Clinical review: Genome-wide association studies of skeletal phenotypes: what we have learned and where we are headed

Abstract

Context: The primary goals of genome-wide association studies (GWAS) are to discover new molecular and biological pathways involved in the regulation of bone metabolism that can be leveraged for drug development. In addition, the identified genetic determinants may be used to enhance current risk factor profiles.

Evidence acquisition: There have been more than 40 published GWAS on skeletal phenotypes, predominantly focused on dual-energy x-ray absorptiometry-derived bone mineral density (BMD) of the hip and spine.

Evidence synthesis: Sixty-six BMD loci have been replicated across all the published GWAS, confirming the highly polygenic nature of BMD variation. Only seven of the 66 previously reported genes (LRP5, SOST, ESR1, TNFRSF11B, TNFRSF11A, TNFSF11, PTH) from candidate gene association studies have been confirmed by GWAS. Among 59 novel BMD GWAS loci that have not been reported by previous candidate gene association studies, some have been shown to be involved in key biological pathways involving the skeleton, particularly Wnt signaling (AXIN1, LRP5, CTNNB1, DKK1, FOXC2, HOXC6, LRP4, MEF2C, PTHLH, RSPO3, SFRP4, TGFBR3, WLS, WNT3, WNT4, WNT5B, WNT16), bone development: ossification (CLCN7, CSF1, MEF2C, MEPE, PKDCC, PTHLH, RUNX2, SOX6, SOX9, SPP1, SP7), mesenchymal-stem-cell differentiation (FAM3C, MEF2C, RUNX2, SOX4, SOX9, SP7), osteoclast differentiation (JAG1, RUNX2), and TGF-signaling (FOXL1, SPTBN1, TGFBR3). There are still 30 BMD GWAS loci without prior molecular or biological evidence of their involvement in skeletal phenotypes. Other skeletal phenotypes that either have been or are being studied include hip geometry, bone ultrasound, quantitative computed tomography, high-resolution peripheral quantitative computed tomography, biochemical markers, and fractures such as vertebral, nonvertebral, hip, and forearm.

Conclusions: Although several challenges lie ahead as GWAS moves into the next generation, there are prospects of new discoveries in skeletal biology. This review integrates findings from previous GWAS and provides a roadmap for future directions building on current GWAS successes.

Fig. 1.

GWAS design.

Fig. 2.

Biological pathways and functional interaction network analyses for BMD GWAS loci. Molecular pathways and functional gene groups were obtained from KEGG, Biocarta, and the Ingenuity knowledge database (canonical pathways and gene ontology). We performed a gene-set enrichment analysis on 66 BMD GWAS loci. Boxes with orange borders are skeletal pathways or skeletal gene groups. Blue boxes are other pathways or gene groups. Genes in red boxes and red circles may be involved in known biological pathways but were not enriched (P > 0.05) in any pathways/gene groups among 66 BMD GWAS loci. The edges (lines) connecting genes, boxes, or circles are represented as functional interactions among genes with P values <0.05 (after multiple testing corrections) from GRAIL analyses.

Fig. 3.

The area under receiver operating characteristics (ROC) curves of genetic risk scores predicting the risk of osteoporosis (T-score ≤ −2.5) in 2836 postmenopausal EU women (Supplementary Fig. 8, Ref. 30).

Fig. 4.

Relation between sample size and number of GWAS loci identified. The blue line is the linear trajectory (linear regression excluded the study with the largest sample size); and the red line is the exponential trajectory (exponential regression included the study with largest sample size).