The approach to predictive medicine that is taking genomics research by storm

Matthew Warren in Nature:

6.6 million — that’s how many spots on the human genome Sekar Kathiresan looks at to calculate a person’s risk of developing coronary artery disease. Kathiresan has found that combinations of single DNA-letter differences from person to person in these select locations could help to predict whether someone will succumb to one of the leading causes of death worldwide. It’s anyone’s guess what the majority of those As, Cs, Ts and Gs are doing. Nevertheless, Kathiresan says, “you can stratify people into clear trajectories for heart attack, based on something you have fixed from birth”. Kathiresan, a geneticist at Massachusetts General Hospital in Boston, isn’t alone in counting outrageously high numbers of variants. The polygenic risk scores he has developed are part of a cutting-edge approach in the hunt for the genetic contributors to common diseases. Over the past two decades, researchers have struggled to account for the heritability of conditions including heart disease, diabetes and schizophrenia. Polygenic scores add together the small — sometimes infinitesimal — contributions of tens to millions of spots on the genome, to create some of the most powerful genetic diagnostics to date. This approach has taken off thanks to a number of well-resourced cohort studies and large data repositories, such as the UK Biobank (see pages 194203 and 210), which collect vast quantities of health information alongside DNA data from hundreds of thousands of people. And some studies published in the past year or so have been able to analyse more than a million participants by combining information from such sources, increasing scientists’ ability to detect tiny effects.

Supporters say that polygenic scores could be the next great stride in genomic medicine, but the approach has generated considerable debate. Some research presents ethical quandaries as to how the scores might be used: for example, in predicting academic performance. Critics also worry about how people will interpret the complex and sometimes equivocal information that emerges from the tests. And because leading biobanks lack ethnic and geographic diversity, the current crop of genetic screening tools might have predictive power only for the populations represented in the databases.

More here.

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