genetics of osteoporosis from genome-wide association ... · genetics of osteoporosis from...

Osteoporosis is a common disease characterized by an increased propensity to fracture owing to decreased bone mass and bone quality1. Over 9 million oste-oporotic fractures occur worldwide, and of these approximately 2 million occur per year in the United States, incurring US$17 billion in direct costs annu-ally: a burden that is projected to increase by 50% by 2025 (REF. 2). Therefore, osteoporotic fractures are common and inflict a substantial economic, social and clinical burden.

Clinically, osteoporosis is diagnosed when a patient presents with a fracture that has resulted from mini-mal trauma (such as a fall from standing height). Preventive therapies exist for fractures, and so indi-viduals are screened through measurement of bone mineral density (BMD). In combination with clinical risk factors, osteoporosis is commonly diagnosed, for the purposes of preventive therapy, through measure-ment of BMD3. Thus, although low BMD is only a risk factor for fracture, much like hypertension is a risk for myocardial infarction, therapeutic decisions that are aimed at preventing fracture are often based on BMD measurement.

BMD has a high heritability: estimates lie between 50 and 85%4–6. Although BMD is the most important clinical risk factor for osteoporotic fracture, apart from age and sex, most individuals who develop osteoporo-tic fractures do not have BMD-defined osteoporosis7,8, suggesting that factors that are unrelated to BMD have a strong impact on the risk of fracture. Osteoporotic

fractures themselves have moderate heritability, with estimates of 54% and 68% for wrist and hip fractures, respectively, in peri-menopausal women9,10. However, this heritability appears to decrease dramatically with age, such that after 79 years, estimates of the heritability for hip fractures drop to 3%10.

Linkage studies have identified loci for BMD11–14, but these findings have not been replicated between studies, and when nine linkage studies were meta-analysed, involving a total of 11,842 subjects, no loci were associated15 (reviewed elsewhere16). Therefore, it seemed that the allelic architecture of BMD would not be amenable to the large effect sizes that are required for linkage studies. This led investigators to seek osteoporosis loci by focusing on candidate regions, but again most of these studies have not been subse-quently replicated by larger and more rigorous studies with systematic phenotypic and genotypic definitions across cohorts17. By contrast, genome-wide association studies (GWASs) have enjoyed considerable success in identifying replicated loci that are associated with osteoporosis.

In this Review, we will first introduce the various GWAS designs that have allowed the identification of osteoporosis loci that will serve to provide insights into pathophysiologic mechanisms. Then, novel drug tar-gets and the ability to identify people who are at risk of fracture through genotypic profiles will be discussed. Finally, the possible future directions for research into the genetics of osteoporosis will be presented.

1Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada.2Twin Research and Genetic Epidemiology, King’s College London, London, UK.Correspondence to J.B.R. e-mail: [email protected]:10.1038/nrg3228Corrected online 7 August 2012

Genetics of osteoporosis from genome-wide association studies: advances and challengesJ. Brent Richards1,2, Hou-Feng Zheng1 and Tim D. Spector2

Abstract | Osteoporosis is among the most common and costly diseases and is increasing in prevalence owing to the ageing of our global population. Clinically defined largely through bone mineral density, osteoporosis and osteoporotic fractures have reasonably high heritabilities, prompting much effort to identify the genetic determinants of this disease. Genome-wide association studies have recently provided rapid insights into the allelic architecture of this condition, identifying 62 genome-wide-significant loci. Here, we review how these new loci provide an opportunity to explore how the genetics of osteoporosis can elucidate its pathophysiology, provide drug targets and allow for prediction of future fracture risk.

D I S E A S E M E C H A N I S M S

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576 | AUGUST 2012 | VOLUME 13 www.nature.com/reviews/genetics

© 2012 Macmillan Publishers Limited. All rights reserved

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deCODE geneticsAn Icelandic company that specializes in the identification of genetic risk factors for disease.

GWASs for BMDBefore GWASs, studies of osteoporosis involved link-age and candidate gene association studies. Given the failure of linkage studies, researchers turned their focus to candidate gene studies. This candidate-based strategy was comprehensively tested by assessing all candidate genes implicated in osteoporosis and/or fracture in a large consortium of five population-based studies involving 19,195 participants17. In this study, only 9 of the 150 previously tested candidate loci harboured common variants that are associated with BMD, and only four loci harboured common variants for fracture. Although this study was limited insofar as it was able to test only SNPs that were cap-tured by HapMap SNP proxies, these findings suggest that most previously studied candidate genes were not replicated in a well-powered study with standardized phenotyping and genotyping. An exception to this arose from candidate gene studies for low-density lipoprotein receptor-related protein 5 (LRP5) for BMD and fracture18,19.

Whereas candidate gene and linkage studies have not contributed substantial gains in osteoporo-sis genetics, GWASs have identified 62 loci that are genome-wide-significant (which, for the purpose of this Review, is defined as P < 5 × 10−8) for BMD at either the lumbar spine or the femoral neck. This Review focuses on GWASs that use SNP data from >200,000 SNPs and that include a replication sample (we there-fore do not intend to review copy-number-variant- or pathway-based analyses using GWAS data). To date, 14 such GWASs have been published for BMD, which is the clinically relevant measure of osteoporosis. TABLE 1 summarizes all of these GWASs, and TABLE 2 describes the loci identified.

Initial GWASs. In 2008, two GWASs were published using discovery phase data from the TwinsUK/Rotterdam20 and deCODE Genetics21 studies. Together, these independent studies identified five loci that were associated at a genome-wide-significant level (P < 5 × 10−8) with BMD (TABLES 1,2). In addition, LRP5, zinc finger and BTB domain containing 40 (ZBTB40) and spectrin, beta, non-erythrocytic 1 (SPTBN1) were associated with a risk of osteoporotic fracture.

deCODE Genetics identified two new loci that were genome-wide-significant for BMD, both of which had significant effects on the risk of fracture22. The first GWAS reported for BMD in children identified the SP7 locus, which encodes the transcription factor osterix, as being associated with BMD, and replication was subse-quently achieved in three additional adult populations. Interestingly, variants in osterix were also associated with height in children23.

Multi-ethnic studies. Multi-ethnic studies permit the identification of alleles that are shared across populations. The first multi-ethnic BMD GWAS used Europeans as a discovery population, with follow-up in Europeans, Asians and subjects of African ancestry from Tobago. This particular GWAS identified genome-wide-significant variants in ADAM metallopeptidase with thrombospon-din type 1 motif, 18 (ADAMTS18)24, but this locus was not found to be genome-wide-significant in two larger meta-analyses of European and Asian populations from the Genetic Factors for Osteoporosis (GEFOS) consor-tium25,26 (which is discussed in more detail below). A dif-ferent multi-ethnic GWAS, this time involving Chinese women as the discovery cohort with replication in six independent populations of European and Asian descent, identified a novel locus with marginal genome-wide

Table 1 | GWASs for BMD and osteoporotic fracture

Study group (year) Total maximum sample size

Total number of GWAS-identified loci achieving genome-wide significance*

Number of new GWAS-identified loci achieving genome-wide significance

Refs

Richards et al. (2008) 8,557 2 2 20

Styrkarsdottir et al. (2008) 13,786 4 3 21

Styrkarsdottir et al. (2008) 15,375 7 2 22

Timpson et al. (2009) 5,275 1 1 23

Xiong et al. (2009) 9,109 1 1 24

Rivadeneira et al. (2009) 19,195 20 13 25

Guo et al. (2010) 11,568 1 1 35

Kung et al. (2010) 18,898 1 1 81

Hsu et al. (2010) 11,290 4 0 30

Koller et al. (2010) 2,193 0 0 82

Duncan et al. (2011) 21,798 2 2 29

Estrada et al. (2012) 83,894 56 32 26

Zheng et al. (2012) 5,672 1 0 31

Medina-Gomez et al. (2012) 13,712 1 0 32

*Genome-wide significance is defined as P < 5×10−8. Studies include only genome-wide association studies (GWASs) using SNP data from >200,000 SNPs and including a replication sample or a meta-analysis of several cohorts. BMD, bone mineral density.

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significance (P = 5.3 × 10−8), and these results were rep-licated in the larger multi-ethnic GEFOS-2 analysis26. Thus, multi-ethnic studies have been helpful in iden-tifying risk variants that are shared across populations. However, further work could be done to leverage the dif-ferential allelic distributions across different ethnicities to identify further loci, as has been described recently27,28.

The use of extreme phenotypes and expression data. Using an alternative design from a case–control study of extremely high versus extremely low hip BMD, two novel loci (namely, UDP-N-acetyl-alpha-d-galactosamine:polypeptide N-acetylgalactosaminyltransferase 3 (GALNT3) and R-spondin 3 (RSPO3)) were identified to be genome-wide-significant for hip BMD29. These loci were also genome-wide-significant in subsequent meta-analysis, including in data from REFS 25,26. Although it ranks among the smallest discovery cohorts used in GWASs for BMD, this study showed that smaller dis-covery cohorts using individuals from the extremes of the phenotypic continuum could be used to generate findings that were replicated in the rest of the pheno-typic distribution, thereby providing a paradigm for cost-efficient GWASs of continuous traits in which the cost of selection and screening is cheaper than geno-typing. Integrating functional data can also help to pri-oritize candidates. Using expression data from humans and animal models in addition to GWAS signals, the Framingham cohort was able to prioritize SNPs aris-ing from GWASs at tumour necrosis factor receptor superfamily, member 11b (TNFRSF11B; commonly known as OPG), wntless homologue (WLS; also known as GPR177) and the transcription factor SOX6 (REF. 30). Although these signals were identified in previous GWASs, their prioritization method outlined an efficient way of selecting SNPs for further validation.

Other BMD sites. Recently, the WNT16–FAM3C locus was identified to be associated with forearm BMD, corti-cal bone thickness, osteoporotic fracture risk and bone strength in a mouse knockout of Wnt16 (REF. 31). These findings strongly suggest an important role for WNT16 in cortical bone strength, as the BMD site (namely, the forearm) is rich in cortical bone, and SNPs at this locus strongly influenced forearm fracture risk in humans (odds ratio = 1.33, P = 7.3 × 10−9). This same locus was identified to be associated with total-body and skull BMD in children and adults32.

Large-scale meta-analyses. Combining data from five genome-wide association studies involving 19,195 sub-jects of European descent, the first large-scale meta-anal-ysis for BMD was undertaken. This GEFOS consortium identified 13 novel regions that were genome-wide-significant for BMD25. This effort therefore more than doubled the number of loci to be associated with BMD at a genome-wide-significant level.

Further insights into the allelic architecture and genetic determinants of BMD arose from the second GEFOS meta-analysis, which involved 32,961 individu-als in the discovery phase and was replicated in 50,933

Table 2 | Loci associated with BMD at genome-wide-significant levels*

Locus Nearest gene or candidate

Best BMD P value

Fracture odds ratio

Fracture P value

Refs

1p31.3 WLS (also known as GPR177)

2.6 × 10−13 25,29,30

1p36 ZBTB40 7.4 × 10−57 1.07 3.6 × 10−6 22,25,26,29

1p36.12 WNT4 9.6 × 10−11 1.09 1.4 × 10−7 22,25,26,29

1q24.3 DNM3 8.5 × 10−15 26

2p16 SPTBN1 2.3 × 10−18 1.06 2.6 × 10−8 25,26

2p21 PKDCC 1.3 × 10−9 26

2q13 ANAPC1 1.5 × 10−9 26

2q14.2 INSIG2 1.2 × 10−10 26

2q24 GALNT3 3.9 × 10−30 26,29

3p22 CTNNB1 4.4 × 10−25 1.06 2.9 × 10−7 25,26

3q13.2 KIAA2018 4.1 × 10−10 26

3q25.31 LEKR1 4.5 × 10−12 26

4p16.3 IDUA 5.2 × 10−15 26

4q21.1 MEPE, SPP1 and IBSP

1.2 × 10−27 1.06 1.7 × 10−8 28,29

5q14 MEF2C 4.5 × 10−61 25,26,29

5q31 ALDH7A1 6.4 × 10−6 2.25 2.1 × 10−9 35

6p21.1 SUPT3H and RUNX2

5.6 × 10−11 26

6p22.3 CDKAL1 and SOX4

2.7 × 10−13 26

6q22 RSPO3 8.1 × 10−12 26,29

6q25 C6ORF97 and ESR1

4.0 × 10−35 21,22,25,26

7p14.1 STARD3NL 3.8 × 10−38 1.05 7.2 × 10−5 25,26

7q21.3 FLJ42280 and SHFM1

9.4 × 10−12 25,26

7q21.3 SLC25A13 8.1 × 10−48 1.08 5.9 × 10−11 26

7q31.31 WNT16 and FAM3C

3.2 × 10−51 1.06 7.3 × 10−9 26,31,32

7q36.1 ABCF2 7.3 × 10−9 26

8q13.3 XKR9 and LACTB2 1.9 × 10−8 26

8q24 OPG 3.2 × 10−39 20,21,25,26

9q34.11 FUBP3 3.4 × 10−22 1.05 3.5 × 10−5 26

10p11.23 MPP7 2.4 × 10−16 26

10q21.1 MBL2 and DKK1 1.6 × 10−12 1.1 9.0 × 10−9 26

10q22.3 KCNMA1 5 × 10−19 26

10q24.2 CPN1 9 × 10−10 26

11p12 LRP4, ARHGAP1 and F2

5.1 × 10−18 25,26

11p14.1 DCDC5 2.2 × 10−11 25,26

11p14.1 LIN7C and DCDC5 4.9 × 10−8 1.05 3.3 × 10−5 26

11p15 SOX6 1.1 × 10−32 25,26,30

11q13.2 LRP5 2.1 × 10−26 1.09 1.4 × 10−8 20,25,26

12p11.22 KLHDC5 and PTHLH

1.9 × 10−12 26

12p13.33 ERC1 and WNT5B 5.6 × 10−12 26

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independent subjects26. All subjects were of European or East Asian ancestry. This study was made possi-ble through coordination of the analysis of discovery cohorts and centralization and standardization of DNA, genotyping and phenotyping for all follow-up cohorts. This study identified 32 novel loci for BMD. It also was the first study to interrogate the X chromosome sys-tematically in a large-scale study, identifying one locus on the X chromosome to be genome-wide-significant. Although such large sample sizes can introduce hetero-geneity, very few significant SNPs showed heterogeneity of effect across 51 cohorts.

Genome-wide significance. Several of the above GWASs have reported ‘significant’ findings arising from SNPs that failed to achieve a P value of less than 5 × 10−8. We feel that this rigorous threshold for statistical significance should be used in GWASs, as it accounts for the number of statistically independent common variants present in HapMap33. SNPs that fail to meet this threshold, but that are declared as significant, have subsequently been shown to be false positives in larger meta-analyses; such was the case for the major histocompatibility complex (MHC) locus, which was reported to be associated with BMD21, but did not achieve a P value <5 × 10−8 in either GEFOS meta-analysis25,26.

Summary of GWASs for BMD. GWASs have clarified why linkage studies were underpowered for identifying regions that are associated with BMD; the effect sizes of these common variants are small. Remarkably, however, despite the large number of loci from GWASs, only 5.8% of the variance in femoral neck BMD has been explained by genome-wide-significant SNPs, even in the GEFOS-2 meta-analysis26. Importantly, the effect sizes of novel loci have been decreasing as sample size increases (average allelic effect per risk allele was −0.09 standard devia-tions in early studies20,21 and decreased to −0.04 standard deviations for novel alleles in GEFOS-2).

Thus, GWASs have begun to describe the allelic architecture of osteoporosis. The fact that small effects from common variants are replicable across large studies and that no locus contributed to a substantial amount of the variance in this trait suggests that osteo-porosis may either have an infinitesimal allelic archi-tecture, wherein a large number of alleles across the allele frequency spectrum have a small effect on risk, or that rare variants contribute substantially to the phe-notype34. Undoubtedly, larger, better-powered GWASs will identify more novel loci, but it seems quite likely that the variance explained by common genome-wide-significant SNPs is likely to remain the minority herit-ability. Nonetheless, the overall goals of the genetics of osteoporosis are not to explain its heritability, but rather to contribute to clinical care.

GWASs for fractureBMD is a risk factor for fracture, which is highly clini-cally relevant when deciding which patients to treat with primary prevention for fracture. Although BMD has been helpful as a tool for understanding the pathways

Table 2 (cont.) | Loci associated with BMD at genome-wide-significant levels*

Locus Nearest gene or candidate

Best BMD P value

Fracture odds ratio

Fracture P value

Refs

12q13 SP7 3.0 × 10−20 22,23,25,26

12q13.12 DHH 1.2 × 10−15 26

12q23.3 C12ORF23 9.6 × 10−10 26

13q14 RANKL 2.0 × 10−21 21,22,25,26

14q32 MARK3 5.2 × 10−16 1.09 0.0038 22,25,26

14q32.12 RPS6KA5 2 × 10−15 1.05 7.2 × 10−5 26

16p13.11 NTAN1 1.7 × 10−10 26

16p13.3 AXIN1 1 × 10−16 26

16p13.3 C16ORF38 and CLCN7

1.5 × 10−16 26

16q12.1 CYLD 1.9 × 10−22 26

16q23 ADAMTS18 2.1 × 10−8 24

16q24 FOXL1 and FOXC2

1.0 × 10−14 25,26

17p13.3 SMG6 9.8 × 10−19 26

17q12 CRHR1 1.4 × 10−8 25

17q21 SOST 2.0 × 10−11 1.07 6.9 × 10−6 22,26

17q21 HDAC5 1.7 × 10−8 25

17q24.3 SOX9 1.9 × 10−11 26

18p11.21 C18ORF19 and FAM210A

4.9 × 10−8 1.08 8.8 × 10−13 26

18q21.33 RANK 1.6 × 10−17 22,25,26

19q13.11 GPATCH1 6.6 × 10−11 26

20p12 JAG1 3.1 × 10−19 1.42 0.009 26,81

Xp22.31 FAM9B and KAL1

1.2 × 10−8 26

*Genome-wide significance is defined as P < 5 × 10−8. Note that some loci are repeated in subsequent studies to show their relationship with fracture. ABCF2, ATP-binding cassette, sub-family F; ADAMTS18, ADAM metallopeptidase with thrombospondin type 1 motif, 18; ALDH7A1, aldehyde dehydrogenase 7 family, member A1; ANAPC1, anaphase promoting complex subunit 1; ARHGAP1, rho GTPase activating protein 1; BMD, bone mineral density; C12ORF23, chromosome 12 open reading frame 23; CDKAL1, CDK5 regulatory subunit associated protein 1-like 1; CLCN7, chloride channel, voltage-sensitive 7; CPN1, carboxypeptidase N, polypeptide 1; CRHR1, corticotropin releasing hormone receptor 1; CTNNB1, catenin (cadherin-associated protein), beta 1; CYLD, cylindromatosis (turban tumour syndrome); DCDC5, doublecortin domain containing 5; DHH, desert hedgehog; DKK1, dickkopf 1; DNM3, dynamin 3; ERC1, ELKS/RAB6-interacting/CAST family member 1; ESR1, oestrogen receptor 1; F2, coagulation factor II (thrombin); FAM210A, family with sequence similarity 210, member A; FAM3C, family with sequence similarity 3, member C; FAM9B, family with sequence similarity 9, member B; FOXC2, forkhead box C2; FUBP3, far upstream element (FUSE) binding protein 3; GALNT3, UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 3; GPATCH1, G patch domain containing 1; HDAC5, histone deacetylase 5; IBSP, integrin-binding sialoprotein; IDUA, alpha-L-iduronidase; INSIG2, insulin-induced gene 2; JAG1, jagged 1; KAL1, Kallmann syndrome 1 sequence; KCNMA1, potassium large conductance calcium-activated channel, subfamily M, alpha member 1; KLHDC5, kelch domain containing 5; LACTB2, lactamase, beta 2; LEKR1, leucine, glutamate and lysine rich 1; LIN7C, lin‑7 homologue C; LRP4, low-density lipoprotein receptor-related protein 4; MARK3, MAP/microtubule affinity-regulating kinase 3; MBL2, mannose-binding lectin (protein C) 2, soluble; OPG, also known as TNFRSF11B; MEF2C, myocyte enhancer factor 2C; MEPE, matrix extracellular phosphoglycoprotein; MPP7, membrane protein, palmitoylated 7; NTAN1, N-terminal asparagine amidase; PKDCC, protein kinase domain containing, cytoplasmic homologue; PTHLH, parathyroid hormone-like hormone; RANK, also known as TNFRSF11A; RANKL, also known as TNFSF11; RPS6KA5, ribosomal protein S6 kinase, 90kDa, polypeptide 5; RSPO3, R-spondin 3; RUNX2, runt-related transcription factor 2; SHFM1, split hand/foot malformation (ectrodactyly) type 1; SLC25A13, solute carrier family 25; SMG6, smg‑6 homologue, nonsense mediated mRNA decay factor; SOST, sclerostin; SOX4, sex-determining region Y (SRY) box 4; SPP1, secreted phosphoprotein 1; SPTBN1, spectrin, beta, non-erythrocytic 1; STARD3NL, STARD3 N-terminal like; SUPT3, suppressor of Ty 3; WLS, wntless; WNT4, wingless-type MMTV integration site family, member 4; XKR9, Kell blood group complex subunit-related family, member 9; ZBT40, zinc finger and BTB domain containing 40.

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and potential drug targets involved in fracture risk, it is fractures themselves that are the most clinically rel-evant endpoint, as they inflict considerable morbidity and mortality.

Fracture has a considerably lower heritability than BMD and decreases with age, as reviewed above. Paradoxically, the age range at which most fractures occur is the age range at which the disease is least herita-ble. This is particularly challenging for population-based studies that tend to accrue fracture cases disproportion-ately from this low heritability age range. Specific study designs that overcome this limitation by focusing on younger individuals suffering fractures are likely to be required to investigate the contribution of genetic vari-ants to fracture risk fully, and they have already identi-fied new genome-wide-significant hits for fracture, even with small sample sizes74.

The lack of GWAS results from large-scale studies testifies to this problem. We are aware of only one published GWAS that used fracture as the discovery phenotype35. One SNP in aldehyde dehydrogenase 7 family, member A1 (ALDH7A1) was associated with hip fracture at a genome-wide-significant level, with a high odds ratio of 2.25 (P = 2.1 × 10−9) (TABLE 2), and was genome-wide-suggestive for hip BMD in Chinese and European subjects. This locus did not associate with BMD in either GEFOS effort or in other GWASs performed.

Another path towards identifying the genetic deter-minants of fracture is first to identify loci that are asso-ciated with BMD through GWASs and then to test their association with fracture. Although this paradigm is limited, as most individuals who suffer osteoporotic fractures do not have a low BMD7,8, it can yield loci for a common disease that is otherwise difficult to study. This approach has worked well for the identification of loci that are associated with fracture. Specifically, targeting highly heritable fractures, such as peri-men-opausal wrist fractures, even small numbers of cases and controls have generated genome-wide-significant findings31.

The largest study for assessing the impact of BMD SNPs on fracture was the GEFOS-2 effort, which involved 31,016 cases and 102,444 controls from 50 independent studies26. The fracture definition used was any type of fracture — thus it was designed to be inclu-sive but was imprecise for fragility fractures — with the intention of increasing statistical power. Although this may produce results that are not specific to a skeletal site or mechanism, it did demonstrate improved power when compared to subclassification of fractures into vertebral or non-vertebral fractures. Fourteen loci were associated with any type of fracture, accounting for the number of loci tested, of which six reached a genome-wide-significant level (P < 5 × 10−8). Interestingly, the RANK–RANKL–OPG pathway, which is important to BMD genetics, did not appear to have an influence on risk of fracture. However, therapeutic targeting of the RANKL–RANKL–OPG pathway clearly influences fracture risk36, highlighting that false negatives do occur in GWASs.

Summary of GWASs for fracture. In summary, the genetics of fracture risk remains poorly understood, and much progress is likely to be made through the dissection of fracture risk, independently of BMD. Future large-scale studies are required to address this issue using fractures from the most heritable age range. It is currently unclear which fracture phenotype will bear the most fruit and at which site, but preliminary evidence from GEFOS-2 suggests that an inclusive phenotype definition, permitting inclusion of differ-ent sites and different levels of fracture validation, with an intent to increase sample size, may yield the most power. However, given the age-dependent heritability of fracture, it is likely that the most heritable fractures will provide the greatest insights into the genetics of this common and costly disease.

Insights into pathophysiologyThe first promise of osteoporosis genetics was to highlight proteins and pathways that are crucial to its pathophysiology, thereby increasing our under-standing of this condition and providing potential treatments37. As it is still too early to understand the function of novel proteins identified by GWASs, we will discuss the sequestering of the identified loci into general pathways for proteins of better-known func-tion, which were generally described before GWASs. In general, the non-novel pathways highlighted by GWASs include WNT, RANK–RANKL–OPG and endochondral ossification. This Review is not meant to describe these pathways and their interactions in detail, and readers are referred elsewhere for these purposes38–43. The importance of these pathways in osteoporosis pathophysiology has been highlighted both through loss-of-function mutations and gain-of-function mutations can of course act in oppo-site directions. Although other pathways certainly influence bone physiology, we will restrict our focus to these three pathways as they were most clearly highlighted by GWASs. TABLE 3 lists genes that are present in other pathways, which have no previous association with bone physiology. Below we outline the gene expression, rodent knockouts and Mendelian diseases that have improved our understanding of the central importance of these pathways in bone physiology.

Although below we have highlighted the relevance of these pathways to bone biology, there is an impor-tant body of work demonstrating the relevance of the endocrine system as well as the central role of the immune system in the development of osteopo-rosis, among other tissues. This is of particular rel-evance, as tissues such as lymphocytes and adipose cells are more easily accessible than bone tissues that are normally procured through bone biopsy. Thus, although bone cells are of clear importance in under-standing the relationship of identified alleles with cis-expression quantitative trait loci (cis-eQTLs), it is important to consider whether other cell types may be of more physiologic relevance for the transcript that is under study.

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Osteoporosis-pseudoglioma syndromeAn autosomal recessive disorder conferring juvenile osteoporosis and juvenile-onset blindness that is caused by mutations in lipoprotein-receptor-related protein 5 (LRP5).

WNT. The WNT signalling pathway is crucial to bone biology, as it is important for embryogenesis and limb development. WNT factors exert their mechanism by binding to cell surface receptors, activation of which leads to accumulation of β-catenin and its subse-quent translocation to the nucleus, where it acts as a transcriptional co-activator38. GWASs for osteoporo-sis have identified the following likely WNT-related genes: catenin (cadherin-associated protein), beta 1 (CTNNB1), sclerostin (SOST), low-density lipopro-tein receptor-related protein 4 (LRP4), LRP5, GPR177, wingless-type MMTV integration site family, mem-ber 4 (WNT4), WNT5B, WNT16, dickkopf 1 (DKK1), secreted Frizzled-related protein 4 (SFRP4), R-spondin 3 (RSPO3), jagged 1 (JAG1), myocyte enhancer factor 2C (MEF2C) and AXIN1. FIGURE 1 shows the probable mechanism of the WNT pathway and displays that most of the key proteins of the canonical WNT signalling pathway were identified through GWASs.

The first unequivocal demonstration of the impor-tance of the WNT pathway in human bone physiology was through a human genetics study that identified inactivating mutations of LRP5 as causing the recessive osteoporosis-pseudoglioma syndrome, which pre-sents with low bone mass and fractures44. Although

high-bone-mass syndromes have been associated with activating mutations of LRP5 (REF. 45), inactivating mutations of sclerostin (SOST) also present with high-bone-mass syndromes46, and MEF2C potentiates SOST47 expression, thus demonstrating the relevance of both gain- and loss-of-function mutations in understanding osteoporosis aetiology. Of the loci in the WNT pathway identified by GWASs, transcript levels of DKK1 and SOST in trans-iliac bone biopsies are shown to correlate with BMD at the lumbar spine and femoral neck in direc-tions that are consistent with their presumed function26. At locus 1p36.12, cis-variant rs6426749[G] correlated with reduced WNT4 expression in fibroblasts, osteo-blasts and adipose tissue, and at 11p11.2, rs7932354[C] correlates with increased LRP4 cis-expression in adipose tissue26. Other members of the WNT pathway identi-fied by GWASs have monogenic skeletal phenotypes in humans and/or mice. TABLE 3 catalogues the human and mouse knockout data for GWAS-identified genes.

The WNT pathway also directly interacts with other important bone pathways. For example, jagged 1 (JAG1) is not only a WNT and β-catenin target but is also an important component of the NOTCH signalling path-way48. Mutations in JAG1 — a NOTCH ligand — that lead to haploinsufficiency cause Alagille’s syndrome49,

Table 3 | Evidence for the role of GWAS-identified genes in generating skeletal phenotypes

Pathway Gene Human monogenic skeletal syndrome Mouse knockout

WNT SOST Sclerosteosis MGI:1921749

LRP5 Osteoporosis-pseudoglioma syndrome, osteopetrosis autosomal dominant 1

MGI:1278315

WLS (GPR177) MGI:1915401

CTNNB1 MGI:88276

RSPO3 MGI:1920030

DKK1 MGI1329040

LRP4 Cenani–Lenz syndactyly syndrome MGI:2442252

AXIN1 MGI:1096327

WNT3 Tetra-amelia, autosomal recessive

JAG1 Alagille’s syndrome

RANK–RANKL–OPG RANKL Osteopetrosis, autosomal recessive 2 MGI:1100089

OPG Paget’s disease MGI:109587

RANK Paget’s disease MGI:1314891

Endochondral ossification

PTHLH (encodes PTHRP) Brachydactyly, type E2 MGI:97800

RUNX2 MGI:99829

SP7 Osteogenesis imperfecta, type XII MGI:2153568

IBSP (encodes BNSP2) MGI:96389

SPP1 (encodes osteopontin)

MGI:98389

SOX6 MGI:98368

SOX9 Acampomelic campomelic dysplasia MGI:98371

This table is modified from REF. 26. This list is not meant to be exhaustive of all genome-wide association study (GWAS)-identified genes but rather is intended to highlight certain genes clustering in the pathways discussed. BNSP2, bone sialoprotein 2; DKK1, dickkopf 1; JAG1, jagged 1; LRP4, low-density lipoprotein receptor-related protein 4; OPG, also known as TNFRSF11B; PTHLH, parathyroid hormone-like hormone; PTHRP, parathyroid hormone-related protein; RANK, also known as TNFRSF11A; RANKL, also known as TNFSF11; RSPO3, R-spondin 3; RUNX2, runt-related transcription factor 2; SOST, sclerostin; TNFRSF11, tumour necrosis factor receptor superfamily, member 11; WLS, wntless; WNT3, wingless-type MMTV integration site family, member 3.

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Nature Reviews | Genetics

DKK1

SOST

SFRP4

*‡ *‡

‡

‡‡RSPO3

FrizzledWNT

‡

AXIN1*‡

*‡

Frizzled

LRP5LRP6

WNT

*‡

WNT factors identifiedby GWASs: WNT3, WNT4,WNT5B, WNT16, involved in WNTsecretion WLS (GPR177)

Cell membrane

β-catenin‡β-catenin‡

APC

AXIN1

P P

Transcriptional co-activation of genes including JAG1Nucleus

‡β-catenin

N

N CP P

a Inhibited state b Activated state

LRP5LRP6

C

N C

N CGSK3β

and the NOTCH pathway directly influences members of the endochondral ossification pathway (such as runt-related transcription factor 2 (RUNX2) and osterix (also known as SP7))50, as described in more detail below.

RANK–RANKL–OPG. Bone is a constantly remodel-ling organ that is dependent on a cyclical resorption and reforming process involving osteoclasts and osteoblasts coordinated by the RANK–RANKL–OPG pathway. Osteoblasts and osteocytes secrete a soluble factor called RANKL (also known as TNFSF11)51. RANKL then interacts with the RANK (also known as TNFRSF11A) receptor, which is present on the osteoclast precursor cell, leading to the migration, differentiation and fusion of osteoclastic lineage cells52 (FIG. 2). However, to modu-late this process intricately, osteoblasts and osteocytes also produce a decoy receptor for RANKL called OPG (also known as TNFRSF11B), which prevents its binding to RANK, thereby halting the bone resorption

cycle53. GWASs for BMD have identified all three of these important proteins in bone biology.

Murine knockout of OPG leads to severe osteopo-rosis54. Furthermore, recent conditional deletion of RANKL in mice showed that osteocytes are the inte-gral source of RANKL, and its absence led to a twofold increase in femoral cancellous bone volume51, which is the expected direction of effect, as RANKL leads to increased bone resorption (FIG. 2).

At locus 8q24.12, the expression of OPG in lympho-cytes was positively correlated with BMD at the lum-bar spine20. Likewise, allelic expression indicated that SNPs in this region were associated with OPG expres-sion in lymphocytes but not in primary osteoblasts. Furthermore, for carriers of the top-risk allele near OPG, expression of this protein was reduced by half in lymphoblast cell lines. Although lymphoblast cell lines are not bone tissue, lymphocytes are the main source of OPG expression in bone marrow55. Similarly, leptin

Figure 1 | Simplified depiction of members of the canonical WNT signalling pathway identified through genome-wide association studies for bone mineral density. Proteins identified through genome-wide association studies (GWASs) are indicated in bold font and with a bold outline. Inhibitory proteins of the WNT pathway are in red, and activators of the WNT pathway are in green. a | The main role of the WNT signalling pathway is to control the stability and subsequent abundance of β-catenin, the role of which is to activate gene transcription in the nucleus. In the absence of WNT factors, β-catenin is phosphorylated by glycogen synthase kinase 3β (GSK3β), preventing the translocation of β-catenin to the nucleus. Sclerostin (SOST), dickkopf 1 (DKK1) and secreted Frizzled-related protein (SFRP4) act by inhibiting the interaction between Frizzled family members, low-density lipoprotein receptor-related protein 5 (LRP5) and WNT. b | WNT proteins bind to the G-protein-coupled receptor Frizzled and LRP5 to form a complex that ultimately leads to the recruitment of AXIN1 to the LRP5 co-receptor. R-spondin 3 (RSPO3) acts to disrupt DKK1 association to LRP6 (REF. 78). This inhibits the degradation of the AXIN1–β-catenin complex and promotes the translocation of β-catenin to the nucleus. Jagged 1 (JAG1) has been reported to be a target of β-catenin transcriptional control and is also an important component of the NOTCH signalling pathway. *Indicates the relevance of the gene to human monogenic skeletal disease. ‡Indicates genes with evidence arising from mouse knockouts. The figure is adapted, with permission, from REF. 38 © (2009) Macmillan Publishers Ltd. All rights reserved.

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*‡

*‡

Osteoblast

Osteocyte

OPG

Macrophage

Bone formation Bone resorption

RANKL

RANK

Pre-fusionosteoclast

Multinucleated osteoclast

Activated osteoclast

OsteopetrosisA syndrome of high bone mass caused by an imbalance in the constant remodelling of bone, which favours bone formation, or mutations leading to increased bone formation, such as activating mutations in low-density lipoprotein receptor-related protein 5 (LRP5).

Paget’s diseaseFocalized bone lesions characterized by enhanced bone remodelling and resultant overgrowth of bone, leading to an increased risk of fracture.

Cleidocranial dysplasiaAn autosomal dominant condition characterized by defective differentiation of osteoblasts, resulting in impaired bone formation, short stature and abnormal teeth owing to mutations of runt-related transcription factor 2 (RUNX2), which encodes core-binding factor alpha 1.

is produced only in adipocytes, and its administration to patients with congenital leptin deficiency increases bone mineral mass considerably while simultaneously reducing body weight56. Thus, we are reminded that the effect of altered expression in tissues apart from bone must be considered.

Osteopetrosis is a syndrome of failed osteoclastic bone resorption and leads to a high bone mass through a disruption of the bone-remodelling cycle. Inactivating mutations in both RANK, RANKL cause osteopetro-sis57,58. Interestingly, mutations in TNFRSF11B cause juvenile Paget’s disease59, and a recent GWAS for adult Paget’s disease identified variants in TNFRSF11A60.

Endochondral ossification. Mature bone is formed through the ossification of the cartilaginous skeleton (reviewed elsewhere61). The parathyroid hormone-related peptide (PTHRP), a ligand that binds the parathyroid hormone 1 receptor encoded by parathy-roid-hormone-like hormone (PTHLH) is important for the development of the cartilage growth plate62, and the transcription factor SOX6 is required for the estab-lishment of the cartilage growth plate, allowing for the development of endochondral bone63. The transcription factor SOX9 regulates expression of collagen, type II, alpha 1 (COL2A1), which encodes the major cartilage matrix protein. All of these genes — which are essential for cartilage development, except COL2A1 — were iden-tified through BMD GWASs (FIG. 3). After cartilage has formed, it is then ossified through the deposition of min-eral. Intriguingly, disruption of a single transcription fac-tor — namely, RUNX2 — is able to prevent ossification

of the cartilaginous skeleton completely40. Osterix acts downstream of RUNX2 and is required for full differ-entiation of osteoblasts64. Integrin-binding sialoprotein (IBSP, which encodes bone sialoprotein 2 (BNSP2)) and secreted phosphoprotein 1 (SPP1, which encodes osteopontin (OPN)) both bind strongly to calcium and hydroxyapatite and may have a role in the adherence of osteoclasts to the bone surface. GWASs also identified all of the above proteins (FIG. 3).

Disruption of RUNX2 results in the human monoge-netic syndrome of cleidocranial dysplasia, which is char-acterized by delayed skeletal development and absent clavicles65, whereas murine knockouts die at birth owing to a softened ribcage that is unable to support respira-tion. In addition, osterix-null mice do not form bone64. At the GWAS-identified locus 4q22.1 (TABLE 2), cis-SNP rs6532023[G] was correlated with reduced SPP1 expression in adipose tissue26. These data suggest that identified GWAS loci are involved in endochondral ossification.

Identification of drug targetsAnother promise of osteoporosis genetics has been the delivery of clinically relevant drug targets that can be manipulated pharmaceutically to prevent osteoporotic fractures. Osteoporotic fractures represent a reasonable disease class for preventive intervention as they occur later in life, are common and incur a substantial financial burden. Furthermore, BMD and clinical risk factors are able to identify groups of individuals who are at high risk for fracture, thus justifying preventive therapy for individuals at risk3.

Figure 2 | Simplified depictions of members of the RANK–RANKL–OPG signalling pathway identified through genome-wide association studies for bone mineral density. Proteins identified as genome-wide-significant through genome-wide association studies (GWASs) are indicated in bold font and with a bold outline. RANK is encoded by tumour necrosis factor receptor superfamily, member 11a (TNFRSF11A), its ligand RANKL is encoded by TNFSF11, and the decoy receptor OPG is encoded by TNFRSF11B. To generate activated osteoclasts, RANKL is secreted by osteoblasts and osteocytes in bone, and these bind to its natural receptor, RANK, on the surface of pre-fusion osteoclasts. To fine-balance this activation system, osteoblasts and osteocytes also secrete OPG, which is a natural decoy receptor for RANKL and prevents binding of RANKL to RANK. *Indicates the relevance of the gene to human monogenic skeletal disease. ‡Indicates genes with evidence arising from mouse knockouts. The figure is adapted, with permission, from REF. 79 © (2008) Health Plexus Ltd.

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Periostealbone collar

Primarycentre ofossification

CartilageBoneBone marrow (includingblood vessels)

PTHLHSOX6SOX9COL2A1*‡

*‡

‡*‡ RUNX2

SP7BNSP2OPN‡

‡*‡

‡

OssificationCartilageformation

Because the development of a novel therapeu-tic requires over 10 years in a drug pipeline, it is too early to judge whether genetic studies for osteoporo-sis have identified loci for which manipulation will influence patient care. However, a simplistic way to address this question theoretically is to ask whether GWASs for osteoporosis have been able to identify

targets for drugs that have been shown to be effica-cious in phase III trials or that are in preclinical tri-als. If GWASs are then able to identify known drug targets, it remains possible that among the novel loci identified, there will be potential targets for the phar-maceutical industry.

The current drugs that are available for the treat-ment of osteoporosis, and their most probable targets, are listed in TABLE 4. All of these drug targets were iden-tified in the pre-GWAS era and therefore provide an opportunity to test whether GWASs can identify clini-cally validated drug targets. Of the eight drugs classes currently used to treat osteoporosis, or showing prom-ise in drug development (such as DKK1 inhibitors66, sclerostin inhibitors67 and cathepsin K inhibitors68), the targets of five of these drugs were directly identi-fied through GWASs for BMD. The pathway of a sixth drug class, parathyroid hormone analogues, was high-lighted through the identification of the PTHLH. This shows that among the GWAS loci, there is a ~267-fold enrichment for validated drug targets in humans (P = 2.5 × 10−15), assuming there are 20,000 genes in the genome. Thus, GWASs for BMD have been able to iden-tify most clinically relevant drug targets for osteoporosis through an agnostic scan of the genome. It therefore is possible that there are useful drug targets among the dozens of loci that have not yet been thoroughly investigated.

Some observers have suggested that GWAS findings will be unlikely to be helpful in the design of therapies or in the understanding of common disease, partly because of the small effect sizes that have been attributed to the common variants under study69. However, these com-ments miss the point. What is of interest is not the effect of any single common variant that has survived selec-tion pressure to become common, but rather the demon-stration that even a marginal modification of a protein’s function through a change in one allele can influence a disease phenotype. The findings from GWASs of osteo-porosis suggest that the identification of such proteins using tools as blunt as common variants can identify clinically relevant drug targets.

Osteoporosis and fracture predictionThe third general objective of osteoporosis genet-ics has been to identify a set of variants that can be measured to allow identification of groups at high risk for future fracture. This is of particular relevance to osteoporosis as safe interventions exist for osteoporo-sis, and such preventive therapy years before disease onset could decrease the population health burden of this disease.

Because many genome-wide-significant alleles have been identified, a weighted allele score can be implemented, which simply counts the number of deleterious alleles per person, weighting each allele by the effect size that is attributed to this allele in an independent population cohort. Individuals with more deleterious alleles would therefore have a higher risk score. Indeed, using 15 genome-wide-significant SNPs for lumbar spine BMD in the GEFOS-1 GWAS, this

Figure 3 | Simplified depiction of members of the endochondral ossification pathway identified through genome-wide association studies for bone mineral density. Proteins identified as genome-wide-significant through genome-wide association studies (GWASs) are indicated in bold font. Bone is generated in the developing skeleton by first forming cartilage, which is then ossified, starting at the primary centre of ossification and moving outwards to the peripheral bone. Arrows indicate genes that are involved in the promotion of a process. Genes involved in the formation of cartilage are indicated in the left panel. Parathyroid-hormone-related protein (PTHRP), which is encoded by the gene PTHLH, binds to the PTHRP receptor to promote development of the cartilage growth plate. The transcription factor SOX6 is involved in the establishment of cartilage growth plate, allowing for the development of endochondral bone. The transcription factor SOX9 regulates collagen, type II, alpha 1 (COL2A1) expression, the product of which is a structural protein that is the main component of cartilage. Genes involved in ossification are depicted in the right-hand panel. RUNX2 is a transcription factor that is a regulator of ossification of cartilaginous skeleton, SP7 is a transcription factor that permits differentiation of osteoblasts, and BNSP2 is a major noncollagenous structural protein. Osteopontin (OPN) is a secreted protein that permits the attachment of osteoclasts to mineralized bone. *Indicates the relevance of the gene to human monogenic skeletal disease. ‡Indicates genes with evidence arising from mouse knockouts. The figure is adapted, with permission, from REF. 80 © (2001) Society for Endocrinology.

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consortium was able to show a difference between the highest risk group and lowest risk group of approxi-mately 0.7 standard deviations25. Increasing the num-ber of SNPs by including those identified in the larger GEFOS-2 effort to 63 autosomal SNPs increased this gradation of effect to approximately 0.86 standard deviations26. Comparing these two findings sug-gests that a substantial increase in the number of risk alleles (of decreasing effect size) does not dramatically improve the ability of a weighted allelic risk score to partition individuals.

More clinically relevant, however, was the dem-onstration of the effect of this allelic risk score on the receiver operating characteristic (ROC) curve (BOX 1) for fracture risk. A considerable body of work has already gone into understanding the relevance of clinical risk factors (such as age, sex and weight) and BMD in risk stratification for fracture. Using only well-validated clinical risk factors, irrespective of genotype, the area under the ROC curve for osteoporotic hip fracture is 0.83 (REF. 70).

However, the area under the ROC curve for frac-ture risk using the allelic risk score without any clinical information was marginally better than chance alone and had a value of 0.57 (95% confidence interval: 0.55–0.59). Similarly, the allelic risk score for a diagnosis of osteoporosis (BMD T score ≤ −2.5) had an area under the curve of 0.59 (95% confidence interval: 0.56–0.61), which was again worse than a risk score including just age and weight (0.75 (95% confidence interval: 0.73–0.77)) and improved only marginally when add-ing the allelic risk score to age and weight (0.76 (95% confidence interval: 0.74–0.78)) (BOX 1).

Some observers have suggested that to predict risk of disease, 150 genes with odds ratios of 1.5 or 250 genes with odds ratios of 1.25 will be needed71. Recent data that show the combined effect of many common variants on the explanation of trait variance may improve our ability to prognosticate osteoporosis72. However, the published data from the field of osteoporosis suggest that simple clinical risk factors, such as age, weight and height, outperform an allelic risk score that is comprised of susceptibility alleles.

These findings may be improved in the future by identifying a set of alleles that influence risk of fracture independently of BMD or by identifying less common variants that have a large effect on the risk of fracture and/or BMD. However, the highly polygenic allelic architecture of BMD and the low variance explained of this trait suggest, at present, that the reliable predic-tion of individuals at risk for fracture or osteoporosis using genetic information is not feasible.

Conclusions, perspectives and future studiesIn the past 4 years since the first GWASs for osteopo-rosis were published, great advances have been made, with the identification of 62 genome-wide-significant loci for this common and costly disease. Many of the identified proteins have clear and relevant mechanisms of action for osteoporosis pathophysiology. Further, the genes identified have mirrored many of the cur-rent therapeutic drug targets, suggesting that among the novel genes identified, there are opportunities for new therapeutic approaches. Despite these successes, the use of these identified variants for diagnosis and risk prediction has been disappointing.

However, the clinical use of these findings is dem-onstrated by the ability of GWASs for BMD to high-light loci that are validated drug targets in humans. Through an agnostic scan of the genome, GWASs were able to identify the drug target for five of eight validated drugs for osteoporosis and highlighted the pathway of a sixth. This tremendous enrich-ment (~250-fold) of the human genome for known drug targets suggests that among the novel BMD loci there are likely to be other clinically relevant drug targets.

Almost all of these advances have been made by first identifying variants that are associated with BMD. However, paradoxically, much of the risk of the clini-cally relevant endpoint — that is, fracture — is inde-pendent of BMD. Therefore, well-powered consortia GWASs that focus on the heritable age range for frac-ture will further help to address the use of genetics in understanding the pathophysiology, therapeutic options and prognosis of this burdensome disease.

Table 4 | Drugs, drug targets and whether the locus encoding the target was identified through GWASs

Drug class Drug target Target locus identified through GWASs Refs

Denosumab RANKL RANKL 36

Sclerostin inhibitors Sclerostin (SOST) SOST 67

Selective oestrogen receptor modulators

Oestrogen receptor ESR1 83

Parathyroid hormone analogues

Parathyroid hormone receptor

Not identified, but the pathway has been highlighted through PTHLH (encodes PTHRP)

84,85

Bisphosphonates Farnesyl pyrophosphate Not identified 86

Oestrogen Oestrogen receptor ESR1 87

Cathepsin K inhibitors Cathepsin K Not identified 68

DKK1 inhibitors DKK1 DKK1 66

DKK1, dickkopf 1; GWASs, genome-wide association studies; PTHLH, parathyroid hormone-like hormone; PTHRP, parathyroid hormone-related protein.

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Sens

itiv

ity

1 − specificity

0.4 0.6 1.00.2 0.80

1.0

0.8

0.6

0.4

0.2

0


Osteoporosis: genetic scoreOsteoporosis: age weight

On the basis of current evidence, it is tempting to suggest that BMD has an infinitesimal allelic architec-ture, such that there are many thousands of genetic variants at hundreds to thousands of genes that all impart a small influence on BMD. If this were the case, then larger and therefore better powered GWASs would continue to uncover loci that are of relevance to the three aims of osteoporosis genetics. These stud-ies should be done but with the prior appreciation that the alleles identified from such additional studies will probably have smaller and smaller effects. The current evidence from GWASs, however, does not preclude the important contribution of rare genetic variation to osteoporosis.

Researchers should therefore turn their attention away from common genetic variation and explore the extent to which rare genetic variation underpins the her-itability of osteoporosis. However, for such studies, large sample sizes will also be required, given the reduced power for single SNPs, even those of large effect73. Fine-mapping studies for GWAS loci will also be helpful in improving our understanding of the pathophysiology of this disease, particularly when there are several genes at a highlighted locus. Currently, large-scale sequenc-ing studies that involve thousands of individuals are underway in several centres, and these should address this question shortly.

Low-coverage whole-genome sequencing, such as that progressing within the UK10K project, will pro-vide direct insights into the role of rare variation in non-coding regions. This may have particular rele-vance to osteoporosis as many of the disease-associated SNPs have resided outside coding sequences. However, in the short term, there will be more whole-exome sequence data generated, considering the substan-tially lower cost of these studies. Whichever region of the genome is interrogated for rare variation, it is of paramount importance that these findings are well replicated, given the modest statistical power of most rare variant association methods73. This is of particular concern for non-coding regions, in which the unit of analysis is less circumscribed than in cod-ing regions, leading to a more arbitrary set of multiple tests. The ‘exome chip’, which is designed to genotype amino-acid-changing polymorphisms, may be of par-ticular relevance as this study design obviates the need for sequence alignment and calling and will provide a standardized set of variants across studies. Finally, extreme phenotypes may be helpful for rare variant studies and have already been collected for fracture and BMD29,74.

Regardless of which approach is taken for rare vari-ants for osteoporosis, their results will be of particular interest to the wider genomics community, given the success of mapping this trait to regions of the genome, its medical use and the availability of centralized DNA collections for replication.

Epigenetics is another potential source of miss-ing heritability in all areas of age-related diseases, and osteoporosis is no exception75. This field has recently expanded from rare diseases to common complex traits with the advent of methylation arrays and immunopre-cipitation methods, such as methylated DNA immuno-precipitation sequencing (MeDIP-seq). For osteoporosis, there is a paucity of studies mainly owing to difficult access to bone tissue. However, there appears to be some overlap between methylation changes across dif-ferent tissues — with correlations of approximately 70% between blood and buccal tissue76. Advances in epige-netics may alter approaches towards drug therapy and may provide insight into therapeutic responses earlier than changes in bone density. How these advances will alter care will depend, however, on the relative costs of the drugs and prognostic tests used.

Other possible genetic avenues worth exploring include the role of copy number variants, which despite having been difficult to identify so far have shown promise in neuro-developmental diseases and could still have large effects in complex traits such as osteoporosis. Finally, the use of more sophisticated associated endo-phenotypes, such as high-throughput metabolomics and proteomics, could dramatically increase power to detect genes in GWASs77.

Given the history of collaboration and coordination of large-scale studies within the osteoporosis genetics community, these future studies have a high likelihood of dissecting the contribution of rare and epigenetic variants for osteoporosis.

Box 1 | Receiver operating characteristic curves for risk of osteoporosis

Clinicians who are faced with a multitude of tests to help discern whether a patient is diseased or at risk of disease are often aided by identifying a cutoff for a diagnostic test that minimizes both false positives and false negatives. A measure of the ability of a screening test to reduce both false positives and false negatives for binary outcomes, such as fracture or diagnosis of osteoporosis, is the receiver operating characteristic (ROC) curve. The ROC curve compares the proportion of truly diseased individuals correctly identified as diseased (that is, the sensitivity) against the proportion of false positives (that is, one minus the specificity). Therefore, an ideal test would have a threshold that maximizes sensitivity while minimizing the proportion of false positives. Simply measuring the area under this ROC curve can then provide a gauge of the clinical use of such a test. If the test were no better than chance alone, then the area under the ROC curve would be 0.5, and a near-perfect test would have an area under the ROC curve of 0.99. The figure shows a ROC curve for risk of osteoporosis as derived from a weighted allelic risk score, where weights were derived from 61 autosomal genome-wide-significant SNPs for bone mineral density and applied to an exterior cohort. Note that the area under the ROC curve is better when age and weight are used alone than the genetic risk score. The figure is adapted, with permission, from REF. 26 © (2012) Macmillan Publishers Ltd. All rights reserved.

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http://www.uk10k.org

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20. Richards, J. B. et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371, 1505–1512 (2008).Together with reference 21, these papers were the first GWASs for osteoporosis, and they made it clear that there were no common genetic variants of large effect for osteoporosis.

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23. Timpson, N. J. et al. Common variants in the region around Osterix are associated with bone mineral density and growth in childhood. Hum. Mol. Genet. 18, 1510–1517 (2009).This is the only GWAS for BMD that has been conducted in children.

24. Xiong, D. H. et al. Genome-wide association and follow-up replication studies identified ADAMTS18 and TGFBR3 as bone mass candidate genes in different ethnic groups. Am. J. Hum. Genet. 84, 388–398 (2009).

25. Rivadeneira, F. et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nature Genet. 41, 1199–1206 (2009).This is the first large-scale consortium-style GWAS for osteoporosis. This paper identified many clinically relevant drug targets for osteoporosis and substantially expanded the number of loci associated with BMD.

26. Estrada, K. et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nature Genet. 44, 491–501 (2012).This is the largest global effort to describe the genetic determinants of BMD and fracture. Using BMD loci, this paper was also the first to assess the contribution of BMD GWAS loci to fracture in a large sample size.

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AcknowledgementsThis work has been supported by the Canadian Institutes of Health Research, the Lady Davis Institute for Medical Research, Ministère de Développement économique, de l’Innovation et de l’Exportation du Québec, the Arthritis Research Campaign, the Wellcome Trust, Guy’s & St. Thomas’ NHS Foundation Trust and the King’s College London Biomedical Centre. We would like to acknowledge the contri-butions of F. Rivadeneira, C. Greenwood and C. Polychronakos for their input on this Review.

Competing interests statementThe authors declare no competing financial interests.

FURTHER INFORMATIONJ. Brent Richards’ homepage: http://www.mcgill.ca/genepiGEFOS: http://www.gefos.orgTwinsUK: http://www.twinsuk.ac.ukUK10K: http://www.uk10k.org

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