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American Journal of Epidemiology
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Vol. 182, No. 1
DOI: 10.1093/aje/kwv102
Advance Access publication:
May 29, 2015
Practice of Epidemiology
Use of the Microbiome in the Practice of Epidemiology: A Primer on -Omic
Technologies
Betsy Foxman* and Emily T. Martin
* Correspondence to Dr. Betsy Foxman, Department of Epidemiology, School of Public Health, University of Michigan, M5108 SPH II,
1415 Washington Heights, Ann Arbor, MI 48109-2029 (e-mail: [email protected]).
Initially submitted January 7, 2015; accepted for publication April 10, 2015.
The term microbiome refers to the collective genome of the microbes living in and on our bodies, but it has colloquially come to mean the bacteria, viruses, archaea, and fungi that make up the microbiota ( previously known as
microflora). We can identify the microbes present in the human body (membership) and their relative abundance
using genomics, characterize their genetic potential (or gene pool) using metagenomics, and describe their ongoing functions using transcriptomics, proteomics, and metabolomics. Epidemiologists can make a major contribution
to this emerging field by performing well-designed, well-conducted, and appropriately powered studies and by including measures of microbiota in current and future cohort studies to characterize natural variation in microbiota
composition and function, identify important confounders and effect modifiers, and generate and test hypotheses
about the role of microbiota in health and disease. In this review, we provide an overview of the rapidly growing
literature on the microbiome, describe which aspects of the microbiome can be measured and how, and discuss
the challenges of including the microbiome as either an exposure or an outcome in epidemiologic studies.
bioinformatics; diversity; genomics; microbiome; microbiota
Abbreviation: rRNA, ribosomal RNA.
hypothesized to cause periodontal disease (2), cause inflammatory bowel disease (3), and potentially increase the risk of
cancer (4).
Unlike other reviews in this series on -omic technologies,
the microbiome is not a technique but a reconceptualization
of humans as superorganisms consisting of human cells and
microorganisms. We argue that microbiota can be a marker of
exposure and a prognostic factor as well as a factor in disease
etiology. However, this will require the incorporation of laboratory analyses that generate data characterizing the presence and
function of microbes in epidemiologic studies, assessments of
the reliability and validity of these analyses and the putative biomarkers, and knowledge about how to best use these data to
address questions of clinical and public health importance.
Microbiota are dynamic, and the variation within an individual can be high. As yet, we do not know what magnitude
of difference in microbial membership and relative abundance ( jointly known as community structure) or function
corresponds to a clinically meaningful difference. This lack
The term microbiome refers to the collective genome of the
microbes living in and on our bodies, but it has colloquially
come to mean the bacteria, viruses, archaea, and fungi that
make up the microbiota ( previously known as microflora).
“-Omic” technologies have transformed our perception of the
microbiota by characterizing the microbes present and their
relevant abundance, as well as their ongoing functions. We can
identify the microbes present in the body (membership) and
their relative abundance using genomics, characterize their genetic potential (or gene pool) using metagenomics, and describe
their ongoing functions using transcriptomics, proteomics, and
metabolomics.
The field of microbiomics is very new, and its application
in epidemiology has barely begun, but excitement about its
potential is high. Every day, new articles appear in the scientific literature—and often in the newspaper—touting the role
of the microbiome in human health. Microbiota have been associated with obesity, the metabolic syndrome, and even autism (1). Disruptions in the microbiota, termed dysbioses, are
1
Am J Epidemiol. 2015;182(1):1–8
2 Foxman and Martin
of knowledge creates challenges for good study design and
sample size estimation. Further, because our understanding of
the factors that affect the microbiome is limited, so too is our
understanding of what factors might confound or modify observed associations between the microbiome and health and
disease. This makes it difficult to differentiate between risk
markers and causal factors and between microbiomic changes
that result from human disease and those that cause human
disease. Well-conducted, population-based longitudinal studies
are essential to filling these knowledge gaps. In this review, we
provide an overview of the rapidly growing literature on the
microbiome, describe which aspects of the microbiome can
be measured and how, and discuss the challenges of including
the microbiome as either an exposure or an outcome in epidemiologic studies.
WHY ALL THE EXCITEMENT ABOUT THE MICROBIOME?
Through the miracle of genetic sequencing, we now have the
tools with which to identify the myriad bacteria, viruses, archaea, and fungi that live in and on our bodies—the microbiota.
The ability to conduct a census of human microbiota is unprecedented; until the development of -omics technologies, we were
able to identify only those microbes that could be grown in the
laboratory (as a point of reference, an estimated 95%–99% of all
bacteria cannot be grown in pure culture in the laboratory (5)).
The results of these -omics microbiota censuses have given us a
different perspective on ourselves. All surfaces of our bodies
with portals to the outside are either covered in microbes or
are subject to a variety of mechanisms designed to limit microbial growth. These surfaces include parts of our anatomy previously believed to be sterile, such as the blood (6), uterus (7), and
lung (8). Further, it is now clear that we are outnumbered: Each
of us carries 10 times more bacterial cells than human cells, and
100 times more viral particles. Good estimates are not available
for eukaryotes, but we all have mites living on the sides of our
noses and fungi in our hair. We are infested with microbes.
However, this is generally a good thing. We depend upon our
microbial communities to help us digest food, resist invasion by
pathogens, and synthesize essential vitamins. Microbes are also
essential for immune system development and response (9).
This new self-perspective has also led to new explanations
for diseases previously resistant to treatment. There are several disease conditions (e.g., bacterial vaginosis, periodontitis, and inflammatory bowel disease) for which the disease
course suggests an infectious etiology but no necessary and
sufficient microbe has been identified. An alternative hypothesis is that rather than a single causal factor, there is a disruption in the system that causes and maintains the disease. A
disrupted microbiotic system is termed dysbiosis. Dysbiosis
may manifest as a microbial community that is either much
more diverse (e.g., periodontitis) or much less diverse (e.g.,
antibiotic-associated diarrhea) than a healthy microbial community in the same biological niche, resulting in increased
(or chronic) inflammation and reduced pathogen resistance.
One area of active study is the identification of factors sufficient to perturb a healthy system and cause disease or sufficient
to perturb a dysbiotic system and regenerate health.
Use of broad-spectrum antibiotics, even for short periods
of time (3–5 days), is sufficient to perturb the gut microbiota
in ways that are identifiable as much as 6 months later (10,
11). Repeated exposure to antibiotics can decrease the ability
of microbiota to resist invasion by other microbes, increasing
risk of infection. For example, broad-spectrum antibiotic use
can result in antibiotic-associated diarrhea, most often caused
by an overgrowth of Clostridium difficile (12). The primary
treatment for C. difficile infection is (ironically) antibiotics,
but C. difficile infection that is unresponsive to antibiotic
therapy can be successfully treated by fecal transplantation.
A fecal transplant can replace the dysbiotic gut microbiota
with healthy ones (13–15).
The vast majority of published studies on the microbiome
have been limited to studies of bacteria. However, the field
of viromics is rapidly growing (16). Similarly to the bacterial
microbiome, the virome varies by body site (Figure 1), is
modified by host factors (17, 18), and may be correlated with
host disease states (19–21). Notably, we now know that the
vast majority of viruses in healthy individuals are bacteriophages, viruses that infect bacterial cells and may play a role
in the regulation of microbial communities in humans (22–
24). This and other insights derived from sequencing technologies have led to the notion that viruses, like bacteria,
may be part of the normal microbiota of healthy individuals
(25, 26). Even less developed are studies of the mycobiome
(the fungal microbiome) and archaea, but all existing evidence
suggests that they are also part of the normal microbiota of
healthy individuals (27, 28).
There are many excellent reviews of the current state of
knowledge on the microbiota of the human gut (29), skin (30),
lung (8), and vagina (31). These reviews are primarily descriptive, and many are the result of studies funded by the
National Institutes of Health Human Microbiome Project (32)
and the European Microbiome Project (33). There also have
been studies of previously isolated human populations (34)
and a few studies of twins (35, 36), particularly with respect
to obesity. Nonetheless, our understanding of the breadth and
variation of human microbiota and how it relates to risk of
human health and disease is very limited. The most interesting and compelling findings have been results from mouse
models (e.g., see Cox et al. (37)) and human fecal transplant
studies (e.g., see Kassam et al. (14), Van Nood et al. (15), and
Youngster et al. (38)). However, mice are not humans, and human fecal transplant studies have been limited to treatment of
antibiotic-associated diarrhea (although there are case reports
of fecal transplants for other conditions) (39).
Although we have much descriptive information about the
microbes included in the microbiome, what they do and how
they do it is less clear. We propose that the major functions of
microbiota in the human body can be divided into 3 categories (Figure 2). First, microbiota can act as a shield. The types
of species present, their relative abundance, and their ongoing
functions can influence the body’s ability to resist invasion
by other microbes ( pathogen resistance) or neutralize the effects of exposure to a drug or toxin. These influences can be
either direct or indirect via interactions with the host. Second,
the microbiota can act as a filter, keeping out some things but
allowing others to enter. Lastly, microbiota can enhance the
effects of exposures. Microbiota can facilitate invasion by
pathogens and, via microbial processes, amplify the effects
of drugs or other exposures.
Am J Epidemiol. 2015;182(1):1–8
The Microbiome and Epidemiology
Oral Cavity
3,000–11,500 bacterial species
1–7 viral genera
1–4 fungal phylotypes
3
Nares
2,000 bacterial species
2–5 viral genera
Skin
2,000–3,000 bacterial species
3–5 viral genera
1–6 fungal genera
Vagina
2,000 bacterial species
1–5 viral genera
4–15 fungal OTUs
Stool
30,000–40,000 bacterial species
0–2 viral genera
1–3 fungal genera
Figure 1. Estimated diversity of bacterial, viral, and fungal microorganisms in the human body as described in the literature (70–76), by body site.
OTU, operational taxonomic unit.
The generation time of microbes is rapid; for Escherichia
coli in the human gut, it is estimated to average 40 hours (40),
but it can be as short as 20 minutes in the laboratory. This
means that microbiota can rapidly adapt to short-term changes
in habitat that occur when we bathe, eat different foods, or
change from tight-fitting clothing to looser clothing. A shortterm change in nutrient sources changes the relative abundance of existing microbes but probably not which microbes
are present (membership) (41). It might also change their ongoing functions. These changes in microbiota abundance and
function may be short-term following a transient exposure,
such as eating a hamburger (42), or may be identifiable weeks
or months later following exposure to broad-spectrum antibiotics (43). Since microbiota respond quickly to the environment, changes in microbial function or the microbes present
or their relative abundance can be a sensitive indicator of
exposure or disease pathogenesis. Additionally, because microbiota act as a barrier, filter, or transformer, respond quickly
to perturbation, and perform essential functions, they can
Exposures
Microbiota
Host Responses
Figure 2. Conceptual model of the potential roles of microbiota in the
causal pathway between exposures and host response. The widths of
the lines correspond to the magnitudes of the exposures.
Am J Epidemiol. 2015;182(1):1–8
serve as a diagnostic or prognostic indicator or a component
of a disease pathway. Therefore, our understanding of the
contribution of the microbiota to an exposure or outcome
of interest determines which attribute of the microbiota is relevant for study.
MICROBIOTIC GENETICS ARE MORE DYNAMIC THAN
HUMAN GENETICS
The term “microbiota” refers to which organisms are present,
and “microbiome” refers to the genomes of those organisms.
That collective genome, and even the genome of individual
microbes, is not directly analogous to the human genome
(Table 1). There is only 1 human genome; although gene deletions may occur, these genetic changes usually have health
consequences (44). By contrast, the microbiome has a dynamic gene pool. Each of the microorganisms present has
its own genes, so as new species join and others leave, the
gene pool changes. Mobile genetic elements (e.g., plasmids)
move between microbes of the same species and microbes of
different species, making it possible for very different species
to have identical genes and functions. Further, within a given
microbial species, the gene pool might be quite diverse: The
percentage of genes shared among all E. coli is estimated to
be less than 30% (45). Therefore, even microbiomes with similar microbial community structures can have different functions, and microbiomes with different community structures
can have similar functions. For now, at least, the function of
most sequenced microbial genes is unknown.
Human reproduction results in gene exchange and the potential for mutation, but once a person is born, his or her functional capacity becomes essentially fixed. By contrast, although
mutation occurs in bacteria during reproduction, bacteria exchange genes independently of reproduction. Their functional
4 Foxman and Martin
Table 1. Characteristics of the Human Microbiome As Compared
With the Human Genome
Human Microbiome
Table 2. Key Differences Between Taxonomic (Microbes Present)
and Functional (Microbial Functions) Analyses of the Human
Microbiome
Human Genome
Microbes Present
Microbial Functions
Many genomes
A single genome
Recombination occurs
independently of reproduction
Recombination occurs only
during reproduction
High variation among individuals
Less variation among
individuals
Functional capacity is dynamic
Basic functions are always
present
Colonization dynamics over time
and space
Results of microbial
interactions
Changes in response to the
environment
Stable in response to the
environment
Key microbes associated with
health
Key functions associated
with health
Highly mutable
Mutations are rare
Selection, evolution, succession
Diagnosis, prognosis
Generation time approximately
30 minutes
Generation time
approximately 30 years
capacity is dynamic. Ignoring their ability to acquire new functions outside of reproduction, microbes’ short generation time
allows them to rapidly adapt (on the human time scale) to new
environments, even relying solely on mutation. The implication is that if we lose parts of our microbiota, we may or may
not lose some functions essential for health. Alternatively, we
might be able to gain desirable functions by adding to our microbial communities or their gene pools.
Although many (perhaps most) of us have descendants of
microbes we obtained from our mothers during birth (46),
from breastfeeding (47), and from daily interactions (48), our
microbiome is in a constant state of flux (49). Our microbes are
constantly reproducing, mutating, and fighting infection by viruses. They also adapt to changes in their environment and nutrient sources caused by antibiotic therapy, diet, and invasion
by pathogens and our body’s host response to invasion.
Despite the dynamic nature of microbiota, the environment
of the human host—temperature, nutrients, and the immune
response—limits which microbes can live in the various ecological niches found in and on us. Further, given that microbiota co-evolved with their human hosts, human genetic variation
may select for certain microbial communities. A study of the
fecal microbiota of 416 United Kingdom twin pairs suggests
that the presence of some taxa is influenced by host genetics
(50). Moreover, it seems that each of us has his or her own microbiome, and this—like the human genome—can be uniquely
identifying (48, 51).
MICROBIAL COMMUNITY STRUCTURE OR MICROBIAL
FUNCTIONS?
Studies colloquially referred to as studies of the “microbiome” can be directed at detecting which microbes are present at various levels of resolution (from phyla to species)
and their relative abundance, the genes present (metagenomics), ongoing gene expression (transcriptomics, proteomics),
and ongoing processes (metabolomics). These levels of investigation accordingly come with their own terminology
and have their own advantages and disadvantages for addressing specific research questions.
The technologies can be dichotomized into those that identify which microbes are present (taxonomic screens, most
commonly using the sequence of ribosomal RNA for bacteria,
archaea, and eukaryotes) and those that identify what the microbes are doing (shotgun sequencing of the entire genome
(metagenomics), transcriptomics, proteomics, and metabolomics) (Table 2). Using a taxonomic screen, every person has
a unique microbiome (especially when resolved to the species/
strain level), but when they are compared among people, microbiota from specific body sites are similar between people
(52). Depending on the body site, the similarities are modified
by sociodemographic factors, health behaviors, and medical
history.
The human body has several distinct biological niches that
vary widely in terms of the nutrients available, surveillance
by the human host, and exposure to the environment. Although
every human has a unique microbiome specific to each site, the
characteristics of a specific site require that the microbial communities present perform certain functions. Note that microbial functions are not uniquely defined by species or even
within species. Therefore, classification by function should be
less variable than classification by species (53). Although this
has not been definitely shown—and one study using inferred
bacterial function suggested that this may not be the case (54)—
measuring functions can give us insight into ongoing microbial interactions, functions associated with health or disease,
and potential diagnostics or prognostics.
HOW DO YOU MEASURE THE MICROBIOME?
Taxonomic screens, which are relatively inexpensive, permit characterization of colonization dynamics over time and
space, the identification of microbial communities associated
with health or disease, and the collection of information for
addressing questions regarding selection, evolution, and succession. At the time of this writing, the most common method
used for characterizing the bacteria present is a taxonomic
screen based on variable portions of the genetic sequence that
codes for the ribosome (16S ribosomal RNA (rRNA)).
All cells have ribosomes, and because ribosomes perform
the essential function of translating messenger RNA into protein, the genetic sequence is highly conserved. This makes
it possible to use primer sets that cross most of the bacterial species present (universal primers) and phylogenetics to classify
bacteria into taxonomic groups (55). The 16S rRNA sequence
data are compared with sequence databases to determine the
genus and species of bacteria that are present. Relative quantities of different 16S rRNA sequences can also be obtained to
Am J Epidemiol. 2015;182(1):1–8
The Microbiome and Epidemiology
establish the relative abundance of each species. These data
provide a snapshot of who is present from the bacterial world
and in what quantities, but they are unable to characterize the
relationships between community members. However, the ability to resolve the obtained genetic sequence to the species level
varies with the region chosen; depending on the body site and
bacterial composition, different regions are preferred. Further,
while 16S rRNA analysis can characterize the members of a
bacterial community, its use of a single bacterial gene precludes
the detection of potential members from other kingdoms, including viruses, fungi, and archaea, and it can also limit resolution to the species level for bacteria. For fungi, ribosomal
analysis is also used, but 18S rRNA is sequenced. Other methods are required to capture the viruses and archaea present (56).
Broader strategies for sequencing the genetic material of
microbiota allow investigators to describe all organisms present in a community, encompassing bacteria, viruses (57, 58),
fungi (59), and archaea (28). This set of all genomes from a
diverse set of microbial sources (i.e., the “metagenome”) can
be viewed as the gene pool of the functioning of the microbial
community at that particular body site (i.e., the “functional potential”). In addition, like taxonomic screens, metagenomics
can provide a sense of the relative abundance of different organisms. The usual strategy for whole-microbiome sequencing
is to randomly sequence genomic fragments and then compile
them to represent whole genomes (shotgun sequencing). Because these methods do not target a single region, they require
additional care to ensure that both RNA (which must be
reverse-transcribed) and DNA present in small-sized genomes
in small quantities are appropriately captured from the sample
(60) and that human DNA is not processed and mistaken for
organism data. Microbial community structure can also be estimated with metagenomic data, using the ribosomal genes.
Metagenomic data sets are very large and the analysis is challenging, but appropriate software is increasingly becoming
available (61).
Measuring function is considerably more expensive than
conducting taxonomic screens, and each of the different methods for assessing function has strengths and limitations. Measuring the metabolic products present (metabolomics) is the
only way to directly assess the ongoing interactions among all
of the microbes present and with the human host, but there is
considerable technical variation. Transcriptomic studies require targeting transcripts from specific groups (e.g., bacteria)
and do not directly correspond to functions. Human messenger
RNA is much larger and more stable, and thus (since transcripts are sequenced for detection) can overwhelm the microbial transcriptome. Metagenomics enables characterization of
gene potential but not ongoing functions.
Analytical software packages are available for analyzing
the results of taxonomic screens (e.g., see Schloss et al. (62)
and Hamady et al. (63)), and there is a software package that
allows one to infer bacterial functions from taxonomy (64).
Software with which to analyze the metagenome is also increasingly available (65). However, beyond the ability to process huge amounts of data from microbiomic studies, the real
challenge lies in the best way to achieve the data reduction
needed to use these data as an epidemiologic parameter. Epidemiologists can make an important contribution to microbiomic research by working to develop and evaluate methods
Am J Epidemiol. 2015;182(1):1–8
5
of producing meaningful parameters from complex microbiomic data.
WHAT ARE THE CHALLENGES FOR EPIDEMIOLOGIC
STUDIES?
Recent advances in molecular and computing technology
make it possible to incorporate assessments of the microbiome into clinical and epidemiologic studies. However, the
learning curve for “-omics” technologies is steep, and the
technology for measurement is continually developing. It is
essential to have outstanding collaborators who understand
the necessity of ensuring the validity and reliability of the
methods and the implementation of ongoing quality control
and quality assurance procedures. There are considerable technical challenges in this regard (Appendix 1). Collection, storage, and preparation of specimens for testing can affect the
results, sometimes to a large degree (66). The polymerase chain
reaction method is not without error; sequencing technologies
vary in sequence length, error rates, and costs. The high sensitivity
of polymerase chain reaction makes contamination across wells
or even across runs a continuing worry in a high-throughput
setup. For example, a recent study (67) identified DNA in extraction kits and other laboratory reagents. These sequences
could be identified in 16S rRNA taxonomic screens and shotgun sequencing, leading to erroneous study results. These
findings, among others, have prompted several recommendations for minimizing bias due to differential contamination,
including the use of technical controls throughout all processes and the importance of processing samples in a blinded
fashion and in random order (67).
The amounts of data involved are large, with correspondingly large computation requirements for initial data cleaning
and classification. There is no consensus in the literature on
the best methods for binning, for comparing community structures, or for data reduction (Appendix 2). Further, because the
measures are highly discriminatory, it is possible to obtain statistically significant results from relatively small samples for
which correspondence to meaningful clinical differences may
be questionable.
THE WAY FORWARD
Some of the most promising applications of microbiomic
research to human health have arisen from studies with substantial rigor in both their technical methods and their epidemiologic design. We highlight 2 exemplar studies here. Antony
et al. (68) used metagenomics to compare placental microbial
community composition and putative functions between preterm pregnancies and term pregnancies and explored the potentially modifying effects of excess gestational weight gain and
obesity. There was significant variation in the placental microbial communities of preterm births by excess gestational weight
gain but not by obesity, suggesting that the association between
gestational weight gain and preterm birth may be mediated specifically through changes in the placental microbiota. Further,
excess gestational weight gain was associated with decreased
microbial folate biosynthesis pathways and decreased butanoate metabolism. Because of this study’s relatively large sample size (n = 320), the authors were able to make a robust
6 Foxman and Martin
epidemiologic comparison between women with obesity and
women with gestational weight gain with respect to an outcome
(preterm birth) that clearly followed the exposure.
Longitudinal follow-up of study participants is particularly
important when evaluating whether microbial diversity is
a risk factor for future disease. A notable example can be
found in a study of the association between intestinal diversity and increased patient mortality following allogenic hematopoietic stem-cell transplantation (69). Patients who died
following transplantation had significantly lower intestinal
diversity, suggesting that the maintenance of intestinal microbiota at the time of transplantation may have an impact on
posttransplant survival. While the study was small (n = 80),
the longitudinal design allowed for a clear epidemiologic
analysis of the association of microbiota changes with the
subsequent outcome (mortality).
These carefully designed studies highlight the power of
microbiomic research to determine the mechanistic processes
by which exposures contribute to disease. By evaluating host
microbiota within the context of carefully selected study
groups and temporally appropriate outcomes, such studies
serve as models for optimal use of the microbiome in future
epidemiologic research.
CONCLUSIONS
Our newfound ability to characterize individual microbiota
in a relatively quick and inexpensive manner is truly exciting,
and the opportunities for productive interdisciplinary collaboration are great. Epidemiologists can make a major contribution to this emerging field by performing well-designed,
well-conducted, and appropriately powered studies and by including measures of microbiota in current and future cohort
studies to characterize natural variation in microbiota composition and function, identify important confounders and effect
modifiers, and generate and test hypotheses about the role of
microbiota in health and disease. However, we have a long
way to go before the promise of understanding of the microbiome translates into new diagnostic, therapeutic, and preventive
measures (70). To achieve that promise requires well-conducted
prospective studies that characterize the distribution of microbial communities’ structure and function over time and space
and identify the determinants of those distributions. We also
must temper the enthusiasm that arises from each new characterization by critically appraising each new microbiomic study
and placing it within a greater public health context. Since the
time of Semmelweiss, millions of lives have been saved—and
continue to be saved (Ebola is a case in point) through good
public health practices: hand hygiene, sanitation, vaccination,
and antibiotics. It is incumbent upon us, as public health workers, to have a firm understanding of the role of microbiota before
suggesting any policy changes that might undo sound public
health practice.
ACKNOWLEDGMENTS
Author affiliations: Department of Epidemiology and Center for Molecular and Clinical Epidemiology of Infectious
Diseases, School of Public Health, University of Michigan,
Ann Arbor, Michigan (Betsy Foxman, Emily T. Martin).
This work was supported by the Center for Molecular and
Clinical Epidemiology of Infectious Diseases at the University of Michigan (B.F.) and by the National Institute of Allergy and Infectious Diseases, National Institutes of Health,
under award K01AI099006 (E.T.M.).
We thank Anna Cronenwett for manuscript preparation.
Conflict of interest: none declared.
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APPENDIX 1
Technical Challenges in Microbiomic Studies
•
•
•
•
•
•
Data management
Variation due to differences in sequencing technologies
Quality control and assurance
Computational requirements
Reference databases
Analytical strategies are under development
APPENDIX 2
Epidemiologic Challenges in Microbiomic Studies
• High level of variation within and between individuals
(dynamic)
• Determining which microbiomic differences are clinically
significant
• Differentiating between microbiomic differences that result
from rather than cause a disease process
Am J Epidemiol. 2015;182(1):1–8