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Journal of the American College of Cardiology
© 2013 by the American College of Cardiology Foundation
Published by Elsevier Inc.
Vol. 61, No. 20, 2013
ISSN 0735-1097/$36.00
http://dx.doi.org/10.1016/j.jacc.2012.12.054
STATE-OF-THE-ART PAPER
Genomics in Cardiovascular Disease
Robert Roberts, MD,*† A. J. Marian, MD,‡ Sonny Dandona, MD,§ Alexandre F. R. Stewart, PHD储
Ottawa, Ontario, and Montreal, Quebec, Canada; and Houston, Texas
A paradigm shift toward biology occurred in the 1990s and was subsequently catalyzed by the sequencing of the
human genome in 2000. The cost of deoxyribonucleic acid (DNA) sequencing has gone from millions to thousands of dollars with sequencing of one’s entire genome costing only $1,000. Rapid DNA sequencing is being
embraced for single gene disorders, particularly for sporadic cases and those from small families. Transmission
of lethal genes such as associated with Huntington’s disease can, through in vitro fertilization, avoid passing it
on to one’s offspring. DNA sequencing will meet the challenge of elucidating the genetic predisposition for common
polygenic diseases, especially in determining the function of the novel common genetic risk variants and identifying
the rare variants, which may also partially ascertain the source of the missing heritability. The challenge for DNA sequencing remains great, despite human genome sequences being 99.5% identical, the 3 million single nucleotide
polymorphisms responsible for most of the unique features add up to 40 to 60 new mutations per person which, for
7 billion people, is 300 to 400 billion mutations. It is claimed that DNA sequencing has increased 10,000-fold while
information storage and retrieval only 16-fold. The physician and health user will be challenged by the convergence of
2 major trends, whole genome sequencing, and the storage/retrieval and integration of the data. (J Am Coll Cardiol
2013;61:2029–37) © 2013 by the American College of Cardiology Foundation
Captain Cook wrote in his log upon reaching Australia that “I
have not only travelled farther than any other man, but I have
travelled as far as man can travel” (1). Thus, by the 18th
century, all the continents had now been discovered and
named. It appeared logical and perhaps appropriate for mankind to pursue the inner treasures of the planet. This coincided
with the industrial revolution that led to the harnessing of
energy from coal, electricity, and oil as well as the discovery of
all the marvelous elements including uranium, which enabled
many human endeavors, from cancer therapy to the invention
of the atomic bomb. While this trend continues, in the 1990s
a major worldwide shift occurred in which mankind became
interested in the inner workings of human biology. The word
“biology” is today often associated with excitement and activity,
not just in science but also in medicine and commerce. This
revolutionary concept received a major boost with the sequencing of the human genome in 2000 (2). In fact, sequencing of
From the *University of Ottawa Heart Institute, Ottawa, Ontario, Canada; †John &
Jennifer Ruddy Canadian Cardiovascular Genetics Centre, Ottawa, Ontario, Canada;
‡Institute of Molecular Medicine, Center for Cardiovascular Genetic Research,
University of Texas Health Sciences Center, Houston, Texas; §Department of
Medicine, McGill University, Montreal, Quebec, Canada; and the 储John & Jennifer
Ruddy Canadian Cardiovascular Genetics Centre, and University of Ottawa Heart
Institute, Ottawa, Ontario, Canada. Dr. Roberts receives grant support from CIHR
#MOP82810 (RR)/Canada, CIHR #MOP77682 (AFRS)/Canada, and CFI #11966
(RR)/Canada; and is a consultant to Cumberland Pharmaceuticals. Dr. Marian
receives grant support from R01-088498/PHS HHS/United States, R21 AG03859701/AG/NIA NIH HHS/United States, and R34HL105563/HL/NHLBI NIH
HHS/United States. Dr. Stewart receives grant support from CIHR #MOP82810
(RR) & CIHR #MOP77682 (AFRS)/Canada. All other authors have reported that
they have no relationships relevant to the contents of this paper to disclose.
Manuscript received November 12, 2012; revised manuscript received January 29,
2013, accepted February 19, 2013.
the human genome may be to the 21st century as invention of the
vowels and development of democracy was to the 6th century BC
or the industrial revolution was to the 18th century.
The Human Genome: New Developments
The double stranded human genome of each cell contains 6.4
billion nucleotides. While proteins are the molecules that do
the work, only about 1% of the human genome sequences are
designated to encode messenger ribonucleic acids (RNAs)for
protein coding (3). Until recently, most of deoxyribonucleic
acid (DNA) was considered junk (3), but we now know that
virtually all of DNA is transcribed into RNA (3). The
ENCODE (Encyclopedia of DNA Elements) project has
enabled us to assign biochemical functions for 80% of the
genome (4). It is of note that only a small proportion of the
transcribed RNAs are translated into protein with the remainder performing a host of functions, affecting those sequences
(genes) that encode for protein. These RNAs that do not code
for protein are as a group referred to as noncoding RNA. Most
genes coding for protein are in some way regulated by these
noncoding RNAs (5). These noncoding RNAs are very promiscuous— each RNA can affect multiple different genes on
the same or different chromosomes.
The Source of Human Genetic Biodiversity
All genomes from all species share most of their DNA
sequences, having acquired them over a 3.8-billion-year
evolutionary history since the origin of life. Despite the
common sequence ancestry, each individual genome within
each species has maintained itself as unique. The develop-
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Genomics in Cardiovascular Disease
ment of biodiversity and unique
sequences of each genome
whether within or between speCAD ⴝ coronary artery
cies is due primarily to the errors
disease
in the process of copying DNA.
DNA ⴝ deoxyribonucleic
Copying errors during the repliacid
cation of one’s DNA induce priDSV ⴝ deoxyribonucleic
marily single base changes
acid sequence variants
through substitution of a single
GWAS ⴝ genome-wide
association studies
base (nucleotide) for another
(e.g., thymine for adenine).
NGS ⴝ next generation
DNA sequencing
These substitutions are passed on
from generation to generation
nsSNV ⴝ nonsynonymous
single-nucleotide variations
and are referred to as single nuRNA ⴝ ribonucleic acid
cleotide polymorphisms (SNPs).
These SNP substitutions account
SNP ⴝ single nucleotide
polymorphism
for 94% of the errors from copyWES ⴝ whole exome
ing or replicating DNA, while
sequencing
deletions of 1 to 4 bp account for
WGS ⴝ whole genome
4.5%, and the remainder are due
sequencing
to insertions of 1 to 4 bp (6,7).
Other types of DNA variation
exist such as chromosomal rearrangements, duplications
(copy number variants), and translocations. The mutations
induced by DNA copying errors, if beneficial, are conserved
and their frequency increases, while deleterious mutations
remain rare or are eliminated. Fortunately, many of these
SNPs have modest to minimal effects or are neutral. The
human DNA (6 billion bases) replicates itself every few days,
and although it only makes 1 error per 1 billion bases created,
it can accumulate a significant number of mutations over
generations. Kruglyayk and Nickerson (8) estimated with a
mutation rate of 2 ⫻ 10⫺8 per base pair per generation and a
human genome of over 3 billion base pairs, each genome
carries 60 new mutations per generation. Sun et al. (9) estimated
a mutation rate of 1.4 ⫻ 10⫺8 which would give a mutation rate
of about 40 new mutations per generation. The world population
of 7 billion has about 300 to 400 billion new mutations in the
current generation. The genetic diversity of mankind is exemplified by the observation that the exons (protein coding regions) of
each individual genome, referred to as the exome, encompasses
⬃13,000 nonsynonymous and ⬃7,000 potentially functional variants, posing considerable challenges in identification of disease
causing DNA sequence variants (DSVs) (10,11). Despite the
sequence of the human genome being 99.5% identical, the
remaining 0.5% is more than adequate to provide each of us a
unique genome that until sequenced will have many hidden
surprises. Current knowledge indicates there are 3 million SNPs
per genome, which account for over 80% of human phenotype
variation, whether it is the color of one’s eyes or the susceptibility
to disease (12).
Abbreviations and
acronyms
The Search for Disease Related Genes
A major goal is to identify DNA regions that predispose or
cause cardiovascular disease. This refers to the ongoing
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studies that correlate physical or biochemical features (phenotype) to that of the genotype. Defining the phenotype
precisely is fundamental to the discovery of the associated or
causal genotype. The role of the clinician in detecting the
phenotype has been crucial to this pursuit and will continue
to be even more so as we further refine and specify
subphenotypes. DNA can be obtained from the blood, other
body fluids such as saliva, or body tissue. The approach to
identify the causal genes and variants has evolved dramatically over the past 3 decades. The conventional approach of
genetic linkage analysis in large families, which was very
successful in linking causal DNA mutations to rare single
gene disorders, has all but been replaced with the newer
approaches of genome-wide association studies (GWAS)
and next generation DNA sequencing (NGS) in small families
and individual cases. The newer approaches not only have
partially overcome a major limitation of genetic linkage in
identifying the causal variant in small size families but also have
afforded the opportunity to identify the causal alleles in
sporadic cases with single gene diseases and the susceptibility
(risk) alleles in those with the complex phenotypes.
Single Gene Disorders:
The Success of Genetic Linkage Analysis
Single gene disorders are the phenotypic consequences of
rare DSVs that impart large effect sizes. The mutation is
both necessary and sufficient to induce the disease. Familial
hypertrophic cardiomyopathy was the first cardiovascular
single gene disorder for which the responsible mutation was
discovered. The responsible mutation was a missense mutation in the gene that encodes the beta-cardiac myosin
heavy chain (13). Introducing the human mutant gene as a
transgene induced the disease in both the mouse (14) and
the rabbit (15). While the rare variant is sufficient to cause
the disease, there is often variable expressivity (severity
of the phenotype), determined by other genetic and nongenetic factors. The conventional approach for mapping the
chromosomal location (locus) of the gene responsible for a
single gene disorder has been genetic linkage analysis. In
this technique, DNA of members of a 2- generation to
3-generation pedigree affected with the disease are genotyped using a few hundred short tandem repeat DNA
markers. DNA markers that are inherited more commonly
than by chance by the affected members of the family
indicate the markers are in close physical proximity to the
DNA region containing the responsible gene. Sequencing
of candidate genes at the mapped locus usually identifies the
causal variant. This approach has been exceedingly successful in mapping the causal genes for various single gene
disorders, typically in large and moderate size families. It is
estimated there are about 6,000 single gene disorders of
which causative genes have been discovered for over 3,500
(16). Accordingly, several dozen genes for hereditary cardiomyopathies, including dilated, hypertrophic, and arrhythmogenic cardiomyopathies; hereditary arrhythmias,
Roberts et al.
Genomics in Cardiovascular Disease
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such as atrial fibrillation, long QT syndromes, short QT
syndromes, and catecholaminergic polymorphic ventricular
tachycardia; and cardiac conduction defects have been identified (17). In addition to linkage analysis, the candidate
gene approach, guided by the biological and functional
similarities between the known causal genes and the candidate gene, has been used to screen and identify new causal
genes for single gene disorders. Both approaches are limited
by not offering sufficient resolution to identify the causal
genes in small families or in sporadic cases.
Single Gene Disorders:
DNA Sequencing, a Paradigm Shift
The advent of NGS platforms, has eased 1 of the bottlenecks to complete elucidation of the genetic causes of single
gene disorders (10) including those occurring in small
families or sporadically and has emerged as the preferred
method. The unbiased approach of whole exome sequencing (WES), sequencing all of the exons in the genome, or
whole genome sequencing (WGS) enables identification of
all DSVs and hence, the opportunity for not only discovering the causal variants but also modifier variants that
influence phenotypic expression of the disease.
The NGS technologies are based on parallel sequencing
of millions of DNA fragments simultaneously. The sequencing reads are relatively short, typically comprising 35
to 100 bases but could be as long as 1,000 bases, depending
on the platform. The reads are aligned with the reference
sequence and multiple reads of the same DNA fragments
are compared to identify the variants. The existing technologies afford the opportunity to generate up to ⬃600 Gbp
sequences per run in about 1 to 2 weeks. Given that each
genome is ⬃3.2 Gbp and each exome is ⬃30 Mbp, such
platforms afford the opportunity to sequence 1 genome or a
dozen or so exomes at a high mean coverage rate (⫻100).
The coverage rate refers to the number of times each DNA
fragment is sequenced and mapped to the reference sequence. A new approach to sequencing is being developed
based on the nanopore technology, whereby a pore is small
enough to enable only a single strand of DNA to pass
through it. Detection of the specific nucleotide is based on
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the changes in conductivity as each specific DNA (or RNA)
nucleotide that passes through the pore (18). There is no
need for fluorescence or chemicals, hence it should be
relatively inexpensive. Oxford nanopore technologies (18)
recently announced the generation of a plastic pore with an
attached enzyme that pulls the single strand of DNA
through at a given speed. It is estimated that 25,000 of these
pores would fit into the diameter of a human hair. The
simultaneous operation of a large number of nanopores
makes it possible to sequence a human genome within hours
at ⬍$1,000 per genome. The machine would be a small
laptop device and also relatively inexpensive. The company
has announced that it will deliver testing machines before
the end of 2012 and mass production is expected in the year
2013.
The most commonly used approach is sequencing of the
approximately ⬃180,000 protein coding exons in the 21,000
genes in the genome, which encompass approximately 30
Mbp of genomic DNA. The approach is referred to as
WES as opposed to WGS wherein the entire genome is
sequenced. In view of the large number of DNA sequence
variants in each exome/genome, skillful interpretation of the
genetic data utilizing various bioinformatics and genetic
resources as well as exquisite phenotyping are necessary to
reduce the number of putative causal variants. WES has
other shortcomings including incomplete capture, and inadequate coverage (per read) of all exons as well as incorrect
mapping of the reads. In general, approximately 500 of the
21,000 genes may not be correctly sequenced due to
inherent errors in WES. For medical sequencing, i.e.,
genetic testing, all DSVs identified by the NGS platforms
should be validated either by repeat independent NGS
reactions, Sanger sequencing or at least by genotyping.
The NGS platforms have already been successful for
many Mendelian disorders (10) as shown in Table 1.
Utilizing this approach, TTN, encoding the giant protein
Titin, was identified as a major causal gene for hereditary
and sporadic dilated cardiomyopathy (19). While WES and
WGS are useful for identification of the causal genes/
variants in small families, robust study design is necessary to
filter out the large number of variants that typically segre-
Using NextofGeneration
Examples
Recent Discoveries
Sequencing
Genetic Etiology
of Monogenic
Diseases
Examples
of
Recent of
Discoveries
of Genetic
Etiology ofCardiovascular
Monogenic Cardiovascular
Diseases
Table 1
Using Next Generation Sequencing
Single Gene Disorder
Gene
Function
Ref. #
Familial dilated cardiomyopathy
TTN
Titin is a large sarcomere protein spanning half of sarcomere length
(19)
Familial and sporadic dilated cardiomyopathy
BAG3
Cochaperon protein
(23)
Autosomal recessive dilated cardiomyopathy
GATAD1
GATA zinc finger domain containing protein 1 transcription factor
(24)
Hypertrophic cardiomyopathy (mitochondrial)
MRPL3
Abnormal assembly of mitochondrial respiratory chain
(25)
Cantú syndrome (patent ductus arteriosus, cardiac hypertrophy,
pulmonary hypertension, and pericardial effusion in
conjunction with noncardiac manifestations)
ABCC9
An ATP-sensitive potassium channel
(26)
(27)
Thoracic aortic aneurysm
SMAD3
Signal transduced of TGF-beta
Familial pheochromocytoma
MAX
Neural crest development
(28)
Congenital heart defects
MYH6
Sarcomeric myofibril formation
(29)
ATP ⫽ adenosine triphosphate; TGF ⫽ transforming growth factor.
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gate with the phenotype in small families, which renders
identification of the true causal variants challenging. Various study design and approaches have been suggested to
strengthen the likelihood of success (10). Various bioinformatics programs, such as PolyPhen2 (20) and SIFT (21) as
well as genetic databases, such as National Institutes of
Health Heart, Lung, and Blood Institute Exome Sequencing Project and 1,000 Genomes, are available to filter out
the DNA sequence variants identified by WES or WGS
experiments that would restrict the number of putative
candidate causal genes.
Determining the causal mutation in autosomal recessive
disorders is facilitated by the fact that the causal mutation
must be homozygous to induce the disease as opposed to
heterozygous in autosomal dominant disease. In autosomal
dominant disease, WES typically leads to identification of
several dozen putative candidates that cosegregate with the
phenotype in small or medium size families and hence, it is
difficult to discern the causal variant. Despite the advantage
of ascertaining the significance of polymorphisms within
families, there will remain many polymorphisms that cannot
be annotated definitively as causative for disease. While
techniques such as bioinformatics and filtering mechanisms
can reduce the number of putative causal variants, for some
it will ultimately require extensive in vitro and in vivo
studies to delineate biological and functional significance of
these variants. Identification of non-synonymous variants
by WES has the advantage of being in a protein coding
region, which considerably facilitates functional analysis
and the search for a corresponding phenotype. These
points have been discussed in greater detail in a recent
review by Marian (22).
DNA Sequencing as a
Genetic Screen for Single Gene Disorders
Targeted subgenomic sequencing approach as opposed to
WES may be used to screen for mutations in the known
genes for single gene disorders (30). However, the approach
is restricted to the known genes and does not lend itself to
identification of the novel genes. It might also be used to
identify double or triple causal mutations and as a part of
cascade screening of family members. Cascade screening
refers to genetic testing of family members of a proband in
whom the causal mutation has already been identified. The
cascade screening may entail simple genotyping for the
presence of the specific mutation, Sanger sequencing, subgenomic sequencing, and even WES. While the latter seems
excessive for cascade screening and currently not covered by
the insurance companies, it affords the opportunity for
identification of potentially additional mutations that might
contribute to the phenotype and define the genetic structure
of the individual. Technical aspects of WES, as a genetic
screening tool in autosomal dominant diseases are similar to
those relevant to gene discovery by NGS. Typically, a much
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higher coverage is demanded for medical sequencing than
for gene discovery studies.
Genetics of CAD:
An Archetypical Polygenic Disorder
It has been recognized for some time (31) that genetic
predisposition to common diseases such as CAD would be
due to multiple common genes, each with minimal to
modest effect on the phenotype. In polygenic disorders,
unlike single gene disorders, one gene is not sufficient or
necessary to induce the phenotype (32). Genetic linkage
analysis, which utilizes a few hundred DNA markers, lacks
the necessary resolution to identify the predisposing genes
in polygenic disorders. It was recognized that the casecontrol association would be the better approach, but would
require hundreds of thousands of DNA markers to span the
genome, which were not available (33). In 2005, HapMap
(34) annotated the chromosomal location of millions of
SNPs, which provided the necessary DNA markers to
perform GWAS. At the same time, platforms for highthroughput genotyping were developed (32,35) which enabled mapping of the first genetic variant for CAD, 9p21 in
2007 (36,37). This was followed by 1 of the largest
collaborative efforts (38) in cardiology involving 2 continents, CARDIoGRAM, with a sample size of 143,000
dedicated to mapping genes for CAD, followed by
CARDIoGRAMplusC4D with a sample of 193,000. In just
5 years, 36 genetic variants have been confirmed to be
associated with increased risk for CAD (39). Each of these
36 genetic risk variants for CAD was confirmed in populations independent of the discovery population and most
recently underwent a meta-analysis in a total sample size of
190,000 (40). Based on this sample size, the chances of even
1 of these loci being false is very unlikely (41). It is
important to realize that the DNA risk region is indicated
by a SNP. This SNP serves as a marker and in most cases is
not the SNP causing the disease risk. Thus, the actual
sequence responsible for the risk in most cases is yet to be
identified but will be markedly facilitated by the availability
of rapid and inexpensive sequencing. Furthermore, most of
the SNPs (23 of 36) mediate their risk independent of
known risk factors (e.g., hypertension and cholesterol)
through mechanisms as yet unknown. Functional analysis of
the independent 23 risk variants for CAD is currently being
pursued. Functional analysis is confounded by the observation that most of these SNPs are in non–protein-coding
regions. Determining the function and identifying the
polymorphism will be extremely difficult because the effect
of any one risk variant is small and its specific intermediary
phenotype that contributes to coronary atherosclerosis or
myocardial infarction is unknown. Functional analysis is
further confounded by the many contributing components
to atherosclerosis such as macrophage formation, plaque
rupture, platelet adhesiveness or thrombosis to name just a
few. One approach to function is the pursuit of network
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modeling techniques (42– 44) in an attempt to identify
DNA, RNA, and protein pathways that involve the DNA
region containing the disease associated DNA marker. This
is pursued along with conventional analysis of in vitro (cells)
and in vivo (animal) expression studies.
The common risk variants for CAD discovered by
GWAS have several features in common, as reviewed in
detail in a recent review (39): 1) the common genetic risk
variants occur frequently, with 10 of the variants occurring
in ⱖ75% of the population and one-half of them in ⱖ50%
of the population; 2) the risk effect per variant is small,
averaging a risk increase of about 18%; 3) 10 of the variants
act through known conventional risk factors: 7 through
cholesterol (SORT1, PCSK9, LPA, ZNF259/APOA5,
TRIBI, APOE, ABCGA, LDLR), 2 through hypertension
(CYP17A1 and SH2B3), and the ABO locus (9q34)
through increased propensity for coronary thrombosis;
4) two-thirds of the genetic risk variants act through
mechanisms independent of conventional risk factors;
5) most of the SNPs signaling a risk variant are found in
non–protein coding regions; 6) risk is proportional to the total
number of risk variants inherited by an individual, rather than
a specific risk variant; and 7) in our analysis of 23 risk variants
for CAD, we observed that while the maximum present in any
1 individual could be 46, the average was 17 with a maximum
observed of 26 and a minimum of 7.
Genetic Risk of CAD and Clinical Application
The clinical application of the genetic risk factors for
complex diseases such as CAD is yet to be recommended.
One approach is to wait until we have specific therapy
related to these genetic risk variants before recommending
genetic testing. This is likely to require many years, as drug
therapy as a rule could require a minimum of 10 years for
development and approval. Another approach would be to
incorporate them as risk factors into the current preventionrecommended guidelines. For example, current guidelines,
for prevention of CAD recommend lowering low-density
lipoprotein cholesterol to 160 mg/dl if one has no conventional risk factors, but if another independent risk factor
such as hypertension is present, low-density lipoprotein
cholesterol should be decreased to 140 mg/dl. Since genetic
risk variants such as 9p21 have been proven to be independent risk factors, it could be incorporated into current
guidelines and would lead to more intense treatment of
known risk factors such as cholesterol. Currently, the
genetic risk variants for CAD are not recommended for
routine prevention and treatment of CAD. The independent genetic risk factors imply several mechanisms involved
in the pathogenesis of atherosclerosis, which have yet to be
discovered. While GWAS has not specifically identified the
culprits, the implications for the pathogenesis and biology of
atherosclerosis provide tremendous potential for development of new drug targets and innovative therapy.
Roberts et al.
Genomics in Cardiovascular Disease
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Missing Heritability:
The Need for DNA Sequencing
Despite the many common genetic risk variants for CAD,
they only account for a small percentage of the expected
heritability (45). It is estimated that about 50% of predisposition for CAD is genetic (31), yet, the 36 risk variants
only account for about 10% of the expected heritability.
There are several possibilities to account for this discrepancy: rare risk variants (mean allele frequency ⱕ5%), undiscovered common variants, epistasis (gene-gene interactions), or miscalculations. GWAS has the resolution to
detect common SNPs, but not rare SNPs, so they can only
be detected by direct DNA sequencing. An ongoing approach instead of WGS is WES. This is a National
Institutes of Health Heart, Lung, and Blood Institute–
sponsored project “Exome Sequencing Project” for rare
variants that involves sequencing about 30 million bases
encompassing all 180,000 exons in the 23,000 genes in the
genome (30). The initial results confirm the expectation
that there are many more rare variants (46) than common
variants. Based on a sample of 202 genes in 14,000
Europeans, investigators observed 1 base pair per 21 base
pairs had undergone mutation to a rare polymorphism.
These variants are very rare (mean allele frequency ⬍1%)
with 75% of these rare variants having a frequency of only 1
per 200 to 300 individuals (47).
While sequencing is necessary to detect rare polymorphisms it does not determine their function or whether they
are disease related. Once a rare polymorphism or SNP is
identified, one must, through case control association studies determine if the SNP is statistically more common in
cases than controls. The advantage of functional rare variants associated with disease is that they occur primarily in
protein coding regions and are associated with several-fold
increased risk (47,48). Rare variants that cause single gene
diseases such as hypertrophic cardiomyopathy (3) and
Wolff-Parkinson-White syndrome (49) are associated with
several fold increased risk and are in themselves potent
enough to induce the phenotype as shown in transgenic
animals (50). The sample size required for 30 rare risk
variants with an average frequency of 1% and power ⱖ80%
is over 6,000 cases and controls, if risk is increased 2-fold. If
one is assessing 30 rare risk variants with an average
frequency of 0.1%, it would require 60,000 cases and
controls.
In determining the biological or pathological function,
rare variants have certain advantages over common variants. Common variants occur primarily in non–protein
coding regions (51) as opposed to disease related rare
variants, which predominantly occur in protein coding
regions (47,48). Thus, WES, in which only the protein
coding regions are sequenced, is appropriate for rare
variants and is much more economical than WGS (52).
Because most of the rare variants occur in known proteins, detection of the phenotype will be greatly facili-
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Genomics in Cardiovascular Disease
tated with prior knowledge of the protein expressed,
whether performed in vitro or in vivo.
It remains to be determined whether rare variants, despite
their greater effect contribute significantly to the missing
heritability. It is important to emphasize that the frequency
of the genetic risk variant has nothing to do with its
importance as a therapeutic target. Their importance as a
therapeutic target is highly enriched by the greater effect
over that of common variants. This is illustrated by the
cholesterol receptor that was identified back in the 1970s by
Brown and Goldstein (53). This inherited defect referred to
as familial hypercholesterolemia only occurs in 1 in 5,000
people, yet this rare disorder was the tipping point to
recognize that cholesterol played a major role in precipitating premature CAD in these individuals. This led to the
development of statins, which inhibit the synthesis of
cholesterol, and, today, statins are the mainstay in the
prevention of CAD (54). A more relevant and recent
example of the potency of rare variants is the rare polymorphism discovered in PCSK9, which has a frequency of about
1% (55). An antibody to PCSK9 was associated with a 60%
further reduction in low-density lipoprotein cholesterol over
that of statin therapy (55).
The other possibility is the overly stringent statistical
requirement demanded by GWAS of p ⱕ 5 ⫻ 10⫺8. There
is considerable evidence, as indicated by Visscher et al.
(56,57) that common variants of less than GWAS may
account for much of the genetic “missing heritability.” In
genetics of height, Yang et al. (57) showed that they can
account for more than 40% of the expected heritability
utilizing less significant common variants. A more recent
study by Simonson et al. (58) also indicates that common
variants of less than genome-wide significance do account
for some of the missing heritability.
The current approach to assessing the total risk effect
of common variants is by simply adding their individual
effects. A major proportion of this missing heritability
may be due to epistasis or gene-to-gene interaction,
which is not accounted for in our current calculations
(59). In their natural state, genes exert their effect
through combined networks rather than as single units
and likely have synergistic effects over and above that of
their individual effects. As more genes are discovered and
their functions elucidated together with their interacting
networks, it should be possible to ascertain and confirm
the source of the missing heritability. To resolve this
issue, it will be necessary to have genome sequencing in
massive sample sizes to identify the rare variants and
elucidate their function.
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farin. Since the establishment of dual antiplatelet therapy as
the gold standard therapy following coronary stent placement, clopidogrel has become 1 of the most widely prescribed cardiac drugs. SNPs in the gene encoding cytochrome P450 2C19 have been shown to affect the degree to
which clopidogrel attenuates platelet aggregation. In both
the PLATO (Platelet Inhibition and Patient Outcomes)
trial (60) and the TRITON⫺TIMI 38 (TRial to assess
Improvement in Therapeutic Outcomes by optimizing
platelet InhibitioN with prasugrel–Thrombolysis In Myocardial Infarction 38) (61) carriage of CYP P450 2C19 polymorphisms was associated with major adverse cardiac events
including the potentially catastrophic outcome of stent thrombosis. It is unclear at this point as to whether tailoring
antiplatelet therapy can favorably modify outcomes in those
individuals that are carriers. However, it has been demonstrated that identification of carriers by point-of-care testing
and tailored prescription of a dual-platelet regimen successfully
eliminates high residual platelet activity (62). A randomized
study using such technology needs to be executed to determine
whether such testing reduces adverse outcomes.
Polymorphisms in cytochrome P450 2C9 and vitamin K
epoxide reductase have been shown to modify warfarin
response. Several pharmacogenetic models have been developed in order to predict warfarin-dosing requirements.
These include CYP P450 2C9 and vitamin K epoxide
reductase 1 genotype, smoking status, relevant medications,
age, sex, and body mass index (63). The application of these
algorithms has been investigated in several prospective
studies demonstrating feasibility of this approach. However,
only a few were randomized and all suffered from small
sample sizes. A recent publication compared standard dosing regimen with 2 genotype-guided algorithms (64). Primary outcomes were percent out of range international
normalized ratios and time in therapeutic range at 3
months. The combined genotype-guided prescription cohort demonstrated superior outcomes with respect to both
primary endpoints. Moreover, serious events were significantly less frequent in the genotype-guided cohort (4.5% vs.
9.4% of patients; p ⬍ 0.001). It should be noted that there
was no difference in the primary outcome between the 2
genotype-based algorithms. As a consequence, routine use
of such algorithms has not been endorsed in the guidelines.
The primary thrust of pharmacogenomic inquiry has been to
define sequence variation that modifies drug efficacy, some
work has been done with respect to sequence variation that
predisposes to adverse effects. One striking example is the
identification of SLCO1B1 polymorphism and HMG CoA
reductase inhibitor induced myopathy, where homozygosity
confers a relative risk of 16.9 relative to noncarriers (65).
Pharmacogenetics
Pharmacogenetics is rapidly expanding in defining the
relationship of DNA sequence variation and drug response.
This has been most notable with 2 drugs commonly
prescribed for cardiovascular therapy, clopidogrel and war-
Individual Genome Sequencing: A New Reality
While a draft of the human genome sequence was completed in 2000 (2), the first individual human genome
completed in its entirety was that of Venter et al. (66), when
Roberts et al.
Genomics in Cardiovascular Disease
JACC Vol. 61, No. 20, 2013
May 21, 2013:2029–37
Current
Genome-Wide
Sequencing Sequencing
Table 2References
Current on
References
on Genome-Wide
Title
Ref. #
Targeted next-generation sequencing for the molecular genetic
diagnostics of cardiomyopathies
(30)
Genome sequencing: search for pore-fection
(18)
DNA sequencing clinical applications of new DNA dequencing
technologies
(48)
Whole-genome sequencing: the new standard of care?
(68)
Secrets of the human genome disclosed
(67)
The ultimate genetic test
(69)
Accurate whole-genome sequencing and haplotyping from
10 to 20 human cells
(70)
Next steps in cardiovascular disease genomic research:
sequencing, epigenetics, and transcriptomics
(71)
sequencing the individual genomes cost millions of dollars.
Introduction of the NGS (30) revolutionized the rate and
cost of DNA sequencing, with the sequence of the human
genome today costing $5,000 and expected to be less than
$1,000 within 1 to 2 years. It is estimated that over 30,000
individuals will have had their whole genome sequenced by
the end of this year (67). Recent reviews on genome
sequencing are listed in Table 2.
What Does It Mean to
Have One’s Genome Sequenced?
If one simply follows through with parallel sequencing, it
will be feasible, inexpensive, rapid and expected to be
routine within the next 5 years and maybe sooner if the
nanopore approach is robust. What does it mean on the
basis of a venipuncture, a buccal smear or a sample of one’s
human hair to have one’s genome completely sequenced?
Knowing one’s DNA disease risk fragments from such a
single measurement is overwhelming considering that these
variants will not change in one’s lifetime. These DNA risk
variants are not influenced by meals, the time of the day, age
gender or medications. A permanent record of one’s DNA
variants can be stored and attached to one’s medical record
as a permanent unchanging blueprint of the individual’s
genetic makeup. The National Institutes of Health has
already launched a project referred to as “eMERGE” involving 5 medical centers in the United States whereby the
individual’s DNA sequencing and his or her medical record
will be analyzed for genotype correlations (72). This could
be the prototype for the future whereby one’s buccal smear,
blood, or tissue is stored in a biorepository and genetic
analysis correlated with the stored electronic phenotypic
data. Similar such projects are ongoing for other diseases
such as cancer. This information will be routinely available
and be part of the hospital record. A couple known to carry
a gene for a lethal disease, such as Huntington’s disease, can
avoid transmitting it to their children through in vitro
fertilization selecting their own egg and sperm without the
mutation, thus avoiding what might be life-threatening
mutations. Having your genome sequenced avoids misin-
2035
terpretation and immediately determines whether you have
one or more mutations proven to be associated with disease.
Despite the utility of the GWAS and NGS platforms in
offering robust strategies to elucidate the genetic basis of
complex diseases, clinical applications of such discoveries
confront a number of challenges. Among them is the
daunting task of identifying the true causal allele from the
vast number of variants that are present in each genome or
exome, including nonsynonymous single-nucleotide variations (nsSNVs) and even insertion/deletion variants. Bioinformatics algorithms might offer information about potential pathogenicity of the variants but such predictions are
often discordant across different platforms. Likewise, largescale high through screening tools to identify the pathogenic
variants are currently not available. The focus on identification of the risk or causal variants by NGS is on the rare
alleles, which are expected to exert larger effect size that the
common alleles. However, a significant number of rare
variants also are not expected to be pathogenic. Therefore,
a practical approach is to identify the variants that have been
already linked to the phenotype. Such variants are typically
rare and are often non-sense, missense, or frame-shift
mutations that either have been shown to cause cardiovascular pathology or are located in a gene that is known to be
a causal gene for a Mendelian disease. Each genome
comprises a handful of such variants that might be used for
early identification of those at risk. However, whether
NGS-based early identification and interventions could
influence the outcome in cardiovascular disease is an empiric
question remains to be tested.
The Convergence of 2 Technologies:
A Challenge for Personalized Medicine
A major challenge to the healthcare policy makers, physicians, care givers, and end users are being created by the
convergence of 2 major technologies: cost-effective DNA
sequencing of the whole genome and digitization of patient
data. The progress of DNA sequencing is said to have
improved 10,000-fold in the past 8 years (73), while our
ability to store, retrieve, and analyze data has only improved
16-fold (73,74). Some claim that the convergence of these 2
technologies is the tipping point for personalized medicine.
It could be costly not to realize we are at the cusp of the new
era of personalized medicine. The detailed genome knowledge is rapidly being made available as DNA sequencing is
accelerating much faster than our ability to store and analyze
the data. Interpreting the data will probably require elucidation
of the function of the DNA risk variants. The era of
population medicine where “1 drug fits all” will be replaced
by medicine based on one’s genetic composition, molecular
makeup, and how it affects the particular disease phenotype
in that individual. Given the etiological and phenotypic
complexity of the common cardiovascular disorders and in
view the difficulties in identifying the true risk alleles, one
has to avoid a cavalier approach in assigning clinical impli-
2036
Roberts et al.
Genomics in Cardiovascular Disease
cations to the genetic data. Experienced clinicians with
training and expertise in medical genetics and/or in conjunction with medical geneticists should carefully assess the
clinical significance of the genetic discoveries. The field is
clearly not ready for a direct-to-customer approach, which
has the potential to offer false information with considerable
medical and psychological implications.
The human genome’s effects have hardly been felt by
some, but 1 effect is obvious to all of society and was best put
by Leroy Hood, one of the pioneers “Revolutions that have
been generated by the first draft of the Human Genome
Project, have barely been felt, but there is 1 profound
change that has already occurred and that is the realization
that biology is fundamentally an informational science” (75).
This informational revolution could not be more unlike the
industrial revolution. It has minimal, if any unfavorable
effects on the environment, being performed in cybernetic
space that most of us believe is intangible, untouchable, and
lily-white clean. The immense nature of the informational
revolution was recently summarized in a book by Firestein
(76). From 5,000 years ago until 2003, humanity created a
total of 5 exabytes (a billion gigabytes) of information. From
2003 to 2010, we created this amount every 2 days and in
2013 we create this amount every 10 min. Another way of
stating this is to realize that every few hours, we create more
information than all of the information created by humanity
since the start of civilization.
Acknowledgment
The authors acknowledge Peggy Offley for her assistance in
the preparation of this manuscript.
Reprint requests and correspondence: Dr. Robert Roberts,
University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa,
Ontario K1Y 4W7, Canada. E-mail: [email protected].
REFERENCES
1. Boorstin D. The Discoverers. New York, NY: Random House, 1983.
2. Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis
of the human genome. Nature 2001;409:860 –921.
3. Roberts R, McNally EM. Genetic basis for cardiovascular disease. In:
Fuster V, Walsh RA, Harrington RA, editors. Hurst’s the Heart. 13th
edition. New York, NY: McGraw Hill, 2011:195–205.
4. Dunham I, Kundaje A, Aldred SF, et al. An integrated encyclopedia
of DNA elements in the human genome. Nature 2012;489:57–74.
5. Amaral PP, Dinger ME, Mercer TR, Mattick JS. The eukaryotic
genome as an RNA machine. Science 2008;319:1787–9.
6. Carlson C. Considerations for SNP selection. In: Winer MP, editor.
Genetic Variation: A Laboratory Manual. Cold Spring Harbor, NY:
Cold Spring Harbor Laboratory Press, 2007:263– 81.
7. Bhangale TR, Rieder MJ, Livingston RJ, Nickerson DA. Comprehensive identification and characterization of diallelic insertiondeletion polymorphisms in 330 human candidate genes. Hum Mol
Genet 2005;14:59 – 69.
8. Kruglyak L, Nickerson D. Variation is the spice of life. Nat Genet
2001;27:234 – 6.
9. Sun JX, Helgason A, Masson G, et al. A direct characterization of
human mutation based on microsatellites. Nat Genet 2012;44:1161–5.
10. Marian AJ, Belmont J. Strategic approaches to unraveling genetic
causes of cardiovascular diseases. Circ Res 2011;108:1252– 69.
JACC Vol. 61, No. 20, 2013
May 21, 2013:2029–37
11. Ng PC, Levy S, Huang J, Stockwell TB, et al. Genetic variation in an
individual human exome. PLoS Genet 2008;4:e1000160.
12. Stranger BEFM, Dunning M, Ingle CE, et al. Relative impact of
nucleotide and copy number variation on gene expression phenotypes.
Science 2007;315:848 –53.
13. Geisterfer-Lowrance A, Kass S, Tanigawa G, et al. A molecular basis
for familial hypertrophic cardiomyopathy: A beta cardiac myosium
heavy chain missense mutation. Cell 1990;62:999 –1006.
14. Lim DS, Oberst L, McCluggage M, et al. Decreased left ventricular
ejection fraction in transgenic mice expressing mutant cardiac troponin
T-Q(92), responsible for human hypertrophic cardiomyopathy. J Mol
Cell Cardiol 2000;32:365–74.
15. Marian AJ, Wu Y, Lim DS, et al. A transgenic rabbit model for
human hypertrophic cardiomyopathy. J Clin Invest 1999;104:
1683–92.
16. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA.
Online Mendelian Inheritance in Man (OMIM), a knowledgebase of
human genes and genetic disorders. Nucleic Acids Res 2002;30:52–5.
17. Marian AJ, Brugada R, Roberts R. Cardiovascular diseases caused by
genetic abnormalities. In: Fuster V, Walsh RA, Harrington RA,
editors. Hurst’s the Heart. 13th edition. New York, NY: McGraw
HIll, 2011:1783– 826.
18. Pennisi E. Genome sequencing. Search for pore-fection. Science
2012;336:534 –7.
19. Herman DS, Lam L, Taylor MR, et al. Truncations of titin causing
dilated cardiomyopathy. N Engl J Med 2012;366:619 –28.
20. Adzhubel I, Schmidt S, Peshkin L, et al. A method and server for
predicting damaging missense mutations. Nat Methods 2010;7:248 –9.
21. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding
non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009;4:1073– 81.
22. Marian AJ. Challenges in medical applications of whole exome/
genome sequencing discoveries. Trends Cardiovasc Med 2012;22:
219 –23.
23. Norton N, Li D, Rieder MJ, et al. Genome-wide studies of copy
number variation and exome sequencing identify rare variants in
BAG3 as a cause of dilated cardiomyopathy. Am J Hum Genet
2011;88:273– 82.
24. Theis JL, Sharpe KM, Matsumoto ME, et al. Homozygosity mapping
and exome sequencing reveal GATAD1 mutation in autosomal
recessive dilated cardiomyopathy. Circ Cardiovasc Genet 2011;4:
585–94.
25. Galmiche L, Serre V, Beinat M, et al. Exome sequencing identifies
MRPL3 mutation in mitochondrial cardiomyopathy. Hum Mutat
2011;32:1225–31.
26. Harakalova M, van Harssel JJ, Terhal PA, et al. Dominant missense
mutations in ABCC9 cause Cantu syndrome. Nat Genet 2012;44:
793– 6.
27. van de Laar IM, Oldenburg RA, Pals G, et al. Mutations in SMAD3
cause a syndromic form of aortic aneurysms and dissections with
early-onset osteoarthritis. Nat Genet 2011;43:121– 6.
28. Comino-Mendez I, Gracia-Aznarez FJ, Schiavi F, et al. Exome
sequencing identifies MAX mutations as a cause of hereditary pheochromocytoma. Nat Genet 2011;43:663–7.
29. Granados-Riveron JT, Ghosh TK, Pope M, et al. Alpha-cardiac
myosin heavy chain (MYH6) mutations affecting myofibril formation
are associated with congenital heart defects. Hum Mol Genet 2010;
19:4007–16.
30. Meder B, Haas J, Keller A, et al. Targeted next-generation sequencing
for the molecular genetic diagnostics of cardiomyopathies. Circ Cardiovasc Genet 2011;4:110 –22.
31. Chan L, Boerwinkle E. Gene-environment Interactions and gene
therapy in atherosclerosis. Cardiol Rev 1994;2:130 –7.
32. Roberts R. A customized genetic approach to the number one killer:
coronary artery disease. Curr Opin Cardiol 2008;23:629 –33.
33. Kruglyak L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat Genet 1999;22:139 – 44.
34. The International HapMap Consortium. A haplotype map of the
human genome. Nature 2005;437:1299 –320.
35. Roberts R, Stewart AF, Wells GA. Identifying genes for coronary
artery disease: An idea whose time has come. Can J Cardiol 2007;23
Suppl A:7A–15A.
JACC Vol. 61, No. 20, 2013
May 21, 2013:2029–37
36. McPherson R, Pertsemlidis A, Kavaslar N. A common allele on
chromosome 9 associated with coronary heart disease. Science 2007;
316:1488 –91.
37. Helgadottir A, Thorleifsson G, Manolescu A, et al. A common variant
on chromosome 9p21 affects the risk of myocardial infarction. Science
2007;316:1491–3.
38. Preuss M, Konig IR, Thompson JR, et al. Design of the Coronary
ARtery DIsease Genome-Wide Replication and Meta-Analysis
(CARDIoGRAM) Study: a genome-wide association meta-analysis
involving more than 22,000 cases and 60,000 controls. Circ Cardiovasc
Genet 2010;3:475– 83.
39. Roberts R, Stewart AF. Genes and coronary artery disease: Where are
we? J Am Coll Cardiol 2012;60:1715–21.
40. The CARDIoGRAMplusC4D Consortium. Coronary artery disease
risk loci identified in over 190,000 individuals implicate lipid metabolism and inflammation as key causal pathways. Nat Genet 2013;45:
25–33.
41. Dudbridge F, Gusnanto A. Estimation of significance thresholds for
genomewide association scans. Genet Epidemiol 2008;32:227–34.
42. Califano A, Butte AJ, Friend S, et al. Leveraging models of cell
regulation and GWAS data in integrative network-based association
studies. Nat Genet 2012;44:841–7.
43. Zhong H, Yang X, Kaplan LM, et al. Integrating pathway analysis and
genetics of gene expression for genome-wide association studies. Am J
Hum Genet 2010;86:581–91.
44. Ravasi T, Suzuki H, Cannistraci CV, et al. An atlas of combinatorial
transcriptional regulation in mouse and man. Cell 2010;140:744 –52.
45. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing
heritability of complex diseases. Nature 2009;461:747–53.
46. Tennessen JA, Bigham AW, O’Connor TD, et al. Evolution and
functional impact of rare coding variation from deep sequencing of
human exomes. Science 2012;337:64 –9.
47. Nelson MR, Wegmann D, Ehm MG, et al. An abundance of rare
functional variants in 202 drug target genes sequenced in 14,002
people. Science 2012;337:100 – 4.
48. Dewey FE, Pan S, Wheeler MT, et al. DNA sequencing: clinical
applications of new DNA sequencing technologies. Circulation 2012;
125:931– 44.
49. Gollob MH, Green MS, Tang AS, et al. Identification of a gene
responsible for familial Wolff-Parkinson-White syndrome. N Engl
J Med 2001;344:1823–31.
50. Sidhu JS, Rajawat YS, Rami TG, et al. Transgenic mouse model of
ventricular preexcitation and atrioventricular reentrant tachycardia
induced by an AMP-activated protein kinase loss-of-function mutation responsible for Wolff-Parkinson-White syndrome. Circulation
2005;111:21–9.
51. Hindorff LA, Sethupathy P, Junkins HA, et al. Potential etiologic and
functional implications of genome-wide association loci for human
diseases and traits. Proc Natl Acad Sci U S A 2009;106:9362–7.
52. Kiezun A, Garimella K, Do R, et al. Exome sequencing and the
genetic basis of complex traits. Nat Genet 2012;44:623–30.
53. Brown MS, Goldstein JL. Expression of the familial hypercholesterolemia gene in heterozygotes: mechanism for a dominant disorder in
man. Science 1974;185:61–3.
54. Shepherd J, Cobbe SM, Ford I, et al. Prevention of coronary heart
disease with pravastatin in men with hypercholesterolemia. N Engl
J Med 1995;333:1301– 8.
55. Stein EA, Mellis S, Yancopoulos GD, et al. Effect of a monoclonal
antibody to PCSK9 on LDL cholesterol. N Engl J Med 2012;366:
1108 –18.
Roberts et al.
Genomics in Cardiovascular Disease
2037
56. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of
GWAS discovery. Am J Hum Genet 2012;90:7–24.
57. Yang J, Benyamin B, McEvoy BP, et al. Common SNPs explain a
large proportion of the heritability for human height. Nat Genet
2010;42:565–9.
58. Simonson MA, Wills AG, Keller MC, McQueen MB. Recent
methods for polygenic analysis of genome-wide data implicate an
important effect of common variants on cardiovascular disease risk.
BMC Med Genet 2011;12:146.
59. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing
heritability: Genetic interactions create phantom heritability. Proc
Natl Acad Sci U S A 2012;109:1193– 8.
60. Wallentin L, James S, Storey RF, et al. Effect of CYP2C19 and
ABCB1 single nucleotide polymorphisms on outcomes of treatment
with ticagrelor versus clopidogrel for acute coronary syndromes: a
genetic substudy of the PLATO trial. Lancet 2010;376:1320 – 8.
61. Mega JL, Close SL, Wiviott SD, et al. Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med 2009;360:354 – 62.
62. Roberts JD, Wells GA, Le May MR, et al. Point-of-care genetic
testing for personalisation of antiplatelet treatment (RAPID GENE):
a prospective, randomised, proof-of-concept trial. Lancet 2012;379:
1705–11.
63. Carlquist JF, Anderson JL. Using pharmacogenetics in real time to
guide warfarin initiation: a clinician update. Circulation 2011;124:
2554 –9.
64. Anderson JL, Horne BD, Stevens SM, et al. A randomized and
clinical effectiveness trial comparing two pharmacogenetic algorithms
and standard care for individualizing warfarin dosing (CoumaGen-II).
Circulation 2012;125:1997–2005.
65. Link E, Parish S, Armitage J, et al., The SEARCH Collaborative
Group. SLCO1B1 variants and statin-induced myopathy–a genomewide study. N Engl J Med 2008;359:789 –99.
66. Levy S, Sutton G, Ng PC, et al. The diploid genome sequence of an
individual human. PLoS Biol 2007;5:e254.
67. Hayden EC. Secrets of the human genome disclosed. Nature 2011;
478:17.
68. Brunham LR, Hayden MR. Medicine. Whole-genome sequencing:
the new standard of care? Science 2012;336:1112–3.
69. Drmanac R. Medicine. The ultimate genetic test. Science 2012;336:
1110 –2.
70. Peters BA, Kermani BG, Sparks AB, et al. Accurate whole-genome
sequencing and haplotyping from 10 to 20 human cells. Nature
2012;487:190 –5.
71. Schnabel RB, Baccarelli A, Lin H, et al. Next steps in cardiovascular
disease genomic research—sequencing, epigenetics, and transcriptomics. Clin Chem 2012;58:113–26.
72. McCarty CA, Chisholm RL, Chute CG, et al. The eMERGE
Network: a consortium of biorepositories linked to electronic medical
records data for conducting genomic studies. BMC Med Genomics
2011;4:13.
73. Stein LD. The case for cloud computing in genome informatics.
Genome Biol 2010;11:207.
74. Zerbino DR, Paten B, Haussler D. Integrating genomes. Science
2012;336:179 – 82.
75. DeSalle R YM. After the genome: where should we go? In: DeSalle R,
Yudell, Y, editors. The Genomic Revolution: Unveiling the Unity of
Life. Washington, DC: Joseph Henry Press, 2002:64 –74.
76. Firestein S. Ignorance: How it Drives Science. Oxford, England:
Oxford University, 2012.
Key Words: data y genomics y sequence.