Genome Res. 13:1855-1862, 2003
©2003 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/03 $5.00
Letter
Allelic Variation in Gene Expression Is Common in the Human Genome
H. Shuen Lo,
Zhining Wang,
Ying Hu,
Howard H. Yang,
Sheryl Gere,
Kenneth H. Buetow and
Maxwell P. Lee1
Laboratory of Population Genetics, National Cancer Institute,
Bethesda, Maryland 20892, USA
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ABSTRACT
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Variations in gene sequence and expression underlie much of human
variability. Despite the known biological roles of differential allelic gene
expression resulting from X-chromosome inactivation and genomic imprinting, a
large-scale analysis of allelic gene expression in human is lacking. We
examined allele-specific gene expression of 1063 transcribed single-nucleotide
polymorphisms (SNPs) by using Affymetrix HuSNP oligo arrays. Among the 602
genes that were heterozygous and expressed in kidney or liver tissues from
seven individuals, 326 (54%) showed preferential expression of one allele in
at least one individual, and 170 of those showed greater than fourfold
difference between the two alleles. The allelic variation has been confirmed
by real-time quantitative PCR experiments. Some of these 170 genes are known
to be imprinted, such as SNRPN, IPW, HTR2A, and PEG3. Most
of the differentially expressed genes are not in known imprinting domains but
instead are distributed throughout the genome. Our studies demonstrate that
variation of gene expression between alleles is common, and this variation may
contribute to human variability.
Polymorphism and variations in gene expression provide the genetic basis
for human variation. Mendelian inheritance assumes that genes from maternal
and paternal chromosomes contribute equally to human development. X-chromosome
inactivation silences gene expression from one of the two X chromosomes, thus
providing an exception to mendelian inheritance
(Gartler and Goldman 2001 ). In
addition, -50 human autosomal genes are known to be imprinted and thus are
expressed from only one chromosome (Tycko
and Morison 2002). However, it is unknown whether variations in
allelic gene expression affect only the X chromosome and imprinted genes or
whether it affects human genes generally. Recently, a group from Johns Hopkins
University reported that six out of 13 genes showed significant difference in
gene expression between the two alleles, and this variation in allelic gene
expression was transmitted by mendelian inheritance
(Yan et al. 2002b).
Furthermore, they had previously shown that the allelic variation in the APC
gene expression plays a critical role in colon cancer
(Yan et al. 2002a). To address
the issue of whether allelic variation in gene expression is a wide-spread
phenomenon, we modified an existing genotyping technology, the Affymetrix
HuSNP chip system, to analyze allele-specific gene expression.
The HuSNP chip was designed for simultaneous typing of 1494 SNPs of the
human genome. It has been successfully applied to study loss of heterozygosity
in lung cancer (Lindblad-Toh et al.
2000). The HuSNP chip contains 16 probes for each SNP locus (see
Methods), with four matching perfectly to allele A and four matching perfectly
to allele B. The other eight probes are identical to the first eight but with
each having one mismatched base in the center of the probe. In this report, we
performed both genotyping and allele-specific gene expression by using HuSNP
chips. Our result shows that the HuSNP chip system is a reliable way to
simultaneously measure allele-specific gene expression for hundreds of
genes.
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RESULTS
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HuSNP Chips Can Reliably Analyze Allele-Specific Gene Expression
To measure allele-specific gene expression quantitatively, we first needed
to find out (1) which of the 1494 SNPs on the chip are located in a
transcribed region, and (2) whether the system can measure allele-specific
expression accurately. By using BLAST searches and annotations in dbSNP, we
found that 1063 SNPs are located in transcribed regions. To address the second
issue, we developed a computational method to extract the fluorescent
intensity for each probe and to quantify the ratio of expression of the two
alleles. To assess the precision of the system, we performed experiments in
duplicates for both genomic DNA and for cDNA derived from polyA RNA from three
fetuses. As shown in Figure 1A,
the correlation between the repeated experiments was very high, with Pearson
correlation coefficients of 0.98 for genomic DNA and 0.95 for RNA. The
P values for both correlation coefficients were <0.0001. Our
analysis indicated that we could reliably identify differences between the
expression of two alleles that differ by greater than twofold (for details,
see Methods).
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Figure 1 Evaluation of Affymetrix HuSNP chip for analysis of allelic gene
expression. (A) Scatter plots for duplicate experiments. In this
example, we performed duplicate experiments by using the same genomic DNA
(left) or cDNA made from kidney RNA (right). Each
circle represents a pair of intensity values in the two experiments
for one SNP. The mean fluorescent intensities of the perfect match probe minus
the mismatch probe from experiment 2 are plotted against the mean fluorescent
intensity of the perfect match probe minus the mismatch probe from experiment
1. The Pearson correlation coefficients of the duplicate experiments for
genomic DNAs and cDNA are 0.98 and 0.95, respectively. The P values
for both correlation coefficients are <0.0001. In other duplicate sample
tests, Pearson correlation coefficients ranged from 0.98–0.99 for
genomic DNA and from 0.88 to 0.96 for cDNA, and the P values for
these correlation coefficients are also <0.0001. (B) Probe images
of the PEG3 gene (SNP rs3143). The probe images were generated by
using the Affymetrix MAS 4.0 software. The genomic DNA, kidney cDNA, and liver
cDNA from the same fetus are each represented by a set of 16 hybridization
signals. Within each set, each individual grid corresponds to a probe. The
eight probes for allele A are on the left, and the eight probes for
allele B are on the right. The top eight probes are for
perfect match probes, whereas the bottom eight probes are for
mismatch probes. The genomic DNA hybridized strongly to the perfect match
probes for both alleles, whereas cDNAs hybridized strongly to the perfect
match probes of allele B only. (C) SNPs/genes on the HuSNP chip are
summarized in this diagram. There are 1494 SNPs on the HuSNP chip. We mapped
1063 SNPs to transcribed regions and 431 SNPs outside of the transcribed
regions. Of the 1063 SNPs, 602 SNPs were analyzed (for selection criteria, see
Methods). Among these 602 SNPs, 277 of them showed almost equal expression
levels between the two alleles, whereas 156 SNPs had a ratio of gene
expression between twofold and fourfold, and 170 SNPs had a ratio exceeding
fourfold for at least one individual.
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Altogether, we performed genotyping and allele-specific gene expression in
kidney and liver for seven fetuses. Genotype calls were obtained by using the
Affymetrix MAS 4.0 software, and quantitative allele-specific gene expression
was obtained as described in the Methods. The average call rate for genomic
DNAs from seven individuals was 71% of the 1494 SNPs on the chip. To be
included in our analysis, each SNP had to meet the following criteria: (1) At
least one fetus is heterozygous for the SNP; (2) the SNP is among the 1063
mapped within a transcribed region, and (3) the gene containing the SNP is
expressed in kidney or liver. We found that 602 SNPs met all three criteria.
RNA from kidney and liver of each individual was used to synthesize cDNA,
which was hybridized to HuSNP chips. We computed the relative expression of
the two alleles.
Differential Allelic Gene Expression Is Common in the Human
Genome
Among the 602 genes analyzed, we found that 326 genes (54%) displayed a
significant difference (at least a twofold difference; for details, see
Methods) in at least one individual. Moreover, 170 genes (28%) showed a
difference greater than fourfold. The number of genes that could be analyzed
and showed differential expression between the two alleles is summarized in
Figure 1C. We compiled a
complete list of the allelic expression of all 326 genes identified
(Supplemental Table 1,
available online at
www.genome.org).
A subset of the genes showing differential allelic expression is given in
Table 1. For 119 genes that
showed differential allelic expression, there was more than one heterozygous
fetus for the SNP. The degree of difference in the expression between the two
alleles varied from individual to individual
(Table 1). This result is
consistent with a recent article that reported that six out of 13 genes had
significant differences between expression of the two alleles, but these
differences varied among individuals (Yan
et al. 2002b).
The overall distribution of variation in gene expression between the two
alleles of all 602 genes examined in this study is shown in
Figure 2. The log of the ratios
between the intensity of the two alleles in genomic DNA centers at zero
(Fig. 2, solid line). The log
of the ratios between gene expression of the two alleles in the cDNAs also
center at 0 but with a much wider distribution of the data points
(Fig. 2, dashed lines),
indicating that there were many genes that showed allelic variation in gene
expression.
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Figure 2 Distribution of ratios of the fluorescent intensities between the two
alleles for genomic DNA and cDNA. The ratios were computed as (PMA - MMA)/(PMB
- MMB) for each SNP for every sample. From the ratios in genomic DNA samples,
the 1-SD interval around the mean is between -1.27 and 1.17 in log scale. The
interval in log scale corresponds to the interval between 0.28 and 3.22 for
the ratios. We selected 602 SNPs for analysis (for selection criteria, see
Methods). To compare the distributions of ratios in genomic DNA and cDNA, we
plotted frequency of samples against the log ratio. Density functions for
genomic DNA, kidney cDNA, and liver cDNA are represented by a black line, blue
line, and purple line, respectively. Black triangles, from
left to right, indicate X coordinates at log(0.25),
log(0.5), log(2), and log(4). The coordinates at log(0.5) and log(2) represent
twofold ratios, and log(0.25) and log(4) represent fourfold ratios. The
density functions for the kidney cDNA ratios and the liver cDNA ratios are
similar. Both have a wider spread compared to the density function for the
genomic DNA.
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Imprinted genes and genes subject to X-chromosome inactivation are expected
to display skewed allelic expression. We compiled a list of 44 known imprinted
genes from the literature (Supplemental
Table 2). The HuSNP chip
contains SNPs in six known imprinted genes: DLK1, HTR2A, PEG3, SNRPN,
WT1, and IPW. Five of them had heterozygous fetuses among the
seven fetuses analyzed. SNRPN, IPW, and PEG3 showed
mono-allelic expression in both kidney and liver, whereas HTR2A
showed mono-allelic expression in liver. WT1 showed bi-allelic
expression, which is consistent with the previous reports that WT1
imprinting is restricted to certain tissues such as placenta and brain
(Little et al. 1992;
Nishiwaki et al. 1997). The
probe images for PEG3 are shown in
Figure 1B. Genomic DNA gave a
uniformly strong signal to the probes for both alleles of PEG3,
whereas cDNAs from kidney and liver hybridized strongly only to allele B.
Some of the genes that displayed preferential expression of one allele are
located in regions that contain other imprinted genes. TRIM22, for
example, showed mono-allelic expression in both kidney and liver, and it is
located in a cluster of at least 10 imprinted genes at 11p15.
LOC145622, which is mapped next to SNRPN, an imprinted gene
located in the imprinting domain at 15q11, showed mono-allelic expression in
kidney. Four of the seven genes on the X chromosome also displayed skewed
allelic expression (Supplemental Fig.
1).
Mapping of Genes That Show Differential Allelic Gene Expression
To understand the genomic organization of allelic gene expression, we
mapped all SNPs onto the human chromosomes. Chromosomes 9, 13, and 15 are
shown (Fig. 3), and the
complete map for the entire genome can be found in the Supplemental Figure 1.
Some of the genes that showed skewed allelic expression are located next to
each other, and a subset of these genes is located in known imprinting
domains. HTR2A, LOC51131, and FLJ13639 are located at 13q14,
and all three show mono-allelic expression
(Table 1). SNRPN, IPW,
and LOC145622 are located in the imprinting domain at 15q12, and all
three genes preferentially express one allele
(Fig. 3;
Table 1). There are also
regions that contain several allele-biased genes outside of the known
imprinting domains. For example, RASGRF2 and RAI14 are
located at 5q13 (Table 1), and
GRF2 and VAV2 are located at 9q34
(Fig. 3;
Table 1). Thus, these might be
novel regions undergoing allele-specific gene regulation. It is also
interesting to note that RASGRF2 and GRF2 both showed
mono-allelic expression, whereas a homologous gene, RASGRF1, is a
known imprinted gene (Plass et al.
1996). However, the vast majority of the genes that show
preferential expression of one allele are scattered
(Fig. 3), indicating that
allelic variation occurs throughout the human genome.
Validation of Allelic Variation in Gene Expression by Real-Time
Quantitative PCR
To validate the results of the HuSNP experiment, we performed
allele-specific quantitative PCR for seven genes: two known imprinted genes
(PEG3 and SNRPN), four genes (TAP2, ELAC2,
DKFZP727G051, and UGDH) that displayed allelic variation in gene
expression, and one gene (C11orf23) that expressed almost equally
between two alleles in the HuSNP experiment. We first performed genotyping by
using TaqMan genotyping assay for the seven fetuses analyzed in the HuSNP
experiment. The resulting genotype calls were identical to the HuSNP
experiment. We then performed genotyping for additional fetuses in order to
identify more samples that are heterozygous or homozygous. An example of the
genotyping calls for ELAC2 is shown in
Figure 4A. We then mixed
genomic DNAs of homozygous AA and BB individuals with seven different ratios
in TaqMan assays to establish a linear regression line for the log of
fluorescent intensity ratio (FAM/VIC) versus the log of allele ratio for each
gene (see Methods). Figure 4B
shows an example of such a linear regression line for ELAC2. For each
of the seven genes, we established these standard curves. By using real-time
quantitative PCR and these standard curves, we were able to deduce the ratios
of gene expression between the two alleles by measuring the fluorescent
intensity of the two alleles in cDNA samples as shown in
Figure 4, C and D. The
real-time quantitative PCR results for the seven genes are summarized in
Table 2. The two known
imprinted genes displayed more than eightfold difference between two alleles
(the linear regression lines were established for difference within
eightfold). The allele ratio for C110rf23, which displayed nearly
equal expression in the HuSNP experiment, also showed nearly equal expression
between the two alleles (ratio between 1.2 and 1.5;
Table 2). UGDH did not
show much difference between the two alleles, although a difference between
the two alleles was detected in the HuSNP experiments. TAP2, ELAC2,
and DKFZP727G051 showed significant differences in gene expression
between the two alleles, which confirmed the results of the HuSNP experiment.
Thus, the results of real-time quantitative PCR are consistent with the HuSNP
experiments in six out of seven genes
(Table 2).
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Figure 4 Validation of allele-specific gene expression using real-time quantitative
PCR. (A) Genotyping of ELAC2 in 23 fetuses. Genomic DNAs
from homozygous AA fetuses are at top left corner (blue), and genomic
DNAs from homozygous BB fetuses are at bottom right corners (red).
Genomic DNAs from heterozygous fetuses are located near the diagonal line
(green). The black square represents no template control (NTC). The
X-axis is for allele labeled by the VIC dye, and the Y-axis
is for allele labeled by the FAM dye. (B) The log2 of (FAM
intensity/VIC intensity) for ELAC2 was plotted against
log2 of (FAM allele/VIC allele) of mixing homozygous DNAs at seven
different ratios (8: 1, 4: 1, 2: 1, 1: 1, 1: 2, 1: 4, 1: 8; VIC allele/FAM
allele). (C) Real-time quantitative PCR amplification of a cDNA
sample from liver for ELAC2. The X-axis is the number of PCR
amplification cycles, and the Y-axis is the fluorescence intensity.
The red and blue curves represent alleles labeled with FAM and VIC,
respectively. (D) Same as C except that the data are from
kidney.
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DISCUSSION
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Affymetrix HuSNP chip array provides a very effective platform for the
simultaneous analysis of large numbers of genes to analyze allele-specific
gene expression. Our study indicates that variation in expression between two
alleles is common and that these genes are distributed throughout the entire
genome, although some of them are clustered. Variations in allelic expression
can be caused by genomic imprinting, X-chromosome inactivation, or other
mechanisms. One example of this latter class is provided by a recent study
(Yan et al. 2002b), which
demonstrated that allelic variation can be transmitted by mendelian
inheritance. Their earlier work also linked the reduced expression of an
affected allele of the APC gene to colon cancer
(Yan et al. 2002a).
Our studies identified 326 genes that showed preferential expression of one
allele, with 170 of those showing greater than fourfold difference between the
two alleles. There are six imprinted genes with SNPs represented on the HuSNP
chip, and five of them had at least one heterozygous fetus among the samples
that we analyzed. Among these five genes, four of them showed differential
expression between the two alleles, whereas the fifth gene, WT1, is
known not to be imprinted in fetal kidney or liver tissues. The fact that
these known imprinted genes were identified by our method indicates that
additional novel imprinted genes can be identified from our list of genes that
showed differential expression between the two alleles. Thus, these genes
provide a rich source to identify novel imprinted genes and to study the role
of allelic variation in gene expression in normal physiology and in
diseases.
Real-time quantitative PCR is an established method for measuring
quantitatively gene expression and genotyping. By mixing DNAs with various
ratios from homozygous AA and BB individuals, it is possible to define a
region of linear response between the log of allele ratio and the log of
fluorescent intensity ratio and to use this linear regression line to
determine allele-specific gene expression. By using real-time quantitative
PCR, we found that the status of allelic gene expression variation in six of
the seven genes was in agreement with what we found in the HuSNP experiments
(Table 2).
The consistency of the HuSNP experiment system has been demonstrated in
Figure 1A. High degree of
correlation between duplicate experiments was observed
(Fig. 1A). We have taken three
approaches to further address the issue of consistency among individuals and
tissues. First, we selected 51 SNPs for which there were at least four
heterozygous individuals. Among the 51 SNPs, 29 (57%) showed skewed allelic
expression in at least two fetuses, and 10 (20%) showed skewed allelic
expression in at least three fetuses. To examine consistency of preferentially
expressing the same allele, we compared the genes that contained the 29 SNPs
and expressed preferentially one allele in both kidney and liver. We found
that 26 sample sets had ratios for both kidney and liver, and 19 out of 26
(73%) preferentially expressed the same allele. Seven out of 26 (27%)
expressed different alleles, and this appeared to be due to marginal
hybridization signals in one of the tissues. This is not unexpected for this
type of experiment that performs hybridization for several hundreds of genes.
Second, we extended the analysis to the 326 genes that showed preferential
expression of one allele, and there were multiple samples in which allelic
variation in gene expression was observed. We found that 272 (83%) out of the
326 genes preferentially expressed the same allele among different fetuses and
tissues (preferentially expressed allele was labeled with "+" in
Table 1 and in Supplemental
Table 1). Third, we measured allelic gene expression in seven individuals in
four tissues for three genes in TaqMan assay
(Table 2). We found that the
consistency for skewed allelic expression is 95% in cross-individual
comparison and cross-tissue comparison.
Our method will enable large-scale analysis of allelic gene expression of
clinical samples such as those from human cancers. The method can be easily
scaled up with a higher density chip such as the 10K SNP chip, which may be
available from Affymetrix in 2003. Our study indicates that the two alleles of
human genes are not always expressed "equally." On the contrary,
allelic variation in gene expression is common and may affect 20% to 50% of
human genes. This may be the basis for variation in the transmission of some
diseases, and it provides a potential mechanism for generating human
variation.
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METHODS
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HuSNP Experiments
Fetal tissues were obtained from the Birth Defects Research Laboratory,
University of Washington. The tissues were snap-frozen after surgery and were
stored in liquid nitrogen. Five fetuses were male, and two were female. The
ages of the fetuses ranged from 78 to 103 d. Fetal genomic DNA was prepared by
using the QIAamp DNA mini kit (Qiagen, Inc.). RNAs were isolated from fetal
tissues by using RNAzol B (Tel-Test, Inc.) according to the manufacturer's
protocol. Poly-A RNAs were isolated by using the Micro-Fast Track kit
(Invitrogen Corp.). cDNA was synthesized by using AMV reverse transcriptase
(Invitrogen Corp.).
The subsequent steps of multiplex PCR amplification, chip hybridization,
chip staining, and chip scanning were all conducted according to the GeneChip
HuSNP mapping assay manual (P/N 700308, Affymetrix, Inc.). Briefly, 120 ng of
genomic DNA or 6 ng of cDNA was used for each set of 24 multiplex PCR
reactions, and the resulting biotinylated amplicons were concentrated to 60
µL. Half (30 µL) of the concentrated amplicon was used for hybridization
to a HuSNP chip for 16 h at 44°C. The chip was then washed and stained
with a complex of streptavidin phycoerythrin (SAPE) and biotinylated
anti-streptavidin IgG antibody on a GeneChip fluidics station, followed by
scanning in a HP GeneArray Scanner (Affymetrix, Inc.). Genotyping calls were
made by using the Affymetrix MicroArray Suite (MAS) software, version 4.0.
Computational Analysis of the HuSNP Data
We downloaded the sequence of each of the 1494 SNPs and performed BLAST
search against dbEST. We also mapped SNPs by using the annotation in dbSNP. We
were able to map 1063 SNPs to the transcribed regions of genes. The criteria
for mapping SNPs to the transcribed regions were (1) at least two EST hits,
(2) E value <10-10, and (3) alignment >40 bp.
We extracted the intensity values for each probe from the .CEL files
generated by Affymetrix MAS 4.0. The .CEL files contain the fluorescent
intensity values for each of the probes. The HuSNP chip contains 16 probes for
each SNP locus. Four of the 16 probes match perfectly to allele A, four match
to allele B, four have one mismatch to allele A, and the other four have one
mismatch to allele B. Allele A and allele B represent the two alleles of the
SNP. Allele A and allele B are assigned alphabetically. For example, if a SNP
has C and T bases, the C base is defined as A allele and the T base is defined
as allele B (for allele information, see Supplemental
Table 1). Each probe contains
20 nucleotides. The center of the nucleotide probes is located at positions
-4, -1, 0, and 1 relative to the SNP. The four mismatch probes are identical
to the perfect match probes, except for one mismatched base, which is always
located in the center of the probe. There are typically four probe pairs for
each of the allele A and the allele B, except for 95 SNPs that have five probe
pairs. The value for each probe pair was computed by subtracting the mismatch
intensity from the perfect match intensity. A t test was used to
calculate a P value for the presence of signal (intensity greater
than zero) for each allele of each SNP. We considered a signal to be present
if at least one allele had signal (P < 0.01, t test).
Affymetrix defines a mini-block as a group of four probes that include a
perfect match probe for allele A (PMA), a mismatch probe for allele A (MMA), a
perfect match probe for allele B (PMB), and a mismatch probe for allele B
(MMB). We set (PMA - MMA) = 50 if (PMA - MMA) is <50 for each mini-block.
Similarly, baseline for allele B was set at 50. An allele A fraction, defined
as f = (PMA - MMA)/(PMA - MMA + PMB - MMB), was computed for each
mini-block, and the mean of the allele A fraction f from mini-blocks
was computed for each SNP. The gene expression difference between the two
alleles from a heterozygous individual can be quantified by using the ratio of
allele A to allele B, computed from f/(1 - f). For each
chip, we have intensities from two scans called scan A and scan B. Generally,
we used the intensity values from scan A. We used the intensity values from
scan B if the t test showed that both alleles have no signal in scan
A, and at least one of the alleles from scan B had signal. The ratio was
further normalized by the ratio of genomic DNAs for the SNP. Among the 602
SNPs analyzed in our studies, 39 had at least five heterozygous fetuses. We
computed the 95% confidence interval for the allelic ratio of genomic DNA for
each of these 39 SNPs, and the average confidence interval was between 0.5 and
2.0. This value was used to select those genes that show significant
difference in the expression between the two alleles.
To evaluate concordance between two duplicate experiments, we computed the
Pearson correlation coefficient between the two experiments by using the mean
intensity of the probe pairs from each allele of a SNP.
Real-Time Quantitative PCR Experiments
We used the ABI PRISM 7900HT Sequence Detection System and Assays-on-Demand
SNP Genotyping products for genotyping and allele-specific gene expression
(TaqMan assay). We followed the manufacturer's protocol for the preparation of
the PCR reactions. Sequence Detection Systems software (SDS 2.0) was used to
automatically collect and analyze the data and to generate the genotype calls.
We mixed the genomic DNAs from the two homozygous individuals, one with
genotype of AA and the other with genotype of BB, with the following ratios:
8: 1, 4: 1, 2: 1, 1: 1, 1: 2, 1: 4, and 1: 8 (VIC allele/FAM allele). TaqMan
assays were conducted, and the fluorescent intensity data were exported as
tab-delimited text files from the SDS software. For each mixing ratio of a
given gene, we calculated the log of (FAM intensity/VIC intensity) at the last
PCR cycle (cycle 40). We generated a standard curve (linear regression line),
y = a + bx, where y is the log of (FAM
intensity/VIC intensity) at a given mixing ratio, x is the log of
mixing ratio, a is the intercept, and b is the slope. We
then measured allele-specific gene expression by using real-time quantitative
PCR. We extrapolated the allele ratio on gene expression by intercepting log
of (FAM intensity/VIC intensity) on the standard curve.
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Acknowledgements
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We thank University of Washington Fetal Tissue Bank for providing fetal
samples. We like to thank Dr. Jeffery Struewing and Jenny Kelley for critical
reading of the manuscript.
The publication costs of this article were defrayed in part by payment of
page charges. This article must therefore be hereby marked
"advertisement" in accordance with 18 USC section 1734 solely to
indicate this fact.
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Footnotes
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Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.1006603.
1 Corresponding author. E-MAIL
leemax{at}mail.nih.gov;
FAX (301) 402-9325. 
[Supplemental material is available online at www.genome.org.]
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WEB SITE REFERENCES
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ftp://ftp.ncbi.nih.gov/snp/human;
National Center for Biotechnology Information (NCBI) dbSNP FTP
site.
Received November 18, 2002;
accepted in revised format June 9, 2003.
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ABSTRACT
RESULTS
