## 1. The Human Genome Project: From Genomics to Postgenomics


### 1.1 Map First, Sequence Later



The idea of sequencing the entire human genome arose in the U.S. in
the mid-1980s and is attributed to University of California at Santa
Cruz chancellor Robert Sinsheimer, Salk Institute researcher Renato
Dulbecco, and the Department of Energy's (DOE's) Charles
DeLisi. While the idea found supporters among prominent molecular
biologists and human geneticists such as Walter Bodmer, Walter
Gilbert, Leroy Hood, Victor McKusick, and James D. Watson, many of
their colleagues expressed misgivings. There were concerns among
molecular biologists about the routine nature of sequencing and the
amount of "junk DNA" that would be sequenced, that the
expense and big science approach would drain resources from smaller
and more worthy projects, and that knowledge of gene sequence was
inadequate to yield knowledge of gene function (Davis and Colleagues
1990).



Committees established to study the feasibility of a publicly funded
project to sequence the human genome released reports in 1988 that
responded to these concerns. The Office for Technology Assessment
report, *Mapping Our Genes: Genome Projects: How Big, How
Fast?* downplayed the concerns of scientist critics by emphasizing
that there was not one but many genome projects, that these were not
on the scale of the Manhattan or Apollo projects, that no agency was
committed to massive sequencing, and that the study of other organisms
was needed to understand human genes. The National Research Council
report, *Mapping and Sequencing the Human Genome*, sought to
accommodate the scientists' concerns by formulating
recommendations that genetic and physical mapping and the development
of cheaper, more efficient sequencing technologies precede large-scale
sequencing, and that funding be provided for the mapping and
sequencing of nonhuman ("model") organisms as well. Genome
projects were underway even before the Office for Technology
Assessment and National Research Council reports were released. The
DOE made the first push toward a "big science" genome
project, with DeLisi advancing a five-year plan in 1986. The DOE
undertaking produced consternation among biomedical researchers who
were traditionally supported by the National Institutes of
Health's (NIH's) intramural and extramural programs, and
James Wyngaarden, head of the NIH, was persuaded to lend his
agency's support to the project in 1987. Congressional funding
for both agencies was in place in time for fiscal year 1988. The
National Research Council report estimated the total cost of the HGP
at $3 billion.



The DOE and NIH coordinated their efforts with a Memorandum of
Understanding in 1988 that agreed on an official launch of the Human
Genome Project on October 1, 1990 and an expected date of completion
of 2005. The DOE established three genome centers in 1988-89: at
Lawrence Berkeley, Lawrence Livermore, and Los Alamos National
Laboratories. David Smith led the DOE-HGP at the outset; he was
followed by David Galas from 1990 to 1993, and Ari Patrinos for the
remainder of the project. The NIH instituted a university grant-based
program for human genome research and placed Watson, co-discoverer of
the structure of DNA and director of Cold Spring Harbor Laboratory, in
charge in 1988. In October 1989, Watson assumed the helm of the newly
established National Center for Human Genome Research (NCHGR) at the
NIH. During 1990 and 1991, Watson expanded the grants-based program to
fund seven genome centers for five-year periods to work on large-scale
mapping projects: Washington University, St. Louis; University of
California, San Francisco; Massachusetts Institute of Technology;
University of Michigan; University of Utah; Baylor College of
Medicine; and Children's Hospital of Philadelphia. Francis
Collins succeeded Watson in 1993, establishing an intramural research
program at the NCHGR to complement the extramural program of grants
for university-based research that already existed. In 1997, the NCHGR
was elevated to the status of a research institute and renamed the
National Human Genome Research Institute (NHGRI).



Although the HGP's inceptions were in the U.S., it did not take
long for mapping and sequencing the human genome to become an
international venture (see Cook-Deegan 1994). France began to fund
genome research in 1988 and had developed a more centralized, although
not very well-funded, program by 1990. More significant were the
contributions of Centre d'Etudes du Polymorphisme Humain (CEPH)
and Genethon. CEPH, founded in 1983 by Jean Dausset,
maintained a collection of DNA donated by intergenerational families
to help in the study of hereditary disease; in 1991, with funding from
the French muscular dystrophy association, CEPH director Daniel Cohen
oversaw the launching of Genethon as an industrial-sized
mapping and sequencing operation. The U.K.'s genome project
received its official start in 1989, though Sydney Brenner had
commenced genome research at the Medical Research Council laboratory
several years before this. Medical Research Council funding was
supplemented with private monies from the Imperial Cancer Research
Fund and, later, the Wellcome Trust. The Sanger Centre, led by John
Sulston and funded by Wellcome and the Medical Research Council,
opened in October 1993. Japan, ahead of the U.S. in having funded the
development of automated sequencing technologies since the early
1980s, was the major genome player outside the U.S. and Europe with
several government agencies beginning small-scale genome projects in
the late-1980s and early-1990s (Swinbanks 1991). Germany and China
subsequently joined the U.S., France, U.K., and Japan in the publicly
funded international consortium that was ultimately responsible for
sequencing the genome.



The NIH and DOE released a joint five-year plan in 1990 that set
specific benchmarks for mapping, sequencing, and technological
development. The plan was updated in 1993 to accommodate progress that
had been made, with the new five-year plan in effect through 1998
(Collins and Galas 1993). As the National Research Council report had
recommended, priority at the outset of the project was given to
mapping rather than sequencing the human genome. HGP scientists sought
to construct two kinds of maps: genetic maps and physical maps.
Genetic maps order polymorphic markers linearly on chromosomes; the
aim is to have these markers densely enough situated that linkage
relations can be used to locate chromosomal regions containing genes
of interest to researchers. Physical maps order collections (or
"libraries") of cloned DNA fragments that cover an
organism's genome; these fragments can then be replicated in
quantity for sequencing. Technological progress was needed to make
sequencing more efficient and less costly for any significant progress
to be made. For the meantime, efforts would focus on sequencing the
smaller genomes of less complex model organisms (Watson 1990). The
model organisms selected for the project were the bacterium
*Escherichia coli*, the yeast *Saccharomyces
cerevisiae*, the roundworm *Caenorhabditis elegans*, the
fruitfly *Drosophila melanogaster*, and the mouse *Mus
musculans*.



As 1998, the last year of the revised five-year plan and midpoint of
the project's projected 15-year span, approached, many mapping
goals had been met. In 1994, Genethon completed a
genetic map with more than 2,000 microsatellite markers at an average
spacing of 2.9 centimorgans (cM) and only one gap larger than 20 cM
(Gyapay et al. 1994); the goal was a resolution of 2 to 5 cM by 2005.
The genetic mapping phase of the project came to a final close in
March 1996 with Genethon's completion of a genetic
map containing 5,264 microsatellite markers located to 2,335 positions
with an average spacing of 1.6 cM (Dib et al. 1996). In 1995, a
physical map with 94 percent coverage of the genome and 15,086
sequence-tagged site (STS) markers at average intervals of 199
kilobases (kb) was published (Hudson et al. 1995); the initial goal
was STS markers spaced approximately 100 kb apart by 1995, a deadline
the revised plan extended to 1998. In 1998, a physical map of 41,664
STS markers was published (Deloukas et al. 1998). Sequencing presented
more of a challenge, despite ramped-up sequencing efforts over the
previous several years at the U.K.'s Wellcome Trust-funded
Sanger Centre in Cambridge and the NHGRI (previously NCHGR)-funded
centers at Houston's Baylor College of Medicine, Stanford
University, The Institute for Genomic Research (TIGR), University of
Washington-Seattle, Washington University School of Medicine in St.
Louis, and Whitehead Institute for Biomedical Research/MIT Genome
Center. The genomes of the smallest model organisms had been
sequenced. In April 1996, an international consortium of mostly
European laboratories published the sequence for *S.
cerevisiae* which was the first eukaryote completed, with 12
million base pairs and 5,885 genes and at a cost of $40 million
(Goffeau et al. 1996). In January 1997, University of Wisconsin
researchers completed the sequence of *E. coli* with 4,638,858
base pairs and 4,286 genes (Blattner et al. 1997). However, with only
three percent of the human genome sequenced, sequencing costs hovering
at $.40/base, and the desired high output not yet achieved by the
sequencing centers, and about $1.8 billion spent, doubts existed about
whether the HGP's target date of 2005 could be met.


### 1.2 Race to the Genome



Suddenly, the publicly funded HGP faced a challenge from the private
sector. In May 1998, TIGR's J. Craig Venter announced a
partnership with Applied Biosystems to sequence the entire genome in
three short years and for a fraction of the cost. The new company,
based in Rockville, MD and later named Celera Genomics, planned to use
"whole-genome shotgun" (WGS) sequencing, an approach
different from the HGP's. The HGP confined the shotgun method to
cloned fragments already mapped to specific chromosomal regions: these
are broken down into smaller bits then amplified by bacterial clones,
sequences are generated randomly by automated machines, and
computational resources are used to reassemble sequence using
overlapping areas of bits. Shotgunning is followed by painstaking
"finishing" to fill in gaps, correct mistakes, and resolve
ambiguities. What Celera was proposing for the shotgun method was to
break the organism's entire genome into millions of pieces of
DNA with high-frequency sound waves, sequence these pieces using
hundreds of Applied Biosystem's new capillary model machines,
and reassemble the sequences with one of the world's largest
civilian supercomputers without the assistance provided by the
preliminary mapping of clones to chromosomes. When WGS sequencing was
considered as a possibility by the HGP, it was rejected because of the
risk that repeat sequences would yield mistakes in reassembly (Green
1997; Venter et al. 1996; Weber and Myers 1997). But Venter by this
time had successfully used the method to sequence the 1.83 million
nucleotide bases of the bacterium *Hemophilus
influenzae*--the first free-living organism to be completely
sequenced--in a year's time (Fleischmann et al. 1995).



HGP scientists downplayed the media image of a race to sequence the
genome often over the next couple of years, but they were certainly
propelled by worries that funding would dry up before the sequence was
complete given private sector willingness to take over and that the
sequence data would become proprietary information. Wellcome more than
doubled its funds to the Sanger Centre (to PS205 million) and the
center changed its goal from sequencing one-sixth of the genome to
sequencing one-third, and possibly one-half (Dickson 1998). The NHGRI
and DOE published a new five-year plan for 1998-2003 (Collins et al.
1998). The plan moved the final completion date forward from 2005 to
2003 and aimed for a "working draft" of the human genome
sequence to be completed by December 2001. This would be achieved by
delaying the finishing process, no longer going clone-by-clone to
shotgun, reassemble, and finish the sequence of one clone before
proceeding to the next. With only six percent of the human genome
sequence completed, the plan called for new and improved sequencing
technologies that could increase the sequencing capacity from 90 Mb
per year at about $.50 per base to 500 Mb per year at no more than
$.25 per base. Goals for completing the sequencing of the remaining
model organisms were also set: December 1998 for *C. elegans*
which was 80 percent complete, 2002 for *D. melanogaster* which
was nine percent complete, and 2005 for *M. musculus* which was
still at the physical mapping stage.



An interim victory for the publicly funded project followed when, on
schedule, the first animal sequence, that of *C. elegans* with
97 million bases and 19,099 genes, was published in *Science*
in December 1998 (The *C. elegans* Sequencing Consortium 1998).
This was the product of a 10-year collaboration between scientists at
Washington University in St. Louis (headed by Bob Waterston) and the
Sanger Centre (headed by John Sulston), carried out at a
semi-industrial scale with more than 200 people employed in each lab
working around the clock. In March 1999, the main players--the
NHGRI, Sanger Centre, and DOE--advanced the date of completion of
the "working draft": five-fold coverage of at least 90
percent of the genome was to be completed by the following spring
(Pennisi 1999; Wadman 1999). This change reflected improved output of
the new model of automated sequencing machines, diminished sequencing
costs at $.20 to $.30 per base, and the desire to speed up the release
of medically relevant data. NHGRI would take responsibility for 60
percent of the sequence, concentrating these efforts at Baylor,
Washington University, and Whitehead/MIT; 33 percent of the sequence
would be the responsibility of the Sanger Centre; and the remaining
sequence would be supplied by the DOE's Joint Genome Institute
(JGI) in Walnut Creek, CA into which its three centers had merged in
January 1997.



The first chromosomes to be completed (this was to finished, not
working draft, standards) were the two smallest: the sequence for
chromosome 22 was published by scientists at the Sanger Centre and
partners at University of Oklahoma, Washington University in St.
Louis, and Keio University in Japan in December 1999 (Dunham et al.
1999); the sequence for chromosome 21 was published by an
international consortium of mostly Japanese and German labs--with
half the sequencing carried out at Japan's RIKEN--in May
2000 (Hattori et al. 2000). The remaining chromosomes lagged behind.
On 26 June 2000, when Collins, Venter, and the DOE's Patrinos
joined U.S. President Bill Clinton (and British Prime Minister Tony
Blair by satellite link) at a White House press conference (see
Clinton, et al. 2000) to announce that the human genome had been
sequenced, this was more an arranged truce than a tie for the prize.
An editorial in *Nature* described the fanfare of 26 June as an
"extravagant" example--one reaching "an all-out
zenith or nadir, according to taste"--of scientists making
public announcements not linked to peer-reviewed publication, here to
bolster share prices (Celera) and for political effect (the HGP) given
the "months to go before even a draft sequence will be
scientifically useful" (Anonymous 2000, p. 981). Neither of the
two sequence maps was complete (Pennisi 2000). The HGP had not met its
previous year's goal of a working draft covering 90 percent of
the genome. Assisted by its researchers' access to HGP data
stored on public
 databases,[1]
 Celera's efforts were accepted as being further along: the
company's press release that day announced 99 percent coverage
of the genome.



Peer-reviewed publications came almost eight months later. Negotiated
plans for joint publication in *Science* broke down when terms
of agreement over data release could not be negotiated, with the
journal's editors willing to publish Celera's findings
without Venter meeting the standard requirement that the sequence data
be submitted to GenBank. Press conferences in London and Washington,
D.C. on 12 February preceded publications that week--by HGP
scientists in *Nature* on 15 February 2001 and by
Venter's team in *Science* on 16 February 2001. The HGP
draft genome sequence covered about 94 percent of the genome, with
about 25 percent in the finished form already attained for chromosomes
21 and 22. Indeed, the authors themselves described it as "an
incomplete, intermediate product" which "contains many
gaps and errors" (International Human Genome Sequencing
Consortium 2001, p. 871). The results published by Celera had
84-90 percent of the genome covered by scaffolds at least 100 kb
in length, with the composition of the scaffolds averaging 91-92
percent sequence and 8-9 percent gaps (Venter et al. 2001). In
the end, Celera's published genome assembly made significant use
of the HGP's publicly available map and sequence data, which
left open for debate the question whether WGS sequencing alone would
have worked (see Waterston et al. 2002; Green 2002; and Myers et al.
2002).



Since the gaps in the sequence were unlikely to contain genes, and
only genes as functional segments of DNA have potential commercial
value, Celera was happy to leave the gaps for the HGP scientists to
fill in. Despite being timed to coincide with celebrations of the
50th anniversary of the Watson-Crick discovery of the
double-helical structure of DNA, there was less fanfare surrounding
the official date of completion of the HGP in April 2003, two years
earlier than had been anticipated at the time of its official launch
in October 1990, and several months earlier than called for in the
most recent five-year plan. In the end, sequencing--the third
phase of the publicly-funded project--was carried out at 16
centers in six countries by divvying up among them sections of
chromosomes for sequencing. 85 percent of the sequencing, however, was
done at the five major sequencing centers (Baylor, Washington
University, Whitehead/MIT, Sanger Center, and DOE's JGI), with
the Sanger Centre responsible for nearly one-third. The cost was lower
than anticipated, with $2.7 billion spent by U.S. agencies and
PS150 million spent by Wellcome Trust. The "finished"
reference DNA sequence for *Homo sapiens* was made publicly
accessible on the Internet. However, for various technical reasons,
the human genome's 3.1 billion nucleotide bases had not yet been
completely sequenced at the close of the HGP in
 2003.[2]


### 1.3 Aftermath



Public support was won for the HGP through scientists' promises
of the revolutionary benefits of genome-based research for
pharmaceutical and other biomedical applications. At the outset of the
HGP, these promises were sometimes alarmingly deterministic,
reductionistic, and overblown, such as when *Science* editor
Daniel Koshland (1989) submitted that genes are responsible not only
for manic-depression and schizophrenia but also poverty and
homelessness, and that sequencing the genome represented "a
great new technology to aid the poor, the infirm, and the
underprivileged" (p. 189). More circumspect claims by
scientist-proponents of the HGP were no less optimistic. Leroy Hood
expressed the belief that "we will learn more about human
development and pathology in the next twenty-five years than we have
in the past two thousand" (1992, p. 163). Hood expected the HGP
to facilitate movement from a reactive to preventive mode of medicine,
which would "enable most individuals to live a normal, healthy,
and intellectually alert life without disease" (p. 158). Francis
Collins predicted that sequencing the genome would "dramatically
accelerate the development of new strategies for the diagnosis,
prevention, and treatment of disease, not just for single-gene
disorders but for the host of more common complex diseases (e.g.,
diabetes, heart disease, schizophrenia, and cancer)" (1999, p.
29). Collins envisioned that, by 2010, genetically-based
"individualized medicine" would be a reality: physicians
would routinely take cheek swabs from patients and send their DNA out
for testing; based on results of genetic testing (returned within a
week), physicians would be able to advise their patients about their
absolute and relative risks for contracting various adult-onset
diseases; by taking preventive measures (e.g., quitting smoking,
having an annual colonoscopy, etc.), patients would be able to prevent
the onset of any such diseases or minimize their effects; and the
field of pharmacogenomics would have "blossomed"
sufficiently for physicians to be able to prescribe prophylactic
medications tailored precisely to the genetic make-up of their
patients, so to promote efficacy and prevent adverse reactions.



Genome-wide association studies (GWAS), in which single nucleotide
polymorphisms (SNPs) across the genome are compared in
case-control fashion, are the main approach used to investigate
the genetic bases of complex traits. The importance of developing
rapid, inexpensive methods of genome sequencing and building a
database of single nucleotide polymorphisms (SNPs) to support the
investigation of complex traits was recognized by the project's
leadership even before completion of the HGP. Worried about the
private sector's efforts to patent SNPs, which would make them
costly to use for research, the NHGRI-DOE's five-year plan for
1998-2003 included the goal of mapping 100,000 SNPs by 2003
(Collins et al. 1998). The development of a public database of SNPs
received a $138 million push from the International HapMap Project, a
three-year public-private partnership completed in 2005 that mapped
variation in four population groups (The International HapMap
Consortium 2005). The 1000 Genomes Project, which ran between 2008 and
2015 (Birney and Soranzo 2015), sought to identify genetic variants
that occur with a frequency of at least one percent in the populations
studied, with the final data set consisting of 2,504 individuals from
26 populations from five continental regions.



The first genome-wide association study was published in 2005; by
2010, 500 genome-wide association studies had been published (Green et
al. 2011); and by 2018, more than 5,000 genome-wide association
studies had been published and their results added to the GWAS Catalog
(Buniello et al. 2019). Despite these efforts, variants isolated by
GWAS for complex traits account for a low percentage of heritability
associated with the traits, a phenomenon known as "missing
heritability" (Maher 2008). Although knowledge of the
pathogenesis of common complex diseases such as diabetes, heart
disease, schizophrenia, and cancer that arise due to the interaction
of numerous genetic and nongenetic factors is lacking despite the
numerous GWAS completed, the pathogenesis of so-called single-gene
disorders is far better understood. Aided by the HGP's dense map
of genetic markers for use in positional mapping and subsequent
development of genome-wide sequencing technologies, since the draft
sequence was published 20 years ago, the number of Mendelian diseases
with a known genetic basis has increased from 1,257 to 4,377 (Alkuraya
2021). This progress speeds up clinical diagnosis and facilitates
prenatal genetic testing. Prevention and treatment remain challenging,
however. There are only 59 "actionable genes" on the
American College of Medical Genetics and Genomics' most recent
list of genes that are highly penetrant and associated with
established interventions (Kalia et al. 2017). Gene therapy, touted as
a potential cure for such disorders, yielded discouraging results in
trials; only with the discovery of CRISPR, a novel technology of gene
editing, has optimism been restored, though how well this basic
science translates into clinical applications is yet to be seen
(Doudna and Sternberg 2017; Baylis 2019).



The confident claims of leading genome scientists such as Hood and
Collins proved to be overly so. Commentary at the time of the 10-year
anniversary of completion of the HGP showed unison among scientists in
recognizing that the promised revolution had not yet arrived. The HGP
has been advanced as a case study in support of the "social
bubble" hypothesis: the hypothesis that people will dive into
new opportunities that present without due regard for potential risks
because they are carried along by social interactions driven by
enthusiasts who generate high expectations of returns--for the
HGP, projections of commercially lucrative pharmaceutical and other
biomedical applications advanced by project proponents (Gisler et al.
2010). The scientific consensus appears to be that although the HGP
failed at least in the short term to fulfill proponents' overly
optimistic prognostications for clinical applications, it has been a
boon for basic science (Evans 2010). In the HGP's early years,
Norton Zinder, who chaired the NIH's Program Advisory Committee
on the Human Genome, characterized it in this way: "This Project
is creating an infrastructure for doing science; it's not the
doing of the science per se. It will provide the biological community
with the basic materials for doing research in human biology"
(in Cooper 1994, p. 74).



Indeed, the infrastructure of mapping and sequencing technologies and
bioinformatics that was developed as part of the HGP--especially
the ability to sequence entire genomes of organisms and traffic in big
data--has changed the way biology, not just human biology, is
done (Stevens 2013). It is recognized that genome structure by itself
tells us only so much. Functional genomics places interest in how
entire genomes--not just individual genes--function. A
surprising discovery of the HGP was that the number of coding genes in
humans is many orders smaller than what scientists had assumed at the
outset of the project--that is, around 20,000, as in other
vertebrates, rather than 80,000-100,000, though the final number
remains an open question (Salzberg 2018). The majority of the
genome's DNA is transcribed but not translated and serves a
regulatory function, with causal processes at the molecular level
associated with interactive networks not linear pathways. The
deterministic and reductionistic assumptions underlying the HGP that
portrayed the genome as a blueprint for organismal development have
been undermined by the research in molecular biology the project made
possible (Keller 2000). Systems biology has emerged as a new
discipline that seeks to understand this complexity by using
computational methods (Hood 2003). In fact, since completion of the
HGP, discovery of non-protein-coding elements of the genome and their
contributions to regulatory networks has far exceeded the discovery of
protein-coding genes (Gates et al. 2021). Evolutionary studies are
aided by the ability of scientists to compare the human genome
reference sequence to reference sequences for close relatives, such as
Neandertals (Green et al. 2010), bonobos (Mao et al. 2021), and
chimpanzees (The Chimpanzee Sequencing and Analysis Consortium
2005).



For scientists to deliver on their promises for a revolution in
medicine, genome sequencing would need to be far faster, easier, and
cheaper for its use to become routine in both research and clinical
settings. In the closing years of the HGP, the cost of sequencing a
human genome using existing Sanger technology was about $100 million.
With high-throughput next-generation sequencing technology, in 2007,
Venter's diploid genome was sequenced at a cost of $10 million,
and by 2013, the cost of sequencing an average genome had been lowered
to $5,000. These developments were aided by a NHGRI grant program that
set its sights on a $1,000 genome, a price point believed to place the
"personal genome" within reach for routine use (Check
Hayden 2014). Early in 2014, Illumina, a Californian company, claimed
a win in the contest, with availability of its HiSeq X Ten system for
population-scale whole-genome sequencing initiatives (Sheridan 2014);
in March 2016, Veritas Genetics, a company cofounded by Harvard
medical geneticist George M. Church, announced commercial availability
of whole-genome sequencing for individuals, including interpretation
and counseling, for $999 (Veritas Genetics 2016). Church had initiated
the Personal Genome Project in 2005 (Church 2005); there are now
Personal Genome Projects in Canada, the U.K., Austria, and China as
well. The projects recruit volunteers who are willing to support
research by releasing their genomes and health and physical
information publicly. Research that takes this longitudinal approach
combining genetic and clinical data is considered crucial for the
promise of genomics to be fulfilled: with faster, easier, and cheaper
genome sequencing technologies, genetic data are readily obtainable,
but analysis of the data, without which there can be no revolution in
medicine, remains challenging.



The National Research Council's 2011 report saw the route to
"personalized medicine," or "precision
medicine" as it was renamed (see Juengst et al. 2016 and
Ferryman and Pitcan 2018 for an account of that change), proceeding
via a "New Taxonomy" of disease informed by two data
repositories: an "Information Commons" that stores
molecular data (genome, transcriptome, proteome, metabolome, lipidome,
and epigenome) and additional information (phenotypes, treatment
outcomes, test results, etc.) gleaned from the electronic health
records of millions of individuals; and a "Knowledge Network of
Disease" that integrates this information with
"fundamental biological knowledge." Disease
generalizations would be "built up from" this large number
of individuals, a departure from studies that group individuals based
on particular characteristics (e.g., GWAS). Research efforts
approaching the scale called for include the *All of Us*
Research Program in the U.S. and the UK Biobank. The data-intensive
approach to molecular biology made possible by information technology
need not stop at electronic health records but could also include
other electronic records such as credit card purchases and social
media postings (Weber et al. 2014), and biometric measurements from
mobile apps and fitness trackers (Shi and Wu 2017). The increased
importance of "big data" is illustrated by contrasting
futuristic scenarios envisioned by Collins (1999) and Hood and Rowen
(2013) almost 15 years apart. The "individualized
medicine" circa 2010 forecasted by Collins is centered in the
physician's office and assumes a traditional view of the
doctor-patient relationship. Hood and Rowen foresee individual
genome sequences playing a larger role in medical practice and a
changed doctor-patient relationship, driven by
"patients" who are likely to bring consumer genetic data
to their appointments and understand themselves to be active
participants in their medical care. The new "P4 medicine"
will be not only predictive, preventive, and personalized, but
participatory, and based on a data-driven systems approach to disease.
Write Hood and Rowen, "We envision a time in the future when all
patients will be surrounded by a virtual cloud of billions of data
points, and when we will have the analytical tools to reduce this
enormous data dimensionality to simple hypotheses to optimize wellness
and minimize disease for each individual" (p. 83). In the
meanwhile, as Jenny Reardon (2017) tells us, the vast expanse between
data and meaning characterizes "the postgenomic
condition."



Physicians are likely to encounter patients who bring consumer genetic
data to their appointments because of a development largely
unanticipated by HGP proponents and critics alike: "personal
genomics" or "recreational genomics." Efforts to
compile SNP databases and develop rapid, inexpensive, whole-genome
sequencing technologies have not yet supported the dawn of a new era
of personalized medicine and drug development guided by
pharmacogenomics, but direct-to-consumer (DTC) genomics has taken off
as an industry, with profit-making seemingly unhampered by the lack of
treatments for diseases based on knowledge of DNA sequences. The first
DTC whole-genome test was marketed in 2006 (Green at al. 2011). By
2018, more than 10 million people had ordered DTC personal genomics
tests (Khan and Mittelman 2018), and, that year, the NHGRI,
celebrating the HGP's 15th anniversary, identified
DTC genetic testing as one of the "15 for 15" ways in
which genomics is influencing the world. In 2019, the global DTC
genetic testing market was valued at over $1 billion and forecast to
climb to $3.4 billion by 2028 (Ugalmugle and Swain 2020). Although
health and ancestry are the most common genetic tests sought, there is
a broad range of tests available, and DTC companies usually offer more
than one service (Phillips 2016). 23andMe and Ancestry.com, for
example, offer both health and ancestry tests. The family match
function offered by these tests allows biological parentage to be
discovered in cases of adoption and gamete donorship/sale. For people
who want to confirm paternity, out a cheating spouse, ascertain
athletic ability, identify nutritional needs, or find a romantic
partner, there are genetic tests and companies for those interests
too.


## 2. Philosophy and the Human Genome Project



At an October 1988 news conference called to announce his appointment,
Watson, in an apparently off-the-cuff response to a reporter who asked
about the social implications of the project, promised that a portion
of the funding would be set aside to study such issues (Marshall
1996b). The result was the NIH/DOE Joint Working Group on Ethical,
Legal, and Social Implications (ELSI) of Human Genome Research,
chaired by Nancy Wexler, which began to meet in September 1989. The
Joint Working Group identified four areas of high priority:
"quality and access in the use of genetic tests; fair use of
genetic information by employers and insurers; privacy and
confidentiality of genetic information; and public and professional
education" (Wexler in Cooper 1994, p. 321). The NIH and DOE each
established ELSI programs: philosopher Eric T. Juengst served as the
first director of the NIH-NCHGR ELSI program from 1990 to 1994. ELSI
was funded initially to the tune of three percent of the HGP budget
for both agencies; this was increased to four and later five percent
at the NIH, a huge boost in bioethics funding, on the order of tens of
millions of dollars each year.



Ethical issues such as genetic privacy, access to genetic testing, and
genetic discrimination were not the only considerations of interest to
philosophers, and besides ethicists, philosophers of science,
political theorists and philosophers working in other areas benefited
from ELSI-related funding. There is now a vast literature on human
genome-related topics. From among these topics, this section attempts
to provide a synopsis of those that are most directly associated with
the HGP itself, of greatest concern and enduring interest to
philosophers, and not covered in other *SEP* entries. Since
there is interest in exporting the ELSI model to other biomedical
contexts, such as neuroscience, consideration is also given to its
legacy.


### 2.1 Geneticization, Genetic Reductionism, and Genetic Determinism



Various HGP proponents told us that we would discover our human
essence in the genome. According to Dulbecco (1986), "the
sequence of the human DNA is the reality of our species" (p.
1056); Gilbert was quoted as saying "sequencing the human genome
is like pursuing the holy grail" (in Lee 1991, p. 9); on the
topic of his decision to dedicate three percent of HGP funds to ELSI,
Watson wrote: "The Human Genome Project is much more than a vast
roll call of As, Ts, Gs, and Cs: it is as precious a body of knowledge
as humankind will ever acquire, with a potential to speak to our most
basic philosophical questions about human nature, for purposes of good
and mischief alike" (with Berry 2003, p. 172).



"Geneticization" is a term used to describe the phenomenon
characterized by an increasing tendency to reduce human differences to
genetic ones (Lippman 1991). The several billion dollars of funding
for the HGP was justified by the belief that genes are key
determinants of not only rare Mendelian diseases like
Huntington's disease or cystic fibrosis but common
multi-factorial conditions like cancer, depression, and heart disease.
Wrote an early critic of the HGP: "Without question, it was the
technical prowess that molecular biology had achieved by the early
1980s that made it possible even to imagine a task as formidable as
that of sequencing what has come to be called 'the human
genome.' But it was the concept of genetic disease that created
the climate in which such a project could appear both reasonable and
desirable" (Keller 1992, p. 293). Given that the development of
any trait involves the interaction of both genetic and nongenetic
factors, on what bases can genes be privileged as causes to claim that
a particular disease or nondisease trait is "genetic" or
caused by a "genetic susceptibility" or "genetic
predisposition"? This question has led philosophers of science
to grapple with appropriate definitions for terms such as
"genetic disease" and "genetic susceptibility"
and how best to conceptualize genetic causation and
gene-environment interaction (e.g., Kitcher 1996; Gannett 1999;
Kronfeldner 2009). Closely related to the concepts of geneticization
and genetic disease/ susceptibility/ predisposition are assumptions
about genetic reductionism and genetic determinism.



Genetic reductionism can be understood as governing the
whole-part relation in which organismal properties are explained
solely in terms of genes and organisms are identified with their
genomes. Definitions of health and disease attach to organisms and
their physiological and developmental processes in particular contexts
(provided by populations and environments) and cannot simply be
relocated to the level of the genome (Griesemer 1994; Limoges 1994;
Lloyd 1994), however, and diseases do not become more objectively
defined entities once they receive a genetic basis since social and
cultural values implicated in designations of health and disease can
become incorporated at the level of the genome, in what counts as a
normal or abnormal gene (Gannett 1998). In contrast to physical
reductionism, which does not privilege DNA but considers it on par
with proteins, lipids, and other molecules (Sarkar 1998), genetic
reductionism assumes that genes are in some sense more causally
efficacious. Genetic determinism concerns such assumptions about the
causal efficacy of genes.



In a public lecture held to celebrate completion of the HGP, Collins
characterized the project as "an amazing adventure into
ourselves, to understand our own DNA instruction book, the shared
inheritance of all humankind" (see National Human Genome
Research Institute, 2003). At the cellular level, the book is said to
contain "the genetic instructions for the entire repertoire of
cellular components" (Collins et al. 2003, p. 3). At this level,
genetic determinism is sustained by metaphors of Weismannism and DNA
as "code" or "master molecule" (Griesemer
1994; Keller 1994), which accord DNA causal priority over other
cellular components. This may be in a physical sense: Weismannism
assumes (falsely) that intergenerational continuity exists only for
germ cell nuclei whereas somatic cells and germ cell cytoplasm arise
anew in each generation. It may also be in the sense of a point of
origin for the transfer of information: the central dogma of molecular
biology, which represents a 1950s reformulation of Weismannism in
terms of information theory, asserts that information travels
unidirectionally from nucleic acids to protein, and never vice versa.
It is contentious, however, whether amongst the cell's
components only nucleic acids can be said to transmit information: for
some philosophers, genetic coding plays a theoretical role at least at
this cellular level (Godfrey-Smith 2000); for others, genetic coding
is merely (and misleadingly) metaphorical, and all cellular components
are potential bearers of information (Griffiths 2001; Griffiths and
Gray 1994; Sarkar 1996).



At the organismal level, new research in functional genomics may lead
to less deterministic accounts even of so-called single gene
disorders. For these, the concepts of penetrance and expressivity
operate in ways that accommodate the one-one genetic determinist
model where the mutation is necessary and/or sufficient for both the
presence of the condition and confounding patterns of phenotypic
variability. But the severity of even a fully penetrant condition like
Huntington's disease seems to depend on not just genetic factors
like the number of DNA repeats in the mutation but epigenetic factors
like the sex of the parent who transmitted the mutation (Ridley et al.
1991). For complex conditions to which both genetic and environmental
differences contribute--for example, psychiatric disorders or
behavioral differences--genetic determinism is denied, and
everyone is an interactionist these days, in some sense of
"interaction." Both genes and environment are recognized
to be necessary for development: by themselves, genes cannot determine
or do anything. Yet, theorists still seem to give the nod to one or
the other, suggesting that it is mostly genes or mostly the
environment, mostly nature or mostly nurture, that make us what we
are. This implies that it is possible to apportion the relative
contributions of each. Gilbert (1992) suggests this in his dismissal
of a more simplistic version of genetic determinism: "We must
see beyond a first reaction that we are the consequences of our genes;
that we are guilty of a crime because our genes made us do it; or that
we are noble because our genes made us so. This shallow genetic
determinism is unwise and untrue. But society will have to wrestle
with the questions of how much of our makeup is dictated by the
environment, how much is dictated by our genetics, and how much is
dictated by our own will and determination" (pp. 96-97).
However, the assertion that the relative contributions of genes and
environment can be apportioned in this way is misleading if not
outright false. Building on R. C. Lewontin's (1974) classic
paper on heritability, work in developmental systems theory (DST)
undermines any such attempts to apportion causal responsibility in
organismal development: traits are jointly determined by multiple
causes, each context-sensitive and contingent (Griffiths and Gray
1994; Griffiths and Knight 1998; Oyama 1985; Oyama et al. 2001; Robert
2004).



Geneticization, genetic reductionism, and genetic determinism helped
to sell the HGP. Gilbert (1992) endorsed the reduction of individual
humans to their genes: "The information carried on the DNA, that
genetic information passed down from our parents," he wrote,
"is the most fundamental property of the body" (p. 83), so
much so, in fact, that "one will be able to pull a CD out of
one's pocket and say, 'Here is a human being; it's
me!'" (p. 96). Cancers that we consider to be
environmental in their origins were recast as genetically determined.
In Watson's words: "Some call New Jersey the Cancer State
because of all the chemical companies there, but in fact, the major
factor is probably your genetic constitution" (in Cooper 1994,
p. 326). In Bodmer's words: "Cancer, scientists have
discovered, is a genetic condition in which cells spread
uncontrollably, and cigarette smoke contains chemicals which stimulate
those molecular changes" (Bodmer and McKie 1994, p. 89). (See
Proctor 1992 and Plutynski 2018 for discussions of cancer as a genetic
disease.) Marking the 20th anniversary of the release of
the draft sequences, Richard Gibbs, director of the sequencing center
at Baylor, admits that "there was plenty of hype that was shared
with the media and the wider community" and that such
"outlandish visions" as personalizing therapies, revealing
the "mysteries of the architecture of common complex
diseases," and predicting criminality have not been realized.
But Gibbs excuses the hype as necessary for generating support for the
project: "The hyperbole that we look back on did not, however,
come from the front line. It came from those who championed the
programme, mindful of its long-term benefits. Thanks to them, they
generated the enthusiasm to fund this transformative work"
(2020, p. 575).



Like Gibbs, bioethicist Timothy Caulfield (2018) finds the hype and
hyperbole of scientists understandable: "Enthusiasm and
optimistic predictions of near-future applications are required in
order to mobilize the scientific community and potential funders, both
public and private. This is particularly so in areas like genomics,
where large amounts of sustained funding are required in order to
achieve the hoped for scientific and translational goals" (p.
561). However, unlike Gibbs, Caulfield details possibilities of
"real harm," which include "potentially eroding
public trust and support for science; inappropriately skewing research
priorities and the allocation of resources and funding; creating
unrealistic expectations of benefit for patients; facilitating the
premature uptake of expensive and potentially harmful emerging
technologies by health systems; misinforming policy and ethics
debates; and accelerating the marketing and utilization of unproven
therapies" (p. 567). The hype and hyperbole used to promote
personalized (or precision) medicine carry the risks Caulfield
mentions.



Approaching 20 years since completion of the HGP, genome science has
not revolutionized medicine or markedly improved human health.
Progress has been made on rare diseases (e.g., spinal muscular
atrophy) and some forms of cancer (e.g., non-small cell lung cancer),
though these interventions can be prohibitively expensive (Tabery
2023). For most complex diseases, however, predictions based on
"family history, neighborhood, socioeconomic circumstances, or
even measurements made with nothing more than a tape measure and a
bathroom scale" outperform predictions based on the possession
of genetic variants identified by GWAS. Pressing public health
problems such as increasing obesity, the opiate epidemic, and mental
illness fail to be addressed by the "human genome-driven
research agenda" to which the lion's share of resources go
(Joyner and Paneth 2019). So even though the deterministic and
reductionistic assumptions underlying the HGP have been undermined by
the research in molecular biology the project made possible (Keller
2000), the critics' worries about geneticization, genetic
reductionism, and genetic determinism remain relevant, in particular
their belief that embracing a reductionist approach to medicine that
conceives of human health and disease in wholly molecular or genetic
terms individualizes these and detracts attention from risks factors
associated with our shared social and physical environments (Nelkin
and Tancredi 1989; Hubbard and Wald 1993; Tabery 2023).


### 2.2 Genetic Testing, Genetic Discrimination, and Genetic Privacy



Genetic testing is carried out for a range of purposes: diagnostic,
predictive, and reproductive. Genetic testing carried out at the
population level for any of these purposes is referred to as genetic
screening. Diagnostic genetic testing is performed on individuals
already experiencing signs and symptoms of disease as part of their
clinical care. Newborn screening programs to diagnose conditions such
as PKU and hemoglobinopathies based on blood components and
circulating metabolites (thus providing indirect genetic tests) have
been carried out for many decades. Predictive genetic testing is
performed on individuals who are at risk for inheriting a familial
condition, such as cystic fibrosis or Huntingdon's disease, but
do not yet show any signs or symptoms. Reproductive genetic testing is
carried out through carrier screening, prenatal testing of the fetus
in utero, and preimplantation genetic diagnosis (PGD) of embryos
created by in vitro fertilization (IVF). In carrier screening,
prospective parents find out whether they are at risk for passing on
disease-related genes to their offspring. Prenatal genetic testing of
fetuses in utero is conducted using blood tests early in a
woman's pregnancy, chorionic villus sampling (CVS) at
10-12 weeks, and amniocentesis at 15-18 weeks. Testing is
increasingly offered to all women who are pregnant, not just those for
whom risk is elevated because of age or family history; based on the
results, women can elect to continue the pregnancy or abort the fetus.
In PGD, a single cell is removed from the 8-cell embryo for testing;
based on the results, a decision is made about which embryo(s) to
implant in the woman's uterus.



There are significant ethical issues associated with genetic testing.
These issues are informed by empirical studies of the psychosocial
effects of testing (Wade 2019). Increased knowledge that comes from
predictive genetic testing is not an unmitigated good: denial may be a
coping mechanism; individuals may feel guilty for passing on harmful
mutations to their offspring or stigmatized as having the potential to
do so; survivor guilt may arise in those who find out they are not at
risk for a disease such as Huntingdon's after all, or they may
become at a loss about how to live their lives differently; those who
find out they are destined to develop Huntingdon's or
early-onset Alzheimer's disease may become depressed or even
suicidal; paternity may not be what it is assumed to be; decisions
about disclosing results have implications for family members. During
debates about the HGP, many authors appealed to the history of
eugenics to warn about the dangers of reproductive genetic testing and
urge caution as we move forward--so much so that historian Diane
Paul (1994) characterized eugenics as the
"'approved' project anxiety" (p. 143). Paul
noted that attempts to draw lessons from the history of eugenics are
confounded by disagreements about how to define
"eugenics"--whether to characterize eugenics
according to a program's intentions or effects, its use of
coercive rather than voluntary means, or its appeals to social and
political aims that extend beyond the immediate concerns of individual
families. The label "liberal eugenics" has become
increasingly accepted for characterizing offspring selection based on
parental choice. Reproductive rights are no longer just about the
right not to have a child (to use contraception, to have an abortion)
or the right to bear a child (to refuse population control measures).
Reproductive rights have come to encompass the right to access
technological assistance to procreate and to have a certain kind of
child (Callahan 1998).



Concerns about genetic discrimination resulting from genetic testing
were frequently expressed at the outset of the HGP. Concerns focused
mostly on insurance companies and employers, but possibilities for
genetic discrimination occurring in other institutional settings were
raised as well (Nelkin 1992; Nelkin and Tancredi 1989). A number of
general arguments have been made against institutional forms of
genetic discrimination: we don't choose our genes and ought not
be punished for what is outside our control (Gostin 1991); the social
costs of creating a "biologic" or "genetic
underclass" of people who lack health care and are unemployed or
stuck in low-wage jobs are too great (Lee 1993; Nelkin and Tancredi
1989); people's fears of genetic discrimination, whether
realistic or not, may lead them to forego genetic testing that might
benefit their lives and be less inclined to participate in genetic
research (Kass 1997); people have the right not to know their genetic
risk status (Kass 1997). Genetic discrimination may also occur in less
formal circumstances. Mate choice could increasingly proceed based on
genetic information, with certain people being labeled as undesirable.
As more and more fetuses are aborted on genetic grounds, families of
children born with similar conditions, and people with disabilities
and their advocates more broadly, worry that increased stigmatization
will result. In addition, group-based genetic research into diseases
or behavioral differences risks stigmatizing people based on racial,
ethnic, and gender differences, with such risks informed by the
troubling history of the study of the genetics of intelligence (Tabery
2015).



In the U.S., where unlike other industrialized countries there is no
publicly funded system of universal health care, genetic
discrimination by insurance companies and employers has been a
particularly serious worry; existing or prospective employees found to
be at genetic risk could be fired or not hired by employers to reduce
costs of providing health care coverage. ELSI research relating to
genetic privacy and the risk of genetic discrimination is credited
with bringing about changes in federal law with "far
reaching" effects on society (McEwen et al. 2014)--in
particular, passage of the Genetic Information Nondiscrimination Act
(GINA) in May 2008, which prohibits U.S. health insurance companies
and employers from discriminating based on genetic information,
defined to include genetic test results and family history but not
manifest disease. The Affordable Care Act, passed in 2010, by
prohibiting discrimination by health insurers based on preexisting
conditions, which include genetic test results and manifest disease,
fills in that gap and negates the need for GINA in the context of
health insurance. As for employment, there remains a gap: employees
who are substantially impaired are covered by the Americans with
Disabilities Act and employees who are asymptomatic with genetic tests
showing a predisposition for disease are covered by GINA, but
employees with manifest disease who are not substantially impaired are
covered by neither (Green et al. 2015). GINA does not prohibit use of
genetic information in underwriting for life, disability, or mortgage
insurance. Discrimination takes the form of refusing coverage on the
basis that the genetic susceptibility counts as a "preexisting
condition," charging high premiums for the policy, limiting
benefits, or excluding certain conditions. In 2020, Florida became the
first state to prohibit use of genetic test results by life insurance
companies.



The insurance industry argues that there is no principled reason to
treat genetic information any differently from other medical
information used in underwriting. They point to the problem of
"adverse selection": people who know themselves to be at
high risk are more likely to seek insurance than people who know
themselves to be at low risk, which threatens the market when insurers
are deprived of the same information (Meyer 2004; Pokorski 1994).
Taking an approach to legislation and policy that singles out genetic
information for protection has also been criticized philosophically
for being based on "misconceptions [that] include the
presumption that a clear distinction exists between genetic and
nongenetic information, tests, and diseases and the genetic
essentialist belief that genetic information is more definitive, has
greater predictive value, and is a greater threat to our privacy than
is nongenetic medical information" (Beckwith and Alper 1998, p.
208; see also Rothstein 2005). The approach has been dubbed
"genetic exceptionalism" and is criticized for drawing
from, and in turn fostering, myths of genetic determinism and genetic
reductionism (Murray 1997, p. 61; see also O'Neill 2001). Rather
than assuming the binarism implicated in here--that genetic
information is unique and targeted policies are necessary or that
genetic information is not unique and targeted policies are
unnecessary--"genomic contextualism" has been
recommended as alternative approach (Garrison et al. 2019a). This
approach recognizes that there are both similarities and differences
between genomic and other types of clinically relevant information and
that the specific context in which the policy or practice is
implemented determines how best to proceed. Since completion of the
HGP, the contexts in which genomics is practiced have changed
sufficiently that while privacy concerns remain pressing, the debate
has been largely recast.



At the outset of the HGP, concerns about genetic privacy focused on
how to protect the public from intrusive governments, employers, and
insurance companies. But the explosion of DTC genomics has raised
privacy concerns caused by the very public in need of protection! In
DTC genomics, Y-chromosomal, mitochondrial, and autosomal DNA ancestry
tests are used to provide familial matches. These matches enable
adoptees in closed adoptions and offspring of anonymous gamete donors
to track down biological parents and other family members, raising
obvious privacy concerns for those who gave up children for adoption
or agreed to donate or sell eggs or sperm expecting that their
anonymity would be protected. Additional privacy concerns arise as the
result of genetic genealogy's use of matches to cousins of
various degrees to fill out missing branches in family trees. Genome
scientists rely on cell lines, DNA sequence data, and clinical data
sets that have been di-identified to protect the anonymity of
volunteers; however, access to genetic genealogy databases that
combine genetic and traditional genealogical information makes
re-identification possible. In genetic genealogy, surname projects
based on the co-inheritance of surnames and Y-chromosomal haplotypes
furnish candidate surnames when sequence information is available;
using Internet searches to match surnames with year of birth and U.S.
state of residence, researchers were able to identify individuals who
had participated in the 1000 Genomes Project; by extension, they also
identified family members who had not participated in the project or
consented to share information (Gymrek et al. 2013). Based on
population genetic modelling, researchers suggest that with a genetic
genealogy database that covers two percent of a target population, a
third cousin match can be obtained for 99 percent of the population.
With this match, family trees constructed using traditional
genealogical methods and additional sources of information can be used
to identify an unknown individual for whom DNA is available: this is
the "long range familial search" approach that law
enforcement is using in an increasing number of active as well as cold
cases (Erlich et al. 2018).



A response to the inability to guarantee anonymity for participants in
genomic research is to consider concerns about genetic privacy and
privacy more generally to be passee. Such concerns are
increasingly seen to stand in the way of scientific progress. Watson
and Venter have promoted the idea that there is nothing to fear by
making one's sequence public rather than protecting it as
private. Venter's diploid genome was fully sequenced and the
findings published in the October 2007 issue of PLoS Biology (Levy et
al. 2007); this was followed by the publication of Watson's
"complete" genome in the 17 April 2008 issue of Nature
(Wheeler et al. 2008). Relevant to the privacy question, Watson did
not bare all: at Watson's request, the APOE gene which is linked
to Alzheimer's disease was omitted from his sequence. Along
similar lines, the volunteers Church recruits for the Personal Genome
Project agree to release their genomes and health and physical
information publicly, a model of "open consent" replacing
genetic privacy (Lunshof et al. 2008). Says Church: "Ideally,
everybody on the planet would share their medical and genomic
information" (in Dizikes 2007). Large-scale biobanking
initiatives such as the *All of Us* Research Program,
originally called the Precision Medicine Initiative or PMI, appeal to
collective altruism. The Internet has radically changed people's
expectations of privacy, the boundary between their personal and
public lives, and their expectations of accessing information, and
these changes are welcomed by genome scientists. To ensure "the
free flow of research data," the 2011 National Research Council
report calls for the "[g]radual elimination of institutional,
cultural, and regulatory barriers to widespread sharing of the
molecular profiles and health histories of individuals, while still
protecting patients' rights" (p. 60).



With this free flow of research data, across institutions and
globally, with varying degrees of oversight, people's ability to
consent to use of their biospecimens and data for some but not other
purposes becomes impossible (Zarate et al. 2016). Privacy concerns are
amplified insofar as genomics is a science driven by big data. The
promise of personalized and precision medicine is premised, for some,
on amassing all obtainable data on individuals, whether mined from
electronic health records, government databases, DTC genomics,
genealogy sites, mobile devices, social media, credit card
transactions, fitbits, etc. Data-driven biology forgoes hypotheses for
algorithms, but these algorithms are not innocuous. Hallam Stevens
(2021) describes "an emerging medical-industrial complex"
that presents "substantial challenges for privacy, data
ownership, and algorithmic bias," which, if not addressed, will
lead to a genomic science that operates in the interests of
"surveillance capitalism" and the corporate tech giants
(pp. 565-566). Governments, especially authoritarian ones, are
building databases that supplement DNA with biometics and social media
posts to carry out genomic surveillance that often targets minorities
(Moreau 2019).



Of course, significant social privilege attaches to some
people's ability not to worry that genetic testing offered as an
employee benefit (Singer 2018) or their virtual cloud of billions of
data points will cause them harm. Or that they will regret spitting
into a tube and sending it off to 23andMe. As Anna Jabloner (2019)
argues, "molecular identification technologies ... tell a
tale of two molecular Californias: one is a tale of an unchanged
biological determinism that continues to mark some bodies as risky and
criminal, the other tale is of individual empowerment through the
consumption of molecular knowledge" (p. 15). Black and Latino
men are overrepresented in CAL-DNA, which is one of the largest
criminological DNA databases in the world, while 23andMe's even
larger database contains the DNA of mostly wealthy white Americans.
Similarly, Reardon (2017) comments on the tension between the
democratizing impulse of the open-data model of Church's
Personal Genome Project and the overwhelming Whiteness, affluence, and
maleness of the tech-savvy volunteers who have contributed their
genomes



Indigenous groups resist this open consent model that appeals to
collective altruism and seek to maintain control over biospecimens
contributed and data generated. As participants in scientific studies,
they have experienced lack of support for their interests and
priorities, failure to share benefits of research, disrespect for
cultural and spiritual beliefs, theft of traditional knowledge,
conduct of unapproved secondary research, and opportunistic
commercialization (Garrison et al. 2019b). Genetics and genomics bring
specific concerns, as "genomic data are commonly seen by Indigenous
communities as more sensitive than other types of health data,
particularly with regard to genealogy and ancestry research that can
influence traditionally held beliefs, cultural histories and identity
claims affecting rights to land and other resources " (Hudson et al.
2020, p. 378). Given distrust of funding agencies, universities, and
researchers arising from these experiences and concerns, Indigenous
underrepresentation in biobanks, DNA sequence databases, and clinical
datasets is not surprising. Genome scientists desire access to
biospecimens and data of Indigenous peoples for a range of purposes:
geographical isolation of populations over tens of thousands of years
can yield genetic variants of physiological and clinical interest
associated with adaptive responses to environments; comparative
genomics and ancient DNA studies contribute to knowledge of human
evolutionary history; and efforts to identify genetic contributions to
complex traits using GWAS and admixture mapping depend on access to
populations that incorporate the genetic diversity of the species. Any
progress in the prevention and treatment of disease that arises
through precision medicine will be weighted towards populations most
studied. Indigenous peoples make up only 0.022% of participants in
GWAS conducted worldwide (Mills and Rahal 2019).



In recognition of the overrepresentation of people of European descent
in biobanks, DNA sequence databases, and clinical datasets, the
NIH's *All of Us* initiative seeks to include at least 50
percent underrepresented minorities, motivated by the goal that
precision medicine benefit everyone. Keolu Fox (2020) points out that
NIH plans to include Indigenous communities in the *All of Us*
initiative fail to appreciate that the open-source data approach used
for previous government-funded, large-scale human genome sequencing
efforts such as the International HapMap Project and 1000 Genomes
Project facilitates the commodification of data by pharmaceutical and
ancestry-testing companies. Nanibaa' A. Garrison et al. (2019b)
suggest that development of alternative models for genetic and genomic
research involving Indigenous peoples begin by recognizing Indigenous
sovereignty, which is "the inherent right and capacity of Indigenous
peoples to develop culturally, socially, and economically along lines
consistent with their respective histories and values" (pp.
496-497). There should be tangible benefits for Indigenous communities
(e.g., support for health promotion, meaningful results), with
equitable sharing of profits should commercialization occur (Hudson et
al. 2020). Community engagement is crucial; this may extend to
community-based participatory research that views Indigenous
communities as partners in research, not merely subjects of research.
Individual consent is insufficient and should be preceded by
collective consent, and consent needs to be an ongoing process for any
subsequent research contemplated (Garrison et al. 2019b; Tsosie et al.
2019). Indigenous control over the use and disposal of DNA samples is
needed: on the "DNA on loan" approach developed in Canada
(Arbour and Cook 2006), the participant or community retains ownership
of biological materials and entrusts these to researchers or research
institutions as stewards. As for data gleaned from biological
materials, the model of open consent is counter to the concept of
Indigenous data sovereignty, which is "the inherent and
inalienable rights and interests of indigenous peoples relating to the
collection, ownership and application of data about their people,
lifeways and territories" (Kukutai and Taylor 2016, p. 2).


### 2.3 Identity and Difference: The "Normal" Human Genome



Early in the debates surrounding plans for the HGP, questions arose
concerning what it means to map and sequence *the* human
genome--"get the genome," as Watson (1992) put it.
About these concerns, McKusick (1989) wrote: "The question often
asked, especially by journalists, is 'Whose genome will be
sequenced?' The answer is that it need not, and surely will not,
be the genome of any one person. Keeping track of the origin of the
DNA that is studied will be important, but the DNA can come from
different persons chosen for study for particular parts of the
genome" (p. 913). The HGP and Celera reference sequences are
indeed composites based on chromosomal segments that originate from
different individuals: the sequence in any given region of the genome
belongs to a single individual, but sequences in different regions of
the genome belong to different individuals. However, in both cases,
the majority of the sequence originates from just one person. As HGP
sequencing efforts accelerated, concerns arose that only four genomes,
a couple of which belonged to known laboratory personnel, were being
used for physical mapping and sequencing (Marshall 1996a). The
decision was made to construct 10 new clone libraries for sequencing
with each library contributing about 10 percent of the total DNA. In
the end, 74.3 percent of the total number of bases sequenced was
derived from a single clone library--that of a male, presumably
from the Buffalo area; seven other clone libraries contributed to an
additional 17.3 percent of the sequence (International Human Genome
Sequencing Consortium 2001, p. 866). A similar proportion--close
to 71 percent--of the Celera sequence belongs to just one male
even though five ethnically diverse donors were selected; incredibly
enough, rumors were eventually confirmed that this individual is
Venter himself (McKie 2002).



The deeper question, of course, is how we might understand a single
human genome sequence, a composite that belongs to no actual
individual in its entirety and only a handful of individuals in its
parts, to be representative of the entire species. This seems to
ignore the extensive genetic variability that exists. Early critics of
the HGP pointed out numerous faults with the concept of a
representative or putatively normal genome: many DNA polymorphisms are
functionally equivalent (Sarkar and Tauber 1991); the genome sequence
will contain unknown defective genes (since no one, including donors,
is free of these), and it is impossible to identify the genetic basis
of a disorder simply by comparing the sequences of sick and well
people since there will be many differences between them (Lewontin
2000 [1992]); and from an evolutionary viewpoint, mutations are not
"errors" in the genetic code or "damage" to
the genome's structure, but the genetic variants that provide
the raw materials that make it possible for new species to arise
(Limoges 1994, p. 124). There were related worries that the human
genome reference sequence would arbitrate a standard of genetic
normality; for example, the application of concepts like
"genetic error" and "damage" to the genome
institutes a call for correction or repair (Limoges 1994; also Murphy
1994). Indeed, the 1988 Office for Technology Assessment report on the
HGP recommended the "eugenic use of genetic information ...
to ensure ... that each individual has at least a modicum of
normal genes" (p. 85).



*Science* named "human genetic variation" as
"Breakthrough of the Year" for 2007. Humans have been
found to be 99.9 percent alike genetically, but notable for the
magazine was the extent to which individuals had been found to differ
genetically from one another--in SNPs, insertions, deletions, and
other structural elements--and the promise this apparently
unexpected amount of variation holds for using genome-wide association
studies (GWAS) to discover the genetic bases for complex traits, both
disease and non-disease traits, to which multiple genetic and
nongenetic factors contribute. The human genome reference sequence has
been useful as a tool for discovering and cataloguing that genetic
variation by providing a standard shared by the scientific community:
"The current reference genome assembly works as the foundation
for all genomic data and databases. It provides a scaffold for genome
assembly, variant calling, RNA or other sequencing read alignment,
gene annotation, and functional analysis. Genes are referred to by
their loci, with their base positions defined by reference genome
coordinates. Variants and alleles are labeled as such when compared to
the reference (i.e., reference (REF) versus alternative (ALT)).
Diploid and personal genomes are assembled using the reference as a
scaffold, and RNA-seq reads are typically mapped to the reference
genome" (Ballouz et al. 2019, p. 159). However, what had
portrayed as a journalist's or philosopher's question is
now being asked by genome scientists too. Even as a tool, there are
challenges to overcome. If alleles included in the reference sequence
are relatively rare, "reference bias" is introduced:
genomes that resemble the reference genome are easier to assemble and
align, and variants are missed or misidentified (Ballouz et al. 2019).
And when entire stretches of sequence are missing in the reference
sequence, those sequences will be discarded and missed entirely
because of the reliance on the genome reference sequence for
assembling and aligning sequenced genomes (Sherman and Salzberg
2020).



Since 2003, there have been ongoing efforts to update the human genome
reference sequence by filling gaps, correcting errors, and replacing
minor alleles (the current version of the reference sequence is
GRCh38). Diversity has been incorporated in the reference sequence by
tacking on additional sequences, but by maintaining a linear
representation, this loses location information (Kaye and Wasserman
2021). Suggestions have been made for ways to further improve the
genome reference sequence. Recently developed long read sequencing
technologies facilitate the discovery of large structural variants
(SVs) and not just genetic variants (i.e., SNPs and smaller insertions
and deletions, or "indels"). One suggestion calls for
"reconstructing a more precise canonical human reference
genome" by using those SVs to correct misassemblies in the
reference sequence and adding the more common SVs to improve variant
detection (Yang et al. 2019). Another suggestion recommends adopting a
"consensus sequence" approach, in which the most common
alleles and variants in the population are chosen for inclusion
(Ballouz et al. 2019). Problems remain, however: these remain
composite genomes and may contain sequences that would not be found
together in any individual, and ongoing updates undermine the
stability of the reference sequence as a reference (Kaye and Wasserman
2021). Suggestions have also been made for the replacement of the
genome reference sequence. Long read sequencing allows the
"*de novo* assembly" of genomes, which obviates
need of the reference sequence for scaffolding (Chiasson et al. 2015).
A human "pan-genome" would accommodate variation by
serving as a collection of all the DNA sequences found in the species,
both SVs and genetic variants, and replace a linear representation of
the genome with more complex genome graphs (Sherman and Salzberg 2020;
Miga and Wang 2021). Another possibility is "The Genome
Atlas," which foregoes use of the reference sequence even as a
coordinate system, with entries in the atlas instead features of the
genome assigned unique feature object identifiers (FOIs). The database
generates blueprint genomes based on selected features for use in
sequencing reads (Kaye and Wasserman 2021).



Philosophical concerns about whether a human genome reference sequence
arbitrates a standard of genetic normality remain, though these may be
mitigated by the pan-genome and genome atlas approaches. An
empirically validated consensus sequence approach that includes the
most common alleles and variants in the population in the genome
reference sequence does not imply that those alleles and variants are
of biomedical significance because they are conducive to health or of
evolutionary significance because they are ancestral.
*Science*'s 18 February 2011 issue in celebration of the
10-year anniversary of publication of the draft human genome sequence
contains an essay by genome scientist Maynard V. Olson, which asks, in
its title, "What Does a 'Normal' Human Genome Look
Like?" Although the HGP was criticized as anti-evolutionary,
pre-Darwinian, typological, and essentialist for seemingly instituting
a standard of genetic normality, it was also argued that the HGP might
be seen instead as incorporating a specific set of evolutionary
assumptions (Gannett 2003). Indeed, from an evolutionary perspective,
Olson contends that genetic variability among relatively healthy
humans is largely composed of deleterious mutations, rather than
adaptive mutations due to balancing or diversifying selection, and
that, consequently, "there actually is a 'wild-type'
human genome--one in which most genes exist in an evolutionarily
optimized form" (p. 872), though individual humans inevitably
"fall short of this Platonic ideal." Judgments about what
constitutes a "normal," "wild-type," or
"ideal" human genome do not escape the socio-cultural
contexts in which they arise. Social and cultural values that attach
to judgements at the phenotypic level are simply embedded in the
genome, where they are less visible as such (Gannett 1998).



In promoting the HGP, Gilbert (1992) suggested that we will find
answers to the age-old question about human nature in our genome:
"At the end of the genome project, we will want to be able to
identify all the genes that make up a human being.... So by
comparing a human to a primate, we will be able to identify the genes
that encode the features of primates and distinguish them from other
mammals. Then, by tweaking our computer programs, we will finally
identify the regions of DNA that differ between the primate and the
human--and understand those genes that make us uniquely
human" (p. 94). Although philosophers challenge the species
essentialism that defines species in terms of genetic properties
shared by all and only their members (Gannett 2003; Robert and Baylis
2003), genome scientists are indeed comparing the human genome
reference sequence to chimpanzee, bonobo, and Neandertal genome
reference sequences to explore questions about human nature. Already
in 1969, Sinsheimer foresaw the promise of molecular biology to remake
human nature: "For the first time in all time, a living creature
understands its origin and can undertake to design its future"
(in Kevles 1992, p. 18). Remaking human nature is likely to begin with
genetic modifications that convey the possibility of resistance to a
serious disease, like HIV/AIDS, or minimize the effects of aging to
extend lifespan, but transhumanists who view human nature as "a
work-in-progress, a half-baked beginning that we can learn to remold
in desirable ways" welcome improvements in memory, intelligence,
and emotional capacities as well (Bostrom 2003, p. 493). Theories of
justice are typically based on conceptions of human nature; although
the new field of sociogenomics continues to favor nature over nurture
(Bliss 2018), with the capacity to remake human nature, this
foundation disappears (Fukuyama 2002; Habermas 2003).


### 2.4 Identity and Difference: Race, Ethnicity, and the Genome



Concerns about race, ethnicity, and the genome were raised in the
early years of the HGP. Racial profiling in the legal system was one
such concern: if people belonging to particular racial and ethnic
groups are more likely to be arrested, charged, or convicted of a
criminal offense, they are more likely to be required to provide DNA
samples to forensic databases, and therefore more likely to come back
into the system with future offenses (Kitcher 1996). Another concern
was that if genetic discrimination by insurers and employers creates a
"genetic underclass" (Lee 1993; Nelkin and Tancredi 1989),
then to the extent that race and ethnicity correlate with
socioeconomic status, some groups--already affected by
disparities in health outcomes unrelated to genetic
differences--will be disproportionately represented among this
"genetic underclass." A further concern was the social
stakes involved when group-based differences are identified, whether
these involve sequences localized to particular groups or varying in
frequency among groups (Lappe 1994). And given the history of
using biological explanations to provide ideological justification for
social inequalities associated with oppressive power structures, the
prospective use of molecular genetics to explain race differences was
met with caution (Hubbard 1994). These concerns were dismissed by HGP
proponents, who argued that mapping and sequencing the human genome
celebrate our common humanity. At the June 26, 2000 White House press
conference announcing completion of "a working draft" of
the sequence of the human genome, Venter announced that the results
show that "the concept of race has no genetic or scientific
basis" (see Clinton, et al. 2000).



Post-HGP genetics and genomics have not lived up to the mantra that
because we are 99.9 percent the same, there is no such thing as race.
Indeed, a predominantly African American racial identity has been
ascribed to the human genome reference sequence itself (Reich et al.
2009), and the *de novo* assembly of genomes permitted by long
read sequencing has led to several countries, such as China, Korea,
and Denmark, producing their own "ethnicity-specific reference
genomes" (Kowal and Llamas 2019). The International HapMap
Project, which was initiated in 2002 with the goal of compiling a
haplotype map adequately dense with SNP markers to permit the
identification of genes implicated in common diseases and drug
responses, sampled the DNA of four populations (European-Americans in
Utah; Yoruba in Ibadan, Nigeria; Japanese in Tokyo; and Han Chinese in
Beijing). This reproduction of racial categories (here, European,
African, and Asian) at the level of the genome has been characterized
as the "molecular reinscription of race" (Duster 2015).
DTC ancestry testing appeals to a range of group categories, which are
defined by geography, nationality, ethnicity, and race: Ancestry.com
advertises tests for Irish ancestry in early March each year;
FamilyTreeDNA confirms Jewish ancestry, whether Ashkenazi or Sephardi;
African Ancestry, Inc. finds ancestral ties to present-day African
countries and ethnic groups dating back more than 500 years; and
DNAPrint's panels of ancestry informative markers determine
proportions of (Indo)European, East Asian, sub-Saharan African, and
Native American heritage.



The resurgence in biological thinking about race and ethnicity since
the HGP is due in large part to the postgenomic use of racial and
ethnic categories of difference to try to capture patterns of group
genetic differences in various fields of research. The revolutionary
benefits of postgenomic "personalized" or
"precision" medicine were supposed to focus on individual
genetic differences within populations, not group genetic differences
across populations. Pharmaceuticals, a powerful engine driving
post-HGP research into human genetic differences, were supposed to be
tailored to individual genomes. In 2003, Venter opposed the U.S. Food
and Drug Administration (FDA) proposal to carry out pharmaceutical
testing using the Office of Management and Budget (OMB) racial and
ethnic classification system, arguing that these are
"social" not "scientific" categories of race
and ethnicity and that the promise of pharmacogenetics lies in its
implementation as individualized medicine given the likelihood that
variation in drug responses will vary more within racial and ethnic
groups than among them (Haga and Venter 2003). However, en route to a
"personalized" or "precision" medicine based
on individual genetic differences and pharmaceuticals tailored to
individual genomes, a detour via research into group genetic
differences has been taken. Now that group genetic differences have
become of interest to more than just evolutionary biologists and
population geneticists, impetus is provided to debates to which
philosophers of science have contributed: longstanding debates about
whether race is biologically real or socially constructed (Andreasen
2000; Pigliucci and Kaplan 2003; Gannett 2010; Hochman 2013; Spencer
2014) and more recent ones concerning the appropriateness of the use
of racial categories in biomedical research (Root 2003; Gannett 2005;
Kaplan 2010; Hardimon 2013).



Despite the detour via group genetic differences en route to
"personalized" or "precision" medicine,
genome-based research into common diseases and drug responses has
focused predominantly on Europeans. For GWAS, a 2009 analysis showed
that 96 percent of participants were of European descent; by 2016,
though the proportion of participants of European descent had
decreased to 81 percent, the change was mostly accounted for by a
greater number of studies being carried out in Asian countries
(Popejoy and Fullerton 2016). Even though African populations are the
most genetically diverse in the world, by 2018, only 2 percent of GWAS
participants had been African in origin (Sirugo et al. 2019). This
European bias makes it more difficult to isolate rare genetic variants
contributing to disease, to provide accurate and informative genetic
test results to nonEuropeans, and to ensure that any clinical benefits
that arise from genomic research will be equitably distributed within
the U.S. and globally. Sociologist Dorothy E. Roberts (2021) calls for
genetic researchers to "stop using a white, European standard
for human genetics and instead study a fuller range of human genetic
variation," which will "give scientists a richer resource
to understand human biology" as well as promoting equitable
access to the benefits of research. That study of human genetic
variation, Roberts argues, should abandon use of race "as a
biological variable that can explain differences in health, disease,
or responses to therapies," as it obscures "how structural
racism has biological effects and produces health disparities in
racialized populations" (p. 566). Structural racism is advanced
as a key determinant of population health: for example, the racial and
economic segregation of neighborhoods contributes to health
disparities because of differences in quality of housing, exposure to
pollutants and toxins, good education and employment opportunities,
and access to decent health care (Bailey et al. 2017).



The NHGRI has affirmed its commitment to improve the inclusion of
participants from diverse populations in research and begun to
appreciate that genomic studies are designed in ways that fail to
consider the contribution of social and physical environments to
disease (Hindorff et al. 2018). However, as sociologist Steven Epstein
(2007) has argued more generally, the "inclusion-and-difference
paradigm" that has prevailed in U.S. research over the past
couple of decades, though correcting researchers' previously
held default assumption of the white, middle-aged, white male as the
normative standard, serves to amplify the role of biology in health
and disease while drawing attention away from society. Genome
scientists understand the ramifications attached to their use of
racial and ethnic categories in research, and despite longstanding,
well-considered ELSI-funded research that urges care be attached to
the use of these categories (Sankar and Cho 2002; Sankar et al. 2007),
problems are ongoing and attended to by NHGRI leaders concerned about
"the misuse of social categories of race and ethnicity as a
proxy for genomic variation" (Bonham et al. 2018, p. 1534). The
"weaponization" of genomics by White nationalists and the
alt-right has raised the stakes for genome scientists. The
self-described fascist, White supremacist, racist, and anti-Semite who
murdered 10 African Americans at a Buffalo supermarket in 2022 posted
writings that cited dozens of scientific studies, including use of a
GWAS of educational attainment to support hereditarian views of racial
differences in intelligence and use of a principal components analysis
(PCA) that resolved human genetic diversity into continental-level
clusters to support realism about biological race (Carlson 2022).



White supremacists chug milk to celebrate their origins in European
populations that evolved the ability to digest lactose in adulthood
(Harmon 2018), and they appeal to traces of Neanderthal DNA in their
genomes to celebrate their origins in populations that evolved outside
Africa (Wolinsky 2019). While it seems incumbent on genome scientists
to confront racist misuses of their research, they face challenges in
doing so. Misuse may result from misunderstanding science, but not
always: lay experts among White nationalists capably use cutting-edge
genomics research to justify hereditarian views, thereby building a
counter-knowledge (Doron, in press) or citizen science of sorts
(Panofsky and Donovan 2019). In the U.S., White nationalists take DTC
ancestry tests to prove their genetic purity, that they are 100%
European/White/non-Jewish (Panofsky and Donovan 2019), while in
Europe, this assumed homogeneity of Whiteness comes into question with
Nordicists and Mediterraneans using genetic admixture mapping as they
vie to prove themselves the most European of Europeans (Doron, in
press). When scientists engage with racists, even ones who are
scientifically literate, there is the risk of unintentionally helping
their cause. "Furthermore," as Aaron Panofsky et al.
(2021) argue, "many of the findings about human evolution and
variation are genuinely complex, ambiguous, contested, changing, and
involve historically contingent judgments" (p. 396); hence, it
may be difficult to claim that research has been misconstrued. As
Claude-Olivier Doron notes, insofar as lay experts exploit ambiguities
constitutive of scientific discourse in population genetics as they
transfer scientific findings to a White supremacist ideological
framework, these findings operate within the framework without
distortion.



Genome scientists have made recommendations about sampling protocols
and standards for visualizations in population genomics to discourage
the misappropriation of research by racists who draw conclusions
inconsistent with the intentions of scientists (Carlson et al. 2022).
However, these recommendations portray population genetic structure,
unlike race, as wholly biological, and as science studies scholarship
suggests, the challenge of accessing the biological without recourse
to the social--and the interests, biases, and imaginaries
associated with the social--may be impossible to overcome.
Historians, sociologists, and anthropologists of science have
insightfully documented how social, political, and cultural
constructions of identity are incorporated in, and become defined by,
genetics and genomics research in ways specific to their locations:
reenactment of continentally-defined races as biogeographical ancestry
in the U.S. (Fullwiley 2008; Gannett 2014); influence of population
genetics on Irish origin stories and genealogy of the Irish Travellers
(Nash 2008; Nash 2017); naturalization and even pathologization of
caste and regional differences in India (Egorova 2010);
geneticization/genomicization of Mestizo identity in the context of
Mexico's own genome mapping project (Lopez Beltran
2011); post-apartheid South Africa's "genomic
archive" bound to apartheid's racialized subjectivities
despite advancing nonracial unity through common origins (Schramm
2021); genetic ancestry testing as a basis for Jewishness (El-Haj
2012); Native American DNA as proof of tribal identity (TallBear
2013); and nationalism's role in interpreting differences
between the Korean Reference Genome (KOREF) and HGP's genome
reference sequence as occurring at the population rather than
individual level (Kowal and Llamas 2019).



Use of "ancestry" as a category for genetics and genomics
research is considered a means of averting problems associated with
the use of "race" and "ethnicity"; for
example, for the mapping of complex traits, rather than relying on
self-identified race, it has been recommended that population
structure be assessed empirically by genotyping individuals to
determine their "continental ancestry" proportions
(Shields et al. 2005). Critics contend, however, that
"continental ancestry" belies the continuous pattern with
which genetic variation is distributed across the species and reenacts
race as it has been traditionally defined, thus contributing to its
reification (Fullwiley 2008; Gannett 2014; Lewis et al. 2022). In
2023, the National Academy of Sciences published a Consensus Study
Report, "Using Population Descriptors in Genetics and Genomics
Research: A New Framework for an Evolving Field," in response to
a request by the National Institutes of Health to assess the status of
use of race, ethnicity, ancestry, and other population descriptors.
The report recommends against use of racial labels in genetics and
genomics research, a possible exception being studies of health
disparities with genomic data, in which race serves as a proxy for
environmental variables (e.g., racism). A distinction is drawn between
genetic ancestry (paths through which an individual's DNA is
inherited from specific ancestors, known as the "ancestral
recombination graph") and genetic similarity (a quantitative
measure of genetic resemblance among individuals that reflects shared
genetic ancestry). The report recognizes geographic origins,
ethnicity, and genetic ancestry as appropriate categories for
reconstructing human evolutionary history, but advocates use of
genetic similarity to constitute groups in most other research
contexts, including gene discovery for complex traits.



Relying on genetic similarity does not necessarily lead researchers
away from race, ethnicity, and ancestry. In an ethnographic study,
sociologists Joan H. Fujimura and Ramya Rajagopalan (2011) found that
although the statistical machinery associated with GWAS allows
researchers to avoid race and ethnic categories by analyzing samples
based wholly on genetic similarity, there was "slippage"
from genetic similarity to shared ancestry, which, in turn, since
mediated by geography and genealogy, became interpreted as racial or
ethnic. While ancestral recombination graphs situate individuals in
the context of their genealogical relations without assigning them to
geographically or culturally defined populations or groups, in
practice, these categories are almost always incorporated in genetic
ancestry estimation (Lewis et al. 2022). Population geneticist Graham
Coop (2022) favours replacing genetic ancestry with genetic
similarity, arguing that describing a sampled individual's
genetic similarity to a reference panel (e.g., "*X* is
genetically similar to the GBR 1000 Genome samples") is
preferable to attributing genetic ancestry to that individual (e.g.,
"*X* has Northwestern European genetic ancestry"),
as it recognizes the conventionalism of the reference panel and
continuity of genetic variation (pp. 11-12). However, given that the
1000 Genomes Project's 26 populations across five continental
regions are named using language that reflects "both the
ancestral geography or ethnicity of each population and the geographic
location where the samples from that population were collected"
(Coriell Institute)--e.g., "British from England and
Scotland"--the slippage remarked upon by Fujimura and
Rajagopalan is encouraged. Nevertheless, as sociologists Aaron
Panofsky and Catherine Bliss (2017) observe, "Geneticists face a
complex set of pressures regarding population labeling" (p. 75).
Ambiguous labels for populations that conflate geography, race, and
ethnicity may offer geneticists the flexibility to fulfill their own
research goals while accessing repositories of preclassified
biospecimens and data, collaborating with researchers pursuing quite
different agendas, and maintaining goodwill with populations studied.
These pressures compete with pressures about labels imposed by funders
and journals, which may provoke skepticism and resistance.


### 2.5 ELSI and Its Legacy



Although ELSI may have had an inauspicious argued beginning in
Watson's apparent off-the-cuff remarks at a 1988 news
conference, the research program has outlasted the HGP itself. ELSI
funding is mandated through the National Institutes of Health
Revitalization Act of 1993, which calls for a minimum of 5 percent of
the NIH budget for the HGP--the monies directed to the
NCHGR-NHGRI--to be set aside to study the ethical, legal, and
social implications of the science of genomics (McEwen et al. 2014).
The Division of Genomics and Society at the NIH's NHGRI, created
in 2012, maintains the Ethical, Legal and Social Implications (ELSI)
Research Program as an extramural grant funding initiative to the
present day; the division also includes an intramural bioethics
program. Input concerning the ELSI program is provided by the NHGRI
Genomics and Society Working Group through the National Advisory
Council for Human Genome Research.



A review article by NIH staff (McEwen et al. 2014) characterizes the
ELSI program as "an ongoing experiment." Since it was
established in 1990, the ELSI program has supported empirical and
conceptual research carried out by researchers from a broad range of
disciplines: "genetics and genomics, clinical medicine,
bioethics, the social sciences (e.g., psychology, sociology,
anthropology, political science, and communication science), history,
philosophy, literature, law, economics, health services, and public
policy" (p. 485). This research is considered to have had
impacts on genomics studies (e.g., requirements for informed consent,
protection of the privacy of subjects, and nomenclature for socially
defined groups), genomic medicine (e.g., personal impacts of acquiring
genetic information from screening and testing carried out in
clinical, research, and direct-to-consumer settings), and wider
society (e.g., federal legislation prohibiting genetic discrimination
in health insurance and employment, increased awareness about DNA
forensics, and policies on gene patenting). The experimental aspect
remarked upon refers to the organizational and physical situation of
ELSI, a program charged with critically evaluating the implications
genomics research, within the very agency that funds that research.
While this institutional arrangement supports the growing trend to
integrate ELSI research with genomics research and policy formulation,
ensuring that ELSI research is scientifically informed and practically
relevant, excessive proximity also risks compromising "the
autonomy, objectivity, and intellectual independence of ELSI
investigators" (McEwen et al. 2014).



Since the early years of the HGP, bioethicists have criticized ELSI on
various institutional grounds: for a lack of independence from
scientist-overseers (Murray 1992; Yesley 2008), for an absence of
structure conducive to providing guidance regarding policy (Hanna
1995), and for a negative impact on bioethics in narrowing the range
of topics covered and creating an isolated subspecialty within the
field (Annas and Elias 1992; Hanna et al. 1993). And from history and
philosophy of science quarters, broader philosophical concerns have
been raised about ELSI's focus on the ethical, legal, and social
*implications* of genetic research. Such a focus promotes a
"downstream" rather than "upstream" framework
for understanding the relationship between science and ethics that
fails to appreciate that foundational concepts in genetic research
such as normality and mutation are themselves evaluative and operate
as directives to action (Limoges 1994). ELSI's European
counterpart, Ethical, Legal and Social Aspects or ELSA, chose to use
the term "aspects" in order to avoid the connotations of
narrowness, linearity, and determinism attached to the term
"implications" (Hilgartner et al. 2016).



The "ongoing experiment" at the NIH's NHGRI has come
to define a particular model for doing research in bioethics that is
being exported to other rapidly developing scientific fields, as
expressed in the title of a target article published in *AJOB
Neuroscience*, "To ELSI or Not to ELSI Neuroscience: Lessons
for Neuroethics from the Human Genome Project" (Klein 2010).
Indeed, although "ELSI" entered the lexicon as an acronym
for the specific extramural research program supported by US
government funding set aside for the HGP (elsewhere in the world,
similar programs received their own appellations and
acronyms--e.g., Genomics and its Ethical, Environmental,
Economic, Legal, and Social Aspects or GE3LS in Canada),
with that program offering a possible model for other emerging
sciences, the term has come to receive a broader meaning that refers
instead to a field of research, defined by its "research and
scholarship content, rather than a particular set of funding
sources" (Morrissey and Walker 2012, p. 52). Given the interest
in exporting the ELSI model, its merits as a field of research, as
bioethicists Clair Morrissey and Rebecca L. Walker argue (Morrissey
and Walker 2012; Walker and Morrissey 2014), need to be examined.
Investigating the content and methods of ELSI as a field of research,
Morrissey and Walker combed through hundreds of articles and book
chapters published between 2003 and 2008 (Morrissey and Walker 2012,
Walker and Morrissey 2014). They found that funding sources influenced
what research is carried out: though only 17 percent of all
publications involved empirical research, for publications whose
authors received US government funding, 30 percent of those with
non-NHGRI support and 52 percent of those with NHGRI support were
empirically based. They found that institutional and professional
forces, irrespective of funding sources, promoted the coverage of
topics of greatest interest to affluent populations (e.g.,
"genomics and clinical practice," "intellectual
property," "genetic enhancement," and
"biorepositories"). They found that the vast majority (89
percent) of publications were prescriptive, recommending to diverse
actors (scientists, clinicians, bioethicists, government, etc.) that
certain policies or practices be pursued. Given this overwhelmingly
prescriptive posture, they were dismayed to find that publications
made use of multiple bioethical methods in piecemeal fashion with
little depth. For the most part (77 percent), publications did not
reflect on methods.



ELSI itself has become an object of research in science and technology
studies (STS) scholarship. The ELSI model, as incorporated more
recently in areas such as nano and synthetic biology, is understood as
serving as "a new governance tool built on the prior
institutionalization of 'bioethics' as a way to manage
problems of moral ambiguity and disagreement in biomedicine"
(Hilgartner et al. 2016, p. 824). At the outset of the HGP, ELSI
relied on a governance model in which ethicists and social scientists
lent their expertise by producing a body of scholarship that could
inform public policy; subsequently, especially in Europe, social
scientists were expected to facilitate mechanisms for making public
policy in more democratic ways by engaging stakeholders and the
broader public. Hilgartner et al. (2016) argue that STS scholarship
sits somewhat uneasily alongside ELSI scholarship inasmuch as STS
scholarship problematizes elements of the "traditional
imaginaries of orderly science-society relations" to which ELSI
subscribes, such as the fact/value distinction, the
"neutrality" of science and technology, and "the
self-evidence of power relations" (p. 832). Criticisms are also
made that as a tool of governance, bioethics exercises power in ways
often unseen, thereby foreclosing questions asked and debates
had--for e.g., taking the boundary between facts and values as
given not made, presenting rational moral arguments as outside
politics to dismiss issues of public concern, or circumventing
legislation by justifying the extension of existing regulations.