The Personal Genome Project

Article (PDF Available)inMolecular Systems Biology 1(1):2005.0030 · February 2005with28 Reads
DOI: 10.1038/msb4100040 · Source: PubMed
EDITORIAL
The Personal Genome Project
Molecular Systems Biology 13 December 2005; doi:10.1038/msb4100040
Large potential benefits for systems biology reside in applica-
tions to human health and identity. To develop our commu-
nity’s skills in these directions, ready access to highly
integrated and comprehensive human genome and phenome
data sets is extremely important and increasingly feasible
technically. The few human ‘functional genomics’ data sets
available today tend to be isolated from one another. Some of
the tools needed to break through this impasse are addressed
below in the context of a Personal Genome Project (PGP) as a
natural successor to the Human Genome Project (HGP)—two
recent buds in the ancient field of genetics.
From my first interaction with Wally Gilbert in 1976, it
seemed that a large (but appealing) leap would be to go from
his new method for sequencing 30 bp segments to a method to
get everyone’s full genome sequenced. Six billion base pairs
for six billion people had a nice ring to it. This was still merely
a fantasy when we published a paper called ‘Genomic
Sequencing’ in 1984 (Church and Gilbert, 1984) and conspired
to create a 3 billion dollar HGP later that year (Cook-Deegan,
1989). For the subsequent 16 years, radical technology
development (while kept alive in a few ‘back-rooms’) was
clearly a minor funding priority relative to ‘production’
sequencing. However, by 2001, the criticisms of the old
technology grew and the call for affordable personal genomes
became irresistible (Jonietz, 2001). In early 2004, the NIH-
NHGRI posted a request for applications, and in October 2004
and August 2005, announced grant awards totaling $70 million
for technology leading to human genome sequences for
$100 000 in 5 years and $1000 in 10 years (http://www.nih.gov/
news/pr/aug2005/nhgri-08.htm). As if the motivation were
not already high enough, at the recent Genome Sequencing &
Analysis Conference in Hilton Head (October 18), the prospect
of a new X-prize arose to encourage this new Personal
Genomics field. (The first X-prize, $10 million for re-usable
spacecraft, was awarded in October 4, 2005 and is followed by
a $50 million prize for orbiting.) Amid all of this positive
reinforcement, some key points were left fuzzy—What exactly
is meant by sequencing a human genome? What is the utility
of personal genomes? What are the ethical, legal, and social
implications (ELSI)? The time has come to sharpen these
points up. As we begin to purchase personal genomes, we
want to know what we are paying for.
What is meant by sequencing a human genome? To
quantitatively assess progress in this field, we need engineer-
ing specifications for the quality and cost of genomes and
practical ways for standardizing and validating progress. The
quality of genomes can be stated by the fraction of the genome
sequenced and the accuracy of those covered regions. Both of
these measures have nuances. The fraction of the genome can
be of the whole genome or just the protein-coding or just the
euchromatic regions. In our current rate of discovering new
genetic elements and phenomena that can affect human
health, how close we can get to a whole genome sequence is
what we want to measure. The quality can count an insertion
or deletion of 4 bp as one error or four errors. The former seems
like an appropriate measure for tandem repeats. An erroneous
‘crossover’ from one haplotype to another would count as a
single error rather than counting up all downstream errors. An
individual genome can be estimated to within 1E3 simply by
guessing that the individual is the same as the hodgepodge
HGP genome currently in GenBank. It can be guessed to within
1E4 by using occasional SNPs to drag in 10 kb or so of linked
common SNPs. However, an error rate of 1E4 means that
there will be 600 000 errors in a diploid human genome
sequence each of which would require additional sequencing
or functional tests (more base-pair errors than some entire
genomes). The genomic differences between a cancer cell and
a normal cell might be around 1E6 per base pair (Wang,
2004) (only some of those changes being causative for the
pathological proliferation). So a reasonable X-prize challenge
would be to sequence 100 diploid (and some aneuploid)
human genomes at the same coverage and accuracy as today’s
haploid mosaic, that is, 99% of the euchromatin and 93%
of the whole genome at an error rate better than 1E6. The
results could be checked by the other teams using a shared set
of DNAs (see discussion of cell lines below).
The utility of the first personal genome is analogous to the
first fax machine, web page, or computer. Until communities of
resources build up, these revolutionary new tools serve mainly
the ‘early adopters’. These initial participants are heroes and
human guinea-pigs paving the way for potentially increasing
utility for the general public. For personal genomics, we are
already seeing market activity in genetic diseases, cancer, and
pharmacogenomics, for example, tests for BRCA1/2, EGFR
that impact diagnostics and drug choice and dose. This is
expanding rapidly to include personalized nutrition and life-
style decisions. As DNA is only a small part of destiny, personal
genomics might fruitfully de-emphasize ‘prediction’ and focus
on augmenting systems biology interpretations and prioritiza-
tions of actual day-to-day measurements of our physiological
states (Hood et al, 2004). Even before the PGP is fully ready
for medical practice, it will aid research in human functional
genomics and systems biology. These topics need the
perspective of inter-individual variation (Cheung, 2005). A
system model that describes a canonical or consensus cell
is not as informative as one that embraces natural genetic
variation and epigenetic stochastics as well.
What would we do if we were given our full genome
tomorrow? We would first rank our 6 million differences
relative to a reference genome, looking for mutations which
(a) affect known disease genes (or genes related by function or
homology), (b) affect conserved genetic elements (Ramensky,
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Article number: 2005.0030
2002; Vitkup, 2003), and/or (c) create homozygous or
(co)dominant alleles. Then, we generate hypotheses about
potential consequences of the top ranked changes including
customizing existing system models. Then, we would want to
test these hypotheses with focused population association
studies, animal models, and functional genomics on the cells
from the PGP subjects. Results from those studies will allow us
to better personally prioritize clinical diagnostics, therapeu-
tics, lifestyle, and nutritional changes.
What are the ELSI of personal genomics? Although easy to
get carried away by the excitement of the technology, we need
to also ask what is needed to help it play out in society
positively. Will acceptance be rapid, like the worldwide web in
the year 1993, or go through a series of major setbacks as
has occurred with genetically modified organisms? Two top
issues that come up are privacy and insurance. First, privacy.
An ideal systems biology resource would be a highly inte-
grated data set for all aspects of human phenotype for
a genetically diverse set of subjects. This would include
full medical records, omics data, and potentially identifying
data like craniofacial MRI and 3D photogrammetry. Facial
features are among the most noticeable and socially sig-
nificant of human phenotypes. Correlating them with geno-
type is of huge significance for forensics and security
applications and will encourage early visualization of positive
and negative uses. Research on human subjects, especially
when de-identification is impractical (as above), requires
Institutional Review Board (IRB) approval not only for
collection of the data, but also for accessing it. Occasionally,
researchers deviate from the filed IRB plans or evade the
IRB entirely, risking loss of funding, loss of access to
clinical resources, and potential harm to the subjects. It is
clearly far more beneficial to communicate the full study
plan to the IRB and the public in advance to catch
broader ramifications than single researchers typically
imagine (Kennedy, 2002; Church, 2005a).
The more popular and broadly distributed the PGP genome
and phenome data, the more likely that accidental or
deliberate release to a public part of the internet (and Google)
or re-identification (Kohane and Altman, 2005) will occur.
If the study subjects are consented with the promise of
permanent confidentiality of their records, then the exposure
of their data could result in psychological trauma to the
participants and loss of public trust in the project. On the other
hand, if subjects are recruited and consented based on
expectation of full public data release, then the above risks
to the subjects and the project can be avoided. How many
volunteers are willing to participate initially and how many as
the study expands (each noting the cumulative experiences
of the earlier participants) is what we can call PGP-ELSI
question #1. The fraction of people willing to volunteer may
surprise us. Like pioneers, health-care workers, and astro-
nauts, they will put themselves (and their families) at risk, but
with rewards for society (and their families). The number
of personal facts considered stigmatizing has been dropping
since the 1960s when cancer, depression, sexual dysfunc-
tion, and sexually transmitted diseases were taboo topics,
while today discussion of personal decisions on Iressa,
Viagra, Prozac, and AZT are common. The key point is that
an open-PGP might be able scale up to thousands of subjects
without relying on perfect data security, and whether it will
is not a theoretical question. As full exposure is a new concept,
the PGP will stimulate ELSI research examining broader
implications including insurance, workplace discrimination,
and profiling.
How do we minimize risk to the participants? The initial
participants should be diverse, yet very familiar with research
on human subjects, genetics, information technology, and
ELSI. They should have thoroughly considered a variety of
worst-case scenarios. The subject ideally (and close relatives
too) would not be hesitant about knowing and sharing ANY
part of the subject’s genome or Personal Health Records (Sands
and Halamka, 2004; Kohane and Altman, 2005), as whatever
makes that part scary to them could make some other part
scary at a later date (after it is too late to remove the data
from the public domain). The goal of the PGP should be to
slowly ramp up the number and diversity of participants
including outreach and broad-audience genetics education.
At each stage, a Data Safety Monitoring Committee will assess
the need for discontinuation or mid-course corrections. Initial
trust may be higher if the PGP is a non-profit entity closely
affiliated with medical schools and teaching hospitals
rather than a commercial enterprise. To increase trust further,
PGP question #2 will be whether it is feasible to protect
participants (beyond their normal health, life, and employ-
ment insurance) specifically for genetic discrimination
(Geller et al, 1996) consequential to the PGP study itself.
Some initial participants may be healthy senior geneticists and
hence intrinsically low risk of being surprised, but in the
interests of generalizability, subjects should also be chosen to
be closer to normal risk levels. Nevertheless, the PGP
insurance fund could still be ‘practical’ as it does not have to
be immediately ‘profitable’. It could leverage donations,
possibly including participation from insurance companies
recognizing a potential sea change.
What happens if subjects learn about predispositions
to diseases without cures? One classic approach to this
problem has been to hide the data from the subject. An
alternative is that the subjects can become expert advocates for
research on the disease afflicting their families. Notable
examples of this are Augusto Odone (adrenoleukodystrophy
and Lorenzo’s oil), Doug Melton (diabetes and the
Harvard Stem Cell Institute), Nancy Wexler (Huntington’s
disease), and Mike Milken (prostate cancer). This might be
PGP question #3.
Scaling up genetic epidemiology. Initially, the PGP will have a
small number of participants and hence might focus on the
hypothesis generation and the cell-based testing described
above. Epidemiological and genetic association studies will
benefit from thousands or even millions of participants, and an
early successful PGP experience might open up new study
design options for multidisease studies on large populations
like the Nurses Health Study and Health Professionals’ Follow-
up Study (Forman, 2005). Radically new social and business
models of data sharing might emerge, analogous to peer-to-
peer, wiki, and blog phenomena. So, question #4 of the PGP—
What new software will arrive to utilize the ‘openness’ of the
PGP that would not (or did not) apply to previous (closed)
studies? Once approved for publication by the primary IRB, the
PGP data would be available for further research just like any
Editorial
GM Church
2 Molecular Systems Biology 2005
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2005 EMBO and Nature Publishing Group
other public domain data without reapproval by other IRBs.
Software developers and users could be contacted to assess the
impact they felt from such a policy.
In order to standardize and validate the new techno-
logies, we need standard genomic DNA (and other molecules
and cells) from a reliable, renewable source, that is, cell
lines. Ideally, these cells would correspond to individuals
participating in an IRB-approved ‘open’ project like the
PGP. The industry-standard for genomic resources is an
EBV-transformed B-cell line. The PGP has initiated a colla-
boration with the Coriell NIGMS repository to provide
community access to the cells and DNA corresponding to
each PGP volunteer (Church, 2005a). To aid in collecting
transcriptome and proteome data from a variety of cell
types for each individual, a pluripotent cell line would also
be extremely useful (Hwang, 2005). These cells would
represent the fusion of analytic and synthetic (Church,
2005b) tools at the cutting edge of personalized medicine
where histocompatible stem cells are established for each
individual and vetted for full genomic and epigenomic
quality in advance of use.
In summary, biological and medical research need not
only new ‘omics’ technology and systems models, but
also new ways to assess technology and to obtain low-risk,
‘open’, integrated data sets focused on inter-individual
variation. The PGP and projects like it are works in progress
and likely to change and diversify in response to a variety
of inputs.
Acknowledgements
This work has been supported in part by the ELSI component of an
NIH-NHGRI CEGS grant with many helpful discussions over the past
year with the Harvard Medical School-IRB, members of the Personal
Health Records and ELSI communities including Ting Wu, John Aach,
Zak Kohane, Esther Dyson, John Halamka, Eric Juengst, Lynn Dressler,
Mildred Cho, and Vivian Ota Wang. This note of thanks is not meant to
imply that they agree with this paper.
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GM Church
Department of Genetics, Harvard Medical School, Boston, MA, USA
Editorial
GM Church
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2005 EMBO and Nature Publishing Group Molecular Systems Biology 2005 3
    • "However, recent genome wide association studies suggest that the lack of data for individual's medical records is an important limitation to fully understand the genetic basis for many genomic disorders [16, 17]. Initiatives such as the Personal Genomes Project (PGP) [18], Genomics England (http://www.genomicsengland.co.uk/) and the Precision Medicine program [19] aim to provide descriptive records and associated genomic data accessible for research. These datasets, however, are still unavailable or pose different challenges when looking into genetic association studies: e.g., lack of sizable data (e.g., PGP) or too restrictive access (e.g., Genomics England). "
    [Show abstract] [Hide abstract] ABSTRACT: Background Network medicine is a promising new discipline that combines systems biology approaches and network science to understand the complexity of pathological phenotypes. Given the growing availability of personalized genomic and phenotypic profiles, network models offer a robust integrative framework for the analysis of "omics" data, allowing the characterization of the molecular aetiology of pathological processes underpinning genetic diseases. Methods Here we make use of patient genomic data to exploit different network-based analyses to study genetic and phenotypic relationships between individuals. For this method, we analyzed a dataset of structural variants and phenotypes for 6,564 patients from the DECIPHER database, which encompasses one of the most comprehensive collections of pathogenic Copy Number Variations (CNVs) and their associated ontology-controlled phenotypes. We developed a computational strategy that identifies clusters of patients in a synthetic patient network according to their genetic overlap and phenotype enrichments. Results Many of these clusters of patients represent new genotype-phenotype associations, suggesting the identification of newly discovered phenotypically enriched loci (indicative of potential novel syndromes) that are currently absent from reference genomic disorder databases such as ClinVar, OMIM or DECIPHER itself. Conclusions We provide a high-resolution map of pathogenic phenotypes associated with their respective significant genomic regions and a new powerful tool for diagnosis of currently uncharacterized mutations leading to deleterious phenotypes and syndromes. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2569-6) contains supplementary material, which is available to authorized users.
    Full-text · Article · Mar 2016
    • "The decreasing cost and turn-around time of next generation sequencing (NGS) is accelerating the availability of clinical personal genomes and exomes (Church, 2005; Altshuler et al., 2010). However, data on the predictive clinical utility of whole genome sequencing (WGS) or whole exome sequencing (WES) are minimal, particularly among unselected patients. "
    [Show abstract] [Hide abstract] ABSTRACT: Whole exome sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28-93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic medical record via chart review. Variants [Single Nucleotide Variant (SNV) and insertion/deletion] were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency <0.1) and available HGMD/OMIM phenotype information. Variants were defined as Tier-1 (nonsense, splice or frame-shifting) and Tier-2 (missense, predicted-damaging) and evaluated in 56 ACMG-reportable genes, 57 cancer-predisposition genes, along with examining overall genotype-phenotype correlations. Following variant filtering, 7046 total variants were identified (~79/person, 644 Tier-1, 6402 Tier-2), 161 among 56 ACMG-reportable genes (~1.8/person, 13 Tier-1, 148 Tier-2), and 115 among 57 cancer-predisposition genes (~1.3/person, 3 Tier-1, 112 Tier-2). The number of variants across 57 cancer-predisposition genes did not differentiate individuals with/without invasive cancer history (P > 0.19). Evaluating genotype-phenotype correlations across the exome, 202(3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical records, while 3710(53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES.
    Full-text · Article · Aug 2015
    • "Such a patient/participant-centered approach would be respectful of participant autonomy and dignity, focusing on education and transparency, and not promising unrealistic levels of protection. The Personal Genome Project (PGP) pioneered a route for openly sharing integrated genomic, environmental and medical or trait data [52] in 2005, which was subsequently implemented in four countries (USA, Canada, UK and Austria). PGP successfully addressed many issues using an innovative open consent protocol [53]. "
    [Show abstract] [Hide abstract] ABSTRACT: Large-scale epigenome mapping by the NIH Roadmap Epigenomics Project, the ENCODE Consortium and the International Human Epigenome Consortium (IHEC) produces genome-wide DNA methylation data at one base-pair resolution. We examine how such data can be made open-access while balancing appropriate interpretation and genomic privacy. We propose guidelines for data release that both reduce ambiguity in the interpretation of open-access data and limit immediate access to genetic variation data that are made available through controlled access. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0723-0) contains supplementary material, which is available to authorized users.
    Full-text · Article · Jul 2015
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