Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes

Abstract

More than 1100 genetic loci have been correlated with drug response outcomes but disproportionately few have been translated into clinical practice. One explanation for the low rate of clinical implementation is that the majority of associated variants may be in linkage disequilibrium (LD) with the causal variants, which are often elusive. This study aims to identify and characterize likely causal variants within well-established pharmacogenomic genes using next-generation sequencing data from the 1000 Genomes Project. We identified 69,319 genetic variations within 160 pharmacogenomic genes, of which 8207 variants are in strong LD (r2>0.8) with known pharmacogenomic variants. Of the latter, eight are coding or structural variants predicted to have high impact, with 19 additional missense variants that are predicted to have moderate impact. In conclusion, we identified putatively functional variants within known pharmacogenomics loci that could account for the association signals and represent the missing causative variants underlying drug response phenotypes.

Full text

The Pharmacogenomics Journal (2019) 19:127–135
https://doi.org/10.1038/s41397-018-0048-y
ARTICLE
Whole genome sequencing identifies high-impact variants in well-
known pharmacogenomic genes
Jihoon Choi1,2 ●Kelan G. Tantisira3,4 ●Qing Ling Duan1,2
Received: 5 July 2017 / Revised: 10 July 2018 / Accepted: 10 August 2018 / Published online: 14 September 2018
© Springer Nature Limited 2018
Abstract
More than 1100 genetic loci have been correlated with drug response outcomes but disproportionately few have been
translated into clinical practice. One explanation for the low rate of clinical implementation is that the majority of associated
variants may be in linkage disequilibrium (LD) with the causal variants, which are often elusive. This study aims to identify
and characterize likely causal variants within well-established pharmacogenomic genes using next-generation sequencing
data from the 1000 Genomes Project. We identified 69,319 genetic variations within 160 pharmacogenomic genes, of which
8207 variants are in strong LD (r2>0.8) with known pharmacogenomic variants. Of the latter, eight are coding or structural
variants predicted to have high impact, with 19 additional missense variants that are predicted to have moderate impact. In
conclusion, we identified putatively functional variants within known pharmacogenomics loci that could account for the
association signals and represent the missing causative variants underlying drug response phenotypes.
Introduction
The current paradigm of drug therapy follows a “trial-and-
error”approach where patients are prescribed a drug at a
standardized dose with the expectation that alternative
therapies or doses will be given during a return clinical visit
(s) [1]. Not surprisingly, this is inefficient and potentially
hazardous for patients who require urgent care or are sus-
ceptible to adverse events, which may result in prolonged
suffering and fatalities [2]. A better understanding of the
modulators of drug response will improve and hopefully
replace our current trial-and-error approach of drug therapy
with more precise methods that are based on scientific
knowledge [3].
To date, more than 1100 genetic loci have been corre-
lated with drug response phenotypes (The Pharmacoge-
nomics Knowledgebase (PharmGKB): www.pharmgkb.org)
but only a small fraction of these genomic findings have
been implemented into clinical practice. In 2009,
PharmGKB partnered with the Pharmacognomics Research
Network (PGRN) to establish the Clinical Pharmacoge-
netics Implementation Consortium (CPIC)) [4–6]. The goal
of CPIC is to provide specific guidelines that instruct clin-
icians on how to use or interpret a patient’s genetic test
results to determine the optimal drug and dosage to each
patient. As of June 2017, there are 36 drug–gene pairs with
CPIC guidelines published, although there are 127 well-
established pharmacogenomic genes identified as CPIC
genes and 64 additional genes labeled as very important
pharmacogenes (VIP) by the PharmGKB curators, which
totals to 160 unique genes.
An example of a CPIC guideline is one that instructs
physicians on how to interpret genomic information from
clinical assays to determine a therapeutic dosage for war-
farin, a commonly used drug for the prevention of throm-
bosis [7]. Warfarin is known to have a narrow therapeutic
These authors contributed equally: Kelan G. Tantisira, Qing Ling
Duan
*Qing Ling Duan
qingling.duan@queensu.ca
1Department of Biomedical and Molecular Sciences, Queen’s
University, Kingston, ON, Canada
2School of Computing, Queen’s University, Kingston, ON, Canada
3Channing Division of Network Medicine, Brigham and Women’s
Hospital and Harvard Medical School, Boston, MA, USA
4Division of Pulmonary and Critical Care Medicine, Brigham and
Women’s Hospital and Harvard Medical School, Boston, MA,
USA
Electronic supplementary material The online version of this article
(https://doi.org/10.1038/s41397-018-0048-y) contains supplementary
material, which is available to authorized users.
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index and wide effect variances among patients. For
example, a conventional dose of warfarin may not be an
effective anticoagulant in some patients or induce adverse
events (e.g. excessive bleeding) in others [8]. Thus, it is
often difficult to achieve and maintain a targeted effect by
administering conventional doses. Recent advancement in
pharmacogenomics helped to facilitate genetic tests of two
genes that can be used to predict a patients’sensitivity to the
drug prior to administration. Specifically, the therapeutic
dosage of warfarin may be calculated based on one’s gen-
otypes at these loci, which has resulted in a significant
improvement in drug safety [8,9].
Despite the successful translation of a small fraction of
pharmacogenomics findings into clinical practice, the rate of
clinical implementation has been slow [6]. One explanation
is that the majority of pharmacogenomics loci are correlated
with drug response but do not represent the actual, causal
variants themselves [10–12]. We hypothesize that the
majority of known pharmacogenomics loci are genetic
markers that tag causal variants, which have yet to be
identified and are likely to be in linkage disequilibrium
(LD) with the associated markers. The use of associated
variants instead of the causal variants in clinical tests is
limiting in that it may not reliably predict drug response
[13].
The primary objective of this study is to identify
potentially causal variants in well-established pharmacoge-
nomics-associated genes, which may account for the
reported association signals. Specifically, we used whole-
genome sequencing data from the 1000 Genomes Project
[14,15] to derive all genetic variations identified within the
160 unique CPIC and VIP pharmacogenomics genes. Next,
we tested the LD with known pharmacogenomic variants,
and determined the predicted function of these LD variants
using annotation databases and clinical outcome databases.
Our results include a catalog of potentially functional
variants that are in LD with well-established
pharmacogenomics variants and could represent the causa-
tive mutations within these loci.
Results
Selection of pharmacogenomics loci and annotation
of variants
We selected 127 CPIC genes and 64 VIP genes (total of 160
unique loci) from PharmGKB, which we deemed as “well-
established”pharmacogenomics loci (Supplemental data 1).
Next, we identified 887,980 variants within these loci using
next-generation sequencing data from the 1000 Genomes
Project Phase I, of which 69,319 were variants with minor
allele frequencies >1% (Supplemental data 2). Annotation
analysis using SnpEff [16] (genetic variant annotation and
effect prediction toolbox) revealed that 65,333 (94%) of
these variants were single-nucleotide polymorphisms
(SNPs), 1404 (2%) were insertions, and 2582 (4%) were
deletions. As shown in Fig. 1, the majority of these occur
within intronic regions (~75%), with the remainder located
3′or downstream (~11%), 5′or upstream (~9%), and
exonic (~2%). Of the coding variants, approximately half of
these variants are missense (~49%), or synonymous muta-
tions (~50%) with some occurrences of nonsense (~1%)
mutations. We compared our findings with annotation
results of whole-genome sequencing data of 1000 Genome
Project phase I dataset (http://snpeff.sourceforge.net/1kg.
html) and confirmed that the results of variant annotation
within 160 PGx genes are within an expected range (Sup-
plemental Fig. 1).
LD analysis
We assessed the LD between associated variants within
known pharmacogenomics loci and variants identified in
Intron
74.55%
Upstream
9.33%
Downstream
11.08%
Exon
1.83%
Intergenic
1.60%
UTR 3 Prime
0.80%
UTR 5 Prime
0.21%
Tran sc ri pt
0.46%
Splice Site
0.11%
Mof
0.03%
Fig. 1 Genomic regions of all
variants identified from the 1000
Genomes Project database
within 160 known
pharmacogenomics genes.
Locations of all the single-
nucleotide variants identified
within the 160
Pharmacogenomics loci using
sequence data from the 1000
Genomes Project
128 J. Choi et al.

our study. Analysis of LD was done in each of the four
populations (American, European, East Asian, African)
from Phase I of 1000 Genomes Project. This resulted in
8207 novel variants forming 21,256 instances of LD (r2>
0.8) with 859 known pharmacogenomics variants (Supple-
mental Data 3).
High-impact variations
We identified eight variants predicted to have a high-impact
using SNPEff from the 1000 GP database that were in LD
(r2> 0.8) with 22 known pharmacogenomics variants.
These included potentially functional variants that code for
an alternative splice donor site, structural interaction, fra-
meshift mutation, stop gain, or stop lost variation. Table 1
lists these new LD variants along with the corresponding
pharmacogenomics variants, the majority of which are
predicted to be non-coding located within introns, up/
downstream, and synonymous, with only few instances of
missense and frameshift variants.
Moderate-impact variations
We identified 19 missense variants that are in LD with 32
pharmacogenomics variants, which are predicted to have a
moderate, low, or modifying effects by SNPEff (Table 2).
Among the newly identified variants, two are regulatory variants
that could potentially affect protein binding, and one has been
associated with neural tube defects and spina bifida cystica.
Low-impact variations
From the total of 8207 variants in LD, 7751 variants are
classified by SNPEff as variants with unpredictable impact
or “modifier”variants. These are in LD with 920 known
pharmacogenomics variants with similar impact features. Of
these, 324 modifier variants were potential regulatory var-
iants affecting gene expression, protein binding, or tran-
scription factor binding.
In this study, we will focus on modifier variants that are
classified under category 1 of RegulomeDB database, which
are known eQTLs or variants correlated with variable gene
expression. Among 324 modifier variants with Reg-
ulomeDB scores, 84 variants were classified as category 1,
forming 213 instances of LD with 73 pharmacogenomics
variants which are predicted to have low or modifying
effects (Supplemental data 4).
Variants associated with clinical outcomes
Using SNPedia database, we discovered 46 variants in LD
that are correlated with clinical phenotypes as documented
in Supplemental data 5.
Discussion
This manuscript reports the identification of potentially
functional genetic variants within genes previously corre-
lated with drug response outcomes. We show that some of
the novel variants identified from next-generation sequen-
cing (NGS) of whole genomes (Phase I of the 1000
Genomes Project) are in LD with well-known pharmaco-
genomics variants and could account for the functional
basis underlying the association signals. Many of these LD
variants code for non-synonymous amino acid substitutions,
frameshift mutations introduce a splice variant that results in
alternative splicing of the transcript, or located in non-
coding regions but are correlated with gene expression
levels (expression quantitative trait loci or eQTL) or other
clinical phenotypes.
In this study, we used LD analysis to determine the
correlation between novel genetic variants identified from
the 1000 Genomes Project database and known pharma-
cogenomics variants. We reasoned that any variant(s) in
strong LD (r2> 0.8) with the known pharmacogenomics
loci could account for the association signal and have
potential to be the actual causal variants at these genomic
loci. In order to prioritize the identified variants, we used a
popular annotation toolbox (SNPEff) to predict the function
of each variant. In addition, we used additional information
such as RegulomeDB and SNPedia to prioritize the variant
(s) of higher impact from those with low impact.
Many of the variants we identified are “novel”in that
these have not been reported in earlier pharmacogenomics
studies. For example, we identified a splice donor variant
(rs28364311) located on a VIP gene ADH1A. This variant is
in LD with a pharmacogenomics-associated variant,
rs6811453, which is associated with increased resistance to
cytarabine, fludarabine, gemtuzumab ozogamicin, and
idarubicin in patients with acute myeloid leukemia [17].
The associated pharmacogenomics variant is non-coding
and have no known biological function as it is located
downstream (3′) of the gene. Considering the potential
impact of rs28364311 on splicing and its strong LD with the
associated pharmacogenomics variant, it is plausible that the
splice variant identified is the functional variant that
accounts for the original association signals at this locus.
Moreover, we identified that a stop gain variant rs4330
from the VIP gene ACE, encoding the angiotensin-
converting enzyme, is in LD with six known pharmacoge-
nomics variants (rs4341, rs4344, rs4331, rs4359, rs4363,
and rs4343), whereas the latter are intronic or code for
synonymous changes, which are less likely to have detri-
mental effects on the gene product, the identified rs4330
codes for a truncated protein that is likely to have detri-
mental effects.
Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes 129

Table 1 Variants with high-impact predictions, which are in LD with known pharmacogenomics variants
Chr New variant Gene Functional annotation PharmGKB variant Gene Functional annotation EUR r2EAS r2AMR r2AFR r2
3 rs13146 UMPS Structural interaction variant rs1801019 UMPS Missense variant 0.98 1.00 1.00 0.98
4 rs28364311 ADH1A Splice donor variant & intron variant rs6811453 ADH1A Downstream gene variant 0.99 1.00 1.00 1.00
6 rs677830 OPRM1 Stop gained rs558025 OPRM1 Downstream gene variant 1.00 1.00 0.98 <0.8
7 rs6977165 CYP3A5 Stop lost rs41303343 CYP3A5 Frameshift variant <0.8 <0.8 <0.8 0.80
17 rs4330 ACE Stop gained rs4341 ACE 3 prime UTR variant 0.99 0.95 1.00 0.95
rs4343 ACE Synonymous variant 0.95 0.95 0.87 <0.8
rs4344 ACE Upstream gene variant 0.99 0.96 1.00 0.90
rs4331 ACE Synonymous variant 0.86 <0.8 0.88 0.84
rs4359 ACE Intron variant 0.96 <0.8 0.91 <0.8
rs4363 ACE Splice region variant & intron variant 0.93 <0.8 0.86 <0.8
19 rs11322783 IFNL4 Frameshift variant rs12980275 IFNL3P1 Upstream gene variant <0.8 0.87 0.87 <0.8
rs8105790 IFNL3P1 Upstream gene variant <0.8 0.94 <0.8 <0.8
rs4803217 IFNL3 Downstream gene variant 0.83 0.97 0.87 <0.8
rs11881222 IFNL4 Downstream gene variant 0.87 0.94 0.84 <0.8
rs28416813 IFNL3 5 prime UTR variant 0.88 0.86 0.94 <0.8
rs12979860 IFNL3 Upstream gene variant 0.94 0.87 0.93 <0.8
rs8109886 IFNL4 Upstream gene variant <0.8 0.89 <0.8 <0.8
rs8113007 IFNL4 Upstream gene variant 0.88 0.97 0.84 <0.8
rs8099917 IFNL4 Upstream gene variant <0.8 0.94 <0.8 <0.8
rs7248668 IFNL4 Upstream gene variant <0.8 0.94 <0.8 <0.8
21 rs881712 CBR3 Structural interaction variant rs8133052 CBR3 Missense variant 0.94 1.00 0.83 <0.8
22 rs3761423 ADORA2A-AS1 Splice donor variant & intron variant rs5996696 ADORA2A Upstream gene variant <0.8 0.90 <0.8 <0.8
Chr Chromosome, EUR r2linkage disequilibrium in the European Population of 1000 Genomes project measured in r-squared, EAS r2linkage disequilibrium in the Eastern Asian Population of
1000 Genomes project measured in r-squared, AMR r2linkage disequilibrium in the American Population of 1000 Genomes project measured in r-squared, AFR r2linkage disequilibrium in the
African Population of 1000 Genomes project measured in r-squared. Annotation definitions:structural interaction variant =These are “within protein”interaction loci, which are likely to be
supporting the protein structure. They are calculated from single protein PDB entries, by selecting amino acids that are: (a) atom within 3Å of each other and (b) are far away in the AA sequence
(over 20 AA distance). The assumption is that, since they are very close in distance, they must be “interacting”and thus important for protein structure. For more information, see http://snpeff.
sourceforge.net/SnpEff_manual.html.
130 J. Choi et al.

Table 2 Variants predicted with moderate impact identified in this study, which are in LD with known pharmacogenomics variants
Chr New variant Gene Functional annotation PharmGKB variant Gene Annotation EUR r2EAS r2AMR r2AFR r2
18 rs2853533* C18orf56 Missense variant & TFBS variant rs2853741 RP11–806L2.5 Upstream gene variant <0.8 0.85 <0.8 <0.8
1 rs55867221 C1orf167 Missense variant & TFBS variant rs17367504 CLCN6 Upstream gene variant <0.8 0.9 <0.8 <0.8
rs3737967 C1orf167 Missense variant <0.8 0.98 0.87 <0.8
rs2274976 MTHFR Missense variant <0.8 0.96 0.87 <0.8
1 rs1537514 C1orf167 Missense variant rs3737967 C1orf167 Missense variant <0.8 0.98 0.87 <0.8
rs2274976 MTHFR Missense variant <0.8 0.96 0.87 <0.8
rs17367504 CLCN6 Upstream gene variant <0.8 0.9 <0.8 <0.8
1 rs1800595 F5 Missense variant rs6018 F5 <Missense variant 1 1 1 1
1 rs6027 F5 Missense variant rs6018 F5 Missense variant 0.94 0.89 0.97 <0.8
1 rs6033 F5 Missense variant rs6018 F5 Missense variant <0.8 0.83 <0.8 <0.8
3 rs3732765 MED12L Missense variant rs9859538 MED12L Intron variant <0.8 0.97 <0.8 <0.8
rs10935842 P2RY12 Upstream gene variant 1 0.99 0.97 <0.8
rs6798637 P2RY12 upstream gene variant 0.89 <0.8 <0.8 <0.8
4 rs1693482 ADH1C Missense variant rs1662060 ADH1C Downstream gene variant 1 1 0.96 1
rs698 ADH1C Missense variant 1 1 0.96 1
4 rs4963 ADD1 Missense variant rs4961 ADD1 Missense variant 0.88 0.99 0.96 <0.8
7 rs2307040 CALU Missense variant rs1043550 CALU 3 prime UTR variant 0.82 <0.8 0.96 0.89
rs11653 CALU 3 prime UTR variant 0.82 <0.8 0.96 0.89
9 rs56350726 SLC28A3 Missense variant rs10868138 SLC28A3 Missense variant 0.81 <0.8 0.83 <0.8
11 rs11604671 ANKK1 Missense variant rs2734849 ANKK1 Missense variant 0.97 1 0.98 <0.8
rs6277 DRD2 Synonymous variant <0.8 1 0.88 <0.8
rs2587548 DRD2 Upstream gene variant <0.8 1 <0.8 <0.8
rs2734833 DRD2 Upstream gene variant <0.8 1 <0.8 <0.8
rs1076563 DRD2 Upstream gene variant <0.8 0.97 <0.8 <0.8
16 rs115629050 CES1 Missense variant rs2307240 CES1 Missense variant <0.8 <0.8 0.9 <0.8
16 rs2307227 CES1 Missense variant rs2307240 CES1 Missense variant <0.8 <0.8 0.9 <0.8
16 rs79711700 CES1 Missense variant rs2307240 CES1 Missense variant 0.88 <0.8 1 <0.8
19 rs2336219 CD3EAP Missense variant rs967591 CD3EAP 5 prime UTR variant 0.83 1 0.96 <0.8
rs735482 CD3EAP Missense variant 1 1 0.96 0.93
19 rs12971396 IFNL4 Missense variant rs12980275 IFNL3P1 Upstream gene variant <0.8 0.84 <0.8 <0.8
rs8105790 IFNL3P1 Upstream gene variant 0.92 0.97 0.97 <0.8
rs4803217 IFNL3 Downstream gene variant <0.8 0.94 <0.8 <0.8
rs11881222 IFNL4 Downstream gene variant <0.8 0.91 <0.8 <0.8
rs28416813 IFNL3 5 prime UTR variant <0.8 0.83 <0.8 <0.8
Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes 131

Table 2 (continued)
Chr New variant Gene Functional annotation PharmGKB variant Gene Annotation EUR r2EAS r2AMR r2AFR r2
rs12979860 IFNL3 Upstream gene variant <0.8 0.84 <0.8 <0.8
rs8109886 IFNL4 Upstream gene variant <0.8 0.86 <0.8 <0.8
rs8113007 IFNL4 Upstream gene variant <0.8 0.94 <0.8 <0.8
rs8099917 IFNL4 Upstream gene variant 0.93 0.97 0.86 <0.8
rs7248668 IFNL4 Upstream gene variant 0.93 0.97 0.86 <0.8
19 rs4803221 IFNL4 Missense variant rs12980275 IFNL3P1 Upstream gene variant <0.8 0.84 <0.8 <0.8
rs8105790 IFNL3P1 Upstream gene variant 0.93 0.97 0.95 0.81
rs4803217 IFNL3 Downstream gene variant <0.8 0.94 <0.8 <0.8
rs11881222 IFNL4 Downstream gene variant <0.8 0.91 <0.8 <0.8
rs28416813 IFNL3 5 prime UTR variant <0.8 0.83 <0.8 <0.8
rs12979860 IFNL3 Upstream gene variant <0.8 0.84 <0.8 <0.8
rs8109886 IFNL4 Upstream gene variant <0.8 0.86 <0.8 <0.8
rs8113007 IFNL4 Upstream gene variant <0.8 0.94 <0.8 <0.8
rs8099917 IFNL4 Upstream gene variant 0.95 0.97 0.89 <0.8
rs7248668 IFNL4 Upstream gene variant 0.95 0.97 0.89 <0.8
19 rs762562 CD3EAP Missense variant rs967591 CD3EAP 5 prime UTR variant 0.83 1 0.92 <0.8
rs735482 CD3EAP Missense variant 1 1 1 1
rs2853533*–phenotype association (SNPedia): Neural Tube Defects & Spina Bifida Cystica (The G variant of rs2853533 was associated with Spina Bifida in a transmission disequilibrium test.
Study size: 610 families (329 trios, 281 duos), Study population/ethnicity: Patients affected with Spina Bifida and their parents; Houston, TX; Los Angeles, CA; Toronto, ON, Canada Significance
metric(s): p=0.0213). Chr Chromosome, EUR r2linkage disequilibrium in the European Population of 1000 Genomes project measured in r-squared, EAS r2linkage disequilibrium in the Eastern
Asian Population of 1000 Genomes project measured in r-squared, AMR r2linkage disequilibrium in the American Population of 1000 Genomes project measured in r-squared, AFR r2linkage
disequilibrium in the African Population of 1000 Genomes project measured in r-squared
132 J. Choi et al.

Another example is a modifier variant (rs2854509),
which we report to be in LD with a pharmacogenomics
variant (rs3213239) that is associated with decreased overall
survival and progression-free survival when treated with
platinum compounds in patients with non-small-cell lung
carcinoma. Our identified variant rs2854509 is located
at downstream, whereas pharmacogenomics variant
rs3213239 is located upstream of gene encoding X-Ray
Repair Cross Complementing 1 protein (XRCC1). Our
analysis revealed that variant rs2854509 is a cis-eQTL
variant acting on CPIC gene XRCC1, which is associated
with variable efficacy in in platinum-based chemotherapy
agents. Additional findings from RegulomeDB showed a
direct evidence of binding-site alteration through ChIP-seq
and DNase with a matched position weight matrix to the
ChIP-seq factor and a DNase footprint. These findings
suggest the possibility that rs2854509 has regulatory effects
on the gene XRCC1, which could modulate response to
platinum-based chemotherapy treatments.
Our proof of principle study demonstrates that many of
the well-known pharmacogenomics loci from PharmGKB
are genetic markers that may tag causal variants. Often the
latter remain elusive and are likely to be in LD with the
associated markers. Using NGS data, we identified a number
of sequence variants in LD with these pharmacogenomics
loci with supporting functional evidence from current
annotation softwares. These findings, pending experimental
evidence, will ultimately facilitate the translation of
improved clinical assays to predict response for a particular
drug or dosage prior to administration. The implementation
of these clinical tests promises to improve efficacy of drug
therapy while reducing the incidence of adverse events [18].
One limitation of the approach taken is the exclusion of
rare variants (minor allele frequency <0.01). While rare
variants are more likely to be functional and clinically
relevant, our decision to exclude them from this study was
based on the limited sample size (approx. 200–400 in each
of the four main populations: American, European, East
Asian, African) of 1KGP Phase 1. Specifically, we would
not be able to determine LD among rare variants (MAF <
0.01) in such small populations. Another limitation is that
this study was based on bioinformatics methods and we did
not experimentally validate the potentially functional var-
iants identified, nor confirm their correlation with drug
response outcomes. Instead our study was proof of concept
that associated variants in well-established pharmacoge-
nomics genes could represent markers of drug response
rather than the casual variants. Further studies are needed to
identify and ultimately validate the often elusive functional
variants in these loci. These additional studies include
genotyping of these potentially functional variants (identi-
fied in LD with the associated variants) and testing them
directly for correlation with drug response outcomes in
clinical trials. Other experiments are needed to confirm the
biological impact of these variants on the resultant RNA
transcripts or proteins, which depends on the predicted
impact of the variants identified. For example, variants of
high impact (Table 1) include splicing effects, premature
stop codons, and structural interactions, which could be
validated through direct sequencing of transcripts and mass
spectrometry to detect truncated and mis-folded proteins.
Our study identified novel genetic variations located in
well-established pharmacogenomics genes, which could
account for the association signals at these loci and have
strong impact on the resulting gene products. We applied an
innovative approach that combined bioinformatics resour-
ces such as PharmGKB, sequencing data from the 1000 GP,
population annotation software such as SNPEff as well as
databases such as RegulomeDB to identify novel variants
and predict their functional effects within pharmacoge-
nomics loci. Moreover, we determined that a number of
these potentially functional variants are in LD with known
pharmacogenomics variants and could account at least in
part for the original association signals. Identification of
these elusive causal variants could facilitate more accurate
genetic tests to predict treatment response prior to drug
administration. The improved accuracy results from direct
testing instead of relying on LD, which varies among
populations (as noted by our study of LD across four
populations in the 1000 GP). Thus, identification of causal
variants will improve the translation of pharmacogenomics
findings into clinical practice and ultimately replace the
current trial-and-error approach for drug therapy, moving us
closer towards precision medicine.
Methods
Pharmacogenomic genes
We selected 160 unique pharmacogenomics-associated loci,
containing 127 CPIC genes (5 June 2017 release) and 64
VIP genes (1 May 2017 release) from the PharmGKB
database. Then, we identified the genomic coordinates of
each gene from the GRCh37/hg19 assembly of the human
reference genome using the University of Santa Cruz
(UCSC) Genome Browser [19]. Next, genomic coordinates
were padded with 5000 bp both 5′and 3′of each gene to
include potential regulatory regions. All variants that appear
in at least 1% of the 1000 Genomes Project Phase I popu-
lation (February 2009 release) were extracted.
Functional annotations
After reviewing many annotation tools (including annoVar,
VEP, Polyphen/SIFT, CADD), we decided that SnpEff best
Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes 133

meets our needs as it allows a great degree of compatibility
with various input formats, offers high flexibility in search
settings, can annotate a full exome set in seconds, based on
up-to-date transcript and protein databases, and has the
ability to be integrated with other tools. SnPEff (version 4.2,
build 2015-12-05) was used with the GRCh37.75 assembly
to predict the effects of identified variants. For variants with
multiple annotations (e.g. variant affects multiple genes or
have varying effects depending on the transcript), only the
most severe consequence was selected and used to represent
each variant in tables to ease the comparison of impacts
among variants. To standardize terminology used for
assessing sequence changes, SNPEff uses sequence ontol-
ogy (http://www.sequenceontology.org/)definitions to
describe functional annotations.
LD analysis
LD between the well-established pharmacogenomics var-
iants (1151 variants annotated by PharmGKB retrieved on
16 June 2017, that are found within 160 PGx loci and 1000
Genomes project phase 1 dataset) and identified variants
from the 1000 Genomes Project phase 1 dataset using Plink
(version 1.09) [20]. Distance window for the LD analysis
were set to 1 Mb and an r2threshold of >0.8.
SNPs associated with regulation and phenotypes
For each variant identified to be in LD with an established
pharmacogenomic variant, we used RegulomeDB [21]to
evaluate and score those that have the potential to cause
regulatory changes, such as eQTL, regions of DNAase
hypersensitivity, binding sites of transcription factors and
proteins. RegulomeDB uses GEO [22], the ENCODE [23]
project, and various published literatures to assess these
information. In addition to that, we used SNPedia [24], a
database of over 90,000 SNPs and associated peer-reviewed
scientific publications, to identify variants that are pre-
viously associated with phenotypes. (Fig. 2)
Code Availability
Code and data used in this manuscript can be accessed from
a public repository https://github.com/12jc59/DuanlabPha
rmacogenomicsProject.
Acknowledgements We would like to thank Dr. Alan R. Shuldiner
and support from the Translational Pharmacogenomics Project. This
manuscript was supported by NIH grants U01 HL65899, U01
HL105198, and K99 HL116651. Computations were performed on
resources and with support provided by the Centre for Advanced
Computing (CAC) at Queen’s University in Kingston, Ontario. The
CAC is funded by the Canada Foundation for Innovation, the
Government of Ontario, and Queen’s University. QLD receives
funding from the Canadian Institutes of Health Research and Queen’s
University.
Author contribution All authors contributed to the writing of the
manuscript. JC performed the data analyses and drafted the manu-
script. QLD supervised data analyses and assisted in the writing of the
manuscript. QLD and KGT designed the research project.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
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Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes 135