Genome-wide association studies in pharmacogenomics: untapped potential for translation

Despite large public investments in genome-wide association studies of common human diseases, so far, few gene discoveries have led to applications for clinical medicine or public health. Genome-wide association studies in the context of clinical trials of drug safety and efficacy may be quicker to yield clinical applications. Certain methodological concerns, such as selection bias and confounding, may be mitigated when genome-wide association studies are conducted within clinical trials, in which randomization of exposure, prospective evaluation of outcome and careful definition of phenotype are incorporated by design

The impact of genomics on precision public health: beyond the pandemic.

Precision public health has been defined in many ways. It can be viewed as an emerging multidisciplinary field that uses genomics, big data, and machine learning/artificial intelligence to predict health risks and outcomes and to improve health at the population level. Just like precision medicine seeks to provide the right intervention to the right patient at the right time, the aim of precision public health is to provide the right intervention to the right population at the right time, with the goal of improving health for all. Genomic technologies have been at the leading edge of applications in clinical medicine and have the potential to revolutionize public health. We are pleased to introduce this special issue of Genome Medicine on the impact of genomics on precision public health, which highlights the utility of genomic tools in public health research and practice in the fight against communicable and noncommunicable diseases. This is particularly timely, given the battle against the COVID-19 pandemic, which has necessitated the application of genomic approaches to track the origin, transmission and evolution of the SARS-CoV-2 virus globally, as well as to understand differential host susceptibility, response, severity, and outcomes. Beyond genomics, granular data from population surveillance approaches are being used to target public health interventions. In addition, big data, digital technologies, and mobile health applications have been instrumental in defining the natural history of COVID-19 and identifying prognostic factors through machine learning and artificial intelligence

Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

Background: Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.Methods: We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.Results: We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.Conclusions: The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC

The need for genetic variant naming standards in published abstracts of human genetic association studies

We analyzed the use of RefSNP (rs) numbers to identify genetic variants in abstracts of human genetic association studies published from 2001 through 2007. The proportion of abstracts reporting rs numbers increased rapidly but was still only 15% in 2007. We developed a web-based tool called Variant Name Mapper to assist in mapping historical genetic variant names to rs numbers. The consistent use of rs numbers in abstracts that report genetic associations would enhance knowledge synthesis and translation in this field

Looking back at genomic medicine in 2011

Genomic medicine, in its broadest sense of being medical developments informed by ‘omic ’ advances, has continued to move towards the clinic in 2011. To mark the end of the year and the beginning of 2012, the editors of the six sections within Genome Medicine were invited to provide their highlights of the past year and to hint at the developments that we are likely to see in the near future. Six different areas of progress are covered here, but the core of genomic medicine continues to be intrinsically linked to improvements in the underlying technology, and two obvious examples are sequencing and mass spectrometry. Technological advances have enabled larger studies and more complex analyses, allowing researchers and clinicians to track changes within a single cell and yet spot patterns across a whole population an