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Additional file 1: Document containing supplementary Figures S1-S7 and supplementary Tables S1-S9. Fig. S1. Selection of genome-wide significant loci and overlapping eQTL/sQTL features for colocalization testing. Fig. S2. Example LocusCompare plots for each quartile of the CLPP-mod score. Fig. S3. Characteristics of candidate and colocalized genes. Fig. S4. Three separate loci with multiple colocalized genes. Fig. S5. Effects of increased gene expression on cardiometabolic trait level / risk, according to the alignment of GWAS and eQTL directions at colocalized loci. Fig. S6. All uniquely colocalized genes that are differentially expressed (DE) under at least one perturbation condition. Fig. S7. Proximal interaction networks for each of the monogenic IR or T2D genes that directly interacts with at least one of the uniquely colocalized genes in perturbation conditions. Table S1. GWAS used for colocalization analysis. Table S2. GTEx QTL tissues used for colocalization analysis. Table S3. List of metabolic perturbations and their abbreviations. Table S4. Genetic studies-based IR/T2D genes used in PPI network analysis. Table S5. Monogenic IR/T2D genes used in PPI network analysis. Table S6. Number of candidate/colocalized genes and loci per GWAS. Table S7. Number of candidate/colocalized genes and loci per QTL type / tissue. Table S8. Fraction of tissue-specific, single-gene colocalizations in each tissue / disease combination. Table S9. Integrative summary table for all uniquely colocalized genes at loci with a WHR, TG, and/or HDL colocalization, but not an insulin sensitivity, T2D, fasting glucose, or fasting insulin colocalization.

Background Head circumference is associated with intelligence and tracks from childhood into adulthood. Methods We performed a genome-wide association study meta-analysis and follow-up of head circumference in a total of 29,192 participants between 6 and 30 months of age. Results Seven loci reached genome-wide significance in the combined discovery and replication analysis of which three loci near ARFGEF2, MYCL1, and TOP1, were novel. We observed positive genetic correlations for early-life head circumference with adult intracranial volume, years of schooling, childhood and adult intelligence, but not with adult psychiatric, neurological, or personality-related phenotypes. Conclusions The results of this study indicate that the biological processes underlying early-life head circumference overlap largely with those of adult head circumference. The associations of early-life head circumference with cognitive outcomes across the life course are partly explained by genetics.

Funder: The NIHR Biomedical Research Centre in Cambridge, Funder: Finnish Innovation Fund Sitra, Funder: Foundation for Paediatric Research, Funder: Ministry of Social Affairs and Health of Finland, Funder: Yrjö Jahnsson Foundation, Funder: Research Committee of the Kuopio University Hospital Catchment Area (State Research Funding), Funder: The city of Kuopio, Funder: The UK Medical Research Council, Funder: Finnish Cultural Foundation, Funder: Ministry of Education and Culture of Finland, Funder: Juho Vainio Foundation, Funder: Paavo Nurmi Foundation, Funder: Diabetes Research Foundation in Finland, Funder: Finnish Foundation for Cardiovascular Research, Funder: Social Insurance Institution of Finland, Aims/hypothesis: We studied for the first time the long-term effects of a combined physical activity and dietary intervention on insulin resistance and fasting plasma glucose in a general population of predominantly normal-weight children. Methods: We carried out a 2 year non-randomised controlled trial in a population sample of 504 children aged 6–9 years at baseline. The children were allocated to a combined physical activity and dietary intervention group (306 children at baseline, 261 children at 2-year follow-up) or a control group (198 children, 177 children) without blinding. We measured fasting insulin and fasting glucose, calculated HOMA-IR, assessed physical activity and sedentary time by combined heart rate and body movement monitoring, assessed dietary factors by a 4 day food record, used the Finnish Children Healthy Eating Index (FCHEI) as a measure of overall diet quality, and measured body fat percentage (BF%) and lean body mass by dual-energy x-ray absorptiometry. The intervention effects on insulin, glucose and HOMA-IR were analysed using the intention-to-treat principle and linear mixed-effects models after adjustment for sex, age at baseline, and pubertal status at baseline and 2 year follow-up. The measures of physical activity, sedentary time, diet and body composition at baseline and 2 year follow-up were entered one-by-one as covariates into the models to study whether changes in these variables might partly explain the observed intervention effects. Results: Compared with the control group, fasting insulin increased 4.65 pmol/l less (absolute change +8.96 vs +13.61 pmol/l) and HOMA-IR increased 0.18 units less (+0.31 vs +0.49 units) over 2 years in the combined physical activity and dietary intervention group. The intervention effects on fasting insulin (regression coefficient β for intervention effect −0.33 [95% CI −0.62, −0.04], p = 0.026) and HOMA-IR (β for intervention effect −0.084 [95% CI −0.156, −0.012], p = 0.023) were statistically significant after adjustment for sex, age at baseline, and pubertal status at baseline and 2 year follow-up. The intervention had no effect on fasting glucose, BF% or lean body mass. Changes in total physical activity energy expenditure, light physical activity, moderate-to-vigorous physical activity, total sedentary time, the reported consumption of high-fat (≥60%) vegetable oil-based spreads, and FCHEI, but not a change in BF% or lean body mass, partly explained the intervention effects on fasting insulin and HOMA-IR. Conclusions/interpretation: The combined physical activity and dietary intervention attenuated the increase in insulin resistance over 2 years in a general population of predominantly normal-weight children. This beneficial effect was partly mediated by changes in physical activity, sedentary time and diet but not changes in body composition. Trial registration: ClinicalTrials.gov NCT01803776 Graphical 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.

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease.

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., We are grateful to all families and participants who took part in these studies. We also acknowledge and appreciate the unique efforts of the research teams and practitioners contributing to the collection of this wealth of data. C. M. M. is supported by funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 721567. J. F. F. has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 633595 (DynaHEALTH) and 733206 (LifeCycle). R. M. F. and R. N. B. are supported by Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150). D. L. C. is funded by the American Diabetes Association Grant 1-17-PDF-077. D. O. M-K. was supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023). N. M. W. is supported by an Australian National Health and Medical Research Council Early Career Fellowship (APP1104818). T. S. A. was partially funded by the Gene-Diet Interactions in Obesity (GENDINOB) project on behalf of GOYA male cohort data management and analyses and acknowledges the same. S. D. was supported by National Institute of Health Research. T. M. F. is supported by the European Research Council grant: 323195 SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk).H.H.is funded by The Children's Hospital of Philadelphia Endowed Chair in Genomic Research. A. T. H. is supported by the Wellcome Trust Senior Investigator Awards (WT098395), National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611-10219). J. Hebebrand received grants from German Research Society, German Ministry of Education and Research. M-F. H. is currently supported by an American Diabetes Association (ADA) Pathway Program Accelerator Early Investigator Award (1-15-ACE-26). S. J. is supported by Helse Vest no. 23929, Bergen Forskningsstiftelse and KG Jebsen Foundation and University of Bergen. J. K. has been supported by the Academy of Finland Research Professor program (grants 265240 & 263278). J. P. K. is funded by a University of Queensland Development Fellowship (UQFEL1718945). H. L. has served as a speaker for Eli-Lilly and Shire and has received research grants from Shire; all outside the submitted work. C. M. L is supported by the Li Ka Shing Foundation, WT-SSI/John Fell funds and by the NIHR Biomedical Research Centre, Oxford, by Widenlife and NIH (5P50HD028138-27). S. E. M. was funded by an NHMRC Senior Reseach Fellowship (APP1103623). K. Panoutsopoulou is funded by a career development fellowship (grant 20308) and by the Wellcome Trust (WT098051). C. P. at UCL Institute of Child Health, with support from the National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. I. P. was funded in part by the Wellcome Trust (WT205915), and the European Union's Horizon 2020 research, the European Union FP7-IDEAS-ERC Advanced Grant (GEPIDIAB, ERC-AG -ERC-294785), and innovation programme (DYNAhealth, H2020-PHC-2014-633595). R. C. R. is supported by CRUK (grant number C18281/A19169). J. G. S. is supported by an NHMRC Practitioner Fellowship Grant (APP1105807). J. T.; r is funded by the European Regional Development Fund (ERDF), the European Social Fund (ESF), Convergence Programme for Cornwall and the Isles of Scilly and the Diabetes Research and Wellness Foundation Non-Clinical Fellowship. N. V-T. is funded by a pre-doctoral grant from the Agencia de Gestio d'Ajuts Universitaris i de Recerca (AGAUR) (2015 FI_B 00636), Generalitat de Catalunya. T. G. M. V was supported by ZonMW (TOP 40-00812-98-11010. J. F. W. is supported by the MRC Human Genetics Unit quinquennial programme QTL in Health and Disease. H. Y. is funded by Diabetes UK RD Lawrence fellowship (grant: 17/0005594). M. H. Z was supported by BBMRI-NL (CP2013-50). E. Z. is supported by the Wellcome Trust (098051). B. F. is supported by Novo Nordisk Foundation (12955) and an Oak Foundation Fellowship. S. S. and M-R. J. have received funding from the European Union's Horizon 2020 research and innovation programme [under grant agreement No 633595] for the DynaHEALTH action. P. R. N. was supported by the European Research Council (ERC), University of Bergen, KG Jebsen and Helse Vest. G. D. S. works within the MRC Integrative Epidemiology Unit at the University of Bristol (MC_UU_12013/1). D. A. L was supported by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement (Grant number 669545; DevelopObese), US National Institute of Health (grant: R01 DK10324), the UK Medical Research Council (grant: MC_UU_00011/6), Wellcome Trust GWAS grant (WT088806), an NIHR Senior Investigator Award (NF-SI-0611-10196) and the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. L. Paternoster was supported by the UK Medical Research Council Unit grants MC_UU_12013_5. N. J. T. is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 102215/2/13/2), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC) and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). V.W.V.J. received an additional grant from the Netherlands Organization for Health Research and Development (NWO, ZonMw-VIDI 016.136.361), a European Research Council Consolidator Grant (ERC-2014-CoG-648916) and funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 633595 (DynaHEALTH) and 733206 (LifeCycle). D. M. E. is funded by the UK Medical Research Council Unit grant MC_UU_12013_4, Australian Research Council Future Fellowship (FT130101709) and a NHMRC Senior Research Fellowship (GNT1137714). S. F. A. G. is funded by the Daniel B. Burke Endowed Chair for Diabetes Research and R01 HD056465. D. I. B. is supported by Spinozapremie (NWO-56-464-14192) and the Royal Netherlands Academy of Science Professor Award (PAH/6635) to DIB. M. I. M. is a Wellcome Senior Investgator and NIHR Senior Investigator supported by the Wellcome (090532, 098381, 203141), NIHR (NF-SI-0617-10090) and the US National Institute of Health (grant: R01 DK10324), the UK Medical Research Council (grant: MCiabetes UK RD Lawrence fellowship (grant: 17/0005594). M. H. Z was supported by BBMRI-NLNIHR Biomedical Research Centre, Oxford. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.

OBJECTIVE: This study aimed to investigate the effect of a genetic risk score (GRS) comprising 15 single-nucleotide polymorphisms, previously shown to associate with childhood BMI, on the baseline cardiometabolic traits and the response to a lifestyle intervention in Danish children and adolescents. METHODS: Children and adolescents with overweight or obesity (n = 920) and a population-based control sample (n = 698) were recruited. Anthropometric and biochemical measures were obtained at baseline and in a subgroup of children and adolescents with overweight or obesity again after 6 to 24 months of lifestyle intervention (n = 754). The effects of the GRS were examined by multiple linear regressions using additive genetic models. RESULTS: At baseline, the GRS associated with BMI standard deviation score (SDS) both in children and adolescents with overweight or obesity (β = 0.033 [SE = 0.01]; P = 0.001) and in the population-based sample (β = 0.065 [SE = 0.02]; P = 0.001). No associations were observed for cardiometabolic traits. The GRS did not influence changes in BMI SDS or cardiometabolic traits following lifestyle intervention. CONCLUSIONS: A GRS for childhood BMI was associated with BMI SDS but not with other cardiometabolic traits in Danish children and adolescents. The GRS did not influence treatment response following lifestyle intervention.