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Nat Genet. Author manuscript; available in PMC 2013 September 16.
Published in final edited form as:
PMCID: PMC3773913
EMSID: EMS54331

Common variants at 12q15 and 12q24 are associated with infant head circumference

H Rob Taal,#1,2,3 Beate St Pourcain,#4 Elisabeth Thiering,#5 Shikta Das,#6 Dennis O Mook-Kanamori,#1,2,3,7 Nicole M Warrington,8,9 Marika Kaakinen,10,11 Eskil Kreiner-Møller,12 Jonathan P Bradfield,13 Rachel M Freathy,14 Frank Geller,15 Mònica Guxens,16,17,18 Diana L Cousminer,19 Marjan Kerkhof,20 Nicholas J Timpson,4 M Arfan Ikram,1,21 Lawrence J Beilin,22 Klaus Bønnelykke,12 Jessica L Buxton,23 Pimphen Charoen,6,24 Bo Lund Krogsgaard Chawes,12 Johan Eriksson,25,26,27 David M Evans,4 Albert Hofman,1,3 John P Kemp,4 Cecilia E Kim,13 Norman Klopp,28,29 Jari Lahti,30 Stephen J Lye,9 George McMahon,4 Frank D Mentch,13 Martina Müller,31,32,33 Paul F O’Reilly,34 Inga Prokopenko,35,36 Fernando Rivadeneira,1,37 Eric A P Steegers,38 Jordi Sunyer,16,17,18,39 Carla Tiesler,5,40 Hanieh Yaghootkar,14 Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium,41 Monique M B Breteler,1 Stephanie Debette,42 Myriam Fornage,43 Vilmundur Gudnason,44,45 Lenore J Launer,46 Aad van der Lugt,21 Thomas H Mosley,47 Sudha Seshadri,42 Albert V Smith,44,45 Meike W Vernooij,1,21 Early Genetics & Lifecourse Epidemiology (EAGLE) consortium,41 Alexandra IF Blakemore,23 Rosetta M Chiavacci,13 Bjarke Feenstra,15 Julio Fernandez-Benet,48 Struan F A Grant,13,49,50 Anna-Liisa Hartikainen,51 Albert J van der Heijden,2 Carmen Iñiguez,18,52 Mark Lathrop,53,54 Wendy L McArdle,55 Anne Mølgaard,12 John P Newnham,8 Lyle J Palmer,9,56 Aarno Palotie,19,57,58,59 Annneli Pouta,60 Susan M Ring,55 Ulla Sovio,6,61 Marie Standl,5 Andre G Uitterlinden,1,37 H-Erich Wichmann,5,31,33 Nadja Hawwa Vissing,12 Charles DeCarli,62 Cornelia M van Duijn,1 Mark I McCarthy,35,36,63 Gerard H. Koppelman,64 Xavier Estivill,18,39,65 Andrew T Hattersley,66 Mads Melbye,15 Hans Bisgaard,12 Craig E Pennell,8 Elisabeth Widen,19 Hakon Hakonarson,13,49,50 George Davey Smith,4, Joachim Heinrich,5, Marjo-Riitta Jarvelin,10,60,67, Early Growth Genetics (EGG) Consortium,41 and Vincent W V Jaddoe1,2,3,

Abstract

To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association (GWA) studies (N=10,768 from European ancestry enrolled in pregnancy/birth cohorts) and followed up three lead signals in six replication studies (combined N=19,089). Rs7980687 on chromosome 12q24 (P=8.1×10−9), and rs1042725 on chromosome 12q15 (P=2.8×10−10) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height1, their effects on infant head circumference were largely independent of height (P=3.8×10−7 for rs7980687, P=1.3×10−7 for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P=3.9×10−6). SNPs correlated to the 17q21 signal show genome-wide association with adult intra cranial volume2, Parkinson’s disease and other neurodegenerative diseases3-5, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.

MAIN TEXT

Head circumference in infancy is used as a measure for brain size and development6-7. Normal variation in head circumference seems to be associated with cognitive and behavioral development8-10. Larger head circumference in infancy is associated with higher IQ scores in childhood10-12. The underlying mechanisms however, are poorly understood. Head circumference is a complex trait with a high heritability of around 0.7-0.913. Several rare mutations with large effects on head circumference have been identified14-17, including those resulting in microcephaly and intellectual disability15-17. Common genetic variants that influence normal variation in head circumference in early life have not yet been identified.

To search for common genetic variants associated with head circumference in infancy, we performed a meta-analysis of GWA studies. We reasoned that finding such common variants might lead to enhanced understanding of molecular mechanisms important for variation in brain development.

We meta-analyzed association statistics from ~2.5 million directly-genotyped and imputed SNPs in infants of European descent from seven discovery GWA studies (N=10,768; Supplementary Table 1). In all studies head circumference in infancy (age 18 months, range 6 to 30 months) was measured from the occipital protuberance to the forehead, using a flexible, non-stretching measure tape following standardized procedures. If multiple measurements were available for one individual in this time window, only the measurement performed closest to the age of 18 months was used (Supplementary Tables 1 and 2). Since the relationship between head circumference and age during infancy is non-linear and the variance increases with age, we calculated sex- and age-adjusted SD-scores of head circumference in each study separately18.

In the discovery phase we identified three lead signals (Manhattan plot is shown in Supplementary Fig. 1); two independent loci on chromosome 12 and one on chromosome 17, which showed suggestive evidence for association with head circumference in infancy. These three loci represent the first three independent loci of the discovery analysis and were at 12q24.31, in SBNO1 (rs7980687, Pdiscovery=3.3×10−7; Figure 1a), at 12q15, near HMGA2 (rs1042725, Pdiscovery=6.6×10−7; Figure 1b) and at 17q21.1, near CRHR1/MAPT (rs11655470, Pdiscovery=1.4×10−6; Figure 1c). Other loci, suggesting an association with infant head circumference (P<1×10−5) are described in Supplementary Table 3.

Figure 1Figure 1Figure 1
Directly genotyped and imputed SNPs are plotted using filled circles with their meta-analysis P values (as −log10 values) as a function of genomic position (NCBI Build 36). In each plot, the discovery-stage SNP taken forward to replication stage ...

Table 1 shows the associations of these three lead SNPs in each cohort. We followed up these three associations in six independent replication samples of European descent (N=8,321; Supplementary Table 2). We genotyped the most strongly associated SNP from each locus (rs7980687 from 12q24.31; rs1042725 from 12q15; rs11655470 from 17q21.1), or a closely-correlated proxy (HapMap R2). Consistent associations were observed for both signals on chromosome 12 in the replication samples (P=0.003 and P=8.1×10−5 for rs7980687 and rs1042725 respectively). Marginal evidence of association for rs11655470 was seen in the replication samples (P=0.093). Genomic control correction was applied during the discovery meta-analysis stage to adjust the statistics generated within each cohort (λ-values ranging from 1.007-1.054, Supplementary Table 1). Results from the replication cohorts were combined with the genomic control corrected discovery results to get the overall meta-analysis results. Combining discovery and replication samples (N=19,089; Table 1), each A allele of rs7980687 in SBNO1 was robustly associated with a 0.074 SD larger head circumference (95% CI: 0.049, 0.099; P=8.1×10−9, explained variance 0.24%) and each T allele of rs1042725 near HMGA2 with a 0.065 SD smaller head circumference (95% CI: −0.085, −0.045; P=2.8×10−10, explained variance 0.33%). This reflects a difference of around 1.2 and 1.0 mm in head circumference respectively. The effect of each T allele of rs11655470 near CRHR1/MAPT did not reach genome-wide significance in the combined analysis (effect 0.048 SD larger head circumference; 95%CI: 0.028, 0.068; P=3.8×10−6, explained variance 0.21%). These three associations showed low heterogeneity (P>0.1, I2=5-33%).

Table 1
Individual association results by study and meta-analysis

Additionally, the signals in SBNO1 and near HMGA2, but not the one near CRHR1/MAPT, were associated with height measured at the same visit as head circumference (Supplementary Table 4). When we adjusted the model for current height, the associations of rs7980687 and rs1042725 with head circumference were slightly attenuated (effect size 0.057 SD; 95%CI: 0.035, 0.080; P=3.8×10−7 and −0.048 SD; 95%CI: −0.066, −0.030; P=1.3×10−7 for rs7980687 and rs1042725 respectively, Supplementary Table 5). The association of the third signal near CRHR1/MAPT was unaffected. In depth mediation analysis showed that the effects of rs7980687 and rs1042725 on head circumference were only partly (12% and 24% respectively) explained by height (Supplementary Fig. 2, Supplementary Table 6). The effect of rs11655470 was a completely direct effect of the SNP on head circumference (Supplementary Table 6). To further adjust for possible population stratification we added principal components to the model, in cohorts where these measures were available (total N = 12,763). This did not materially change the effect on head circumference, indicating that the utilized association tests are robust to population stratification (Supplementary Table 7). The three variants were not associated with other covariates such as breast feeding, socioeconomic status or educational level (data not shown). We did not find evidence for an interaction of these variants with infant sex or breastfeeding after Bonferroni correction (P>0.017, Supplementary Table 8 and 9).

In order to further investigate an effect of the three lead signals on fetal head growth, we assessed the associations of the variants with head circumference using third trimester fetal ultrasound data (n=3,781) and head circumference measured at birth (n=13,775), in discovery and replication cohorts that had these data available (Supplementary Table 2). All three signals showed evidence of association with head circumference in third trimester of pregnancy and at birth (Table 2). The directions of the effects were consistent with those in infancy.

Table 2
Association of the three lead signals related to head circumference with additional phenotypes

Next, we assessed the associations of the three lead signals with intra-cranial volume (ICV) in adulthood, measured by magnetic resonance imaging (MRI), in 8,175 individuals in the CHARGE-consortium2. There was evidence of association between the signals near HMGA2 and CRHR1/MAPT and ICV (Table 2). For the signal near CRHR1/MAPT, a variant further downstream (rs9915547; r2 0.22 HapMap CEU) showed a genome-wide significant association (P<5×10−8). All directions of the effects were consistent with the observed associations for head circumference in infancy (Table 2).

We also assessed if there were possibly functional common variants in LD (r2 > 0.50) with our three lead SNPs, being either non-synonymous SNPs or eQTLs. One variant, rs1060105, in high LD with our lead signal (rs7980687 with HapMap r2 0.89), was a non-synonymous SNP located in exon 5 of SBNO1 (missense; AGT(Ser) => AAT(Asn)). The minor allele (A) of rs1060105 was associated with an increased head circumference in infancy (effect size 0.081 SD; 95%CI: 0.048, 0.115; P=2.4×10−6 (N=10,768)). The underlying mechanism is unknown. Considering that transcription regulation is highly cell-type specific, we next evaluated whether we could find eQTLs established in brain tissue19. We did not find eQTLs in publicly available brain expression data19. Subsequently, we also explored eQTL databases from other tissues and identified three SNPs in LD with rs7980687 (r2 > 0.7 HapMap CEU) associated with gene transcript expression of CDK2AP1 and MPHOSPH9 in liver tissue, monocytes and lymphoblastoid cell lines20-22. Little is known on these genes except that both CDK2AP1 and MPHOSPH9 are involved in cell-cycle regulation (Supplementary Table 10)23-24.

To our knowledge, this is the first genome-wide association study on head circumference in infancy. The top two signals (rs7980687 in SBNO1 and rs1042725 near HMGA2) associated with infant head circumference have previously been associated with adult height1. Therefore, we also assessed the association between the 180 known height variants and head circumference during infancy1. A strong deviation from the null-line was observed on the QQ-plot (Supplementary Fig. 3). Besides SBNO1 and HMGA2, 23 other height variants were nominally associated with head circumference in infancy (Supplementary Table 11). After applying Bonferroni correction for multiple testing in this candidate gene analysis (P<2.8×10−4), markers in/near ZNFX1 (P=6.1×10−6), OR2J3 (P=1.8×10−5) and ZBTB38 (P=1.8×10−4) remained statistically significant associated with head circumference in infancy.

The relative effect size of rs1042725 near HMGA2 was similar for infant head circumference (0.065 SD) and adult height (0.060 SD). However, the effect size of rs7980687 in SBNO1 on infant head circumference (0.074 SD) was considerably larger than for adult height (0.035 SD). As head size is correlated with total body size25, it might be that the top two loci have a more general regulating role in skeletal growth and bone development. It also could be that variants in SBNO1 affect brain growth and concurrent head circumference, or that they affect skull growth rather than skeletal growth. The SBNO1-gene is involved in the Notch signaling pathway26. In Drosophila, a similar gene (sno) is required for early embryogenesis, and absence of this gene leads to maldevelopment of the central nervous system26. In humans SBNO1 has been implicated in oncogenic processes27-28.

The variant near HMGA2 was one of the first to be associated with adult height. Deletions and truncations in the HMGA2-gene in mice and humans have been associated with small and large stature29-30. The effect of HMGA2 is similar for head circumference and adult height, thus it seems likely that it has a more general role in skeletal growth.

A third variant (rs11655470), in the promoter region of CRHR1/MAPT, was also related to head circumference, though this signal did not reach genome-wide significance. Rs11655470 lies within the 17q21 inversion, but is not strongly correlated with the inversion (r2 0.22 HapMap CEU). This 900kb region, corresponding to the conversion, contains several genes. The SNP is closely related to the CRHR1-gene (r2 0.59 HapMap CEU with rs171440). Variants in/near CRHR1 have been associated with brain development and bone mineral density31-32, although the underlying mechanisms are largely unknown. Another gene included in the 17q21 inversion is MAPT (r2 0.22 HapMap CEU). Both common variants and mutations in MAPT are known to be associated with Parkinson’s disease and other neurodegenerative diseases3-5,33-34. Other genes in this region are saitohin (STH) and granulin (GRN). STH has been associated with progressive supranuclear palsy and increased risk of late-onset Alzheimer’s disease35-36. Mutations in GRN have been shown to cause fronto-temporal degeneration37. It might be that common genetic variants in/near CRHR1/MAPT affect early brain development, by altering the stability and assembly of microtubules. Ikram et al. showed that a correlated SNP in the same region (rs9303525, HapMap r2 0.22 with rs11655470) is associated with adult intra cranial volume, reaching genome-wide significance2. Since the LD between the variants is low, it could be that they represent separate independent effects on different phenotypes. When we adjusted the effect of rs11655470 on infant head circumference for the CHARGE ICV signal (rs9915547), the effect was attenuated but remained significant (0.059 SD (P=1.0×10−5) and 0.037 SD (P=7.3×10−3) before and after adjustment for rs9915547 respectively), suggesting that these signals both tag a third marker influencing both phenotypes (Supplementary Table 12). However, although the association attenuates after conditioning on the CHARGE ICV signal, the two signals might still independently tag different causal markers in the region and the attenuation might be due to the weak LD, because of proximity, between the two signals. The marker associated with head circumference is in low LD with the chromosome 17q21 inversion, while the CHARGE ICV signal is in high LD with the inversion. Therefore, it does not seem likely that the 17q21 inversion is causally related to infant head circumference. The biological mechanisms underlying these associations are largely unknown.

Our study highlights early effect of variants in/near SBNO1 and HMGA2 on head circumference in fetal life and infancy, and shows that a variant near CRHR1/MAPT is marginally associated with head circumference in infancy. Our findings suggest that the genetic variants in the CRHR1/MAPT region might link early brain growth with neurological disease in later life. Further research is needed to elucidate whether these variants influence brain growth and neurodevelopment in early life.

ONLINE METHODS

Stage 1: GWA meta-analysis of head circumference

Discovery samples, genotyping and imputation

We selected seven population-based studies with head circumference measured in infancy (study cohort specific median age range 11-18 months) and GWA data available by the beginning of March 2010 (combined N=10,768): the Avon Longitudinal Study of Parents And Children (ALSPAC; N=1,748); The Children’s Hospital of Philadelphia (CHOP; N=1,008); the Copenhagen Study on Asthma in Childhood (COPSAC; N=369); The Generation R Study (Generation R; N=2,240); the Lifestyle – Immune System – Allergy Study (LISA; N=357); the Northern Finland 1966 Birth Cohort (NFBC1966; N=4,287) and the Western Australian Pregnancy study (RAINE; N=759). Genotypes were obtained using high-density SNP arrays, and then imputed for ~2.4 million HapMap SNPs (Phase II, release 21/22, http://hapmap.ncbi.nlm.nih.gov/). The basic characteristics, exclusions (e.g. samples of non-European ancestry), genotyping, quality control and imputation methods for each discovery sample are presented in Supplementary Table 1.

Statistical analysis within discovery samples

Head circumference was measured in infancy (age window: 6-30 months). If multiple measurements were available for one individual within this age window, the measurement closest to 18 months was used. Sex- and age-adjusted standard deviation scores (SD score) were constructed using Growth Analyser 3.0 (http://www.growthanalyser.org; Dutch Growth Research Foundation, Rotterdam, the Netherlands) in each study separately18. The association between each SNP and head circumference was assessed in each study sample using linear regression of head circumference SD score against genotype, assuming an additive model. Imputed genotypes were only used where directly-assayed genotypes were unavailable.

Meta-analysis of discovery samples

Data exchange was facilitated by the SIMBioMS platform (simbioms.org)38. Prior to meta-analysis, SNPs with a minor allele frequency <1% and poorly-imputed SNPs (proper_info ≤0.4 [SNPTEST]; r2 ≤0.3 [MACH2QTL]) were filtered. Fixed effects meta-analyses were independently conducted by two investigators (H.R.T., D.O.M-K.). Meta-analysis was performed using the software package: METAL (http://www.sph.umich.edu/csg/abecasis/metal/index.html); Genomic control39 was applied during the meta-analysis stage to adjust the statistics generated within each cohort (see Supplementary Table 1 for individual study λ-values, discovery meta-analysis λ-value: 1.043). Meta-analysis was done using the inverse-variance method; a fixed effects model was assumed. SNPs available in less than four discovery cohorts were excluded. Final meta-analysis results were obtained for 2,449,806 SNPs. We considered the top three lead signals (representing 3 distinct genomic regions on chromosomes 12 and 17) in the discovery analysis for further follow-up in additional samples. The two loci at chromosome 12 reached the threshold of P<1×10−6 and were therefore selected for replication and the third locus at chromosome 17 was just above that threshold (P=1.4×10−6) and was selected because of prior knowledge of the nearby genome wide significant hit on intra cranial volume as described by Ikram et al.2

Stage 2: Follow-up of three lead signals in additional samples

Follow-up samples, genotyping and analysis

We used 6 independent study samples (combined N=8,321) to follow up the three lead signals from the GWA meta-analysis (represented by index SNPs rs7980687, rs1042725 and rs11655470). Details of these study samples are presented in Supplementary Table 2. If the index SNP was unavailable, a closely correlated proxy was substituted (rs12322888 or rs12316131 for rs7980687 [HapMap r2=0.95]; rs7970350 or rs1351394 for rs1042725 [HapMap r2=1 and 0.91 respectively]; rs12938031 for rs11655470 [HapMap r2=0.58]). In 3 of the replication studies, the index SNPs were imputed from genome-wide genotype data (see Supplementary Table 2). The head circumference analysis (as described above) was performed within each study sample.

Statistical analysis

Meta-analyses of discovery and replication samples

We performed fixed effects inverse variance meta-analyses of the head circumference association results for the three lead signals in the seven discovery samples and six replication samples combined. Fixed effects meta-analyses were conducted independently by two investigators (H.R.T., D.O.M-K.), using RMeta in R [v.2.7.0]). We used the Cochran Q test and the I2 statistic40 to assess evidence of between-study heterogeneity of effect sizes.

Informed consent (or parental consent, as appropriate) was obtained from all discovery and follow-up study participants and study protocols were approved by the local ethics committees.

Analyses of potential confounders

To verify that the investigated lead SNPs were not associated with other covariates which could theoretically confound the observed associations with head circumference (including height, weight and age at measurement; breastfeeding; maternal educational level; and sex), we used linear or logistic regression models to assess the associations between each covariate and genotype, in all discovery and replication samples. For height and weight, we constructed sex- and age-adjusted SD scores using Growth Analyser 3.0 (http://www.growthanalyser.org; Dutch Growth Research Foundation, Rotterdam, the Netherlands) in each study separately, similar to the head circumference SD score. To investigate possible effects of the three lead signals on head circumference through height, we first conducted linear regression analysis with and without adjustment for height SD score. Second, we conducted a mediation analysis and assessed the direct SNP effects and indirect SNP effects (mediated through height) on head circumference for each of the signals using a seemingly unrelated regression model (STATA, StataCorp LP, College Station, TX, USA) or a simple path analysis model (MPLUS, Muthen & Muthen, Los Angeles, CA, USA), which provide identical effect estimates. To investigate whether the associations between genotypes and infant head circumference were similar in the sexes, we repeated the analyses in males and females separately. Furthermore, we evaluated possible effect modification by breastfeeding status for each of the SNPs. Where possible, we meta-analyzed results to assess overall evidence of association.

Analysis of fetal head circumference and intra cranial volume

We explored associations of rs7980687, rs1042725 and rs11655470 with third trimester fetal head circumference and head circumference at birth, assuming an additive model using linear regression. Fetal head circumference was measured by ultrasound in three studies (combined N=3,781 singleton pregnancies) in third trimester of pregnancy (gestational age window 27-36 weeks). Only one measurement per subject was included in the time window. If multiple measurements were available within the time-window, the one closest to the median of 32 weeks of the gestation was used. We calculated gestational age specific SD scores using previously published growth charts41. This analysis was adjusted for sex. Head circumference was measured at birth, or within the 31st day of life, in 12 studies (N=13,775; Supplementary table 2). We created SD scores for head circumference within each of the cohorts and assessed the association with each SNP, adjusted for sex and gestational age. If head circumference was measured in the first month, we used gestational age at birth + age (weeks) at measurement in the first month. Combined effect estimates were calculated using fixed effects meta-analyses.

We used the meta-analysis on intracranial volume in adults, measured by MRI, in the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) consortium42 as a third additional phenotype. Data collection methods, phenotype definition, baseline characteristics, and results of the meta-analysis are described elsewhere in this issue2,43.

Analysis of known adult height variants with infant head circumference

We used the discovery meta-analyses to assess the associations of the previously identified 180 known adult height loci1 with head circumference in infancy, using the same model as described above. We also checked whether very closely related SNPs (HapMap r2 >0.95) showed higher significance levels than the originally reported SNPs. SNPs with a P-value lower than 2.8×10−4 (0.05/180) were considered significant.

Variance explained

To estimate the percentage of variation in birth weight explained by each of the associated loci, we obtained the adjusted-R2 from univariate linear regression models of head circumference against genotype. We then calculated a mean value from all discovery and replication studies, weighted by sample size.

Non-synonomous SNPs and eQTLs

We assessed SNPs in LD with the three lead signals and checked for non-synonomous SNPs or eQTLs to identify possible functional variants explaining the associations with head circumference. First, we used the SNP Annotation and Proxy search developed by the Broad institute (http://www.broadinstitute.org/mpg/snap/) to select all SNPs in LD (r2 > 0.50) with our three lead signals. We used the 1000 Genomes Pilot 1 set as SNP dataset for rs7980687 and rs1042725 and the HapMap r22 as SNP dataset for rs11655470 (r2 > 0.50) since this SNP was not available on the 1000 Genomes dataset. Next, we evaluated whether these SNPs were non-synonomous using dbSNP search engine from NCBI. To evaluate whether there were cis-eQTLs in LD with our lead signals we searched publicly available eQTL databases through the NCBI GTEx (Genotype-Tissue Expression) eQTL Browser (http://www.ncbi.nlm.nih.gov/gtex/test/GTEX2/gtex.cgi) and the Generic Genome Browser (http://eqtl.uchicago.edu/cgi-bin/gbrowse/eqtl/). In total, these browsers search nine databases for eQTLs. Only cis-associations (defined as genes within 1Mb) that reached the P-value threshold for significance, as used in the original papers describing the gene expression datasets, were included in Supplementary Table 10. The statistics behind the eQTL analysis and calculation of the threshold for declaring significance of the associations are described in the published and validated eQTL datasets20-22.

Supplementary Material

Supplementary Table 1

Supplementary Table 2

Supplementary Text and Figures

ACKNOWLEDGMENTS

See also Supplementary Note for detailed acknowledgments by study.

Major funding for the research in this paper is as follows: Academy of Finland (project grants 104781, 120315, 129269, 1114194 and Center of Excellence in Complex Disease Genetics); Biocentrum Helsinki; Biocenter, University of Oulu, Finland; British Heart Foundation; Canadian Institutes of Health Research (grant MOP 82893); The Children’s Hospital of Philadelphia (Institute Development Award); Cotswold Foundation (Research Development Award); Darlington Trust; Dutch Asthma Foundation; Dutch Ministry of the Environment; Erasmus Medical Center Rotterdam; Erasmus University Rotterdam; The European Community’s Seventh Framework Programme (FP7/2007-2013), ENGAGE project, grant agreement HEALTH-F4-2007-201413; Exeter NHS Research and Development; Fundació La Marató de TV3; Helmholtz Zentrum Muenchen - German Research Center for Environment and Health; Institute of Epidemiology Neuherberg; Instituto de Salud Carlos III (FIS PI081151, and PS09/00432); IUF-Institut für Umweltmedizinische Forschung Düsseldorf; Marien-Hospital Wesel; Medical Research Council UK (G0500539, G0600331, PrevMetSyn/Salve/MRC, G0600705); Municipal Health Service Rotterdam; National Health and Medical Research Council of Australia (ID 403981 and ID 003209); National Public Health Institute, Helsinki, Finland; Netherlands Organisation for Scientific Research (NOW)/Netherlands Organisation for Health Reseacrh and Development (ZonMw) (grants SPI 56-464-14192, 904-61-090, 904-61-193, 480-04-004, 400-05-717); NHLBI (grant 5R01HL087679-02 through the STAMPEED program (1RL1MH083268-01)); NIH (grant 1R01HD056465-01A1); Peninsula NIHR Clinical Research Facility; Raine Medical Research Foundation; Rotterdam Homecare Foundation; South West NHS Research and Development; Stichting Astmabestrijding; Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR) Rotterdam; Technical University Munich; Telethon Institute for Child Health Research; UFZ-Centre for Environmental Research Leipzig-Halle; University Hospital Oulu, Finland; University of Bristol; University of Leipzig; Wellcome Trust (project grant GR069224); Western Australian DNA Bank; Western Australian Genetic Epidemiology Resource; ZonMW (grant 21000074).

The data exchange and deposition has been facilitated by the SIMBioMS platform (simbioms.org).

Personal funding is as follows: H.R.T by the Dutch Kidney Foundation (C08.2251), S.D. by the Medical Research Council UK (G0500539, PrevMetSyn, and PS0476), R.M.F by a Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust grant: 085541/Z/08/Z), D.M.E. by a Medical Research Council New Investigator Award (MRC G0800582 to D.M.E.), J.P.K. by a Wellcome Trust 4-year PhD studentship (WT083431MA), I.P. and J.F.B. in part supported by the European Community’s ENGAGE grant HEALTH-F4-2007-201413, A.T.H. is employed as a core member of the Peninsula NIHR Clinical Research Facility, V.W.V.J by the Netherlands Organization for Health Research (ZonMw 90700303, 916.10159).

Early Growth Genetics Consortium (EGG) Membership and Affiliations

Linda S. Adair 1, Wei Ang 2, Mustafa Atalay 3, Toos van Beijsterveldt 4, Nienke Bergen 5-6, Kelly Benke 2, Diane Berry 7, Jonathan Bradfield 8, Pimphen Charoen 9-10, Lachlan Coin 9, Diana Cousminer 11, Shikta Das 9, Oliver S.P. Davis 12, Paul Elliott 13, Dave M. Evans 14, Bjarke Feenstra 15, Claudia Flexeder 16, Tim Frayling17, Rachel Freathy 14,17, Romy Gaillard 5-6, Frank Geller 15, Maria Groen-Blokhuis 4, Liang-Kee Goh 18-19, Mònica Guxens 20-22, Claire M.A. Haworth 12, Dexter Hadley 8, Johannes Hedebrand 23, Anke Hinney 23, Joel N. Hirschhorn 24-26, John W. Holloway 27-28, Claus Holst 29, Jouke Jan Hottenga 4, Momoko Horikoshi 30-31, Ville Huikari 32-33, Elina Hypponen7,34, Carmen Iñiguez 21,35, Marika Kaakinen 32-33, Tuomas O. Kilpeläinen 36, Mirna Kirin 37, Mattew Kowgier 2, Hanna-Maaria Lakka 38, Leslie A. Lange 39, Debbie A. Lawlor 14, Terho Lehtimäki 40-41, Alex Lewin 9, Cecilia Lindgren 42, Virpi Lindi 3, Reedik Maggi 42-43, Julie Marsh 2, Christel Middeldorp 4, Iona Millwood 9,44, Dennis O. Mook-Kanamori 5-6,45-46, Jeffrey C. Murray 47, Michel Nivard 4, Ellen Aagaard Nohr 29, Ioanna Ntalla48, Emily Oken 24-26, Paul O’Reilly 9, Lyle Palmer 49-50, Kalliope Panoutsopoulou 51, Jennifer Pararajasingham 12, Inga Prokopenko 30-31, Alina Rodriguez 9,12,52, Rany M. Salem 24-26, Sylvain Sebert 9, Niina Siitonen 53, Ulla Sovio 9,54, Beate St Pourcain 14, David P. Strachan 55, Jordi Sunyer 20-22,56, H. Rob Taal 5-6,45, Yik-Ying Teo 19, Elisabeth Thiering 16, Carla Tiesler 16,57, Andre G. Uitterlinden 6,58, Beatriz Valcárcel 9, Nicole Warrington 2,49, Scott White 2, Gonneke Willemsen 4, Hanieh Yaghootkar 17, Eleftheria Zeggini 51, Dorret I. Boomsma 4, Cyrus Cooper 59, Xavier Estivill 21,56,60, Matthew Gillman 61, Struan F. Grant 8,62-63, Hakon Hakonarson 8,62-63, Andrew T. Hattersley 64, Joachim Heinrich 16, Berthold Hocher 65-66, Vincent W.V. Jaddoe 5-6,45, Marjo-Riitta Jarvelin 13,32-33,67, Timo A. Lakka 3, Mark I. McCarthy 30-31,68, Mads Melbye 15, Karen L. Mohlke 39, George V. Dedoussis 48, Ken K. Ong 69, Ewan R. Pearson 70, Craig E. Pennell 2, Thomas S. Price 12, Chris Power 7, Olli T. Raitakari 53,71, Seang-Mei Saw 18-19,72, Andre Scherag 73, Olli Simell 53,74, Thorkild I.A. Sørensen 29,75, Nicholas J. Timpson 14, Elisabeth Widen 11, James F. Wilson 37,76

  1. Department of Nutrition, University of North Carolina, Chapel Hill, NC.
  2. School of Women’s and Infants’ Health, The University of Western Australia, Perth, Australia
  3. Department of Physiology, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.
  4. Department of Biological Psychology, VU University, Amsterdam, The Netherlands.
  5. The Generation R Study Group, Erasmus Medical Center, Rotterdam, The Netherlands.
  6. Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
  7. Centre For Paediatric Epidemiolgy and Biostatistics/MRC Centre of Epidemiology for Child Health, University College of London Institute of Child Health, London, UK.
  8. Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
  9. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK.
  10. Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
  11. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
  12. MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, UK.
  13. MRC-HPA Center, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
  14. MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK.
  15. Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.
  16. Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
  17. Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Magdalen Road, Exeter, EX1 2LU, UK.
  18. Duke-NUS Graduate Medical School, Singapore.
  19. Saw Swee Hock School of Public Health, National University of Singapore.
  20. Hospital del Mar Research Institute (IMIM), Barcelona, Catalonia, Spain.
  21. CIBER Epidemiologia y Salud Pública (CIBERESP), Barcelona, Catalonia, Spain.
  22. Center for Research in Environmental Epidemiology (CREAL), Barcelona, Catalonia, Spain.
  23. Department of Child and Adolescent Psychiatry, University of Duisburg-Essen, Essen, Germany.
  24. Divisions of Genetics and Endocrinology and Program in Genomics, Children’s Hospital, Boston, Massachusetts 02115, USA.
  25. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
  26. Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA.
  27. Human Genetics and Medical Genomics, Human Development & Health, Faculty of Medicine, University of Southampton.
  28. Clinical & Experimental Sciences, Faculty of Medicine, University of Southampton.
  29. Institute of Preventive Medicine, Copenhagen University Hospital, Copenhagen, Denmark.
  30. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, UK.
  31. Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.
  32. Institute of Health Sciences, University of Oulu, Finland
  33. Biocenter Oulu, University of Oulu, Finland.
  34. Department of Genomics of Common Disease, School of Public Health, Imperial College London.
  35. Division of Environment and Health, Center for Public Health Research-CSISP, Valencia, Spain.
  36. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
  37. Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland.
  38. Department of Public Health, Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Finland.
  39. Department of Genetics, University of North Carolina, Chapel Hill, NC.
  40. Department of Clinical Chemistry, Tampere University Hospital, Tampere, Finland.
  41. Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
  42. Genetic and Genomic Epidemiology Unit, The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
  43. Estonian Genome Center, University of Tartu, Tartu, Estonia.
  44. Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), University of Oxford, UK.
  45. Department of Paediatrics, Erasmus Medical Center, Rotterdam, the Netherlands.
  46. Department of Physiology and Biophysics, Weill Cornell Medical College - Qatar, Doha, Qatar.
  47. Department of Pediatrics, University of Iowa, Iowa City, Iowa, USA.
  48. Department of Dietetics - Nutrition, Harokopio University of Athens, Athens, Greece.
  49. Samuel Lunenfeld Research Institute, University of Toronto, Toronto, Canada
  50. Genetic Epidemiology and Biostatistics Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
  51. Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
  52. Department of Psychology, Mid Sweden University, Sweden.
  53. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
  54. Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
  55. Division of Population Health Sciences and Education, St George’s, University of London
  56. Pompeu Fabra University (UPF), Barcelona, Catalonia, Spain.
  57. Division of Metabolic Diseases and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-University of Munich, Munich, Germany.
  58. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.
  59. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom.
  60. Genes and Disease Program, Center for Genomic Regulation (CRG-UPF), Barcelona, Catalonia, Spain.
  61. Obesity Prevention Program, Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Obesity Prevention Program, Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA 02215 USA.
  62. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
  63. Department of Pediatrics, University of Pennsylvania, Philadelphia PA 19104, USA.
  64. Peninsula NIHR Clinical Research Facility, Peninsula College of Medicine and Dentistry, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK.
  65. Institute of Nutritional Science, University of Potsdam, D-14558 Nuthetal Potsdam, Germany.
  66. Center for Cardiovascular Research/Institute of Pharmacology, Charité, Berlin, Germany.
  67. National Institute of Health and Welfare, Oulu, Finland.
  68. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, UK.
  69. MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, CB2 0QQ, United Kingdom.
  70. Biomedical Research Institute, University of Dundee.
  71. Department of Clinical Physiology, University of Turku and Turku University Hospital, Turku, Finland.
  72. Singapore Eye Research Institute.
  73. Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany.
  74. Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland.
  75. The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copnahgen.
  76. MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, Scotland

Early Genetics & Lifecourse Epidemiology (EAGLE) Membership and Affiliations

Wei Ang 1, Toos van Beijsterveldt 2, Nienke Bergen 3-4, Kelly Benke 1, Diane Berry 5, Jonathan Bradfield 6, Pimphen Charoen 7-8, Lachlan Coin 7, Diana Cousminer 9, Shikta Das 7, Paul Elliott 10, Dave M. Evans 11, Claudia Flexeder 12, Tim Frayling13, Rachel Freathy 11,13, Romy Gaillard 3-4, Maria Groen-Blokhuis 2, Dexter Hadley 6, Jouke Jan Hottenga 2, Ville Huikari 14-15, Elina Hypponen5,16, Marika Kaakinen 14-15, Mattew Kowgier 1, Debbie A. Lawlor 11, Alex Lewin 7, Cecilia Lindgren 17, Julie Marsh 1, Christel Middeldorp 2, Iona Millwood 7,18, Dennis O. Mook-Kanamori 3-4,19-20, Michel Nivard 2, Paul O’Reilly 7, Lyle Palmer 21-22, Inga Prokopenko 23-24, Alina Rodriguez 7,25-26, Sylvain Sebert 7, Ulla Sovio 7,27, Beate St Pourcain 11, David P. Strachan 28, H. Rob Taal 3-4,19, Elisabeth Thiering 12, Carla Tiesler 12,29, Andre G. Uitterlinden 4,30, Beatriz Valcárcel 7, Nicole Warrington 1,21, Scott White 1, Gonneke Willemsen 2, Hanieh Yaghootkar 13, Dorret I. Boomsma 2, Struan F. Grant 6,31-32, Hakon Hakonarson 6,31-32, Andrew T. Hattersley 33, Joachim Heinrich 12, Vincent W.V. Jaddoe 3-4,19, Marjo-Riitta Jarvelin 10,14-15,34, Mark I. McCarthy 23-24,35, Craig E. Pennell 1, Chris Power 5, Nicholas J. Timpson 11, Elisabeth Widen 9

  1. School of Women’s and Infants’ Health, The University of Western Australia, Perth, Australia
  2. Department of Biological Psychology, VU University, Amsterdam, The Netherlands.
  3. The Generation R Study Group, Erasmus Medical Center, Rotterdam, The Netherlands.
  4. Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
  5. Centre For Paediatric Epidemiolgy and Biostatistics/MRC Centre of Epidemiology for Child Health, University College of London Institute of Child Health, London, UK.
  6. Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
  7. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK.
  8. Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
  9. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
  10. MRC-HPA Center, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
  11. MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK.
  12. Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
  13. Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Magdalen Road, Exeter, EX1 2LU, UK.
  14. Institute of Health Sciences, University of Oulu, Finland
  15. Biocenter Oulu, University of Oulu, Finland.
  16. Department of Genomics of Common Disease, School of Public Health, Imperial College London.
  17. Genetic and Genomic Epidemiology Unit, The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
  18. Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), University of Oxford, UK.
  19. Department of Paediatrics, Erasmus Medical Center, Rotterdam, the Netherlands.
  20. Department of Physiology and Biophysics, Weill Cornell Medical College - Qatar, Doha, Qatar.
  21. Samuel Lunenfeld Research Institute, University of Toronto, Toronto, Canada
  22. Genetic Epidemiology and Biostatistics Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
  23. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, UK.
  24. Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.
  25. Department of Psychology, Mid Sweden University, Sweden.
  26. MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, UK.
  27. Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
  28. Division of Population Health Sciences and Education, St George’s, University of London
  29. Division of Metabolic Diseases and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-University of Munich, Munich, Germany.
  30. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.
  31. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
  32. Department of Pediatrics, University of Pennsylvania, Philadelphia PA 19104, USA.
  33. Peninsula NIHR Clinical Research Facility, Peninsula College of Medicine and Dentistry, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK.
  34. National Institute of Health and Welfare, Oulu, Finland.
  35. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LJ, UK.

Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Membership and Affiliations

M. Arfan Ikram 1,2,3, Myriam Fornage 4, Albert V. Smith 5,6, Sudha Seshadri 7,8,9, Reinhold Schmidt 10, Stèphanie Debette 7,8,11, Henri A. Vrooman 2,12, Sigurdur Sigurdsson 5, Stefan Ropele 10, Laura H. Coker 16, W.T. Longstreth Jr. 17, Wiro J. Niessen 2,12,18, Anita L. DeStefano 7,8,9, Alexa Beiser 7,8,9, Alex P. Zijdenbos 19, Maksim Struchalin 1, Clifford R. Jack Jr. 20, Mike A. Nalls 21, Rhoda Au 7,10, Albert Hofman 1,3, Haukur Gudnason 5, Aad van der Lugt 2, Tamara B. Harris 22, William M. Meeks 23, Meike W. Vernooij 1,2, Mark A. van Buchem 24, Diane Catellier 25, Vilmundur Gudnason 5,6, B. Gwen Windham 23, Philip A. Wolf 7,9, Cornelia M. van Duijn 1,3, Thomas H. Mosley Jr. 23, Helena Schmidt 26, Lenore J. Launer 22, Monique M.B. Breteler 1,3,27, Charles DeCarli 28

  1. Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
  2. Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
  3. Netherlands Consortium for Healthy Aging, The Netherlands.
  4. Institute of Molecular Medicine and Human Genetics Center, University of Texas, Houston Health Sciences Center, Houston, TX, USA.
  5. Icelandic Heart Association, Kopavogur, Iceland.
  6. Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
  7. Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
  8. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
  9. The National Heart Lung and Blood Institute’s Framingham Heart Study, Framingham, MA, USA.
  10. Department of Neurology, Medical University Graz, Austria.
  11. INSERM, U708, Neuroepidemiology, Paris, France.
  12. Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
  13. Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
  14. Department of Neurology and Epidemiology, University of Washington, Seattle, WA, USA.
  15. Faculty of Applied Sciences, Delft University of Technology, The Netherlands.
  16. Biospective Inc, Montreal, Canada.
  17. Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  18. Laboratory of Neurogenetics, National Institute on Aging, National Institute of Health, Bethesda, MD, USA.
  19. Laboratory of Epidemiology, Demography, and Biometry, National Institute of Health, Bethesda, MD, USA.
  20. Department of Medicine (Geriatrics) and Neurology, University of Mississippi Medical Center, Jackson, MS, USA.
  21. Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
  22. Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
  23. Institute of Molecular Biology and Biochemistry, Medical University Graz, Austria.
  24. German Center for Neurologic Diseases (DZNE), Bonn, Germany.
  25. Department of Neurology and Center of Neuroscience, University of California at Davis, Sacramento, CA, USA.

Footnotes

COMPETING INTERESTS STATEMENT

The authors declare no competing financial interests

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