Second, researchers and clinicians need to be able to consider both genetic and non-genetic risk factors (for type 2 diabetes, for example, these would encompass hundreds of genetic markers and measures of diet, exercise and socio-economic status alongside measures of current clinical state, such as glucose levels). First, researchers need to expand measures of genetic risk by embracing more-diverse populations, cataloguing the full spectrum of variants, and understanding the environmental context in which these variants act. To gain a more accurate assessment of individual health risks (that is, to make medicine truly personalized), researchers and clinicians must integrate disparate types of data from a wider diversity of populations. is the deputy director-general of the European Molecular Biology Laboratory (EMBL) and director of the EMBL European Bioinformatics Institute near Cambridge, UK, and has played a pivotal part in the design and analysis of multiple genome projects. is an endocrinologist who has focused on understanding the genetics of type 2 diabetes, and leads human genetic research at the biotechnology firm Genentech in South San Francisco, California. Risk estimation on the basis of polygenic scores alone also fails to incorporate real-time measurements of clinical state that are especially important in diseases linked to ageing.īoth authors are strongly invested in the value of human genetics as a tool for understanding disease mechanisms, and are enthusiastic about the contribution that genetic profiling will make to personalizing care. The emphasis on genetic risk diverts attention away from non-genetic factors that might be equally important for disease risk and progression. They leave out many sources of relevant data, and work best for the predominantly white, wealthy populations in which most genetic studies have been performed. Here, we argue that clinical medicine must learn to develop more-holistic measures of individual risk, both genetic and non-genetic, and to combine these with clinical data over time to deliver better care.Īlthough current polygenic scores hold clinical promise, they come with several limitations. This debate often fails to recognize that the challenge is not merely to improve understanding of genetic risk, but to capture more about the interwoven, multifaceted factors that play into disease risk (see ‘Path to personalization’). Others argue that the clinical benefits have been massively overstated 3. These clues to disease risk can be combined to generate ‘polygenic scores’, which provide a measure of the degree to which an individual is genetically predisposed to developing each such disease 1.Ī growing chorus of scientists and clinicians emphasize the value of such genetic profiling as an integral part of a person’s medical record 2. Genomic data, gathered across millions of individuals, have revealed thousands of DNA sequence variants associated with common diseases such as diabetes, heart disease, schizophrenia and cancer. Information on genetic risk represents one promising approach to providing these answers. The goal is to have personalized answers when people need to know whether, for instance, preventive surgery makes sense, a given medicine is likely to be risky or a certain diet should be recommended. Providing the best possible care for an individual means having a better understanding of their risks of developing disease.
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