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GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.

ABSTRACT: Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

SUBMITTER: Couto Alves A

PROVIDER: S-EPMC6904961 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.

Couto Alves Alexessander A De Silva N Maneka G NMG Karhunen Ville V Sovio Ulla U Das Shikta S Taal H Rob HR Warrington Nicole M NM Lewin Alexandra M AM Kaakinen Marika M Cousminer Diana L DL Thiering Elisabeth E Timpson Nicholas J NJ Bond Tom A TA Lowry Estelle E Brown Christopher D CD Estivill Xavier X Lindi Virpi V Bradfield Jonathan P JP Geller Frank F Speed Doug D Coin Lachlan J M LJM Loh Marie M Barton Sheila J SJ Beilin Lawrence J LJ Bisgaard Hans H Bønnelykke Klaus K Alili Rohia R Hatoum Ida J IJ Schramm Katharina K Cartwright Rufus R Charles Marie-Aline MA Salerno Vincenzo V Clément Karine K Claringbould Annique A J AAJ van Duijn Cornelia M CM Moltchanova Elena E Eriksson Johan G JG Elks Cathy C Feenstra Bjarke B Flexeder Claudia C Franks Stephen S Frayling Timothy M TM Freathy Rachel M RM Elliott Paul P Widén Elisabeth E Hakonarson Hakon H Hattersley Andrew T AT Rodriguez Alina A Banterle Marco M Heinrich Joachim J Heude Barbara B Holloway John W JW Hofman Albert A Hyppönen Elina E Inskip Hazel H Kaplan Lee M LM Hedman Asa K AK Läärä Esa E Prokisch Holger H Grallert Harald H Lakka Timo A TA Lawlor Debbie A DA Melbye Mads M Ahluwalia Tarunveer S TS Marinelli Marcella M Millwood Iona Y IY Palmer Lyle J LJ Pennell Craig E CE Perry John R JR Ring Susan M SM Savolainen Markku J MJ Rivadeneira Fernando F Standl Marie M Sunyer Jordi J Tiesler Carla M T CMT Uitterlinden Andre G AG Schierding William W O'Sullivan Justin M JM Prokopenko Inga I Herzig Karl-Heinz KH Smith George Davey GD O'Reilly Paul P Felix Janine F JF Buxton Jessica L JL Blakemore Alexandra I F AIF Ong Ken K KK Jaddoe Vincent W V VWV Grant Struan F A SFA Sebert Sylvain S McCarthy Mark I MI Järvelin Marjo-Riitta MR

Science advances 20190904 9

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap ...[more]

PMID: 31840077

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Project description:Background: The prevalence of obesity and cardiometabolic diseases continues to rise globally and obesity is a significant risk factor for cardiometabolic diseases. However, to our knowledge, evidence of the relative roles of genes and the environment underlying obesity and cardiometabolic disease traits and the correlations between them are still lacking, as is how they change with age. Method: Data were obtained from the Chinese National Twin Registry (CNTR). A total of 1421 twin pairs were included. Univariate structural equation models (SEMs) were performed to evaluate the heritability of BMI and cardiometabolic traits, which included blood hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). Bivariate SEMs were used to assess the genetic/environmental correlations between them. The study population was divided into three groups for analysis: ≤50, 51–60, and >60 years old to assess the changes in heritability and genetic/environmental correlations with ageing. Results: Univariate SEMs showed a high heritability of BMI (72%) and cardiometabolic traits, which ranged from 30% (HbA1c) to 69% (HDL-C). With age increasing, the heritability of all phenotypes has different degrees of declining trends. Among these, BMI, SBP, and DBP presented significant monotonous declining trends. The bivariate SEMs indicated that BMI correlated with all cardiometabolic traits. The genetic correlations were estimated to range from 0.14 (BMI and LDL-C) to 0.39 (BMI and DBP), while the environmental correlations ranged from 0.13 (BMI and TC/LDL-C) to 0.31 (BMI and TG). The genetic contributions underlying the correlations between BMI and SBP and DBP, TC, TG, and HDL-C showed a progressive decrease as age groups increased. In contrast, environmental correlations displayed a significant increasing trend for HbA1c, SBP, and DBP. Conclusions: The findings suggest that genetic and environmental factors have essential effects on BMI and all cardiometabolic traits. However, as age groups increased, genetic influences presented varying degrees of decrement for BMI and most cardiometabolic traits, suggesting the increasing importance of environments. Genetic factors played a consistently larger role than environmental factors in the phenotypic correlations between BMI and cardiometabolic traits. Nevertheless, the relative magnitudes of genetic and environmental factors may change over time.

Project description:BackgroundThe influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.MethodsOur study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.ResultsWe identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.ConclusionsAssociations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

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GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.

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GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.

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