The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia : design, results and future prospects

Vol.:(0123456789) 1 3 European Journal of Epidemiology (2019) 34:279–300 https://doi.org/10.1007/s10654-019-00502-9 CONSORTIUM The Early Growth Genetics (EGG) andEArly Genetics andLifecourse Epidemiology (EAGLE) consortia: design, results andfuture prospects ChristelM.Middeldorp1,2,3 · JanineF.Felix4,5,6· AnubhaMahajan7,8· EArly Genetics Lifecourse Epidemiology (EAGLE) consortium· Early Growth Genetics (EGG) consortium· MarkI.McCarthy7,8,9 Received: 20 November 2018 / Accepted: 25 January 2019 / Published online: 18 March 2019 © The Author(s) 2019 Abstract The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites. Keywords Genetics· Consortium· Childhood traits and disorders· Longitudinal Background In countries with a high-sociodemographic index, the major contributors to burden of disease during childhood and adolescence are non-communicable diseases such as obesity, The full author list for this manuscript, including affiliations, includes all current active members of both consortia and is listed at the end of the paper followed by the membership lists of the EGG and EAGLE consortia as well as the acknowledgments and disclosures of interests. * Christel M. Middeldorp c.middeldor[email protected] * Mark I. McCarthy 1 Child Health Research Centre, University ofQueensland, Brisbane, QLD, Australia 2 Child andYouth Mental Health Service, Children’s Health Queensland Hospital andHealth Service, Brisbane, QLD, Australia 3 Department ofBiological Psychology, Vrije Universiteit Amsterdam, 1081BTAmsterdam, TheNetherlands 4 The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, 3015CERotterdam, TheNetherlands 5 Department ofEpidemiology, Erasmus MC, University Medical Center Rotterdam, 3015CERotterdam, TheNetherlands 6 Department ofPediatrics, Erasmus MC, University Medical Center Rotterdam, 3015CERotterdam, TheNetherlands 7 Wellcome Centre forHuman Genetics, University ofOxford, OxfordOX37BN, UK 8 Oxford Centre forDiabetes, Endocrinology andMetabolism, University ofOxford, OxfordOX37LE, UK 9 Oxford National Institute forHealth Research (NIHR) Biomedical Research Centre, Churchill Hospital, OxfordOX37LE, UK 280 C.M.Middeldorp et al. 1 3 asthma or allergies, and psychiatric disorders. These have a large cumulative impact on individuals, families and society [1]. Moreover, many early-life traits track throughout childhood and adolescence into adulthood. Childhood obesity, for example, is associated with adult obesity and cardiovascular disease [2]. Several childhood psychiatric disorders persist into adolescence and adulthood or precede severe mental illness such as schizophrenia, which usually starts at late adolescence or early adulthood [3, 4]. Low birth weight, as a proxy for a suboptimal intrauterine environment, has been shown to be robustly associated with many later-life non-communicable traits, including cardiovascular, respiratory and psychiatric disorders (see e.g., 5–7). This prompted researchers, including those within the Developmental Origins of Health and Disease (DOHaD) field, to investigate the basis for the early origins of later life differences in health and disease. Insight into the etiology of childhood and adolescent traits and disorders may provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. Individual differences in developmental phenotypes, such as body weight and composition, behavioral problems, language skills, and their stability across ages are partly influenced by genetic factors [8–13]. Identifying the specific genetic variants that influence these traits, and the biological pathways through which they operate, can therefore help to unravel etiological mechanisms. Genetic studies can also define whether the relationships between childhood and adult traits, for example, birth weight and cardiovascular disease, are causally mediated by early life exposures. In addition, genetics can support how specific environmental factors contribute to variation in these traits, i.e., whether there is gene-environment interaction with the increase in risk depending on an individual’s genetic risk. It is increasingly recognized that large sample sizes are essential in genetic research [14] and studies performed in large international consortia have become the norm. Two such consortia with a particular focus on the genetics of early life phenotypes are the Early Growth Genetics (EGG) consortium (http://egg-conso r tium .org/) and the EArly Genetics and Lifecourse Epidemiology (EAGLE) consortium (http://www.wikig enes.org/e/art/e/348.html) (Fig.1). This paper describes these two consortia as they have shared objectives and the participating cohorts partly overlap. We also highlight the results so far and outline the directions of future research. Description andaims oftheEGG andEAGLE consortia Both consortia arose in 2009 out of the EU-funded European Network for Genetic And Genomic Epidemiology (ENGAGE). The EGG consortium focuses on the genetic basis of growth-related phenotypes spanning from fetal life into adolescence, including birth weight, childhood obesity and pubertal development. EAGLE was established to investigate the genetic basis of the wide range of further phenotypes collected by these cohorts from fetal life into adolescence, such as those relevant to asthma and eczema, childhood psychopathology, cognition, and neurodevelopment. The collective objectives of EGG and EAGLE are: 1. to characterize the genetic background of traits and diseases in fetal life, childhood and adolescence by facilitating collaboration between pregnancy, birth, childhood Fig. 1 Logo’s 281 The Early Growth Genetics (EGG) andEArly Genetics andLifecourse Epidemiology (EAGLE)… 1 3 Table 1 Participating cohorts Short name Full name cohort Website References ABCD Amsterdam Born Children and their Development https ://abcd-studi e.nl/ 20813863 ALSPAC Avon Longitudinal Study on Parents and Children www.brist ol.ac.uk/alspa c/ 22507743, 22507742 B58C 1958 British Birth Cohort www.cls.ioe.ac.uk/page.aspx?&sites ectio nid=724&sites ectio ntitl e=Welco me+to+the+1958+Natio nal+Child +Devel opmen t+Study 16155052, 17255346 BAMSE Children, Allergy, Milieu, Stockholm, Epidemiology https ://ki.se/en/imm/bamse -proje ct 26505741 BMDCS Bone Mineral Density in Childhood Study https ://bmdcs .nichd .nih.gov/ 17311856 Breathe BRain dEvelopment and Air polluTion ultrafine particles in scHool childrEn https ://www.isglo bal.org/en/-/breat he-brain -devel opmen t-and-airpollu tion-ultra fine-parti cles-in-schoo l-child ren 25734425, 27656889 CATSS Child and Adolescent Twin Study in Sweden https ://ki.se/en/meb/the-child -and-adole scent -twin-study -in-swede n-catss 22506305 CHOP Children’s Hospital of Philadelphia https ://www.cagla b.org/ 22138692 CHS Children’s Health Study https ://healt hstud y.usc.edu/ 10051249, 10051248, 17307103,25738666, 28103443, 27115265 CLHNS Cebu Longitudinal Health and Nutrition Survey http://www.cpc.unc.edu/proje cts/cebu 20507864 COPSAC Copenhagen Prospective Studies on Asthma in Childhood www.copsa c.com 15521375, 24118234, 24241537 DNBC Danish National Birth Cohort https ://www.ssi.dk/Engli sh/RandD /Resea rch%20are as/Epide miolo gy/DNBC.aspx EFSOCH Exeter Family Study of Childhood Health 16466435 Finntwin12 Finnish Twin Cohort Study https ://wiki.helsi nki.fi/displ ay/twine ng/Twins tudy 23298696,17254406, 12537860 Gen3G Genetics of Glucose regulation in Gestation and Growth n/a 26842272 Generation R Study https ://www.gener ation r.nl/ 28070760; 25527369 GINIplus German Infant Study on the influence of Nutrition Intervention PLUS environmental and genetic influences on allergy development https ://www.helmh oltz-muenc hen.de/epi/resea rch/resea rch-group s/aller gy-epide miolo gy/proje cts/ginip lus/index .html 20082618 GLAKU Glycyrrhizin in Licorice https ://blogs .helsi nki.fi/depsy -group /resea rch/ 19808634; 17076756; 11390327 HBCS Helsinki Birth Cohort Study https ://thl.fi/en/web/thlfi -en/resea rch-and-exper twork /proje ctsand-progr ammes /helsi nki-birth -cohor t-study -hbcs-idefi x 11312225 Health2006 Helbred2006 https ://clini caltr ials.gov/ct2/show/NCT00 31666 7 23615486 INMA INfancia y Medio Ambiente http://proye ctoin ma.org/en_index .html 21471022 Inter99 The Inter99 Study https ://www.regio nh.dk/rcph/popul ation -based -epide miolo gy/ Pages /The-Inter 99-Study .aspx 14663300 LISA Influence of life-style factors on the development of the immune system and allergies in East and West Germany https ://www.helmh oltz-muenc hen.de/epi/resea rch/resea rch-group s/aller gy-epide miolo gy/proje cts/lisa/index .html 12358337 MAAS Manchester Asthma and Allergy Study http://maas.org.uk/ 25805205, 15029579, 12688622 MOBA Norwegian Mother and Child Cohort Study https ://fhi.no/studi er/moba/ 27063603, MUSP Mater University Study of Pregnancy https ://socia l-scien ce.uq.edu.au/mater -unive rsity -queen sland -study -pregn ancy 25519422 NTR Netherlands Twin Register http://www.tweel ingen regis ter.org/ 23186620; 23265630 282 C.M.Middeldorp et al. 1 3 and adolescent cohort studies, as well as adult biobanks (such as UK Biobank) with relevant information; 2. to define the causal relationships between early life exposures and related early life phenotypes and major sources of morbidity and mortality in later life; 3. to develop and improve statistical methods for analyzing complex, high-dimensional and longitudinal phenotypic data; 4. to provide training opportunities for junior researchers to develop in the field of genetic epidemiology. The EGG and EAGLE consortia started as collaborations of population-based pregnancy and birth cohort studies, each of which has collected longitudinal data across a wide range of developmental phenotypes. As the collaboration developed, cohorts that started data collection during childhood and adolescence were also included. Almost all participating studies have genome-wide genotype data available. In addition, early life data collected through self-report and/ or record linkage in adult biobanks, such as UK Biobank or the population based cohorts listed in Table1 that have an adult counterpart, have been brought into the genome-wide association (GWA) meta-analyses for phenotypes such as birth weight. Both consortia welcome new collaborations, and they are keen to add data from longitudinal cohorts that are currently in the process of obtaining genotype data. Tables1 and 2 provides a summary of the participating studies and their design, as of April 2018. Table3 gives further details on the extensive data available, indicating, per cohort, whether data collection has taken place at least once at preschool, school, adolescent and adult age. However, many cohorts have had multiple follow-up rounds within any given period or follow-up data collection is ongoing, through research clinic assessments, questionnaires or record linkage. The majority of the cohorts have around equal numbers of males and females included. Most cohorts were established with the aim of investigating risk and protective factors for a broad range of developmental phenotypes. They have collected data on physical traits, cognition, emotional and behavioral problems, as well as on lifestyle and environmental factors, such as smoking during pregnancy and physical exercise. Other cohorts were set up with a specific focus, such as asthma research, but many of these have collected ancillary information on a wider range of phenotypes. Table2 gives an indication as to whether data collection was focused on a specific phenotype. Additional details on many of these studies will be available from cohort websites and publications (see Table1). Participating cohorts have obtained DNA from blood samples, saliva or buccal swabs. A variety of different genotyping arrays have been used over the years, but meta-analysis has been facilitated by imputation of directly genotyped data using reference panels such as those generated by 1000 Table 1 (continued) Short name Full name cohort Website References NFBC1966 and NFBC1986 Northern Finland Birth Cohort http://www.oulu.fi/nfbc/ 750195; 19060910; 9246691 PIAMA Preventie en Incidentie van Astma en Mijt Allergie http://piama .iras.uu.nl/ 12688626, 23315435 Project Viva http://dacp.org/viva/ 24639442 Qtwin Queensland Twin Registry http://www.qimrb ergho fer.edu.au/qtwin / DOI: 10.1080/00049530410001734865 Raine The Western Australian Pregnancy Cohort (Raine) Study https ://www.raine study .org.au/ 8105165; 23230915; 23301674l; 26169918; 28064197; 28662683 SKOT Småbørns Kost Og Trivsel https ://skot.ku.dk/om-proje ktet/engli sh/ 28947836 STRIP Special Turku Coronary Risk Factor Intervention Project http://strip study .utu.fi/engli sh.html 18430753 TCHAD Twin Study of Child and Adolescent Development https ://ki.se/en/meb/twin-study -of-child -and-adole scent -devel opmen t-tchad 17539366 TDCOB The Danish Childhood Obesity Biobank https ://clini caltr ials.gov/ct2/show/NCT00 92847 3 TEDS Twins Early Development Study http://teds.ac.uk/ 23110994 TRAILS TRacking Adolescents’ Individual Lives Survey https ://www.trail s.nl/ 25431468 Young Finns The Cardiovascular Risk in Young Finns Study http://young finns study .utu.fi/ 18263651 283 The Early Growth Genetics (EGG) andEArly Genetics andLifecourse Epidemiology (EAGLE)… 1 3 Genomes or the Haplotype Reference Consortium [15, 16]. Moreover, an increasing number of cohorts have, or plan to get, additional ‘omics data including parental genotypes, DNA methylation profiles, RNA expression levels, metabolomics and/or microbiome data. Results ofthegenetic studies performed intheEGG andEAGLE consortia The implementation of GWA meta-analyses for each of the phenotypes of interest to EGG or EAGLE has usually been championed and organized at the level of a working group, formed by a subset of motivated investigators and analysts, who have assumed responsibility for assembling, combining and interpreting the genetic data. The wide range of phenotypes available to study across these consortia has provided fertile ground for many such working groups and has resulted in a large number of peer-reviewed papers across this wide range of phenotypes [17–45]. These are typically GWA meta-analyses, focusing on the effects of individual genetic variants, but increasingly now extend to multivariate, polygenic analyses, that evaluate the joint effects of multiple associated genetic variants and apply this information to address questions of causality. Amongst the many GWA analyses led by EGG and EAGLE, the traits for which the largest numbers of genetic loci reached genome-wide statistical significance (p < 10−8) have been birth weight (65 loci), atopic dermatitis (31), childhood BMI (15), allergic sensitization (10), and pubertal growth (10) [17, 19, 23, 26, 28, 36]. For other phenotypes with a large number of genome wide hits, such as age at menarche (108 loci) or ADHD (16 loci), the association analysis has involved collaborations with other consortia [25, 37]. The summary statistics for many of the genomewide association studies undertaken by EGG and EAGLE investigators can be found on consortium websites (http:// egg-conso rtium .org/; http://www.wikig enes.or g/e/art/e/348. html) or are available from corresponding authors. As with adult phenotype GWA studies, the number of association signals recovered by these studies is influenced heavily by sample size (N = 182,416 for age at menarche, N = 153,781 for birth weight) and, to a lesser extent, by phenotype characteristics (somatic or behavioral traits, continuous or binary outcomes). In addition to cross-sectional GWA analyses, there have been many examples of projects that have investigated genetic relationships within childhood traits or between childhood traits and related adult phenotypes, often revealing shared genetic factors. For example, genetic overlap was found among related atopic conditions during childhood, and between atopic conditions and auto-immune disorders [19, 36]; among puberty-related phenotypes, and between puberty-related phenotypes and BMI [23, 24, 37]; between childhood and adult blood pressure [41]; between preschool internalizing symptoms and adult psychiatric disorders [18]; and between childhood and adult anthropometric traits [21, 26, 40, 44]. The development of statistical methods that support the calculation of genetic correlations from summary GWAS results [46] and the easy availability of such data from a growing number of GWA meta-analyses for adult traits have enabled these analyses to be undertaken with adequate statistical power. Figure2 shows genetic correlations, calculated exclusively from GWAS data, between birth weight and a range of continuous and disease phenotypes [28], generated using the linkage disequilibrium score regression approach [46] as implemented in the LDHub web utility [47]. For many cardiometabolic and anthropometric traits measured in late adult life, there is evidence of substantial sharing of genetic variation with birth weight. In line with the wider epidemiological data, the genetic correlations between birth weight and adult cardiometabolic traits (including type 2 diabetes, blood pressure, and coronary artery disease) tend to be negative. These data indicate that a substantial proportion of the observed covariance between birth weight and cardiometabolic disease predisposition is likely to be driven by genetic rather than environmental factors. However, the potential for more complex causal relationships (such as those that connect fetal genotype to adult disease via the correlation with maternal genotype and altered maternal environment) also needs to be considered. Full characterization of these complex relationships requires the application of statistical methods that enable partitioning of genetic effects into maternal and fetal components both at the level of individual SNPs [48] and genome-wide [49]. Using the M-GCTA method [49], for example, it has been reported that maternal genotypes contribute more to gestational weight gain in the mother, while offspring genotypes contribute more to birth weight [45]. Another critical advantage of genetic studies is the potential to characterize causal relationships using Mendelian randomization approaches [50]. Tyrrell etal. [42] found evidence of a positive causal effect of maternal BMI and fasting glucose levels on offspring birth weight but inverse effect of maternal systolic blood pressure on offspring birth weight. Despite bringing together the largest number of studies at the time with relevant data, there was insufficient power to dissect how the opposing effects of maternal glucose and systolic blood pressure are reflected in the maternal BMI effect (one reason why we are keen to extend the collaboration to any new cohorts). Crucially, however, appropriate application and interpretation of studies that seek to elucidate the mechanisms underlying associations between maternal and offspring phenotypes require investigators to 284 C.M.Middeldorp et al. 1 3 Table 2 Study designs a Includes individuals from non-European descent Cohort Study design Years of recruitment Country ABCD Population based pregnancy cohort 2003–2004 The Netherlands ALSPAC Population based birth cohort 1990–1992 UK B58C Population based birth cohort 1958 UK BAMSE Population based cohort 1994–1996 Sweden BMDCS Multi-center observational cohort 2002–2009 United States Breathe Population based cohort 2002–2006 Spain CATSS Populaton based twin birth cohort 1992-ongoing Sweden CHOP Population based cohort 1988-Present USA CHS Community based children cohort 1993–2002 United States CLHNS Population based birth cohort 1983–1984 Philippines COPSAC-2000 Asthma risk birth cohort From 2000Denmark COPSAC-2010 Population based birth cohort Ongoing From 2010 COPSAC-REGISTRY Severe asthma cases (children) Ongoing DNBC-GOYA Population based pregnancy cohorts From 1997 Ongoing Denmark DNBC-PTB EFSOCH Community-based pregnancy cohort of parent– offspring trios 2000–2004 United Kingdom Finntwin12 Population-based twin-family cohort 1983–1987 Finland Gen3G Population based birth cohort 2010–2013 Canada Generation RaPopulation-based birth cohort 2002–2006 The Netherlands GINIplus Population based birth cohort 1995–1998 Germany GLAKU Population-based birth cohort 1998 Finland HBCS Population-based birth cohort 1934–1944 Finland Health2006 General population study 2006–2008 Denmark INMA Population-based birth cohort 1997–2008 Spain Inter99 Population-based randomized intervention study 1999–2006 Denmark LISA population based birth cohort 1997–1999 Germany MAASaPopulation-based birth cohort 1996/1997 UK MOBA Population based birth cohort 1999–2008 Norway MUSP Pregnancy general population 1981–1984 Australia NTRaBirth general twin population From 86—ongoing Netherlands NFBC1966 and NFBC1986 longitudinal birth cohort 1966 and 1986 Finland PIAMA Population based birth cohort, enriched for high risk allergy children (allergic mother) 1996–1997 Netherlands Project VivaaPopulation based birth cohort 1999–2002 USA Qtwin Longitudinal twin study 1980–2004 Australia Raine Longitudinal pregnancy cohort study 1989–1991 Australia SKOT Observational cohort study, monitoring healthy young children from 9 to 36months of age. 2006–2007 (SKOT I); 2011–2013 (SKOT II) Denmark STRIP Prospective randomized life-style intervention trial 1990–1992 Finland TCHAD Birth general twin population 1985–1987 Sweden TDCOB Case–control study Children and adolescence with obesity: 2007–2013; Population-based sample: 2010–2013 Denmark TEDS Population based twin birth cohort From 1994—Ongoing UK TRAILS-pop Population based 2001/2002 Netherlands TRAILS-CC High risk 2004 Netherlands Young Finns Population based follow-up from childhood to adulthood 1980 Finland 285 The Early Growth Genetics (EGG) andEArly Genetics andLifecourse Epidemiology (EAGLE)… 1 3 Table 3 Data collected Cohort N genotyped childrena Phenotypes Age periods data available Pregnancy Pre-school School Adolescence Adult ABCD 1192 Broad x x x x ALSPAC 10,000 Broad x x x x x B58C 6491 Broad x x x x x BAMSE 2500 Broad x x x x x BMDCS 1885 Broad x x x x Breathe 1667 Broad x CATSS 13,576 Broad, focus on psychiatry x, information from registers x x x CHOP 43,320 Broad x x x CHS 3986 Broad, focus on respiratory and metabolic health x x CLHNS 1779 Broad x x x x COPSAC-2000 411 Broad x x x x x COPSAC-2010 700 Broad x x x COPSAC-REGISTRY 1240 Broad x x DNBC -GOYA DNBC -PTB 1500 Broad x x x x 1500 EFSOCH 812 Anthropometric and glycemic traits x x Parents only Finntwin12 1264 Broad Retrospective Retrospective x x x Gen3G 582 Broad, focus on metabolic/ adiposity x on-going Generation R 5731 Broad x x x x GINIplus 835 broad x x x x Ongoing GLAKU 357 Broad x x x x x HBCS 1566 Broad x x x x Health2006 2802 Cardiovascular disease, type 2 diabetes, and other lifestyle related diseases x INMA 1517 Broad x x x Ongoing Inter99 6184 Cardiovascular disease, type 2 diabetes, other lifestyle related diseases, glucose tolerance x LISA 674 Broad x x x x Ongoing MAAS 919 asthma and allergy focused x x x x Ongoing MOBA 17,000 Broad x x x x x MUSP 1200 Broad x x x x x NTR 7750 Broad x x x x Ongoing NFBC1966 NFBC1986 5402 Broad x x x x x 3743 PIAMA 2113 Broad, focus on respiratory health x x x x Project Viva 1580 Broad x x x x Qtwin 4500 Broad x x x Raine 1500 Broad x x x x Ongoing 286 C.M.Middeldorp et al. 1 3 consider diverse complicating factors including the correlation between maternal and fetal genetic instruments, and to account for these sources of potential bias in the Mendelian randomization analyses wherever possible [51]. The longitudinal data collected in EGG and EAGLE cohorts provide the means to investigate whether the influence of genetic variants changes over time. This has only recently been explored given the need for large numbers a Some cohorts also have genotype data on parents Table 3 (continued) Cohort N genotyped childrena Phenotypes Age periods data available Pregnancy Pre-school School Adolescence Adult SKOT I 260 Dietary intake, growth, cognitive development, overweight and lifestyle related diseases x SKOT II 112 STRIP 666 Broad x x x x x TCHAD 990 Broad x x x TDCOB 1771 Overweight and Obesity x x x x TEDS 10,346 Broad x x x x TRAILS-pop 1354 Broad Retrospective Retrospective Retrospective x x TRAILS-CC 341 Young Finns 2442 Broad x x x x x Fig. 2 Genome-wide genetic correlation between birth weight and a range of traits and diseases in later life. Genome-wide genetic correlations between birth weight and traits and diseases evaluated in later life. The figure (adapted from Horikoshi etal. 2016 [28] with permission of the authors) displays the genetic correlations between birth weight and a range of traits and diseases in later life as estimated using LD Score regression. Traits selected were those for which genome-wide association summary statistics were available in suitably large sample sizes, and the analyses were typically performed on the largest meta-analyses available as of early 2016. The genetic correlation estimates (rg) are colour coded according to phenotypic area. Allelic direction of effect is aligned to increased birth weight. Size of the circle denotes the significance level for the correlation (per the key). Correlations with a lower significance level are not depicted. Further detail on the methods and studies involved is available in Horikoshi etal. 2016 [28]. Diameter of circles is proportional to genetic correlation p value 287 The Early Growth Genetics (EGG) andEArly Genetics andLifecourse Epidemiology (EAGLE)… 1 3 of studies with repeated measures. We have found that genetic variation in FTO, one of the first BMI increasing genetic variants to be identified in GWAS and one of the variants most strongly associated with mean BMI (in adults) is inversely associated with BMI in infancy only becoming positive in later childhood and adult [38], indicating the value of research that explores gene-by-age interactions. On a genome-wide scale, using meta-regression methods, polygenic risk scores generated from adult schizophrenia data yielded associations with variation in childhood and adolescent psychiatric symptom scores, which strengthened in magnitude with increasing age [52]. Strengths andweaknesses The aggregation of data in consortia such as EGG and EAGLE provides vastly improved sample sizes and a powerful way to overcome the major weakness of many of the early GWAS, which were, in hindsight, underpowered to detect the generally small genome-wide significant associations. This has brought multiple robust association signals across many traits, and provided a valuable basis for dissecting the, often complex, causal relationships between epidemiologically-correlated traits. A clear strength of the EGG and EAGLE consortia is the wealth of data available. This encompasses not only repeated measures for physical and behavioral traits, but also copious information on lifestyle and environmental circumstances. Moreover, some of the cohorts have collected data for several decades, and now provide repeated measures well into adulthood. This enables developmental research as well as analyses of the interplay between genes and environment. To date, one of the limitations has been that the majority of participating cohorts have data based on Europeanancestry populations (see Table2 for exceptions). There is a clear need for equivalent data to be generated in samples from other ethnic groups, so that the genetic contribution to reproducible ethnic differences in the distribution of early life phenotypes can be explored and the implications for adult disease risk quantified. Since the cohorts are population-based and lack a particular disease-focus, the consortia are not so well-suited to investigate conditions with a low prevalence. They are better-placed to analyze common traits, particularly those that can be measured on continuous scales and analyzed as quantitative measures, such as blood pressure instead of hypertension and ADHD symptom score instead of ADHD diagnosis [32, 34]. Power analyses demonstrate that identification of a genetic variant is, in most circumstances, more powerful for continuous traits than for dichotomous variables based on clinical cut-offs [53]. Future Considerable progress is to be expected from ongoing increases in sample sizes, especially for traits such as childhood aggression, ADHD-related traits and internalizing symptoms, where the number of identified genetic variants has been limited so far. Access to new data sets can motivate efforts to tackle phenotypes that have not hitherto been subject to detailed genetic analysis. The results emerging from many of these studies provide a timely reminder that analysis of early life phenotypes often requires researchers to consider the joint impacts of multiple genomes (e.g., those of the fetus and the mother) together with the web of environmental influences as potential contributors to individual variation. They also highlight the need to take into account the changes happening throughout development. This is now possible because of large, rich and complex datasets that support use of novel statistical methods for the analysis of causality or gene-by-age interaction [48, 49, 51, 54]. There have already been several examples of papers performing such analyses and this will only increase with the number of identified genetic variants. In addition, existing gender differences in the associations between early life and adult factors (such as cardiometabolic risk) suggest a need for more thorough analysis of the effects of gender on these early acting mechanisms. The focus to date on the role of maternal and offspring GWAS information indicates a failure to properly consider the contribution of genetic variation in the father that will be remedied as more data from complete trios and pedigrees becomes available. We are also planning to expand these consortia to accommodate access to the increasing amount of ‘omics data now becoming more available. Combining the results from EGG and EAGLE GWA analyses with those from DNA methylation analyses performed by the Pregnancy And Childhood Epigenetics (PACE) consortium [55] and with the pregnancy/child cohorts in the COnsortium of METabolomic Studies (COMETS; https ://epi.grant s.cance r.gov/comte ts/) will shed further light on the biological mechanisms underlying associations of early-life risk factors and childhood, adolescent and adult health outcomes. The focus on translating this knowledge to clinical and public health settings represents a major motivation. Insight into genetic factors underlying stability in traits such as obesity and psychiatric disorders may aid in providing targeted interventions to the groups at highest need. A more complete understanding of the contributions of genetic and non-genetic factors in the relationships between early life and later life traits may focus attention on the most effective strategies for behavioural or environmental modification. Acknowledgements cohorts We are grateful to all families and participants who took part in these studies. We also acknowledge and 294 C.M.Middeldorp et al. 1 3 Sciences, The Chinese University of Hong Kong, Hong Kong, China. 125. Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China. 126. Estonian Genome Center, University of Tartu, Tartu, 50090, Estonia. 127. Institute for Social Science Research, University of Queensland, QLD, Australia. 128. QIMR Berghofer Medical Research Institute, QLD, Australia. 129. Amsterdam Reproduction and Development, Amsterdam, The Netherlands. 130. Centre for Genetic Origins of Health and Disease (GOHaD), The University of Western Australia, Crawley, WA, 6000, Australia. 131. Department of Medicine, Stanford School of Medicine, Stanford, CA 94305, USA. 132. Sachs’ Children’s Hospital, Sweden. 133. Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA. 134. Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Copenhagen, DK-1014, Denmark. 135. Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK. 136. Department of Pediatrics, University of Iowa, Iowa City, IA 52242, USA. 137. School of Public Health, The University of Queensland, QLD, Australia. 138. Research Unit forGynaecology andObstetrics, Institute ofClinical Research, University ofSouthern Denmark, Odense, DK-5000, Denmark. 139. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 140. William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK. 141. Medical Research Council (MRC), Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK. 142. Division of Pediatric Endocrinology, Diabetes, and Metabolism, Department of Pediatrics, Columbia University Medical Center, New York, NY 10032, USA. 143. Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. 144. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20014, Finland. 145. Paavo Nurmi Centre, Sports and Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland. 146. Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1HH, UK. 147. School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Callaghan, NSW, 2308, Australia. 148. Centre for Occupational and Environmental Medicine, Stockholm County Council, Sweden. 149. MRC Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK. 150. Section of Genomics of Common Disease, Department of Medicine, Imperial College London, London, SW7 2AZ, UK. 151. Department of Psychology, University of Jyväskylä, Jyväskylä, Finland. 152. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20520, Finland. 153. BHF Centre for Cardiovascular Science, University of Edinburgh, Queen’s Medical Research Institute, Edinburgh, EH16 4TJ, UK. 154. Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 155. Department of Psychology, Mid Sweden University, Östersund, SE-831 25, Sweden. 156. Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA. 157. Department of Medicine, Division of Endocrinology, Boston Children’s Hospital, Boston, MA 02115, USA. 158. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. 159. Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA 02115, USA. 160. Subdirección de Salud Pública y Adicciones de Gipuzkoa, San Sebastián, Spain. 161. Instituto de Investigación Sanitaria Biodonostia, San Sebastián, Spain. 162. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, 119077, Singapore. 163. Singapore Eye Research Institute, Singapore, 168751, Singapore. 164. Metro North Mental Health Service, QLD, Australia. 165. Queensland Centre for Mental Health Research, QLD, Australia. 166. Department of Epidemiology, Cancer Center, University of Hawaii (Manoa), Honolulu, Hawaii, 96813, USA. 167. Department of Obstetrics and Gynecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 168. Population Health Research Institute, St George’s University of London, London, SW17 0RE, UK. 169. School of Physiotherapy and Exercise Science, Faculty of Health Sciences, Curtin University, Perth, WA, Australia. 170. Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland. 171. Clinicum, University of Helsinki, Helsinki, Finland. 172. Department of Statistics and Applied Probability, National University of Singapore, Singapore, 117546, Singapore. 173. Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore. 174. Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, 80337, Germany. 175. ib-salut, Area de Salut de Menorca, Spain. 176. Fundació Institut d’Investigació Sanitària Illes Balears – IdISBa, Spain. 177. European Centre for Environment and Human Health, University of Exeter, Truro, TR1 3HD, UK. 178. Department of Medicine, University of Turku, Turku, Finland. 179. Division of Medicine, Turku University Hospital, Turku, Finland. 180. Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Spain. 181. Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. 182. Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands. 183. Department of Public Health, Amsterdam Public Health Research Institute, Academic Medical Center (AMC), University of Amsterdam, Amsterdam, 1105 AZ, The Netherlands. 184. Institute of Medical Statistics and Epidemiology, Technical University Munich, Munich, D-80333, Germany. 185. Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, 81377, Germany. 186. Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9AG, UK. 187. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. 188. Queensland Brain Institute, University of Queensland, QLD, Australia. 189. Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK. 190. Department of Clinical Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands, 1105 AZ, The Netherlands. 191. Division of Gastroenterology, Hepatologyand Nutrition, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA. 192. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, 70210, Finland. 193. Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland. 194. IMIM (Hospital del Mar Medical Research Institute), Barcelona, 08003, Spain. 195. Biocenter Oulu, University of Oulu, Oulu, 90220, Finland. 196. Department of Genomics of Complexe Diseases, Imperial College, London, UK. 197. Department of Obstetrics and Gynecology, Sahlgrenska Academy, University of Gothenburg, Diagnosvägen 15, SE-416 85 Gothenburg, Sweden. 198. Norwegian Institute of Public Health, Norway. 199. Bristol NIHR Biomedical Research Centre, Bristol, UK. 200. Department of Paediatrics, University of Cambridge, Cambridge, CB2 0QQ, UK. 201. Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands. 202. Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Harvard University, Boston, USA. 203. Unit of Primary Care, Oulu University Hospital, Oulu, 90220, Finland. 204. Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Middlesex, UB8 3PH, UK. 205. Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LE, UK. 295 The Early Growth Genetics (EGG) andEArly Genetics andLifecourse Epidemiology (EAGLE)… 1 3 EGG Membership Members as of July 2018 are listed in alphabetical order. Linda S Adair1, Emma Ahlqvist2, Tarunveer S Ahluwalia3,4,5, Peter Almgren2, Wei Ang6, Mustafa Atalay7, Robin N Beaumont8, Jacques S Beckmann9, Hans Bisgaard3, Tom Bond10, Klaus Bønnelykke3, Dorret I Boomsma11,12,13,14, Judith B Borja15,16, Jonathan P Bradfield17,18, Mariona Bustamante19,20,21, Alana Cavadino22,23, Pimphen Charoen10,24, Lachlan Coin25, Cyrus Cooper26, Diana L Cousminer27,28, John A Curtin29, Adnan Custovic30, Shikta Das23, Felix R Day31, N Maneka De Silva10, George V Dedoussis32, Paul Elliott10, Johan G Eriksson33,34,35, David M Evans36,37,38, João Fadista39, Bjarke Feenstra39, Janine F Felix40,41,42, Timothy M Frayling8, Rachel M Freathy8,36, Romy Gaillard41, Frank Geller39, Vincente Gilsanz43, Struan FA Grant17,27,28,44, Niels Grarup4, Leif Groop2,45, Monica Guxens19,20,21, Dexter Hadley46, Hakon Hakonarson17,27,44, Torben Hansen4, Andrew T Hattersley8,47, M Geoffrey Hayes48, Johannes Hebebrand49, Joachim Heinrich50,51, Øyvind Helgeland52,53,54, Tine B Henriksen55, Anke Hinney49, Joel N Hirschhorn56,57,58, Marie-France Hivert59,60,61, Berthold Hocher62,63, John W Holloway64, Momoko Horikoshi65,66,67, Jouke-Jan Hottenga11,12,14, Elina Hyppönen23,68,69, Bo Jacobsson54,70, Vincent WV Jaddoe40,41,42, Marjo-Riitta Järvelin10,71,72,73,74, Stefan Johansson52,75, Heidi J Kalkwarf76, Marjan Kerkhof77, Antje Körner78,79, Sailesh Kotecha80, Eskil Kreiner-Møller3,81, Benard Kulohoma65, Zoltán Kutalik82,83, Timo A Lakka7,84,85, Joan M Lappe86, Debbie A Lawlor36,37,87, Terho Lehtimäki88,89, Alexandra M Lewin10, Cecilia M Lindgren65,90,91, Virpi Lindi7, Allan Linneberg92,93, Xueping Liu39, Jun Liu41, William L Lowe Jr48, Ronald CW Ma94,95,96, Aurélien Macé82, Reedik Mägi97, Per Magnus98, Anubha Mahajan65,66, Nina S McCarthy99, Mark I McCarthy65,66,100, Mads Melbye39,101, Karen L Mohlke102, Claire Monnereau40,41,42, Dennis O Mook-Kanamori103,104, Camilla S Morgen105, Andrew P Morris65,97,106, Jeffrey C Murray107, Ronny Myhre108, Pål R Njølstad52,53, Ellen A Nohr109, Ioanna Ntalla110, Paul O’Reilly111, Sharon E Oberfield112, Emily Oken113, Ken K Ong31,114, Kalliope Panoutsopoulou115, Oluf Pedersen4, Craig E Pennell116, John RB Perry31, Niina Pitkänen117, Beate St Pourcain36,118, Christine Power23, Rashmi B Prasad2, Inga Prokopenko65,119, Olli T Raitakari117,120, Rebecca M Reynolds121, Rebecca C Richmond36,37, Alina Rodriguez10,122, Rany Salem57,123,124,125, Seang-Mei Saw126,127, Theresia M Schnurr4, Sylvain Sebert10,71,73,128, John A Shepherd129, Angela Simpson29, Line Skotte39, Thorkild IA Sørensen4,36,105, Marie Standl50, Eric AP Steegers130, David P Strachan131, Jordi Sunyer19,20,21,132, Michelle Taylor36,37, Yik-Ying Teo126,133,134, Elisabeth Thiering50,135, Nicholas J Timpson36,37, Jessica Tyrrell8,136, André G Uitterlinden40,41,137, Cornelia M van Duijn41, Suzanne Vogelezang40,41,42, Tanja GM Vrijkotte138, Carol A Wang116, Nicole M Warrington38, William J Watkins80, H-Erich Wichmann50,139,140, Elisabeth E Widén45, Gonneke Willemsen11,12,14, James F Wilson141,142, Hanieh Yaghootkar143, Mohammad Hadi Zafarmand138,144, Eleftheria Zeggini115, Babette S Zemel145 1. Department of Nutrition, University of North Carolina, Chapel Hill, NC 27599, USA. 2. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, SE-205 02, Sweden. 3. COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, 2900 Hellerup, Denmark. 4. Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2200, Denmark. 5. Steno Diabetes Center Copenhagen, Gentofte, 2820, Denmark. 6. Division of Obstetrics and Gynaecology, The University of Western Australia, Crawley, WA, 6009, Australia. 7. Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, 70211, Finland. 8. Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, EX2 5DW, UK. 9. University of Lausanne, Lausanne, CH-1015, Switzerland. 10. Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, W2 1PG, UK. 11. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, 1081 BT, The Netherlands. 12. Amsterdam Public Health, Amsterdam, The Netherlands. 13. Amsterdam Reproduction and Development, Amsterdam, The Netherlands. 14. Netherlands Twin Register, Department of Biological Psychology, VU University, Amsterdam, 1081 HV, The Netherlands. 15. USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu City, 6000, Philippines. 16. Department of Nutrition and Dietetics, University of San Carlos, Cebu City, 6000, Philippines. 17. Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA. 18. Quantinuum Research LLC, San Diego, CA, 92101, USA. 19. ISGlobal, Institute for Global Health, Barcelona, 08003, Spain. 20. Universitat Pompeu Fabra (UPF), Barcelona, 08003, Spain. 21. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, 28029, Spain. 22. Section of Epidemiology & Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand. 23. Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, WC1N 1EH, UK. 24. Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand. 25. Institute for Molecular Bioscience, University of Queensland, QLD, Australia. 26. Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, SO17 1BJ, UK. 27. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA. 28. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA. 29. Division of Infection Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, M13 9NT, UK. 30. Department of Paediatrics, Imperial College London, London, SW7 2AZ, UK. 31. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK. 32. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, 17671, Greece. 33. National Institute for Health and Welfare, Helsinki, 00271, Finland. 34. Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, 00014, Finland. 35. Folkhälsan Research Center, Helsinki, 00250, Finland. 36. Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK. 37. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK. 38. University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, 4072, Australia. 39. Department of Epidemiology Research, Statens Serum Institute, Copenhagen, DK-2300, Denmark. 40. The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 41. Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 42. Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 43. Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA. 44. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. 45. Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland. 46. Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco, CA 94143, USA. 47. NIHR Exeter Clinical Research Facility, University of Exeter Medical School and Royal Devon and Exeter NHS Foundation Trust, Exeter, EX2 5DW, UK. 48. Department of Medicine, Division of Endocrinology, Metabolism, and Molecular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. 49. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, 45141, Germany. 50. Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for 296 C.M.Middeldorp et al. 1 3 Environmental Health, Neuherberg, Germany. 51. Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital of Ludwig Maximilians University, Munich, Germany. 52. KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, N-5020, Norway. 53. Department of Pediatrics, Haukeland University Hospital, Bergen, 5021, Norway. 54. Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, N-0473, Norway. 55. Department of Paediatrics and Adolescent Medicine, Aarhus University Hospital, Aarhus N, DK-8200, Denmark. 56. Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. 57. Department of Genetics, Harvard Medical School, Boston, MA 02115, USA. 58. Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, 02115, USA. 59. Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA 02215, USA. 60. Diabetes Center, Massachusetts General Hospital, Boston, MA 02114, USA. 61. Department of Medicine, Universite de Sherbrooke, Sherbooke, QC J1K 2R1, Canada. 62. Institute of Nutritional Science, University of Potsdam, Nuthetal, 14558, Germany. 63. The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China. 64. Human Development & Health, Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK. 65. Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK. 66. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LE, UK. 67. RIKEN, Centre for Integrative Medical Sciences, Laboratory for Endocrinology, Metabolism and Kidney diseases, Yokohama, Kanagawa, 230-0045, Japan. 68. Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, SA, 5001, Australia. 69. South Australian Health and Medical Research Institute, Adelaide, SA, 5001, Australia. 70. Department of Obstetrics and Gynecology, Sahlgrenska Academy, University of Gothenburg, Diagnosvägen 15, SE-416 85 Gothenburg, Sweden. 71. Biocenter Oulu, University of Oulu, Oulu, 90220, Finland. 72. Unit of Primary Care, Oulu University Hospital, Oulu, 90220, Finland. 73. Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, FI-90014, Finland. 74. Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Middlesex, UB8 3PH, UK. 75. Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway. 76. Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA. 77. Observational & Pragmatic Research Institute Pte Ltd, Singapore, Singapore. 78. Pediatric Research Center, Department of Women´s & Child Health, University of Leipzig, Leipzig, 04109, Germany. 79. IFB Adiposity Diseases, University of Leipzig, Leipzig, 04109, Germany. 80. Department of Child Health, School of Medicine, Cardiff Univeristy, Cardiff, CF10 3AT, UK. 81. Danish Pediatric Asthma Center, Copenhagen University Hospital, Gentofte, DK-2100, Denmark. 82. Institute of Social and Preventive Medicine, Lausanne University Hospital (CHUV), Lausanne, 1011, Switzerland. 83. Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland. 84. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, 70210, Finland. 85. Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland. 86. Division of Endocrinology, Department of Medicine, Creighton University, Omaha, NE 68178, USA. 87. Bristol NIHR Biomedical Research Centre, Bristol, UK. 88. Department of Clinical Chemistry, Fimlab Laboratories, Tampere University Hospital, Tampere, Finland. 89. Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33520, Finland. 90. Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK. 91. The Broad Institute of Harvard and MIT, Cambridge, USA. 92. Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Frederiksberg, 2000, Denmark. 93. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 94. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China. 95. Li Ka ShingInstitute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China. 96. Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China. 97. Estonian Genome Center, University of Tartu, Tartu, 50090, Estonia. 98. Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, N-0403, Norway. 99. Centre for Genetic Origins of Health and Disease (GOHaD), The University of Western Australia, Crawley, WA, 6000, Australia. 100. Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LE, UK. 101. Department of Medicine, Stanford School of Medicine, Stanford, CA 94305, USA. 102. Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA. 103. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands. 104. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands. 105. Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Copenhagen, DK-1014, Denmark. 106. Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK. 107. Department of Pediatrics, University of Iowa, Iowa City, IA 52242, USA. 108. Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, N-0403, Norway. 109. Research Unit forGynaecology andObstetrics, Institute ofClinical Research, University ofSouthern Denmark, Odense, DK-5000, Denmark. 110. William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK. 111. Medical Research Council (MRC), Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK. 112. Division of Pediatric Endocrinology, Diabetes, and Metabolism, Department of Pediatrics, Columbia University Medical Center, New York, NY 10032, USA. 113. Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. 114. Department of Paediatrics, University of Cambridge, Cambridge, CB2 0QQ, UK. 115. Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1HH, UK. 116. School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Callaghan, NSW, 2308, Australia. 117. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20014, Finland. 118. Max Planck Institute for Psycholinguistics, Nijmegen, 6525 XD, The Netherlands. 119. Section of Genomics of Common Disease, Department of Medicine, Imperial College London, London, SW7 2AZ, UK. 120. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20520, Finland. 121. BHF Centre for Cardiovascular Science, University of Edinburgh, Queen’s Medical Research Institute, Edinburgh, EH16 4TJ, UK. 122. Department of Psychology, Mid Sweden University, Östersund, SE-831 25, Sweden. 123. Department of Medicine, Division of Endocrinology, Boston Children’s Hospital, Boston, MA 02115, USA. 124. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. 125. Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA 02115, USA. 126. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, 119077, Singapore. 127. Singapore Eye Research Institute, Singapore, 168751, Singapore. 128. Department of Genomics of Complexe Diseases, Imperial College, London, UK. 129. Department of Epidemiology, Cancer Center, University of Hawaii (Manoa), Honolulu, Hawaii, 96813, USA. 130. Department of Obstetrics and Gynecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 131. 297 The Early Growth Genetics (EGG) andEArly Genetics andLifecourse Epidemiology (EAGLE)… 1 3 Population Health Research Institute, St George’s University of London, London, SW17 0RE, UK. 132. IMIM (Hospital del Mar Medical Research Institute), Barcelona, 08003, Spain. 133. Department of Statistics and Applied Probability, National University of Singapore, Singapore, 117546, Singapore. 134. Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore. 135. Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, 80337, Germany. 136. European Centre for Environment and Human Health, University of Exeter, Truro, TR1 3HD, UK. 137. Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 138. Department of Public Health, Amsterdam Public Health Research Institute, Academic Medical Center (AMC), University of Amsterdam, Amsterdam, 1105 AZ, The Netherlands. 139. Institute of Medical Statistics and Epidemiology, Technical University Munich, Munich, D-80333, Germany. 140. Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, 81377, Germany. 141. Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9AG, UK. 142. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. 143. Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, UK. 144. Department of Clinical Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands, 1105 AZ, The Netherlands. 145. Division of Gastroenterology, Hepatology and Nutrition, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA. EAGLE Membership Members as of July 2018 are listed in alphabetical order. Tarunveer S Ahluwalia1,3, Juha Auvinen4, Meike Bartels5,6,7, Jose Ramon Bilbao8,9,10, Hans Bisgaard1, Klaus Bønnelykke1, Dorret I Boomsma5,6,7,11, Jonathan P Bradfield12,13, Mariona Bustamante14,15,16, Zhanghua Chen17, John A Curtin18, Adnan Custovic19, George Davey Smith20,21, Gareth E Davies22, Liesbeth Duijts23,24,25, Peter R Eastwood26,27, Anders U Eliasen28,29, Xavier Estivill30, David M Evans20,21,31, Iryna O Fedko5,6, Janine F Felix23,32,33, W James Gauderman17, Frank Gilliland17, Raquel Granell21, Struan FA Grant12,34,35,36, Monica Guxens14,15,16, Hakon Hakonarson12,34,35, Catharina A Hartman37, Joachim Heinrich38,39, Anjali K Henders40, John Henderson21, Patrick Holt41, Jouke-Jan Hottenga5,6,7, Elina Hyppönen42,43,44, Carmen Iñíguez16,45,46, Bo Jacobsson47,48, Vincent WV Jaddoe23,32,33, Marjo-Riitta Järvelin4,49,50,51,52, Astanand Jugessur53,54,55, Mika Kähönen56,57, Jaakko Kaprio58,59, Ville Karhunen60, John P Kemp20,21,31, Gerard H Koppelman61, Ashish Kumar62,63, Jari Lahti64,65, Henrik Larsson66,67, Debbie A Lawlor20,21,68, Terho Lehtimäki69,70, Jin Li12, Paul Lichtenstein67, Sebastian Lundström71,72, Leo-Pekka Lyytikäinen69,70, Per Magnus54, Abdullah A Mamun73, Minna Mannikko4, Nicholas G Martin74, Hamdi Mbarek5,6,7,11, Sarah E Medland74, Erik Melén62,75, Christel M Middeldorp6,76,77, Jackob M Najman78, Michel G Nivard5,6, Ilja M Nolte79, Albertine J Oldehinkel37, Katja Pahkala80,81, Teemu Palviainen58, Lavinia Paternoster20,21, Craig E Pennell82, Göran Pershagen62,83, Niina Pitkänen80, Robert Plomin84, Beate St Pourcain20,85, Christine Power44, Lea Pulkkinen86, Katri Räikkönen65, Olli T Raitakari80,87, Rebecca C Richmond20,21, Fernando Rivadeneira23,32,88, Richard J Rose89, Loreto Santa-Marina16,90,91, James G Scott78,92,93, Sylvain Sebert4,49,50,94, Saskia Selzam84, Angela Simpson18, Patrick MA Sleiman12,35, Harold Snieder79, Marie Standl38, Camilla Stoltenberg95, David P Strachan96, Leon Straker97, Timo Strandberg4,98,99, Jordi Sunyer14,15,16,100, Elisabeth Thiering38,101, Henning Tiemeier102,103, Nicholas J Timpson20,21, Maties Torrent16,104,105, André G Uitterlinden23,32,88, Toos van Beijsterveldt5,6, Peter J van der Most79, Cornelia M van Duijn32, Jorma Viikari106,107, Natalia Vilor-Tejedor108,109, Judith M Vonk79,110, Tanja GM Vrijkotte111, Eero Vuoksimaa58, Carol A Wang82, Andrew JO Whitehouse41, Gonneke Willemsen5,6,7, Gail M Williams78, Naomi R Wray40,112, Shujing Xu17, Cheng-Jian Xu61, Lu Yi67, Mohammad Hadi Zafarmand111,113 1. COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, 2900 Hellerup, Denmark. 2. Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2200, Denmark. 3. Steno Diabetes Center Copenhagen, Gentofte, 2820, Denmark. 4. Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, FI-90014, Finland. 5. Amsterdam Public Health, Amsterdam, The Netherlands. 6. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, 1081 BT, The Netherlands. 7. Netherlands Twin Register, Department of Biological Psychology, VU University, Amsterdam, 1081 HV, The Netherlands. 8. University of the Basque Country (UPV/ EHU), Spain. 9. Biocruces Health Research Institute, Spain. 10. CIBER Diebetes y Enfermedades Metabólicas (CIBERDEM), Spain. 11. Amsterdam Reproduction and Development, Amsterdam, The Netherlands. 12. Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA. 13. Quantinuum Research LLC, San Diego, CA, 92101, USA. 14. ISGlobal, Institute for Global Health, Barcelona, 08003, Spain. 15. Universitat Pompeu Fabra (UPF), Barcelona, 08003, Spain. 16. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, 28029, Spain. 17. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. 18. Division of Infection Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, M13 9NT, UK. 19. Department of Paediatrics, Imperial College London, London, SW7 2AZ, UK. 20. Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK. 21. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK. 22. Avera Institute for Human Genetics, Sioux Falls, South Dakota, USA. 23. The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 24. Department of Pediatrics, Division of Respiratory Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. 25. Department of Pediatrics, Division of Neonatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. 26. School of Human Sciences, The University of Western Australia, WA, Australia. 27. West Australian Sleep Disorders Research Institute, Department of Pulmonary Physiology & Sleep Medicine, Sir Charles Gairdner Hospital, WA, Australia. 28. Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Denmark. 29. Department of Bio and Health Informatics, Technical University of Denmark, Denmark. 30. Sidra Medicine Research Center, Sidra Medicine, Doha, Qatar. 31. University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, 4072, Australia. 32. Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 33. Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 34. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA. 35. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. 36. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA. 37. Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 38. Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. 39. Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital of Ludwig 298 C.M.Middeldorp et al. 1 3 Maximilians University, Munich, Germany. 40. Institute for Molecular Bioscience, University of Queensland, QLD, Australia. 41. Telethon Kids Institute (TKI), The University of Western Australia, WA, Australia. 42. Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, SA, 5001, Australia. 43. South Australian Health and Medical Research Institute, Adelaide, SA, 5001, Australia. 44. Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, WC1N 1EH, UK. 45. Department of Statistics and Computational Research, Universitat de València, Spain. 46. Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Spain. 47. Department of Obstetrics and Gynecology, Sahlgrenska Academy, University of Gothenburg, Diagnosvägen 15, SE-416 85 Gothenburg, Sweden. 48. Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, N-0473, Norway. 49. Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, W2 1PG, UK. 50. Biocenter Oulu, University of Oulu, Oulu, 90220, Finland. 51. Unit of Primary Care, Oulu University Hospital, Oulu, 90220, Finland. 52. Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Middlesex, UB8 3PH, UK. 53. Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, N-0403, Norway. 54. Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, N-0403, Norway. 55. Department of Global Public Health and Primary Care, University of Bergen, Norway. 56. Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland. 57. Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland. 58. Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland. 59. Department of Public Health, University of Helsinki, Helsinki, Finland. 60. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. 61. University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children’s Hospital, GRIAC Research Institute Groningen, Groningen, The Netherlands. 62. Institute of Environmental Medicine, Karolinska Institutet, Sweden. 63. Department of Public Health Epidemiology, Unit of Chronic Disease Epidemiology, Swiss Tropical and Public Health Institute, Basel, University of Basel, Switzerland. 64. Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland. 65. Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 66. School of Medicine Sciences, Örebro University, Sweden. 67. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden. 68. Bristol NIHR Biomedical Research Centre, Bristol, UK. 69. Department of Clinical Chemistry, Fimlab Laboratories, Tampere University Hospital, Tampere, Finland. 70. Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33520, Finland. 71. Centre for Ethics, Law and Mental Health, University of Gothenburg, Sweden. 72. Gillberg Neuropsychiatry Centre, University of Gothenburg, Sweden. 73. Institute for Social Science Research, University of Queensland, QLD, Australia. 74. QIMR Berghofer Medical Research Institute, QLD, Australia. 75. Sachs’ Children’s Hospital, Sweden. 76. Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia. 77. Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, Brisbane, QLD, Australia. 78. School of Public Health, The University of Queensland, QLD, Australia. 79. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 80. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20014, Finland. 81. Paavo Nurmi Centre, Sports and Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland. 82. School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Callaghan, NSW, 2308, Australia. 83. Centre for Occupational and Environmental Medicine, Stockholm County Council, Sweden. 84. MRC Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK. 85. Max Planck Institute for Psycholinguistics, Nijmegen, 6525 XD, The Netherlands. 86. Department of Psychology, University of Jyväskylä, Jyväskylä, Finland. 87. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20520, Finland. 88. Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, 3015 CE, The Netherlands. 89. Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA. 90. Subdirección de Salud Pública y Adicciones de Gipuzkoa, San Sebastián, Spain. 91. Instituto de Investigación Sanitaria Biodonostia, San Sebastián, Spain. 92. Metro North Mental Health Service, QLD, Australia. 93. Queensland Centre for Mental Health Research, QLD, Australia. 94. Department of Genomics of Complexe Diseases, Imperial College, London, UK. 95. Norwegian Institute of Public Health, Norway. 96. Population Health Research Institute, St George’s University of London, London, SW17 0RE, UK. 97. School of Physiotherapy and Exercise Science, Faculty of Health Sciences, Curtin University, Perth, WA, Australia. 98. Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland. 99. Clinicum, University of Helsinki, Helsinki, Finland. 100. IMIM (Hospital del Mar Medical Research Institute), Barcelona, 08003, Spain. 101. Divisionof Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, 80337, Germany. 102. Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands. 103. Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Harvard University, Boston, USA. 104. ib-salut, Area de Salut de Menorca, Spain. 105. Fundació Institut d’Investigació Sanitària Illes Balears – IdISBa, Spain. 106. Department of Medicine, University of Turku, Turku, Finland. 107. Division of Medicine, Turku University Hospital, Turku, Finland. 108. Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Spain. 109. Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. 110. Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands. 111. Department of Public Health, Amsterdam Public Health Research Institute, Academic Medical Center (AMC), University of Amsterdam, Amsterdam, 1105 AZ, The Netherlands. 112. Queensland Brain Institute, University of Queensland, QLD, Australia. 113. Department of Clinical Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands, 1105 AZ, The Netherlands. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. References 1. Global Burden of Disease Pediatrics Collaboration. Global and national burden of diseases and injuries among children and adolescents between 1990 and 2013: findings from the global burden of disease 2013 study. 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