How the Intended Use of Polygenic Risk Scores Guides the Design and Evaluation of Prediction Studies

Understanding polygenic models, their development and the potential application of polygenic scores in healthcare

The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.

It is time to get real when trying to predict educational performance

A study of 3,500 children in the UK shows that data on socioeconomic background and previous educational achievements can better predict how students will perform at school than genetic data.

La responsabilidad: un principio para retomar en la reflexión bioética

La ciencia y su brazo, la tecnología, pueden centrarse en una lógica antropocéntrica en la medida en que se empecinen en satisfacer únicamente las necesidades humanas a expensas del dominio (o destrucción) de la naturaleza y las especies coexistentes. También es posible que se pongan al servicio de poderes económicos y políticos y se investigue únicamente bajo una lógica centrada en la ganancia. En medio de este panorama, se hace un llamado a un acto de conciencia para retomar la propuesta del filósofo alemán Hans Jonas sobre el principio de responsabilidad, según el cual, es necesaria una reflexión más allá de las relaciones inmediatas, que incluya a la naturaleza, los animales y las generaciones futuras en la formulación de una nueva ética que debe enseñarse y practicarse desde la academia.

Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?

Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should respect customer autonomy and informed decision making and maximize opportunities for transparency. The algorithm itself is only one piece of the information that is deemed essential for understanding how prediction algorithms are developed and evaluated. Companies should be encouraged to disclose everything else, including the expected risk distribution of the algorithm when applied in the population, using a benchmark DNA dataset. A standardized presentation of information and risk distributions allows customers to compare test offers and scientists to verify whether the undisclosed algorithms could be valid. A new model of oversight in which stakeholders collaboratively keep a check on the commercial market is needed.