Jianbiao Zhenga,1, Martin Moorheada,1, Li Wenga, Farooq Siddiquia, Victoria E. H. Carltona, James S. Irelanda, Liana Leea,
Joseph Petersona, Jennifer Wilkinsa, Sean Lina, Zhengyan Kanb, Somasekar Seshagirib, Ronald W. Davisc,2,
and Malek Fahama,2,3
aAffymetrix Inc., 3420 Central Expressway, Santa Clara, CA 95051;bGenentech, 1 DNA Way, South San Francisco, CA 94080; andcStanford Genome
Technology Center, 855 California Avenue, Palo Alto, CA 94304
Contributed by Ronald W. Davis, February 25, 2009 (sent for review December 23, 2008)
Although genomewide association studies have successfully iden-
tified associations of many common single-nucleotide polymor-
phisms (SNPs) with common diseases, the SNPs implicated so far
account for only a small proportion of the genetic variability of
tested diseases. It has been suggested that common diseases may
often be caused by rare alleles missed by genomewide association
studies. To identify these rare alleles we need high-throughput,
high-accuracy resequencing technologies. Although array-based
genotyping has allowed genomewide association studies of com-
mon SNPs in tens of thousands of samples, array-based resequenc-
ing has been limited for 2 main reasons: the lack of a fully
multiplexed pipeline for high-throughput sample processing, and
failure to achieve sufficient performance. We have recently solved
both of these problems and created a fully multiplexed high-
throughput pipeline that results in high-quality data. The pipeline
consists of target amplification from genomic DNA, followed by
allele enrichment to generate pools of purified variant (or nonva-
riant) DNA and ends with interrogation of purified DNA on rese-
quencing arrays. We have used this pipeline to resequence ?5 Mb
of DNA (on 3 arrays) corresponding to the exons of 1,500 genes in
>473 samples; in total >2,350 Mb were sequenced. In the context
of this large-scale study we obtained a false positive rate of ?1 in
500,000 bp and a false negative rate of ?10%.
multiplex ? mismatch repair detection ? single nucleotide
polymorphism (SNP) ? mutation
common and rare alleles has been hotly debated (2–4). Recent
advances in array technologies have allowed large-scale associ-
ation studies (with thousands to tens of thousands of samples) of
common alleles in common diseases and confirmed that com-
mon alleles play a role (5–7). Generally these common alleles
have low to moderate genetic effects. Although technology has
been lacking to perform large-scale association studies of rare
allele, there are several reasons to believe that they, too, play a
fundamental role in common disease. First, studies of several
candidate genes have shown that rare alleles can contribute to
common diseases (8–11). Second, for many common diseases
the failure of linkage studies to generate robust linkage signals
argues against the presence of common alleles with large relative
risk; the data are instead consistent with rare alleles of large or
small relative risks or common alleles with small relative risks
(12). Finally, although exhaustive common allele studies have
been performed for several diseases, only a small fraction of the
genetic contribution is explained (13). The remaining genetic
contribution is likely caused by both common alleles with
extremely small effects (which will require even larger associa-
tion studies) and rare alleles (which may have substantial genetic
effects) (14, 15).
Array-based platforms have allowed the study of tens of
thousands of samples in whole-genome studies. Unfortunately,
even though resequencing arrays have long been available (16)
enetic variability is clearly a major contributor to many
common diseases (1), although the relative importance of
their use has been much more limited for 2 reasons: the lack of
a fully multiplexed pipeline that allows high-throughput process-
ing and the inability of the performance to reach the low false
positive rate (?10?5) necessary for large-scale resequencing-
based association studies (see SI Text and Fig. S1).
We have developed solutions to both of these limitations and
propose arrays as a reasonable method of high-throughput
resequencing. We have developed a pipeline that uses target
amplification by capture and ligation (TACL) to amplify the
specific loci of interest. TACL is then followed by an allele
amplicons (average size 160 bp) carrying variant and nonvariant
alleles are separated into almost pure homozygous states. En-
riched alleles are then sequenced on the array with an automated
algorithm making sequencing calls. This pipeline is fully multi-
plexed with 10,000 amplicons processed in a single reaction, and
sample handling (except the array handling steps) is done in
With this pipeline, we have recently resequenced exons from
1,500 genes (?5 Mb) in 473 samples; in total we sequenced 2,351
Mb. This technology enables the generation of high-quality
sequencing data over many megabases in thousands of samples;
these type of data are necessary for resequencing-based associ-
We have developed and implemented a resequencing pipeline
that consists of target capture, allele enrichment, array detec-
tion, and automated variant calling. The target capture method,
TACL, is capable of jointly amplifying many thousands of
amplicons. The allele enrichment method, MRD, efficiently
separates variant and nonvariant alleles from thousands of
amplicons simultaneously. The resequencing array then enables
the identification of the variant through an automatic base-
Target Capture by TACL. TACL uses probes to capture specific
sequences of interest. Probes were made once and used with
every sample studied. These dU probes are dsDNA containing
J.W., and S.L. performed research; M.M., F.S., and J.S.I. contributed new reagents/analytic
Conflict of interest statement: This work was part of the research done at Affymetrix
Laboratory, the research arm of Affymetrix. However, there are no current products or
specific plans to make products of work described in this manuscript.
Freely available online through the PNAS open access option.
1J.Z. and M.M. contributed equally to this work.
2To whom correspondence may be addressed. E-mail: email@example.com or
3Present address: MLC Dx, 3000 Sand Hill Road, Menlo Park, CA 94025.
This article contains supporting information online at www.pnas.org/cgi/content/full/
April 21, 2009 ?
vol. 106 ?
‘‘target’’ sequences unique to each dU probe flanked by 2
sequences common to all dU probes (Fig. 1 shows probe
manufacture). The unique sequences range in size between 70
sequence to be captured from the genome except that all of the
deoxythymidines (T) in the dU probe sequences are replaced by
The dU probes were then pooled together and used to capture
sequences of interest from genomic DNA; dU probes were
and common oligonucleotides and then incubated with 5? flap
endonuclease and ligase. The hybridization generates a structure
in which one strand comes from the dU probe and the other
comes from the common primers and genomic DNA (Fig. 2).
This process creates a duplex in which one strand is the dU probe
and the other is genomic DNA flanked by common primers. The
dU probe is then removed with UNG.
Given that a RE site is required at the 3? end of the target
fragment, this method is not completely flexible, and it is not
possible to capture some specific sequences (those on very large
or very small restriction fragments). With 1 RE, however, ?84%
of the exonic bases are in restriction fragments of the correct size
(between 70 and 1,350 bp), and with 3 enzymes, 99% of exonic
bases are in such fragments. To study the exons of 1,500 genes,
we constructed 3 panels each using a different RE (AluI, DdeI,
and HpyCH4V). In each of the panels there were slightly
?10,000 amplicons covering ?1.65 Mb; in total the panels
covered ?5 Mb.
For each of 3 REs, we performed TACL to amplify simulta-
neously 10,000 amplicons encompassing ?1.65 Mb of DNA. The
capture was very specific. When DNA such as herring sperm
DNA was used instead of human, no amplification product was
obtained, which is indicative of very low intrinsic background,
because protocols that involve amplification often amplify some
material even in the absence of proper targets. Because of the
high specificity small amounts of input DNA could be success-
fully amplified. We typically used 150 ng per reaction, but we
have successfully used as little as 3 ng of DNA. Capture success
did not have a substantial dependence on amplicon or flap size
flap ?1,000 bp).
Allele Enrichment by MRD. The allele enrichment technology using
MRD has been described (17–19); it relies on the exquisitely-
precise ability of bacteria to detect and repair mismatches.
Bacterial repair of a mismatch (which only occurs when there is
a variant) triggers the corepair of a marker gene, cre, which in
transformation 2 pools are generated through bacterial selec-
tion, the variant pool (VP) and the nonvariant pool (NVP).
These pools are hybridized on separate resequencing arrays. The
utilization of MRD leads to dramatic improvement of the data
quality from arrays for 2 main reasons: the information on
whether a fragment was present in the VP or NVP, and the
A G C U
dUdU dUdU dU dU
Amplified target DNA
sequences specific to the region of interest (gray) and 13-bp sequences com-
mon to all primers (orange and red) appended the 5? end. After amplification
of target DNA, all of the PCR products are pooled. A small aliquot is then used
for a secondary amplification with common primers (orange and red). The
common primers are 30 nt long and are able to amplify all of the amplicons
double-stranded sequences with all of the Ts replaced by Us. Each dU probe
has a unique sequence flanked by 30 nt common to all of the dU probes.
Schematic of dU probe manufacturing process. Individual PCRs are
Ligase closes nicks
UNG removes dU probes, leaving
target DNA with common primers
Situation 2: RE digested
DNA ends perfectly
match dU probe ends
Situation 1: RE
digested DNA 5’ end
has a flap past dU
…5’ FLAP endonuclease
removes the flap leaving
dU dUdUdU dU
RE sites at each end (Situation 1), the digested DNA will hybridize with dU
To increase genomic coverage, some dU probes were designed so that the 3?
a flap up to 1,000 bp long (Situation 2). The use of 5?flap endonuclease makes
the 5? end a substrate for a thermostable ligase that is able to close the nicks.
UDG and heat treatment destroy the dU probes, leaving only genomic DNA
liagated to common primers that can be later amplified by using common
TACL. Genomic DNA (black) is digested with RE and hybridized with
Zheng et al.PNAS ?
April 21, 2009 ?
vol. 106 ?
no. 16 ?
All steps including transformation are performed in a 96-well
Standards. The allele enrichment by MRD relies on hybridization
of the test sequences with cloned references (standards). The
same pools of individual PCR products that were used in making
the dU probes were also used to make standards (details in SI
Text). Making the standards used the same labor-intensive step
of individual PCR as for making the dU probes, but once
constructed, they could be used with every sample that was
studied, making the investment in the generation of these
reagents reasonable when many hundreds or thousands of
samples are to be studied. Many steps (PCR with U for the dU
probes and ligation and 2 transformations of the standards) were
method for standard construction that eliminates the labor
intensive step of individual PCR is discussed in Fig. S1.
Allele enrichment through bacterial selection. All of the standards
were hybridized to the TACL PCR products from test samples
and vector sequences lacking inserts (17). For our study of the
exons of 1,500 genes, ?10,000 amplicons covering ?1.65 Mb
were hybridized in each of the 3 panels (one per RE used in
TACL). The hybridization formed heteroduplex molecules with
2 nicks that were closed by a thermostable ligase. This hetero-
coli strain engineered to respond in a specific way the presence
of a mismatch. In the presence of a mismatch (or variant) the
bacterium grew in 1 medium (VP) and in its absence it grew in
another (NVP). The VP and NVP were prepared and the
plasmid inserts were amplified through the use of common
primers. The contents of these inserts were analyzed by hybrid-
ization on arrays.
Bacterial cloning is often criticized as inefficient. Inefficiency
of this process is caused by the colony picking and processing
steps and inefficiency of intermolecular ligation. We largely
avoided both problematic steps. No individual colony was pro-
cessed in the standard making or the MRD assay; instead we
used cultures of bacteria carrying many inserts. Whereas making
MRD assay the hybridization event forms the appropriate
molecule requiring only the more efficient intramolecular nick
closure to generate the heteroduplex molecule. The ligation for
making the standards can be less efficient as it is performed only
Differential detection of different types of mismatches. E. coli detects
single-base mismatches and small insertions/deletion (indels)
1–3 bp in length (there is partial detection of 4-bp indels). We
evaluated the enrichment of the different classes of single-base
variants. We demonstrated that we are able to robustly detect all
of the different types of mismatches (SI Text).
Detection by Resequencing Array. Array design is shown in Fig. S3.
On each chip, there were ?1.65 M perfect match (PM) probes
that matched the reference sequence of the human genome.
Each reference base in the genome had a PM probe in which it
lay in the middle position (13th as probes were 25 bp long). Each
PM probe had 3 matching mismatch (MM) probes in which the
middle base had been replaced with each of the 3 other bases
(e.g., if the PM probe had C in the middle position the MM
probes were identical except they had A, G, or T in the middle).
(Each chip has ?4.95 M MM probes.) Because probes were tiled
along the genome, each position in the genome was represented
in 25 different PM probes (at the first position on one probe, the
second position in another probe, . . . the 25th position in yet
The probes were complementary to only one of the strands,
and the strand switched at every adjacent position. We previ-
ously found that correlation of signals from probes in different
strands is lower than for neighboring probes on the same strand,
and therefore the switching provides more information than
solely using 1 strand while requiring only half as many probes as
standard resequencing arrays that tile both strands at each
Finally, to obtain more information in regions near known
SNPs, we made extra tiling probes in these regions. For each PM
probe, if there was a known SNP in the single nucleotide
polymorphism database (dbSNP) within the 25-mer sequence,
then there was 1 additional probe for each allele of the SNP. It
was identical to the PM except that at the SNP position, the base
matched the nonreference allele of the SNP allele. The use of
these probes is discussed below.
Automated Variant Calling. The variant calling pipeline had 3
layers of analysis. Each analysis was performed separately and
generated a score. Results from each layer of analysis were then
combined to generate a final combination score. The combina-
tion score was compared with a threshold number to determine
whether the call was variant or not.
Ratio analysis was performed at the scale of the whole
amplicon. If an amplicon contained a variation, it was enriched
in the VP, and hence the ‘‘ratio’’ of the amplicon in the VP to
that in the NVP was greatly increased relative to samples with no
variation for that amplicon (example in Fig. 3). In this analysis,
only the PM probes were considered. For each amplicon, the VP
signal (Vs) and NVP signal (NVs) were computed as the median
signal among all of the PM probes. For an amplicon of size X bp,
there were X ? 16 PM probes used in this analysis (probe at the
ends were ignored). The minimum value of X was 70, and the
average was 160. Hence ratio score was based on signal from at
least 54 probes and was fairly robust. This analysis requires data
from the VP and NVP from every sample. The other layers of
analysis focus on the VP and do not require NVP data for each
sample. The use of the NVP data in the ratio analysis helps
discriminate heterozygous samples (present in both VP and
NVP) from homozygous samples (present only in VP).
Dip analysis localized that variant to a region of the amplicon.
Variants were enriched to near homozygosity in the VP. There-
fore, variant sequences exhibit markedly reduced hybridization
to the PM probes. This reduction in hybridization occurs for the
PM probes when the variant base is within 10 bp of the center
of the probe (i.e., for probes with the variant base at positions
?3–23 in the probe); the most dramatic reduction is seen when
the variant is up to 6 bases from the middle position. In most
cases this ‘‘dip’’ in hybridization localizes the variant to ?3 bp,
but for some variants the uncertainty is as much as ?8 bp. Like
the VP/NVP ratio analysis, this dip analysis uses only the PM
probes. However, unlike the ratio analysis, the dip is focused
mainly on the VP. Only a small number (?20) of NVP samples
are analyzed and these serve as a reference to normalize the
-1 0 1
NVs. The contrast is computed as (Vs ? NVs)/(Vs ? NVs). Therefore, if the
fragment is nonvariant, variant, or heterozygous, the contrast is expected to
be ?1, ?1, or 0, respectively. The y axis is the signal sum (Vs ? NVs).
Ratio analysis. The x axis shows the contrast between the Vs and the
www.pnas.org?cgi?doi?10.1073?pnas.0901902106Zheng et al.
signal from different probes as shown in Fig. 4. Because the dip
in signal is measured over multiple probes (15–20), it is reason-
Base analysis identifies the exact sequence change within the
dip using all probes (PM and MM) within 8 bp of the center of
the dip. At each of 17 positions, there are 3 MM probes and for
each we computed the contrast value (MM ? PM)/PM ? MM),
near homozygozity, in the VP there will be little DNA that
matches the PM probe, resulting in low PM signal. If there is a
variant, however, the matching MM probe should show much
stronger signal that the PM probe. The MM probe with the
largest contrast value was used to identify the variation.
Performance. We have recently finished a project resequencing
?5 Mb of DNA in each of 473 genomic samples. The 5 Mb
covered exons of ?1,500 genes that were chosen based on their
potential role in cancer. All of the exons and 10 bp of the
surrounding introns were targeted. The targets were covered in
at least 1 of 3 pools, each with a specific RE at the TACL step.
Each pool had slightly ?10,000 amplicons.
We assessed amplicon performance by measuring several
metrics (e.g., average amplicon signal, percentage passed sam-
ples for amplicon) and then assessed data quality in the top 84%
of amplicons. Twenty-four HapMap samples were used to
identify false positives and false negatives. Fig. 5 shows the
receiver operator curve (ROC) analysis representing the false
positive/false negative tradeoff. As can be seen, at a false positive
rate of ?1 per 500,000 bp, ?90% sensitivity can be obtained. Of
called variants, for ?98% we could identify the specific base
change; for the remaining 1–2% we localized the variant to
either an amplicon or a small dip. Dataset S1 provides variant
calls for the 24 HapMap samples at the false positive rates shown
in Table S1.
Our process and algorithms have no bias toward detecting
high-frequency alleles as opposed to low-frequency alleles (ex-
cept for the fact that a sample with the minor allele must be
tested). For example, we did not perform clustering, a process
that tends to perform better when there are numerous variant
and nonvariant calls. Instead for ratio, dip, and base, we defined
to that cluster to generate a score. To confirm the absence of
bias, we looked at the performance for SNPs at different
frequency bins and found essentially no difference in perfor-
mance (Table S2).
Variants Identified. This experiment was designed primarily to
the best sample set for the study of germ-line variants and
population genetics. For instance, tumors often have large
genomic regions with loss of heterozygosity, greatly decreasing
the number of variants they carry. However, given the large size
of variations identified. In total, there were 817,521 variant calls,
among which 735,578 (90%) were in previously known SNP sites
(Table S1). There were 3.5 novel (i.e., not present in dbSNP)
The 817,521 variant calls corresponded to 36,939 unique vari-
ants, 29,519 of which (80%) were novel (Table S3). As expected,
the allele frequency distribution of the novel SNPs was strongly
shifted toward the rarer alleles when compared with known
SNPs (Fig. S4).
We demonstrate here a high-throughput, high-quality array-
based pipeline for resequencing; TACL allowed us to eliminate
singleplex PCR, generating a scalable process; MRD dramati-
cally improved array data quality by providing the ratio infor-
mation on amplicons and enriching variant alleles to near
homozygosity. The use of TACL and MRD ameliorated the 2
hurdles that have plagued the large-scale use of resequencing
arrays. Genotyping arrays have enabled large association studies
through genotyping tens of thousands of samples. By creating
created the potential to conduct similar large-scale resequenc-
ing-based association studies. In this work we studied ?5 Mb of
DNA representing exonic sequences of ?1,500 genes in 473
samples with high accuracy, generating sequence for 2,351 Mb in
total. Most of the samples studied were tumor samples in which
we detected ?2,000 nonsynonymous somatic mutations vali-
dated by independent methods. These confirmed mutations
provide additional validation to the technology.
We previously used MRD with tag arrays to identify variant
fragments; identifying the exact sequence change required fol-
low-up sequencing. Our current study’s use of resequencing
arrays streamlines this process. In addition ?1 variant can now
be identified in a fragment.
Although our current process has allowed us to efficiently
generate 2,351 Mb of sequence data, there are 5 ways in which
we hope to improve the process in the future (details in SI Text
and Fig. S5). Reducing the number of arrays by 1/3 through
combining the NVP arrays should be straightforward, as should
improving amplicon pass rate by optimizing hybridization con-
ditions for AT-rich amplicons. Preliminary results for the other
3 methods (reducing dU probe and standard cost through array
Log Norm Signal + Fit
data from some NVP samples can be used to build a model of the expected signals for each position and data from the VP can be compared to find regions of
poor hybridization that may contain a variant. (B) The y axis shows the comparison of the VP pool with the model generated from the NVP samples; data from
both strands are shown (solid red and blue circles). Open circles show processed data after dip fitting.
Dip analysis. The x axes show position in the amplicon for a 200-bp amplicon. (A) The y axis shows the NVPs obtained at each position with data shown
Zheng et al.PNAS ?
April 21, 2009 ?
vol. 106 ?
no. 16 ?
synthesis followed by cleavage; reducing array size/cost through
enzymatic base discrimination rather than hybridization; and
further reducing array size/cost through decreasing feature size)
Recently, several parallel sequencing techniques have been
developed (22). So far, these techniques have not yet been
applied for large-scale resequencing (thousands of genes in
hundreds to thousands of samples). Even though the costs of
these techniques are dropping, all require high levels of redun-
dancy for highly-accurate detection of heterozygous variants, in
part because different genomic fragments will be present at
different concentration after amplifications. We note that the 3
modules of our technology (TACL, MRD, and array) are all
independent of each other; hence TACL and/or MRD can be
used in conjugation with the parallel sequencing technologies as
these techniques become more cost effective.
Materials and Methods
Analysis. After the ratio, dip, and base analyses are completed, calls are made
through the use of a combo score based on all 3 scores.
each analysis, a score is given for each amplicon in every sample. A combined
score is then generated from all 3 scores, and then a threshold is set above
which data points are called variants and below which the data points are
called nonvariants. In a small fraction (2%) of the calls with positive combo
calls, the base could not be determined.
Double combo analysis. When ?1 variant is present in the same sample for the
same amplicon, a modification of the algorithm is required. The simple
modification is after identification of the first variant, one can look for
another variant using the second and third analysis layers (dip and base) but
not the ratio because the ratio pertains to the amplicon as a whole and is thus
already affected by the first variant. In this case we can only combine the dip
the search for ?1 variant in the same sample and amplicon to those cases
where one of the variants corresponds to a variation at a SNP already known
for known SNPs. In the array there are extra probes for all of the SNPs (and
insertion/deletions) in dbSNP. Each of the 2 alleles of all SNPs is tiled in all 25
positions with respect to location of the SNP within the probe sequence. We
use the 15 probes where the SNP is ?8 bases from the probe center to do the
SNP analysis. The status of the allele can be surmised through the median
ratio to be nonvariant. This SNP analysis is done for all known SNPs. In those
samples with identified variant alleles in the known SNPs, a search is done for
any other variants at all other potential sites in the same amplicons.
Variant Detection. The pipeline for detecting variants in samples of interest is
as follows: (i) Do SNP analysis on all of the known SNPs. This will find variants
that occur at known SNPs. (ii) Based on this result use 1 of 2 strategies to find
variants that occur at unknown SNPs.
For triple combo analysis, combine the ratio, dip, and base analyses to-
gether for all cases where a sample has no variant alleles in the known SNPs
at unknown SNPs, because in most cases there is no variation present at the
known SNPs for each amplicon.
In double combo analysis, for those cases where a sample has 1 (or more)
do a double combo analysis (dip and base only) to search for additional
variants at other sites than the known SNPs. This only represents ?6% of the
variants that occur at unknown SNPs, because in most cases there is no
variation present at the known SNPs for each amplicon.
Calibration. To calibrate the efficiency of the above variant detection pipeline
we run 3 separate kinds of calibration analyses:
SNP calibration. Use known genotypes of HapMap SNPs to determine the
sensitivity and false positive rate of the SNP analysis.
Triple combo calibration. We restrict this analysis to amplicons with at most 1
sample is variant at the SNP we perform the triple combo analysis (ratio, dip,
and base) and ignore the SNP analysis. This effectively determines the sensi-
tivity for detecting a new variant that is not in the presence of a variant at a
of the variant detection pipeline above. The false positive rate is determined
by using amplicons that have been Sanger-sequenced to find samples where
there are no variants across the entire amplicon. These nonvariant cases are
then subjected to the triple combo analysis (ratio, dip, and base). Because we
variants are false positives.
Double combo calibration. For finding new variants in the presence of variants
at known SNPs (for the given amplicon and sample), i.e., as in double combo
analysis) of the variant detection pipeline above, we must use a different
approach to calibrate our efficiency of detecting the second (new) variant. In
this case we use amplicons with exactly 2 HapMap SNPs and no other known
SNPs. We then perform, at both SNPs, a double combo analysis (dip and base
only) that ignores the SNP analysis and the ratio analysis. This allows us to
calibrate the sensitivity for finding the second variant because we are using
variant detection pipeline. The false positive rate is determined by using the
same Sanger-sequenced nonvariant data as in triple combo calibration but
5E-6 1E-5 5E-5 1E-4
False positive rate (per bp)
5E-6 1E-5 5E-5 1E-4
False positive rate (per bp)
positive and negative for the 3 different enzyme panels/arrays. The 3 panels/
arrays have somewhat different performance with the best performance
(blue) seen for the Dde panel that carries the MutS-overexpressing strain. The
average performance among the 3 panels shows a false positive rate of
1/500,000 bp at a sensitivity of ?90%. (B) This plot shows more specific
performance data for the intermediate performance in A (Hpy). The combi-
same as the green line in A). Much of the power comes from the robust ratio
from fewer features, the dip (green) and the base (red) analyses have lower
power. However, they add to the power of the ratio particularly at low false
base could not be determined).
ROC analysis. (A) The ROC curve showing tradeoff between false
www.pnas.org?cgi?doi?10.1073?pnas.0901902106 Zheng et al.
instead applying the double combo analysis (dip and base only) to find any Download full-text
At first glance it might seem troubling to use known (HapMap) SNPs to
calibrate the sensitivity for detecting variants at unknown SNPs. However, in
both the triple and double combo analyses, no information from the SNP
analysis or any of the SNP specific probes are used. In addition, all of the
metrics used in the ratio, dip, and base analyses are explicitly chosen so that
they do not depend on the frequency of the variation within the sample
population. This ensures that the sensitivity does not depend on allele fre-
quency, which would otherwise create a bias because known SNPs are of
generally higher allele frequency than unknown SNPs. Nonetheless, it is a fair
they have been found to perform well on at least 1 genotyping platform.
Given the similarity of different genotyping platforms, detection of these is
potentially ‘‘easier’’ to detect than ‘‘average’’ SNPs. The effect is expected to
be small, particularly because the allele enrichment is unlikely to be well
correlated with the ability to genotype on other platforms.
false negative rates we needed data representing true positives and true
negatives. The true positives were easily available through the HapMap.
total). To avoid potential confounding when 2 variants were in the same
reported additional dbSNP variants. (When there are 2 variants in an ampli-
con, both contribute to the ratio score.) The 38,561 known variants from
HapMap represent true positives allowing us to compute the false negative
rate of this technology. (Although the array contained probes specific for
known SNPs to allow us to identify variant near SNPs, for the false positive
analysis we called SNPs using the same method used to detect other variants,
a combination of ratio, dip, and base scores. Using the SNP-specific probes
HapMap may be present in the studied samples. We have used Mendelian
inheritance to identify true negatives. Fragments that are clearly not carrying
a variant in the 2 parents are expected to have no variant in the child and
hence are treated as true negatives. The data shown in Fig. 5 were obtained
by using 10 Mb per enzyme per panel from this true negative data. We have
to obtain a true negative data for ?750,000 bp. Data from this smaller set of
true negative data were consistent with what was obtained by using the
were extra probes corresponding to the SNP sites, we did not use them in this
analysis and hence the performance should be the same as for previously-
ACKNOWLEDGMENTS. We thank Sumathi Venkatapathy, Laura Miller, and
Wipapat Kladwang for assistance with laboratory work and Francisco Useche
for bioinformatics support.
1. Chakravarti A, Little P (2003) Nature, nurture, and human disease. Nature 421:412–414.
2. Reich DE, Lander ES (2001) On the allelic spectrum of human disease. Trends Genet
Am J Hum Genet 69:124–137.
4. Pritchard JK, Cox NJ (2002) The allelic architecture of human disease genes: Common
disease–common variant or not? Hum Mol Genet 11:2417–2423.
5. Plenge RM, et al. (2007) Two independent alleles at 6q23 associated with risk of
rheumatoid arthritis. Nat Genet 39:1477–1482.
6. Saxena R, et al. (2007) Genomewide association analysis identifies loci for type 2
diabetes and triglyceride levels. Science 316:1331–1336.
7. Zeggini E, et al. (2007) Replication of genomewide association signals in UK samples
reveals risk loci for type 2 diabetes. Science 316:1336–1341.
8. Cohen JC, et al. (2004) Multiple rare alleles contribute to low plasma levels of HDL
cholesterol. Science 305:869–872.
9. Vaisse C, et al. (2000) Melanocortin-4 receptor mutations are a frequent and hetero-
geneous cause of morbid obesity. J Clin Invest 106:253–262.
10. CHEK2 Breast Cancer Case-Control Consortium (2004) CHEK2*1100delC and suscepti-
9,065 controls from 10 studies. Am J Hum Genet 74:1175–1182.
11. Hugot JP, et al. (2001) Association of NOD2 leucine-rich repeat variants with suscep-
tibility to Crohn’s disease. Nature 411:599–603.
12. Jones HB, Faham M (2005) Evidence and implications for multiplicative interactions
among loci predisposing to human common disease. Hum Hered 59:176–184.
13. Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science
14. Iyengar SK, Elston RC (2007) The genetic basis of complex traits: Rare variants or
‘‘common gene, common disease?’’ Methods Mol Biol 376:71–84.
15. Polychronakos C (2008) Common and rare alleles as causes of complex phenotypes.
Curr Atheroscler Rep 10:194–200.
16. Hacia JG (1999) Resequencing and mutational analysis using oligonucleotide microar-
rays. Nat Genet 21(Suppl 1):42–47.
of patients using mismatch repair detection (MRD) on tag arrays. Proc Natl Acad Sci
18. Peters BA, et al. (2007) Highly efficient somatic-mutation identification using Esche-
richia coli mismatch-repair detection. Nat Methods 4:713–715.
19. Bentivegna S, et al. (2008) Rapid identification of somatic mutations in colorectal
and breast cancer tissues using mismatch repair detection (MRD). Hum Mutat
cancers. Science 314:268–274.
21. Greenblatt MS, Bennett WP, Hollstein M, Harris CC (1994) Mutations in the p53 tumor
suppressor gene: Clues to cancer etiology and molecular pathogenesis. Cancer Res
22. Holt RA, Jones SJ (2008) The new paradigm of flow cell sequencing. Genome Res
Zheng et al.PNAS ?
April 21, 2009 ?
vol. 106 ?
no. 16 ?