A novel common variant in DCST2 is associated with length in early life and height in adulthood

A novel common variant in DCST2 is associated with length in early life and height in adulthood Ralf J.P. van der Valk1,2,3, { , Eskil Kreiner-Møller5, { , Marjolein N. Kooijman1,2,3, { ,Mo `nica Guxens6,7,8, { , Evangelia Stergiakouli9, { , Annika Sa ¨a ¨f12, Jonathan P. Bradfield13, Frank Geller15, M. Geoffrey Hayes16,17, Diana L. Cousminer18, Antje Ko ¨rner20, Elisabeth Thiering21,22, John A. Curtin25, Ronny Myhre26, Ville Huikari28, Raimo Joro31, Marjan Kerkhof33,34, Nicole M. Warrington37,38, Niina Pitka ¨nen39, Ioanna Ntalla41,42, Momoko Horikoshi43,44, Riitta Veijola45, Rachel M. Freathy47, Yik-Ying Teo48,49,50, Sheila J. Barton51, David M. Evans9,38, John P. Kemp9,38, Beate St Pourcain9,10,11, Susan M. Ring9,10, George Davey Smith9, Anna Bergstro ¨m12, Inger Kull53,54, Hakon Hakonarson13,55,14, Frank D. Mentch13, Hans Bisgaard5, Bo Chawes5, Jakob Stokholm5, Johannes Waage5, Patrick Eriksen5, Astrid Sevelsted5, Mads Melbye15,56, Early Genetics and Lifecourse Epidemiology (EAGLE) Consortium, Cornelia M. van Duijn1, Carolina Medina-Gomez1,3,4, Albert Hofman1,3, Johan C. de Jongste2,3, H. Rob Taal1,2, Andre ´G. Uitterlinden1,3,4, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Loren L. Armstrong16,17, Johan Eriksson18, Aarno Palotie18,57,59,58, Mariona Bustamante6,7,8,61, Xavier Estivill7,8,61,62, Juan R. Gonzalez6,7,8, Sabrina Llop7,63, Wieland Kiess20, Anubha Mahajan43, Claudia Flexeder22, Carla M.T. Tiesler21,22, Clare S. Murray25, Angela Simpson25, Per Magnus27, Verena Sengpiel64, Anna-Liisa Hartikainen29, Sirkka KeinanenKiukaanniemi28, Alexandra Lewin65, Alexessander Da Silva Couto Alves65, Alexandra I. Blakemore66, Jessica L. Buxton66, Marika Kaakinen28,65,30, Alina Rodriguez65,67, Sylvain Sebert28, Marja Vaarasmaki46, Timo Lakka31,68,69, Virpi Lindi31, Ulrike Gehring70, Dirkje S. Postma34,35, Wei Ang37, John P. Newnham37, Leo-Pekka Lyytika ¨inen71,72, Katja Pahkala39,38, Olli T. Raitakari39,74, Kalliope Panoutsopoulou76, Eleftheria Zeggini76, Dorret I. Boomsma77,78,79, Maria Groen-Blokhuis77,78,79, Jorma Ilonen40,32, Lude Franke80, Joel N. Hirschhorn81,60,82, Tune H. Pers81,60,83, Liming Liang89, Jinyan Huang89,85, Berthold Hocher86,87,88, Mikael Knip19,89,90, Seang-Mei Saw48,91,92, John W. Holloway52, ErikMele ´n12,54, StruanF.A. Grant13,55,14, Bjarke Feenstra15, William L. Lowe16,17,ElisabethWide ´n18, Elena Sergeyev20, Harald Grallert23,24, Adnan Custovic25, Bo Jacobsson26,64, Marjo-Riitta Jarvelin28,65,30,93,94, Mustafa Atalay31, Gerard H. Koppelman34,36, Craig E. Pennell37, Harri Niinikoski39,75, George V. Dedoussis42, Mark I. Mccarthy43,44,95, Timothy M. Frayling47, Jordi Sunyer6,7,8,62, { , Nicholas J. Timpson9, { , Fernando Rivadeneira1,3,4, { , Klaus Bønnelykke5, { and Vincent W.V. Jaddoe1,2,3, { ,∗for the Early Growth Genetics (EGG) Consortium † These authors have contributed equally to this work. ‡ The authors wish it to be known that, in their opinion, J.S., N.J.T., F.R., K.B. and V.W.V.J. should be regarded as joint Lead Senior Authors; these authors jointly directed this work. ∗ To whom correspondence should be addressed at: Generation R Study Group, Department of Epidemiology, Erasmus Medical Center, Sophia’s Children’s Hospital, Postbus 2060, 3000 CB Rotterdam, The Netherlands. Tel: +31 107043405; Fax: +31 10 4089382; Email: [email protected] #The Author 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/),whichpermitsunrestrictedreuse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperly cited. Human Molecular Genetics, 2015, Vol. 24, No. 4 1155–1168 doi:10.1093/hmg/ddu510 Advance Access published on October 3, 2014 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from 1 Department of Epidemiology, 2 Department of Paediatrics, 3 The Generation R Study Group, 4 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands, 5 Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health Sciences, University of Copenhagen & Danish Pediatric Asthma Center, Copenhagen University Hospital, Gentofte, Denmark, 6 Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain, 7 CIBER Epidemiologı ´a y Salud Pu ´blica (CIBERESP), Spain, 8 Pompeu Fabra University (UPF), Barcelona, Catalonia, Spain, 9 MRC Integrative Epidemiology Unit , 10 Avon Longitudinal Study of Parents and Children (ALSPAC), School of Social and Community Medicine, 11 School of Oral and Dental Sciences, University of Bristol, Bristol, UK, 12 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, 13 Center for Applied Genomics, Abramson Research Center, 14 Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA, 15 Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark, 16 Division of Endocrinology, Metabolism and Molecular Medicine, 17 Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA, 18 Institute for Molecular Medicine Finland, 19 Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland, 20 Center of Pediatric Research, University Hospital Center Leipzig, University of Leipzig, Leipzig, Germany, 21 Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, Germany, 22 Institute of Epidemiology I, 23 Institute of Epidemiology II, 24 Research Unit for Molecular Epidemiology, Helmholtz Zentrum Mu ¨nchen – German Research Center for Environmental Health, Neuherberg, Germany, 25 Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester and University Hospital of South Manchester, Manchester Academic Health Sciences Centre, Manchester, UK, 26 Division Epidemiology, Department Genes and Environment, 27 Division Epidemiology, Norwegian Institute of Public Health, Oslo, Norway, 28 Institute of Health Sciences, 29 Institute of Clinical Medicine/Obstetrics and Gynecology, 30 Biocenter Oulu, University of Oulu, Oulu, Finland, 31 Institute of Biomedicine, Physiology, 32 Department of Clinical Microbiology, University of Eastern Finland, Kuopio, Finland, 33 Department of Epidemiology, 34 Groningen Research Institute for Asthma and COPD, 35 Department of Pulmonology, 36 Beatrix Children’s Hospital, Pediatric Pulmonology and Pediatric Allergy, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 37 School of Women’s and Infants’ Health, The University of Western Australia, Perth, Australia, 38 University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia, 39 Research Centre of Applied and Preventive Cardiovascular Medicine, 40 Immunogenetics Laboratory, University of Turku, Turku, Finland, 41 Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK, 42 Department of Nutrition and Dietetics, Harokopio University of Athens, Athens 11527, Greece, 43 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK, 44 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK, 45 Department of Pediatrics, Medical Research Center, 46 Department of Obstetrics and Gynecology and MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland, 47 University of Exeter Medical School, Royal Devon and Exeter Hospital, Barrack Road,Exeter EX2 5DW, UK, 48 Saw Swee Hock School of Public Health, 49 Life Science Institute, National University of Singapore, Singapore, 50 Genome Institute of Singapore, Agency for Science, Technology and Research, 51 MRC Lifecourse Epidemiology Unit, 52 Human Genetics and Genomic Medicine, Human Development & Health, Faculty of Medicine, University of Southampton, UK, 53 Department of Clinical Science and Education, So ¨dersjukhuset, Stockholm, Sweden, 54 Sachs’ Children’s Hospital, Stockholm, Sweden, 55 Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 56 Department of Medicine, Stanford School of Medicine, Stanford, USA, 57 Analytic and Translational Genetics Unit, Department of Medicine, 58 Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA, 59 Program in Medical and Population Genetics, 60 Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA, 61 Centre for Genomic Regulation (CRG), Barcelona, Spain, 62 IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain, 63 Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, FISABIO-Public Health, Valencia, Spain, 64 Department Obstetrics and Gynecology, Sahlgrenska Academy, Sahlgrenska University Hospital, Gothenburg, Sweden, 65 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, MRC Health Protection Agency (HPE) Centre for Environment and Health, 66 Section of Investigative Medicine, Division of Diabetes, Endocrinology, and Metabolism, Faculty of Medicine, Imperial College, London W12 0NN, UK, 67 Department of Psychology, Mid Sweden University, O ¨stersund, Sweden, 68 Kuopio Research Institute of Exercise Medicine, Kuopio, Finland, 69 Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland, 70 Institute for Risk Assessment 1156 Human Molecular Genetics, 2015, Vol. 24, No. 4 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from Sciences, Utrecht University, Utrecht, The Netherlands, 71 Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland, 72 Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland, 73 Sports and Exercise Medicine Unit, Department of Physical Activity and Health, Paavo Nurmi Centre, Turku, Finland, 74 Department of Clinical Physiology and Nuclear Medicine, 75 Department of Pediatrics, Turku University Hospital, Turku, Finland, 76 Wellcome Trust Sanger Institute, The Morgan Building, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1HH, UK, 77 Department of Biological Psychology, VU University, Amsterdam, The Netherlands, 78 EMGO Institute for Health and Care Research, Amsterdam, The Netherlands, 79 Neuroscience Campus Amsterdam, The Netherlands, 80 Department of Genetics, University of Groningen, University Medical Centre Groningen, The Netherlands, 81 Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, USA, 82 Department of Genetics, Harvard Medical School, USA, 83 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Denmark, 84 Department of Biostatistics and Epidemiology, Harvard School of Public Health, Boston, USA, 85 Shanghai Institute of Hematology, Rui Jin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China, 86 Institute of Nutritional Science, University of Potsdam, Germany, 87 The First Affiliated Hospital of Jinan University, Guangzhou 510630, China, 88 Center for Cardiovascular Research/Institute of Pharmacology, Charite ´, Berlin, Germany, 89 Department of Pediatrics, Tampere University Hospital, Tampere, Finland, 90 Children’s Hospital, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland, 91 Singapore Eye Research Institute, Singapore, 92 Duke-NUS Graduate Medical School, Singapore, 93 Unit of Primary Care, Oulu University Hospital, Kajaanintie 50, P.O.Box 20, FI-90220, Oulu 90029 OYS, Finland, 94 Department of Children and Young People and Families, National Institute for Health and Welfare, Aapistie 1, Box 310, Oulu FI-90101, Finland and 95 Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK Received July 3, 2014; Revised and Accepted September 29, 2014 Common genetic variants have been identified for adult height, but not much is known about the genetics of skeletal growth in early life. To identify common genetic variants that influence fetal skeletal growth, we metaanalyzed 22 genome-wide association studies (Stage 1; N528 459). We identified seven independent top single nucleotide polymorphisms (SNPs) (P<1310 26 ) for birth length, of which three were novel and four were in or near loci known to be associated with adult height (LCORL,PTCH1,GPR126 and HMGA2). The three novel SNPs were followed-up in nine replication studies (Stage 2; N511 995), with rs905938 in DCSTAMP domain containing 2 (DCST2) genome-wide significantly associated with birth length in a joint analysis (Stages 1 12; b 50.046, SE 50.008, P52.46 310 28 , explained variance 50.05%). Rs905938 was also associated with infant length (N528 228; P55.54 310 24 ) and adult height (N5127 513; P51.45 310 25 ). DCST2 is a DC-STAMP-like protein family member and DC-STAMP is an osteoclast cell-fusion regulator. Polygenic scores based on 180 SNPs previously associated with human adult stature explained 0.13% of variance in birth length. The same SNPs explained 2.95% of the variance of infant length. Of the 180 known adult height loci, 11 were genome-wide significantly associated with infant length (SF3B4,LCORL,SPAG17,C6orf173, PTCH1,GDF5,ZNFX1,HHIP,ACAN,HLA locus and HMGA2). This study highlights that common variation in DCST2 influences variation in early growth and adult height. INTRODUCTION Fetal and infancy length growth are important measures of development in early life. Early length growth seems to be associated with height in adulthood (1). It has been shown that fetal and infant growth are independently associated with higher risks of cardiovascular disease, type 2 diabetes and many other complex diseases. Previous findings suggested genetic links between fetal growth and metabolism (2,3). However, these studies mainly focused on birth weight as early growth measure. Skeletal growth is a different measure of development in early life. Skeletal growth during fetal life and infancy is a complex trait with heritability estimates of 26–72% (4). Although correlated with each other, fetal, infant and adult skeletal growth may be influenced by different genetic factors. Many common genetic variants have been identified for adult height (5), but not much is known about the genetics of skeletal growth in early life. Although, several rare genetic defects with large effects on length at birth and during infancy have been found (6,7), common genetic variants that influence normal variation in birth and infant length have not yet been identified. Therefore, we aimed to identify common genetic variants influencing early length growth, also in perspective of their effect on adult stature. Human Molecular Genetics, 2015, Vol. 24, No. 4 1157 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from RESULTS To identify common genetic variants associated with birth length, we examined 2 201 971 million directly genotyped and imputed SNPs with birth length in 22 independent discovery studies with genome-wide association (GWA) or Metabochip data (Stage 1; N¼28 459; Fig. 1). Birth length was measured using standardized procedures (Supplementary Material, Tables S1 and S2). Studies with self-reported measurements were excluded a priori. Birth length was standardized using growth analyzer (http://www.growthanalyser.org), transforming birth length into sexand age-adjusted standard deviation scores (SDS). We used the North-European 1991 reference panel to compare results between studies. We applied linear regression between number of alleles or dosages obtained from imputations and standardized birth length (full details in Materials and Methods). Gene identification In the discovery phase (Stage 1), we found seven independent top SNPs with suggestive evidence of association (P,1×10 26 ) with birth length (Supplementary Material, Figs. S1 and S2, QQand Manhattan plot). Four SNPs mapped to loci already known to be associated with adult height (Supplementary Material, Table S3, LCORL,PTCH1,GPR126 and HMGA2)(5). The 3 SNPs reflecting potentially novel associations were taken forward in nine independent replication studies (Stage 2; N¼11 995; Fig. 1). Only one of the three SNPs displayed significant evidence for replication in Stage 2 and reached genome-wide significance in the joint analysis (Stages 1 +2; P,5×10 28 ; Table 1). This novel association arose from SNP rs905938, mapping to chromosome 1q22 in DC-STAMP domain containing 2 (DCST2) (Fig. 2, regional association plot). Each C allele [minor allele frequency (MAF) ¼0.24] of rs905938 was associated with an increase (standardized) of 0.046 SDS in birth length (standard error ¼0.008, P¼2.46 ×10 28 ; explained variance ¼0.05%). The genome-wide significantly associated SNP showed low degree of heterogeneity between the discovery studies (P¼0.93, I 2 ¼0%). Figure 3shows the forest plot of the associations between rs905938[C] and birth length across all studies. Other suggestive loci in the discovery analysis are shown in Supplementary Material, Table S3 (P,1×10 25 ). Summary statistics of all SNPs are available at http://eggconsortium.org. Functional analyses We assessed common variants with deleterious functional implications in linkage disequilibrium (LD, r 2 .0.80) with rs905938 using HaploReg (8). There were no non-synonymous variants in LD with rs905938. We found three putative functional intronic variants in high LD with rs905938. Details are depicted in Supplementary Material, Table S4. Subsequently, we assessed whether variants in the identified locus were involved in the Figure 1 Study design. 1158 Human Molecular Genetics, 2015, Vol. 24, No. 4 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from regulation of messenger RNA expression (eQTLs) in genomewide expression datasets of lymphoblastoid cell lines (LCLs, N¼1830) (9,10). We found cis eQTLs [false discovery rate (FDR) ,1% account for all SNP-probe pairs that were within 1 Mb of each other) for transcripts of PBXIP1,GBA and ADAM15. Yet, rs905938 and the cis eQTL SNPs were not in perfect LD (r 2 ,0.80, Supplementary Material, Table S5). Therefore, we cannot exclude that multiple independent effects arise from the same region of association. DCST2 and growth phenotypes We tested the associations of rs905938[C] with ‘fetal growth’ measures in the 1st, 2nd and 3rd trimester of pregnancy in the Table 1 Summary statistics of the three novel SNPs at P,1×10 26 in the discovery analysis and the replication follow-up results Marker MAF b SE PnI 2 HetP Discovery (Stage 1) rs905938[C] at 1q22 (DCST2) 0.24 0.050 0.010 2.59 ×10 27 28 327 0.0 0.930 rs12545524[G] at 8q22.1 (near GDF6) 0.14 0.078 0.014 1.54 ×10 28 22 170 6.6 0.376 rs11037473[A] at 11p11.2 (nearest genes TTC17-HSD17B12) 0.06 20.109 0.021 2.17 ×10 27 22 259 0.0 0.735 Replication (Stage 2) rs905938[C] at 1q22 (DCST2) 0.23 0.035 0.015 1.99 ×10 22 11 908 – – rs12545524[G] at 8q22.1 (near GDF6) 0.11 20.012 0.017 4.67 ×10 21 17 614 – – rs11037473[A] at 11p11.2 (nearest genes TTC17-HSD17B12) 0.08 20.035 0.020 8.06 ×10 22 17 606 – – Discovery +replication (Stages 1 +2) rs905938[C] at 1q22 (DCST2) 0.24 0.046 0.008 2.46 ×10 28 40 235 – – rs12545524[G] at 8q22.1 (near GDF6) 0.13 0.042 0.011 9.08 ×10 25 39 784 – – rs11037473[A] at 11p11.2 (nearest genes TTC17-HSD17B12) 0.07 20.069 0.014 1.49 ×10 26 39 865 – – SNPs markers are identified accordingto their standard rs numbers (NCBI build 36). Independent novel SNPs with a strong suggestiveeffect in the discovery analysis on birth length are shown (P,1×10 26 ). SNPs in loci that are known to be associated with adult height were excluded for replication efforts (adult height loci: LCORL,PTCH1,GPR126 and HMGA2). MAF, minor allele frequency; SE, standard error. b reflects differences in standardized birth length per minor allele. Pvalues are obtained from linear regression of each SNP against standardized birth length adjustedfor sex and gestational age. We included bothGWA and metabochip cohorts in our discovery analysis,rs905938 is on the metabochip, and rs12545524 and rs11037473 are not, this explainsthe differences in numbers (n). Derived inconsistency statistic I 2 and HetP values reflect heterogeneity across discovery studies with the use of Cochran’s Qtests. Figure 2 Regionalassociation plot of1q22 inthe 22 birth lengthdiscovery studies(N¼28 459).SNPsare plottedwith theirPvalues (as 2log 10 values;left y-axis) as a function of genomic position (x-axis). Estimated recombination rates (right y-axis) taken from HapMap are plotted to reflect the local LD-structure around the top associatedSNP(‘whiteopendiamond’)and thecorrelatedproxies(‘circles’according toablack-to-grayscalefrom r 2 ¼0to 1).Thejointanalysis Pvalueofdiscovery and replication studies is reported with the ‘white square’ (N¼40 235). Human Molecular Genetics, 2015, Vol. 24, No. 4 1159 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from Generation R Study (N¼5756) (11), infant length at 1 year of age (range 6–18 months; N¼28 228) in the Early Growth Genetics (EGG) consortium (12), and adult height in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (N¼127 513) (5). Rs905938[C] was not associated with ‘fetal growth’ measures, but was associated with infant length and adult height (P,0.05; Table 2). Known adult height loci in relation to birth and infant length We also explored whether common genetic variants known to be associated with adult height (5) influenced birth length variation. We found that 17 out of 180 known adult height loci were associated with birth length (FDR ,5%, Supplementary Material, Table S6; Fig. 4,QQ-plot of 180 SNPs and birth length). We then calculated a height-increasing-alleles score of the 180 known height loci (5) to predict birth length in the Generation R Study (N¼2085; Fig. 5). The score composed of variants associated with adult height explained 0.13% of the variance in birth length (P¼0.1), in contrast to the 10% of the phenotypic variation in adult height reported in the original manuscript (5). To evaluate whether different common genetic variants influenced both birth and infant length, we tested 2 193 675 million SNPs for association with infant length in almost the same set of samples used for the analysis of birth length (19 studies, N¼28 238; Supplementary Material, Table S7). We identified genome-wide significant associations at 11 genetic loci (Supplementary Material, Figs S3 and S4, QQand Manhattan plot), which all are known to be associated with adult height (Table 3, SNPs in or near SF3B4,LCORL,SPAG17,C6orf173, PTCH1,GDF5,ZNFX1,HHIP,ACAN,HLA locus and HMGA2) (5,13). In addition, we found that variants in 58 of the adult height loci were associated with infant length at an FDR of 5% (Supplementary Material, Table S8; Fig. 4,QQ-plot of 180 SNPs and infant length). Next, we tested in the Generation R Study (N¼2385) how much of the phenotypic variance in infant length was explained by the score composed of heightincreasing-alleles. Variants from the 180 known adult height loci together explained 2.95% of the variance in infant length (P¼3.10 ×10 217 , Fig. 5). DEPICT analysis of birth and infant length Finally, we used a pathway analysis tool called DEPICT (Pers et al., unpublished data) to prioritize genes at associated regions, search for reconstituted gene sets that were enriched in genes near associated variants, and identify tissue and cell types in which genes from loci associated with birth and infant length were highly expressed (full details in Materials and Methods). For both traits, we used independent SNPs (r 2 ,0.05) associated at P,1×10 25 , from 21 birth length and 44 infant length loci. There were no pathways significantly overrepresented in the birth length results. In contrast, for infant length DEPICT significantly prioritized nine genes which were overrepresented (FDR ,5%, Supplementary Material, Table S9), including three known Mendelian human stature genes (ACAN, GDF5 and PTCH1) as well as several relevant reconstituted Figure 3 Forest plot of the associations between rs905938[C] and birth length. ∗Replication studies. The ‘black diamond’ indicates the overall effect size and the confidence interval of the 31 studies. Table 2 Associations of rs905938[C] in DCST2 related to birth length with ‘fetal growth’ measures, infant length and adult height b SE P Generation R: fetal growth (N¼5756) First trimester Crown-rump length (n¼1126) 0.003 0.045 0.952 Second trimester Femur length (n¼5361) 20.035 0.023 0.129 Third trimester Femur length (n¼5532) 20.015 0.022 0.490 EGG: infant length Infant length at 1 year of age (N¼28 228) 0.035 0.010 5.54 ×10 24 GIANT: adult height Adult height (N¼127 513) 0.024 0.006 1.45 ×10 25 rs905938 C-allele with a genome-wide significant effect on birth length is shown (P,5×10 28 ) in relation to ‘fetal growth’ measures, infant length and adult height. SE, standard error. b reflects difference in standard deviation scores per minor allele. 1160 Human Molecular Genetics, 2015, Vol. 24, No. 4 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from gene sets (e.g. abnormal sternum ossification, regulation of osteoblast proliferation and WNT signaling, Supplementary Material, Table S10). There was no significant enrichment for particular tissue or cell types for any of the two traits. DISCUSSION In the present study we identified one previously unknown locus (rs905938 in DCST2 at 1q22) to be associated with birth length at a genome-wide significant level. This common genetic variant was also associated with infant length and adult height. It was not possible to identify eQTLs for transcripts of DCST2 in the MRCA and MRCE databases, as there were no probes available (9). Also, there was no significant eQTL of DCST2 in immortalized LCLs (10). However, DCST2 is a DC-STAMPlike protein family member and DC-STAMP is an important regulator of osteoclast cell-fusion in bone homeostasis (14–16). The transcripts of PBXIP1,GBA and ADAM15 were in weak LD with our lead SNP rs905938. The PBXIP1 protein is known to regulate estrogen receptor functions (17). Mutations in the GBA gene cause Gaucher disease, and strong associations with Parkinson’s disease and dementia with Lewy bodies have been described (18–21). ADAM15 is prominently expressed in osteoblasts and to a lesser extent in osteoclasts (22). A study in mice showed that ADAM15 is required for normal skeletal homeostasis and that its absence causes increased nuclear translocation of b-catenin in osteoblasts leading to increased osteoblast proliferation and function, which results in higher trabecular and cortical bone mass (23). The 1q22 locus is a complex region harboring multiple interesting genes that could affect birth length. We emphasize that we could not specifically pinpoint the causal gene(s) as our lead SNP (rs905938) was not in perfect LD with our cis eQTL SNPs. Although, there is some overlap between adult height loci and birth length, which is illustrated by 17 shared loci, the genetic architecture of adult height seems more similar to the genetic architecture of infant length than birth length [58 shared loci for infant length, based on conservative statistical method (FDR)]. One point of consideration for the interpretation of our findings is the potential of measurement error for birth length (24). This may lead to less power to detect novel genetic variants as standard errors of SNPs could be increased. The estimate of the risk-allele score slope of Figure 5is not influenced by measurement error and the differences in the slopes suggest that birth and infant length are influenced by distinct genetic variants. We found that the SNP effects for birth length of 137 of the 180 established height loci were in the same direction as reported in the GIANT paper (5) (Supplementary Material, Table S6; probability of success ¼0.761, P¼6.25 ×10 213 ). One hundred sixty-two of the 180 loci were in the same direction for infant length (Supplementary Material, Table S8; probability of success ¼0.900, P¼2.20 ×10 216 ). Four SNPs associated with birth length (P,1×10 25 ) are in or near loci known to be associated with birth weight (LCORL, HMGA2, ADCY5 and ADRB1). LCORL is associated with birth weight, birth length, infant length and adult height, but we could not find an obvious link between the gene and adult-onset diseases. HMGA2 is associated with aortic root size (25), type 2 diabetes (26), and many other traits like tooth development, head circumference and brain structure (12,27). ADCY5 is also associated with type 2 diabetes and ADRB1 with adult blood pressure (2,3). These findings highlight genetic links between fetal growth and metabolism (2,3,26). As we found overlap between genetic variants of birth weight and birth length, we looked-up the effect of rs905938 in DCST2 on birth weight in a previous EGG study (3).Rs905938 was associated with birth weight, but weaker as compared with birth length ( b ¼0.035 SDS, SE ¼0.010, P¼2.35 ×10 24 ,N¼26 558). In conclusion, in the present study we identified one novel locus (rs905938 in DCST2 at 1q22) associated with birth length at a genome-wide significant level. This common Figure 4 QQ-plots of the 180 known adult height SNPs with birth and infant length. QQ-plot of the 180 known adult height SNPs in association with birth length (upper panel) in 22 studies (N¼28 459) and with infant length (lower panel) in 19 studies (N¼28 238). The black dots represent observed Pvalues and the diagonal lines represent the expectedPvalues under the null distribution. Human Molecular Genetics, 2015, Vol. 24, No. 4 1161 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from genetic variant was also associated with infant length and adult height, with decreasing magnitude of the associations in later life (0.046 SDS for birth length, 0.035 SDS for infant length and 0.024 SDS for adult height). To our knowledge, no phenotype has been previously associated with the DCST2 gene and while the gene is expressed in osteoclasts, its function should be further studied. MATERIALS AND METHODS Stage 1: discovery genome-wide association analyses of birth length We combined 21 population-based studies with GWA or Metabochip data and birth length available (total N¼28 459 individuals). One of our discovery cohorts had two independent sub-samples within their study leading to a total of 22 independent GWA/Metabochip sub-samples for our analysis: one subsamplefromtheAvonLongitudinalStudyofParentsandChildren (ALSPAC, GWA, n¼4816); Children, Allergy, Milieu, Stockholm, Epidemiology [Swedish] (BAMSE, GWA, n¼423); Children’s Hospital Of Philadelphia (CHOP, GWA, n¼432); Copenhagen Study on Asthma in Childhood 2000 (COPSAC2000, GWA, n¼348); Copenhagen Study on Asthma in Childhood Registry (COPSAC-Registry, GWA, n¼1111); Danish National Birth Cohort (DNBC, GWA, n¼932); Generation R Study (Generation R, GWA, n¼2085); Hyperglycemia and Adverse Pregnancy Outcomes study (HAPO, GWA, n¼1325); Helsinki Birth Cohort Study (HBCS, GWA, n¼1572); Infancia y Medio Ambiente (INMA, GWA, n¼848); Leipzig Childhood Obesity cohort (LEIPZIG, Metbochip, n¼607); Lifestyle Immune System Allergy study (LISA, GWA, n¼552); Figure 5 Height-increasing-alleles score of known adult height SNPs predicting birth and infant length. Genetic risk-allele scores (sum of height-increasing alleles weighted by known effect on adult height(5) transformed to standard deviation Z-scores) in the Generation R study plotted against length adjusted for sex and age. The distribution of the genetic risk-allele score is depicted as bars. (A) Mean birth length plotted against the genetic score (N¼2085). (B) Mean infant length plotted against the genetic score (N¼2385). Table 3 Summary statistics of the eleven known adult height SNPs in association with infant length at P,5×10 28 Marker MAF b SE PnI 2 HetP rs7536458[G] at 1p12 (SPAG17) 0.25 20.064 0.010 9.61 ×10 211 28234 0.0 0.403 rs11205303[C] at 1q21.2 (SF3B4) 0.34 0.087 0.011 1.79 ×10 216 26559 0.0 0.864 rs1380294[T] at 4p15.31 (LCORL) 0.15 20.108 0.014 2.54 ×10 214 23079 13.7 0.184 rs1812175[A] at 4q28-q32(HHIP) 0.18 20.068 0.011 2.33 ×10 29 28227 0.0 0.398 rs592229[G] at (HLA locus) 0.43 0.048 0.009 2.22 ×10 28 28223 0.6 0.326 rs9385399[T] at 6q22.32 (C6orf173) 0.46 0.055 0.009 1.68 ×10 210 28224 0.0 0.943 rs1984119[C] at 9q22.3 (PTCH1) 0.26 20.063 0.010 1.77 ×10 210 28197 0.0 0.490 rs7970350[T] at 12q15 (HMGA2) 0.49 20.047 0.009 2.90 ×10 28 28226 0.0 0.426 rs2280470[A] at 15q26.1 (ACAN) 0.36 0.053 0.009 6.43 ×10 29 27443 0.0 0.436 rs143384[G] at 20q11.2 (GDF5) 0.44 0.058 0.009 2.87 ×10 210 28232 0.0 0.996 rs1567865[T] at 20q13.13 (ZNFX1) 0.21 0.063 0.010 1.10 ×10 29 28229 22.5 0.104 SNPs markers are identified according to their standard rs numbers (NCBI build 36). The total sample includes data of 19 independent datasets (N¼28 238). MAF, minorallelefrequency;SE, standarderror. b reflectsdifferencesinstandardized infantlengthperminorallele. Pvaluesareobtainedfrom linearregressionofeach SNP against standardized infant length adjusted for sex and age. We included both GWA and metabochip cohorts in our discovery analysis, this explains the differences in numbers (n). Derived inconsistency statistic I 2 and HetP values reflect heterogeneity across discovery studies with the use of Cochran’s Qtests. 1162 Human Molecular Genetics, 2015, Vol. 24, No. 4 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from Manchester Asthma and Allergy Study (MAAS, GWA, n¼402); Norwegian Mother and Child Cohort study (MOBA, GWA, n¼832); Northern Finland Birth Cohorts 1966 (NFBC66, GWA, n¼4642); Northern Finland Birth Cohorts 1986 (NFBC86, Metabochip, n¼4652); Physical Activity and Nutrition in Children study (PANIC, Metabochip, n¼319); two subsamples from the Prevention and Incidence of Asthma and Mite Allergy birth cohort study (PIAMA1, GWA, n¼283; PIAMA2, GWA, n¼195); The Western Australian Pregnancy Cohort Study (RAINE, GWA, n¼1272); Special Turku Coronary Risk Factor Intervention Project (STRIP, Metabochip, n¼614); and TEENs of Attica: Genes and Environment (TEENAGE, GWA, n¼197). While no systematic phenotypic differences were observed between the sub-samples of the PIAMA birth cohort study, they were analyzed separately due to genotyping on different platforms and at different time periods. Genotypes within each study were obtained using highdensity SNP arrays and then imputed for 2.5 M HapMap SNPs (Phase II, release 22; http://hapmap.ncbi.nlm.nih.gov/). The basic characteristics, exclusions applied (for example, individuals of non-European ancestry, family related individuals), genotyping, quality control and imputation methods for each discovery study are presented in Supplementary Material, Table S1. Statistical analysis within discovery studies In all studies, birth length was measured using standardized procedures.Studieswithself-reportedmeasurementswereexcluded a priori. Birth length was standardized using growth analyzer (http://www.growthanalyser.org), transforming birth length into sexand age-adjusted SDS. We used the North-European 1991 reference panel to compare results between studies. Multiple births and twins were excluded from all analyses. We applied linear regression between number of alleles or dosages obtained from imputations and standardized birth length. The GWA analysis per study was performed using MaCH2qtl (28), SNPTEST (29), PLINK (30) or PropABEL (31). The secured data exchange and storage were facilitated by the Erasmus Medical Center, Department of Internal Medicine (32). Meta-analysis of discovery studies Priorto meta-analysis, SNPswith aMAF ,0.01 andpoorly imputed SNPs [r2hat ,0.3 (MaCH); proper_info ,0.4 (IMPUTE2); R2_BEALE ,0.4 (BEAGLE)] were filtered. Genomic control (GC) (33) was applied to adjust the statistics generated within each cohort (see Supplementary Material, Table S1 for individualstudy l values). Four out of the twenty-two sub-samples were genotyped on Metabochips. These SNP-arrays were enriched with ‘adult height SNPs’. Normal variation in early length growth seems to be associated with height in adulthood (1). Therefore, we assumed more true-positive hits in these studies and did not apply GC in these studies (GIANT et al., unpublished data). Details of any additional corrections for study specific population structure are given in the Supplementary Material, Table S1. Inverse variance fixed-effects meta-analyses were analyzed using METAL (released 2010-08-01) (34) by two meta-analysts in parallel and blinded to obtain identical results. After the METAL meta-analysis, we filtered SNPs with a MAF ,0.05 and SNPs that were not available in at least 12 subsamples to avoid false-positive findings. We used Cochran’s Q test and the derived inconsistency statistic I 2 to assess evidence of between-study heterogeneity of the effect sizes. The meta-analysis results were obtained for a total of 2 201 971 SNPs. SNPs that crossed the threshold of P≤1×10 26 were considered to represent strong suggestive evidence of association with birth length. SNPs that were already known to be associated with adult height were excludedfor the replicationanalysis (5). The explainedvariance of the top SNPs were calculated inone of the largest cohorts, the Generation R Study (n¼2085). Stage 2: replication analysis of top birth length SNPs In the discovery phase, we found seven independent SNPs with strong suggestive evidence of association (P,1×10 26 ) with birth length. Four SNPs were already known to be associated with adult height (5). These SNPs were excluded for followup analyses. The three remaining novel SNPs were followed-up in replication studies. We included both GWA and Metabochip studies in our discovery analysis. Rs905938 was on our Metabochips, and rs12545524 and rs11037473 were not. This results in differences in numbers for our top SNPs in the discovery and replication analyses. rs905938 was taken forward in 9 independent replication studies (N¼11 995), rs12545524 and rs11037473 in 13 independent replication studies including the four discovery Metabochip studies (N¼17 679). Details of the replication studies are presented in Supplementary Material, Table S2. Within the replication studies, we analyzed the association between number of alleles and standardized birth length. Combined effect estimates and heterogeneity between cohorts was calculated using fixed effects meta-analyses in R Version 2.8.1 (The R foundation for Statistical Computing, library rmeta). Top SNPs that crossed the significant threshold of P-replication ≤0.05 and the widely accepted genome-wide significance threshold of P≤5×10 28 for all studies combined were consideredtorepresent robustevidence of associationwith birthlength. The institutional review boards for human studies approved the protocols and written consent was obtained from the participating subjects or their caregivers if required by the institutional review board. DEPICT analysis We used the novel Data-driven Expression-Prioritized Integration for Complex Traits (DEPICT) method (Pers et al., unpublished data). DEPICT is designed to systematically identify the most likely causal gene at a given locus, gene sets that are enriched in genetic associations, and tissues and cell types in which genes from associated loci are highly expressed. First, DEPICT assigns genes to associated SNPs using LD r 2 .0.5 distance to define locus boundaries, merges overlapping loci and discards loci mapping within the extended major histocompatibility complex region (chromosome 6, base pairs 25 000– 35 000). Next, the DEPICT method prioritizes genes within a given associated locus based on the genes’ functional similarity to genes from other associated loci. Genes that are highly similar to genes from other loci obtain low prioritization Pvalues, and simulated GWAS results are used to adjust for gene length bias as well as other potential confounders. There can be several prioritized genes in a given locus. Next, DEPICT conducts gene set enrichment analysis by testing whether genes in associated loci enrich for reconstituted versions of known pathways, gene Human Molecular Genetics, 2015, Vol. 24, No. 4 1163 at Tampere University Library. Department of Health Sciences on September 27, 2016http://hmg.oxfordjournals.org/Downloaded from