An estimated 200 million individuals worldwide are affected by osteoporosis, with the numbers expected to rise to approximately 212 million by 2050.1 In addition, therapeutic options for osteoporosis are limited and outcomes generally poor. Owing to the increasing older populations globally, this presents a significant health burden.

The completion of the World Genome Project in 20032 and the identification of the vast number of single-nucleotide polymorphisms (SNPs) have led to the development of post hoc analyses based on the effect of individual and combined patterns of genetic traits on disease susceptibility and outcomes.1,2

In 2007, the “first” genome-wide association study (GWAS) of the most commonly measured osteoporosis-related phenotypes was published by Kiel et al.3 Hundreds of loci and SNPs with associations to osteoporosis have since been identified.1,4,5 In a recent review, Zhu et al1 summarized the clinical use of GWAS findings in the bone field, which included identification of risk factors, development of drug targets, and disease prediction.

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Although a range of modifiable factors contribute to bone loss, including diet, exercise, medications, and comorbidities, osteoporosis is largely mediated by genetic factors, making it a promising field for GWAS.1

Limitations to First-Generation GWAS

Early research studies were focused on the incidence of changes in bone mineral density (BMD), osteoporosis, and/or osteoporotic fractures. While data from these analyses were clinically insightful, they had limitations, which may explain the lack of new therapies in osteoporosis.1,3

One such study, conducted by Kiel et al, included 1141 individuals from the Framingham Heart Study, most of whom were European and White.1,3

Another limitation of these studies has been the focus on the associations with BMD compared with other aspects of osteoporosis and fracture. Studies conducted before the discovery of GWAS demonstrated a significant correlation between decreased BMD and actual risk for fracture at 46% to 66%.1 The majority of these studies used dual energy x-ray absorption (DXA) scans to evaluate BMD parameters, such as periosteal expansion, trabecular volume, thickness and number, and cortical volume and thickness.1,4 An important finding of the GWAS conducted on BMD is the consistent support of the direct correlation between decreasing BMD and the occurrence of fractures.5

Discovering Markers of Fracture Risk Using GWAS

Studies conducted on fracture risk, using data from the Genetic Factors of Osteoporosis (GEFOS) consortium and the Genetic Factors for Osteoporosis consortium, had large sample sizes.1 The first study of the GEFOS cohort resulted in the identification of 56 loci for fracture risk associated with BMD. Results of GEFOS-2 produced 6 more associations, although both the definitions of fracture and the demographics of the sample populations were too varied to be conclusive; further research is needed in this area.1

A large-scale GWAS meta-analysis by Trajanoska et al6 including 25 cohorts with genome-wide genotyping and fracture data identified 15 genetic determinants of fracture, such as genetic predispositions to lower vitamin D and calcium intake, as well as clinical risk factors from comorbidities, such as diabetes and rheumatoid arthritis. These factors were shown to influence BMD, but they did not independently influence fracture risk, and, in fact, the total effect of all other SNPs on fracture risk was smaller than that for BMD alone.6 Authors of this study reported, “Using Mendelian [randomization] analyses, we demonstrated that genetically decreased [BMD] was the only clinical risk factor among those tested, with evidence for an effect on fracture risk.”6

Possible suggested markers of specific types of osteoporosis fractures were also demonstrated in the presence of obesity and serum estradiol concentrations, and with early menopause among adult women. Late puberty in adolescent girls and adiposity in children were also causally linked to fracture risk.1

Most studies did not find a causal relationship between serum levels of urate, homocysteine, thyroid stimulating hormone, alcohol consumption, smoking status, and fracture risk.1 A Mendelian randomization GWAS from 2020 found an inverse correlation between the concentrations of serum parathyroid hormone, which regulates calcium absorption, and BMD of the forearm, femoral neck, and lumbar spine.7

Targeting Pathways for Novel Treatments

The majority of treatments in use for bone loss inhibit osteoclast resorption, based on numerous previous knockout studies in mice.4 While these therapies are important to reduce fracture risk, they cannot replace bone tissue already lost, and so other anabolic therapies need to be sought.

Hundreds of genetic loci discovered by recent GWAS investigations were found to be in locations already known to be active for bone metabolism, and drug targets identified in this manner are approximately twice as likely to be proven successful in clinical trials.4,5 Repurposing existing drugs can also be greatly enhanced by genetic information mined from GWAS; loci relevant to BMD and fracture risk can rapidly be matched to drugs already in use in other therapeutic areas.1

Barriers and Future of GWAS in the Bone Field

Despite an abundance of discoveries of genomes related to bone loss and risks for fractures, the vast majority of GWAS (79%) have included European (White) populations.1 The primary goal moving forward will be to carry out major genetic sequencing studies in other ethnic groups, particularly among Asians, who make up an estimated 20% of the world’s population.1,5

Consistently positive correlations between BMD and future risk for fracture indicate an important pathway as a target for novel therapeutics. Further investigation into other causal pathways can lead to the discovery of multiple therapeutic interventions, and yet, these studies have not been undertaken in bone tissue. A review by Sabik et al4 in 2017 observed that one of the main barriers efforts was the lack of repositories of large-population samples taken from the bone tissue and bone cells that can provide the extensive range of transcript and genetic omics data needed to fuel high-quality GWAS.

Anabolic therapeutic agents that build new bone are needed to reverse bone loss and prevent future osteoporosis.3

In Summary

The enormous potential of GWAS to change the treatment paradigm in osteoporosis and fracture risk has not yet been met. Although the previous 12 years of GWAS have yielded vast new information about the genetics and mechanisms of bone loss and the consequences of fractures, they have not resulted in new treatment options, or more importantly, the prevention of age-related bone loss. Focus on the issues addressing study design and access to the broadest cohorts will produce more concentrated results that may open doors to these new therapies.


  1. Zhu X, Bai W, Zheng H. Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications. Bone Res. 2021;9(1):23. doi:10.1038/s41413-021-00143-3
  2. NIH National Human Genome Research Institute. 2003: Human Genome Project completed. Updated November 26, 2014. Accessed July 14, 2021.
  3. Kiel DP, Demissie S, Dupuis J, Lunetta KL, Murabito JM, Karasik D. Genome-wide association with bone mass and geometry in the Framingham Heart Study. BMC Med Genet. 2007;8(Suppl 1):S14. doi:10.1186/1471-2350-8-S1-S14
  4. Sabik OL, Farber CR. Using GWAS to identify novel therapeutic targets for osteoporosis. Transl Res. 2017;181:15-26. doi:10.1016/j.trsl.2016.10.009
  5. Trajanoska K, Rivadeneira F. The genetic architecture of osteoporosis and fracture risk. Bone. 2019;126:2-10. doi:10.1016/j.bone.2019.04.005
  6. Trajanoska K, Morris JA, Oei L, et al. Assessment of the genetic and clinical determinants of fracture risk: genome wide association and Mendelian randomisation study. BMJ. 2018;362:k3225. doi:10.1136/bmj.k3225
  7. Qu Z, Yang F, Hong J, Wang W, Yan S. Parathyroid hormone and bone mineral density: a Mendelian randomization study. J Clin Endocrinol Metab. 2020;105(11):579. doi:10.1210/clinem/dgaa579