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• W2080996579 abstract "•Technical advances utilising genomics promise personalised, evidence-based healthcare. •Overstatements for common diseases risk/outcome prediction triggered regulations. •Risk-stratified intervention is a realistic strategy for targeting common diseases. •Electronic health records can aid curating cohorts for whole-of-life risk predictors. Genome sequencing has the potential for stratified cancer treatment and improved diagnostics for rare disorders. However, sequencing needs to be utilised in risk stratification on a population scale to deepen the impact on the health system by addressing common diseases, where individual genomic variants have variable penetrance and minor impact. As the accuracy of genomic risk predictors is bounded by heritability, environmental factors such as diet, lifestyle, and microbiome have to be considered. Large-scale, longitudinal research programmes need to study the intrinsic properties between both genetics and environment to unravel their risk contribution. During this discovery process, frameworks need to be established to counteract unrealistic expectations. Sufficient scientific evidence is needed to interpret sources of uncertainty and inform decision making for clinical management and personal health. Genome sequencing has the potential for stratified cancer treatment and improved diagnostics for rare disorders. However, sequencing needs to be utilised in risk stratification on a population scale to deepen the impact on the health system by addressing common diseases, where individual genomic variants have variable penetrance and minor impact. As the accuracy of genomic risk predictors is bounded by heritability, environmental factors such as diet, lifestyle, and microbiome have to be considered. Large-scale, longitudinal research programmes need to study the intrinsic properties between both genetics and environment to unravel their risk contribution. During this discovery process, frameworks need to be established to counteract unrealistic expectations. Sufficient scientific evidence is needed to interpret sources of uncertainty and inform decision making for clinical management and personal health. utilizing EHRs for the cost-effective recruitment of a precise and well-annotated patient cohort. a study designed to determine the association between a specific phenotype and environmental factors. combination of physical, chemical, nutritional, lifestyle, and psychosocial exposure factors. determining the genotype of a known disease marker. different treatment based on a person's actual or assumed genetic makeup. obtaining the genomic profile of an individual. a study designed to determine the association between a specific phenotype and the genotype of common genetic variants. set of genetic variations observed in an individual. medical data and treatment history in a machine-readable format shared with all relevant healthcare providers of the patient. adapting food and nutritional intake depending on the individual's genomic profile, life stage, and lifestyle to prevent disease or delay its onset. a study designed to determine the association between specific genomic regions and the observed phenotypes. proportion of a population found to have a specific disease condition. visualisation of sensitivity and specificity of a prediction method. subdividing a population into groups based on genetic profiles." @default.
• W2080996579 created "2016-06-24" @default.
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• W2080996579 date "2014-09-01" @default.
• W2080996579 modified "2023-10-06" @default.
• W2080996579 title "Genomics and personalised whole-of-life healthcare" @default.
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• W2080996579 doi "https://doi.org/10.1016/j.molmed.2014.04.001" @default.
• W2080996579 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24801560" @default.
• W2080996579 hasPublicationYear "2014" @default.
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