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Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes

Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John Kemp, John A Morris, John A Kanis, Douglas P. Kiel, Eugene V McCloskey, Fernando Rivadeneira, Helena Johannson, Nicholas Harvey, Cyrus Cooper, David M Evans, Joelle Pineau, William D Leslie, Celia MT Greenwood, J Brent Richards
doi: https://doi.org/10.1101/413716
Vincenzo Forgetta
1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
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Julyan Keller-Baruch
2Department of Human Genetics, McGill University, Montréal, Québec, Canada
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Marie Forest
1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
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Audrey Durand
3School of Computer Science, McGill University, Montréal, Québec, Canada
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Sahir Bhatnagar
1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
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John Kemp
4University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
5MRC Integrative Epidemiology Unit, University of Bristol, UK
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John A Morris
1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
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John A Kanis
6Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, UK, and Australian Catholic University, Melbourne, Australia
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Douglas P. Kiel
7Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Broad Institute of MIT & Harvard University, Boston, USA
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Eugene V McCloskey
8Mellanby Centre for Bone Research, Centre for Integrated Research in Musculoskeletal Ageing, University of Sheffield, and Sheffield Teaching Hospitals Foundation Trust, Sheffield, UK
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Fernando Rivadeneira
9Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Netherlands
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Helena Johannson
6Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, UK, and Australian Catholic University, Melbourne, Australia
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Nicholas Harvey
10Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
11National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
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Cyrus Cooper
10Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
11National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
12National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
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David M Evans
4University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
5MRC Integrative Epidemiology Unit, University of Bristol, UK
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Joelle Pineau
3School of Computer Science, McGill University, Montréal, Québec, Canada
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William D Leslie
13Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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Celia MT Greenwood
1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
2Department of Human Genetics, McGill University, Montréal, Québec, Canada
14Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
15Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada
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J Brent Richards
1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
2Department of Human Genetics, McGill University, Montréal, Québec, Canada
16Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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Abstract

Background Genomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)—a risk factor for osteoporotic fracture.

Methods We used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic pre-screening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.

Results gSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], P = 10-28) than that for measured SOS (OR = 1.60 [1.50-1.69], P = 10-52) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], P = 10-6). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10-18) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10-14). Introducing a genomic pre-screening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%.

Interpretation The use of genotypes in a machine learning algorithm resulted in a clinically-relevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.

Evidence Before this Study Genome-wide association studies have identified many loci associated with risk of clinically-relevant fracture risk factors, such as SOS. Yet, it is unclear if such information can be leveraged to identify those at risk for disease outcomes, such as osteoporotic fractures. Most previous attempts to predict disease risk from genotypes have used polygenic risk scores, which may not be optimal for genomic-prediction. Despite these obstacles, genomic-prediction could enable screening programs to be more efficient since most people screened in a population are not determined to have a level of risk that would prompt a change in clinical care. Genomic pre-screening could help identify individuals whose risk of disease is low enough that they are unlikely to benefit from screening.

Added Value of this Study Using a large dataset of 426,811 individuals we trained and tested a machine learning algorithm to genomically-predict SOS. This metric, gSOS, had performance characteristics for predicting fracture risk that were similar to measured SOS and femoral neck BMD. Implementing a gSOS-based pre-screening step into the UK-based osteoporosis treatment guidelines reduced the number of individuals who would require screening clinical visits and skeletal testing by approximately 50%, while having little impact on the sensitivity to identify individuals at high risk for osteoporotic fracture.

Implications of all of the Available Evidence Clinically-relevant genomic prediction of heritable traits is feasible using the machine learning algorithm presented here in large sample sizes. Genome-wide genotyping is now less expensive than many clinical tests, needs to be performed once over a lifetime and could risk stratify for multiple heritable traits and diseases years prior to disease onset, providing an opportunity for prevention. The implementation of such algorithms could improve screening efficiency, yet their cost-effectiveness will need to be ascertained in subsequent analyses.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 12, 2018.
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Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes
Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John Kemp, John A Morris, John A Kanis, Douglas P. Kiel, Eugene V McCloskey, Fernando Rivadeneira, Helena Johannson, Nicholas Harvey, Cyrus Cooper, David M Evans, Joelle Pineau, William D Leslie, Celia MT Greenwood, J Brent Richards
bioRxiv 413716; doi: https://doi.org/10.1101/413716
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Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes
Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John Kemp, John A Morris, John A Kanis, Douglas P. Kiel, Eugene V McCloskey, Fernando Rivadeneira, Helena Johannson, Nicholas Harvey, Cyrus Cooper, David M Evans, Joelle Pineau, William D Leslie, Celia MT Greenwood, J Brent Richards
bioRxiv 413716; doi: https://doi.org/10.1101/413716

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