Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits

The genetic background of childhood body mass index (BMI), and the extent to which the well-known associations of childhood BMI with adult diseases are explained by shared genetic factors, are largely unknown. We performed a genome-wide association study meta-analysis of BMI in 61,111 children aged between 2 and 10 years. Twenty-five independent loci reached genome-wide significance in the combined discovery and replication analyses. Two of these, located nearNEDD4LandSLC45A3, have not previously been reported in relation to either childhood or adult BMI. Positive genetic correlations of childhood BMI with birth weight and adult BMI, waist-to-hip ratio, diastolic blood pressure and type 2 diabetes were detected (R(g)ranging from 0.11 to 0.76, P-values Author summary Although twin studies have shown that body mass index (BMI) is highly heritable, many common genetic variants involved in the development of BMI have not yet been identified, especially in children. We studied associations of more than 40 million genetic variants with childhood BMI in 61,111 children aged between 2 and 10 years. We identified 25 genetic variants that were associated with childhood BMI. Two of these have not been implicated for BMI previously, located close to the genesNEDD4LandSLC45A3. We also show that the genetic background of childhood BMI overlaps with that of birth weight, adult BMI, waist-to-hip-ratio, diastolic blood pressure, type 2 diabetes, and age at menarche. Our results suggest that the biological processes underlying childhood BMI largely overlap with those underlying adult BMI. However, the overlap is not complete. Additionally, the genetic backgrounds of childhood BMI and other cardio-metabolic phenotypes are overlapping. This may mean that the associations of childhood BMI and later cardio-metabolic outcomes are partially explained by shared genetics, but it could also be explained by the strong association of childhood BMI with adult BMI.Peer reviewe

The LifeCycle Project-EU Child Cohort Network : a federated analysis infrastructure and harmonized data of more than 250,000 children and parents

Early life is an important window of opportunity to improve health across the full lifecycle. An accumulating body of evidence suggests that exposure to adverse stressors during early life leads to developmental adaptations, which subsequently affect disease risk in later life. Also, geographical, socio-economic, and ethnic differences are related to health inequalities from early life onwards. To address these important public health challenges, many European pregnancy and childhood cohorts have been established over the last 30 years. The enormous wealth of data of these cohorts has led to important new biological insights and important impact for health from early life onwards. The impact of these cohorts and their data could be further increased by combining data from different cohorts. Combining data will lead to the possibility of identifying smaller effect estimates, and the opportunity to better identify risk groups and risk factors leading to disease across the lifecycle across countries. Also, it enables research on better causal understanding and modelling of life course health trajectories. The EU Child Cohort Network, established by the Horizon2020-funded LifeCycle Project, brings together nineteen pregnancy and childhood cohorts, together including more than 250,000 children and their parents. A large set of variables has been harmonised and standardized across these cohorts. The harmonized data are kept within each institution and can be accessed by external researchers through a shared federated data analysis platform using the R-based platform DataSHIELD, which takes relevant national and international data regulations into account. The EU Child Cohort Network has an open character. All protocols for data harmonization and setting up the data analysis platform are available online. The EU Child Cohort Network creates great opportunities for researchers to use data from different cohorts, during and beyond the LifeCycle Project duration. It also provides a novel model for collaborative research in large research infrastructures with individual-level data. The LifeCycle Project will translate results from research using the EU Child Cohort Network into recommendations for targeted prevention strategies to improve health trajectories for current and future generations by optimizing their earliest phases of life.Peer reviewe

Analysis of DNA methylation at birth and in childhood reveals changes associated with season of birth and latitude

This is the final version. Available from BMC via the DOI in this record. Individual cohort-level data can be obtained from the respective cohort (see Additional file 1: Table S1 and Additional file 2 for cohort details).BACKGROUND: Seasonal variations in environmental exposures at birth or during gestation are associated with numerous adult traits and health outcomes later in life. Whether DNA methylation (DNAm) plays a role in the molecular mechanisms underlying the associations between birth season and lifelong phenotypes remains unclear. METHODS: We carried out epigenome-wide meta-analyses within the Pregnancy And Childhood Epigenetic Consortium to identify associations of DNAm with birth season, both at differentially methylated probes (DMPs) and regions (DMRs). Associations were examined at two time points: at birth (21 cohorts, N = 9358) and in children aged 1-11 years (12 cohorts, N = 3610). We conducted meta-analyses to assess the impact of latitude on birth season-specific associations at both time points. RESULTS: We identified associations between birth season and DNAm (False Discovery Rate-adjusted p values < 0.05) at two CpGs at birth (winter-born) and four in the childhood (summer-born) analyses when compared to children born in autumn. Furthermore, we identified twenty-six differentially methylated regions (DMR) at birth (winter-born: 8, spring-born: 15, summer-born: 3) and thirty-two in childhood (winter-born: 12, spring and summer: 10 each) meta-analyses with few overlapping DMRs between the birth seasons or the two time points. The DMRs were associated with genes of known functions in tumorigenesis, psychiatric/neurological disorders, inflammation, or immunity, amongst others. Latitude-stratified meta-analyses [higher (≥ 50°N), lower (< 50°N, northern hemisphere only)] revealed differences in associations between birth season and DNAm by birth latitude. DMR analysis implicated genes with previously reported links to schizophrenia (LAX1), skin disorders (PSORS1C, LTB4R), and airway inflammation including asthma (LTB4R), present only at birth in the higher latitudes (≥ 50°N). CONCLUSIONS: In this large epigenome-wide meta-analysis study, we provide evidence for (i) associations between DNAm and season of birth that are unique for the seasons of the year (temporal effect) and (ii) latitude-dependent variations in the seasonal associations (spatial effect). DNAm could play a role in the molecular mechanisms underlying the effect of birth season on adult health outcomes.Wellcome TrustBiotechnology and Biological Sciences Research Council (BBSRC)Biotechnology and Biological Sciences Research Council (BBSRC)European Union’s Horizon 2020Economic and Social Research Council (ESRC)Medical Research Council (MRC)Medical Research Council (MRC)European UnionSwedish foundation for strategic research (SSF)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)Environmental Protection Agency (EPA)National Cancer Institute Cancer CenterNational Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)Environmental Protection Agency (EPA)Environmental Protection Agency (EPA)European UnionEuropean UnionEuropean UnionEuropean UnionEuropean Union’s Horizon 2020European Research Council (ERC)German Ministry of Education and ResearchNational Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)Autism SpeaksNational Institutes of Health (NIH)National Institutes of Health (NIH)European UnionEuropean UnionEuropean UnionEuropean UnionEuropean UnionEuropean UnionEuropean UnionEuropean UnionEuropean UnionEuropean Research Council (ERC)Flemisch Scientific Research CouncilFlemisch Scientific Research CouncilFlemisch Scientific Research CouncilEuropean UnionFonds de recherche du Québec - Santé (FRQS)Canadian Institute of Health Research (CIHR)Canadian Institute of Health Research (CIHR)Netherlands Organisation for Scientific Research (NWO)National Institute of Child and Human DevelopmentEuropean Union’s Horizon 2020European Union’s Horizon 2020European Union’s Horizon 2020ZonMwZonMwMedical Research Council Integrative Epidemiology Unit (University of Bristol)Netherlands Heart FoundationNetherlands Heart FoundationNetherlands Organisation for Scientific Research (NWO)European UnionNational Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)Spanish Ministry of ScienceNational Institute for Health and Care Research (NIHR)Wellcome TrustNorwegian Ministry of Health and the Ministry of Education and ResearchNorwegian Ministry of Health and the Ministry of Education and ResearchNorwegian Ministry of Health and the Ministry of Education and ResearchLithuanian Agency for Science Innovation and TechnologySpanish Ministry of HealthSpanish Ministry of HealthSpanish Ministry of HealthSpanish Ministry of HealthSpanish Ministry of HealthInstituto de Salud Carlos IIIInstituto de Salud Carlos IIIEuropean Research Council (ERC)CDMRP/Department of DefenseNIGMSNational Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Asthma Campaign, UKNational Institutes of Health (NIH)Medical Research Council (MRC)National Institutes of Health (NIH)Norwegian Research CouncilNational Institute of Environmental Health SciencesResearch Council of NorwayNational Institute of Environmental Health SciencesNational Institute of Environmental Health SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Environmental Health SciencesNational Institute of Environmental Health SciencesSwedish Research CouncilSwedish Initiative for research on Microdata in the Social And Medical Sciences (SIMSAM)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)Medical Research Council Integrative Epidemiology Unit (University of Bristol)Medical Research Council Integrative Epidemiology Unit (University of Bristol)Medical Research Council Integrative Epidemiology Unit (University of Bristol)Swedish Heart-Lung FoundationUniversity of MunichFoundation for Medical Research (FRM)National Agency for ResearchNational Institute for Research in Public HealthFrench Ministry of HealthFrench Ministry of ResearchInserm Bone and Joint Diseases National Research (PRO-A) and Human Nutrition National Research ProgramsParis–Sud UniversityNestléFrench National Institute for Population Health SurveillanceFrench National Institute for Health EducationFrench Agency for Environmental Health SafetyMutuelle Générale de l’Education NationaleFrench National Agency for Food SecurityFrench-speaking association for the study of diabetes and metabolismItalian National Centre for Disease Prevention and ControlItalian Ministry of HealthGreek Ministry of HealthFlemish Government (Department of Economy, Science and Innovations, Agency for Care and Health and Department of Environment)The Research Foundation-FlandersFlemish Institute for Technological ResearchDiabète QuébecErasmus University RotterdamNetherlands Organization for Health Research and Development and the Ministry of Health, Welfare and SportErasmus MCDanish National Research FoundationDanish Regional CommitteesNovo Nordisk FoundationLundbeck FoundationHelmholtz Center for Environmental ResearchGerman Cancer Research CentreAcademy of FinlandEraNetEVOUniversity of Helsinki Research FundsSigne and Ane Gyllenberg foundationEmil Aaltonen FoundationFinnish Medical FoundationJane and Aatos Erkko FoundationJuho Vainio foundationYrjö Jahnsson foundationJalmari and Rauha Ahokas foundationPaivikki and Sakari Sohlberg FoundationSigrid Juselius FoundationSir Jules Thorn Charitable TrustSwedish Asthma and Allergy Association's Research FoundationStiftelsen Frimurare Barnhuset Stockhol

Mendelian randomization analysis does not support causal associations of birth weight with hypertension risk and blood pressure in adulthood

Epidemiology studies suggested that low birthweight was associated with a higher risk of hypertension in later life. However, little is known about the causality of such associations. In our study, we evaluated the causal association of low birthweight with adulthood hypertension following a standard analytic protocol using the study-level data of 183,433 participants from 60 studies (CHARGE-BIG consortium), as well as that with blood pressure using publicly available summary-level genome-wide association data from EGG consortium of 153,781 participants, ICBP consortium and UK Biobank cohort together of 757,601 participants. We used seven SNPs as the instrumental variable in the study-level analysis and 47 SNPs in the summary-level analysis. In the study-level analyses, decreased birthweight was associated with a higher risk of hypertension in adults (the odds ratio per 1 standard deviation (SD) lower birthweight, 1.22; 95% CI 1.16 to 1.28), while no association was found between genetically instrumented birthweight and hypertension risk (instrumental odds ratio for causal effect per 1 SD lower birthweight, 0.97; 95% CI 0.68 to 1.41). Such results were consistent with that from the summary-level analyses, where the genetically determined low birthweight was not associated with blood pressure measurements either. One SD lower genetically determined birthweight was not associated with systolic blood pressure (β = − 0.76, 95% CI − 2.45 to 1.08 mmHg), 0.06 mmHg lower diastolic blood pressure (β = − 0.06, 95% CI − 0.93 to 0.87 mmHg), or pulse pressure (β = − 0.65, 95% CI − 1.38 to 0.69 mmHg, all p > 0.05). Our findings suggest that the inverse association of birthweight with hypertension risk from observational studies was not supported by large Mendelian randomization analyses

Association of Birth Weight With Type 2 Diabetes and Glycemic Traits: A Mendelian Randomization Study

IMPORTANCE Observational studies have shown associations of birth weight with type 2 diabetes (T2D) and glycemic traits, but it remains unclear whether these associations represent causal associations.OBJECTIVE To test the association of birth weight with T2D and glycemic traits using a mendelian randomization analysis.DESIGN, SETTING, AND PARTICIPANTS This mendelian randomization study used a genetic risk score for birth weight that was constructed with 7 genome-wide significant single-nucleotide polymorphisms. The associations of this score with birth weight and T2D were tested in a mendelian randomization analysis using study-level data. The association of birth weight with T2D was tested using both study-level data (7 single-nucleotide polymorphisms were used as an instrumental variable) and summary-level data from the consortia (43 single-nucleotide polymorphismswere used as an instrumental variable). Data from 180 056 participants from 49 studies were included.MAIN OUTCOMES AND MEASURES Type 2 diabetes and glycemic traits.RESULTS This mendelian randomization analysis included 49 studies with 41 155 patients with T2D and 80 008 control participants from study-level data and 34 840 patients with T2D and 114 981 control participants from summary-level data. Study-level data showed that a 1-SD decrease in birth weight due to the genetic risk score was associated with higher risk of T2D among all participants (odds ratio [OR], 2.10; 95% CI, 1.69-2.61; P=4.03 x 10-5), among European participants (OR, 1.96; 95% CI, 1.42-2.71; P=.04), and among East Asian participants (OR, 1.39; 95% CI, 1.18-1.62; P=.04). Similar results were observed from summary-level analyses. In addition, each 1-SD lower birth weight was associated with 0.189 SD higher fasting glucose concentration (beta=0.189; SE=0.060; P=.002), but not with fasting insulin, 2-hour glucose, or hemoglobin A1c concentration.CONCLUSIONS AND RELEVANCE In this study, a genetic predisposition to lower birth weight was associated with increased risk of T2D and higher fasting glucose concentration, suggesting genetic effects on retarded fetal growth and increased diabetes risk that either are independent of each other or operate through alterations of integrated biological mechanisms