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Nutrigenomics and nutrigenetics

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Nutrigenomics and Nutrigenetics 
Technological worksheet September 2008 
Catherine Golstein and Michel Caboche, Technologies of the Future, INRA 
 
1. Introduction, background 
How to eat according to our genes? 
 
According to the World Health Organisation (WHO), there were approximately 1.6 
billion overweight adults (Body Mass Index (BMI) !25 kg.m
-2
), with at least 400 
million obese (BMI !30 kg.m
-2
) in 2005; WHO further projects that by 2015, 
approximately 2.3 billion adults will be overweight and more than 700 million will be 
obese. In contrast, about 800 million did not have enough to eat, indicating that 
overweight people now outnumber the under-nourished. Today, obesity is a problem 
mainly in rich countries, but the WHO estimates that by 2010 the developing world 
will have more than caught up, facing the paradoxical double burden of under-
nourishment and obesity. 
 
 
 
 
Fig. 1 Current analyses of the escalating obesity rates in different countries. From W. James, J. 
Int. Med., 2008, after the International Obesity TaskForce (IOTF) statistics (James, 2008). 
 
Overweight and obesity lead to serious health consequences, increasing the risk 
for multifactorial chronic diseases such as cardiovascular diseases (world number one 
killer diseases), hypertension, osteoarthritis, some cancers (eg colon cancer), or the 
fast-growing epidemics of type 2 diabetes (171 million cases today, expected to 
double by 2030). 
 
These epidemics may reflect the fact that the human population genetic makeup is not 
adapted to modern diets and sedentary lifestyle. Rather, human genes have adapted 
 
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through millions of years of food scarcity to protect against starvation and defend 
stored body fat. The relatively recent adoption of agriculture and sedentary life 10,000 
years ago and its evolution up to our modern obesogenic environment coupled with 
fast-improving medical care reduced the possibility for natural selection and 
responsive genetic adaptation. 
 
It is also recognised that human beings vary for food requirements and response to 
diet. For example, the ethnic minority populations of African Americans and Mexican 
Americans are at greater risk for cardiovascular diseases compared to Americans of 
Northern European origin under the same diet and environment, indicating an 
underlying genetic variation among individuals for food impact on health. 
 
A few monogenic food-associated genetic disorders have been elucidated and some 
have found simple cures, as exemplified by phenylketonurea, systematically screened 
at birth and treated by a phenylalanine-free diet. However, identifying the 
combination of genetic loci and diet components involved in multifactorial 
polygenic chronic diseases is proving more challenging. 
 
Consequently, the stakes of nutrition science are shifting from covering nutritional 
requirements towards enabling evidence-based personalised nutrition or diet 
intervention in order to reduce the risk of food-associated diseases, or to improve 
overall well-being and performance. Adapting diet to genetic makeup requires new 
research to unravel the complex interactions between genetics, diet and health. 
Towards this goal, taking advantage of the advent of human genomic resources and 
high-throughput ‘omics technologies, two new complementary fields of nutrition 
science –nutrigenomics and nutrigenetics– are emerging under high expectations. 
 
 
2. What are nutrigenomics and nutrigenetics? 
Two meanings of nutrigenomics coexist in the scientific literature. The first one refers 
to the application of high-throughput ‘omics technologies to the field of nutrition 
(Müller et al., 2003). The second one is more specific and used when nutrigenomics is 
referred along with nutrigenetics. Nutrigenomics then addresses how nutrients affect 
the expression of our genes, and nutrigenetics addresses how our genetic makeup 
affects our response to nutrients. These new meanings associated to genomics and 
genetics are unexpected for outsider geneticists, but well adopted by insiders working 
in this area. The same terminology is used for pharmacogenomics/genetics, with 
medical drugs replacing nutrients. 
2.1 Nutrigenomics 
More than simple building blocks for proteins, fats and sugars in our body, nutrients 
are increasingly recognised as fine modulators of our health status through interaction 
with our genes. To gain a better understanding of nutrition at the molecular level, 
nutrigenomics studies the effects of nutrients or diets on our transcriptome, proteome 
and metabolome. 
 
 
 3 
Technologies and strategies in nutrigenomics 
1) Global profiling for molecular mechanisms and biomarkers discovery 
High-throughput profiling technologies have been applied to reveal global changes 
induced by specific nutrients and diets on gene expression, protein and metabolite 
production. This hypothesis-free approach provides unbiased clues as to their 
molecular targets (genes, proteins, metabolites), signalling pathways, response genes 
and resulting metabolites for a deeper understanding of their overall impact on health. 
 
 
 
Fig. 2 The complexity of the interplay of diet and foods with the genome orchestrates human 
metabolism. The particular composition of a diet, in addition to individual foods or food constituents, 
can affect every step from epigenetic modifications of genes to changes in transcription, translation, 
protein degradation and the metabolome, by direct and indirect routes (e.g., diet-dependent hormone 
secretion, allosteric regulation). Profiling technologies, such as transcriptomics, proteomics and 
metabolomics, should enable the elucidation of the interactions of dietary constituents with the genome 
at every level of complexity and provide insights into the connectivity among the different layers of the 
biological processes. After Rist et al., Trends Biotech., 2006 (Rist et al., 2006). 
 
An emerging transcriptomics approach to assess gene expression is described in the 
worksheet on high-throughput sequencing technologies (“RNA-seq” type analysis for 
transcript discovery and profiling by deep sequencing). Other technologies include the 
broadly used microarray-based transcriptome profiling through hybridisation. The 
latest developments in proteomics and metabolomics are reported in the worksheet on 
mass spectroscopy (MS) and nuclear magnetic resonance (NMR) spectroscopy-based 
technologies. Finally, biostatistics and bioinformatics are becoming essential to 
extract relevant information from ‘omics profiles and to integrate complex data, 
enabling to consider nutrient effects in a global and holistic way at the system’s level. 
These analytical tools are described in the worksheet on systems biology. 
 
These profiling experiments are typically performed on cell lines or on animal 
models, which guarantee access to appropriate quantities of experimental material, 
 
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permit invasive sampling for targeted analysis in specific organs, and allow the 
control of genetic and overall environmental factors including food intake. Such a 
controlled, multi-organ analysis of mouse models humanised with human baby flora 
enabled to show complex metabolic effects of probiotic intake, including the 
modification of lipid metabolism through modulation of the gut microbiome (Martin 
et al., 2008). 
 
Molecular profiling analysis on human cohorts are increasing, however, using 
relatively accessible material such as blood or urine: transcriptomic analysis of the 
pathway responsive to increases in plasma free fatty acids following fasting was 
successfully performed using RNA extractsfrom human peripheral blood 
mononuclear cells (Bouwens et al., 2007); large scale metabolomic profiling has 
proven urine to be not only a practical source of metabolites for high-throughput 
analysis, but also a rich source of information combining influences of environment, 
gut microbes and genetics (Holmes et al., 2008). 
 
In addition to providing insight into the molecular mechanisms of response to food 
components, ‘omics profiling can be used to identify biomarkers indicative of the 
development of chronic disease. Thanks to the high throughput of metabolic profiling 
of urine and the sophistication of chemometric analysis, the new approach of 
“metabolome-wide association” has successfully taken this concept to proof, 
identifying urine metabolites associated with high blood pressure, a major risk factor 
for cardiovascular disease (Holmes et al., 2008). 
2) Functional genomics for candidate gene validation and analysis 
Functional genomics on cell cultures or animal models help validate candidate genes 
by analysing the effects of targeted inactivation by knock-out through homologous 
recombination, targeted reduction of gene expression by knock-down through RNAi-
mediated silencing, or conditional expression of candidate genes through inducible 
systems. Humanised mouse models engineered to express human versions of specific 
proteins in place of mouse ones are increasingly used to overcome the divergence of 
ligand specificity between species and facilitate the extrapolation from mice to 
humans of the results of studies of food component or drug bioavailability, toxicity 
and/or efficacy (Scheer et al., 2008). 
2.2 Nutrigenetics 
Nutrigenetics addresses how genetic makeup affects responses to diet. These 
responses can be positive, such as improved health, well-being and performance, or 
negative such as allergies, intolerances or increased risk of chronic diseases. 
 
Nutrigenetics is expected to be instrumental in food-related disease prevention based 
on the following rationale. If diet is a risk factor for disease in some individuals, and 
if diet components affect gene expression, then specific alleles must exist whose 
direct or indirect interaction with diet components affect the incidence, onset and 
progression of such disease. Consequently, evidence-based personalised dietary 
advice may help prevent, delay or reduce the symptoms of chronic disease in 
individuals with a genetic predisposition. This requires robust prognostic markers of 
disease, and a validated intervention diet to act on the information. 
 
 
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For the sensu stricto application of the nutrigenetics paradigm, prognostic markers of 
disease should be genetic variants responsible for genetic predisposition, but interest 
is rising for the exploitation of robust disease biomarkers to guide personalised diet 
interventions. 
Technologies and strategies in nutrigenetics 
1) Identification of genetic loci associated with food-related response or disease 
The recent development of high-throughput genotyping platforms allows the 
discovery of genetic variants associated with specific traits in a blind, systematic yet 
cost-effective approach, by genome-scan or genome-wide association (GWA) 
analysis. Human genetic studies have benefited from the completion of the human 
genome sequence, and the subsequent mapping of common genetic variants in the 
HapMap project: the analysis of above 3 million common single-nucleotide 
polymorphisms (SNPs) in human populations of different ancestry led to the release 
of commercial genotyping arrays enabling to assay up to 600,000 genome-wide tag 
SNPs in one go, sufficient to capture most known SNP variants by imputation (see 
worksheet on high-throughput genotyping technologies) (Frazer et al., 2007). High-
throughput accurate phenotyping has become the limiting factor for the statistic 
association of a genetic variant with a disease trait. 
 
As highlighted in Figure 3 for type 2 diabetes, genome-wide association analysis is 
not only uncovering a number of unsuspected genes (“non-candidate genes”) 
associated with the disease, but it is performing more efficiently than candidate-gene 
linkage and/or association studies, promising faster progress in unravelling genes 
predisposing to chronic disease (Frayling, 2007). 
 
 
 
Fig. 3 Effect sizes of the 11 common variants confirmed to be involved in type 2 diabetes risk. The 
x axis gives the year that published evidence reached the levels of statistical confidence that are now 
accepted as necessary for genetic association studies. CDKAL1, CDK5 regulatory subunit associated protein 1-like 
1; CDKN2, cyclin-dependent kinase inhibitor 2A; FTO, fat mass and obesity-associated; HHEX, haematopoietically expressed 
homeobox; IDE, insulin-degrading enzyme; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; KCNJ11, potassium 
inwardly-rectifying channel, subfamily J, member 11; PPARG, peroxisome proliferator-activated receptor-g gene; SLC30A8, 
solute carrier family 30 (zinc transporter), member 8; TCF2, transcription factor 2, hepatic; TCF7L2, transcription factor 7-like 2 
(T-cell specific, HMG-box); WFS1, Wolfram syndrome 1. From Frayling, Nat. Rev. Genet., 2007. 
 
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Candidate gene association studies, however, remain a successful strategy in 
correlating specific nutrient responses with variants of genes suspected to be involved 
in the digestion and metabolism of these nutrients: variants of genes involved in lipid 
metabolism, suspected to be involved in intestinal uptake and metabolism of vitamin 
E, were indeed found to be associated with different plasma levels of vitamin E (Borel 
et al., 2007); one among several variants of genes involved in folate metabolism was 
effectively associated with serum folate concentration — genotyping at this locus 
could pinpoint which individuals should benefit from folic acid fortification (Yang et 
al., 2008); independent mutations affecting lactase gene expression were associated 
with lactase persistence, resulting in adult lactose tolerance (Tishkoff et al., 2007). 
2) Identification of food components that interfere with genetic predisposition 
and metabolic status associated to food-related disorder or chronic disease 
Efforts are emerging to identify specific food components and dietary habits that are 
either incriminated in transforming a genetic predisposition into disease development, 
or that can counteract this evolution in susceptible individuals. Together with the 
development of nutraceuticals or functional foods that antagonise negative health 
effects of mutations, this approach will be essential to be able to propose appropriate 
nutritional intervention or recommendations depending on genetic information. 
 
3. Current and prospective applications of nutrigenomics and 
nutrigenetics 
 
1. Fundamental research: human and animal nutrition and health 
- Unravelling of the mode of action of food components and diets at a molecular 
and system’s level: 
• effects of specific food components such as macro and micronutrients, 
functional foods, prebiotics and probiotics, food supplements 
• effects of whole diets such as hyper/hypo-protein, high/low-fat, high/low 
caloric, meat versus vegetarian, Cretan, Japanese diet, etc. 
• impact of different genotypes on this mode of action 
• impact of different metabolic groups (defined by gender, age, health status, 
lifestyle, environment, etc.) on this mode of action 
- Association of genetic variation at candidate genes with a diet response phenotype 
- Identification of genetic loci predisposing to diet-related chronic disease 
- Identification of biomarkers in response to a given foodcomponent or diet 
- Identification of biomarkers associated with diet-related chronic disease 
2. Feed/food industry and regulation 
- Validation of health claims of functional foods and notorious diets. 
- Test for bioavailability and action of nutrients and food supplements 
- Test for potential toxicity of food additives, GM-derived foods and contaminants 
from food processing and packaging 
- Development of new functional feed products 
- Personalised nutrition, involving: 
 
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• diagnostic services or toolkits for typing genetic markers or other kinds of 
markers to guide personalised interventions for optimal health 
• Discovery and rational development of new functional foods and 
supplements for personalised nutrition by screening for specific functions 
based on growing knowledge of molecular mechanisms of nutrition and 
food-related disorders 
3. Plant breeding industry 
Definition of new breeding objectives and strategies to increase specific plant 
compounds identified as beneficial in nutrigenomic analysis. 
4. Government, ministry of health 
Improve dietary guidelines of the general population and specific nutritional 
recommendations for distinct sub-populations with which one can easily identify 
(e.g., metabolic groups based on gender, age group, ancestry, genetic 
predisposition, environment, lifestyle, etc.), in order to improve well-being and 
reduce the risks of developing food-related diseases. 
 
4. Current limitations and challenges 
 
- Issues in nutrigenomics and nutrigenetics research: 
1) Challenges specific to the different ‘omics technologies used in nutrigenomics 
are developed in the corresponding worksheets. 
2) Challenges in genome-wide association analysis (see also genotyping 
worksheet): 
- issues around genotyping-phenotyping associations: large population and 
replication studies required for robust statistical analysis, phenotyping 
challenges due to heterogeneity of environmental context including diet, 
population stratification and risk of spurious association due to epistasis 
(Kaput, 2008), 
- commercial platforms for human genotyping use SNP sets selected from 
the HapMap project. Consequently, those SNPs that are absent from the 
HapMap populations will be overlooked (identification of new 
polymorphisms modulating lactase expression was only made possible by 
re-sequencing the region from distinct African subpopulations); as will 
other types of polymorphisms such as insertions/deletions and copy 
number variants. 
3) Issues related with studies on human subjects: 
- High cost of large population studies 
- Limited possibilities for tissue biopsies; restriction to non-invasive 
techniques 
- Uncontrollable heterogeneity in human population (genetics and 
environment) 
- Limitations of dietary surveys: 
• complexity of nutrition, including chronic exposure, varying doses 
and varying mixtures or matrices 
• problems associated to false statements 
 
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4) Extra sources of variability are not taken into account in genetic tests: the 
environment, the epigenome, and the microbiome, all three playing significant 
roles in shaping our response to diet. 
- The epigenome consists of epigenetic modifications, or chemical marking 
of the DNA and associated proteins, upon development and environmental 
factors. Although not affecting the genetic code itself, these marks are a 
major determinant of gene expression and have been implicated in the 
aetiology of some diseases like cancer. Epigenetic marks may be inherited 
from previous generations through meiosis, but they may also be acquired 
during lifetime, maintained through mitosis or lost during development, 
resulting in a spatial and temporal mosaic of epigenome sectors in an 
organism. 
- The microbiome consists of all the genomes of microbial symbionts that 
live inside and on humans, and contributes significantly to human genetic 
and physiological diversity, notably to processes of digestion and 
metabolism. A nutrigenetic assay should therefore take into account the 
influence of the microbiome. Considering the microbiome contains at least 
100 times as many genes as the human genome, the task may appear 
daunting, but high-throughput sequencing technologies are emerging that 
make microbiome analysis feasible (see worksheet on metagenomics). 
Metagenomic analysis showing a correlation between obesity and specific 
distribution of gut microbes are promising. Nutrigenomic studies 
integrating microbiome and metabolome data have indeed demonstrated 
the significant contribution of microbiome to human metabolism. 
- Because it integrates influences from environment, microbiome, genetic 
makeup and expression, including epigenetic control, metabolic phenotype 
is a better indicator of health status than genetics. In fact it has been shown 
that high blood pressure correlates with diet-associated urine metabolites 
more than with genetics (Holmes et al., 2008). 
5) Challenges in experimental design, measurements and data interpretation 
- Definition of “healthy” status and associated range of references in ‘omic 
signature profiles 
- Need for standardisation of measurements for phenotype assessments 
- Data interpretation is often limited by issues with poor gene annotation 
6) Bioinformatics challenges: 
- Storage and management of information 
- Statistical analysis and treatment of information 
- Integration of different types of data 
 
- Issues in nutrigenomics and nutrigenetics application: 
1) The challenges specific to genetic testing are well described in the 2007 report 
of the UK Human Genetics Commission, “More Genes Direct”: 
a. Analytical validity, i.e., accuracy of the test in identifying the biomarker, 
b. Health validity, i.e., relationship between biomarker and health status, 
c. Health utility, i.e., likelihood that the test will lead to an improved 
outcome for the test subject, 
d. Ethical, legal and social implications, i.e., whether the use of the test 
involves additional considerations for individuals, certain groups or for 
society more generally, e.g., genetic privacy. 
 
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While a. can be achieved in nutrigenomics, b. and c. remain controversial: 
although clearly achievable for the rare mutations conditioning monogenic 
disorders, they are disputed for most chronic diseases for which a large 
number of genes have each a minor contribution to disease. Furthermore, the 
development of diet recommendations and functional foods designed to reduce 
the negative effects of specific gene variants is in its infancy. Understandably, 
genetic tests have led to criticism: “Four personalized nutrition companies - 
Sciona, Genelex, Market America, and Suracell - were last July named by the 
US Government Accountability Office for having "misled consumers by 
making predictions that are medically unproven and so ambiguous that they 
do not provide meaningful information" by offering over the counter 
nutrigenomic tests.” 
2) Part of the food industry also expresses concern about the difficulty to provide 
individual solutions for optimal health impact based on personal genetics, and 
the financial feasibility of such a fragmented investment. Specific diet 
adjustments for sub-population groups will be more realistic to achieve and a 
significant progress in itself. 
3) The initial question “how to eat according to our genes?” is restrictive and 
addresses only the genetic component that conditions our response to diet. 
Specific genetic predispositions exist but seem to play a minor role for most 
people in terms of conditioning significant food impact on health. Obesity is a 
population-wideepidemic mostly reflecting overall changes in diet and 
lifestyle rather than individual genetic predispositions. Over 200 loci have 
been implicated in obesity: when validated, individual genetic variants at some 
of these loci represent minor risk factors compared to environmental factors. 
The impact of combinations of genetic variants are currently being tested. 
Genetics is determinant for the minority of individuals suffering from 
monogenic disorders such as celiac disease, phenylketonurea or lactose 
intolerance. But for most of the population, even though nutrigenetics is 
identifying more and more genetic variants responsible for variation in food 
responses, their impact on overall health has not yet proved significant enough 
to justify investments in personal genetics for personal nutrition. The 
application of nutrigenomics and nutrigenetics for public health will make 
more sense in specifying diet recommendations and developing specific 
functional foods for well-defined subgroups that represent a significant portion 
of the population. Nutrigenomics and nutrigenetics will contribute to the 
selection of criteria to define these groups and the corresponding diet 
recommendations. These will likely include characteristics that influence 
metabolism, namely gender, age and lifestyle, with specific conditions (e.g., 
pregnancy, illness), and a few selected genetic predispositions for monogenic 
disorders that may be screened at birth. 
 
 
5. Glossary 
 
Nutrigenomics (broad definition): application of genomic resources and high-throughput ‘omics 
technologies in the field of nutrition. 
 
Nutrigenomics (specific definition): research field addressing how nutrients affect the expression of 
our genes. 
 
 10 
 
Nutrigenetics: research field addressing how our genetic makeup affects our response to diet. 
 
‘Omics technologies: high-throughput technologies for the massive parallel analysis of various kinds 
of macromolecules (DNA molecules, transcripts, proteins, metabolites). 
(see origin of ‘omes and ‘omics?) 
 
Transcriptomics: simultaneous analysis of many transcripts, aiming at the full complement of 
transcripts (the transcriptome) of an individual, an organ, a tissue or a cell. 
 
Proteomics: simultaneous analysis of many proteins, aiming at the full complement of proteins (the 
proteome) of an individual, an organ, a tissue or a cell. 
 
Metabolomics: simultaneous analysis of many metabolites, aiming at the full complement of 
metabolites (the metabolome) of an individual, an organ, a tissue or fluid, or a cell. 
 
Mass spectroscopy (MS): a technique for separating ions based on their mass to charge ratios. 
 
Nuclear Magnetic Resonance Spectroscopy (NMR): a form of spectroscopy that depends on the 
absorption by and emission of energy from changes in spin states of the nucleus of an atom. 
Absorption and emission are affected by local chemical environment. 
 
Genome-wide association (GWA), whole-genome association or genome-scan analysis: statistical 
analysis linking specific genetic variants with a phenotype. Genetic variants are determined using 
commercial arrays of genetic markers representing a substantial proportion of common variation in 
DNA sequence throughout the genome. 
 
Single-Nucleotide Polymorphism (SNP): most common type of genetic variant, consisting of a single 
nucleotide difference between two individuals at a particular site in the DNA sequence. The human 
population is estimated to vary at about 10 “common” million SNPs. “Common” SNPs are 
observed at a frequency above 1% and represent about 90% of the variation in the population. The 
remaining 10% variation is due to additional variants that are each rare in the population. Other 
types of genetic variants exist such as insertions and deletions or copy number variants. 
 
Haplotype: a particular combination of alleles at nearby SNPs. DNA polymorphisms can strongly 
associated because of co-inheritance, leading to the constitution of a block of SNPs in linkage 
disequilibrium. A tag SNP can then be selected to represent a whole haplotype, reducing the 
amount of typing required to genotype whole genomes. 
 
Epistasis: direct or indirect interaction between genes, which may result in the alteration or complete 
masking of the phenotypic effect of one locus by the effects of another locus. Epistasis adds a layer of 
complexity in quantitative genetic analysis performed to detect association between genes and 
phenotypes. 
 
Functional food, medicinal food or nutraceutical (from nutrient and pharmaceutical): any food 
claimed to have a health-promoting and/or disease-preventing property beyond the basic nutritional 
function of supplying nutrients (e.g., yogurt containing probiotics). 
 
Probiotics: live organism food supplements –usually bacteria or yeasts– that confer a health benefit on 
the host when administered in adequate amounts (e.g., strains of Lactobacillus or Bifidobacterium). 
 
Prebiotics: non-digestible food ingredients that beneficially affect the host by selectively stimulating 
the growth and/or activity of one or a limited number of bacteria in the colon, and thus improve 
host health. The most prevalent forms of prebiotics are nutritionally classified as soluble fibre 
(example of sources of prebiotics: soybean, inulin sources, raw oats, unrefined wheat or barley). 
 
Epigenetics: the study of epigenetic modifications. 
 
Epigenetic modifications: Development and environment-induced chemical “marking” of chromatin 
(DNA and associated histone proteins), which affects gene expression without affecting the DNA 
 
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sequence. Epigenetic modifications are heritable from cell to cell through mitosis, or from 
generation to generation through meiosis. Modifications typically include DNA methylation at 
some cytosine residues (CpG dinucleotides in animals), post-translational methylation, acetylation, 
phosphorylation or ubiquitylation of histone proteins, and change in nucleosome density and 
positioning. 
 
Epigenomics: simultaneous analysis of many epigenetic modifications, aiming at the full complement 
of epigenetic modifications (epigenome) in an individual, an organ, a tissue, or a cell. 
 
Metagenomics: Analysis of the metagenome, consisting of all the genomes of a microbe community in 
a particular environment, such as the gut. 
 
Microbiome: All the genomes of microbial symbionts that live inside and on humans. 
 
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6. Key references 
Borel, P., Moussa, M., Reboul, E., Lyan, B., Defoort, C., Vincent-Baudry, S., Maillot, 
M., Gastaldi, M., Darmon, M., Portugal, H., Planells, R., and Lairon, D. (2007). 
Human plasma levels of vitamin E and carotenoids are associated with genetic 
polymorphisms in genes involved in lipid metabolism. J Nutr 137, 2653-2659. 
Bouwens, M., Afman, L. A., and Muller, M. (2007). Fasting induces changes in 
peripheral blood mononuclear cell gene expression profiles related to increases in 
fatty acid beta-oxidation: functional role of peroxisome proliferator activated receptor 
alpha in human peripheral blood mononuclear cells. Am J Clin Nutr 86, 1515-1523. 
Frayling, T. M. (2007). Genome-wide association studies provide new insights into 
type 2 diabetes aetiology. Nat Rev Genet 8, 657-662. 
Frazer, K. A., Ballinger, D. G., Cox, D. R., Hinds, D. A., Stuve, L. L., Gibbs, R. A., 
Belmont, J. W., Boudreau, A., Hardenbol, P., Leal, S. M., et al. (2007). A second 
generation human haplotype map of over 3.1 million SNPs. Nature 449, 851-861. 
Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K., Chan, Q., Ebbels, T., De 
Iorio, M., Brown, I. J., Veselkov, K. A., Daviglus, M. L., Kesteloot,H., Ueshima, H., 
Zhao, L., Nicholson, J. K., and Elliott, P. (2008). Human metabolic phenotype 
diversity and its association with diet and blood pressure. Nature 453, 396-400. 
James, W. P. (2008). The epidemiology of obesity: the size of the problem. J Intern 
Med 263, 336-352. 
Kaput, J. (2008). Nutrigenomics research for personalized nutrition and medicine. 
Curr Opin Biotechnol 19, 110-120. 
Martin, F. P., Wang, Y., Sprenger, N., Yap, I. K., Lundstedt, T., Lek, P., Rezzi, S., 
Ramadan, Z., van Bladeren, P., Fay, L. B., Kochhar, S., Lindon, J. C., Holmes, E., 
and Nicholson, J. K. (2008). Probiotic modulation of symbiotic gut microbial-host 
metabolic interactions in a humanized microbiome mouse model. Mol Syst Biol 4, 
157. 
Müller, M., and Kersten, S. (2003). Nutrigenomics: goals and strategies. Nat Rev 
Genet 4, 315-322. 
Rist, M. J., Wenzel, U., and Daniel, H. (2006). Nutrition and food science go 
genomic. Trends Biotechnol 24, 172-178. 
Scheer, N., Ross, J., Rode, A., Zevnik, B., Niehaves, S., Faust, N., and Wolf, C. R. 
(2008). A novel panel of mouse models to evaluate the role of human pregnane X 
receptor and constitutive androstane receptor in drug response. J Clin Invest 118, 
3228-3239. 
Tishkoff, S. A., Reed, F. A., Ranciaro, A., Voight, B. F., Babbitt, C. C., Silverman, J. 
S., Powell, K., Mortensen, H. M., Hirbo, J. B., Osman, M., Ibrahim, M., Omar, S. A., 
Lema, G., Nyambo, T. B., Ghori, J., Bumpstead, S., Pritchard, J. K., Wray, G. A., and 
 
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Deloukas, P. (2007). Convergent adaptation of human lactase persistence in Africa 
and Europe. Nat Genet 39, 31-40. 
Yang, Q. H., Botto, L. D., Gallagher, M., Friedman, J. M., Sanders, C. L., Koontz, D., 
Nikolova, S., Erickson, J. D., and Steinberg, K. (2008). Prevalence and effects of 
gene-gene and gene-nutrient interactions on serum folate and serum total 
homocysteine concentrations in the United States: findings from the third National 
Health and Nutrition Examination Survey DNA Bank. Am J Clin Nutr 88, 232-246. 
 
7. Consulted experts 
 
Pascal Martin 
INRA, 
Toulouse, France 
 
Patrick Borel 
UMR INRA!INSERM-Université Aix!Marseille 
Marseille, France 
 
Marc!Emmanuel Dumas 
CNRS Lyon, France 
Imperial College, London, UK 
 
Jean-Luc Tonneau 
Directeur du Planning Stratégique, 
Danone Research 
Massy-Palaiseau, France 
 
Nicolas Gausserès 
Directeur de la Plateforme Etudes Cliniques & Nutrition 
Danone Research 
Massy-Palaiseau, France

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