Mendelian Randomization Analysis Reveals a Causal Influence of Circulating Sclerostin Levels on Bone Mineral Density and Fractures

In bone, sclerostin is mainly osteocyte‐derived and plays an important local role in adaptive responses to mechanical loading. Whether circulating levels of sclerostin also play a functional role is currently unclear, which we aimed to examine by two‐sample Mendelian randomization (MR). A genetic instrument for circulating sclerostin, derived from a genomewide association study (GWAS) meta‐analysis of serum sclerostin in 10,584 European‐descent individuals, was examined in relation to femoral neck bone mineral density (BMD; n = 32,744) in GEFOS and estimated bone mineral density (eBMD) by heel ultrasound (n = 426,824) and fracture risk (n = 426,795) in UK Biobank. Our GWAS identified two novel serum sclerostin loci, B4GALNT3 (standard deviation [SD]) change in sclerostin per A allele (β = 0.20, p = 4.6 × 10−49) and GALNT1 (β  = 0.11 per G allele, p = 4.4 × 10−11). B4GALNT3 is an N‐acetyl‐galactosaminyltransferase, adding a terminal LacdiNAc disaccharide to target glycocoproteins, found to be predominantly expressed in kidney, whereas GALNT1 is an enzyme causing mucin‐type O‐linked glycosylation. Using these two single‐nucleotide polymorphisms (SNPs) as genetic instruments, MR revealed an inverse causal relationship between serum sclerostin and femoral neck BMD (β = –0.12, 95% confidence interval [CI] –0.20 to –0.05) and eBMD (β = –0.12, 95% CI –0.14 to –0.10), and a positive relationship with fracture risk (β = 0.11, 95% CI 0.01 to 0.21). Colocalization analysis demonstrated common genetic signals within the B4GALNT3 locus for higher sclerostin, lower eBMD, and greater B4GALNT3 expression in arterial tissue (probability >99%). Our findings suggest that higher sclerostin levels are causally related to lower BMD and greater fracture risk. Hence, strategies for reducing circulating sclerostin, for example by targeting glycosylation enzymes as suggested by our GWAS results, may prove valuable in treating osteoporosis. © 2019 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals, Inc.

[1]  Pedro M. Valero-Mora,et al.  ggplot2: Elegant Graphics for Data Analysis , 2010 .

[2]  H. Nakanishi,et al.  Molecular Cloning and Characterization of a Novel Human β1,4-N-Acetylgalactosaminyltransferase, β4GalNAc-T3, Responsible for the Synthesis of N,N′-Diacetyllactosediamine, GalNAcβ1–4GlcNAc* , 2003, Journal of Biological Chemistry.

[3]  D. Kiel,et al.  Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study , 2018, British Medical Journal.

[4]  Nicola J. Rinaldi,et al.  Genetic effects on gene expression across human tissues , 2017, Nature.

[5]  G. Davey Smith,et al.  Orienting the causal relationship between imprecisely measured traits using GWAS summary data , 2017, PLoS genetics.

[6]  M. Oursler,et al.  Sclerostin expression and functions beyond the osteocyte. , 2017, Bone.

[7]  Beth Wilmot,et al.  Edinburgh Explorer Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture , 2022 .

[8]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[9]  Daniel L. Koller,et al.  The role of mutant UDP-N-acetyl-alpha-D-galactosamine-polypeptide N-acetylgalactosaminyltransferase 3 in regulating serum intact fibroblast growth factor 23 and matrix extracellular phosphoglycoprotein in heritable tumoral calcinosis. , 2006, The Journal of clinical endocrinology and metabolism.

[10]  Daniel Sinnett,et al.  Population genomics in a disease targeted primary cell model. , 2009, Genome research.

[11]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[12]  A. Butterworth,et al.  Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data , 2013, Genetic epidemiology.

[13]  T. Frayling,et al.  C-reactive protein levels and body mass index: Elucidating direction of causation through reciprocal Mendelian randomization , 2010, International Journal of Obesity.

[14]  Michael H. Guo,et al.  Epigenetic profiling of growth plate chondrocytes sheds insight into regulatory genetic variation influencing height , 2017, eLife.

[15]  W. März,et al.  Randomized Controlled Trial on the Efficacy and Safety of Atorvastatin in Patients with Type 2 Diabetes on Hemodialysis (4D Study): Demographic and Baseline Characteristics , 2004, Kidney and Blood Pressure Research.

[16]  S. Khosla,et al.  Hormonal and systemic regulation of sclerostin. , 2017, Bone.

[17]  B. Meijers,et al.  Circulating levels of sclerostin but not DKK1 associate with laboratory parameters of CKD-MBD , 2017, PloS one.

[18]  Reedik Mägi,et al.  GWAMA: software for genome-wide association meta-analysis , 2010, BMC Bioinformatics.

[19]  N. Timpson,et al.  Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors , 2015, European Journal of Epidemiology.

[20]  Tom R. Gaunt,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 , 2016, bioRxiv.

[21]  Daniel L. Koller,et al.  Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture , 2012, Nature Genetics.

[22]  Zoltán Kutalik,et al.  Quality control and conduct of genome-wide association meta-analyses , 2014, Nature Protocols.

[23]  Julian M. W. Quinn,et al.  An Atlas of Human and Murine Genetic Influences on Osteoporosis , 2018, bioRxiv.

[24]  N. Napoli,et al.  Serum Sclerostin and Bone Turnover in Latent Autoimmune Diabetes in Adults , 2018, The Journal of clinical endocrinology and metabolism.

[25]  D. Weghuis,et al.  Genomic organization and chromosomal localization of three members of the UDP-N-acetylgalactosamine: polypeptide N-acetylgalactosaminyltransferase family. , 1998, Glycobiology.

[26]  George Davey Smith,et al.  Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology , 2008, Statistics in medicine.

[27]  Gil McVean,et al.  Genetic characterization of Greek population isolates reveals strong genetic drift at missense and trait-associated variants , 2014, Nature Communications.

[28]  Julian M. W. Quinn,et al.  An atlas of genetic influences on osteoporosis in humans and mice , 2018, Nature Genetics.

[29]  Jun Yu,et al.  ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data , 2018, BMC Genomics.

[30]  G. Abecasis,et al.  MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes , 2010, Genetic epidemiology.

[31]  Yurii S. Aulchenko,et al.  Twenty bone mineral density loci identified by large-scale meta-analysis of genome-wide association studies , 2009, Nature Genetics.

[32]  E. Zeggini,et al.  The mountainous Cretan dietary patterns and their relationship with cardiovascular risk factors: the Hellenic Isolated Cohorts MANOLIS study , 2016, Public Health Nutrition.

[33]  Tom R. Gaunt,et al.  Systematic identification of genetic influences on methylation across the human life course , 2016, Genome Biology.

[34]  D. Lawlor,et al.  Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption , 2018, bioRxiv.

[35]  Jacqueline K. White,et al.  Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis , 2017, Nature Genetics.

[36]  D. Lawlor,et al.  Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort , 2012, International journal of epidemiology.

[37]  L. Lanyon,et al.  Sclerostin's role in bone's adaptive response to mechanical loading , 2017, Bone.

[38]  Manuel Mayr,et al.  BAS/BSCR9 Proteomic characterisation of extracellular space components in the human aorta , 2010, Heart.

[39]  D. Lawlor,et al.  Cohort Profile: The ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children , 2012, International journal of epidemiology.

[40]  J. Schousboe,et al.  Romosozumab versus Alendronate and Fracture Risk in Women with Osteoporosis. , 2018, The New England journal of medicine.

[41]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[42]  C. Wallace,et al.  Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics , 2013, PLoS genetics.

[43]  A. Grauer,et al.  Romosozumab Treatment in Postmenopausal Women with Osteoporosis. , 2016, The New England journal of medicine.

[44]  Jack Bowden,et al.  Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression , 2018, International journal of epidemiology.

[45]  J. Starzyk,et al.  Sclerostin and its significance for children and adolescents with type 1 diabetes mellitus (T1D). , 2019, Bone.

[46]  Bjarni V. Halldórsson,et al.  Methylation of Bone SOST, Its mRNA, and Serum Sclerostin Levels Correlate Strongly With Fracture Risk in Postmenopausal Women , 2015, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[47]  Jack Bowden,et al.  Invited Commentary Invited Commentary: Detecting Individual andGlobal Horizontal Pleiotropy in Mendelian Randomization—A Job for the Humble Heterogeneity Statistic? , 2018 .

[48]  J. Marchini,et al.  Fast and accurate genotype imputation in genome-wide association studies through pre-phasing , 2012, Nature Genetics.

[49]  D. Mellström,et al.  Free Testosterone Is a Positive, Whereas Free Estradiol Is a Negative, Predictor of Cortical Bone Size in Young Swedish Men: The GOOD Study , 2005, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[50]  Eurie L. Hong,et al.  Annotation of functional variation in personal genomes using RegulomeDB , 2012, Genome research.

[51]  E. McCloskey,et al.  Elevated Circulating Sclerostin Concentrations in Individuals With High Bone Mass, With and Without LRP5 Mutations , 2014, The Journal of clinical endocrinology and metabolism.

[52]  S. Ebrahim,et al.  'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? , 2003, International journal of epidemiology.

[53]  C. García-Fontana,et al.  Circulating levels of sclerostin are associated with cardiovascular mortality , 2018, PloS one.

[54]  T. Martin,et al.  Modulation of osteoclast differentiation and function by the new members of the tumor necrosis factor receptor and ligand families. , 1999, Endocrine reviews.

[55]  Howard Y. Chang,et al.  Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position , 2013, Nature Methods.

[56]  Valeriia Haberland,et al.  The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.