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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 2 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, 4 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. 5 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. 6 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: 7 • 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 8 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. 9 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 11 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. 12 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 13 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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