Researchers develop guidelines for reporting polygenic risk scores
Researchers hope the new approach will be used as a framework for
publishing studies on polygenic risk scores

Date:
March 10, 2021
Source:
NIH/National Human Genome Research Institute
Summary:
Scientists and healthcare providers are beginning to use polygenic
risk scores for assessing a person's inherited risk for common
complex diseases. But researchers have observed inconsistencies
in how such scores are calculated and reported. To address this
concern, researchers have published a framework that identifies the
minimal polygenic risk score-related information that scientists
should include in their studies.



FULL STORY ========================================================================== Scientists and healthcare providers are beginning to use a new approach
for assessing a person's inherited risk for diseases like Type 2 diabetes, coronary heart disease and breast cancer, which involves calculating a polygenic risk score. The score provides an estimate of an individual's
risk for specific diseases, based on their DNA changes related to those diseases.


========================================================================== Despite the rise in studies using polygenic risk scores, researchers
have observed inconsistencies in how such scores are calculated and
reported. These differences threaten to compromise the adoption of
polygenic risk scores in clinical care.

To address this concern, the research teams, funded primarily by the
National Human Genome Research Institute (NHGRI), have published a 22-item framework in the journal Nature that identifies the minimal polygenic
risk score-related information that scientists should include in their
studies. This framework - - created by NHGRI's Clinical Genome Resource's (ClinGen) Complex Disease Working Group and the Polygenic Score Catalog
(PGS), an open database of polygenic risk scores -- will help promote the validity, transparency and reproducibility of polygenic risk scores. NHGRI
is part of the National Institutes of Health.

To calculate a person's polygenic risk score, researchers survey DNA
variants in over 6 billion locations in the human genome.

"A real challenge is that the research community has not adopted any
universal best practices for reporting polygenic risk scores," said Erin
Ramos, Ph.D., a program director for ClinGen, deputy director of the
NHGRI Division of Genomic Medicine and co-author of the paper. "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." This framework builds off another
best practice model called the Genetic Risk Prediction Studies (GRIPS) statement, published by an international working group in 2011. GRIPS
placed an emphasis on models that included a smaller set of genomic
variants and gene scores. However, genetic risk prediction models
have evolved rapidly since then, and are based on a much larger set of
genomic variants and more complex methodologies. Also, researchers have
not fully adopted the GRIPS framework.

"A renewed emphasis on reporting standards by ClinGen and the Polygenic
Score Catalog comes at a crucial time for polygenic risk scores," said Genevieve Wojcik, Ph.D., M.H.S., an assistant professor of epidemiology
at the Johns Hopkins Bloomberg School of Public Health, Baltimore, and corresponding author of the paper. "It specifies the minimum information
that should be described in a research paper for interpreting a polygenic
risk score, reproducing results and eventually translating the information
into clinical care." Some of the new reporting framework items include detailing the study population and the basis for choosing that population.

"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," said Katrina Goddard, Ph.D., director of Translational and Applied Genomics at the Kaiser Permanente Center for Health Research,
Portland, Oregon, who also co-authored the paper. "Without that
transparency and reproducibility, efforts to use polygenic risk scores
may be undermined." The new framework suggests that scientists should
explain the statistical methods they used to develop and validate the
polygenic risk scores. Without a consistent way of reporting polygenic
risk scores, it is nearly impossible to compare the utility of the scores
for assessing disease risk in people.

According to the new guidelines, researchers should also consider
potential limitations of these scores and how clinicians should use the
scores in patient care.

"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," said
Michael Inouye, Ph.D., director of the Cambridge Baker Systems
Genomics Initiative, U.K., and co-senior author of the paper. "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." ========================================================================== Story Source: Materials provided by NIH/National_Human_Genome_Research_Institute. Original written by Prabarna Ganguly, Ph.D.. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Hannah Wand, Samuel A. Lambert, Cecelia Tamburro, Michael
A. Iacocca,
Jack W. O'Sullivan, Catherine Sillari, Iftikhar J. Kullo,
Robb Rowley, Jacqueline S. Dron, Deanna Brockman, Eric Venner,
Mark I. McCarthy, Antonis C. Antoniou, Douglas F. Easton, Robert
A. Hegele, Amit V. Khera, Nilanjan Chatterjee, Charles Kooperberg,
Karen Edwards, Katherine Vlessis, Kim Kinnear, John N. Danesh,
Helen Parkinson, Erin M. Ramos, Megan C. Roberts, Kelly E. Ormond,
Muin J. Khoury, A. Cecile J. W.

Janssens, Katrina A. B. Goddard, Peter Kraft,
Jaqueline A. L. MacArthur, Michael Inouye, Genevieve
L. Wojcik. Improving reporting standards for polygenic scores
in risk prediction studies. Nature, 2021; 591 (7849): 211 DOI:
10.1038/s41586-021-03243-6 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/03/210310122540.htm

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