Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

Abstract

Osteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.

References

[1] Pouresmaeili F, Kamalidehghan B, Kamarehei M, Goh YM. A comprehensive overview on osteoporosis and its risk factors. Therapeutics Clin. Risk Manag., 2018, 14: 2029-2049

[2] Zhu X, Zheng H. Factors influencing peak bone mass gain. Front Med., 2021, 15: 53-69

[3] Hernlund E et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch. Osteoporos., 2013, 8

[4] Burge R et al. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005-2025. J. Bone Miner. Res., 2007, 22: 465-475

[5] Liu ZH, Zhao YL, Ding GZ, Zhou Y. Epidemiology of primary osteoporosis in China. Osteoporos. Int., 1997, 7 Suppl 3 S84-S87

[6] Cooper C, Campion G, Melton LJ 3rd. Hip fractures in the elderly: a world-wide projection. Osteoporos. Int., 1992, 2: 285-289

[7] Melton LJ 3rd. Adverse outcomes of osteoporotic fractures in the general population. J. Bone Miner. Res., 2003, 18: 1139-1141

[8] Si L, Winzenberg TM, Jiang Q, Chen M, Palmer AJ. Projection of osteoporosis-related fractures and costs in China: 2010–2050. Osteoporos. Int., 2015, 26: 1929-1937

[9] Peacock M, Turner CH, Econs MJ, Foroud T. Genetics of osteoporosis. Endocr. Rev., 2002, 23: 303-326

[10] Zheng HF, Spector TD, Richards JB. Insights into the genetics of osteoporosis from recent genome-wide association studies. Expert Rev. Mol. Med, 2011, 13

[11] Trajanoska K, Rivadeneira F. The genetic architecture of osteoporosis and fracture risk. Bone, 2019, 126: 2-10

[12] Richards JB, Zheng HF, Spector TD. Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet, 2012, 13: 576-588

[13] Styrkarsdottir U et al. Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits. Nature, 2013, 497: 517-520

[14] Zheng HF et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature, 2015, 526: 112-117

[15] Larsson SC, Michaelsson K, Burgess S. Mendelian randomization in the bone field. Bone, 2019, 126: 51-58

[16] Richards JB et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet, 2008, 371: 1505-1512

[17] Rivadeneira F et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat. Genet., 2009, 41: 1199-1206

[18] Kung AW et al. Association of JAG1 with bone mineral density and osteoporotic fractures: a genome-wide association study and follow-up replication studies. Am. J. Hum. Genet., 2010, 86: 229-239

[19] Estrada K et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet., 2012, 44: 491-501

[20] Kanis JA, Melton LJ 3rd, Christiansen C, Johnston CC, Khaltaev N. The diagnosis of osteoporosis. J. Bone Miner. Res., 1994, 9: 1137-1141

[21] Bachrach LK. Acquisition of optimal bone mass in childhood and adolescence. Trends Endocrinol. Metab., 2001, 12: 22-28

[22] Paternoster L et al. Genome-wide association meta-analysis of cortical bone mineral density unravels allelic heterogeneity at the RANKL locus and potential pleiotropic effects on bone. PLoS Genet., 2010, 6

[23] Adams JE. Quantitative computed tomography. Eur. J. Radiol., 2009, 71: 415-424

[24] Gonnelli S et al. Quantitative ultrasound and dual-energy X-ray absorptiometry in the prediction of fragility fracture in men. Osteoporos. Int., 2005, 16: 963-968

[25] Moayyeri A et al. Genetic determinants of heel bone properties: genome-wide association meta-analysis and replication in the GEFOS/GENOMOS consortium. Hum. Mol. Genet., 2014, 23: 3054-3068

[26] Kiel DP et al. Genome-wide association with bone mass and geometry in the Framingham Heart Study. BMC Med. Genet., 2007, 8 Suppl 1

[27] Styrkarsdottir U et al. Multiple genetic loci for bone mineral density and fractures. N. Engl. J. Med., 2008, 358: 2355-2365

[28] Yang TL et al. Genome-wide copy-number-variation study identified a susceptibility gene, UGT2B17, for osteoporosis. Am. J. Hum. Genet., 2008, 83: 663-674

[29] Guo Y et al. Genome-wide association study identifies ALDH7A1 as a novel susceptibility gene for osteoporosis. PLoS Genet., 2010, 6

[30] Kou I et al. Common variants in a novel gene, FONG on chromosome 2q33.1 confer risk of osteoporosis in Japanese. PloS ONE, 2011, 6

[31] Naito T et al. Clinical and genetic risk factors for decreased bone mineral density in Japanese patients with inflammatory bowel disease. J. Gastroenterol. Hepatol., 2018, 33: 1873-1881

[32] Hwang JY et al. Meta-analysis identifies a MECOM gene as a novel predisposing factor of osteoporotic fracture. J. Med. Genet., 2013, 50: 212-219

[33] Hwang JY, Kim YJ, Choi BY, Kim BJ, Han BG. Meta analysis identifies a novel susceptibility locus associated with heel bone strength in the Korean population. Bone, 2016, 84: 47-51

[34] Liu YJ, Zhang L, Papasian CJ, Deng HW. Genome-wide Association Studies for Osteoporosis: A 2013 Update. J. Bone Metab., 2014, 21: 99-116

[35] Zhang L et al. Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Hum. Mol. Genet., 2014, 23: 1923-1933

[36] Trajanoska K et al. Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study. BMJ, 2018, 362: k3225

[37] Bai WY et al. Genotype imputation and reference panel: a systematic evaluation on haplotype size and diversity. Brief Bioinform., 2019, 21: 1806-1817

[38] Kemp JP et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat. Genet., 2017, 49: 1468-1475

[39] Morris JA et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat. Genet., 2019, 51: 258-266

[40] Bai WY et al. Identification of PIEZO1 polymorphisms for human bone mineral density. Bone, 2020, 133: 115247

[41] Bauer DC et al. Broadband ultrasound attenuation predicts fractures strongly and independently of densitometry in older women. A prospective study. Study of Osteoporotic Fractures Research Group. Arch. Intern. Med., 1997, 157: 629-634

[42] Bauer DC et al. Quantitative ultrasound predicts hip and non-spine fracture in men: the MrOS study. Osteoporos. Int., 2007, 18: 771-777

[43] Chesi A et al. A trans-ethnic genome-wide association study identifies gender-specific loci influencing pediatric aBMD and BMC at the distal radius. Hum. Mol. Genet., 2015, 24: 5053-5059

[44] Timpson NJ et al. Common variants in the region around Osterix are associated with bone mineral density and growth in childhood. Hum. Mol. Genet., 2009, 18: 1510-1517

[45] Medina-Gomez C et al. Meta-analysis of genome-wide scans for total body BMD in children and adults reveals allelic heterogeneity and age-specific effects at the WNT16 locus. PLoS Genet., 2012, 8

[46] Koller DL et al. Genome-wide association study of bone mineral density in premenopausal European-American women and replication in African-American women. J. Clin. Endocrinol. Metab., 2010, 95: 1802-1809

[47] Koller DL et al. Meta-analysis of genome-wide studies identifies WNT16 and ESR1 SNPs associated with bone mineral density in premenopausal women. J. Bone Miner. Res., 2013, 28: 547-558

[48] Zheng HF et al. WNT16 influences bone mineral density, cortical bone thickness, bone strength, and osteoporotic fracture risk. PLoS Genet., 2012, 8

[49] Liu CT et al. Assessment of gene-by-sex interaction effect on bone mineral density. J. Bone Miner. Res., 2012, 27: 2051-2064

[50] Chesi A et al. A genomewide association study identifies two sex-specific loci, at SPTB and IZUMO3, influencing pediatric bone mineral density at multiple skeletal sites. J. Bone Miner. Res., 2017, 32: 1274-1281

[51] Liu YZ et al. Identification of PLCL1 gene for hip bone size variation in females in a genome-wide association study. PloS ONE, 2008, 3

[52] Lei SF et al. Genome-wide association study identifies HMGN3 locus for spine bone size variation in Chinese. Hum. Genet., 2012, 131: 463-469

[53] Guo YF et al. Suggestion of GLYAT gene underlying variation of bone size and body lean mass as revealed by a bivariate genome-wide association study. Hum. Genet., 2013, 132: 189-199

[54] Baird DA et al. Identification of novel loci associated with hip shape: a meta-analysis of genomewide association studies. J. Bone Miner. Res., 2019, 34: 241-251

[55] Zhang H et al. Pleiotropic loci underlying bone mineral density and bone size identified by a bivariate genome-wide association analysis. Osteoporos. Int., 2020, 31: 1691-1701

[56] Styrkarsdottir U et al. GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures. Nat. Commun., 2019, 10

[57] Xu XH et al. Molecular genetic studies of gene identification for osteoporosis: the 2009 update. Endocr. Rev., 2010, 31: 447-505

[58] Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat. Genet., 2008, 40: 695-701

[59] Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet., 2010, 11: 415-425

[60] Recker RR, Deng HW. Role of genetics in osteoporosis. Endocrine, 2002, 17: 55-66

[61] Manolio TA et al. Finding the missing heritability of complex diseases. Nature, 2009, 461: 747-753

[62] Farber CR. Systems genetics: a novel approach to dissect the genetic basis of osteoporosis. Curr. Osteoporos. Rep., 2012, 10: 228-235

[63] Huang Q. Genetic study of complex diseases in the post-GWAS era. J. Genet. Genomics, 2015, 42: 87-98

[64] Shi H, Kichaev G, Pasaniuc B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet., 2016, 99: 139-153

[65] Stahl EA et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet., 2012, 44: 483-489

[66] Zeng J et al. Signatures of negative selection in the genetic architecture of human complex traits. Nat. Genet., 2018, 50: 746-753

[67] O’Connor LJ et al. Extreme polygenicity of complex traits is explained by negative selection. Am. J. Hum. Genet., 2019, 105: 456-476

[68] Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell, 2017, 169: 1177-1186

[69] Liu X, Li YI, Pritchard JK. Trans effects on gene expression can drive omnigenic inheritance. Cell, 2019, 177: 1022-1034.e6

[70] Tamai K et al. LDL-receptor-related proteins in Wnt signal transduction. Nature, 2000, 407: 530-535

[71] Gong Y et al. LDL receptor-related protein 5 (LRP5) affects bone accrual and eye development. Cell, 2001, 107: 513-523

[72] Balemans W, Van Hul W. The genetics of low-density lipoprotein receptor-related protein 5 in bone: a story of extremes. Endocrinology, 2007, 148: 2622-2629

[73] Liu W, Zhang X. Receptor activator of nuclear factor-kappaB ligand (RANKL)/RANK/osteoprotegerin system in bone and other tissues (review). Mol. Med. Rep., 2015, 11: 3212-3218

[74] Kichaev G et al. Leveraging polygenic functional enrichment to improve GWAS Power. Am. J. Hum. Genet., 2019, 104: 65-75

[75] Visscher PM et al. 10 years of gwas discovery: biology, function, and translation. Am. J. Hum. Genet., 2017, 101: 5-22

[76] Zhao PP et al. Relationship between alcohol use, blood pressure and hypertension: an association study and a Mendelian randomisation study. J. Epidemiol. Community Health, 2019, 73: 796-801

[77] Sleiman PM, Grant SF. Mendelian randomization in the era of genomewide association studies. Clin. Chem., 2010, 56: 723-728

[78] Xiong A et al. No causal effect of serum urate on bone-related outcomes among a population of postmenopausal women and elderly men of Chinese Han ethnicity–a Mendelian randomization study. Osteoporos. Int., 2016, 27: 1031-1039

[79] Trajanoska K, Rivadeneira F. Using mendelian randomization to decipher mechanisms of bone disease. Curr. Osteoporos. Rep., 2018, 16: 531-540

[80] Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol., 2003, 32: 1-22

[81] Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ, 2005, 330: 1076-1079

[82] VanderWeele TJ, Tchetgen Tchetgen EJ, Cornelis M, Kraft P. Methodological challenges in mendelian randomization. Epidemiology, 2014, 25: 427-435

[83] Holick MF. Vitamin D deficiency. N. Engl. J. Med., 2007, 357: 266-281

[84] Leong A et al. The causal effect of vitamin D binding protein (DBP) levels on calcemic and cardiometabolic diseases: a Mendelian randomization study. PLoS Med., 2014, 11

[85] Li SS et al. Genetically low vitamin d levels, bone mineral density, and bone metabolism markers: a mendelian randomisation study. Sci. Rep., 2016, 6

[86] Larsson SC, Melhus H, Michaelsson K. Circulating serum 25-hydroxyvitamin D levels and bone mineral density: mendelian randomization study. J. Bone Miner. Res., 2018, 33: 840-844

[87] Yang Q et al. Genetically predicted milk consumption and bone health, ischemic heart disease and type 2 diabetes: a Mendelian randomization study. Eur. J. Clin. Nutr., 2017, 71: 1008-1012

[88] Xia J et al. Systemic evaluation of the relationship between psoriasis, psoriatic arthritis and osteoporosis: observational and Mendelian randomisation study. Ann. Rheum. Dis., 2020, 79: 1460-1467

[89] Ahmad OS et al. A mendelian randomization study of the effect of type-2 diabetes and glycemic traits on bone mineral density. J. Bone Miner. Res., 2017, 32: 1072-1081

[90] Wang MC et al. The relative contributions of lean tissue mass and fat mass to bone density in young women. Bone, 2005, 37: 474-481

[91] Clark EM, Ness AR, Tobias JH. Adipose tissue stimulates bone growth in prepubertal children. J. Clin. Endocrinol. Metab., 2006, 91: 2534-2541

[92] Janicka A et al. Fat mass is not beneficial to bone in adolescents and young adults. J. Clin. Endocrinol. Metab., 2007, 92: 143-147

[93] Zhao LJ et al. Correlation of obesity and osteoporosis: effect of fat mass on the determination of osteoporosis. J. Bone Miner. Res., 2008, 23: 17-29

[94] Timpson NJ, Sayers A, Davey-Smith G, Tobias JH. How does body fat influence bone mass in childhood? A Mendelian randomization approach. J. Bone Miner. Res., 2009, 24: 522-533

[95] Kemp JP, Sayers A, Smith GD, Tobias JH, Evans DM. Using Mendelian randomization to investigate a possible causal relationship between adiposity and increased bone mineral density at different skeletal sites in children. Int. J. Epidemiol., 2016, 45: 1560-1572

[96] Warodomwichit D et al. Causal inference of the effect of adiposity on bone mineral density in adults. Clin. Endocrinol., 2013, 78: 694-699

[97] Cousminer DL et al. Genetically determined later puberty impacts lowered bone mineral density in childhood and adulthood. J. Bone Miner. Res., 2018, 33: 430-436

[98] Dalbeth N et al. Mendelian randomization analysis to examine for a causal effect of urate on bone mineral density. J. Bone Miner. Res., 2015, 30: 985-991

[99] van Vliet NA et al. Thyroid stimulating hormone and bone mineral density: evidence from a two-sample mendelian randomization study and a candidate gene association study. J. Bone Miner. Res., 2018, 33: 1318-1325

[100] Guo R, Wu L, Fu Q. Is there causal relationship of smoking and alcohol consumption with bone mineral density? a mendelian randomization study. Calcif. tissue Int., 2018, 103: 546-553

[101] Cerani A et al. Genetic predisposition to increased serum calcium, bone mineral density, and fracture risk in individuals with normal calcium levels: mendelian randomisation study. BMJ, 2019, 366: l4410

[102] Nelson MR et al. The support of human genetic evidence for approved drug indications. Nat. Genet., 2015, 47: 856-860

[103] Gandal MJ, Leppa V, Won H, Parikshak NN, Geschwind DH. The road to precision psychiatry: translating genetics into disease mechanisms. Nat. Neurosci., 2016, 19: 1397-1407

[104] Wray NR, Wijmenga C, Sullivan PF, Yang J, Visscher PM. Common disease is more complex than implied by the core gene omnigenic model. Cell, 2018, 173: 1573-1580

[105] Sanseau P et al. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol., 2012, 30: 317-320

[106] Franke A et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat. Genet., 2010, 42: 1118-1125

[107] Lewiecki EM. Sclerostin: a novel target for intervention in the treatment of osteoporosis. Discov. Med., 2011, 12: 263-273

[108] Winkler DG et al. Osteocyte control of bone formation via sclerostin, a novel BMP antagonist. EMBO J., 2003, 22: 6267-6276

[109] Semenov M, Tamai K, He X. SOST is a ligand for LRP5/LRP6 and a Wnt signaling inhibitor. J. Biol. Chem., 2005, 280: 26770-26775

[110] Recker RR et al. A randomized, double-blind phase 2 clinical trial of blosozumab, a sclerostin antibody, in postmenopausal women with low bone mineral density. J. Bone Miner. Res., 2015, 30: 216-224

[111] Geusens P. New insights into treatment of osteoporosis in postmenopausal women. RMD Open, 2015, 1

[112] Falk SS, Mittlmeier T, Gradl G. Results of geriatric distal radius fractures treated by intramedullary fixation. Injury, 2016, 47 Suppl 7 S31-S35

[113] Saag KG et al. Romosozumab or alendronate for fracture prevention in women with osteoporosis. N. Engl. J. Med., 2017, 377: 1417-1427

[114] Langdahl BL et al. Romosozumab (sclerostin monoclonal antibody) versus teriparatide in postmenopausal women with osteoporosis transitioning from oral bisphosphonate therapy: a randomised, open-label, phase 3 trial. Lancet, 2017, 390: 1585-1594

[115] Cosman F et al. Romosozumab treatment in postmenopausal women with osteoporosis. N. Engl. J. Med., 2016, 375: 1532-1543

[116] Amgen. FDA Approves EVENITY^TM (romosozumab) for the treatment of osteoporosis in postmenopausal women at high risk for fracture. available at https://www.fda.gov/news-events/press-announcements/fda-approves-new-treatment-osteoporosis-postmenopausal-women-high-risk-fracture (2019).

[117] Francisco, E. M. Approval of the marketing authorisation for Evenity (romosozumab). available at https://www.ema.europa.eu/en/documents/medicine-qa/questions-answers-approval-marketing-authorisation-evenity-romosozumab_en.pdf (2019).

[118] Bafico A, Liu G, Yaniv A, Gazit A, Aaronson SA. Novel mechanism of Wnt signalling inhibition mediated by Dickkopf-1 interaction with LRP6/Arrow. Nat. Cell Biol., 2001, 3: 683-686

[119] Mao B et al. Kremen proteins are Dickkopf receptors that regulate Wnt/beta-catenin signalling. Nature, 2002, 417: 664-667

[120] Canalis E. Update in new anabolic therapies for osteoporosis. J. Clin. Endocrinol. Metab., 2010, 95: 1496-1504

[121] Korvala J et al. Rare variations in WNT3A and DKK1 may predispose carriers to primary osteoporosis. Eur. J. Med. Genet., 2012, 55: 515-519

[122] Glantschnig H et al. Generation and selection of novel fully human monoclonal antibodies that neutralize Dickkopf-1 (DKK1) inhibitory function in vitro and increase bone mass in vivo. J. Biol. Chem., 2010, 285: 40135-40147

[123] Glantschnig H et al. A rate-limiting role for Dickkopf-1 in bone formation and the remediation of bone loss in mouse and primate models of postmenopausal osteoporosis by an experimental therapeutic antibody. J. Pharmacol. Exp. Ther., 2011, 338: 568-578

[124] Bodine PV et al. The Wnt antagonist secreted frizzled-related protein-1 is a negative regulator of trabecular bone formation in adult mice. Mol. Endocrinol., 2004, 18: 1222-1237

[125] Betts AM et al. The application of target information and preclinical pharmacokinetic/pharmacodynamic modeling in predicting clinical doses of a Dickkopf-1 antibody for osteoporosis. J. Pharmacol. Exp. Ther, 2010, 333: 2-13

[126] Pepe MS, Gu JW, Morris DE. The potential of genes and other markers to inform about risk. Cancer Epidemiol., Biomark. Prev., 2010, 19: 655-665

[127] Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos. Int., 2008, 19: 385-397

[128] Tran BN et al. Genetic profiling and individualized prognosis of fracture. J. Bone Miner. Res., 2011, 26: 414-419

[129] Ho-Le TP, Center JR, Eisman JA, Nguyen HT, Nguyen TV. Prediction of bone mineral density and fragility fracture by genetic profiling. J. Bone Miner. Res., 2017, 32: 285-293

[130] Pare G, Mao S, Deng WQ. A machine-learning heuristic to improve gene score prediction of polygenic traits. Sci. Rep., 2017, 7

[131] Ho DSW, Schierding W, Wake M, Saffery R, O’Sullivan J. Machine learning SNP based prediction for precision medicine. Front. Genet., 2019, 10: 267

[132] Bellot P, de Los Campos G, Perez-Enciso M. Can deep learning improve genomic prediction of complex human traits? Genetics, 2018, 210: 809-819

[133] Kruppa J, Ziegler A, Konig IR. Risk estimation and risk prediction using machine-learning methods. Hum. Genet., 2012, 131: 1639-1654

[134] Zhang J et al. Computer vision and machine learning for robust phenotyping in genome-wide studies. Sci. Rep., 2017, 7

[135] Isakov O, Dotan I, Ben-Shachar S. Machine learning-based gene prioritization identifies novel candidate risk genes for inflammatory bowel disease. Inflamm. bowel Dis., 2017, 23: 1516-1523

[136] Guo H, Zhang F, Chen J, Xu Y, Xiang J. Machine learning classification combining multiple features of a hyper-network of fMRI data in Alzheimer’s disease. Front. Neurosci., 2017, 11: 615

[137] Lynch CM et al. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int. J. Med. Inform., 2017, 108: 1-8

[138] Jovic S, Miljkovic M, Ivanovic M, Saranovic M, Arsic M. Prostate cancer probability prediction by machine learning technique. Cancer Investig., 2017, 35: 647-651

[139] Awan SE, Sohel F, Sanfilippo FM, Bennamoun M, Dwivedi G. Machine learning in heart failure: ready for prime time. Curr. Opin. Cardiol., 2018, 33: 190-195

[140] Lello L et al. Accurate genomic prediction of human height. Genetics, 2018, 210: 477-497

[141] Forgetta, V. et al. Machine learning to predict osteoporotic fracture risk from genotypes. bioRxiv, 413716 (2018).

[142] Martin AR et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet., 2019, 51: 584-591

[143] Genetics for all. Nat. Genet. 51, 579-579 (2019).

[144] Styrkarsdottir U et al. New sequence variants associated with bone mineral density. Nat. Genet., 2009, 41: 15-17

[145] Xiong DH 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., 2009, 84: 388-398

[146] Liu YZ et al. Powerful bivariate genome-wide association analyses suggest the SOX6 gene influencing both obesity and osteoporosis phenotypes in males. PloS ONE, 2009, 4

[147] Guo Y et al. IL21R and PTH may underlie variation of femoral neck bone mineral density as revealed by a genome-wide association study. J. Bone Miner. Res., 2010, 25: 1042-1048

[148] Hsu YH et al. An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits. PLoS Genet., 2010, 6

[149] Tan L et al. A genome-wide association analysis implicates SOX6 as a candidate gene for wrist bone mass. Sci. China Life Sci., 2010, 53: 1065-1072

[150] Duncan EL et al. Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk. PLoS Genet., 2011, 7

[151] Deng FY et al. Genome-wide association study identified UQCC locus for spine bone size in humans. Bone, 2013, 53: 129-133

[152] Zheng HF et al. Meta-analysis of genome-wide studies identifies MEF2C SNPs associated with bone mineral density at forearm. J. Med. Genet., 2013, 50: 473-478

[153] Oei L et al. Genome-wide association study for radiographic vertebral fractures: a potential role for the 16q24 BMD locus. Bone, 2014, 59: 20-27

[154] Kemp JP et al. Phenotypic dissection of bone mineral density reveals skeletal site specificity and facilitates the identification of novel loci in the genetic regulation of bone mass attainment. PLoS Genet., 2014, 10

[155] Tan LJ et al. Bivariate genome-wide association study implicates ATP6V1G1 as a novel pleiotropic locus underlying osteoporosis and age at menarche. J. Clin. Endocrinol. Metab., 2015, 100: E1457-E1466

[156] Styrkarsdottir U et al. Two rare mutations in the COL1A2 gene associate with low bone mineral density and fractures in Iceland. J. Bone Miner. Res., 2016, 31: 173-179

[157] Styrkarsdottir U et al. Sequence variants in the PTCH1 gene associate with spine bone mineral density and osteoporotic fractures. Nat. Commun., 2016, 7

[158] Mullin BH et al. Genome-wide association study using family-based cohorts identifies the WLS and CCDC170/ESR1 loci as associated with bone mineral density. BMC Genomics, 2016, 17

[159] Taylor KC et al. A genome-wide association study meta-analysis of clinical fracture in 10,012 African American women. Bone Rep., 2016, 5: 233-242

[160] Choi HJ et al. Genome-wide association study in East Asians suggests UHMK1 as a novel bone mineral density susceptibility gene. Bone, 2016, 91: 113-121

[161] Pei YF et al. Genome-wide association meta-analyses identified 1q43 and 2q32.2 for hip Ward’s triangle areal bone mineral density. Bone, 2016, 91: 1-10

[162] Pei YF et al. Association of 3q13.32 variants with hip trochanter and intertrochanter bone mineral density identified by a genome-wide association study. Osteoporos. Int., 2016, 27: 3343-3354

[163] Mullin BH et al. Genome-wide association study meta-analysis for quantitative ultrasound parameters of bone identifies five novel loci for broadband ultrasound attenuation. Hum. Mol. Genet., 2017, 26: 2791-2802

[164] Villalobos-Comparan M et al. A pilot genome-wide association study in postmenopausal Mexican-Mestizo women implicates the RMND1/CCDC170 locus is associated with bone mineral density. Int. J. Genomics, 2017, 2017: 5831020

[165] Peng C et al. Enhanced identification of potential pleiotropic genetic variants for bone mineral density and breast cancer. Calcif. Tissue Int., 2017, 101: 489-500

[166] Lu S et al. Bivariate genome-wide association analyses identified genetic pleiotropic effects for bone mineral density and alcohol drinking in Caucasians. J. Bone Miner. Metab., 2017, 35: 649-658

[167] Alonso N et al. Identification of a novel locus on chromosome 2q13, which predisposes to clinical vertebral fractures independently of bone density. Ann. Rheum. Dis., 2018, 77: 378-385

[168] Inaba H et al. Bone mineral density in children with acute lymphoblastic leukemia. Cancer, 2018, 124: 1025-1035

[169] Pei YF et al. Joint study of two genome-wide association meta-analyses identified 20p12.1 and 20q13.33 for bone mineral density. Bone, 2018, 110: 378-385

[170] Lin X et al. Identifying potentially common genes between dyslipidemia and osteoporosis using novel analytical approaches. Mol. Genet. genomics, 2018, 293: 711-723

[171] Kim SK. Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture. PloS ONE, 2018, 13

[172] Qiu C, Shen H, Fu X, Xu C, Deng H. Meta-analysis of genome-wide association studies identifies novel functional CpG-SNPs associated with bone mineral density at lumbar spine. Int. J. Genomics, 2018, 2018: 6407257

[173] Gregson CL et al. Genome-wide association study of extreme high bone mass: Contribution of common genetic variation to extreme BMD phenotypes and potential novel BMD-associated genes. Bone, 2018, 114: 62-71

[174] Liang X et al. Assessing the genetic correlations between early growth parameters and bone mineral density: a polygenic risk score analysis. Bone, 2018, 116: 301-306

[175] Hsu YH et al. Meta-analysis of genomewide association studies reveals genetic variants for hip bone geometry. J. Bone Miner. Res., 2019, 34: 1284-1296

[176] Oei L et al. Dissecting the relationship between high-sensitivity serum C-reactive protein and increased fracture risk: the Rotterdam Study. Osteoporos. Int., 2014, 25: 1247-1254

[177] Huang JV, Schooling CM. Inflammation and bone mineral density: a Mendelian randomization study. Sci. Rep., 2017, 7

[178] Richards JB, Zheng HF, Spector TD. Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet., 2012, 13: 576-588

[179] Cummings SR et al. Denosumab for prevention of fractures in postmenopausal women with osteoporosis. N. Engl. J. Med., 2009, 361: 756-765

[180] Ettinger B et al. Reduction of vertebral fracture risk in postmenopausal women with osteoporosis treated with raloxifene: results from a 3-year randomized clinical trial. Multiple Outcomes of Raloxifene Evaluation (MORE) Investigators. JAMA, 1999, 282: 637-645

[181] Neer RM et al. Effect of parathyroid hormone (1-34) on fractures and bone mineral density in postmenopausal women with osteoporosis. N. Engl. J. Med., 2001, 344: 1434-1441

[182] Greenspan SL et al. Effect of recombinant human parathyroid hormone (1-84) on vertebral fracture and bone mineral density in postmenopausal women with osteoporosis: a randomized trial. Ann. Intern. Med., 2007, 146: 326-339

[183] Liberman UA et al. Effect of oral alendronate on bone mineral density and the incidence of fractures in postmenopausal osteoporosis. The Alendronate Phase III Osteoporosis Treatment Study Group. N. Engl. J. Med., 1995, 333: 1437-1443

[184] Rossouw JE et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women’s Health Initiative randomized controlled trial. JAMA, 2002, 288: 321-333

[185] Gauthier JY et al. The discovery of odanacatib (MK-0822), a selective inhibitor of cathepsin K. Bioorg. Medicinal Chem. Lett., 2008, 18: 923-928