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Meta-Analysis
. 2012 Apr 15;44(5):491-501.
doi: 10.1038/ng.2249.

Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture

Karol Estrada  1 Unnur StyrkarsdottirEvangelos EvangelouYi-Hsiang HsuEmma L DuncanEvangelia E NtzaniLing OeiOmar M E AlbaghaNajaf AminJohn P KempDaniel L KollerGuo LiChing-Ti LiuRyan L MinsterAlireza MoayyeriLiesbeth VandenputDana WillnerSu-Mei XiaoLaura M Yerges-ArmstrongHou-Feng ZhengNerea AlonsoJoel ErikssonCandace M KammererStephen K KaptogePaul J LeoGudmar ThorleifssonScott G WilsonJames F WilsonVille AaltoMarkku AlenAaron K AragakiThor AspelundJacqueline R CenterZoe DailianaDavid J DugganMelissa GarciaNatàlia Garcia-GiraltSylvie GirouxGöran HallmansLynne J HockingLise Bjerre HustedKaren A JamesonRita KhusainovaGhi Su KimCharles KooperbergTheodora KoromilaMarcin KrukMarika LaaksonenAndrea Z LacroixSeung Hun LeePing C LeungJoshua R LewisLaura MasiSimona Mencej-BedracTuan V NguyenXavier NoguesMillan S PatelJanez PrezeljLynda M RoseSerena ScollenKristin SiggeirsdottirAlbert V SmithOlle SvenssonStella TrompetOlivia TrummerNatasja M van SchoorJean WooKun ZhuSusana BalcellsMaria Luisa BrandiBrendan M BuckleySulin ChengClaus ChristiansenCyrus CooperGeorge DedoussisIan FordMorten FrostDavid GoltzmanJesús González-MacíasMika KähönenMagnus KarlssonElza KhusnutdinovaJung-Min KohPanagoula KolliaBente Lomholt LangdahlWilliam D LesliePaul LipsÖsten LjunggrenRoman S LorencJanja MarcDan MellströmBarbara Obermayer-PietschJosé M OlmosUlrika Pettersson-KymmerDavid M ReidJosé A RianchoPaul M RidkerFrançois RousseauP Eline SlagboomNelson L S TangRoser UrreiztiWim Van HulJorma ViikariMaría T ZarrabeitiaYurii S AulchenkoMartha Castano-BetancourtElin GrundbergLizbeth HerreraThorvaldur IngvarssonHrefna JohannsdottirTony KwanRui LiRobert LubenCarolina Medina-GómezStefan Th PalssonSjur ReppeJerome I RotterGunnar SigurdssonJoyce B J van MeursDominique VerlaanFrances M K WilliamsAndrew R WoodYanhua ZhouKaare M GautvikTomi PastinenSoumya RaychaudhuriJane A CauleyDaniel I ChasmanGraeme R ClarkSteven R CummingsPatrick DanoyElaine M DennisonRichard EastellJohn A EismanVilmundur GudnasonAlbert HofmanRebecca D JacksonGraeme JonesJ Wouter JukemaKay-Tee KhawTerho LehtimäkiYongmei LiuMattias LorentzonEugene McCloskeyBraxton D MitchellKannabiran NandakumarGeoffrey C NicholsonBen A OostraMunro PeacockHuibert A P PolsRichard L PrinceOlli RaitakariIan R ReidJohn RobbinsPhilip N SambrookPak Chung ShamAlan R ShuldinerFrances A TylavskyCornelia M van DuijnNick J WarehamL Adrienne CupplesMichael J EconsDavid M EvansTamara B HarrisAnnie Wai Chee KungBruce M PsatyJonathan ReeveTimothy D SpectorElizabeth A StreetenM Carola ZillikensUnnur ThorsteinsdottirClaes OhlssonDavid KarasikJ Brent RichardsMatthew A BrownKari StefanssonAndré G UitterlindenStuart H RalstonJohn P A IoannidisDouglas P KielFernando Rivadeneira
Affiliations
Meta-Analysis

Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture

Karol Estrada et al. Nat Genet. .

Abstract

Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10(-4), Bonferroni corrected), of which six reached P < 5 × 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

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Figures

Figure 1
Figure 1. Description of Study design
Stage 1: Meta-analysis of 17 Genome-wide association studies for BMD. Stage 2: 96 top independent SNPs (82 autosomal SNPs with P<5×10−6 + 5 X-chromosome SNPs + 9 SNPs from conditional analysis) were followed-up for de-novo and in-silico replication of the BMD association in 34 studies. Stage 3: the same 96 SNPs were tested for association with fracture in 50 studies with de-novo and in-silico data.
Figure 2
Figure 2
A) Phenotype-wide effect for the BMD loci associated with fracture and those part of the OPG/RANK/RANKL pathway. Genetic effect estimates (± 95%CI) are shown for fracture (blue circles), lumbar-spine BMD (yellow rectangles) and femoral-neck BMD (green diamonds) for the 14 loci associated with fracture risk. Horizontal lines represent 95% confidence limits. Effect estimates are shown after transformation of the standardized mean difference(SMD) in the BMD effect to odds ratio equivalents (e.g. a 0.02 SMD in the BMD effect corresponds to an OR of 1.04). Secondary signals for markers rs227584 and rs6426749 are marked with an asterisk and the signals mapping to the OPG (rs2062377), RANK (rs884205), and RANKL (rs9533090) genes are marked with a hash. B) Regional association plot for the 18p11.21 locus displaying the P-values for the top SNP associated with fracture (rs4796995) together with P-values of the BMD discovery (Stage 1) and combined with the BMD replication (Stage 1 + 2). SNPs are plotted by position in a 500Kb window of chromosome 18 against association with FN-BMD (−log10 P). Estimated recombination rates (from HapMap) are plotted in cyan to reflect the local LD structure. SNPs surrounding most significant SNP are color-coded reflecting LD between markers (pairwise r2). Genes, exons and transcription direction are derived from the UCSC genome browser.
Figure 3
Figure 3
Combined effect of BMD-decreasing alleles and fracture risk-increasing risk alleles modelled in the population-based Prospective Epidemiological Risk Factor (PERF) study (n=2,836 women) on A) Baseline FN-BMD standardized residuals (Z-scores), B) Risk for Osteoporosis and C) Risk for Any type of fracture. The genetic score of each individual for A) and B) was based on the 63 SNPs displaying genome-wide significant association with BMD (55 main and 8 secondary signals), and for C) was based on the 16 BMD SNPs associated with fracture. Both genetic scores are weighted for relative effect sizes estimated without the PERF study. Weighted allele counts summed for each individual were divided by the mean effect size making them equivalent to the percent of alleles carried by each individual and binned into 5 categories. Histograms describe counts of individuals in each genetic score category (left axis scale). Diamonds (right axis scale) represent A) mean FN-BMD standardized levels, risk estimates in the form of odds ratio for B) Osteoporosis (defined as NHANES T-score<−2.5) and for C) Any type of Fracture using the middle category as reference (OR=1). Vertical lines represent 95% confidence limits.
Figure 4
Figure 4
Graphic representation of GRAIL connections between SNPs and corresponding genes for the 18 SNPs as determined with GRAIL P<0.01. The top 10 keywords linking the genes were: ‘bone’, ‘catenin’, ‘signaling’, ‘differentiation’, ’rank’, ‘osteoblast’, ‘diacylglycerol’, ‘kappab’, ‘development’, and ‘osteoclast’. Thicker redder lines imply stronger literature-based connectivity. Blue and black boxes depict loci boundaries represented per top-associated marker (outer circle) and per gene in the region (inner circle).

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