Throughout our lives, we choose to a complex mixture of foods, which can have a positive or negative impact on our health. Intricate biochemical processes extract the energy and other useful components that enable us to grow and function, and many compounds, seemingly unimportant in the past, are now recognised as being beneficial. The problem, for scientists and consumers alike, is that the benefits appear not to be the same for everyone. Individual genetic differences in response to dietary components have been evident for years, e.g. cholesterol and saturated fat intake. In the UK alone, one in three die from cardiovascular disease (CVD)—heart attack, stroke etc. High cholesterol causes a third of all CVD worldwide and by 2020 CVD will be the leading cause of death, and disability, worldwide ([20 million deaths per year rising to 24 million in 2030). A 10% reduction in blood cholesterol can halve the risk of CVD in a 40-year-old man; why 10%? Because this is about the extent to which diet alone can have an impact but even that can depend on our genetic make-up. Thus, we need to understand how what we eat interacts with our bodies—or, more specifically, our genes—to affect our health. This is the science of nutrigenomics. The post-genomic technologies allow nutritional research to take a more holistic perspective; using new technologies provided by the sequencing of the human genome as well as adapting existing ones to measure how what we eat interacts with our genes, proteins and metabolism. The long-term aim of nutrigenomics is to understand how the whole body responds to real foods using an integrated approach termed ‘‘systems biology’’. The huge advantage in this approach is that the studies can examine people (i.e. populations, sub-populations—based on genes or disease—and individuals), food, life-stage and lifestyle without preconceived ideas. Genomics seeks to understand the structure and function of our entire DNA sequence—all three billion base-pairs across 23 pairs of chromosomes. Genotyping describes the genes for a particular characteristic but cannot predict phenotype—the result—except in very simple cases, e.g. eye colour. Predictable phenotypes include those associated with disease, e.g. cystic fibrosis, but not generally diet or age-related diseases, which are controlled by many genes and external factors. DNA codes for proteins but DNA and proteins do not ‘‘speak’’ the same language; their interpreter is RNA. Transcriptomics using Affymetrix GeneChip arrays or two-colour microarrays can measure which and how often genes are actively being read. What it does not reveal is whether or not this is having any affect overall. Proteomics is the study of proteins, their structure and their function, and metabolomics the products of our metabolism, which are affected by gene transcription and translation as well as proteins function. Perhaps more easily understood than nutrigenomics, nutrigenetics examines single-gene/single food compound relationships. One of the best-described examples is folate and the gene for MTHFR (5,10-methylenetetrahydrofolate reductase). MTHFR has a role in supplying methionine, which is important in many metabolic pathways include production of neurotransmitters and regulation of gene expression. Folate is essential to the efficient functioning of this MTHFR. There is a common polymorphism in the gene for MTHFR that leads to two forms of protein: the S. B. Astley (&) Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK e-mail: sian.astley@bbsrc.ac.uk