Single nucleotide polymorphisms affect both cis- and trans-eQTLs
Lang Chena, Grier P. Pagea,1, Tapan Mehtaa, Rui Fenga, Xiangqin Cuia,b,⁎
aDepartment of Biostatistics, Section on Statistical Genetics, School of Public Health, University of Alabama at Birmingham, AL 35209, USA
bDepartment of Genetics, School of Medicine, University of Alabama at Birmingham, AL 35209, USA
a b s t r a c ta r t i c l ei n f o
Received 30 May 2008
Accepted 31 January 2009
Available online 25 February 2009
Single nucleotide polymorphisms (SNPs) between microarray probes and RNA targets can affect the
performance of expression array by weakening the hybridization. In this paper, we examined the effect of
the SNPs on Affymetrix GeneChip probe set summaries and the expression quantitative trait loci (eQTL)
mapping results in two eQTL datasets, one from mouse and one from human. We showed that removing
SNP-containing probes significantly changed the probe set summaries and the more SNP-containing probes
we removed the greater the change. Comparison of the eQTL mapping results between with and without
SNP-containing probes showed that less than 70% of the significant eQTL peaks were concordant regardless
of the significance threshold. These results indicate that SNPs do affect both probe set summaries and eQTLs
(both cis and trans), thus SNP-containing probes should be filtered out to improve the performance of eQTL
© 2009 Elsevier Inc. All rights reserved.
Microarray probes are designed to match the sequences of the
target genes in the selected reference genome. However, due to
individual and population variation, some probes may not exactly
match the sequence of the RNA applied to the arrays. The
polymorphisms that are most likely encountered in microarray
experiments are single nucleotide polymorphisms (SNPs) because
they are much more abundant than any other type of polymorphisms
in most species including human and mouse . Therefore, it is safe to
speculate that a substantial number of probes on human and mouse
microarrays overlap with SNPs in population studies, such as
expression quantitative trait loci (eQTL) studies.
Sequence polymorphisms between a probe on the microarray and
its target can affect the hybridization signal . The effect of SNPs on
probe-target hybridization has long been utilized in the Affymetrix
short oligo microarrays. The Affymetrix expression GeneChips use a
single base-pair mismatch in the middle of the probe sequence
(mismatch probe) to estimate non-specific hybridization background
utilized in the Affymetrix SNP Arrays, where each SNP is interrogated
with approximately 40 different probes. The hybridization signal
differences caused by a SNP are used to infer the genotype for an
individual at a given locus .
The effect of SNPs on probe-target hybridization has also enabled
the identification of novel sequence polymorphisms [5,6]. For
example, Borevitz et. al.  used genomic DNA of Arobidopsis theliana
to hybridize to RNA expression GeneChips. They were able to identify
a large number of single feature polymorphisms (SFPs), which ranged
from single nucleotide polymorphisms to large deletions. Comparing
the SFPs and genomic sequences, Rostoks et al.  showed that a large
proportion of the identified SFPs with sequence information available
contain SNPs. Rostoks et. al.  further confirmed that the SNPs
located near the center of the probes are more likely to be identified as
Even though sequence differences can lead to different hybridiza-
tion intensity, it is not clear how this affects the summary score of a
probe set consisting of 11–16 probes on Affymetrix expression micro-
arrays. If the existence of SNPs does affect the probe set summary, it is
genetic factors controlling gene expression when a probe perfectly
matches one allele but mismatches the other in the mapping
population. In an eQTL study, the expression of each gene (the
summary of each probe set for Affymetrix arrays) is considered as a
of eQTL studies identify both cis-eQTLs (eQTLs mapped to the same
genomic location as the expressed gene) and trans-eQTLs (eQTL
mapped to a different genomic location from the expressed gene) [12–
14]. A few eQTL studies have briefly examined the effect of SNPs on
eQTLs. One study indicated that SNPs are enriched in the cis-regulated
eQTLs compared in the trans-regulated eQTLs . Another study 
made similar findings but stated that the net effect of the SNP on the
eQTLs waslikelysmalland“onlya relativelysmallnumberof cis-acting
eQTL can be attributed to probes overlapping SNPs”.
Genomics 93 (2009) 501–508
⁎ Corresponding author. Department of Biostatistics, Section on Statistical Genetics,
Ryals School of Public Health, 327L, University of Alabama at Birmingham, 1665
University Blvd, Birmingham, AL 35209, USA. Fax: +1 205 975 2540.
E-mail address: firstname.lastname@example.org (X. Cui).
1Present address: Statistics and Epidemiology Unit, RTI International, Atlanta GA
0888-7543/$ – see front matter © 2009 Elsevier Inc. All rights reserved.
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In this paper, we aim to evaluate the effect of SNPs on eQTL studies
using a human dataset and a mouse dataset by comparing the results
of eQTL studies with and without the probes that contain SNPs.
Hereafter, we define a probe ‘SNP-containing probe’ if its target
sequence contains one or more SNPs and ‘SNP-free probe’ otherwise.
Similarly, a probe set is‘SNP-containing probe set’ if anyof its probes is
a SNP-containing probe and ‘SNP-free probe set’ otherwise.
Identification of SNPs in probe targeted sequences
SNPs are the most common genetic variation (90%) in various
genomes including human and mouse . It is likely that a substantial
studies overlap with genetic variation in populations. The Microarray lab
of Molecular and Behavioral Neuroscience Institute at University of
Michigan has compared human and mouse SNPs in the NCBI dbSNP
database with the probe sequences on various Affymetrix GeneChips and
identified the probes that overlap with any of the SNPs in the database
(http://arrayanalysis.mbni.med.umich.edu/). We identified the SNPs in
their list that were also found in the HapMap CEPH population and
examined their overlap with probes on the Affymetrix Human Genome
Focus Array, which was used in the human eQTL study using the CEPH
of these probes, 99.3%, overlap with just one SNP and only 0.7% of them
overlap with two SNPs (Fig. 1). At the probe set level, more than 17%
(1543)of the8793probesets onthechipoverlapwithoneormoreSNPs.
Examining the 1543 SNP-containing probes sets showed that 71.6% of
98.1% of the SNP-containing probe sets have one to three SNP-containing
The same type of analysis was conducted for the BXD mouse data
, where the Affymetrix U74Av2 chip was used. The U74Av2 chip
contains12,488 probesets, mostof which(11,820) have 16 probes. The
BXD mouse eQTL population was originated from two inbred line
(C57BL/6J and DBA/2J) , which have been sequenced. Comparing
the SNPs between these two inbred lines and the probe sequences on
the array revealed that 1854 probes harbor SNPs. As observed from the
human dataset, most of these probes, 1689 (91.1%), contain just one
SNP. A much smaller number, 120 (6.5%), have two SNPs and an even
smaller number of probes have more than two SNPs. At the probe set
level, 792 (6.3%), contain SNPs. Similarly to that in the human dataset,
most of the SNP-containing probe sets have just one or two SNP-
containing probes (Fig. 1), 342 (43%) with one SNP-containing probe
Fig.1. Distributions of SNP-containing probes and SNP-containing probe sets on the Affymetrix arraysused in our study. The human datawere generated usingthe Affymetrix Human
Focus Arrays containing probes for 8500 transcripts. The mouse data were generated using the Affymetrix Mouse U74Av2 microarrays, which contain 12,488 probe sets. The SNP-
containing probes of the human array in the studied population were established based on the HapMap Phase 3 data of CEPH population. The mouse SNP-containing probes were
established based on the SNPs between the two parental inbred lines for the BXD RI population in the mouse SNP database. SNP-probe, SNP-containing probe.
SNP position affects the number of cis-eQTLs obtained from the probe level analysis.
SNP positionNo. of probes trans-eQTLacis-eQTLs cis-eQTL percentage (%)
The mouse dataset was analyzed at probe level for the probe sets with only one SNP-
containing probe. The SNP-free probes in these SNP-containing probe sets were used as
negative control (no SNP row). The SNP position indicates the location of the SNP from
the nearest end of the probe.
L. Chen et al. / Genomics 93 (2009) 501–508
and 203 (26%) with two SNP-containing probes. A total of 104 and 143
SNP-containing probes affect the probe level eQTL analysis
It has been shown that the SNP can affect the probe level analysis
of geneexpression and its effect is related tothe position of the SNP on
the probe [5,18]. However, it is not clear whether and how much it
affects the probe level eQTL analysis. If the effect is large in probe level
eQTL analysis, we would expect to see dramatic enrichment of cis-
eQTL peaks from SNP-containing probes, especially from probes with
SNPsnear the center. We used themouse datatotesttheeffectof SNPs
and SNP locations in probe level eQTL analysis for computational
simplicity. For this analysis, we only considered the probes that
contain one SNP for simplicity. If we find strong effects of one SNP,
multiple SNP would likely to have even stronger effects. After RMA
processing, the probe intensities (on log scale) were treated as
quantitative traits and analyzed for QTL mapping as described in
Materials and Methods. We considered a linkage peak as cis-eQTL if
the peak falls within 10 Mb of the probes. The results showed that
collectively only a very small proportions (2.85%) of the QTL peaks
obtained from the SNP-free probes of the SNP-containing probe sets
are cis-eQTLs while a much larger proportion of the eQTL peaks (up to
30%) obtained from the SNP-containing probes in the same probe sets
are cis-eQTL peaks, especially from the probes with SNPs in the center
produce false positive cis-eQTLs in probe level mapping. Fig. 2 shows
an example of false positive cis-QTL as well as an example of false
negative cis-QLT resulted from SNP-containing probes.
In almost all eQTL studies using Affymetrix GeneChips, the
hybridization intensities of probes are first summarized for each
probe set. The probe set summaries are then used for eQTL mapping.
Therefore, it is important to evaluate the effect of SNPs on the probe
Fig. 2. Examples of false positive and false negative cis-QTLs caused by SNP-containing probes. The LOD scores are plotted at the regions surrounding the target gene locations in the
genome for each probe in the probe set. The black and grey lines represent SNP-containing and SNP-free probes, respectively. LOD curves of probe set 104225_at show that 11 SNP-
free probes have a cis-QTL at 90 cM on chromosome 2 while the SNP-containing probe 104225_at1 does not have the QTL at that location, which illustrates the false negative caused
by SNP-containing probe. LOD curves of probe set 95010_at show that SNP-containing probe 95010_at16 has a cis-QTL at 92 cM on chromosome 12 while the SNP-free probes in the
same probe set do not.
Fig. 3. Box plots of Pearson correlation coefficients of probe set summaries. The coefficients were obtained from correlating the original probe set summaries with those after filtering
out probes. The open box plots are for correlations between the original summaries and those after filtering out the SNP-containing probes. The shaded box plots are for correlations
between the original summaries and those from random probe filtering. Correlation coefficients were calculated after normalizing all individuals in the population together.
L. Chen et al. / Genomics 93 (2009) 501–508
set summaries. We examined the difference of the probe set
summaries caused by filtering out the SNP-containing probes. After
preprocessing using RMA based on filtered or unfiltered CDF files, we
calculated the Pearson correlation coefficients of the probe set
summaries for each probe set. Fig. 3 illustrates the observed
distributions forthecorrelationcoefficientsof theexpression between
the two types of probe set summaries. The results showed that the
presence of a SNP in a probe set can change the expression summaries
and the larger the number of SNP-containing probes, the greater the
change. Due to the manner in which RMA normalizes and processes
arrays the expression, values realized from the SNP-free probes could
differ when realized with the filtered or unfiltered ⁎.CDFs; however,
the differences were minimal as expected. For the SNP-containing
probe sets in the human data, the median correlation coefficients are
around 0.96, 0.93, 0.88, and 0.85 for the probe sets with one, two,
three, and more than 3 SNP-containing probes, respectively. For
mouse, a similar reduction of coefficients was observed along with the
increase of the number of SNP-containing probes. The median
correlation coefficients are around 0.98, 0.96, 0.92, and 0.88 for the
probe sets with one, two, three, and more than three SNP-containing
The reduction of the correlation coefficients could also come from
the reduction of the number of probes in the probe sets. To evaluate
the contribution of SNPs independent of the probe set size reduction,
we randomly removed same number of probes from each SNP-
containing probe set and calculated the correlation coefficients for
1000 times (red box plots in Fig. 3). These coefficients are larger
compared to the ones obtained from removing the SNP-containing
probes (black box plots Fig. 3) except the category of probe sets with
more than three SNP-containing probes from human data. The lacking
improvement of correlation in the random removing process could be
due to the small number of probe sets in this category (29), which
resulted in the unstable results. We also obtained p value for each
SNP-containing probe removal based on the empirical distributions of
the correlation coefficients obtained from randomly removing probes.
If theSNPs have significanteffectonthe probe set summary, wewould
expect a left shifted distribution of the p values, else a uniform one.
We indeed observed left shifted distributions of p values when there
are more than one SNP-containing probes in a probe set although the
proportion of excess small p values is relatively small (Fig. 4). From
these results we conclude that SNPs between probe and target
sequences do affect probe set summaries.
SNPs between probe and target sequences affect probe set level eQTL
Our goal in this study is to evaluate whether SNPs have an impact
on the results of eQTL studies. If removing the SNP-containing probes
had no effect, we would expect that the eQTLs identified in the filtered
and unfiltered datasets largely agree with some allowance for the
variability from the reduction of probe numbers and slightly different
results from separate RMA preprocessing. The results from analyzing
the human CEPH data showed that many of the eQTL peaks obtained
after removing the SNP-containing probes agreed with those resulted
from the original data for the SNP-containing probe sets (Table 2). In
total, we found 145 eQTLs and 132 eQTLs for the 1543 SNP-containing
Fig. 4. Histograms of p values obtained from testing the effect of removing SNP-
containing probes on probe set summaries. The extra small p values compared with a
uniform distribution indicate that in some probe sets correlation obtained fromfiltering
out the SNP-containing probes is significantly weaker than that obtained from
randomly filtering out equal number of probes. SNP-probe, SNP-containing probe.
Comparisons between eQTL peaks identified before and after filtering out the SNP-containing probes.
Human CEPH dataMouse BXD data
No. of SNP-p per prs
No. of associated prs
No. of overlapping QTLs
No. of QTLs w/o filtering (overlapping)
No. of QTLs w/ filtering (overlapping)
The significance level for eQTLs is genome-wide 0.05. The filtering was conducted before data preprocessing. All other steps for the filtered data were exactly the same as for the
unfiltered data. Overlapping QTLs were defined as the QTLs located within 10 cM. SNP-p, SNP-containing probe; prs, probe set. PAP, positive agreement proportion defined as the
proportion of overlapping positives among all positives from both analyses.
L. Chen et al. / Genomics 93 (2009) 501–508
probe sets before and after filtering, respectively. Among these eQTLs,
93 were common. When we examined the relationship of the
agreement of results and the number of SNP-containing probes, we
foundthatthediscrepancyoflinkage peaks increases asthenumberof
SNP-containing probes increases. The probe sets with just one SNP-
containing probe gave very similar linkage results (68.3% agreement)
between filtered and unfiltered datasets (Table 2). For the group of
probe sets with two and three SNP-containing probes, the overlap is
reduced to 46.2% and 42.9% of those obtained without filtering,
respectively. For the group of probe sets that have more than three
SNP-containing probes, there is no overlap. A better measurement of
agreement is positive agreement proportion (PAP) , which is
defined as the proportion of agreed results among all the positives
from both analyses. The PAP between eQTLs from the filtered and
unfiltered data decreased from 70% to 46% as the number of the SNPs-
containing probes increased from one to three (Table 2). These results
indicate that the linkage mapping results are relatively robust against
removing just one SNP-containing probe; however, when there is
more than one SNP-containing probe, the linkage mapping result can
change dramatically. Filtering out SNP-containing probes not only
removed some of the eQTL peaks obtained from the unfiltered data
but also produced some new eQTLs. For example, from the probe sets
that contain just one SNP-containing probe in the human data, 39
original eQTLs were lost but 33 new eQTLs were gained after filtering
out the SNP-containing probes (Table 2).
Similar results were obtained from the mouse data. We identified
207 and 166 eQTL peaks from the 792 SNP-containing probe sets
before and after filtering the SNP-probes, respectively. Among the
eQTL peaks identified before filtering,105 (50.7%) were also identified
after filtering. The overlapping eQTL peaks also decrease as the
number of the SNP-containing probes in the probe sets increases. For
the probe sets with just one SNP-containing probe, the overlap of the
eQTL is about 63% of those obtained without filtering. For the probe
sets with two and three SNP-containing probes, the overlap is about
54% and 30%, respectively. These results are consistent for various
significance levels (e.g. LOD 2, 4 etc.). The proportion of the positive
agreement also decreased from 69% to 35% with the number of the
SNP-containing probes in a probe set increased from one to three
(Table 2). Notice that PAP of probe sets with three SNP-containing
probes is slightly lower than that of probe sets with more than three
SNP-containing probes. This could be due to the random fluctuation
with small sample size (only 6 and 13 overlapping QTLs in these two
To examine whether the reduction of overlap is simply due to the
reduction of probe set size we conducted a resampling study for the
mouse data. Similar to what we did in assessing the effect of the SNP
onprobe set summaries, we randomly removed equal number of SNP-
free probes from the SNP-containing probe sets 1000 times and
examined the overlap of linkage peaks between with and without
filtering probes each time. The distribution of the overlapping eQTL is
shown in Fig. 5. Forthe probe sets with justone SNP-containingprobe,
the number of overlapping eQTL peaks between with and without
filtering is 59 (Table 2), which is considerably smaller than most of
those from the resampling processes. This result indicates that
removing the SNP-containing probes have substantial effect on the
eQTL results beyond the effect of probe set size reduction. The
empirical p values are 0.095, 0.296, 0.062, and 0.038 for one, two,
three and more than three SNP-containing probes, respectively
(Fig. 5). To understand the large p value obtained from the probe
sets with two SNP-containing probes, we randomly picked 16 probe
sets from this categoryand compared the microarray probe sequences
with the corresponding sequences of thetwomouse strains. We found
that only 7 probe sets with the two SNP-containing probes matching
the same strain, while 5 probe sets with the two SNP-containing
probes matching neither of the two strains, one probe set with the
SNP-containing probes matching the two opposite strains, and 3
probe sets with one SNP-containing probe matching neither of the
two strains. These findings indicate that only about half of the probe
sets in this category have effects as expected for the SNP-containing
probe sets with two SNP-containing probes.
SNPs affect both cis- and trans-eQTLs
Most previous investigations on the effect of SNPs in eQTL studies
have focused only on the cis-eQTL peaks [14,20]. Our results above
included both cis- and trans-eQTLs. To examine the two types of eQTL
peaks separately using the mouse data, we first established the
baseline byexamining the SNP-free probe sets for the similarity of cis-
and trans-eQTL peaks before and after the filtering procedure.
Although no probe was removed from these probe sets, the removing
of the SNP-containing probes from other probe sets potentially affects
the RMA preprocessing and normalization results; therefore, affects
the SNP-free probe sets too. Our analysis generated 2346 trans-QTLs
and 155 cis-QTLs in common between the two analyses (Table 3A).
Only about 10 unique trans-eQTLs and one or two unique cis-eQTLs
was found in each analysis. For the SNP-containing probes sets, the
differences are much greater. For the cis-eQTLs, there are 15 common
to both analyses but 3 and 4 unique to the without and with filtering,
respectively. In addition, one cis-eQTL became trans-eQTL and one
trans-eQTL became cis-eQTL (Table 3A). Overall, the gain and loss of
cis-eQTLsis about the same.Forthetrans-eQTLs,the difference is even
more dramatic. There are 84 trans-eQTLs in common between the two
analyses; however, there are 50 and 86 trans-eQTLs unique to the
without and with filtering, respectively. The overall gain of new trans-
eQTLs after filtering is much greater than the loss. These results
showed that filtering out SNP-containing probes not only affect the
cis-eQTLs but alsothe trans-eQTL, which indicates that SNPs can cause
both false positive cis- and trans-eQTLs. When we randomly removed
same number of probes from SNP-containing probe sets and
conducted eQTL mapping, we found that a much larger proportion
of trans-eQTL are in common with those obtained from the original
Fig. 5. Histograms for the numbers of overlapping eQTLs between randomly removing
probes and the original mouse data. Probes were randomly filtered out of the SNP-
containing probe sets to generate new datasets. The same preprocess and QTL mapping
methods were applied to the new datasets. The eQTLs from the new datasets were
compared to the eQTLs obtained from the same probe sets in the original dataset. The
triangles point to the number of overlapping eQTLs between the original dataset and
the dataset with all the SNP-containing probes filtered out. Ps, probe sets; SNP-probe,
L. Chen et al. / Genomics 93 (2009) 501–508
data (Table 3B). This result indicates that the low overlap of trans-
eQTLs is indeed partially due to the SNP-probes. For the cis-eQTL, we
actually obtained a smaller overlap from the resampling procedure,
the cause of which is not clear.
and continue to use to indicate the effect of SNP in eQTL studies is the
enrichment of cis-acting eQTL peaks from probe sets with SNPs. Our
mouse study showed that about 4% of the eQTLs obtained from the
SNP-free probe sets were cis-eQTLs while 13% of the eQTLs obtained
fromthe SNP-containing probe sets were cis-eQTLs. However, as Dross
et. al.  pointed out, the excess of cis-eQTL from the SNP-containing
probesets couldbe duetothefactthat SNP-containing probesets tend
to be localized in none identical by decent (IBD) regions, where
sequence is highly polymorphic between the two mouse strains. The
SNPs in the nearby region instead of the SNPs on the probes could be
the cause of cis-acting peaks. To distinguish the effect of these two
types of SNPs, we compared the cis-acting eQTLs before and after
removing the SNP-probes. Our results showed thatonly 4 outof the 19
cis-QTLs disappeared after filtering SNP-containing probes. In addi-
tion, 5 new cis-eQTLs were gained after filtering in the mouse dataset.
Albert et. al.  flagged 25 of the 70 cisB6eQTL (36%) as potentially
false cis-eQTLs, which is higher than our finding that 20% of the cis-
QTLs resulted from the original data analysis without filtering were
potential false cis-QTLs. However, we showed that false positive cis-
eQTLs is just one aspect of the SNP effect. A substantial proportion of
trans-eQTLs (38%) was also lost after filtering out the SNP-containing
probes. On the other hand, both new cis-eQTL's and trans-eQTLs were
also gained after filtering. Interestingly, we observed even greater SNP
effect on the trans-eQTLs. The positive agreement proportion for the
trans-eQTLs is only 40% while that for the cis-eQTL is around 65%. In
addition, a lot more new trans-eQTLs were gained after removing the
SNP-containing probes compared with the ones lost. This could be
significant trans-eQTL peaks. After removing the SNP-containing
probes, the residual variance is reduced and more trans-eQTL peaks
Multiple factors contribute to the difficulty in comparing results
regarding the effect of SNPs in eQTL mapping. One problem is that the
definition of the cis-acting eQTL is inconsistent across studies.
Investigators use Mb or cM to define a location for a cis-eQTL and
the window size can range for 2–20 [8,11,14,22]. Depending on the
sample size and power of the study, it might be more appropriate to
chose different window size for the cis-acting eQTL, such as confident
interval of the eQTL locations. In addition, eQTL studies are the high-
dimensionality in nature. Both the number of the markers and the
number of traits are large. Although most studies can establish
genome-wide significant level fora single trait, it is hardto control the
type I error rate for all traits without losing much power. The low
power results in high false negative rate and thus we could easily
underestimate the effects of SNPs. In our study, we did not adjust for
multiple testing at the gene dimension to avoid extreme low power.
We did try various significant levels and found that our results were
Different microarray data preprocessing methods  can also
cause difference in eQTL studies. Due to the robust nature against the
outlier probes from probe sets, RMA likely minimizes the effect of SNP
effects. Therefore, we choose the RMA preprocessing in this study. We
also tried MAS 5.0  for preprocessing. The results from MAS5 were
similar (results not shown) to those obtained from RMA in respect to
the decrease of overlapping QTLs with the increase of SNP-containing
probes in a probe set. However, removing the SNP-containing probes
resulted in much less overlapping eQTL peaks when MAS5.0 was used
to preprocess data. This indicates that the choice of preprocessing
methods affects the robustness of the analysis against SNP-containing
probes. SNP-probes can be considered as noise in the eQTL analysis;
therefore, down-weighting or filtering out those probes provides
more accurate measurement and improves the analysis.
Our probe level eQTL analyses showed that SNP-probes cause a
dramatic increase in cis-eQTLs compared with the SNP-free probes in
the same probe set. These results indicate that the SNP-containing
probes definitely need to be excluded if eQTL mapping is to be
conducted at probe level. Affymetrix expression GeneChips use probe
sets that contain more than 10 probes to interrogate the expression of
one gene. As expected, the summary of a probe set is less sensitive to
SNPsunlessthere aremultipleSNP-probesin a probeset.However, we
showed that the eQTL mapping results based on probe set summaries
probe (Fig. 5). Therefore, removing the SNP-containing probes is a
worthwhile practice in eQTL studies using Affymetrix GeneChips. For
microarray platforms withlonger probes, the SNP effect onprobe level
eQTL analysis is likely less significant. It has been shown that the SNP
effect on eQTL result is minor in a study using a 60-mer long oligo
arrays . It is not clear how many SNPs it takes to affect the
performance of long oligo probes.
We showed that SNPs affect both probe set summaries and eQTL
results based on the probe set summaries, especially when the SNP-
containing probe sets have more than one SNP-containing probes. For
both the human CEPH data and the mouse data, we only considered
the SNPs found in the population or between the two parental lines,
C57BL/6J (B) and DBA/2J (D) in the mouse case. However, there is still
Comparing the cis- and trans-eQTLs between before and after filtering out the SNP-containing probes from the mouse data.
trans-eQTLs w/o filter cis-eQTLs w/o filter No eQTL
SNP-free probe setstrans-eQTLs w/filter
1SNP-containing probe sets
Resampling the SNP-containing probe setstrans-eQTL
A, Comparisons when the SNP-containing probes were removed from the SNP-containing probe sets. B, Comparisons when equal numbers of probes were randomly removed from
the SNP-containing probe sets. The resampling process was repeated for 1000 times and the averages are shown in B. The significance level used for the eQTLs here is LOD 3. The
common eQTL peaks are defined as within 10 cM.
L. Chen et al. / Genomics 93 (2009) 501–508
the possibility that the probe sequence does not match any of the
alleles as we have shown using a handful of probe sets with two SNP-
containing probes from the mouse data. In these cases, having the
sequence polymorphism betweenprobe and targets will not affect the
eQTL results because the SNP affects the two alleles equally. In
addition, it is also possible that some of the SNPs are fixed in the
mouse RI lines due to the lack of representation of the second parent
in some regions. Removing the probes that contain these SNPs are not
necessary either. All these issues deserve further investigation to fully
characterize the true effect of sequence polymorphism betweenprobe
and targets in eQTL studies. However, removing specific types of SNP-
containing probes will also greatlycomplicate the procedure of probe-
removing before eQTL mapping. Furthermore, SNP is only one type of
sequence polymorphisms, other types of sequence polymorphisms
are worth investigate too. Only after we remove the contributions of
all these sources that affect probe hybridization, we can truly be
confident that we are mapping the factors that affect the gene
Materials and methods
Human eQTL dataset — CEPH Utah families data
The dataset consists of 14 three-generation Centre d'Etude du
Polymorphisme Humain (CEPH) Utah pedigrees  with 194
individuals. The gene expression profile was measured on immorta-
lized B cells using the Affymetrix Human Focus Arrays that contain
probes for 8500 transcripts. Only 82 individuals were profiled with
two microarrays while the rest were profiled with one microarray
each. A total of 2819 autosomal SNP markers were genotyped by
The SNP Consortium (http://snp.cshl.org/linkage_maps/). The aver-
age distance between two adjacent makers is 0.97 megabases (Mb)
with median 0.06 Mb. Only 13 inter-marker distances were greater
that 10 Mb.
Mouse eQTL dataset — mouse BXD bone marrow data
The mouse dataset consists of 22 BXD recombinant inbred lines
. Bone marrow cells were collected from the femurs and tibiae of
three mice. The samples were thenpooled within each inbred line and
hybridized onto two Affymetrix Mouse U74Av2 microarrays, which
contain 12,488 probe sets consisting of 197,993 probes. The marker
genotypes are available for a total of 7636 informative markers that
differ between the parental strains, C57BL/6J (B6) and DBA/2J (DBA).
Marker genotypes were available at http://www.webQTL.com. A
selected subset of 2325 markers that includes all markers with unique
strain distribution patterns were used in the linkage analysis ftp://
We used Probefilter (version 1.4.0) (http://arrayanalysis.mbni.
med.umich.edu/MBNIUM.html) to create customized Affymetrix
probe set definitions (CDFs) with probes that contain SNPs removed.
Probefilter provides the list of SNPs that overlaps the probe sequences
on the Human Focus Array. Unfortunate, their list contains all SNPs
existing in the dbSNP database (ftp://ftp.ncbi.nih.gov/snp/organ-
isms/). For our purpose of removing only the SNPs existing in the
human CEPH population, we identified the SNPs present in the
HapMap Phase 3 data of CEPH population (NCBI build 36, dbSNP b126
at www.hapmap.org). All Probesonthe arraythatoverlap oneormore
SNPs in the HapMap Phase 3 data of CEPH population were identified
as a SNP-containing probes. For the mouse data, the SNPs between the
identify SNP-containingprobes. Forboth datasets, the SNP-containing
probes were eliminated from the Affymetrix CDF files before data
preprocessing. Probe sets containing less than three SNP-free probes
were removed from the analysis due to unstable probe set summaries
Microarray image processing
The Affymetrix CEL files were preprocessed using RMA method
 as implemented in the affy package in Bioconductor (http://
www.bioconductor.org) with default settings. The .cel files were
processed separately with or without filtering the SNP-containing
probes using different versions of CDF files.
Correlation coefficient analyses of probe set summary scores
For each probe set, the Pearson correlation coefficient (robs) was
calculated betweentheprobe setsummaryscoresobtainedbefore and
after filtering the SNP-containing probes based on all individuals in
the population. To establish an empirical null distribution for these
correlation coefficients, we constructed a new filtered datasets by
randomly removing the same number of probes from each SNP-
containing probe set. The new filtered dataset was then processed
using the RMA method with the same setting and a correlation
coefficient (r⁎) was calculated between this new dataset and the
original unfiltered dataset for each probe set. This procedure was
repeated 1000 times toobtain an empirical distribution forcorrelation
coefficients from each probe set. The observed correlation coefficients
were compared to the empirical distributions from corresponding
probe sets to obtain the p values (proportion of r⁎brobs).
For linkage analysis, the probe set summary scores from technical
replicates were averaged for each individual after image processing.
For the human data, we used MERLIN  to detect problematic
genotypes. All genotype errors were set to missing. Linkage analyses
wereconducted usingthe variancecomponents approach andthe
summary score of each probe set was considered as a separate
continuous phenotype.We skippedthosemarkers thathadMendelian
inconsistencies withina pedigree forcalculation of the likelihood. LOD
scores, Chi-square statistics, and their associated nominal p values
were reported. Nominal p values less than or equal to 0.00005 were
considered to be genome-wide significant at 0.05 according to the
Ohrenstein–Uhlenbeck method .
For the mouse data, the average probe set intensities of the two
technical replicates (two chips) after image processing were treated
as quantitative trait. Haley–Knott regression implemented in R/qtl
wasusedforthe QTLmapping[30,31]. Pseudomarkers weregenerated
every 2 centimorgan (cM) and genome-wide significance level was
established using 1000 permutations.
We also performed probe level eQTL analysis for mouse data, the
intensity of each probe was treated as a quantitative trait after
background correction, normalization and log transformation. The
same QTL mapping methods described above for mouse probe set
level data were applied.
Comparison of linkage results
The linkage results were compared at the concordance and
discordance of eQTL peaks at various significance levels. QTL peaks
located within 10 Mb of each other were considered as the same
peaks. This criterionwas chosen based onwhat has been used in other
studies. Mapping was conducted based on genetic distance cM. The
genomic positions (Mb) of pseudo-markers were extrapolated using
linear relation between cM and Mb between two adjacent markers.
Extrapolation was based on the fact that the average genomic
recombination rate is about 1.1 cM per Mb in the human genome,
L. Chen et al. / Genomics 93 (2009) 501–508
although the rate can range from 0.01 cM to 130 cM per Mb . For
mouse, the average recombination rate per Mb was also based on the
average genetic distance per Mb DNA calculated from the total mouse
genome genetic distance, 1355–1450 cM, and the total physical
distance, 2500 Mb [33–36].
We thank Guiming Gao and Nianjun Liu for fruitful discussions.
The authors acknowledge the financial support from the National
Institutes of Health U54CA100949, R01NS043530, and R01ES012933.
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