2.Lander, ES, Linton, LM, Birren, B et al. (2001) Initial sequencing and analysis of the human genome. Nature 409, 860–921.
3.Venter, JC, Adams, MD, Myers, EW et al. (2001) The sequence of the human genome. Science 291, 1304–1351.
4.Roberts, L, Davenport, RJ, Pennisi, E et al. (2001) A history of the human genome project. Science 291, 1195.
5.Collins, FS, Morgan, M & Patrinos, A (2003) The human genome project: lessons from large-scale biology. Science 300, 286–290.
6.Green, ED, Watson, JD & Collins, FS (2015) Human genome project: twenty-five years of big biology. Nature 526, 29–31.
7.Venter, JC, Smith, HO & Adams, MD (2015) The sequence of the human genome. Clin Chem 61, 1207–1208.
9.Mardis, ER (2011) A decade's perspective on DNA sequencing technology. Nature 470, 198–203.
10.The International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437, 1299–1320.
11.Abecasis, GR, Auton, A, Brooks, LD et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65.
12.Pennisi, E (2007) Breakthrough of the year. Human genetic variation. Science 318, 1842–1843.
13.Summerskill, W (2008) Paper of the year 2007. The Lancet 371, 370–371.
14.Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678.
15.Bycroft, C, Freeman, C, Petkova, D et al. (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209.
16.Turnbull, C, Scott, RH, Thomas, E et al. (2018) The 100 000 Genomes Project: bringing whole genome sequencing to the NHS. BMJ 361, k1687.
17.Stevens, EA & Rodriguez, CP (2015) Genomic medicine and targeted therapy for solid tumors. J Surg Oncol 111, 38–42.
18.Prat, A, Pineda, E, Adamo, B et al. (2015) Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast 24 Suppl 2, S26–S35.
20.Erikainen, S & Chan, S (2019) Contested futures: envisioning ‘Personalized,’ ‘Stratified,’ and ‘Precision’ medicine. New Genet Soc 38, 308–330.
21.National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease (2011) Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington, DC: National Academies Press (US) National Academy of Sciences.
22.Yates, LR, Seoane, J, Le Tourneau, C et al. (2018) The European Society for Medical Oncology (ESMO) Precision Medicine Glossary. Ann Oncol 29, 30–35.
23.Lederberg, J & McCray, AT (2001) ‘Ome sweet ‘omics – a genealogical treasury of words. Scientist 15, 8.
24.Dettmer, K, Aronov, PA & Hammock, BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26, 51–78.
25.Cousins, RJ, Blanchard, RK, Popp, MP et al. (2003) A global view of the selectivity of zinc deprivation and excess on genes expressed in human THP-1 mononuclear cells. Proc Natl Acad Sci USA 100, 6952–6957.
26.Moore, JB, Blanchard, RK & Cousins, RJ (2003) Dietary zinc modulates gene expression in murine thymus: results from a comprehensive differential display screening. Proc Natl Acad Sci USA 100, 3883–3888.
27.Cousins, RJ, Blanchard, RK, Moore, JB et al. (2003) Regulation of zinc metabolism and genomic outcomes. J Nutr 133, 1521s–1526s.
28.Abbott, A (2001) Workshop prepares ground for human proteome project. Nature 413, 763.
29.Hanash, S & Celis, JE (2002) The Human Proteome Organization: a mission to advance proteome knowledge. Mol Cell Proteomics 1, 413–414.
30.Wishart, DS (2007) Proteomics and the human metabolome project. Expert Rev Proteomics 4, 333–335.
31.HUPO – the Human Proteome organization (2010) A gene-centric human proteome project. Mol Cell Proteomics 9, 427–429.
32.Omenn, GS, Lane, L, Overall, CM et al. (2019) Progress on identifying and characterizing the human proteome: 2019 metrics from the HUPO human proteome project. J Proteome Res 18, 4098–4107.
33.Stern, CD (2019) The ‘omics revolution: how an obsession with compiling lists is threatening the ancient art of experimental design. Bioessays 41, e1900168.
34.Spanos, C, Maldonado, EM, Fisher, CP et al. (2018) Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease. Proteome Sci 16, 4.
35.Rosen, R (1968) Systems theory and biology. Proceedings of the 3rd systems symposium, Cleveland, Ohio, Oct. 1966. (MD Mesarović, editor) 161, 34–35.
36.Ideker, T, Galitski, T & Hood, L (2001) A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2, 343–372.
37.Kitano, H (2002) Systems biology: a brief overview. Science 295, 1662–1664.
38.Moore, JB & Weeks, ME (2011) Proteomics and systems biology: current and future applications in the nutritional sciences. Adv Nutr 2, 355–364.
39.Fisher, CP, Kierzek, AM, Plant, NJ et al. (2014) Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease. World J Gastroenterol 20, 15070–15078.
40.Kitano, H (2002) Computational systems biology. Nature 420, 206–210.
41.Agrawal, A (1999) New institute to study systems biology. Nat Biotechnol 17, 743–744.
42.Hood, LE (2018) Lessons learned as president of the institute for systems biology (2000–2018). Genom Proteom Bioinf 16, 1–9.
43.Hood, L, Heath, JR, Phelps, ME et al. (2004) Systems biology and new technologies enable predictive and preventative medicine. Science 306, 640–643.
44.Hood, L (2008) A personal journey of discovery: developing technology and changing biology. Annu Rev Anal Chem 1, 1–43.
45.Desiere, F (2004) Towards a systems biology understanding of human health: interplay between genotype, environment and nutrition. Biotechnol Annu Rev 10, 51–84.
46.van Ommen, B & Stierum, R (2002) Nutrigenomics: exploiting systems biology in the nutrition and health arena. Curr Opin Biotechnol 13, 517–521.
47.Stephanou, A, Fanchon, E, Innominato, PF et al. (2018) Systems biology, systems medicine, systems pharmacology: the what and the why. Acta Biotheor 66, 345–365.
48.Moore, JB (2019) From sugar to liver fat and public health: systems biology driven studies in understanding non-alcoholic fatty liver disease pathogenesis. Proc Nutr Soc 78, 290–304.
49.Maldonado, EM, Leoncikas, V, Fisher, CP et al. (2017) Integration of genome scale metabolic networks and gene regulation of metabolic enzymes with physiologically based pharmacokinetics. CPT: Pharmacometrics Sys Pharmacol 6, 732–746.
50.Maldonado, EM, Fisher, CP, Mazzatti, DJ et al. (2018) Multi-scale, whole-system models of liver metabolic adaptation to fat and sugar in non-alcoholic fatty liver disease. NPJ Syst Biol Appl 4, 33.
51.Chen, R, Mias, GI, Li-Pook-Than, J et al. (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307.
52.Schmidt, S (2019) Congratulations to Michael Snyder for receiving the 2019 George W. Beadle Award! Genes to Genomes. Available online: http://genestogenomes.org/snyder-beadle/ (accessed January 2020).
53.Karczewski, KJ & Snyder, MP (2018) Integrative omics for health and disease. Nat Rev Genet 19, 299–310.
54.Gibney, MJ & Walsh, MC (2013) The future direction of personalised nutrition: my diet, my phenotype, my genes. Proc Nutr Soc 72, 219–225.
55.Ordovas, JM, Ferguson, LR, Tai, ES et al. (2018) Personalised nutrition and health. BMJ 361, [Epublication 13 June 2018] k2173.
56.Gibney, ER (2020) Personalised nutrition – phenotypic and genetic variation in response to dietary intervention. Proc Nutr Soc, 79, 236–245.
57.Muller, M & Kersten, S (2003) Nutrigenomics: goals and strategies. Nat Rev Genet 4, 315–322.
58.Trayhurn, P (2003) Nutritional genomics – ‘Nutrigenomics’. Br J Nutr 89, 1–2.
59.Torkamani, A, Wineinger, NE & Topol, EJ (2018) The personal and clinical utility of polygenic risk scores. Nat Rev Genet 19, 581–590.
60.Dib, MJ, Elliott, R & Ahmadi, KR (2019) A critical evaluation of results from genome-wide association studies of micronutrient status and their utility in the practice of precision nutrition. Br J Nutr 122, 121–130.
61.Phillips, AM (2016) Only a click away – DTC genetics for ancestry, health, love…and more: a view of the business and regulatory landscape. Appl Transl Genom 8, 16–22.
62.Khan, R & Mittelman, D (2018) Consumer genomics will change your life, whether you get tested or not. Genome Biol 19, 120.
63.Blell, M & Hunter, MA (2019) Direct-to-consumer genetic testing's red herring: ‘Genetic Ancestry’ and personalized medicine. Front Med 6, 48.
66.Tandy-Connor, S, Guiltinan, J, Krempely, K et al. (2018) False-positive results released by direct-to-consumer genetic tests highlight the importance of clinical confirmation testing for appropriate patient care. Genet Med 20, 1515–1521.
67.Wynn, J & Chung, WK (2017) 23andMe paves the way for direct-to-consumer genetic health risk tests of limited clinical utility. Ann Intern Med 167, 125–126.
68.Kalokairinou, L, Howard, HC, Slokenberga, S et al. (2018) Legislation of direct-to-consumer genetic testing in Europe: a fragmented regulatory landscape. J Community Genet 9, 117–132.
69.Locke, AE, Kahali, B, Berndt, SI et al. (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206.
70.Fuchsberger, C, Flannick, J, Teslovich, TM et al. (2016) The genetic architecture of type 2 diabetes. Nature 536, 41–47.
71.Khera, AV, Chaffin, M, Aragam, KG et al. (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50, 1219–1224.
72.Khera, AV, Chaffin, M, Wade, KH et al. (2019) Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell 177, 587–596.
73.Torkamani, A & Topol, E (2019) Polygenic risk scores expand to obesity. Cell 177, 518–520.
74.Curtis, D (2019) Clinical relevance of genome-wide polygenic score may be less than claimed. Ann Hum Genet 83, 274–277.
75.Genin, E (2020) Missing heritability of complex diseases: case solved? Hum Genet 139, 103–113.
76.Zeevi, D, Korem, T, Zmora, N et al. (2015) Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094.
77.Drew, JE (2020) Challenges of the heterogeneous nutrition response: interpreting the group mean. Proc Nutr Soc 79, 174–183.
78.de Roos, B & Brennan, L (2017) Personalised interventions – a precision approach for the next generation of dietary intervention studies. Nutrients 9, 847.
81.Yetisen, AK, Martinez-Hurtado, JL, Unal, B, et al. (2018) Wearables in medicine. Adv Mater, [Epublication ahead of print].
82.Dias, D & Paulo Silva Cunha, J (2018) Wearable health devices-vital sign monitoring, systems and technologies. Sensors 18, 2414.
83.Isakadze, N & Martin, SS (2019) How useful is the smartwatch ECG? Trends Cardiovasc Med [Epublication ahead of print].
84.Li, X, Dunn, J, Salins, D et al. (2017) Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol 15, e2001402.
85.Witt, D, Kellogg, R, Snyder, M et al. (2019) Windows into human health through wearables data analytics. Curr Opin Biomed Eng 9, 28–46.
86.Forster, H, Walsh, MC, Gibney, MJ et al. (2016) Personalised nutrition: the role of new dietary assessment methods. Proc Nutr Soc 75, 96–105.
87.Hall, H, Perelman, D, Breschi, A et al. (2018) Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol 16, e2005143.
88.Mendes-Soares, H, Raveh-Sadka, T, Azulay, S et al. (2019) Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals. Am J Clin Nutr 110, 63–75.
89.Mendes-Soares, H, Raveh-Sadka, T, Azulay, S et al. (2019) Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw Open 2, e188102.
90.Wolever, TM (2016) Personalized nutrition by prediction of glycaemic responses: fact or fantasy? Eur J Clin Nutr 70, 411–413.
91.Matthan, NR, Ausman, LM, Meng, H et al. (2016) Estimating the reliability of glycemic index values and potential sources of methodological and biological variability. Am J Clin Nutr 104, 1004–1013.
92.Meng, H, Matthan, NR, Ausman, LM et al. (2017) Effect of macronutrients and fiber on postprandial glycemic responses and meal glycemic index and glycemic load value determinations. Am J Clin Nutr 105, 842–853.
93.Meng, H, Matthan, NR, Lichtenstein, AH (2018) Reply to Brighenti F et al. Am J Clin Nutr 107, 846–847.
94.Vega-Lopez, S, Venn, BJ & Slavin, JL (2018) Relevance of the glycemic index and glycemic load for body weight, diabetes, and cardiovascular disease. Nutrients 10, 1361.
95.Price, ND, Magis, AT, Earls, JC et al. (2017) A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol 35, 747–756.
96.Schussler-Fiorenza Rose, SM, Contrepois, K, Moneghetti, KJ et al. (2019) A longitudinal big data approach for precision health. Nat Med 25, 792–804.
97.The All of Us Research Program Investigators (2019) The ‘All of Us’ research program. NEJM 381, 668–676.
98.Prosperi, M, Min, JS, Bian, J et al. (2018) Big data hurdles in precision medicine and precision public health. BMC Med Inform Decis Mak 18, 139.
99.Wang, DD & Hu, FB (2018) Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabetes Endocrinol 6, 416–426.
100.Misra, BB, Langefeld, CD, Olivier, M et al. (2019) Integrated omics: tools, advances, and future approaches. J Mol Endocrinol 62, R21–R45.
101.Vogt, H, Green, S, Ekstrom, CT et al. (2019) How precision medicine and screening with big data could increase overdiagnosis. BMJ 366, l5270.
102.Moore, JB & Boesch, C (2019) Getting energy balance right in an obesogenic world. Proc Nutr Soc 78, 259–261.
103.Moore, JB & Fielding, BA (2019) Taxing confectionery, biscuits, and cakes to control obesity. BMJ 366, l5298.
104.Cousins, RJ (2016) Driving along the zinc road. Annu Rev Nutr 36, 1–15.
105.Mathers, JC (2017) Nutrigenomics in the modern era. Proc Nutr Soc 76, 265–275.
106.Mutch, DM, Wahli, W & Williamson, G (2005) Nutrigenomics and nutrigenetics: the emerging faces of nutrition. FASEB J 19, 1602–1616.
107.Ferguson, LR, De Caterina, R, Gorman, U, et al. (2016) Guide and position of the international society of nutrigenetics/nutrigenomics on personalised nutrition: part 1 - fields of precision nutrition. J Nutrigenet Nutrigenomics 9, 12–27.