reactions. We found significant
differences depending on the origin of the markers: in vitro SNPs
generally showed significantly higher genotyping-success and
conversion rates compared to in silico SNPs (+11.5% and
+18.5% with x2-test P-values of 0.025 and 4.73.1024, respectively).
The distribution of allelic frequencies for in vitro- and in silico
SNPs is shown in Figure 3. Among successfully genotyped SNPs,
monomorphic loci were twice more abundant for in silico SNPs
compared to in vitro SNPs (30.9% versus 16.4%, respectively). Most
of the 22 monomorphic in vitro SNPs corresponded to either SNPs
that were monomorphic in the Aquitaine sequences (10 SNPs),
rare variants (3 SNPs with a MAF below 5% in the Aquitaine
sequencing dataset), or were detected on alignments that did not
include any sequences from south-western France (3 SNPs).
Among the polymorphic SNPs, 35.7% of in vitro and 29.4% of in
silico SNPs corresponded to rare variants (MAF #10%) (Figure 3).
SNP success rate according to SNP functionality score
Prior to the construction of the SNP bead array, a functionality
score was calculated for each candidate SNP using the Illumina
Assay Design Tool. The higher the score, the more likely will the
SNP be successfully genotyped. We could not genotype any of the
five SNPs with functionality scores below 0.5, and only 13 of the
27 SNPs with functionality scores between 0.5 and 0.6 (Figure 4).
SNPs with a predicted functionality score above 0.6 had a much
higher success rate than those below 0.6 (x2-test P-value of 0.0019),
as found in PAVY et al. [23] for white and black spruce. This also
agrees with Illumina’s recommendations of using only SNPs with a
functionality score above 0.6 to ensure a high success rate for the
assay.
Comparison of allele frequency estimated by sequencing
and genotyping
Among the 112 polymorphic in vitro SNPs of the genotyping
assay, 101 were previously identified in alignments containing 10
sequences or more from the Aquitaine population and were used
to assess the reliability of allele frequency estimates based on
sequencing data. The correlation between marker allele frequen-
cies determined by sequencing and genotyping was ,0.83
(considering only the 212 unrelated samples from the Aquitaine
G0 breeding population) (Figure S1), showing that allelic
frequencies estimated by genotyping were generally in the range
of those estimated by sequencing.
Measuring genotyping error rate with pedigree data
For 84 and 81 offsprings of the G1 population, either one or
both parental G0 trees were genotyped in the assay. This dataset
consisted in 36,991 genotyping datapoints corresponding to 222
samples (165 G1 and 57 G0 trees) genotyped for 188 polymorphic
SNPs, after excluding 4,745 missing data. We found a total of 181
Mendelian Inconsistencies (MIs). Most of these errors (75%)
appeared in only nine parents-offspring pairs for which the MI
rate ranged from 4% to 17%, suggesting laboratory errors (either
traceability errors during the controlled pollination, plant material
sampling and handling, wet lab experiment, or DNA contamina-
tion) rather than genotyping errors. In six cases we assumed that
the MIs originated from the offspring genotypes as the parents
were involved in other crosses where no MI was found. For the
other cases we could not tell parents and offspring MIs apart.
Setting aside these possible human errors, 46 MIs were detected
for 35,521 genotyping datapoints. MIs were not significantly more
abundant for samples presenting low call rates (x2-test P-value of
0.51, see also Table S4). To estimate the genotyping error rate P,
we used a subset of 17 unrelated families corresponding to 75 G1
trees and their 26 G0 parents genotyped for 188 polymorphic
SNPs (18,261 genotyping datapoints after removing 727 missing
Figure 2. Distribution of the 200 in silico SNPs according to the
number of ESTs considered for the detection.
doi:10.1371/journal.pone.0011034.g002
Table 2. Success rate of the genotyping assay.
Category
Nb of SNPs
(in vitro/in silico)
% of SNPs
(in vitro/in silico)
Failed1
127 (50/77) 33% (27%/38.5%)
Monomorphic2
60 (22/38)16% (12%/19%)
Polymorphic3
197 (112/85) 51% (61%/42.5%)
Total384 (184/200)100% (100%/100%)
1Failed genotyping, i.e. GenTrain score ,0.25 or cluster compression.
2Genotyping successful but monomorphic SNPs.
3Genotyping successful and polymorphic SNP.
doi:10.1371/journal.pone.0011034.t002
Genotyping Non-Model Species
PLoS ONE | www.plosone.org5 June 2010 | Volume 5 | Issue 6 | e11034

Page 6

data). The observed MI count for this subset was 28, yielding a
global mean genotyping error rate P of 0.54%. At the SNP level, a
total of 181 SNPs showed no MIs and thus a null per SNP
genotyping error rate. Among the seven remaining markers, three
showed error rates ranging from 2.9% to 3.3%, and four (two in
vitro SNPs and two in silico SNPs) showed particularly elevated
error rates (P between 16% and 70%). In these cases (distribution
of error rates skewed owing to four SNPs with very high error
rates), the estimate of the mean error rate tends to be biased
upwards [38]. When removing these four SNPs, the observed MI
count dropped to 3, leading to a mean error rate P of 0.06%.
Discussion
Data summary
A 384-SNPs GoldenGate genotyping array for Pinus pinaster was
built from i/ 448 SNPs originally detected in a set of 41 re-
sequenced candidate genes (in vitro SNPs) and ii/ 9,364 SNPs
screened from ESTs (in silico SNPs). Two different SNP selection
strategies were followed, ‘‘depth vs. breath of SNP coverage’’. For
in vitro SNPs we aimed at validating as many polymorphisms as
technically possible for each fragment (depth), whereas for in silico
SNPs we aimed at validating few SNPs per unigene in a large
number of unigenes (breath). A total of 184 in vitro SNPs were
chosen on the basis of functionality scores, presence of neighboring
polymorphisms, MAF and linkage disequilibrium. Moreover, 200
in silico SNPs were selected based on three parameters that proved
critical for high validation rate of EST-derived SNPs [18]: the
number of ESTs used for SNP detection, the SNP MAF and the
quality of SNP flanking sequences. The global success rate of the
assay was 66.9% (considering monomorphic and polymorphic
SNPs), and a conversion rate of 51% was achieved (considering
only polymorphic SNPs). In vitro SNPs showed significantly higher
genotyping success (+11.5%, P-value 0.025) and conversion
(+18.5%, P-value 4.73.1024) rates than in silico SNPs. The
functionality score estimated for each SNP, which in our case
could not account for sequence redundancy in the genome,
showed a significant relationship with success of genotyping. The
reproducibility of the assay was very good (100%, based on 19
replicated genotypes), and the genotyping error rate very low
(0.54%, dropping down to 0.06% when removing four SNPs
showing elevated error rates).
Conversion rates of in vitro and in silico SNPs for Pinus
pinaster
Data obtained from the GoldenGate assay reported in this
paper suggest that the bead array technology is suitable for the
complex and large genome of P. pinaster: 66.9% of the SNPs were
translated into easily interpreted genotypic clusters. This success
rate is similar to that observed for Pinus taeda [66.9%, 24], but
lower than that observed for Picea glauca or Picea mariana [78.5%
and 81.1% respectively when considering polymorphic and
monomorphic SNPs, 23]. So far, two main causes have been
invoked in the literature for explaining genotyping failures in
GoldenGate assays for non-model species. First, the partial
knowledge of large and redundant genomes can be a limiting
factor to design an efficient SNP genotyping assay. Indeed,
flanking sequences cannot be fully validated for locus specificity
and the possible presence of repetitive elements [23,27,34].
Secondly, the sample size used for SNP discovery in species
presenting a high level of nucleotide diversity may be too small,
possibly leading to the presence of undetected SNPs within
Figure 3. Allele frequency spectrum for 257 successfully genotyped in vitro and in silico SNPs.
doi:10.1371/journal.pone.0011034.g003
Figure 4. Genotyping success rate according to functionality
score for the 384 SNPs of the assay. The number of SNPs in each
functionality score class is indicated above each bar.
doi:10.1371/journal.pone.0011034.g004
Genotyping Non-Model Species
PLoS ONE | www.plosone.org6June 2010 | Volume 5 | Issue 6 | e11034

Page 7

priming sites when larger sample of trees are genotyped [24]. In
the case of Pinus pinaster, both hypotheses can be examined: we
reached a 79.6% success rate when considering a group of 103 in
vitro SNPs that were detected on more than 30 individuals from the
Aquitaine population, which is similar to that observed in Picea
species [78.5% and 81.1% in P. glauca and P. mariana, respectively,
23]. The rate dropped to 55.8% for another group of 43 in vitro
SNPs detected on 10 to 30 samples. We checked that this
difference in success rates was not due to differences between
allelic frequency distributions in both groups (data not shown).
This significant difference (x2-test P-value of 0.006) suggests that
the sample size of the SNP discovery panel has a large impact on
the conversion rate. However, the high conversion rate achieved
using SNPs from well characterized DNA regions (79.6%) still does
not reach that reported for human [.91% in 27,40,41,42]. As
discussed in PAVY et al. [23], the megagenome of conifers may
hinder the development of specific probes for the assay. The nine
cases of cluster compression detected in our assay support this
hypothesis. The shift of a homozygous cluster toward the other
one has previously been observed for a SNP in a gene presenting a
nearly identical paralog in soybean, and is likely the sign of the
targeted-sequence redundancy [29].
We found a significant difference between in vitro SNP and in
silico SNP conversion rates, a lower rate being observed for in silico
SNPs. According to WANG et al. [18], genotyping failures in ESTs-
derived SNPs may come either from sequencing errors that lead to
the identification of false-positive SNPs (pseudo-SNPs), from low
quality of SNPs flanking sequences, or from the presence of an
exon-intron junction near the SNP of interest. In our study, the
selection of false-positive SNPs should have been prevented by the
use of trace data for SNP detection [33], and a set of stringent
criteria including MAF and contig size. Indeed, WANG et al. [18]
achieved a 70.9% conversion rate for catfish in silico SNPs detected
on at least four sequences and with a minor allele present twice,
against a rate of 33.3% for SNPs detected on four or fewer
sequences with minor allele present only once. In our case,
chromatograms have also been checked to ensure high-quality of
flanking sequences for primer design, but the presence of
undetected polymorphisms in these regions is likely as most SNPs
were detected on only ten ESTs or less (Figure 2). We could not
confirm whether or not in silico SNPs were located at exon-intron
borders, as we lack a fully sequenced conifer genome to compare
with. The presence of introns has been identified as a major cause
for in silico SNP genotyping failures [18], and may explain the
conversion rate difference between in vitro (revealed from genomic
DNA sequences) and in silico (discovered from mRNA sequences)
SNPs. We previously defined the conversion rate as the number of
polymorphic SNPs divided by the total number of SNPs in the
assay. Since monomorphic loci were twice more abundant for in
silico SNPs than for in vitro SNPs, this also partly explains their
lower conversion rate. Indeed, the EST database used for in silico
SNP detection included sequences from samples of various origins
(Corsica, Spain and Aquitaine, see Table 1), leading probably to
the detection of a small quantity of population-specific in silico
SNPs. On the other hand, more than 80% of in vitro SNPs
originated from individuals collected in the Aquitaine provenance
region (Table S3), i.e. the same material than the genotyped
population. Therefore one should remain careful to check and
control that the discovery panel for SNPs, whether in silico or in
vitro, matches as closely as possible the genotyped plant material in
order to improve the conversion rate. When material of different
origins needs to be genotyped in a species showing significant
population structure, the genotyping array can only be a
compromise, and this situation is likely to be common with the
development of arrays including thousands of SNPs. Identifying
and better accounting for the provenance of sequences in EST
databases when choosing in silico SNPs thus seem crucial and is
more and more documented either in unigene assemblies or in
SNP databases (see for example the NCBI database available at
http://www.ncbi.nlm.nih.gov/SNP/). This information not being
available upfront in the unigene that we used for in silico SNPs
discovery, we had overlooked its influence initially, but have been
integrating it in future studies.
Surprisingly, six in vitro SNPs were found monomorphic on the
genotyped trees, while they were detected as polymorphic loci with
intermediate frequency estimates in the re-sequenced haploid
panel from the Aquitaine population. Given that we are confident
that they were not sequencing artifacts, this observation could be
explained by either the lack of amplification of one allele due to
polymorphism in the priming site, the presence of gametophyte
selection against deleterious mutations (as sequences were
obtained from haploid megagametophytes while genotyping was
performed on diploid DNA), or the general complexity of the pine
genome as previously discussed. In the latter case, the distinction
between genotyping reaction failures and monomorphic SNPs is
not obvious. In this study we decided to discard nine monomor-
phic SNPs with acceptable GenTrain scores but showing either
subgroups in the homozygous cluster, or normalized theta values
departing from the classical 0/1 values for an homozygous locus.
These patterns might be particular forms of cluster compression
(shift of the BB cluster toward the AA cluster as illustrated in
Figure 1D, or putative shift of the AA and AB clusters toward the
BB cluster, Figure 1E). The main quality metrics for SNP assays
(GenCall and GenTrain scores) measure the capacity to group
samples into genotypic clusters, but to our knowledge no study
have established yet the ability of genotype calling algorithms to
tell apart failed reactions from monomorphic markers, or to detect
cluster compression. Even if geneticists are generally not interested
in failed or monomorphic markers, as they do not carry any
information, detecting cluster compression would be very useful
for non-model species. Markers presenting such patterns should
not be used in highly heterozygous populations such as mapping
pedigrees, as the heterozygous cluster is often indistinguishable
from one or both homozygous ones [29].
Genotyping error rate
All large genotype datasets have errors that can be either due to
sample mishandling, failures of analysis algorithms, or simply
biochemical anomalies. Inclusion of incorrect data in genetic
analysis can lead to an inflation in genetic map distances [43], an
increase in type I error and/or a decrease in statistical power in
association studies [44,45], or to biased estimates of linkage
disequilibrium [46] and other allele-frequency related parameters
[47]. Errors in a dataset can be detected either by comparing
genotypic information obtained from different technologies or by
using Mendelian Inconsistencies (MIs) in family-based samples. In
this study, we identified nine samples that concentrated 75% of all
the observed MIs, which was interpreted as human errors. Sample
mishandling has already been identified as a main issue during the
genotyping process [47,48], and could be reduced by the use of
traceability systems such as Laboratory Information Management
Systems (LIMs), quality insurance standards, and reduced human
manipulation, according to the automation possibilities.
Using pedigree information of unrelated families, we also
estimated a per SNP genotyping error-rate [38], which provides
complementary information and helps to identify error-prone loci
that can be removed from the study to increase its reliability. For
example, the mean error rate per locus dropped from 0.54% to
Genotyping Non-Model Species
PLoS ONE | www.plosone.org7 June 2010 | Volume 5 | Issue 6 | e11034

Page 8

0.06% when removing the four (out of 188) polymorphic loci that
had the highest error rate. These genotyping error-rates are in the
range of those recently reported for tetraploid and hexaploid
wheat [0% and 1%, respectively, 30]. Unfortunately, genotyping
error-rates have seldom been reported for GoldenGate assays in
non-model species. While this technique already proved accurate
for human, the species for which it was developed [27], its
reliability in the complex genomes of plants should be estimated
before extensive use. If moderate error rates can be tolerated in
cases such as QTL studies involving frequent alleles [44], or
identical by descent-based analyses when considering a large
number of markers [38], conversely low error rates can be
dramatic in association-mapping studies [49]. Once the genotyp-
ing error-rate has been estimated, statistical tools that account for
it have been developed for linkage analysis [50], family or
population-based association mapping [51,52,53].
Conclusion and perspectives
In this study, we demonstrated that ESTs provide a resource for
SNP identification in non-model species, which do not require any
additional bench work and little bioinformatics analysis. However,
the time and cost benefits of in silico SNPs are counterbalanced by
a lower conversion rate than in vitro SNPs. This drawback is
acceptable for population-based experiments (in our study, a
42.5% conversion rate was achieved for in silico SNPs, compared to
61% for in vitro SNPs), but could be dramatic in experiments
involving samples from narrow genetic backgrounds. For example,
ECKERT et al. [24] only reached an 18.2% conversion rate in a P.
taeda mapping pedigree, using in vitro SNPs from a database that
did not include any sequences of the parental lines of the mapping
population. In addition, we showed that both the visual inspection
of genotyping clusters and the estimation of a per SNP error rate
should help identify markers that are not suitable to the
GoldenGate technology in species characterized by a large and
complex genome.
Recently, a larger-scale SNP-array was designed for maritime
pine, comprising 1,536 SNPs (826 in vitro SNPs, including 560
SNPs detected from re-sequenced amplicons provided by David
Neale, UC Davis, CA, USA, http://dendrome.ucdavis.edu/crsp/,
and 710 in silico SNPs selected with the same criteria as in this
study). This second generation SNP-array will be used to establish
a species consensus map based on the analysis of seven pedigrees,
and for association mapping for a series of traits (biomass
production, wood and end-use properties, drought stress resis-
tance) measured on clonal and progeny tests on the first and
second breeding populations.
Supporting Information
Figure S1
sequencing and genotyping for 101 in vitro SNPs. The plain lines
and dashed lines correspond to the 95% bootstrap confidence
intervals for allele frequencies estimated on 20 or 50 samples,
respectively.
Found at: doi:10.1371/journal.pone.0011034.s001 (6.54 MB TIF)
Correlation between allele frequencies estimated by
Table S1
SNPs that were polymorphic in the assay.
Found at: doi:10.1371/journal.pone.0011034.s002 (0.03 MB
XLS)
NCBI ss accession numbers for in vitro and in silico
Table S2
detection and associated projects.
Found at: doi:10.1371/journal.pone.0011034.s003 (0.03 MB
XLS)
List of the 41 candidate genes used for in vitro SNPs
Table S3
the total sequencing dataset, in the Aquitaine sequencing dataset
and in the genotyped samples.
Found at: doi:10.1371/journal.pone.0011034.s004 (0.06 MB
XLS)
List of the 184 in vitro SNPs and their frequencies in
Table S4
Found at: doi:10.1371/journal.pone.0011034.s005 (0.02 MB
XLS)
Call rate classes of the genotyped samples.
Acknowledgments
We would like to acknowledge the staff of the ‘‘Genome-Transcriptome’’
facility of the Functional Genomic Center of Bordeaux (France) and the
GenoToul facility of Toulouse (France) for their help in sequencing and
genotyping, respectively. We also thank the Experimental Unit of Pierroton
(UE570) and P. Alazard from FCBA for collecting the samples, and E.
Eveno (Treesnips EU project nu836501), F. Bedon, L. Cancino and F.
Hubert for sequence data production. Finally, we are grateful for the
constructive reviews of Santiago C. Gonzalez-Martinez and Zhanjiang Liu.
Author Contributions
Conceived and designed the experiments: CML PGG CP. Performed the
experiments: CML FS. Analyzed the data: CML. Contributed reagents/
materials/analysis tools: CML JMF PGG MTC BV. Wrote the paper:
CML CP. Assembled the EST data: JMF PGG. Organized the funding of
the study: LH CP.
References
1. Henry RJ (2008) Plant Genotyping II: SNP Technology. New York: Oxford
University Press. 285 p.
2. Rafalski A (2002) Applications of single nucleotide polymorphisms in crop
genetics. Current Opinion in Plant Biology 5: 94–100.
3. Rafalski JA (2002) Novel genetic mapping tools in plants: SNPs and LD-based
approaches. Plant Science 162: 329–333.
4. Gupta PK, Roy JK, Prasad M (2001) Single nucleotide polymorphisms: a new
paradigm for molecular marker technology and DNA polymorphism detection
with emphasis on their use in plants. Current Science 80: 524–535.
5. Gibbs JR, Singleton A (2006) Application of genome-wide single nucleotide
polymorphism typing: simple association and beyond. PLoS Genetics 2(10):
e150.
6. Slate J, Gratten J, Beraldi D, Stapley J, Hale M, et al. (2009) Gene mapping in
the wild with SNPs: guidelines and future directions. Genetica 136: 97–107.
7. Henikoff S, Comai L (2003) Single-nucleotide mutations for plant functional
genomics. Annual Review of Plant Biology 54: 375–401.
8. Edwards D, Forster JW, Cogan NOI, Batley J, Chagne ´ D (2007) Single
nucleotide polymorphism discovery. In: Oraguzie N, Rikkerink E, Gardiner S,
De Silva H, eds. Association Mapping in Plants. New York: Springer. pp
53–76.
9. Schmid KJ, So ¨rensen TR, Stracke R, To ¨rje ´k O, Altmann T, et al. (2003) Large-
scale identification and analysis of genome-wide single-nucleotide polymor-
phisms for mapping in Arabidopsis thaliana. Genome Research 13: 1250–1257.
10. Batley J, Barker G, O’Sullivan H, Edwards KJ, Edwards D (2003) Mining for
single nucleotide polymorphisms and insertions/deletions in maize expressed
sequence tag data. Plant Physiology 132: 84–91.
11. Pindo M, Vezzulli S, Coppola G, Cartwright DA, Zharkikh A, et al. (2008) SNP
high-throughput screening in grapevine using the SNPlex genotyping system.
BMC Plant Biology 8: 12.
12. Deleu W, Esteras C, Roig C, Gonza ´lez-To M, Ferna ´ndez-Silva I, et al. (2009) A
set of EST-SNPs for map saturation and cultivar identification in melon. BMC
Plant Biology 9: 90.
13. Labate JA, Baldo A (2005) Tomato SNP discovery by EST mining and
resequencing. Molecular Breeding 16: 343–349.
14. Pavy N, Parsons L, Paule C, MacKay J, Bousquet J (2006) Automated SNP
detection from a large collection of white spruce expressed sequences:
contributing factors and approaches for the categorization of SNPs. BMC
Genomics 7: 174.
15. Le Dantec L, Chagne D, Pot D, Cantin O, Garnier-Gere P, et al. (2004)
Automated SNP detection in expressed sequence tags: statistical considerations
Genotyping Non-Model Species
PLoS ONE | www.plosone.org8 June 2010 | Volume 5 | Issue 6 | e11034

Page 9

and application to maritime pine sequences. Plant Molecular Biology 54:
461–470.
16. Picoult-Newberg L, Ideker TE, Pohl MG, Taylor SL, Donaldson MA, et al.
(1999) Mining SNPs from EST databases. Genome Research 9: 167–174.
17. Ganal MW, Altmann T, Ro ¨der MS (2009) SNP identification in crop plants.
Current Opinion in Plant Biology 12: 211–217.
18. Wang S, Sha Z, Sonstegard TS, Liu H, Xu P, et al. (2008) Quality assessment
parameters for EST-derived SNPs from catfish. BMC Genomics 9: 450.
19. Wang S, Peatman E, Abernathy J, Waldbieser G, Lindquist E, et al. (2010)
Assembly of 500,000 inter-specific catfish expressed sequence tags and large
scale gene-associated marker development for whole genome association studies.
Genome Biology 11: R8.
20. Sobrino B, Brio ´n M, Carracedo A (2005) SNPs in forensic genetics: a review on
SNP typing methodologies. Forensic Science International 154: 181–194.
21. Syva ¨nen AC (2005) Toward genome-wide SNP genotyping. Nature Genetics 37:
5–10.
22. Chagne ´ D, Batley J, Edwards D, Forster JW (2007) Single nucleotide
polymorphisms genotyping in plants. In: Oraguzie N, Rikkerink E, Gardiner S,
De Silva H, eds. Association Mapping in Plants. New York: Springer. pp 77–94.
23. Pavy N, Pelgas B, Beauseigle S, Blais S, Gagnon F, et al. (2008) Enhancing
genetic mapping of complex genomes through the design of highly-multiplexed
SNP arrays: application to the large and unsequenced genomes of white spruce
and black spruce. BMC Genomics 9: 17.
24. Eckert AJ, Pande B, Ersoz ES, Wright MH, Rashbrook VK, et al. (2009) High-
throughput genotyping and mapping of single nucleotide polymorphisms in
loblolly pine (Pinus taeda L.). Tree Genetics & Genomes 5: 225–234.
25. Murray B (1998) Nuclear DNA amounts in gymnosperms. Annals of Botany 82:
3–15.
26. Oliphant A, Barker DL, Stuelpnagel JR, Chee MS (2002) BeadArray
technology: enabling an accurate, cost-effective approach to high-throughput
genotyping. BioTechniques JUN. pp 56–61.
27. Fan JB, Oliphant A, Shen R, Kermani BG, Garcia F, et al. (2003) Highly
parallel SNP genotyping. Cold Spring Harbor Symposia on Quantitative
Biology 68: 69–78.
28. Rostoks N, Ramsay L, MacKenzie K, Cardle L, Bhat PR, et al. (2006) Recent
history of artificial outcrossing facilitates whole-genome association mapping in
elite inbred crop varieties. Proceedings of the National Academy of Sciences,
USA 103: 18656–18661.
29. Hyten D, Song Q, Choi I-Y, Yoon M-S, Specht J, et al. (2008) High-throughput
genotyping with the GoldenGate assay in the complex genome of soybean.
Theoretical and Applied Genetics 116: 945–952.
30. Akhunov E, Nicolet C, Dvorak J (2009) Single nucleotide polymorphism
genotyping in polyploid wheat with the Illumina GoldenGate assay. Theoretical
and Applied Genetics 119: 507–517.
31. Pot D, McMillan L, Echt C, Le Provost G, Garnier-Gere P, et al. (2005)
Nucleotide variation in genes involved in wood formation in two pine species.
New Phytologist 167: 101–112.
32. Eveno E, Collada C, Guevara MA, Leger V, Soto A, et al. (2008) Contrasting
patterns of selection at Pinus pinaster Ait. drought stress candidate genes as
revealed by genetic differentiation analyses. Molecular Biology and Evolution
25: 417–437.
33. Marth GT, Korf I, Yandell MD, Yeh RT, Gu ZJ, et al. (1999) A general
approach to single-nucleotide polymorphism discovery. Nature Genetics 23:
452–456.
34. Shen R, Fan J-B, Campbell D, Chang W, Chen J, et al. (2005) High-throughput
SNP genotyping on universal bead arrays. Mutation Research 573: 70–82.
35. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local
alignment search tool. Journal of Molecular Biology 215: 403–410.
36. Lin CH, Yeakley JM, McDaniel TK, Shen R (2009) Medium- to high-
throughput SNP genotyping using VeraCode Microbeads. In: Bugert P, ed.
DNA and RNA Profiling in Human Blood. New York: Humana Press. pp
129–142.
37. O’Connell JR, Weeks DE (1998) PedCheck: a program for identification of
genotype incompatibilities in linkage analysis. The American Journal of Human
Genetics 63: 259–266.
38. Saunders IW, Brohede J, Hannan GN (2007) Estimating genotyping error rates
from Mendelian errors in SNP array genotypes and their impact on inference.
Genomics 90: 291–296.
39. Hao K, Li C, Rosenow C, Hung Wong W (2004) Estimation of genotype error
rate using samples with pedigree information - an application on the GeneChip
Mapping 10K array. Genomics 84: 623–630.
40. Montpetit A, Nelis M, Laflamme P, Magi R, Ke X, et al. (2006) An evaluation of
the performance of tag SNPs derived from HapMap in a Caucasian population.
PLoS Genetics 2(3): e27.
41. Garcı ´a-Closas M, Malats N, Real FX, Yeager M, Welch R, et al. (2007) Large-
scale evaluation of candidate genes identifies associations between VEGF
polymorphisms and bladder cancer risk. PLoS Genetics 3(2): e29.
42. Cunningham JM, Sellers TA, Schildkraut JM, Fredericksen ZS, Vierkant RA,
et al. (2008) Performance of amplified DNA in an Illumina GoldenGate
BeadArray Assay. Cancer Epidemiology, Biomarkers & Prevention 17:
1781–1789.
43. Hackett CA, Broadfoot LB (2003) Effects of genotyping errors, missing values
and segregation distortion in molecular marker data on the construction of
linkage maps. Heredity 90: 33–38.
44. Abecasis G, Cherny SS, Cardon LR (2001) The impact of genotyping error on
family-based analysis of quantitative traits. Journal of Human Genetics 9:
130–134.
45. Gordon D, Finch SJ, Nothnagel M, Ott J (2002) Power and sample size
calculations for case-control genetic association tests when errors are present:
application to single nucleotide polymorphisms. Human Heredity 54: 22–33.
46. Akey JM, Zhang K, Xiong M, Doris P, Jin L (2001) The effect that genotyping
errors have on the robustness of common linkage-disequilibrium measures. The
American Journal of Human Genetics 68: 1447–1456.
47. Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors:
causes, consequences and solutions. Nature Reviews: Genetics 6: 847–846.
48. Bonin A, Bellemain E, Bronken Eidesen P, Pompanon F, Brochmann C, et al.
(2004) How to track and assess genotyping errors in population genetics studies.
Molecular Ecology 13: 3261–3273.
49. Kang SJ, Gordon D, Finch JS (2004) What SNP genotyping errors are most
costly for genetic association studies? Genetic Epidemiology 26: 132–141.
50. Go ¨ring HHH, Terwilliger JD (2000) Linkage analysis in the presence of errors
IV: joint pseudomarker analysis of linkage and/or linkage disequilibrium on a
mixture of pedigrees and singletons when the mode of inheritance cannot be
accurately specified. The American Journal of Human Genetics 66: 1310–1327.
51. Rice KM, Holmans P (2003) Allowing for genotyping error in analysis of
unmatched case-control studies. Annals of Human Genetics 67: 165–174.
52. Gordon D, Heath SC, Liu X, Ott J (2001) A Transmission/Disequilibrium Test
that allows for genotyping errors in the analysis of single-nucleotide
polymorphism data. The American Journal of Human Genetics 69: 371–380.
53. Sobel E, Papp JC, Lange K (2002) Detection and integration of genotyping
errors in statistical genetics. The American Journal of Human Genetics 70:
496–508.
Genotyping Non-Model Species
PLoS ONE | www.plosone.org9 June 2010 | Volume 5 | Issue 6 | e11034