Research funded by the National Human Genome Research Institute has produced a 22-item guideline for the minimal polygenic risk score-related information that researchers should include in their studies.
Assessing a person’s inherited disease risk may not be a new concept, but the ways in which physicians can do so have changed drastically with the advent of genomic testing and its increased availability in recent years. One novel approach is to calculate a polygenic risk score, which gauges a person’s risk for diseases like cancer, coronary heart disease, and others based on the identification of genomic variants that are associated with complex diseases.
But as is often the case when new technology is adopted, one potential drawback that investigators have observed is a lack of consistency in the calculation of polygenic risk scores. In an effort to change that, new research funded by the National Human Genome Research Institute (NHGRI) and published in the journal Nature includes a 22-item framework that identifies the minimal polygenic risk score-related information that scientists should include in their studies. Their hope is that this new proposed standard will help ensure the validity, transparency, and reproducibility of polygenic risk scores.
The NHGRI's Clinical Genome Resource's (ClinGen) Complex Disease Working Group collaborated with the Polygenic Score Catalog, an open database of polygenic risk scores, to formulate the new framework. The NHGRI is part of the National Institutes of Health (NIH).
"A real challenge is that the research community has not adopted any universal best practices for reporting polygenic risk scores," said Erin Ramos, PhD, a program director for the ClinGen Complex Disease Working Group, deputy director of the NHGRI Division of Genomic Medicine, and co-author of the paper, in a statement. "With the field growing as fast as it is, we need standards in place so we can meaningfully evaluate these scores and determine which ones are ready to be used in clinical care."
The new framework builds off of a 2011 best practice model, the Genetic Risk Prediction Studies (GRIPS) statement, which was formed by an international working group and emphasized models with a smaller set of genomic variants and gene scores. But nowadays, polygenic risk scores are calculated based on DNA variants in more than 6 billion locations in the human genome—a much larger set than GRIPS.
The broader categories in the suggested minimum reporting standard cover background on the study type and risk model; study population and data; risk model development and application; risk model evaluation; limitations and clinical implications; and data transparency and availability. Each includes more detailed subcategories to ensure thorough reporting.
Researchers hope the new framework, which includes items such as detailing the study population and why that population was chosen, will renew the emphasis on standardized reporting as polygenic risk scores become more widely used.
"If we are to make these scores available to people around the world, the studies need to define who they are studying and why, in the clearest way possible," co-author Katrina Goddard, PhD, director of Translational and Applied Genomics at the Kaiser Permanente Center for Health Research, said. "Without that transparency and reproducibility, efforts to use polygenic risk scores may be undermined."
Explaining the statistical methods used to develop and validate risk scores and noting any potential limitations will be key in research going forward, the framework suggests. Then, the utility of the scores for assessing disease risk can be compared more easily.
"If researchers can follow these guidelines, it will be more straightforward to evaluate published polygenic risk scores and decide which ones are a good fit for the clinical setting," Michael Inouye, PhD, director of the Cambridge Baker Systems Genomics Initiative and co-senior author of the paper, said. "For diseases such as breast cancer and many others, we will be able to responsibly place patients in different risk categories and provide beneficial screening strategies and treatments. Ideally, in the future, we will detect risk early enough to combat the disease effectively."
Wand H, Lambert SA, Tamburro C, et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature. Published online March 10, 2021. doi:10.1038/s41586-021-03243-6