A healthy childhood environment helps to combat inherited susceptibility to obesity

Huels, A.; Wright, M. N.; Bogl, L. H.; Kaprio, J.; Lissner, L.; Molnar, D.; Moreno, L.; De Henauw, S.; Siani, A.; Veidebaum, T.; Ahrens, W.; Pigeot, I.; Foraita, R.; IDEFICS/I.Family consortia,

doi:10.1101/2020.01.13.905125

bioRxiv · 10.1101/2020.01.13.905125

A healthy childhood environment helps to combat inherited susceptibility to obesity

Huels, A.·Wright, M. N.·Bogl, L. H.·Kaprio, J.·Lissner, L.·Molnar, D.·Moreno, L.·De Henauw, S.·Siani, A.·Veidebaum, T.·Ahrens, W.·Pigeot, I.·Foraita, R.·IDEFICS/I.Family consortia,

View original source ↗Export metadata ↓

Abstract

ObjectivesTo investigate the degree by which the inherited susceptibility to obesity is modified by environmental factors during childhood and adolescence. DesignCohort study with repeated measurements of diet, lifestyle factors and anthropometry. SettingThe pan-European IDEFICS/I.Family cohort Participants8,609 repeated observations from 3,098 children aged 2 to 16 years, examined between 2007 and 2014. Main outcome measuresBody mass index (BMI) and waist circumference. Genome-wide polygenic risk scores (PRS) to capture the inherited susceptibility of obesity were calculated using summary statistics from independent genome-wide association studies of BMI. Gene-environment interactions of the PRS with sociodemographic (European region, socioeconomic status) and lifestyle factors (diet, screen time, physical activity) were estimated. ResultsThe PRS was strongly associated with BMI (r2 = 0.11, p-value = 7.9 x 10-81) and waist circumference (r2 = 0.09, p-value = 1.8 x 10-71) in our cohort. The associations with BMI increased from r2=0.03 in 3-year olds to r2=0.18 in 14-year olds and associations with waist circumference from r2=0.03 to r2=0.14. Being in the top decile of the PRS distribution was associated with 3.63 times higher odds for obesity (95% confidence interval (CI): [2.57, 5.14]). We observed significant interactions with demographic and lifestyle factors for BMI as well as waist circumference. The risk of becoming obese among those with higher genetic susceptibility was ~38% higher in children from Southern Europe (BMI: p-interaction = 0.0066, Central vs. Southern Europe) and ~61% higher in children with a low parental education (BMI: p-interaction = 0.0012, low vs. high). Furthermore, the risk was attenuated by a higher intake of dietary fiber (BMI: p-interaction=0.0082) and shorter screen times (BMI: p-interaction=0.018). ConclusionsOur results highlight that a healthy childhood environment might partly offset a genetic predisposition to obesity during childhood and adolescence.

Source connections

Published article ↗

Explore related subjects

genetics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Huels, A., Wright, M. N., Bogl, L. H., Kaprio, J., Lissner, L., Molnar, D., Moreno, L., De Henauw, S., Siani, A., Veidebaum, T., Ahrens, W., Pigeot, I., Foraita, R., IDEFICS/I.Family consortia,. 2020-01-14. A healthy childhood environment helps to combat inherited susceptibility to obesity. https://doi.org/10.1101/2020.01.13.905125

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗

bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗

bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗