CORRESPONDENCE
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
http://www.nature.com/news/
reproducibility-1.17552; https://
f1000research.com/gateways/PRR). The
newly launched International MAQC Society
will strive to work with various scientific
communities to develop consensus on best
practices for enhanced reproducibility in
generation, analysis, and interpretation of
massive data from increasingly innovative
biomedical fields. More information
about the MAQC Society can be found at
http://www.maqcsociety.org.
ACKNOWLEDGMENTS
The first MAQC Society meeting was supported by
the SAS Institute, Burroughs Wellcome Fund, and
the National 863 Program of China (2015AA020104,
to L.S.).
DISCLAIMER
The views presented in this article do not necessarily
reflect current or future opinion or policy of the
US Food and Drug Administration. Any mention
of commercial products is for clarification and not
intended as endorsement.
COMPETING FINANCIAL INTERESTS
The authors declare competing financial interests:
details are available in the online version of the paper.
Leming Shi1,2, Rebecca Kusko3,
Russell D Wolfinger4,
Benjamin Haibe-Kains5–7, Matthias Fischer8,9,
Susanna-Assunta Sansone10,
Christopher E Mason11, Cesare Furlanello12,
Wendell D Jones13, Baitang Ning14 &
Weida Tong14
1State Key Laboratory of Genetic Engineering,
School of Life Sciences and Shanghai Cancer
Center, Fudan University, Shanghai, China.
2Collaborative Innovation Center for Genetics
and Development, Fudan University, Shanghai,
China. 3Immuneering Corporation, Cambridge,
Massachusetts, USA. 4SAS Institute Inc., Cary,
North Carolina, USA. 5Princess Margaret Cancer
Centre, Toronto, Ontario, Canada. 6Departments
of Medical Biophysics and Computer Science,
University of Toronto, Toronto, Ontario, Canada.
7Ontario Cancer Research Institute, Toronto,
Ontario, Canada. 8Department of Experimental
Pediatric Oncology, University Children’s
Hospital of Cologne, Medical Faculty, Cologne,
Germany. 9Center for Molecular Medicine
Cologne (CMMC), University of Cologne,
Cologne, Germany. 10Oxford e-Research Centre,
Engineering Science Department, University of
Oxford, Oxford, UK. 11Weill Cornell Medicine,
New York, New York, USA. 12Fondazione Bruno
Kessler, Trento, Italy. 13Q2 Solutions, Morrisville,
North Carolina, USA. 14National Center for
Toxicological Research, US Food and Drug
Administration, Jefferson, Arkansas, USA.
e-mail: weida.tong@fda.hhs.gov.
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17. Wilkinson, M.D. et al. Sci. Data. 3, 160018 (2016).
Challenges and recommendations
for epigenomics in precision health
To the Editor: In March 2017, US life
insurance company, GWG Life (Minneapolis)
started to require policy owners to submit
saliva samples. The company was not
interested in the genes that their customers
inherited, but in the epigenetic state of genes
in the form of DNA methylation, for which
it had licensed an epigenomic technology to
predict an individual’s health and life span1,2.
This raises the issue of individual epigenomic
profiles being used to charge more or less for
insurance coverage—or to deny life insurance
altogether. The silver lining is that the
epigenetic profile is not fixed in stone; you
may improve your epigenome by changes in
diet, exercise, or other modifications.
Seven years after epigenetics was
featured on the cover of Time magazine, the
development of novel epigenomic techniques
has led to a better understanding of how the
epigenome changes across individuals and
health states3. For example, researchers have
shown that the T cells of the immune system
change at the epigenomic level during aging
or functional exhaustion4,5. The response of
certain cancer patients to a drug treatment
can be predicted from DNA accessibility
of target loci during treatment6. Whether a
person smokes cigarettes, and thus may be
at risk for myriad cancers, can be inferred
from DNA methylation patterns7. The origin
of cell-free DNA, released into the blood
by damaged tissues can be gleaned from
nucleosome positioning in those fragments8.
Thus, in the coming years, we can expect
epigenomic findings to be used increasingly
in determining diagnosis, treatment course,
and even the cost of insurance. Here, we
describe five recommendations for continued
development in this field, formulated with
an interdisciplinary group of experts toward
realizing the full potential of epigenomic
medicine.
The authors’ perspectives for this piece
came together via the Centers of Excellence
in Genomic Science (CEGS), sponsored by
the US National Human Genome Research
Institute (NHGRI). CEGS aim to develop and
disseminate novel genomic and epigenomic
technologies. As technology developers, we
recognize that the field of epigenomics is
rapidly maturing in both the technological
and biological sciences and many further
applications of epigenomic technology
have been proposed for both clinical and
commercial purposes. For the technology
to be optimally designed for discovery
or research purposes as well as robust
application, priorities for use across these
fields should be considered together at the
outset. Thus we sought to bring together an
interdisciplinary group consisting of both
developers and end users of epigenomic
technology to propose priorities for the field.
The Center for Personal Dynamic Regulomes
at Stanford, California, joined with the
CEGS investigators from Harvard Medical
School (Boston), Massachusetts Institute
of Technology (Cambridge, MA, USA),
Dana-Farber Cancer Institute (Boston),
Massachusetts General Hospital (Boston),
the Salk Institute (La Jolla, CA, USA), and
the University of Chicago, as well as thought
leaders in academic medicine, executives
from companies specializing in diagnosis,
lifestyle, and data analysis, and biosecurity
and bioethics experts. Our recommendations
reflect discussions that began with a
consideration of clinical and consumer
needs and moved to technical feasibility,
commercial opportunities, and regulatory
and ethical considerations.
The promise and challenge of
epigenomics
Precision medicine promises to greatly
improve individualized medical care, and
this promise hinges not only on genetic
tests and therapies, but also epigenetic
insights. Although the massive power of
DNA sequencing has largely been applied
to genome and exome sequencing as a
means to trace sequence variants in myriad
diseases, such applications do not capture
VOLUME 35 NUMBER 12 DECEMBER 2017 NATURE BIOTECHNOLOGY
CORRESPONDENCE
Liquid
biopsy
Standard
spike-in
Standard
pipeline
Regulatory
element database
ATCG
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
Tissue
collection
Epigenomic
assay
Data
analysis
Data
integration
Patient engagement
and longitudinal sampling
Figure 1 Summary of challenges and recommendations to advance epigenomics in precision medicine.
the tissue, context, or temporal dynamism
to the epigenome that defines cell state
and drives phenotypes9. Take the case of a
woman who brings her young son to the
pediatrician, explaining a set of symptoms.
After some testing, the doctor confirms that
the child has a fairly common autoimmune
disease. The doctor is intrigued, however,
when the woman explains that her son’s
identical twin does not show any symptoms.
He suggests that although the two boys
share the same genetic information,
their discordance for the disease could
perhaps be explained by differences in
epigenomic states, leading to changes in
gene expression. The woman consents to
giving blood samples from her children for
a research study, which finds a handful of
changes in DNA methylation and chromatin
accessibility in the blood of the two boys
that could be related to the disease. This
study is powerful in its ability to ascertain
how epigenomic changes lead to disease or
other phenotypes because of the controlled
genetic makeup of the twins10,11.
Although much evidence for epigenomic
biomarkers and drivers of disease exist
in cases like the above, their application
to broader clinical medicine has yet to be
fully realized. Before they can be applied
in the context of precision medicine, the
following questions need to be answered.
How does one know the measurements
are accurate? Which changes are transient
versus true biomarkers of disease? How
can results be compared across studies
and individuals? What are the best ways to
compare different emerging technologies
for measurement, and how do we ensure
that these new insights are used for
maximal benefit toward precision health?
Many of the challenges associated with
making personalized genomic medicine a
reality hold true for epigenomic medicine
as well. Beyond the technical challenge of
resolving the complex molecular basis of
clinical phenotypes, these include issues of
patient privacy and identification, ethical
considerations of editing the genome or
epigenome, difficulties in gaining access
to diverse patient cohorts, and the cost
of individualized care. Though critical,
these considerations have been addressed
repeatedly in the literature and in the realm
of policy and thus are not discussed here12.
Instead, we describe the opportunities
unique to epigenomic medicine and the
hurdles that must be overcome for this
vision to become reality.
Epigenomic science
The epigenome encompasses a broad range of
molecular signals associated with DNA, from
modifications to DNA itself, to nucleosomes
and their modifications, to the folding of
chromatin and the accessibility of regulatory
regions to DNA binding proteins. The
epigenome integrates genetic information,
such as inherited sequence variants,
together with environmental information—
propagated via signaling cascades and
pathways—to affect functional outcomes
for the cell. Reading the epigenome, which,
unlike the genome is highly dynamic, can
reveal disease-, condition-, and tissueassociated changes in gene regulation over
time that do not involve changes at the
sequence level13. Whether these epigenomic
modifications are the cause or simply the
consequence of changes in cellular state,
they are potentially valuable as diagnostic
biomarkers and/or as targets for therapeutic
intervention.
Several methods have been developed
in the past few years to make epigenetic
measurements on small, clinical samples on a
reasonable time scale; for example, ATAC-seq
(assay for transposase-accessible chromatin
using sequencing) reveals information about
protein–DNA binding and regulatory activity
NATURE BIOTECHNOLOGY VOLUME 35 NUMBER 12 DECEMBER 2017
of a majority of non-coding functional
elements in the genome on a timescale of one
day. It requires only a small number of cells
as input, making it an attractive technology
for clinical application14–16. Other techniques
that involve sequencing of epigenomic
modifications, such as cytosine and histone
methylation, provide opportunities for both
invasive (i.e., biopsy) and non-invasive (i.e.,
cell-free DNA/liquid biopsy) diagnosis and
prognosis17. The rapid development of these
techniques points toward further integration
of epigenomic metrics, alongside genomic
metrics, in clinical medicine and lifestyle
applications.
The nature of the epigenome also raises
unique challenges related to two key
properties18: dynamism and tissue specificity,
and continuous scale.
The dynamism and tissue-specificity
of epigenomic modification require that
for appropriate interpretation, assays be
performed longitudinally from biopsies or
blood samples from a single person over
the course of disease progression, aging,
or lifestyle intervention. Furthermore,
epigenomic measurements are most often
measured on a continuous scale without a
‘ground truth’ value or standard threshold for
presence or absence of a feature. These facts
bring challenges in assay and data analysis
standardization and/or storage and in longterm patient engagement and feedback. For
those in academia and industry seeking to
tackle these unique challenges, we describe
below five recommended areas of focus:
first, develop a standard chromatinized DNA
sample to benchmark assays; second, create
a standardized analysis pipeline for quality
control of epigenomic data, such as ATACseq data; third, name and catalog regulatory
elements in the genome in a manner
that can evolve as knowledge increases
around regulatory element structure and
function; fourth, develop experimental and
computational biomarker assays to acquire
epigenomic information from all tissues
using non-invasive collection methods; and
fifth, cultivate a large and diverse patient
pool for participation in longitudinal studies
over long time frames. The first three of these
recommendations relate to standardization;
the final two to clinical application (Fig. 1).
Recommendation 1: develop a
commercially available and affordable
standard spike-in chromatin sample
We recommend the development of a
commercially available and affordable
standard chromatinized DNA sample that
can be added from the start of an assay to
1129
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
CORRESPONDENCE
control for differences among laboratories
and over time. Imagine that a researcher
in another country wants to test whether
some children that have developed certain
symptoms in his clinic are in the early
stages of the same autoimmune disease
that presented in the single twin example
described above. These patients likely
would not have healthy identical twins for
comparison, and thus this researcher would
need to compare against other healthy
individuals whose data are available, like
the original healthy twin’s. To compare
the DNA methylation and chromatin
accessibility in these new patients, this
researcher must be able to benchmark his
assay against those done at the previous
institution. This comparison across large
cohorts of longitudinal samples to discover
mechanisms of condition-related gene
regulation requires standardization of
sample preparation and analysis. This type
of standardization would also expedite the
process for regulators to sign off on such
discoveries to be turned into common
clinical practices. Though written protocols
for sample preparation are currently
available, we recommend that a standard
‘lynchpin’ sample be used in all epigenomic
experiments as a benchmark to ensure
assay quality, instrument performance, and
normalization.
The use of standard ‘ground truth’ samples
is common in RNA-seq experiments, which
employ ERCC (External RNA Control
Consortium) spike-in RNA samples for
comparison19. These spike-ins, combined
with unique molecular indexing (UMI)
allow an absolute measurement of molecules
of a given type in a sample. Similar standard
samples are being developed for genomic
DNA sequencing by the Genome In a
Bottle Consortium20. Developing this type
of standard is especially important for
epigenomic markers that are measured as
continuous variables and in reference to an
internal control. In the case of the twins,
imagine that the brother with autoimmune
symptoms showed a fourfold increase in
chromatin accessibility at an important
immune-modulating gene over his brother.
In another laboratory across the country,
technical variables may yield an increase
in a patient of threefold over an unaffected
individual. To determine whether this
difference is significant, a reference sample
is required. Overall enrichment of signal in
assays like chromatin immunoprecipitation
sequencing (ChIP-seq) and ATAC-seq, and
conversion rates for bisulfite sequencing
vary between experiments, even within the
1130
same laboratory. Furthermore, these can
change based on the library preparation and
the sequencing instruments used, which can
affect biases in DNA fragment lengths and
sequence content. An internal standard is
absolutely imperative for defining ‘normal’
versus ‘pathogenic’ in these cases.
A standard chromatin sample could come
from renewable mouse and human sources,
like common ENCODE (Encyclopedia
of DNA Elements) cell lines, and should
have high confidence data available on
DNA accessibility, histone modifications,
nucleosome positions, methylation, and
sequence polymorphisms. It is important
that this type of standard be prepared from
a single batch, or as few batches as possible,
of cells in an industry-standard setting
to eliminate the current problems arising
because of various confounding factors.
These include different passage numbers,
karyotypes, culture conditions, and labto-lab variation. In addition, so that this
program can continue unbroken into the
future, we will need a strategy whereby
new, or renewed, standard cell lines are
periodically brought into the program
and characterized well before established
cell lines are exhausted or too far altered
(genetically or epigenetically). We recognize
the value of normal karyotypes, isogenic
samples, and ancestral diversity. Finally,
coordination of the choice of cell lines with
ongoing efforts to describe the long-range
structure of genomes through chromatin
capture techniques (e.g., Hi-C) and imaging
technologies will greatly facilitate the
integration of three- and four-dimensional
(4D; including time) genome information.
Recommendation 2: create a consensus
analysis pipeline and data repository for
epigenomic assays
There is a need for a standardized analysis
pipeline for quality control of epigenomic
data. At present, to analyze data published
by multiple laboratories, researchers must
start from raw sequencing reads owing
to differences in downstream pipelines,
making comparison inefficient. In
addition, unlike base calls in genomic data,
epigenomic measurements are continuous,
and thus thresholds are arbitrarily chosen.
To confidently assign biological meaning
to epigenomic changes, researchers must
be capable of integrating very large sample
cohorts from multiple institutions analyzed
in the same way. This standardized analysis
would apply to the most upstream steps of
sequence analysis, specifically pertaining
to quality control. As new techniques
become available and multiple institutions
apply them to multiple tissue types, quality
metrics related to signal-to-noise ratios
and bias in the data must be recorded in
a standardized way. This type of pipeline
would still allow novel downstream analyses
in individual laboratories, while improving
reproducibility and comparison across
studies.
Although we recognize that the
development of novel analyses is just as
important to driving research forward as
the development of new assays, analyses for
clinical samples need to be standardized to
facilitate comparisons and meet regulatory
standards. Public pipelines generated by
consortiums like ENCODE are a good
start, but there is currently no requirement
or incentive for individual laboratories
to make use of them. Furthermore, it is
hard to overemphasize the importance of
data visualization methods. Methods for
simplification and visualization of complex
epigenomic data should be developed and
standardized for ease of dissemination and
human interpretability. Finally, a common
data repository with standardized metrics
would allow laboratories to deposit data
analyzed in the same way and compare
new data against high-quality data from
others in the field. Simplifying the process
of comparing data sets via a common data
deposit space and user-friendly interface
would speed this process.
Recommendation 3: catalog and register
regulatory elements in a standardized
index for easy searchability and
comparison across laboratories, genome
builds, and species
Concurrent to standardized analytic
methods, there is an urgent need for a way to
name and catalog regulatory elements in the
genome as knowledge increases concerning
their structure and function. Unlike genes,
regulatory elements are not currently
indexed in a standardized manner, which
makes cross-referencing among studies
both manual and laborious. A gene (or
more precisely a transcript) is identified not
only by its sequence, strand, structure, and
coordinates in the reference genome, but also
by a standardized unique identifier (its gene
name) that links it across genome builds,
species, and assays as well as to its product.
At the gene level, confusion often arises
because there are multiple databases with
unique transcript names and identifiers21–23.
This problem is dramatically expanded in the
regulatory space. Regulatory elements, which
are estimated to comprise ~10–20% of the
VOLUME 35 NUMBER 12 DECEMBER 2017 NATURE BIOTECHNOLOGY
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
CORRESPONDENCE
human genome (aggregated across all cellular
contexts), have no universally accepted
identifiers at present.
Creating a dictionary of regulatory
elements is an essential foundation for the
construction of a body of evidence linking
a given regulatory region to a phenotype
or disease. Take for example, the set of
loci whose accessibility or methylation are
changed between the healthy and diseased
twin. Another study at another institution
finds that in a set of patients, those affected
with a related autoimmune disease, show
changes in the epigenomic state at 20% of
these same loci. Without a common naming
system, these two studies may never link
these regions together, and the latter group
of researchers likely would not be able to find
that these loci had previously been linked to
autoimmunity.
The task of cataloguing regulatory
elements is complicated by several factors.
First, regulatory elements do not have
well-defined starts and stops (unlike, for
example, an open reading frame of a gene).
Thus, setting a convention for choosing the
starts and ends of enhancers, promoters,
and other regulatory elements is necessary.
Second, regulatory elements may expand
or contract in size across different cellular
states or form higher order coordinated units.
Thus, a higher order structure may need to
be imposed on a dictionary of elements that
is flexible enough to capture such complex
relationships. Third, regulatory elements
are not as well conserved as coding genes.
Thus, comparisons across species may
require the use of operational and spatial
identification instead of sequence-based
identification. Fourth, not all accessible
elements in the genome are functional,
and not all nucleotides within a defined
element are functional, necessitating highthroughput methods for assaying necessity
and sufficiency of elements in different
contexts24.
Several collaborative functional genomics
projects, such as the ENCODE Project,
The Roadmap Epigenomics Project,
the International Human Epigenome
Consortium (IHEC), and Functional
Annotation of the Mammalian Genome
(FANTOM) Consortium have made
substantial progress toward identifying
and cataloguing candidate regulatory
elements by integrating multiple types of
functional genomic data (e.g., chromatin
accessibility, RNA-seq, ChIP-seq, cap
analysis gene expression (CAGE), and DNA
methylation) to predict chromatin state for
a given region across multiple common cell
types25–27. Using the ENCODE portal, for
example, regulatory regions can be queried
by coordinate or by nearby gene name,
yielding an output of expression level or
accessibility in a host of tissues. Despite these
major advances, at present this approach
has limitations. We support a convention for
naming and numbering be extended more
broadly in the field such that publications
from all groups report regulatory elements
in a standard way for all samples, not only
those included in a specific consortium.
Furthermore, an ontology, similar to that
employed by ENCODE, would simplify
making functional connections between
samples, regions, cell types, and species28.
A combination of a standardized
chromatin spike-in and analysis framework
and a cataloguing system for the non-coding
genome will expedite understanding of the
relationship between epigenetic changes and
biological meaning and thus its translation
into applications in clinical practice.
Recommendation 4: develop assays for
inferring tissue-of-interest epigenomic
modifications from accessible tissues
such as blood, skin, saliva, and urine
Unlike genome sequencing, which requires
often just one sample from an accessible
tissue at a single time point, epigenomic
assays require multiple samples over time
from tissues of interest. This necessitates
two advances: first, the development of
biomarker assays that can infer the state
of the epigenome in an inaccessible tissue,
such as the brain, from blood, skin, saliva,
or urine; and second, long-term engagement
in epigenomic investigations from enrolled
patients.
For longitudinal studies on healthy
participants to be feasible, as well as for
epigenomic medicine to reach its full
potential, non-invasive collection methods
must be sufficient to acquire epigenomic
information from all tissues. The ultimate
goal is to be able to infer the cell-type specific
state of an epigenomic feature in the forebrain
or in a pancreatic tumor through a surrogate
tissue source that can be collected rapidly and
repeatedly. This is especially important for
measurements in healthy people, who ideally
would not have to visit a hospital for a biopsy
to participate. These kinds of surrogate
tissue assays have proven useful at the DNA
sequence level in both pre-natal diagnosis
and cancer. In these cases, DNA found in the
blood, called cell-free DNA (cfDNA), can
be sequenced and assigned to its tumor or
fetal origin by a mutational signature or sex
chromosomes and genetic polymorphisms,
NATURE BIOTECHNOLOGY VOLUME 35 NUMBER 12 DECEMBER 2017
respectively29,30. Studies in cancer have
shown that the amount of circulating DNA
from a known tumor with a common
mutational signature correlates with tumor
size and thus the number of cells dying and
releasing DNA into the bloodstream.
For conditions that do not involve changes
to the DNA sequence itself, epigenomic
biomarker assays in surrogate tissues would
be highly valuable. These assays would
measure epigenomic features correlated
with health outcomes, or identify the
tissue source of DNA being released as a
result of tissue damage. As the twin with
autoimmune disease progresses through his
life, complications may arise as the immune
system attacks his own tissues. Imagine that
doctors could identify exactly which tissues
of the gut, for example, are being damaged
and releasing their DNA into the blood
based on epigenomic features in cfDNA. This
would allow the doctor to specifically treat
symptoms in that organ.
As described above, DNA methylation
and nucleosome positioning measured in the
blood have been shown to correlate with the
tissue of origin and a number of health traits,
including life expectancy and smoking2,7,8.
Liquid biopsy-based epigenomic assays
have already shown their utility for clinical
application. Researchers in academia and
industry should strive to continue developing
a broader range of biochemical and analytical
methods for inferring or deconvoluting the
epigenetic state of various cell types from
blood, skin, saliva, and urine.
Recommendation 5: improve long-term
participant involvement via enhanced
result reporting and patient-brokered data
sharing
One of the greatest challenges in
biomedical research is cultivation of a large
and diverse patient pool for participation
in longitudinal studies over long time
periods. This is especially important to
capture temporal dynamics in epigenomic
measurements. Such a large-scale
longitudinal study requires an elevated
level of patient participation. One reason
for challenges in this area has been the
fact that patients are not always notified of
and encouraged to engage in the findings
of research studies. One way to improve
participant engagement is via increased
feedback to participants regarding study
results. We also encourage experiments
with a data clearinghouse. Such an online
clearinghouse could allow individuals to
submit their own data (e.g., ATAC-seq data
from blood) to multiple ongoing studies
1131
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
CORRESPONDENCE
in order to gain more information about
their health from a single donation. This
may also facilitate data sharing among
researchers, but with patients controlling
the use of their data by acting as their own
brokers.
Personalized epigenomic measurements
hold enormous and transformative potential
for clinical medicine and even for lifestyle
applications. The epigenome has the power
to reveal why the two identical twins
present differently for diseases, which
medication they may respond to, and the
influence of environmental factors on the
progression of the disease throughout
their lifetime. But the complexity and
dynamic nature of the epigenome over time
and health state bring unique difficulties
surrounding standardization, data analysis
and sharing, sample acquisition, and patient
participation.
Editor’s note: This article has been peer-reviewed.
ACKNOWLEDGMENTS
We thank J.A. Stamatoyannopoulos (U. Washington),
C.-T. Wu (Harvard Medical School) and J. Schloss
(NHGRI) for input. We thank the National Human
Genome Research Institute for support.
COMPETING FINANCIAL INTERESTS
The authors declare competing financial interests:
details are available in the online version of the paper.
Ava C Carter1, Howard Y Chang1,
George Church2, Ashley Dombkowski3,
Joseph R Ecker4, Elad Gil5, Paul G Giresi6,
Henry Greely7, William J Greenleaf 1,8,
Nir Hacohen9, Chuan He10, David Hill11,
Justin Ko12, Isaac Kohane13, Anshul Kundaje14,
1132
Megan Palmer15, Michael P Snyder1,8,
Joyce Tung16, Alexander Urban1,17,
Marc Vidal11 & Wing Wong1,18
1Center for Personal Dynamic Regulomes,
Stanford University, Stanford, California, USA.
2Department of Genetics, Harvard Medical
School, Boston, Massachusetts, USA, and
Wyss Institute, Boston, Massachusetts, USA.
3BEFORE Brands, Menlo Park, California,
USA. 4The Salk Institute for Biological Studies,
La Jolla, California, USA, and Howard Hughes
Medical Institute. 5Color Genomics, Burlingame,
California, USA. 6Epinomics, Menlo Park,
California, USA. 7Center for Law and the
Biosciences, Stanford University, Stanford,
California, USA. 8Department of Genetics,
Stanford University, Stanford, California,
USA. 9Massachusetts General Hospital,
Boston, Massachusetts, USA. 10University of
Chicago, Chicago, Illinois, USA, & Howard
Hughes Medical Institute 11Center for Cancer
Systems Biology, Dana Farber Cancer Institute,
Boston, Massachusetts, USA. 12Department of
Dermatology, Stanford University, Stanford,
California, USA. 13Department of Medical
Informatics, Harvard Medical School, Boston,
Massachusetts, USA. 14Departments of
Genetics and Computer Science, Stanford
University, Stanford, California, USA. 15Center
for International Security and Cooperation,
Stanford University, Stanford, California, USA.
1623andMe, Mountain View, California, USA.
17Department of Psychiatry and Behavioral
Sciences, Stanford University, Stanford,
California, USA. 18Department of Statistics,
Stanford University, Stanford, California, USA.
e-mail: howchang@stanford.edu.
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to collect epigenetic samples to analyze biomarkers
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VOLUME 35 NUMBER 12 DECEMBER 2017 NATURE BIOTECHNOLOGY