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Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study
BACKGROUND: Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331983/ https://www.ncbi.nlm.nih.gov/pubmed/32614825 http://dx.doi.org/10.1371/journal.pmed.1003152 |
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author | Forgetta, Vincenzo Keller-Baruch, Julyan Forest, Marie Durand, Audrey Bhatnagar, Sahir Kemp, John P. Nethander, Maria Evans, Daniel Morris, John A. Kiel, Douglas P. Rivadeneira, Fernando Johansson, Helena Harvey, Nicholas C. Mellström, Dan Karlsson, Magnus Cooper, Cyrus Evans, David M. Clarke, Robert Kanis, John A. Orwoll, Eric McCloskey, Eugene V. Ohlsson, Claes Pineau, Joelle Leslie, William D. Greenwood, Celia M. T. Richards, J. Brent |
author_facet | Forgetta, Vincenzo Keller-Baruch, Julyan Forest, Marie Durand, Audrey Bhatnagar, Sahir Kemp, John P. Nethander, Maria Evans, Daniel Morris, John A. Kiel, Douglas P. Rivadeneira, Fernando Johansson, Helena Harvey, Nicholas C. Mellström, Dan Karlsson, Magnus Cooper, Cyrus Evans, David M. Clarke, Robert Kanis, John A. Orwoll, Eric McCloskey, Eugene V. Ohlsson, Claes Pineau, Joelle Leslie, William D. Greenwood, Celia M. T. Richards, J. Brent |
author_sort | Forgetta, Vincenzo |
collection | PubMed |
description | BACKGROUND: Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)—a heritable risk factor for osteoporotic fracture—can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS: A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed “gSOS”, and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)–based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r(2) = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS: Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention. |
format | Online Article Text |
id | pubmed-7331983 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73319832020-07-14 Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study Forgetta, Vincenzo Keller-Baruch, Julyan Forest, Marie Durand, Audrey Bhatnagar, Sahir Kemp, John P. Nethander, Maria Evans, Daniel Morris, John A. Kiel, Douglas P. Rivadeneira, Fernando Johansson, Helena Harvey, Nicholas C. Mellström, Dan Karlsson, Magnus Cooper, Cyrus Evans, David M. Clarke, Robert Kanis, John A. Orwoll, Eric McCloskey, Eugene V. Ohlsson, Claes Pineau, Joelle Leslie, William D. Greenwood, Celia M. T. Richards, J. Brent PLoS Med Research Article BACKGROUND: Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)—a heritable risk factor for osteoporotic fracture—can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS: A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed “gSOS”, and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)–based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r(2) = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS: Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention. Public Library of Science 2020-07-02 /pmc/articles/PMC7331983/ /pubmed/32614825 http://dx.doi.org/10.1371/journal.pmed.1003152 Text en © 2020 Forgetta et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Forgetta, Vincenzo Keller-Baruch, Julyan Forest, Marie Durand, Audrey Bhatnagar, Sahir Kemp, John P. Nethander, Maria Evans, Daniel Morris, John A. Kiel, Douglas P. Rivadeneira, Fernando Johansson, Helena Harvey, Nicholas C. Mellström, Dan Karlsson, Magnus Cooper, Cyrus Evans, David M. Clarke, Robert Kanis, John A. Orwoll, Eric McCloskey, Eugene V. Ohlsson, Claes Pineau, Joelle Leslie, William D. Greenwood, Celia M. T. Richards, J. Brent Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study |
title | Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study |
title_full | Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study |
title_fullStr | Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study |
title_full_unstemmed | Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study |
title_short | Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study |
title_sort | development of a polygenic risk score to improve screening for fracture risk: a genetic risk prediction study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331983/ https://www.ncbi.nlm.nih.gov/pubmed/32614825 http://dx.doi.org/10.1371/journal.pmed.1003152 |
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