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

Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank

The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2-5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10-3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer's risk APOE- E 4 allele (rs429358) was significantly associated with weight loss ( β  = -0.047 kg per yr, s.e. 0.007, P = 2.2 × 10-11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10-4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.

Fetal influence on the human brain through the lifespan

Human fetal development has been associated with brain health at later stages. It is unknown whether growth in utero, as indexed by birth weight (BW), relates consistently to lifespan brain characteristics and changes, and to what extent these influences are of a genetic or environmental nature. Here we show remarkably stable and lifelong positive associations between BW and cortical surface area and volume across and within developmental, aging and lifespan longitudinal samples (N = 5794, 4-82 y of age, w/386 monozygotic twins, followed for up to 8.3 y w/12,088 brain MRIs). In contrast, no consistent effect of BW on brain changes was observed. Partly environmental effects were indicated by analysis of twin BW discordance. In conclusion, the influence of prenatal growth on cortical topography is stable and reliable through the lifespan. This early-life factor appears to influence the brain by association of brain reserve, rather than brain maintenance. Thus, fetal influences appear omnipresent in the spacetime of the human brain throughout the human lifespan. Optimizing fetal growth may increase brain reserve for life, also in aging.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGS BMI -covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge GWAS effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Understanding risk and causal mechanisms for developing obesity in infants and young children: A National Institutes of Health workshop

Obesity in children remains a major public health problem, with the current prevalence in youth ages 2-19 years estimated to be 19.7%. Despite progress in identifying risk factors, current models do not accurately predict development of obesity in early childhood. There is also substantial individual variability in response to a given intervention that is not well understood. On April 29-30, 2021, the National Institutes of Health convened a virtual workshop on "Understanding Risk and Causal Mechanisms for Developing Obesity in Infants and Young Children." The workshop brought together scientists from diverse disciplines to discuss (1) what is known regarding epidemiology and underlying biological and behavioral mechanisms for rapid weight gain and development of obesity and (2) what new approaches can improve risk prediction and gain novel insights into causes of obesity in early life. Participants identified gaps and opportunities for future research to advance understanding of risk and underlying mechanisms for development of obesity in early life. It was emphasized that future studies will require multi-disciplinary efforts across basic, behavioral, and clinical sciences. An exposome framework is needed to elucidate how behavioral, biological, and environmental risk factors interact. Use of novel statistical methods may provide greater insights into causal mechanisms.

Metabolic trade-offs in childhood: Exploring the relationship between language development and body growth

During human childhood, brain development and body growth compete for limited metabolic resources, resulting in a trade-off where energy allocated to brain development can decrease as body growth accelerates. This preregistered study explores the relationship between language skills, serving as a proxy for brain development, and body mass index at three distinct developmental stages, representing different phases of body growth. Longitudinal data from 2002 children in the EDEN mother-child cohort were analyzed using structural equation modeling. Our findings reveal a compelling pattern of associations: girls with a delayed adiposity rebound, signaling slower growth rate, demonstrated better language proficiency at ages 5-6. Importantly, this correlation appears to be specific to language skills and does not extend to nonverbal cognitive abilities. Exploratory analyses show that early environmental factors contributing to enhanced cognitive development, such as higher parental socio-economic status and increased cognitive stimulation, are positively associated with both language skills and the timing of adiposity rebound in girls. Overall, our findings lend support to the existence of an energy allocation trade-off mechanism that appears to prioritize language function over body growth investment in girls.

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

Meta-regression of genome-wide association studies to estimate age-varying genetic effects

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

PheWAS-based clustering of Mendelian Randomisation instruments reveals distinct mechanism-specific causal effects between obesity and educational attainment

Mendelian Randomisation (MR) estimates causal effects between risk factors and complex outcomes using genetic instruments. Pleiotropy, heritable confounders, and heterogeneous causal effects violate MR assumptions and can lead to biases. To alleviate these, we propose an approach employing a Phenome-Wide association Clustering of the MR instruments (PWC-MR) and apply this method to revisit the surprisingly large apparent causal effect of body mass index (BMI) on educational attainment (EDU): [Formula: see text] = -0.19 [-0.22, -0.16]. First, we cluster 324 BMI-associated genetic instruments based on their association with 407 traits in the UK Biobank, which yields six distinct groups. Subsequent cluster-specific MR reveals heterogeneous causal effect estimates on EDU. A cluster enriched for socio-economic indicators yields the largest BMI-on-EDU causal effect estimate ([Formula: see text] = -0.49 [-0.56, -0.42]) whereas a cluster enriched for body-mass specific traits provides a more likely estimate ([Formula: see text] = -0.09 [-0.13, -0.05]). Follow-up analyses confirms these findings: within-sibling MR ([Formula: see text] = -0.05 [-0.09, -0.01]); MR for childhood BMI on EDU ([Formula: see text] = -0.03 [-0.06, -0.002]); step-wise multivariable MR ([Formula: see text] = -0.05 [-0.07, -0.02]) where socio-economic indicators are jointly modelled. Here we show how the in-depth examination of the BMI-EDU causal relationship demonstrates the utility of our PWC-MR approach in revealing distinct pleiotropic pathways and confounder mechanisms.

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

Development of genome-wide association studies on childhood obesity and its indicators: A scoping review and enrichment analysis

BACKGROUND

The progress of genome-wide association studies (GWAS) in childhood obesity and its indicators is challenging and there are differences in genetic studies in children and adults.

OBJECTIVE

To illustrate the history of the development of GWAS in childhood obesity and its indicators and summarize the GWAS loci.

METHODS

PubMed, Web of Science, Embase and GWAS Catalog databases were systematically searched from 1 January 2005 to 19 October 2022 for literature related to GWAS of childhood BMI, body fatness and obesity. The nearest genes were used as positional genes to perform gene set analyses including the enrichment of pathways, tissues and diseases.

RESULTS

Twenty articles published between 2007 and 2021 were included in this scoping review, which identified 116 SNPs reaching genome-wide significance with childhood BMI (n = 50), body fatness (n = 31) and obesity (n = 35). The study populations were European in 16 studies, non-European in three studies (1 East Asian; 1 American; 1 Mexican) and trans-ancestry in one study. Several enriched pathways, tissues and diseases were identified through enrichment analysis of genes associated with childhood obesity and its indicators.

CONCLUSIONS

The innovations in tools and methods enable GWAS to better explore the genetic characteristics of obesity in children and adolescents. However, the number of GWAS in American, Asian and African populations is limited compared to the European population.

Genetics and Epigenetics in Obesity: What Do We Know so Far?

PURPOSE OF REVIEW

Enormous progress has been made in understanding the genetic architecture of obesity and the correlation of epigenetic marks with obesity and related traits. This review highlights current research and its challenges in genetics and epigenetics of obesity.

RECENT FINDINGS

Recent progress in genetics of polygenic traits, particularly represented by genome-wide association studies, led to the discovery of hundreds of genetic variants associated with obesity, which allows constructing polygenic risk scores (PGS). In addition, epigenome-wide association studies helped identifying novel targets and methylation sites being important in the pathophysiology of obesity and which are essential for the generation of methylation risk scores (MRS). Despite their great potential for predicting the individual risk for obesity, the use of PGS and MRS remains challenging. Future research will likely discover more loci being involved in obesity, which will contribute to better understanding of the complex etiology of human obesity. The ultimate goal from a clinical perspective will be generating highly robust and accurate prediction scores allowing clinicians to predict obesity as well as individual responses to body weight loss-specific life-style interventions.

Dual-systems models of the genetic architecture of impulsive personality traits: neurogenetic evidence of distinct but related factors

BACKGROUND

Dual-systems models, positing an interaction between two distinct and competing systems (i.e. top-down self-control, and bottom-up reward- or emotion-based drive), provide a parsimonious framework for investigating the interplay between cortical and subcortical brain regions relevant to impulsive personality traits (IPTs) and their associations with psychopathology. Despite recent developments in multivariate analysis of genome-wide association studies (GWAS), molecular genetic investigations of these models have not been conducted.

METHODS

Using IPT GWAS, we conducted confirmatory genomic structural equation models (GenomicSEM) to empirically evaluate dual-systems models of the genetic architecture of IPTs. Genetic correlations between dual-systems factors and relevant cortical and subcortical neuroimaging phenotypes (regional/structural volume, cortical surface area, cortical thickness) were estimated and compared.

RESULTS

GenomicSEM dual-systems models underscored important sources of shared and unique genetic variance between top-down and bottom-up constructs. Specifically, a dual-systems genomic model consisting of sensation seeking and lack of self-control factors demonstrated distinct but related sources of genetic influences (rg = 0.60). Genetic correlation analyses provided evidence of differential associations between dual-systems factors and cortical neuroimaging phenotypes (e.g. lack of self-control negatively associated with cortical thickness, sensation seeking positively associated with cortical surface area). No significant associations were observed with subcortical phenotypes.

CONCLUSIONS

Dual-systems models of the genetic architecture of IPTs tested were consistent with study hypotheses, but associations with relevant neuroimaging phenotypes were mixed (e.g. no associations with subcortical volumes). Findings demonstrate the utility of dual-systems models for studying IPT genetic influences, but also highlight potential limitations as a framework for interpreting IPTs as endophenotypes for psychopathology.

A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank

Bone mineral density (BMD) is an essential predictor of osteoporosis and fracture. We conducted a genome-wide trajectory analysis of BMD and analyzed the BMD change.

PURPOSE

This study aimed to identify the genetic architecture and potential biomarkers of BMD.

METHODS

Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform.

RESULTS

A total of 52 genes associated with BMD trajectory mean were identified, of which the top three significant genes were WNT16 (P = 1.31 × 10-126), FAM3C (P = 4.18 × 10-108), and CPED1 (P = 8.48 × 10-106). In addition, 114 genes associated with BMD within-subject variability were also identified, such as AC092079.1 (P = 2.72 × 10-13) and RGS7 (P = 4.72 × 10-10). The associations for these candidate genes were confirmed in the previous GWASs and replicated successfully in the Asian participants. GSEA results of BMD change identified multiple GO terms related to skeletal development, such as SKELETAL SYSTEM DEVELOPMENT (Padjusted = 2.45 × 10-3) and REGULATION OF OSSIFICATION (Padjusted = 2.45 × 10-3). KEGG enrichment analysis showed that these genes were mainly enriched in WNT SIGNALING PATHWAY.

CONCLUSIONS

Our findings indicated that the CPED1-WNT16-FAM3C locus plays a significant role in BMD mean trajectories and identified several novel candidate genes contributing to BMD within-subject variability, facilitating the understanding of the genetic architecture of BMD.

Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children

Importance

Children's exposure to screen time has been associated with poor mental health outcomes, yet the role of genetic factors remains largely unknown.

Objective

To assess the extent of genetic confounding in the associations between screen time and attention problems or internalizing problems in preadolescent children.

Design, Setting, and Participants

This cohort study analyzed data obtained between 2016 and 2019 from the Adolescent Brain Cognitive Development Study at 21 sites in the US. The sample included children aged 9 to 11 years of genetically assigned European ancestry with self-reported screen time. Data were analyzed between November 2021 and September 2023.

Exposure

Child-reported daily screen time (in hours) was ascertained from questionnaires completed by the children at baseline.

Main Outcomes and Measures

Child psychiatric problems, specifically attention and internalizing problems, were measured with the parent-completed Achenbach Child Behavior Checklist at the 1-year follow-up. Genetic sensitivity analyses model (Gsens) was used, which incorporated polygenic risk scores (PRSs) of both exposure and outcomes as well as either single-nucleotide variant (SNV; formerly single-nucleotide polymorphism)-based heritability or twin-based heritability to estimate genetic confounding.

Results

The 4262 children in the sample included 2269 males (53.2%) with a mean (SD) age of 9.9 (0.6) years. Child screen time was associated with attention problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD) and internalizing problems (β = 0.03 SD; 95% CI, 0.003-0.06 SD). The television time PRS was associated with child screen time (β = 0.18 SD; 95% CI, 0.14-0.23 SD), the attention-deficit/hyperactivity disorder PRS was associated with attention problems (β = 0.13 SD; 95% CI, 0.10-0.16 SD), and the depression PRS was associated with internalizing problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD). These PRSs were associated with cross-traits, suggesting genetic confounding. Estimates using PRSs and SNV-based heritability showed that genetic confounding accounted for most of the association between child screen time and attention problems and for 42.7% of the association between child screen time and internalizing problems. When PRSs and twin-based heritability estimates were used, genetic confounding fully explained both associations.

Conclusions and Relevance

Results of this study suggest that genetic confounding may explain a substantial part of the associations between child screen time and psychiatric problems. Genetic confounding should be considered in sociobehavioral studies of modifiable factors for youth mental health.

Genetics and epigenetics in the obesity phenotyping scenario

Obesity is a common complex trait that elevates the risk for various diseases, including type 2 diabetes and cardiovascular disease. A combination of environmental and genetic factors influences the pathogenesis of obesity. Advances in genomic technologies have driven the identification of multiple genetic loci associated with this disease, ranging from studying severe onset cases to investigating common multifactorial polygenic forms. Additionally, findings from epigenetic analyses of modifications to the genome that do not involve changes to the underlying DNA sequence have emerged as key signatures in the development of obesity. Such modifications can mediate the effects of environmental factors, including diet and lifestyle, on gene expression and clinical presentation. This review outlines what is known about the genetic and epigenetic contributors to obesity susceptibility, along with the albeit limited therapeutic options currently available. Furthermore, we delineate the potential mechanisms of actions through which epigenetic changes can mediate environmental influences and the related opportunities they present for future interventions in the management of obesity.

Weight management practices of health center providers in the United States

BACKGROUND

We examined weight management counseling practices of Health Resources and Services Administration-funded health center (HC) providers for patients with overweight (POW) and obesity (POB) status, focusing on weight-related conditions, risk factors, and health care utilization.

METHOD

We used a nationally representative cross-sectional survey of HC patients and multilevel generalized structural equation logistic regression models to assess the association of provider counseling practices for POW and POB and by three obesity classes. Dependent variables included being told by the HC provider that weight was a problem, receiving a diet or exercise recommendation, referral to a nutritionist, or receiving weight loss prescriptions. Independent variables included weight-related conditions such as diabetes and hypertension, risk factors such as smoking, and health service utilization such as five or more primary care visits.

RESULTS

All POB classes had higher odds of receiving all five counseling interventions than POW. Patients with diabetes and high cholesterol had higher odds of diet recommendations (OR = 1.8) and nutritionist referrals (OR = 2.3), while patients with cardiovascular disease had higher odds of nutritionist referral (OR = 2.0) and receiving weight loss prescriptions (OR = 2.6). Respondents with POB class III and diabetes had higher odds of receiving exercise recommendations (OR = 3.4), while POB class 1 and had hypertension had lower odds of nutritionist referral (OR = 0.3).

CONCLUSIONS

Variations in HC primary care providers' weight management counseling practices between POW and POB present missed opportunities for consistent practice and early intervention. Assessing providers' counseling practices for patients with comorbid conditions is essential to the successful management of the obesity crisis.

Taking a lifespan approach to polygenic scores

This commentary is a call to action for researchers to create and use genome-wide association studies (GWASs) with previously missed age groups (e.g., infancy, elderly), which will improve our ability to ask important developmental questions using genetic data to trace pathways across the lifespan.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

The rs1421085 variant within FTO promotes brown fat thermogenesis

One lead genetic risk signal of obesity-the rs1421085 T>C variant within the FTO gene-is reported to be functional in vitro but lacks evidence at an organism level. Here we recapitulate the homologous human variant in mice with global and brown adipocyte-specific variant knock-in and reveal that mice carrying the C-allele show increased brown fat thermogenic capacity and resistance to high-fat diet-induced adiposity, whereas the obesity-related phenotypic changes are blunted at thermoneutrality. Both in vivo and in vitro data reveal that the C-allele in brown adipocytes enhances the transcription of the Fto gene, which is associated with stronger chromatin looping linking the enhancer region and Fto promoter. Moreover, FTO knockdown or inhibition effectively eliminates the increased thermogenic ability of brown adipocytes carrying the C-allele. Taken together, these findings identify rs1421085 T>C as a functional variant promoting brown fat thermogenesis.

Child and adolescent obesity

The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. Obesity arises when a mix of genetic and epigenetic factors, behavioural risk patterns and broader environmental and sociocultural influences affect the two body weight regulation systems: energy homeostasis, including leptin and gastrointestinal tract signals, operating predominantly at an unconscious level, and cognitive-emotional control that is regulated by higher brain centres, operating at a conscious level. Health-related quality of life is reduced in those with obesity. Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. Treatment incorporates a respectful, stigma-free and family-based approach involving multiple components, and addresses dietary, physical activity, sedentary and sleep behaviours. In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. Prevention of obesity requires a whole-system approach and joined-up policy initiatives across government departments. Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities.

GWAS-Identified Variants for Obesity Do Not Influence the Risk of Developing Multiple Myeloma: A Population-Based Study and Meta-Analysis

Multiple myeloma (MM) is an incurable disease characterized by the presence of malignant plasma cells in the bone marrow that secrete specific monoclonal immunoglobulins into the blood. Obesity has been associated with the risk of developing solid and hematological cancers, but its role as a risk factor for MM needs to be further explored. Here, we evaluated whether 32 genome-wide association study (GWAS)-identified variants for obesity were associated with the risk of MM in 4189 German subjects from the German Multiple Myeloma Group (GMMG) cohort (2121 MM cases and 2068 controls) and 1293 Spanish subjects (206 MM cases and 1087 controls). Results were then validated through meta-analysis with data from the UKBiobank (554 MM cases and 402,714 controls) and FinnGen cohorts (914 MM cases and 248,695 controls). Finally, we evaluated the correlation of these single nucleotide polymorphisms (SNPs) with cQTL data, serum inflammatory proteins, steroid hormones, and absolute numbers of blood-derived cell populations (n = 520). The meta-analysis of the four European cohorts showed no effect of obesity-related variants on the risk of developing MM. We only found a very modest association of the POC5_rs2112347G and ADCY3_rs11676272G alleles with MM risk that did not remain significant after correction for multiple testing (per-allele OR = 1.08, p = 0.0083 and per-allele OR = 1.06, p = 0.046). No correlation between these SNPs and functional data was found, which confirms that obesity-related variants do not influence MM risk.

New insights from GWAS on BMI-related growth traits in a longitudinal cohort of admixed children with Native American and European ancestry

Body-mass index (BMI) is a hallmark of adiposity. In contrast with adulthood, the genetic architecture of BMI during childhood is poorly understood. The few genome-wide association studies (GWAS) on children have been performed almost exclusively in Europeans and at single ages. We performed cross-sectional and longitudinal GWAS for BMI-related traits on 904 admixed children with mostly Mapuche Native American and European ancestries. We found regulatory variants of the immune gene HLA-DQB3 strongly associated with BMI at 1.5 - 2.5 years old. A variant in the sex-determining gene DMRT1 was associated with the age at adiposity rebound (Age-AR) in girls (P = 9.8 × 10 - 9 ). BMI was significantly higher in Mapuche than in Europeans between 5.5 and 16.5 years old. Finally, Age-AR was significantly lower (P = 0.004 ) by 1.94 years and BMI at AR was significantly higher (P = 0.04 ) by 1.2 kg/ m 2 , in Mapuche children compared with Europeans.

Maternal prenatal cholesterol levels predict offspring weight trajectories during childhood in the Norwegian Mother, Father and Child Cohort Study

BACKGROUND

Numerous intrauterine factors may affect the offspring's growth during childhood. We aimed to explore if maternal and paternal prenatal lipid, apolipoprotein (apo)B and apoA1 levels are associated with offspring weight, length, and body mass index from 6 weeks to eight years of age. This has previously been studied to a limited extent.

METHODS

This parental negative control study is based on the Norwegian Mother, Father and Child Cohort Study and uses data from the Medical Birth Registry of Norway. We included 713 mothers and fathers with or without self-reported hypercholesterolemia and their offspring. Seven parental metabolites were measured by nuclear magnetic resonance spectroscopy, and offspring weight and length were measured at 12 time points. Data were analyzed by linear spline mixed models, and the results are presented as the interaction between parental metabolite levels and offspring spline (age).

RESULTS

Higher maternal total cholesterol (TC) level was associated with a larger increase in offspring body weight up to 8 years of age (0.03 ≤ P_interaction ≤ 0.04). Paternal TC level was not associated with change in offspring body weight (0.17 ≤ P_interaction ≤ 0.25). Higher maternal high-density lipoprotein cholesterol (HDL-C) and apoA1 levels were associated with a lower increase in offspring body weight up to 8 years of age (0.001 ≤ P_interaction ≤ 0.005). Higher paternal HDL-C and apoA1 levels were associated with a lower increase in offspring body weight up to 5 years of age but a larger increase in offspring body weight from 5 to 8 years of age (0.01 ≤ P_interaction ≤ 0.03). Parental metabolites were not associated with change in offspring height or body mass index up to 8 years of age (0.07 ≤ P_interaction ≤ 0.99).

CONCLUSIONS

Maternal compared to paternal TC, HDL-C, and apoA1 levels were more strongly and consistently associated with offspring body weight during childhood, supporting a direct intrauterine effect.

Genetic risk score for common obesity and anthropometry in Spanish schoolchildren

IntroductionCommon or non-syndromic obesity is a complex polygenic trait conditioned by biallelic or single-base polymorphisms called SNPs (Single-Nucleotide Polymorphisms) that present an additive effect and act synergistically. Most genotype-obese phenotype association studies include body mass index (BMI) or waist-to-height ratio (WtHR), and very few introduce a broad anthropometric profile. ObjectiveTo verify whether a genetic risk score (GRS) developed from 10 SNPs is associated with the obesity phenotype assessed from anthropometric measures indicative of excess weight, adiposity and fat distribution. Material and methodsA series of 438 Spanish schoolchildren (6-16 years old) were evaluated anthropometrically (weight, height, waist circumference, skinfold thickness, BMI, WtHR, body fat percentage [%BF]). Ten SNPs were genotyped from saliva samples, generating a GRS for obesity, establishing genotype-phenotype association. ResultsSchoolchildren categorised as obese by BMI, ICT and %BF had higher GRS than their non-obese peers. The prevalence of overweight and adiposity was higher in subjects with a GRS above the median. Similarly, between 11 and 16 years of age, all anthropometric variables presented higher averages. ConclusionsGRS estimated from the 10 SNPs can be a diagnostic tool for the potential risk of obesity in Spanish schoolchildren and could be useful from the preventive perspective.

The genetic architecture of changes in adiposity during adulthood

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.

Genetically-predicted placental gene expression is associated with birthweight and adult body mass index

The placenta is critical to human growth and development and has been implicated in health outcomes. Understanding the mechanisms through which the placenta influences perinatal and later-life outcomes requires further investigation. We evaluated the relationships between birthweight and adult body mass index (BMI) and genetically-predicted gene expression in human placenta. Birthweight genome-wide association summary statistics were obtained from the Early Growth Genetics Consortium (N = 298,142). Adult BMI summary statistics were obtained from the GIANT consortium (N = 681,275). We used S-PrediXcan to evaluate associations between the outcomes and predicted gene expression in placental tissue and, to identify genes where placental expression was exclusively associated with the outcomes, compared to 48 other tissues (GTEx v7). We identified 24 genes where predicted placental expression was significantly associated with birthweight, 15 of which were not associated with birthweight in any other tissue. One of these genes has been previously linked to birthweight. Analyses identified 182 genes where placental expression was associated with adult BMI, 110 were not associated with BMI in any other tissue. Eleven genes that had placental gene expression levels exclusively associated with BMI have been previously associated with BMI. Expression of a single gene, PAX4, was associated with both outcomes exclusively in the placenta. Inter-individual variation of gene expression in placental tissue may contribute to observed variation in birthweight and adult BMI, supporting developmental origins hypothesis.

Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index

Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.

An optimal growth pattern during pregnancy and early childhood associates with better fertility in men

OBJECTIVE

This study aimed to evaluate the association between birth weight (BW), childhood and adolescent BMI, with reproductive capacity in men.

DESIGN

A prospective, population-based cohort study (Northern Finland birth cohort 1966).

METHODS

Around 6196 men born in 1966 were followed from birth to age 50 years. Weight and height were measured repeatedly by professionals. Reproductive capacity (infertility assessment, male factor infertility and infertility treatment by age 46 years) was evaluated by questionnaires at ages 31 and 46 years. The number of children by the age of 50 years was recovered from registers. After excluding the men who reported never having attempted to have children or not answering the question at age 31 or 46 years (n = 2041), 4128 men were included in the final study population. Results were adjusted for BW, BW for gestational age (GA), mother's smoking status, marital status, educational level and smoking status.

RESULTS

Being small for GA (10.5% vs 8.2%, P = 0.012) or having a lower BW (3495 g vs 3548 g, P = 0.003) were associated with childlessness. The association was however no longer significant after adjusting for marital status. Being underweight in early childhood was associated with an increased risk of infertility assessment (adjusted, aOR: 2.04(1.07-3.81)) and childlessness (aOR: 1.47(1.01-2.17)) compared to the normal weight group. Conversely, overweight or obesity in early childhood was associated with a decreased risk of infertility assessment (aOR: 0.60 (0.41-0.87)), treatment (aOR: 0.42 (0.25-0.70)) and male factor infertility (aOR: 0.45 (0.21-0.97)). BMI in mid-childhood or puberty had no association with infertility or childlessness.

CONCLUSION

In boys, an optimal growth trajectory during pregnancy and early childhood seems to be very important for life-long fertility.

Association between LEPR polymorphism and susceptibility of osteoporosis in Chinese Mulao people

To explore the association between the single nucleotide polymorphism (SNP) of leptin receptor (LEPR) gene and the susceptibility to osteoporosis (OP) among Chinese Mulao people. A total of 738 people were involved. Bone mineral density (BMD) was examined by calcaneus ultrasound attenuation measurement. Six SNPs of LEPR were detected. The genotypes, allele frequencies, linkage disequilibrium, and haplotype were analyzed. BMD decreased with age and males had higher BMD than women. The proportion of normal bone mass decreased with age, and morbidity of OP increased. Three out of six SNPs showed a difference between OP and normal group. Individuals with AA genotype of rs1137100 in OP group outnumber the normal group, AA increased the risk of OP. In rs2767485, CT increased the risk of OP, C allele may be susceptible to OP. TT genotype of rs465555 was susceptible genotype of OP, T locus may be associated with OP. Strong linkage disequilibrium was detected among rs1137100, rs1137101, and rs4655555. Four haplotypes were constructed, among which, AACGCT and GGTGTA increased the risk of OP by 3.9 and 4.2 times, respectively, whereas, GGCGTA reduced 74% of OP susceptibility. The rs1137100, rs2767485, and rs465555 of LEPR were associated with OP in Chinese Mulao people.

Cord blood leptin level and a common variant of its receptor as determinants of the BMI trajectory: The EDEN mother-child cohort

BACKGROUND

Cord blood leptin is an indicator of neonatal fat mass and could shape postnatal adiposity trajectories. Investigating genetic polymorphisms of the leptin receptor gene (LEPR) could help understand the mechanisms involved.

OBJECTIVES

We aimed to investigate the association of cord blood leptin level and the LEPR rs9436303 polymorphism, with body mass index (BMI) at adiposity peak (AP) and age at adiposity rebound (AR).

METHODS

In the EDEN cohort, BMI at AP and age at AR were estimated with polynomial mixed models, for 1713 and 1415 children, respectively. Multivariable linear regression models allowed for examining the associations of cord blood leptin level and LEPR rs9436303 genotype with BMI at AP and age at AR adjusted for potential confounders including birth size groups. We also tested interactions between cord blood leptin level and rs9436303 genotype.

RESULTS

Increased leptin level was associated with reduced BMI at AP and early age at AR (comparing the highest quintile of leptin level to the others). Rs9436303 G-allele carriage was associated with increased BMI at AP and later age at AR but did not modulate the association with leptin level.

CONCLUSION

These results illustrate the role of early life body composition and the intrauterine environment in the programming of adiposity in childhood.

SINE Insertion May Act as a Repressor to Affect the Expression of Pig LEPROT and Growth Traits

Retrotransposon is an important component of the mammalian genome. Previous studies have shown that the expression of protein-coding genes was affected by the insertion of retrotransposon into the proximal genes, and the phenotype variations would be related to the retrotransposon insertion polymorphisms (RIPs). In this study, leptin (LEP), leptin receptor (LEPR), and leptin receptor overlapping transcript (LEPROT), which play important roles in the regulation of fat synthesis and body weight, were screened to search for the RIPs and their effect on phenotype and gene expression, as well as to further study the function of the insertion. The results showed that three RIPs located in intron 1 of LEPROT and intron 2 and 21 of LEPR were identified, and they were all SINEA1, which was one type of retrotransposon. The SINE insertion at the LEPROT was the dominant allele in native pig breeds. The age of 100 kg body weight of SINE+/+ Large White individuals was significantly higher than those of SINE+/− and SINE−/− individuals (p < 0.05). The LEPROT gene expression in the liver and suet of 30-day-old SINE−/− Sujiang piglets were significantly higher than those of SINE+/+ and SINE+/− piglets (p < 0.01). The dual-luciferase reporter gene assay showed that SINE insertion in PK15 and 3T3-L1 cells significantly reduced the promoter activity of the LEPROT gene (p < 0.01). Therefore, SINE insertion can be a repressor to reduce the expression of LEPROT and could be a useful molecular marker for assisted selection of growth traits in pig breeding.

Changing genetic architecture of body mass index from infancy to early adulthood: an individual based pooled analysis of 25 twin cohorts

Background

Body mass index (BMI) shows strong continuity over childhood and adolescence and high childhood BMI is the strongest predictor of adult obesity. Genetic factors strongly contribute to this continuity, but it is still poorly known how their contribution changes over childhood and adolescence. Thus, we used the genetic twin design to estimate the genetic correlations of BMI from infancy to adulthood and compared them to the genetic correlations of height.

Methods

We pooled individual level data from 25 longitudinal twin cohorts including 38,530 complete twin pairs and having 283,766 longitudinal height and weight measures. The data were analyzed using Cholesky decomposition offering genetic and environmental correlations of BMI and height between all age combinations from 1 to 19 years of age.

Results

The genetic correlations of BMI and height were stronger than the trait correlations. For BMI, we found that genetic correlations decreased as the age between the assessments increased, a trend that was especially visible from early to middle childhood. In contrast, for height, the genetic correlations were strong between all ages. Age-to-age correlations between environmental factors shared by co-twins were found for BMI in early childhood but disappeared altogether by middle childhood. For height, shared environmental correlations persisted from infancy to adulthood.

Conclusions

Our results suggest that the genes affecting BMI change over childhood and adolescence leading to decreasing age-to-age genetic correlations. This change is especially visible from early to middle childhood indicating that new genetic factors start to affect BMI in middle childhood. Identifying mediating pathways of these genetic factors can open possibilities for interventions, especially for those children with high genetic predisposition to adult obesity.

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

The relationship of life-course patterns of adiposity with type 2 diabetes, depression, and their comorbidity in the Northern Finland Birth Cohort 1966

OBJECTIVES

Type 2 diabetes (T2D) and comorbid depression challenges clinical management particularly in individuals with overweight. We aim to explore the shared etiology, via lifecourse adiposity, between T2D and depression.

METHODS

We used data from birth until 46years from Northern Finland Birth Cohort 1966 (n = 6,372; 53.8% females). We conducted multivariate analyses on three outcomes: T2D (4.2%), depression (19.2%) and as comorbidity (1.8%). We conducted (i) Path analysis to clarify time-dependent body mass index (BMI) related pathways, including BMI polygenic risk scores (PRS); and (ii) Cox regression models to assess whether reduction of overweight between 7years and 31years influence T2D, depression and/or comorbidity. The models were tested for covariation with sex, education, smoking, physical activity, and diet score.

RESULTS

The odd ratios (OR) of T2D in individuals with depression was 1.68 [95% confidence interval (CI): 1.34-2.11], and no change in estimate was observed when adjusted for covariates. T2D and comorbidity showed similar patterns of relationships in the path analyses (P < 0.001). The genetic risk for obesity (PRS BMI) did not show direct effect on T2D or comorbidity in adulthood but indirectly through measures of adiposity in early childhood and mid-adulthood in the path analysis (P < 0.001). Having early-onset of overweight at 7years and 31years showed highest risk of T2D (OR 3.8, 95%CI 2.4-6.1) and comorbidity (OR 5.0, 95%CI 2.7-9.5), with mild-to-moderate attenuation with adjustments. Depression showed no significant associations.

CONCLUSIONS

We found evidence for overweight since childhood as a risk factor for T2D and co-morbidity between T2D and depression, influenced moderately by lifestyle factors in later life. However, no shared early life adiposity related risk factors were observed between T2D and depression when assessed independently in this Finnish setting.

Age and diet shape the genetic architecture of body weight in diversity outbred mice

Understanding how genetic variation shapes a complex trait relies on accurately quantifying both the additive genetic and genotype–environment interaction effects in an age-dependent manner. We used a linear mixed model to quantify diet-dependent genetic contributions to body weight measured through adulthood in diversity outbred female mice under five diets. We observed that heritability of body weight declined with age under all diets, except the 40% calorie restriction diet. We identified 14 loci with age-dependent associations and 19 loci with age- and diet-dependent associations, with many diet-dependent loci previously linked to neurological function and behavior in mice or humans. We found their allelic effects to be dynamic with respect to genomic background, age, and diet, identifying several loci where distinct alleles affect body weight at different ages. These results enable us to more fully understand and predict the effectiveness of dietary intervention on overall health throughout age in distinct genetic backgrounds.

Body weight is one trait influenced by genes, age and environmental factors. Both internal and external environmental pressures are known to affect genetic variation over time. However, it is largely unknown how all factors – including age – interact to shape metabolism and bodyweight.

Wright et al. set out to quantify the interactions between genes and diet in ageing mice and found that the effect of genetics on mouse body weight changes with age. In the experiments, Wright et al. weighed 960 female mice with diverse genetic backgrounds, starting at two months of age into adulthood. The animals were randomized to different diets at six months of age. Some mice had unlimited food access, others received 20% or 40% less calories than a typical mouse diet, and some fasted one or two days per week.

Variations in their genetic background explained about 80% of differences in mice’s weight, but the influence of genetics relative to non-genetic factors decreased as they aged. Mice on the 40% calorie restriction diet were an exception to this rule and genetics accounted for 80% of their weight throughout adulthood, likely due to reduced influence from diet and reduced interactions between diet and genes. Several genes involved in metabolism, neurological function, or behavior, were associated with mouse weight.

The experiments highlight the importance of considering interactions between genetics, environment, and age in determining complex traits like body weight. The results and the approaches used by Wright et al. may help other scientists learn more about how the genetic predisposition to disease changes with environmental stimuli and age.

Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

Dynamics of ultrafast phase transitions in MgF2 triggered by laser-induced THz coherent phonons

The advent of free-electron lasers opens new routes for experimental high-pressure physics, which allows studying dynamics of condensed matter with femtosecond resolution. A rapid compression, that can be caused by laser-induced shock impact, leads to the cascade of high-pressure phase transitions. Despite many decades of study, a complete understanding of the lattice response to such a compression remains elusive. Moreover, in the dynamical case (in contrast to quasi-static loading) the thresholds of phase transitions can change significantly. Using the third harmonic pump–probe technique combined with molecular dynamics to simulate the terahertz (THz) spectrum, we revealed the dynamics of ultrafast laser-induced phase transitions in MgF₂ in all-optical experiment. Tight focusing of femtosecond laser pulse into the transparent medium leads to the generation of sub-TPa shock waves and THz coherent phonons. The laser-induced shock wave propagation drastically displaces atoms in the lattice, which leads to phase transitions. We registered a cascade of ultrafast laser-induced phase transitions (P42/mnm ⇒ Pa-3  ⇒ Pnam) in magnesium fluoride as a change in the spectrum of coherent phonons. The phase transition has the characteristic time of 5–10 ps, and the lifetime of each phase is on the order of 40–60 ps. In addition, phonon density of states, simulated by molecular dynamics, together with third-harmonic time-resolved spectra prove that laser-excited phonons in a bulk of dielectrics are generated by displacive excitation (DECP) mechanism in plasma mediated conditions.

Fitness, Food, and Biomarkers: Characterizing Body Composition in 19,634 Early Adolescents

Adolescent obesity persists as a major concern, especially in Central and Eastern Europe, yet evidence gaps exist regarding the pivotal early adolescent years. Our objective was to provide a comprehensive picture using a holistic approach of measured anthropometry in early adolescence, including body composition, cardiorespiratory fitness (CRF), and reported lifestyle characteristics. We aimed to elucidate potential sex/gender differences throughout and associations to biomarkers of disease risk for obese adolescents. Methods: Trained nurses measured 19,634 early adolescents (12−14-year-olds), we collected parental reports, and, for obese adolescents, fasting blood samples in four major Polish cities using a cross-sectional developmental design. Results: 24.7% boys and 18.6% girls were overweight/obese, and 2886 had BMI ≥ 90th percentile. With increasing age, there was greater risk of obesity among boys (p for trend = 0.001) and a decreasing risk of thinness for girls (p for trend = 0.01). Contrary to debate, we found BMI (continuous) was a useful indicator of measured fat mass (FM). There were 38.6% with CRF in the range of poor/very poor and was accounted for primarily by FM in boys, rather than BMI, and systolic blood pressure in girls. Boys, in comparison to girls, engaged more in sports (t = 127.26, p < 0.0001) and consumed more fast food (t = 188.57, p < 0.0001) and sugar-sweetened beverages (167.46, p < 0.0001). Uric acid, a potential marker for prediabetes, was strongly related to BMI in the obese subsample for both boys and girls. Obese girls showed signs of undernutrition. Conclusion: these findings show that overweight/obesity is by far a larger public health problem than thinness in early adolescence and is characterized differentially by sex/gender. Moreover, poor CRF in this age, which may contribute to life course obesity and disease, highlights the need for integrated and personalized intervention strategies taking sex/gender into account.

GWAS of longitudinal trajectories at biobank scale

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

Characterization of the genetic architecture of infant and early childhood body mass index

Early childhood obesity is a growing global concern; however, the role of common genetic variation on infant and child weight development is unclear. Here, we identify 46 loci associated with early childhood body mass index at specific ages, matching different child growth phases, and representing four major trajectory patterns. We perform genome-wide association studies across 12 time points from birth to 8 years in 28,681 children and their parents (27,088 mothers and 26,239 fathers) in the Norwegian Mother, Father and Child Cohort Study. Monogenic obesity genes are overrepresented near identified loci, and several complex association signals near LEPR, GLP1R, PCSK1 and KLF14 point towards a major influence for common variation affecting the leptin-melanocortin system in early life, providing a link to putative treatment strategies. We also demonstrate how different polygenic risk scores transition from birth to adult profiles through early child growth. In conclusion, our results offer a fine-grained characterization of a changing genetic landscape sustaining early childhood growth.

Mediterranean Diet and Genetic Determinants of Obesity and Metabolic Syndrome in European Children and Adolescents

Childhood obesity and metabolic syndrome (MetS) are multifactorial diseases influenced by genetic and environmental factors. The Mediterranean Diet (MD) seems to modulate the genetic predisposition to obesity or MetS in European adults. The FTO gene has also been shown to have an impact on the MD benefits to avoid obesity or MetS. Since these interaction effects have been scarcely analyzed in European youth, the aim was to describe the gene–MD interplay, analyzing the impact of the genetic factors to reduce the obesity and MetS risk through MD adherence, and the MD impact in the obesity and MetS genetic profile. From the limited evidence on gene–MD interaction studies in European youth, a study showed that the influence of high MD adherence on adiposity and MetS was only observed with a limited number of risk alleles; the gene–MD interplay showed sex-specific differences, being higher in females. Most results analyzed in European adults elucidate that, the relationship between MD adherence and both obesity and MetS risk, could be modulated by obesity genetic variants and vice versa. Further research is needed, to better understand the inter-individual differences in the association between MD and body composition, and the integration of omics and personalized nutrition considering MD.

Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans

Background

Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species.

Results

In this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs.

Conclusion

Our post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08373-3.

Prospective associations between dietary patterns, screen and outdoor play times at 2 years and age at adiposity rebound: The EDEN mother-child cohort

Although an early adiposity rebound (AR) is an established risk factor for later obesity, little is known regarding its determinants, especially modifiable ones. Using data from the French EDEN mother–child cohort (1903 children born in 2003–2006), we aimed to examine the association between diet and activity-related behaviors at 2 years of age and the timing of the AR. Two-year-old children (n = 1138) with parent-reported data on their foods/drinks intake, TV/DVD watching time, outdoor playtime, and with an estimated (via growth modelling) age at AR were included in the present study. Two dietary patterns, labelled 'Nutrient-dense foods' and 'Processed and fast foods', were identified in a previous study. Multivariable linear and logistic regression models were used to assess the association between dietary patterns and activity-related behaviors and, respectively, the age at AR (continuous) and the likelihood of having a very early AR (before 3.6 years for girls and 3.8 years for boys, i.e., below the 10th percentile of sex-specific distribution). A higher score on the ‘Processed and fast foods’ dietary pattern was associated with a higher likelihood of having a very early AR (OR = 1.23; 95% CI: 1.00 to 1.50). No significant association was observed between the ‘Nutrient-dense foods’ dietary pattern, TV/DVD watching and outdoor playing times and the timing of the AR. This finding emphasizes the importance of reducing nutrient-dense and processed foods from the early years of life, and provides further support for early interventions aimed at helping parents establish healthy eating habits for their growing child from the complementary period.

Mendelian Randomization: Concepts and Scope

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

Personalized approach to childhood obesity: Lessons from gut microbiota and omics studies. Narrative review and insights from the 29th European childhood obesity congress

The traditional approach to childhood obesity prevention and treatment should fit most patients, but misdiagnosis and treatment failure could be observed in some cases that lie away from average as part of individual variation or misclassification. Here, we reflect on the contributions that high-throughput technologies such as next-generation sequencing, mass spectrometry-based metabolomics and microbiome analysis make towards a personalized medicine approach to childhood obesity. We hypothesize that diagnosing a child as someone with obesity captures only part of the phenotype; and that metabolomics, genomics, transcriptomics and analyses of the gut microbiome, could add precision to the term "obese," providing novel corresponding biomarkers. Identifying a cluster -omic signature in a given child can thus facilitate the development of personalized prognostic, diagnostic, and therapeutic approaches. It can also be applied to the monitoring of symptoms/signs evolution, treatment choices and efficacy, predisposition to drug-related side effects and potential relapse. This article is a narrative review of the literature and summary of the main observations, conclusions and perspectives raised during the annual meeting of the European Childhood Obesity Group. Authors discuss some recent advances and future perspectives on utilizing a systems approach to understanding and managing childhood obesity in the context of the existing omics data.

Birth weight and adult income: An examination of mediation through adult height and body mass

This paper examines the causal links between early human endowments and socioeconomic outcomes in adulthood. We use a genotyped longitudinal survey (Cardiovascular Risk in Young Finns Study) that is linked to the administrative registers of Statistics Finland. We focus on the effect of birth weight on income via two anthropometric mediators: body mass index (BMI) and height in adulthood. We find that (i) the genetic instruments for birth weight, adult height, and adult BMI are statistically powerful; (ii) there is a robust total effect of birth weight on income for men but not for women; (iii) the total effect of birth weight on income for men is partly mediated via height but not via BMI; and (iv) the share of the total effect mediated via height is substantial, of approximately 56%.