Xu Z, Taylor JA. SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies. Nucleic Acids Res 37: W600-W605

Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
Nucleic Acids Research (Impact Factor: 9.11). 06/2009; 37(Web Server issue):W600-5. DOI: 10.1093/nar/gkp290
Source: PubMed


We have developed a set of web-based SNP selection tools (freely available at where investigators can specify genes or linkage regions and select SNPs based on GWAS results, linkage disequilibrium (LD),
and predicted functional characteristics of both coding and non-coding SNPs. The algorithm uses GWAS SNP P-value data and finds all SNPs in high LD with GWAS SNPs, so that selection is from a much larger set of SNPs than the GWAS
itself. The program can also identify and choose tag SNPs for SNPs not in high LD with any GWAS SNP. We incorporate functional
predictions of protein structure, gene regulation, splicing and miRNA binding, and consider whether the alternative alleles
of a SNP are likely to have differential effects on function. Users can assign weights for different functional categories
of SNPs to further tailor SNP selection. The program accounts for LD structure of different populations so that a GWAS study
from one ethnic group can be used to choose SNPs for one or more other ethnic groups. Finally, we provide an example using
prostate cancer and demonstrate that this algorithm can select a small panel of SNPs that include many of the recently validated
prostate cancer SNPs.


Available from: Zongli Xu, Jun 25, 2015
W600–W605 Nucleic Acids Research, 2009, Vol. 37, Web Server issue Published online 5 May 2009
SNPinfo: integrating GWAS and candidate gene
information into functional SNP selection for
genetic association studies
Zongli Xu
* and Jack A. Taylor
Epidemiology Branch and
Laboratory of Molecular Carcinogenesis, National Institute of Environmental
Health Sciences, Research Triangle Park, NC 27709, USA
Received February 13, 2009; Revised April 13, 2009; Accepted April 14, 2009
We have developed a set of web-based SNP selec-
tion tools (freely available at http://www.niehs.nih.
gov/snpinfo) where investigators can specify
genes or linkage regions and select SNPs based
on GWAS results, linkage disequilibrium (LD), and
predicted functional characteristics of both coding
and non-coding SNPs. The algorithm uses GWAS
SNP P-value data and finds all SNPs in high LD
with GWAS SNPs, so that selection is from a much
larger set of SNPs than the GWAS itself. The pro-
gram can also identify and choose tag SNPs
for SNPs not in high LD with any GWAS SNP. We
incorporate functional predictions of protein struc-
ture, gene regulation, splicing and miRNA binding,
and consider whether the alternative alleles of a
SNP are likely to have differential effects on func-
tion. Users can assign weights for different func-
tional categories of SNPs to further tailor SNP
selection. The program accounts for LD structure
of different populations so that a GWAS study from
one ethnic group can be used to choose SNPs for
one or more other ethnic groups. Finally, we provide
an example using prostate cancer and demonstrate
that this algorithm can select a small panel of SNPs
that include many of the recently validated prostate
cancer SNPs.
The completion of the International HapMap Project (1)
and the development of advanced genotyping technologies
have made genome-wide association studies (GWAS) pos-
sible. These studies typically genotype more than 1000
cases and 1000 controls for 300 K to 1 million SNPs. A
number of GWAS have been published with many more in
progress (2–4). A number of disease-associated SNPs have
been identified and confirmed by these breakthrough stud-
ies with many more yet to come. Repeating GWAS
in additional individuals has helped to find more dis-
ease-associated SNPs, although doing so is costly.
Interestingly, the SNPs identified and subsequently con-
firmed in large replication samples are not always those
with the smallest P-value in the GWAS, and two GWAS
may have radically different P-values assigned to a con-
firmed SNP. For example, in prostate cancer a confirmed
SNP in MSMB from the initial GWAS had a P-value of
only 0.042, but the P-value was 7.31 10
in a follow up
study (4,5). Thus the list of potential SNPs from any
GWAS remains large. This large SNP list poses a problem
for validation studies where a very large number of people
are genotyped because custom arrays can cost more than
standard GWAS arrays.
For many diseases there exists a rich, diverse and
growing literature that can be used to identify genes and
chromosomal regions of high interest. This literature
includes existing genetic studies of linkage and candidate
genes as well as research on disease pathogenesis. For
example, information about disrupted cell signaling path-
ways and genomic-level expression data from comparisons
of tumor and normal tissues have identified interesting
candidate genes for cancer. Thus investigators may
have a large but finite set of genes and genomic regions
that they feel deserve particular scrutiny or they may
have a special interest in certain genes or chromosomal
Agnostic GWAS data provide a unique opportunity for
hypothesis driven candidate gene exploration, but the
sheer size and complexity of GWAS data can be difficult
to manage. Although it may not be difficult to find which
SNPs of a gene are directly included in a GWAS panel, it
is harder to determine which additional SNPs are tagged
by the panel, particularly when examining multiple ethnic
groups where linkage disequilibrium (LD) structure and
allele frequencies differ. There are a growing array of tools
*To whom correspondence should be addressed. Tel: +1 919 541 4631; Fax: +1 919 541 2511; Email:
Correspondence may also be addressed to Zongli Xu. Tel: +1 919 541 1677; Fax: +1 919 541 2511; Email:
Published by Oxford University Press 2009
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (
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at NIH Library on June 25, 2015 from
Page 1
for gene annotation (e.g. identifying regulatory elements,
alternative splicing, miRNA-binding sites), but many
researchers may find it difficult to gather and employ
these algorithms. Finally, while such tools predict puta-
tive functional regions for the Reference Sequence, they
do not necessarily consider if the alternative alleles of
SNPs in that sequence are likely to have different
Here we describe a comprehensive web server designed
to select SNPs for genetic association studies. In designing
this application we provide 3 pipelines for SNP selection
with options to combine all three pipelines. The candidate
gene pipeline uses both a user-provided list of candidate
genes and disease-specific GWAS data [readily available
from dbGaP (
gap) and elsewhere] to select SNPs that are predicted to
have functional consequences and that are in high LD
with a small P-value GWAS SNP. For genes where a
large proportion of the SNPs were not in LD with any
GWAS SNP and thus are uninvestigated in the GWAS,
the web application can pick LD tag SNPs to evaluate the
untagged SNPs. The second, genomic pipeline selects
SNPs with likely functional consequences from SNPs
with small P-value in a GWAS and from SNPs in high
LD with such SNPs. The third, linkage pipeline uses
a user-provided list of linkage regions to select small
P-value GWAS SNPs for each linkage region. The web
application has information on all SNPs in HapMap and
dbSNP and automatically constructs ethnic-specific LD
relationships from both sources provided that the SNPs
have population data available. In this way, SNPs that
were not genotyped in a GWAS, but are in LD with a
SNP that was genotyped, can be screened appropriately
and GWAS data generated in one ethnic group can be
used to pick SNPs in one or more other ethnic groups.
We illustrate this application using prostate cancer as an
example in which we start with a set of a priori candidate
genes, prostate cancer GWAS data, and a set of linkage
regions, and use the pipelines to select a small panel of
1361 SNPs. We evaluate the utility of the application
against the results of a follow-up validation study that
screened a much larger panel of 27 000 SNPs genotyped
in 8000 cases and controls and find that we included five
of the seven SNPs found to be associated with prostate
Candidate gene pipeline
A list of candidate genes for a particular disease can be
gleaned from published association studies, gene expres-
sion studies, disease pathways and the specific interests of
an investigator. Such lists may be very large, so we first
filter the list against GWAS results as shown in Figure 1.
We use SNPs that have genotype data in dbSNP as our
source of SNPs in and near a gene (for a user-specified
flanking region around the gene). We keep a gene if it has
at least one small P-value SNP (less than or equal to a
user-specified threshold, T1) in the GWAS. We also keep
genes that were not adequately represented by SNPs in the
GWAS panel. The percent of common SNPs (within a
gene and flanking region) in high LD (pairwise r
user-specified threshold) with any GWAS SNP (including
GWAS SNPs outside the gene and flanking region)
is calculated and genes with coverage less than a user-
specified cutoff A% are retained. Genes that do not
have SNPs with small P-value but do have sufficient cov-
erage by GWAS SNPs are excluded from further analysis.
For the candidate genes that pass the above screen we
extract SNPs from dbSNP and process this list as shown
in Figure 1. If a SNP was examined in the GWAS and had
a P-value less than the user-specified threshold T1 it is
retained. If a SNP was not in the GWAS but was in
high LD with a GWAS SNP that had a P-value larger
than T1 it is eliminated because we reason that it was
adequately evaluated by the GWAS and found to have
no association with disease. We then score all retained
SNPs for functional significance and apply different
minor allele frequency (MAF) filters depending on the
functional category of the SNP. These user-specified
MAF filters are provided because functionally important
SNPs often have lower MAF due to natural selection (6)
and we wish to provide extra flexibility to retain functional
SNPs below the MAF filter being applied to SNPs without
such function. The details of the functional predictions
used in this and other pipelines are provided in a separate
section below.
In the final processing step we select LD tag SNPs.
Because there are certain advantages to having functional
and small P-value SNPs directly assessed by the genotyp-
ing panel (instead of being indirectly assessed via LD)
we provide for the assignment of user-specified weights
for different categories of functional SNPs and small
P-value SNPs. If weights are assigned the null value of
1, then tag SNPs are selected simply by rank order, so
that SNPs that are in high LD with the largest number
of SNPs are selected first and SNPs that tag only them-
selves (singleton tags) are selected last. If a functional SNP
has a weight applied, then the weight act as multiplier of
the actual number of SNPs tagged so that it is more likely
to be selected early. For example, a functional SNP with a
weight of two that is in LD with four SNPs (including
itself) would have a weighted tag value of 2 4=8.
Investigators may modify a variety of values (e.g.
P-value threshold T1, LD threshold, or weights) to
adjust selected SNP counts to fit their genotyping panel
size and budget. We provide two options for additional
SNP reduction that we think are useful: (i) Each SNP
must be in LD with a user-specified minimum number of
common SNPs (after multiplied by the user-assigned
weights). For example, this option can be used to elimi-
nate singleton SNPs. (ii) A user can also specify the
maximum number of SNPs that are allowed for any one
gene using a method which is similar to selecting the best
N SNPs to optimize power (7). To insure that each gene
has some coverage, we also provide a user-specified min-
imum number of best SNPs (in terms of number of SNPs
captured at a specific LD threshold) that must be selected
for each gene even if they do not meet the previous
criterion for tag SNPs.
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Genome pipeline
Small P-value GWAS SNPs were considered in the previ-
ous pipeline if they occur within a specified candidate
gene, but for those in the remainder of the genome we
provide additional means of selection based on function
and evolutionary conservation (Figure 2A). In providing
this screen we consider not only all the GWAS SNPs that
were found to have small P-values, but the much larger
set of SNPs in dbSNP that meet two criteria: (i) they
are within a user-specified distance from a small P-value
GWAS SNP; and (ii) they are known to be in high LD
with a small P-value GWAS SNP. From this large pool we
screen SNPs based on functional predictions and apply
MAF filters. Finally, we eliminate redundant SNPs
based on a user-defined LD threshold.
Linkage pipeline
Linkage studies of family-based samples are another val-
uable source for candidate regions of the genome involved
in disease. GWAS panels have much higher SNP density
than linkage studies, and provide finer mapping infor-
mation using large population-based samples. Within
each user-specified linkage region, we select small
P-value GWAS SNPs at a user-specified threshold, rank
them by P-value and select a user-specified number of
non-redundant SNPs (based on pairwise LD) that have
the smallest P-value (Figure 2B).
Functional SNP prediction
Depending on their position and flanking sequence in a
gene, SNPs may have varied functional effects on protein
sequence, transcriptional regulation, RNA splicing or
Figure 1. GenePipe: decision tree to prioritize SNPs for candidate genes based on GWAS results, SNP functional prediction characteristics and pair-
wise LD. The six-sided boxes represent decision points and rectangles represent action steps or end points.
Figure 2. (A) GenomePipe: flow chart for functional SNP selection
from SNPs that are in high LD with small P-value GWAS SNPs. (B)
LinkagePipe: flow chart to prioritize SNPs in linkage loci based on
P-values in GWAS.
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miRNA binding. There are a variety of in silico tools
available for prediction of such functional regions within
genes, and we use these tools to help identify SNPs that
are more likely to affect biological function. In doing so
we examine not only whether a SNP occurs within a likely
functional region, but also whether the alternative alleles
are likely to have differential functional effects.
Coding SNPs. Within the coding region of a gene, we
identify nonsense SNPs that lead to premature termina-
tion of translation and are therefore very likely to affect
protein function. In addition, non-synonymous poly-
morphisms (nsSNPs) that lead to amino acid changes
may also affect protein function depending on the location
and nature of the amino acid substitution. We used two in
silico classification programs, Polyphen (8) and SNPs3D
(9), to predict the effect of an amino acid substitution on
the structure and function of a human protein, and then
classified nsSNPs as possibly or probably damaging or
Transcription-factor-binding sites (TFBS). If a SNP is
located at a TFBS of a gene, it may affect the level or
timing of gene expression. We identified such SNPs
according to the procedure shown in Supplementary
Figure 1. For each SNP within 5 kb upstream or 1 kb
downstream of a transcription start site (TSS), we first
extracted 29 bp DNA sequence on either side of the
SNP, and then used the MATCH (10) method to predict
possible TFBSs in the resulting 59 base pair sequence
using each alternative allele. A SNP was classified as
affecting TFBS activity if MATCH predicted a TFBS
with one allele but not with the other and the difference
in the matrix similarity scores (MSS) or core similarity
scores (CSS) between the two alleles was 0.2. Possible
scores for MSS and CSS range from 0 to 1 (10). We per-
formed predictions using all the 187 position weight matri-
ces classified as high quality non-redundant vertebrate
(mouse, rat and human) matrices in TRANSFAC
Release 12.1 (11). We used the default set of MATCH
score thresholds provided by TRANSFAC to allow for
10% false negative results. We also filtered out SNPs in
non-conserved TFBS. To find conserved TFBS, we first
identified the mouse or rat homolog sequence for each
predicted TFBS in the human genome based on 17-way
vertebrate multiz alignment from UCSC genome bioinfor-
matics web site. We then ran MATCH on these homolog
sequences with the same position weight matrices.
We categorized a TFBS as conserved if both mouse and
rat homolog sequences also have the same predicted
TFBS. Several studies (12–14) show that using both the
predicted conserved TFBS together with the regulatory
potential score (13) can improve predictions, so we also
provide this option on the web server.
Splice sites. SNPs that are located within two base pairs
of an intron–exon junction, or located at exonic splicing
enhancer (ESE) or exonic splicing silencer (ESS)-binding
sites may disrupt mRNA splicing and severely affect pro-
tein function (15). We predicted ESE and ESS sites using
procedure outlined in Supplementary Figure 2. If an exon
was longer than 140 base pairs, only SNPs within the first
and last 70 base pairs of each exon were evaluated because
the effect of alternative alleles on the activity of ESEs and
ESSs decrease with distance from the splice site (16,17).
We only considered a maximum of 10 base pairs on either
side of a SNP because there are no significant compensa-
tory or correlated relationships between non-overlapping
ESE or ESS motifs (18). ESE sites were predicted using
RESCUE ESE (19) or ESEfinder (20) methods. ESS sites
were predicted using the FAS–ESS (21) method. A SNP
was classified as affecting splicing activity if there was
at least one predicted binding site with one allele, but
none with the other allele. In order to reduce false positive
results, we excluded predicted binding sites within an exon
if, based on Ensembl transcripts isoform data, there were
no alternatively spliced transcripts observed involving
the exon. For example, suppose a gene has eight exons
and five different transcript isoforms reported in
Ensembl. If there was a predicted ESE or ESS-binding
site in exon 3 but all five transcripts include exon 3, then
we would exclude the site.
MicroRNA-binding sites. MicroRNAs (miRNA) are
21–23-base single-stranded RNA molecules that bind to
the end of a messenger RNA (mRNA) and can inhibit
protein translation. Human miRNA is usually comple-
mentary to a site in the 3
UTR region of an mRNA.
We extracted the 20 base pair flanking sequence on both
sides of SNPs in the 3
UTR region of genes. We search for
possible miRNA-binding sites on the 41 base pair DNA
sequence for each allele of a SNP using the software
miRanda (22), with default parameter values. Using the
procedure outlined in Supplementary Figures 3 and 4, we
predicted putative miRNA-binding sites for all 677 human
miRNAs in the miRBase database (23). We excluded
SNPs in miRNA-binding sites that were not conserved
in either the mouse or rat homolog sequences. We classi-
fied a SNP as affecting miRNA-binding site activity if the
miRanda scores for the two alleles differed by 16, a value
which is equivalent to that of a SNP in the ‘seed’ region
of a miRNA-binding site.
Web server and usage
We have incorporated these methods into a user-friendly
web server: SNPinfo (
The web utility is supported by a set of optimized
mySQL databases. Depending on the specific pipeline
being used (GenePipe, GenomePipe or LinkagePipe),
an investigator may input several types of data: a list of
candidate genes, a GWAS SNP list of Reference Sequence
(rs) numbers with associated P-value from the GWAS of
interest, or a list of linkage loci.
LD relationships between SNPs may differ between
ethnic groups so we have deposited, as a central resource
of our web server, the information on SNP genotype data
and pairwise LD for each ethnic group. This allows the
user to incorporate the results of a GWAS from one ethnic
group into LD tag SNP selection for one or more different
ethnic groups. To evaluate LD relationships between
SNPs, a user can use not only pair-wise LD data
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calculated from HapMap genotype data for 11 popula-
tions in HapMap Phase III, but also has the option to
use pair wise LD based on all dbSNP genotype data for
each of five population groups (African American, Asian,
European, Hispanics and sub-Saharan African). dbSNP
genotype data includes all deposited HapMap data as
well as additional SNPs, individuals and ethnic groups.
dbSNP includes genotype data from many different geno-
typing and resequencing efforts on sometimes overlapping
sets of individuals. We combined genotypes for indivi-
duals of the same ethnic group. If multiple submitters
genotyped the same SNP in the same person and the gen-
otype calls are inconsistent, we assigned the person the
most commonly called genotype or a missing call
if they are equally split. We employed an efficient
greedy algorithm that was originally implemented in
TAGster (24) to select LD tag SNP for single or multiple
In addition to the three pipelines the server provides
three additional tools. The first of these ‘TagSNP’ allows
a user to combine the SNP lists selected from different
pipelines and eliminate redundant SNPs based on LD
relationships and SNPs with low SNP design scores. It
also allows the user to mandate inclusion of SNPs of spe-
cial interest, or exclusion of undesired SNPs. This same
tool may be used as a stand-alone tool to find and list
SNPs, choose LD tag SNPs, and produce high quality
LD or genotype figures for individual genes or chromo-
some regions. A second stand-alone tool, ‘FuncPred’
allows a user to query functional prediction results and
ethnic group allele frequencies for all of the SNPs in a
gene or chromosomal region, or for a list of input SNPs.
The final tool ‘SNPseq’ allows a user to visualize SNP
related information and CpG regions in DNA sequence
context for an individual SNP, gene, or region of a chro-
mosome. This is particularly useful for PCR primer
Example and validation
We have used the GWAS data from the Cancer Genetics
Markers of Susceptibility (CGEMS) project on prostate
cancer (4) to demonstrate the utility of our method. This
GWAS genotyped 550 K SNPs in 1172 prostate cancer
cases and 1157 controls of European origin. We used
our web utility to construct a small SNP genotyping
panel for a genetic association study on prostate cancer
in African-American and European-American men.
Based on published candidate gene association studies,
gene expression studies, and pathway analysis we con-
structed a list of 848 candidate genes of interest in prostate
cancer. Using GenePipe, 542 genes were excluded because
none of the GWAS SNPs in these genes had a P-value
0.05 and there were sufficient GWAS SNPs to capture
(at r
0.8) more than 50% of common (MAF 0.05)
SNPs in Europeans. For the remaining 306 genes, 822
non-redundant SNPs were selected as outlined in
Figure 1 with the following GenePipe parameter values:
gene upstream region = 5 kb, gene downstream
region = 1 kb, MAF = 0.05 for all SNPs, weight = 3 for
any predicted functional SNP and small P-value SNPs,
weight = 1 for all other SNPs, r
threshold = 0.8, mini-
mum number of SNPs tagged by each selected tag
SNP = 3, minimum number of tag SNPs/gene = 1, and
maximum number of tag SNPs/gene = 5.
The CGEMS GWAS reported 6034 GWAS SNPs with
P 0.01. GenomePipe identified 41755 SNPs that are in
high LD with these GWAS SNPs (r
0.8), and from the
41755 SNPs selected 543 common SNPs (MAF 0.05)
that were predicted to be functional by at least one of
the prediction methods.
Published studies have identified 43 non-overlapping
linkage regions for prostate cancer. As shown in
Figure 2B, we used LinkagePipe to select 266 GWAS
SNPs using the following parameter values (MAF =
0.05, Maximum number of SNPs/linkage locus = 7,
GWAS P threshold = 0.01, LD threshold = 0.8).
The resulting SNP lists from GenePipe, GenomePipe
and LinkagePipe were combined and we used TagSNP
to eliminate duplicate and redundant SNPs, or SNPs
with low assay design scores, yielding a set of 1361
SNPs. Of these, 709 (52%) were GWAS SNPs and the
remaining 48% were new SNPs not in the GWAS which
were selected to provide additional functional examina-
tion of genes.
Although the selection algorithm used the P-value data
for 550 K SNPs from the CGEMS GWAS, we did not, in
this example, use information from other GWAS data sets
or from the validation portion of the CGEMS initial study
(4). The CGEMS follow up study was particularly robust
because it genotyped 26 958 SNPs, including all SNPs with
P-value <0.068 from the initial CGEMS GWAS, in 3941
cases and 3964 controls (5). This provides an unbiased
opportunity to evaluate whether the very small set of
SNPs selected by our algorithm include the SNPs vali-
dated in a genotyping panel that was many times larger.
The CGEMS validation study identified seven prostate
cancer related SNPs which had P-value ranks in the initial
GWAS ranging from 116 (P = 0.0004) to 24407
(P = 0.042). Our algorithms selected five (71%) of those
seven SNPs. Three of the five SNPs were selected by
GenePipe, one was selected by GenomePipe and three
were selected by LinkagePipe. Of the two SNPs that
were missed, rs10486567 in JAZF1 was not in our candi-
date gene list because at the time we constructed the gene
list, JAZF1 had not previously been reported in the liter-
ature as having any association with prostate cancer. The
other SNP, rs10896449, was not located in a known gene
or linkage region. Although the very small panel of SNPs
selected by the algorithm cannot substitute for massive
follow-up genotyping, it performs very well with 2.5%
(709 vs. 26 958) of the GWAS SNPs, and in addition ded-
icates almost half of the SNPs to new functional and can-
didate gene polymorphisms that were unexplored in the
half million GWAS SNP panel.
SNP selection for an association study can be a complex
problem. Decades of diverse investigation provide a tre-
mendous amount of information on genes, pathways, and
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chromosomal regions that appear to be linked to disease.
GWAS offers an agnostic approach to investigating
SNP-disease association, and the results of such studies
offers a wealth of data to inform the next generation of
investigation. Here, we develop a user-friendly web server
to incorporate such clinical, experimental, mechanistic,
and computational information with the results of
GWAS in order to organize, annotate, and select SNPs.
The web server can be used for either small or large-scale
SNP selection and is particularly useful for association
studies. It uses both functional prediction and GWAS
results to select not only SNPs included in the GWAS,
but other functional SNPs in dbSNP that were not in
the GWAS. Considering the varied interests and emphasis
different investigators may bring to a problem, we pro-
vided many tunable parameters in each web utility, so
the algorithm can be adjusted to meet different needs.
We employ several methods for functional sequence
assessment and predict functional consequence of different
alleles of a SNP. To reduce the number of false positive
results, we perform the predictions in only the most prob-
able genomic regions for each category of functional
sequence site (such as the gene promoter region for TFBS
or the 3
UTR for microRNA-binding sites) and use phy-
logenetic footprint information to filter out non-conserved
putative functional sequence. The SNP selection algorithm
uses functional prediction results to prioritize LD tag SNP
selection. These LD tag SNPs capture other unexamined
SNPs in and around a gene, including SNPs with unknown
or unpredicted functional consequences. The web utility
options allow an investigator to choose prediction methods
and assign weights to those predictions for study-specific
SNP selection. Functional sequence prediction is a rapidly
developing field. The web server structure allows rapid
updates as better methods of functional prediction
become available and it allows expansion to include pre-
dictions on other biologic functions.
Supplementary Data are available at NAR Online.
This research was supported by the Intramural Research
Program of the NIH, National Institute of Environmental
Health Sciences. Funding for open access charge:
National Institute of Environmental Health Sciences.
Conflict of interest statement. None declared.
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  • Source
    • "One SNP, rs1800544, in the 5 0 UTR of the ADRA2A gene revealed nominal association with MPH response in adult ADHD patients. Located in a transcription factor binding site, rs1800544 may affect gene expression and, consequently , MPH's pharmacological effect on the symptoms of ADHD (Xu and Taylor 2009). ADRA2A encodes an adrenergic receptor located pre-and post-synaptically in both the central and peripheral nervous system. "
    [Show abstract] [Hide abstract] ABSTRACT: Attention-deficit/hyperactivity disorder (ADHD) is a common childhood onset neuropsychiatric disorder with a complex and heterogeneous symptomatology. Persistence of ADHD symptoms into adulthood is common. Methylphenidate (MPH) is a widely prescribed stimulant compound that may be effective against ADHD symptoms in children and adults. However, MPH does not exert satisfactory effect in all patients. Several genetic variants have been proposed to predict either treatment response or adverse effects of stimulants. We conducted a literature search to identify previously reported variants associated with MPH response and additional variants that were biologically plausible candidates for MPH response. The response to MPH was assessed by the treating clinicians in 564 adult ADHD patients and 20 genetic variants were successfully genotyped. Logistic regression was used to test for association between these polymorphisms and treatment response. Nominal associations (p < 0.05) were meta-analysed with published data from previous comparable studies. In our analyses, rs1800544 in the ADRA2A gene was associated with MPH response at a nominal significance level (OR 0.560, 95 % CI 0.329-0.953, p = 0.033). However, this finding was not affirmed in the meta-analysis. No genetic variants revealed significant associations after correction for multiple testing (p < 0.00125). Our results suggest that none of the studied variants are strong predictors of MPH response in adult ADHD as judged by clinician ratings, potentially except for rs1800544. Consequently, pharmacogenetic testing in routine clinical care is not supported by our analyses. Further studies on the pharmacogenetics of adult ADHD are warranted.
    Full-text · Article · Apr 2016 · Journal of Neural Transmission
  • Source
    • "Rs2228611 is located in exon 17 of DNMT1 and the Gto-A change mediates a synonymous variation (CCG→CCA, Proline→Proline). Bioinformatics tool SNPinfo predicts that rs2228611 is in the region of exonic splicing enhancer (ESE) [38]. The G-to-A variation may alter the binding activity to serine/arginine-rich (SR) protein, change pre-RNA splicing of DNMT1, and therefore lead to alteration of normal DNMT1 expression. "
    [Show abstract] [Hide abstract] ABSTRACT: DNA methyltransferase 1 (DNMT1) plays a pivotal role in maintaining DNA methylation status. Polymorphisms of DNMT1 may modify the role of DNMT1 in prognosis of gastric cancer (GC). Our aim was to test whether polymorphisms of DNMT1 gene were associated with overall survival of GC. Four hundred and forty-seven GC patients who underwent radical tumorectomy were enrolled in the study. Five tagging SNPs (rs10420321, rs16999593, rs2228612, rs2228611, and rs2288349) of the DNMT1 gene were genotyped by TaqMan assays. Kaplan-Meier survival plots and Cox proportional hazard regression were used to analyze the associations between SNPs of DNMT1 and survival of GC. Patients carrying rs2228611 GA/AA genotype tended to live longer than those bearing the GG genotype (HR 0.68, 95% CI: 0.51–0.91, P = 0.007 ). Further multivariate Cox regression analysis showed that rs2228611 was an independent prognostic factor (GA/AA versus GG: OR 0.67, 95% CI 0.49–0.91, P = 0.010 ). Nevertheless, other SNPs did not show any significant associations with survival of GC. Polymorphisms of the DNMT1 gene may affect overall survival of GC. The SNP rs2228611 has the potentiality to serve as an independent prognostic marker for GC patients.
    Full-text · Article · Mar 2016 · Disease markers
  • Source
    • "The GWAVA score ranges 0–1, with higher score indicating variants predicted as more likely to be functional. We then used another tool (SNPinfo) ( htm) (Xu and Taylor 2009 ) to predict the potential functional consequences of the studied SNPs. Finally, we predicted the potential functional consequences of rs3916441 and rs31480 using RegulomeDB (http://www.regulomedb. "
    [Show abstract] [Hide abstract] ABSTRACT: Greatly expanded brain volume is one of the most characteristic traits that distinguish humans from other primates. Recent studies have revealed genes responsible for the dramatically enlarged human brain size (i.e., the microcephaly genes), and it has been well documented that many microcephaly genes have undergone accelerated evolution along the human lineage. In addition to being far larger than other primates, human brain volume is also highly variable in general populations. However, the genetic basis underlying human brain volume variation remains elusive and it is not known whether genes regulating human brain volume variation also have experienced positive selection. We have previously shown that genetic variants (near the IL3 gene) on 5q33 were significantly associated with brain volume in Chinese population. Here, we provide further evidence that support the significant association of genetic variants on 5q33 with brain volume. Bioinformatic analyses suggested that rs31480 is likely to be the causal variant among the studied SNPs. Molecular evolutionary analyses suggested that IL3 might have undergone positive selection in primates and humans. Neutrality tests further revealed signatures of positive selection of IL3 in Han Chinese and Europeans. Finally, extended haplotype homozygosity (EHH) and relative EHH analyses showed that the C allele of SNP rs31480 might have experienced recent positive selection in Han Chinese. Our results suggest that IL3 is an important genetic regulator for human brain volume variation and implied that IL3 might have experienced weak or modest positive selection in the evolutionary history of humans, which may be due to its contribution to human brain volume.
    Full-text · Article · Feb 2016 · Human Genetics
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