## 1. Terminology and Definitions


### 1.1 Gene--Genome--Genomics



The term 'genomics' derives from the term
'genome', which itself derives (in part) from the term
'gene'. The meaning(s) of--and the relationships
between--these different terms is by no means simple.



The term 'gene' was introduced in 1909 by the Danish
biologist Wilhelm Johannsen, who used it to refer to the (then
uncharacterised) elements that specify the inherited characteristics
of an organism (see
 gene
 and
 molecular genetics
 entries for an overview of the complex history of the term
'gene').



The term 'genome' was introduced in 1920 by the German
botanist Hans Winkler (1877-1945) in his publication
"Verbreitung und Ursache der Parthenogenesis im Pflanzen- und
Tierreiche" (Prevalence and Cause of Parthenogenesis in the
Plant and Animal Kingdom). Winkler defined the term as follows:



> 
> 
> Ich schlage vor, fur den haploiden Chromosomensatz, der im Verein
> mit dem zugehorigen Protoplasma die materielle Grundlage der
> systematischen Einheit darstellt, den Ausdruck: das Genom zu verwenden
> [...]. (Winkler 1920: 165)
> 
> 
> 
> I propose to use the expression 'genome' for the haploid
> set of chromosomes that, in conjunction with the associated
> protoplasm, represents the material foundation of the systematic unit
> [often translated as 'species']. (Translation by S.G.)
> 
> 
> 



The etymology of the term is not clear but most authors and
encyclopaedia entries assume that it is a combination of the German
words '*Gen*' and '*Chromosom*',
leading to the composite '*Genom*'. In general, the
origin and the different meanings of the -ome suffix are not entirely
clear and there are now several accounts that try to bring some
structure and/or meaning to the ever flourishing -omes terminology in
contemporary life sciences (see, e.g., Lederberg & McCray 2001;
Fields & Johnston 2002; Yadav 2007; Eisen 2012: Baker 2013; for interesting/entertaining
lists, see -omes and -omics in the 
 Other Internet Resources section below).



The term 'genomics', finally, was invented in 1986 at a
meeting of several scientists who were brainstorming (in a bar) to
come up with a name for a new journal that Frank Ruddle (Yale
University) and Victor McKusick (Johns Hopkins University) were
setting up. The aim of this journal was to publish data on the
sequencing, mapping and comparison of genomes. To capture these
different activities--and in analogy to the well-established
discipline of genetics--Thomas Roderick (Jackson Laboratory)
proposed the term 'genomics' (Kuska 1998). Unbeknownst to
the people involved this was a significant moment in the history of
the life sciences, as it is here that the -omics suffix appears for the
first time.


### 1.2 What *is* a Genome?



Looking at the history and the etymology of a term does not, of
course, necessarily tell us a lot about how it is used in the context
of current science. So what *is* a genome in today's life
sciences? Is it the (haploid) set of chromosomes we find in the
nucleus of a eukaryotic cell, in line with the original definition by
Winkler? Or is it the totality of genes we find in an organism or the
totality of DNA present in a cell? And if so, which DNA? Most
definitions that are currently in circulation are an intricate mix of
different ways of approaching the issue. This can be illustrated by
looking at the definitions given in several key online resources (for
more definitions of the term 'genome' see Table 1 in
Keller 2011).



The term is defined on the genome.gov website glossary:



> 
> 
> The genome is the entire set of genetic instructions found in a cell.
> In humans, the genome consists of 23 pairs of chromosomes, found in
> the nucleus, as well as a small chromosome found in the cells'
> mitochondria. Each set of 23 chromosomes contains approximately 3.1
> billion bases of DNA sequence. (Talking Glossary: genome, in the
> Other Internet Resources)
> 
> 
> 



And this is how the U.S. National Library of Medicine defines it:



> 
> 
> A genome is an organism's complete set of DNA, including all of
> its genes. Each genome contains all of the information needed to build
> and maintain that organism. In humans, a copy of the entire
> genome--more than 3 billion DNA base pairs--is contained in
> all cells that have a nucleus. (NIH 2016)
> 
> 
> 



Similarly the education portal of the journal *Nature*:



> 
> 
> A genome is the complete set of genetic information in an organism. It
> provides all of the information the organism requires to function. In
> living organisms, the genome is stored in long molecules of DNA called
> chromosomes. (Scitable: genome, in the Other Internet Resources)
> 
> 
> 



All of these definitions refer both to information and to instructions
for the development and/or functioning of an organism. In the first
two, the genome is also identified with a material entity, in the
first case the chromosomes, in the second a sequence of base pairs.
*Nature* allows only that the information is "stored
in" the chromosomes.



The combination of these two aspects is highly problematic. The
definition from the U.S. National Library of Medicine implies that
"all of the information needed to build and maintain that
organism" is contained in the
 DNA,[2]
 which is certainly false: many environmental factors, not to mention
factors in the maternal cytoplasm, are required for the first task,
and even more obviously (food, light, etc.) for the second. Moreover
when, as is almost always the case, an organism requires symbiotic
partners for its proper functioning, such a definition will imply that
the DNA of these symbionts is part of the genome of the first
organism, a result that few would
 welcome.[3]
 The *Nature* definition commits the same error in its second
sentence. The genome.gov definition appears to identify the
chromosomes both with a set of instructions and a material entity,
which appears rather problematically to conflate a material object
with an abstract entity. 



The problem is not hard to see. Attempting to combine aspects of the
material base of the genome and its informational content, as all
these definitions do, inevitably assume some simple relation between
these two; but in fact the relationship is extremely complex. Because
the informational content of the genome is dependent in multiple ways
on elements that are not, on any account, part of the genome, an
account in terms purely of informational content seems a hopeless
project.



One commonly held view that can be quickly dismissed, is the idea that
the genome is just the sum total of an organism's genes. The
problem here is just that even passing over the well-known problems
with saying what a gene is (see Barnes & Dupre 2008; SEP
entry on the
 gene),
 on any tenable account of genes, there is far more to the genome than
genes, and only a fraction of the actual DNA contained in the
chromosomes would be part of the genome, at least in the case of
humans and other organisms that have a relatively large amount of
non-coding DNA (Barnes & Dupre 2008:
 76).[4]
 Even if 'gene' is interpreted in the widest possible
sense, including any section of the genome that has some identifiable
function, no one denies that a significant amount of DNA is not
functional. The rest of the DNA would not form part of the genome, an
outcome that contradicts all definitions of the genome of which we are
aware, and makes nonsense of such familiar concepts as
'whole-genome sequencing', which refers to the analysis of
all the DNA found in the chromosomes.



There are, we suggest, two initially tenable approaches to the
 problem.[5]
 The first, and one that is often implicitly or explicitly assumed to
be correct, is to define the genome as the sequence of nucleotides.
This may or may not contain extranuclear DNA, as in mitochondria or
chloroplasts; the genome.gov definition explicitly includes the
former. This last question figured largely in debates over the moral
permissibility of so-called mitochondrial transplants (a designation
that speaks volumes, incidentally, about the almost magical importance
attached to DNA as opposed to the remaining contents of the cell), but
it is not one of great philosophical significance. The alternative
approach is to understand the genome strictly as a material object,
presumably, in most cases, the nuclear chromosomes. 



The problem with the first approach is that it is largely motivated by
the assumption that the nucleotide sequence is what contains all the
important information in the genome. But in fact it has become
increasingly clear that this is not the case, especially as a result
of the growing understanding of epigenetics. Epigenetics is the study
of material modifications of the genome that affect what parts of the
genome sequence are or are not transcribed into RNA, the first stage
of the process by which the genome influences the containing organism.
The two most well-studied classes of epigenetic modification are
methylation, the attachment of a methyl group (-CH3) to one
of the four nucleotides, cytosine, and various chemical modifications
of the histone proteins, proteins that form the core structure of the
chromosomes, and around which the DNA double helix is wrapped
(Bickmore & van Steensel 2013; Cutter & Hayes 2015). The
nucleotide sequence, then, provides the (extremely large) set of
possible transcripts that the genome can produce, but the epigenetic
state of the genome determines which transcripts are actually produced
(Jones 2012). Both features of the genome (*qua* material
object) must be specified, therefore, if we want to understand the
biologically relevant behavior of the whole system.



So if the motivation for defining the genome in terms of sequence is
to capture its informational content, the definition fails to serve
its goal. Indeed, the definition that will come closest to this goal
is that which identifies the genome as the material object, the set of
chromosomes (this interpretation of the genome is defended in detail
in Barnes & Dupre 2008). An implication of this definition
that is often taken to be counterintuitive by biologists is that the
genome will on this account encompass not only DNA, but the histone
proteins that are material parts of the chromosomes. But of course the
point of the preceding discussion is that the variable chemical states
of the histones are, in fact, essential bases for some of the
information inherent in the genome.



The phenomenon of methylation makes a similar point in a slightly
different way. The nucleotides that comprise the familiar sequence are
cytosine, thymine, adenine and guanine). When a methyl group attaches
to the cytosine molecule the resultant nucleotide is not, strictly
speaking, cytosine, but 5-methyl cytosine. So unless one takes the
letter 'C' in the standard representation of sequence to
mean, rather counterintuitively, "cytosine or 5-methyl
cytosine", it is only a partially accurate representation of the
feature of the genome it purports to represent. More importantly, it
is a representation that fails to capture crucial functional aspects
of the genome.



A final telling point is that it has recently become clear that there
are functions of the genome, as material object, that go well beyond
even the broadest interpretation of the genetic (Bustin & Misteli
2016). It appears that the genome plays an essential role in a range
of cellular processes. First, its physical arrangement into domains of
varying sizes plays a central role in the coordination of gene
expression. But much further from the genetic, it is a large object
the mechanical forces of which are involved in various cellular
processes and cellular homeostasis, and the chromatin fiber provides a
scaffolding for both proteins and membranes (Bustin & Misteli
2016). Unless we are to introduce a new word to refer to this
biologically vital entity, only a material conception of the genome
can capture the full range of its activities.



One might be tempted to object to the argument above concerning
methylation, that whereas methylation is a somewhat transitory state,
the underlying four-letter sequence is extremely durable, lasting
across many generations. Richard Dawkins (1976) famously emphasized
the importance of this durability in arguing for the importance of
this stability in evolution, even going so far as to describe genes as
"immortal". So perhaps there is a good reason for
understanding "C" as referring to a disjunction.



This is not the place to address the quasi-theological view of gene
immortality. However, this does point to a fundamental issue about the
nature of genes. Even if genes, somehow, were unchanging immortal
substances, the genome is nothing of the sort. It is an extremely
dynamic entity, constantly changing its properties in generally
adaptive response to it environment. Moreover even the constancy of
its nucleotide sequence is something maintained only by the continuous
application of various editing and repair mechanisms. Indeed, far from
being an eternal substance, we suggest it is much better seen as a
process, a highly complex set of dynamic activities crucial in
maintaining the structural and functional stability not only of the
organism but also, through its role in reproduction, of the lineage.
Importantly, these relations are bi-directional and, specifically, the
organism is also crucial to maintaining the necessary aspects of
stability of the
 genome.[6]


## 2. Reading the Genome



The first genome to be sequenced was that of a virus, namely
bacteriophage PhX174, sequenced by Frederick Sanger in 1977 (Sanger et
al. 1977). Up to about 1985, work on several other viruses was
initiated in different laboratories across the world and even the
sequencing of model organisms such as the bacterium *Escherichia
coli* or the roundworm *Caenorhabditis elegans* was being
 tackled.[7]



Of all the different sequencing efforts at the time the human genome
project (HGP) of course stands out. Not only is the human genome
relatively large (roughly 3.2 billion base pairs (bps)) and of key
interest to us as human beings, but the HGP itself was envisioned as a
diverse large-scale research project with various strands and aims.
Getting the sequence out of this project was the one goal that got the
most attention in the wider media, but surely many would agree that
other findings and practices developed within the HGP were of equal or
even greater importance.



In what follows we will treat the HGP as a pivot around which genomics
developed as a field of research and as a set of techniques. For ease
of exposition we will talk here of a pre-HGP and a post-HGP phase.
Obviously, this is a simplification; there is not just one single
trajectory along which the story of genomics runs and there is not one
clear break between a pre- and a post-genome era (Richardson &
Stevens 2015). Nevertheless, as a way of structuring the discussion
this distinction will be a helpful tool.



### 2.1 The Run-up to the HGP



A decade after Sanger and Maxam and Gilbert published their DNA
sequencing methods in 1975 the first concrete talk of a human genome
project started to appear in writing (Dulbecco 1986) and at different
workshops (Sinsheimer 1989; Palca 1986). The Human Genome Project
(HGP) itself became a reality in 1990 when it was officially launched
as a US federal program (see 1990 in a brief history and timeline
[NHGRI] in the 
 Other Internet Resources section
 below).



In the run-up to the HGP there were high expectations (some would say
"hype") developing, which inevitably also brought critics
of the project onto the scene (Koshland 1989; Luria et al. 1989). As
so often, the issue of funding had a key role to play. When the HGP
was initiated there were no 'big science' projects being
pursued in the life sciences. The HGP therefore was a true first for
biology. But pushing such a large project that absorbed a significant
proportion of the funding allocated to the biological sciences
encountered a lot of resistance from other scientists.



There were three key criticisms: 1) Some claimed that the HGP was a
waste of money because much useless (read: junk) DNA was sequenced;
the focus should be more directly on the functional parts of the
genome, i.e., the genes or regulatory elements, which could be
achieved using simpler and less expensive methods (Brenner 1990;
Weinberg 1991; Rechsteiner 1991; Lewontin 1992; Rosenberg 1994).
Others claimed 2) that the HGP was a waste of money as it was merely a
descriptive and not a hypothesis-driven project. This was an issue
that became much more prominent ten years after the project was
finished, when it became clear that big data science was here to stay
(see, e.g., Weinberg
 2010).[8]



And last but not least there was also the critique 3) that the HGP is
fundamentally misguided as it assumes that by using sequence knowledge
alone we would be able to develop an understanding of how our body
works, how it develops disease, and that this understanding will
eventually lead to cures for many diseases (Lewontin 1992; Tauber
& Sarkar 1992; Kitcher 1994). This more general critique of a
narrowly sequence-focused approach to biomedical issues also comes up
20 years later in discussions about the use of common genetic variants
to learn more about common diseases and traits (see
 Section 3.1.2).
 



It is difficult to evaluate criticisms of the last kind. There is no
doubt that enthusiasm for the HGP and many other successor projects in
genomics has often been grounded in simplistic assumptions about the
power of DNA and its pre-eminent role in biological systems. On the
other hand it is arguable that many unanticipated benefits have
derived from genomics quite independently of such assumptions. For
instance the ability to make very precise comparisons of genome
sequences has led to major advances in unraveling the details of
evolutionary history, not to mention its application to technologies
such as forensic DNA testing. Moreover, it can be argued with Waters
(2007b) that what makes
genomes so central to biological research is not the erroneous belief
that they are the ultimate causes of everything, but rather the unique
possibilities they present for precise intervention in organisms or
cells.


### 2.2 The Results and Impact of the HGP



The main output of the HGP is usually seen as 'the' human
genome sequence. The draft human genome sequence (about 90% complete)
was announced in June 2000, followed in 2001 by the publication of the
draft sequences produced by the HGP (International Human Genome
Sequencing Consortium 2001) and the privately funded initiative
(Venter et al. 2001). The complete (or almost complete (99%)) sequence
of the human genome was released in 2003, which also marked the
official ending of the HGP (International Human Genome Sequencing
Consortium 2004).



But the view that the sequence of 'the' human genome was
the key output is wrong in several ways. First of all there is in
general no such thing as 'the' human genome, as each
individual (except for monozygotic twins) carries their own set of
small and large variations in their genome (and even for twins there
are many differences they accumulate in their genomes during their
lifetime). The sequence that was produced in the HGP is therefore
nothing more than an example of one particular sequence, meaning it
can only serve as a reference genome. Importantly, the reference
sequences that both the HGP and Venter's project delivered did
not correspond to the genome of a single person as the DNA used to
produce them was derived from several
 individuals.[9]
 The genomes that came out of the two sequencing efforts were
therefore composite reference sequences. But the HGP also produced
much more than just a DNA sequence. Here we will highlight three
outcomes or aspects of the HGP that are of particular importance, also
for the period that followed the completion of the project.



One key feature of the HGP was that it involved the sequencing of a
range of different model organisms, an aspect of the HGP that was
often overlooked in discussions of the project in the philosophical
literature and elsewhere (Ankeny 2001; for a searchable list of
sequenced genomes see genome information by organism in the
 Other Internet Resources section
 below). The HGP provided not only a first reference genome of
*Homo sapiens* but also the first bacterial genome
(*Haemophilus influenzae*, Fleischmann et al. 1995), the first
eukaryotic genome (*Saccharomyces cerevisiae*, Goffeau et al.
1996), and the genomes of key model organisms (*Escherichia
Coli*, Blattner et al. 1997; *Caenorhabditis elegans*,
*C. elegans* Sequencing Consortium 1998; *Arabidopsis
thaliana*, Arabidopsis Genome Initiative 2000; *Drosophila
melanogaster*, Adams et al. 2000, Myers et al.
 2000).[10]



A further crucial output was the acceleration in technology
development the HGP brought about. It is safe to say that without the
HGP (and subsequent initiatives such as the Advanced Sequencing
Technology Awards created in 2004 by the National Human Genome
Research Institute (NHGRI) (NIH 2004)) there wouldn't have been
such a rapid development in next-generation sequencing (NGS)
approaches and the cost of whole genome sequencing would not have
dropped as quickly as it has (see Mardis 2011 for a review of the
development of NGS). And these improvements in the sequencing
technology had further consequences, for example allowing scientists
to sample DNA in different ways and from different sources, as new
sequencing methods could process more DNA material more quickly and
work with less starting material. This, finally, made possible whole
new sub-disciplines, such as metagenomics (see
 Section 3.2).



A final noteworthy output of the HGP is what scientists learned about
the structure of the genome. Beginning with the HGP, and building on
further studies, researchers have gained a much more detailed picture
of the fine structure, the dynamics and the functioning of the human
genome. It was not only that there were many fewer genes present than
expected, but there was also much more repetitive DNA and transposable
elements present (it is estimated that about 45% of human DNA consists
of transposable elements or their inactive remnants). These findings
relate to a more general and older discussion about genome size and
complexity to which we next turn.



### 2.3 Genome Size, the C-value Paradox and Junk DNA



It has been known since the 1950s that genome size varies greatly
between different organisms (Mirsky & Ris 1951; see also Gregory
2001), but from the very beginning it was also clear that this
diversity has some surprising features. One of these features is the
absence of correlation between the complexity of an organism and the
size of its genome.


#### 2.3.1 The C-value Paradox



Assuming an informational account of the genome one would expect that
the more complex an organism is, the more DNA its genome should
contain (this is in fact what many biologists assumed at least until
about the 1960s). How to define and assess the complexity of an
organism is a tricky issue, but intuitively it seems reasonable to
assume that a single-celled amoeba is less complex than an onion,
which in turn is less complex than a large metazoan such as a human
being, both in terms of the complexity of the workings and the
structure of the organism. The expectation was that the DNA content of
human cells should be much larger than that of onions or amoebae. As
it turns out, however, both the onion and the amoeba have much larger
genomes than human beings. The onion, for instance, has a genome of
about 16 billion base pairs, meaning it is about five times the size
of the human genome (Gregory 2007). The same lack of
correlation between genome size and complexity can be found in many
other instances (for an overview of different genome sizes see the
animal genome size database in the
 Other Internet Resources section
 below).



It was also found early on that very similar species in the same genus
show large variation in genome size, despite having similar phenotypes
and karyotypes (i.e., number and shape of chromosomes in a genome).
Within the family of buttercups, for instance, DNA content varied up
to 80-fold (Rothfels et al. 1966). Also, Holm-Hansen (1969) showed
that species of unicellular algae display a 2000-fold difference in
DNA content despite all being of similar developmental complexity. It
was findings such as these that gave a real urgency to addressing this
discrepancy that was now labelled the C-value paradox (Thomas 1971).
The term 'C-value' refers to the constant
('C') amount ('value') of haploid DNA per
nucleus and is measured in picograms of DNA per nucleus. The C-value
is a measure of the amount of DNA each genome contains (we can see
here Winkler's original definition of the genome at work).


#### 2.3.2 Junk DNA



These discussions of genome sizes were closely related to concerns
about gene numbers. And this consideration of genome size vs. gene
numbers is what originally gave rise to the concept of 'junk
DNA' (Ohno
 1972).[11]
 The reasoning behind this concept was the following: if one assumes
a) that more complex organisms will have more DNA than less complex
organisms and b) that gene numbers increase in proportion with genome
size, then the genome of the more complex organism should have more
genes than the less complex
 one.[12]
 Human cells, for instance, contain about 750x more DNA than *E.
coli*, meaning that they should turn out to have in the range of
3.7 million genes, as *E. coli* has about 5000 genes. This is
clearly not the case; even in the 1970s it was generally supposed that
the human genome might contain no more than 150,000 genes (Crollius et
al. 2000). This discrepancy leads to the conclusion that the vast
majority of the DNA in our genome cannot be genes and is therefore
what Ohno referred to as
 'junk'.[13]



The problem that the junk DNA discussion brings up has also been
referred to as the 'G-value paradox' ('G'
stands for 'gene'), which directly concerns the
discrepancy between the number of genes in an organism and its
complexity (Hahn & Wray 2002). This paradox has been reinforced by
the findings of the HGP. As Gregory (2005) and other commentators have
pointed out, the finding that the human genome contains many fewer
genes than expected was one of the most surprising outcomes of the
HGP. Initial estimates from before the project were in the range of
50,000 to 150,000. These were reduced to about 30,000--35,000
after the publication of the first sequence draft in 2001 and have now
been further revised to the order of 20,000 (Gregory 2001).



Some researchers assumed that the C-value paradox was fully resolved
by the recognition that there is non-coding DNA in genomes (Gregory
2001). Larger genome size in 'simpler' organisms merely
means that they have large quantities of non-coding DNA. But as
Gregory points out, the fact that the majority of DNA in our genomes
is non-coding might make the C-value discrepancies less of a paradox,
but it gives rise to a whole range of further puzzles (Where does this
extra DNA come from? What is its function? Etc.), which is why he
proposes to talk of the C-value as an *enigma* rather than a
paradox (Gregory 2001). The C-value enigma consists of many different
and layered problems and these require a pluralistic approach to
answering them, or so Gregory claims.



The publication of the draft genome sequence in 2001 and the
conclusion of the HGP in 2003 did not give researchers all the tools
and insights they needed to tackle these long-standing problems. But
after the HGP, building on the initial sequencing effort, researchers
could start to go beyond the mere sequence and gain a deeper
understanding of the workings of the genome. This put them in a
position to tackle issues such as the significance of junk DNA and the
C-value paradox more directly (or at least from a different angle).
The post-HGP phase is also characterized by an intense debate about
the best way of doing research: the question of whether biological
research should best be done on a small or a large scale has come up
again and again in the post-HGP era, especially with the rise of other
post-HGP large scale projects. The next section will address two
projects/research fields that symbolize the various efforts and
aspirations that were characteristic of the post-HGP era and which
will help to illuminate some of the philosophical issues these
developments raised. 


## 3. Beyond Sequencing



The post-HGP phase is marked by a flourishing of different projects,
closely connected in their origins to the HGP, but going beyond it in
many different ways. This section discusses two such post-HGP
projects, namely the International HapMap project and a new field of
research called 'metagenomics'. These examples indicate
some important directions in which the postgenomic era is heading and
identify some, though certainly not all, of the key characteristics
and issues that mark this new period.


### 3.1 The International HapMap Project



The International HapMap project was a multi-centre project launched
in 2002 that came to an initial conclusion in 2005 (NIH 
 2002).[14]
The acronym 'HapMap' stands for 'haplotype map'
and (indirectly) refers to the main goal of the project, namely to map
the common genetic variation in the human genome.


#### 3.1.1 The HapMap Project and Genomic Variation



It is a well-known fact that everyone's genome is different.
There are, however, several ways in which genomes of individuals can
vary from each other, ranging from the deletion, insertion or
rearrangement of longer stretches of DNA to differences in single
nucleotides at specific locations on a chromosome. The latter form of
variation was the focus of the HapMap project. If we align the DNA
sequence of two individuals they will be identical for hundreds of
nucleotides; the DNA of two human beings typically displays about
99.9% sequence identity (Li & Sadler 1991; Wang et al. 1998;
Cargill et al. 1999). But the 0.1% difference means that approximately
every 1000 nucleotides there will be a difference in a single
nucleotide between any two individuals.



Any variation at a specific genomic locus is referred to as an
'allele'. If there are two different versions of a
specific gene that can be found in a population at a specific locus on
a chromosome, then that means that there are two different alleles of
that gene present in that
 population.[15]
 If one of these single nucleotide alleles is found in more than 1% of
a specific population it is treated as a 'common' variant
and researchers speak of a 'polymorphism' or, more
precisely, a 'single nucleotide polymorphism' (abbreviated
'SNP'; pronounced 'snip'). If a variation is
found in less than 1% of the population researchers simply call it a
'mutation' (or also a 'point
 mutation').[16]
 On average there are about 3 million SNPs found in each individual
and there is a pool of more than 10 million SNPs present in the human
population as a whole (HapMap 2005).



Many of these alleles are (or have an increased likelihood of being)
inherited together, meaning that they do not easily become separated
through recombination events during
 meiosis.[17]
 This leads to the non-random association of different alleles at two
or more loci, a phenomenon that has been dubbed 'linkage
disequilibrium' or 'LD'. The concept of LD is key
for the HapMap project as the fact that some SNPs stay associated
(whereas the clusters themselves might get separated from each other
over time by recombination events) explains the haplotype structure of
the genome (Daly et al. 2001). The term 'haplotype' simply
refers to a particular cluster of alleles (in this case SNPs) that a)
are on the same chromosomes and b) are commonly inherited as one. The
aim of the HapMap project was to characterize human SNPs, their
frequency in different populations and the correlations between them
(HapMap 2003). The first haplotype map was published in 2005,
reporting on data from 269 samples derived from four different
populations (HapMap 2005). Five years later, a follow up was
published, now reporting on data from 1184 individuals sampled from 11
different populations (HapMap 2010).



The realization that the structure of genetic variation in the genome
can be understood in terms of haplotypes was important for at least
two reasons. First it opened the door for a relatively easy and
efficient analysis of (single nucleotide) genetic variation in
populations: the clustering of SNPs meant that in principle only one
or a few of the SNPs in each cluster (so-called 'tag
SNPs') would have to be tested to verify the presence of the
cluster of variants as a whole. This made the analysis of genetic
variation at the level of whole genomes from a large number of
subjects feasible at a time when whole-genome sequencing was still too
expensive for such a task (HapMap 2003). The development of a
haplotype map was therefore a crucial step to enable what are now
called 'genome-wide association studies' (GWAS) (see
 Section 3.1.2).



Secondly, as the distribution of haplotypes varies between different
populations, the HapMap project had a strong focus on sampling DNA
from different populations. This is an important aspect of this type
of research as it brought, unwittingly perhaps, the issue of race and
the question of its biological basis right back into genomics. This
point will be revisited in
 Section 3.1.4.


#### 3.1.2 The HapMap, GWAS and the Idea of Personalized Medicine



A key point driving the HapMap project was the fact that SNPs can be
used to uncover connections between an individual's DNA sequence
and specific conditions or traits. At face value an SNP is simply a
distinguishing mark in the genome of a person. Such marks allow
researchers to screen groups of a population with different
phenotypes, for instance those with a condition (e.g., high blood
pressure) and those without. Looking at the frequency of specific SNPs
or haplotypes in either group the researchers can use statistical
analysis to get insight into the association between a particular SNP
or haplotype and a trait (Cardon & Bell 2001). As mentioned above,
this analysis can be focused on tag SNPs that are treated as proxies
for a whole cluster of SNPs (if the cluster has a high LD).



Once a haplotype has been associated with a particular condition,
other people can be screened for the presence of that haplotype and
therefore gain some understanding of the risk groups they belong to.
Although the test will not tell carriers of disease-linked SNPs
whether they will develop the condition or not, it can nevertheless
give them some information about their chances. Furthermore, even
though the tag SNP itself might not be the genetic variation that
causes or contributes to the variation in phenotype, it might be
linked to so-called 'causal SNPs'. Learning about SNPs
associated with a condition or trait therefore can give the researcher
clues as to which genes or regulatory DNA regions might be causally
involved in the development of that condition. Findings from
association studies can therefore in some cases contribute to the
analysis of the condition itself.



The HapMap initially only looked for common variants (SNPs include by
definition only common variants). This was in line with the so-called
common disease/common variant (CD/CV) hypothesis formulated by Lander
(1996); Cargill et al. (1999), and Chakravarti
 (1999).[18]
 This hypothesis postulates, roughly, that common conditions are
linked to genetic variations that are common in a population.



This link between common variants and common diseases also explains
why the HapMap project could be promoted from the very beginning as
the 'next big thing' after the sequence of the human
genome had been determined: it was with the haplotype map that
genomics should really start to have an impact on biomedical research
and ultimately our understanding of
 disease.[19]


#### 3.1.3 The HapMap and its Critics



But the HapMap project was not without its critics; indeed the
biologist David Botstein called it a "magnificent failure"
(cited in Hall
 2010).[20]
 Some commentators, for instance, were worried that the project is
nothing more than a make-work project filling a gap that the finished
HGP left behind, and therefore a waste of precious funds (Couzin
2002). But more often, criticism of the HapMap project was part of
wider debates about the way post-HGP research should be conducted. The
HapMap project can therefore provide a useful window on some of the
key tendencies and disputes that marked (or marred) the post-HGP
era.



One such indirect criticism of the HapMap derives from the apparent
failure of GWAS to lead researchers to clearer information about the
links between our genetic makeup and the different conditions to which
our bodies can succumb. In the eyes of these critics the CD/CV
hypothesis was the key problem, as the common variants simply do not
explain much of the heritability of common diseases. This observation
gave rise to the concept of 'missing heritability'
(Eichler et al. 2010).



The general focus on common variants in genomics was criticized by
other authors who claimed that the focus of geneticists should rather
be on rare variants (McClellan & King 2010). These rare variants,
they claim, are where the missing heritability will be found. The
problem with the rare variants is that they cannot be picked up in
GWAS that use SNP databases, as SNPs are by definition common
variants. Also, finding rare variants is a technical challenge as
researchers have to analyse the genomic data of a very large number of
individuals to do so reliably. This hunt for rare variants is a major
reason behind the current push for the sequencing of millions (rather
than a couple of hundreds or thousands) of genomes. As discussed
earlier, such large-scale approaches have become feasible in recent
years due to the reduced cost and increased speed of next-generation
DNA sequencing.



The current shift to whole-genome sequencing will also help to address
another critique of the GWAS/SNP/HapMap approach, namely its strict
focus on single base pair changes in the genome. Other changes in the
genome, such as variations in the numbers of copies of repeated
elements or rearrangements, deletions or insertions of larger chunks
of genomic DNA, might in many cases be what is at the core of a
disorder, necessitating (again) a shift in focus away from point
mutations and single genes to the genome as a whole (Lupski 1998,
2009).



As one of the first follow-ups to the original HGP, the HapMap project
was a topic that often came up in discussions of the legacy of the
HGP. Such discussions became especially prominent at the tenth
anniversary of the publication of the draft genome sequence. In
general, there was an overwhelming sense of disappointment at what had
come out of the HPG, at least in the medical context. Given the grand
promises that were made both around the start of the project in the
1980s and then again in the year 2000 at the presentation at the White
 House,[21]
 it is not surprising that people were unimpressed by what had been
delivered by 2010/2011. Interestingly, it was not only the usual
suspects, such as Lewontin (2011), but also key proponents of the HGP itself who were
critical and pointed out the minimal medical advances that had been
achieved in the first post-HGP decade (Collins 2010; Venter 2010).



However, one thing that all critics, including the above-mentioned,
agreed on was that even though its effect on medical practice had been
negligible, the HGP had transformed biological research (see for
instance Wade 2010; Varmus 2010; Hall 2010; Butler 2010; Green et al.
2011). One area in which genomic research had fundamentally changed
both concepts and practices was in the understanding of what a gene is
and how gene expression works and is regulated (Keller 2000; Moss
2003; Dupre 2005; Griffiths & Stotz 2006; Stotz et al.
2006; Check 2010). With great foresight, Evelyn Fox Keller pointed out
already in 2000 that the HGP was interesting not so much because of
the raw sequence it produced, but more because of the transformations
it brought about in our expectations when it comes to
'genes' and DNA (Keller 2000).


#### 3.1.4 The HapMap, Genomics and Race



As mentioned above, HapMap's use of samples from different
populations brought the concept of race into discussions of the
project. Studies that looked into the genetic variation between
population groups (of which the HapMap was a key representative) are
among several recent developments (Duster 2015) that reignited
discussion about a) the biological reality of race and b) the question
whether racial classifications should be used in biomedical research
at all. Several authors have picked up the relation between the HapMap
project and a renewed concern with race (see, e.g., Ossorio 2005;
Duster 2005; Hamilton 2008). The question that dominates these
discussions is whether racial classifications reflect a
'biological reality'.



Race has of course been an important topic in epidemiology and
clinical research for a long time (Witzig 1996; Stolley 1999), but it
has been widely perceived as a socially constructed category that has
no biological
 basis.[22]
 And many researchers imagined that as the HGP demonstrated how highly
similar any two human beings are to each other at the DNA level, any
idea of race as serious biological concept would be disposed of once
and for all (see, e.g., Gilbert 1992; Venter 2000). But the concept of
biological race was if anything rejuvenated rather than laid to rest
by the developments in genomics (Kaufman & Cooper 2001; Foster
& Sharp 2002; Hamilton 2008; Roberts 2011). This is exemplified by
the fact that more and more scientists have claimed in recent years
that there *is* a biological basis to our traditional notions
of race, basing their claims on elaborate statistical analyses of data
on genetic variation derived from a large number of human DNA samples.
These developments led for many to what Troy Duster has called a
'post-genomic surprise' (Duster 2015). 



An important point here is that linking genomics and race does not
mean that researchers search for, or even that there are, any
'genes for race', even if we consider the many different
ways in which this term can be interpreted (Dupre 2008). The discussion about the
possible genetic basis for race is now more subtle, as it is not
simply concerned with the presence or absence of specific genes or DNA
elements and hence some sort of biological essence of races, but
rather with the variation in the frequencies of alleles in the
population of interest (Gannett 2001, 2004). The question is therefore
not whether DNA element *X* is absent or present in one
population or the other, but rather which variant of *X* is
present at what frequency in a population (in the context of the
HapMap researchers will talk of SNP frequencies).



Data from population genetics shows that the global distribution of
allele frequencies in the human population is not discontinuous (Jorde
& Wooding 2004; Feldman & Lewontin 2008) but clinal, meaning
that human DNA sequences vary in a gradual manner over geographic
space (Livingstone 1962; Serre & Paabo 2004; Barbujani
& Colonna 2010). Moreover, both genetic and phenotypic traits
display what is called 'nonconcordant' clinal variation,
meaning that different traits do not necessarily co-vary with each
other; the pattern of how trait A varies across geographic space might
be very different from the pattern displayed by trait B (Livingstone
1962; Goodman 2000; Jorde & Wooding 2004).



But despite these widely accepted findings, it is in the discussion of
these distributions that the idea of a biological basis for our
traditional understanding of race classifications has re-emerged.
Based on the analysis of large sets of genetic variants in samples
derived from various locations around the globe, a number of
researchers have made the claim that human genetic variation displays
geographical clustering (see, e.g., Rosenberg et al. 2002; Edwards
2003; Burchard et al. 2003; Bamshad et al. 2003; Leroi 2005; Tang et
al. 2005). Importantly, these findings often also gave rise to, or
were interpreted to support, the claim that this geographical
distribution matches our traditional racial classifications.



Such findings also led a number of authors to claim that race still
has a valid place in biomedical research: since these classifications
are supposed to describe groups that are internally genetically
similar, but genetically different from other groups, they can serve
as useful proxies in estimating, for instance, the group
member's average risk of developing a particular condition (see,
e.g., Xie et al. 2001; Wood 2001; Risch et al. 2002; Rosenberg et al.
2002; Shiao et al. 2012). Some authors are more cautious and claim
that race should only serve as a loose and temporary proxy (Foster
& Sharp 2002; Jorde & Wooding 2004) that should be abandoned
as soon as we know the actual genetic variations that are linked to a
particular condition or trait (Jorde & Wooding 2004; Leroi 2005;
Dupre 2008). Such
critics may note that the most that these genetic studies show is that
there is a correlation between a person's genetic variants and
their geographical origin, if only because variants originate in a
specific place; and there is a loose relation between the socially
constructed concept of race and geographic origin. But given the
tenuous connection that this generates between perceived or
self-identified racial categories and genetic constitution, race is a
poor substitute for any actually salient genetic information that may
eventually be related to disease. 



But there is also a significant group of researchers who are not
convinced by these analyses and who don't think that there is
any biological basis to the race concept (see, e.g., Schwartz 2001;
Duster 2005, 2006; Krieger 2000; Ossorio 2005). All of these authors
criticise the above studies and the geographic clusters of genetic
variation they identify, mainly because of flaws in the way samples
are collected (see, e.g., Duster 2015) and how the data is ultimately
analysed. The latter criticism has mainly focused on the program
'Structure' that is used by a majority of the studies
mentioned above to churn out clusters of genetic variation (Bolnick
2008; Kalinowski, 2011; Fujimura et al. 2014). A telling criticism is
that while Structure can be made to report that there are five main
geographical clusters that show distinctive allele frequencies and
which roughly match traditional notions of race (African, Asian,
European, etc.), the programme can equally be set up to report any
arbitrarily selected number of genetically different groups, as the
user has to specify the number of clusters they are looking for before
the Structure program is applied to any actual dataset.



Two interesting aspects of these discussions are that they a) usually
only deal with one way of analyzing the biological reality of race
classifications (as genetic) and b) adhere to a sharp distinction
between race as biological reality or as social construct. Regarding
a) several philosophers of biology have come up with alternative ways
of thinking about a biological basis for race (for instance race as
clades (Andreasen 1998), inbred lines (Kitcher 1999), or ecotypes (Pigliucci &
Kaplan 2003)). This expansion of concepts brought with it the question
of classificatory monism vs. pluralism, i.e., the question whether
there is one privileged way of classifying race that somehow captures
the 'true nature' of races (natural kinds) or whether
there are several ways of doing so, depending on theoretical or
practical interests/context (Gannett 2010). As Gannet argues, however,
this focus on the monism/pluralism debate and on natural kinds comes
at a cost, as it can mean that questions of practical significance are
systematically ignored (2010). Regarding b), Gannett points out that
drawing a sharp distinction between race as social construct or
biological reality has not only been proven meaningless by recent work
in population genetics but can also mean that the much messier reality
of human history and diversity on this planet (and the complex
interactions between scientific and social concepts of race) is being
overlooked, leading to an impoverished analysis of the problems at
hand (Gannett 2010).


### 3.2 Metagenomics



Metagenomics (also referred to as 'environmental' or
'community' genomics) is a research field that aims to
analyse the collective genomes of microbial communities. These
communities are usually extracted from environmental samples, ranging
from soil to water or even air samples. A major advantage of
metagenomics is that it does not rely on techniques for culturing
microbes. This is important because only an estimated 1%-5% of
all microbes can be cultured at all (Amann et al. 1995), an issue that
has been referred to as the 'great plate count anomaly'
(Staley & Konopka
 1985).[23]



The term 'metagenomics' was first coined in 1998
(Handelsman et al. 1998). The prefix 'meta' in
'metagenomics' can be read in at least three different
ways (O'Malley 2013): 1) As referring to the fact that
metagenomics *transcends* culturing limitations. 2) As
emphasising the *aggregate-level* approach to biology that
characterises metagenomics (looking beyond single entities (cells or
genomes)). And 3) as referring to the goal of creating an overarching
understanding of the genomic diversity of the microbial realm.



The methodology of metagenomics can be described as a four step
process, consisting of: 1) the collection of environmental samples, 2)
the isolation of microbial DNA from these samples, 3a) the direct
analysis of the DNA or 3b) the creation of a genomic DNA library by
fragmentation and insertion of the sampled DNA into suitable vectors
(for instance plasmids that can be propagated in laboratory bacterial
strains). These genomic libraries can then be used to 4a) sequence or
4b) perform a functional screen of the sampled genomic DNA. As the
distinction between steps 4a) and 4b) already implies, metagenomics
can be divided into a sequence- and a function-based approach (Gabor
2007; Sleator et al. 2008). In the former the collected DNA is
sequenced so that potential genes present in the sample can be
identified and, if feasible, the genomes of all the microbes that were
present in the sample can be reconstituted.



The sequence-based approach is feasible due to the vastly reduced
costs of sequencing and the increased computing power available. The
goal of the approach is to get an idea of the diversity and
distribution of microbes present in the sample and to also get an
insight into their functioning (for instance by identifying
metabolism-related enzymes that can give clues about the metabolic
pathways active in the different microbes). This can give insights
into the workings of the microbial ecosystem present in the sampled
environment more generally.



In the functional approach the fragments of DNA that are stored in the
library are used in what is often called a 'functional
screen'. To perform such a screen the researchers introduce the
library plasmids into specific bacterial strains which then read and
express any protein-coding sequence that might be present on the
fragments, thereby producing the protein(s) the fragment codes
 for.[24]
 The key to a functional screen is to create conditions in which only
those bacteria that express a protein with the *function of
interest* can be singled out (for instance by making sure that
only those cells survive). Once the cells are singled out the library
plasmid they contain can be recovered and sequenced allowing the
researcher to identify the protein(s) encoded by that fragment.
Functional metagenomics is often used to identify novel microbial
proteins that can be used in biotechnological and pharmaceutical
contexts and it is not surprising that metagenomics was and still is
of great interest to the biotechnological sector (Streit & Schmitz
2004; Lorenz & Eck 2005; Culligan et al. 2014; Ekkers et al.
2012).



One of the first actual (sequence-based) metagenomics projects was
performed (yet again) by one of the pioneers of genomics, Craig
Venter. The goal of Venter and his team was to sample microbes from
the surface of the nutrient-poor Sargasso sea (Venter et al. 2004).
This particular environment was chosen for this pilot study because it
was expected to have a microbial community with relatively low
diversity. This assumption turned out to be wrong and the project
identified more than a million putative protein-coding sequences
derived from at least 1800 different genomic species extracted from
the sea water.



Another early metagenomics study consisted of the analysis of an
acidophilic biofilm with low microbial diversity from an acid mine
drain in California (Tyson et al. 2004). The analysed biofilm survives
in one of the most extreme environments including a very low pH (i.e.,
high acidity), relatively high temperature and high concentration of
metals. Importantly, this specific biofilm truly displays low
complexity as it is composed of only three bacterial and two archaeal
species. This simplicity greatly aided the analysis effort and allowed
the researchers an almost complete recovery of two of the genomes and
a partial recovery of the other three.



There have been many other metagenomics studies conducted since and
there is little point in listing them here, as the list is growing by
the month. One aspect of the ongoing research that is important to
point out, however, is that the projects are becoming increasingly
ambitious. The trend now is not just to have an integrated view on the
genomes but to combine metagenomics with other techniques such as
metabolomics (the assay of small molecules present in a system),
metatranscriptomics (the analysis of all RNA transcripts of a
community of microbes) and viromics (the analysis of all the viral
genomes present in the system of interest) (see Turnbaugh & Gordon
2008; Bikel et al. 2015). In a sense the field is moving towards a
highly integrated meta-Metagenomics approach (Dupre &
O'Malley (2007) talk of "metaorganismal
metagenomics"). This is also in line with the general trend
towards big-data and discovery-based approaches in the life sciences
(Ankeny & Leonelli 2015; Dolinski & Troyanskaya 2015; Leonelli
2014, 2016).



The rise of metagenomics is also linked to other changes in biological
sciences more generally, especially the rise of systems biology
starting around the year 2000 (which is itself closely linked to the
development of genomics since the 1990s). O'Malley and
Dupre (2005) point out that there is an important distinction
to be made when looking at fields like systems biology, because there
is not only a change in epistemology but also one in ontology. They
therefore distinguish between pragmatic and systems-theoretic
biologists. For the former, the idea of a 'system' is
merely an epistemic tool. For the latter, the system becomes the new
fundamental ontological unit. Doolittle and Zhaxybayeva (2010) claim
that the same can be seen in metagenomics where there is a drive to
see the community or the ecosystem as the new fundamental unit, and
not the single species (see also Dupre & O'Malley
2007).



Moving away from a focus on single organisms or monogenomic species
allows us to make better sense of many recent findings in microbiology
(in which metagenomics has played a key role). Central to all of this
are mobile DNA elements that can travel horizontally, meaning between
different members of a community (including between different kinds of
organisms). Obtaining such mobile DNA elements can have a crucial
effect on the survival and reproduction capacity of the recipient
cell. Mobile DNA can therefore be a key element in the evolutionary
processes as it becomes a 'communal resource' (McFall-Ngai
et al. 2013). Acquired antibiotic resistance is only one of many
benefits cells are known to obtain through acquired DNA elements.



It is then the composition of functional elements that the community
as a whole contains which is preserved over evolutionary time. And the
community could be seen as an assembly of biochemical activities and
not of distinct microbial lineages (see for instance Turnbaugh et al.
2009 and also Burke et al. 2011). The metagenome then becomes a
'genome of communities' and not a 'community of
genomes' (Doolittle & Zhaxybayeva 2010). All of this also
feeds into the more general, and currently very active, discussion
about the problem of individuality in biology (Clarke 2010; Bouchard
& Huneman 2013; Ereshefsky & Pedroso 2013; Guay & Pradeu
2015; SEP entry on
 the biological notion of individual).



Apart from these issues in biological ontology, there are also
epistemological issues raised by metagenomics, namely the discrepancy
between our ability to sequence DNA and to interpret it. These
discussions about the challenges of DNA sequence interpretation are
not just a problem for (meta)genomics and other -omics approaches, but
also for biomedicine more generally and its push towards a truly
personalised medicine. A key issue for this push is the discrepancy
between the (ever-decreasing) costs of obtaining a personal genome
sequence (Bennett et al. 2005; Mardis 2006; Check 2014a,b) and the
high costs of making sure the data can be appropriately interpreted
(Mardis 2006; Sboner et al. 2011; Phillips et al. 2015). This problem
is related to the so-called 'bioinformatic bottleneck',
the handling and the interpretation of the large amounts of sequence
data that provides the main obstacle to progress (Green et al. 2011;
Desai et al. 2012; Scholz et al. 2012; Marx 2013). In the days of
next-generation sequencing the sequencing step itself is no longer the
rate-limiting step.


## 4. Outlook



Genomics is now an integral part of all of the life sciences. Not that
every life scientist is now a genomicist--there are still
researchers who focus on the biochemistry, development, or the
molecular networks of human cells and other organisms. But the DNA
sequences of the human genome and the numerous model organisms that
came out of the HGP enter every laboratory, if not on a daily basis
than at least at some stage of every research project. The same
applies to the maps of genetic variation that were discussed
in Section 3 and to the (somewhat controversial)
data on functional DNA elements that the ENCODE project generated (see
the supplementary document
 The ENCODE Project and the ENCODE Controversy).



And it is not just the quantity of data and the many new
"-omes" that researchers now work with that have
transformed the science. As we have pointed out in several places,
insights into the genome and its functioning have transformed
researchers' understanding of the entities and processes they
are working with in the course of the last few decades. Part of this
was also a transformation in our understanding of what it means to do
'good' science. What the HPG and its various offshoots
have achieved, therefore, is to change the life sciences at the
epistemological, the ontological and also the methodological
level.



As so often, an interesting and even pressing question is where all of
this is going. Predicting the future might not be possible, but there
are trends that can be identified and which can be expected to follow
a similar trajectory in the near future. One such trend is the drive
for big data. 'Big' here refers not only to the quantity
but also to the different types of data collected. A derivative of
this big-data drive is the goal to integrate all of the diverse data
and mould it into models that can further our understanding of
biological systems and the prediction of their behaviour. The
relatively young discipline of systems biology, which could not be
discussed in detail in this entry, will certainly play a key role in
this endeavour.