Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

Hagenbeek, Fiona A.; Dongen, Jenny van; Pool, René; Harms, Amy C.; Roetman, Peter J.; Fanos, Vassilios; Keulen, Britt J. van; Walker, Brian R.; Karu, Naama; Pol, Hilleke E. Hulshoff; Rotteveel, Joost; Finken, Martijn J J; Vermeiren, Robert; Kluft, C.; Bartels, Meike; Hankemeier, Thomas; Boomsma, Dorret I.

doi:10.3390/metabo12060474

Cited by 8 publications

(3 citation statements)

References 61 publications

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“…The metabolomics data (Hagenbeek et al, 2022) were assessed by ultra‐performance liquid chromatography‐mass spectrometry (UPLC‐MS) for 66 amines and 13 steroids (pre‐QC). To measure amines, methanol was added to 5 μL of spiked (with internal standards) urine for protein precipitation and centrifugation of the supernatant.…”

Section: Methodsmentioning

confidence: 99%

Integrative multi‐omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention‐Deficit/Hyperactivity Disorder

Hubers

Hagenbeek

Pool

et al. 2023

American J of Med Genetics Pt B

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The evolving field of multi‐omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non‐transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi‐omics framework to identify biomarkers for Attention‐Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single‐ and next multi‐omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in‐sample prediction through cross‐validation. The multi‐omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out‐of‐sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non‐transmitted PGSs, CpGs, and amino acid levels and show that multi‐omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

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Section: Methodsmentioning

confidence: 99%

Integrative multi‐omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention‐Deficit/Hyperactivity Disorder

Hubers

Hagenbeek

Pool

et al. 2023

American J of Med Genetics Pt B

Self Cite

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“…Since the quantification of D requires considerable statistical power, we have only estimated A and E, with C where appropriate. Heritability (h 2 ) is defined as A/V, and the standardized coefficients e 2 and c 2 denote the part of the variance V explained by E and C, respectively [60,61].…”

Section: Classical Twin Modelingmentioning

confidence: 99%

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Drouard

Hagenbeek

Whipp

et al. 2023

Preprint

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Background: The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain 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. Methods: Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four 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 using 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. The sources of genetic and environmental variation underlying the protein abundances were quantified using 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) using mixed-effect models and correlation networks. Results: We 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 6 and 4 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 many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. Conclusions: Associations 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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Section: Classical Twin Modelingmentioning

confidence: 99%

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Drouard,

Hagenbeek,

Whipp

et al. 2023

BMC Med

Self Cite

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Background The 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. Methods Our 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. Results We 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. Conclusions Associations 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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Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

Cited by 8 publications

References 61 publications

Integrative multi‐omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention‐Deficit/Hyperactivity Disorder

Integrative multi‐omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention‐Deficit/Hyperactivity Disorder

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

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