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Multiple polygenic scores improve bone mineral density prediction in an independent sample of Caucasian women
  1. Xiangxue Xiao1,2,
  2. Qing Wu1,2
  1. 1Nevada Institute of Personalized Medicine, College of Science, University of Nevada Las Vegas, Las Vegas, Nevada, USA
  2. 2Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada Las Vegas, Las Vegas, Nevada, USA
  1. Correspondence to Dr Qing Wu, Nevada Institute of Personalized Medicine, College of Science, University of Nevada Las Vegas, Las Vegas, Nevada, USA; qing.wu{at}unlv.edu

Abstract

Purpose of the study To determine if multiple Genetic Risk Scores (GRSs) improve bone mineral density (BMD) prediction over single GRS in an independent sample of Caucasian women.

Study design Based on summary statistics of four genome-wide association studies related to two osteoporosis-associated traits, namely BMD and heel quantitative ultrasound derived estimated BMD (eBMD), four GRSs were derived for 1205 individuals in the Genome-Wide Scan for Female Osteoporosis Gene Study. The effect of each GRS on BMD variation was assessed using multivariable linear regression, with conventional risk factors adjusted for. Next, the eBMD-related GRS that explained the most variance in BMD was selected to be entered into a multi-score model, along with the BMD-related GRS. Elastic net regularised regression was used to develop the multiscore model, which estimated the joint effect of two GRSs (GRS_BMD and GRS_eBMD) on BMD variation, after being adjusted for conventional risk factors.

Results With the same clinical risk factors having been adjusted for, the model that included GRS_BMD performed best by explaining 32.53% of the variance in BMD; the single-score model that included GRS_eBMD explained 34.03% of BMD variance. The model that includes both GRS_BMD and GRS_ eBMD, as well as the clinical risk factors, aggregately explained 35.05% in BMD variation. Compared with the single GRS models, the multiscore model explained significantly more variance in BMD.

Conclusions The multipolygenic score model explained a considerable amount of BMD variation. Compared with single score models, multipolygenic score model provided significant improvement in explaining BMD variation.

  • calcium & bone
  • genetics

Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study. All data relevant to the study are included in the article or uploaded as online supplemental information.

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Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study. All data relevant to the study are included in the article or uploaded as online supplemental information.

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Footnotes

  • Contributors Guarantor, QW; Conceptualisation, QW; methodology, QW and XX; software, XX; validation, QW and XX; formal analysis, XX; investigation, QW and XX; resources, QW; data curation, XX; writing—original draft preparation, XX; writing—review and editing, QW and XX; visualisation, XX; supervision, QW; project administration, QW; funding acquisition, QW.

  • Funding The research was funded by a grant from the National Institute of General Medical Sciences (P20GM121325), as well as a grant from the National Institute on Minority Health and Health Disparities of the National Institutes of Health (R15MD010475).

  • Disclaimer The funding sponsors were not involved in the analysis design, genotype imputation, data analysis, interpretation of the analysis results, or the preparation, review, or approval of this manuscript.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability The data used in the current study is publically available through the database of Genotype and Phenotype (dbGap) (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000390.v1.p1)

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