A genome-wide scan for pleiotropy between bone mineral density and nonbone phenotypes

Maria A. Christou , Georgios Ntritsos , Georgios Markozannes , Fotis Koskeridis , Spyros N. Nikas , David Karasik , Douglas P. Kiel , Evangelos Evangelou , Evangelia E. Ntzani

Bone Research ›› 2020, Vol. 8 ›› Issue (1) : 26

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Bone Research ›› 2020, Vol. 8 ›› Issue (1) : 26 DOI: 10.1038/s41413-020-0101-8
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A genome-wide scan for pleiotropy between bone mineral density and nonbone phenotypes

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Abstract

Osteoporosis is the most common metabolic bone disorder globally and is characterized by skeletal fragility and microarchitectural deterioration. Genetic pleiotropy occurs when a single genetic element is associated with more than one phenotype. We aimed to identify pleiotropic loci associated with bone mineral density (BMD) and nonbone phenotypes in genome-wide association studies. In the discovery stage, the NHGRI-EBI Catalog was searched for genome-wide significant associations (P value < 5 × 10−8), excluding bone-related phenotypes. SNiPA was used to identify proxies of the significantly associated single nucleotide polymorphisms (SNPs) (r 2 = 1). We then assessed putative genetic associations of this set of SNPs with femoral neck (FN) and lumbar spine (LS) BMD data from the GEFOS Consortium. Pleiotropic variants were claimed at a false discovery rate < 1.4 × 10−3 for FN-BMD and < 1.5 × 10−3 for LS-BMD. Replication of these genetic markers was performed among more than 400 000 UK Biobank participants of European ancestry with available genetic and heel bone ultrasound data. In the discovery stage, 72 BMD-related pleiotropic SNPs were identified, and 12 SNPs located in 11 loci on 8 chromosomes were replicated in the UK Biobank. These SNPs were associated, in addition to BMD, with 14 different phenotypes. Most pleiotropic associations were exhibited by rs479844 (AP5B1, OVOL1 genes), which was associated with dermatological and allergic diseases, and rs4072037 (MUC1 gene), which was associated with magnesium levels and gastroenterological cancer. In conclusion, 12 BMD-related genome-wide significant SNPs showed pleiotropy with nonbone phenotypes. Pleiotropic associations can deepen the genetic understanding of bone-related diseases by identifying shared biological mechanisms with other diseases or traits.

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Maria A. Christou, Georgios Ntritsos, Georgios Markozannes, Fotis Koskeridis, Spyros N. Nikas, David Karasik, Douglas P. Kiel, Evangelos Evangelou, Evangelia E. Ntzani. A genome-wide scan for pleiotropy between bone mineral density and nonbone phenotypes. Bone Research, 2020, 8(1): 26 DOI:10.1038/s41413-020-0101-8

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References

[1]

Papadimitriou N et al. Burden of hip fracture using disability-adjusted life-years: a pooled analysis of prospective cohorts in the CHANCES consortium. Lancet Public health, 2017, 2:e239-e246

[2]

Johnell O et al. Predictive value of BMD for hip and other fractures. J. Bone Miner. Res., 2005, 20:1185-1194

[3]

Arden NK, Baker J, Hogg C, Baan K, Spector TD. The heritability of bone mineral density, ultrasound of the calcaneus and hip axis length: a study of postmenopausal twins. J. Bone Miner. Res., 1996, 11:530-534

[4]

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

[5]

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

[6]

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

[7]

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

[8]

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

[9]

Hackinger S, Zeggini E. Statistical methods to detect pleiotropy in human complex traits. Open Biol, 2017, 7:170125

[10]

Bulik-Sullivan B et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet., 2015, 47:1236-1241

[11]

Chesmore K, Bartlett J, Williams SM. The ubiquity of pleiotropy in human disease. Hum. Genet., 2018, 137:39-44

[12]

Witoelar A et al. Genome-wide pleiotropy between parkinson disease and autoimmune diseases. JAMA Neurol., 2017, 74:780-792

[13]

Nikpay M, Turner AW, McPherson R. Partitioning the pleiotropy between coronary artery disease and body mass index reveals the importance of low frequency variants and central nervous system-specific functional elements. Circ. Genom. Precis. Med., 2018, 11

[14]

Malochet-Guinamand S, Durif F, Thomas T. Parkinson’s disease: a risk factor for osteoporosis. Jt. Bone Spine, 2015, 82:406-410

[15]

Metta V, Sanchez TC, Padmakumar C. Osteoporosis: a hidden nonmotor face of parkinson’s disease. Int. Rev. Neurobiol., 2017, 134:877-890

[16]

Lim JS, Lee JI. Prevalence, pathophysiology, screening and management of osteoporosis in gastric cancer patients. J. Gastric Cancer, 2011, 11:7-15

[17]

Bantz SK, Zhu Z, Zheng T. The atopic march: progression from atopic dermatitis to allergic rhinitis and asthma. J. Clin. Cell Immunol, 2014, 5:202

[18]

Silverberg JI. Association between childhood atopic dermatitis, malnutrition, and low bone mineral density: a US population-based study. Pediatr. Allergy Immunol., 2015, 26:54-61

[19]

Wu CY et al. Osteoporosis in adult patients with atopic dermatitis: a nationwide population-based study. PloS ONE, 2017, 12

[20]

Sweeney J et al. Comorbidity in severe asthma requiring systemic corticosteroid therapy: cross-sectional data from the Optimum Patient Care Research Database and the British Thoracic Difficult Asthma Registry. Thorax, 2016, 71:339-346

[21]

Garg NK, Silverberg JI. Eczema is associated with osteoporosis and fractures in adults: a US population-based study. J. Allergy Clin. Immunol., 2015, 135:1085-1087 e1082

[22]

Yap CX et al. Dissection of genetic variation and evidence for pleiotropy in male pattern baldness. Nat. Commun., 2018, 9

[23]

Urano-Morisawa E et al. Induction of osteoblastic differentiation of neural crest-derived stem cells from hair follicles. PloS one, 2017, 12

[24]

Lango Allen H et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature, 2010, 467:832-838

[25]

Fassio A et al. The obesity paradox and osteoporosis. Eat Weight Disord., 2018, 23:293-302

[26]

Cao JJ. Effects of obesity on bone metabolism. J. Orthop. Surg. Res., 2011, 6

[27]

Dolan E, Swinton PA, Sale C, Healy A, O’Reilly J. Influence of adipose tissue mass on bone mass in an overweight or obese population: systematic review and meta-analysis. Nutr. Rev., 2017, 75:858-870

[28]

Castiglioni S, Cazzaniga A, Albisetti W, Maier JA. Magnesium and osteoporosis: current state of knowledge and future research directions. Nutrients, 2013, 5:3022-3033

[29]

Nicolet-Barousse L et al. Inactivation of the Na-Cl co-transporter (NCC) gene is associated with high BMD through both renal and bone mechanisms: analysis of patients with Gitelman syndrome and Ncc null mice. J. Bone Miner. Res., 2005, 20:799-808

[30]

Wallach S. Effects of magnesium on skeletal metabolism. Magnes. Trace Elem., 1990, 9:1-14

[31]

Arrabal-Polo MA, Cano-Garcia Mdel C, Canales BK, Arrabal-Martin M. Calcium nephrolithiasis and bone demineralization: pathophysiology, diagnosis, and medical management. Curr. Opin. Urol., 2014, 24:633-638

[32]

Ryan LE, Ing SW. Idiopathic hypercalciuria: can we prevent stones and protect bones? Clevel. Clin. J. Med., 2018, 85:47-54

[33]

Liang X et al. Assessing the genetic correlations between blood plasma proteins and osteoporosis: a polygenic risk score analysis. Calcif. Tissue Int., 2019, 104:171-181

[34]

Dolan E, Sale C. Protein and bone health across the lifespan. Proc. Nutr. Soc., 2019, 78:45-55

[35]

Gurevitch O, Slavin S. The hematological etiology of osteoporosis. Med. Hypotheses, 2006, 67:729-735

[36]

Valderrabano RJ, Wu JY. Bone and blood interactions in human health and disease. Bone, 2019, 119:65-70

[37]

Holmberg T et al. Socioeconomic status and risk of osteoporotic fractures and the use of DXA scans: data from the Danish population-based ROSE study. Osteoporos. Int., 2019, 30:343-353

[38]

Du Y, Zhao LJ, Xu Q, Wu KH, Deng HW. Socioeconomic status and bone mineral density in adults by race/ethnicity and gender: the Louisiana osteoporosis study. Osteopor. Int., 2017, 28:1699-1709

[39]

Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet., 2013, 14:483-495

[40]

Andreassen OA et al. Identifying common genetic variants in blood pressure due to polygenic pleiotropy with associated phenotypes. Hypertension, 2014, 63:819-826

[41]

Billings LK et al. Impact of common variation in bone-related genes on type 2 diabetes and related traits. Diabetes, 2012, 61:2176-2186

[42]

Reppe S et al. Genetic sharing with cardiovascular disease risk factors and diabetes reveals novel bone mineral density loci. PloS one, 2015, 10

[43]

Karasik D, Kiel DP. Evidence for pleiotropic factors in genetics of the musculoskeletal system. Bone, 2010, 46:1226-1237

[44]

Evangelou E et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nature Genet., 2018, 50:1412-1425

[45]

Sivakumaran S et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet., 2011, 89:607-618

[46]

Arnold M, Raffler J, Pfeufer A, Suhre K, Kastenmuller G. SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics, 2015, 31:1334-1336

[47]

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

[48]

SIMES RJ. An improved Bonferroni procedure for multiple tests of significance. Biometrika, 1986, 73:751-754

[49]

Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 1995, 57:289-300

[50]

Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics, 2015, 31:3555-3557

[51]

Sudlow C et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med., 2015, 12

[52]

Bycroft C et al. The UK Biobank resource with deep phenotyping and genomic data. Nature, 2018, 562:203-209

[53]

UK Biobank. Ultrasound Bone Densitometry. Version 1.0. (15/04/2011). https://biobank.ctsu.ox.ac.uk/crystal/docs/Ultrasoundbonedensitometry.pdf.

[54]

Cunningham F et al. Ensembl 2019. Nucleic Acids Res., 2019, 47:D745-D751

[55]

Stelzer G et al. The genecards suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinforma., 2016, 54:1 30 31-31 30 33

[56]

Frost ML, Blake GM, Fogelman I. Can the WHO criteria for diagnosing osteoporosis be applied to calcaneal quantitative ultrasound? Osteopor. Int., 2000, 11:321-330

[57]

Stata Statistical Software: Release 12. (College Station, TX: StataCorp LP., 2011). https://www.stata.com/support/faqs/resources/citing-software-documentation-faqs/.

[58]

Chang CC et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 2015, 4

[59]

Zheng J et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics, 2017, 33:272-279

[60]

Staley JR et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics, 2016, 32:3207-3209

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