Genome-wide methylation data improves dissection of the effect of smoking on body mass index

Amador, Carmen; Zeng, Yanni; Barber, M.; Martin, Richard M; Campbell, Archie; McIntosh, Andrew M.; Evans, Kathryn; Porteous, David J.; Hayward, Caroline; Wilson, James F.; Navarro, Pau; Haley, Chris

doi:10.1101/2020.10.08.329672

Cited by 4 publications

(8 citation statements)

References 71 publications

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“…For instance, the availability of methylation data has and continues to increase substantially, and can be used as good, and in some cases far better proxy measures of environments as seen with smoking. 35, 61 Evidence suggests there may be a methylation profile observed in individuals with trauma exposure. 62, 63 Genome-by-environment interaction effects using methylation data, can then be dissected to explore biological pathways with non-additive effects on outcomes that can be directly targeted.…”

Section: Discussionmentioning

confidence: 99%

“…34 Including matrices representing genetic, environmental and interaction effect similarity within a sample, in mixed linear models, can provide estimates of the genetic, environmental, and genome-by-environment interaction components of trait variance. 35 Here, we estimated the contribution of trauma exposure and its interaction with genetic variation to depression as well as neuroticism. We chose to explore neuroticism as this trait has been shown to have a greater genetic component 36,37 and has a strong phenotypic link with MDD suggesting the exploration of neuroticism to be useful in understanding the genetic aetiology of both of these traits.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genome-by-Trauma Exposure Interaction Effects in Depression

Chuong

Adams

Kwong

et al. 2022

Preprint

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Background Self-reported trauma exposure has consistently been found to be a risk factor for Major Depressive Disorder (MDD) and several studies have reported interactions with genetic liability. To date, most studies have examined interaction effects with trauma exposure using genome-wide variants (single nucleotide polymorphisms SNPs) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance. We sought to re-examine genome-by-trauma interaction effects using genetic measures utilising all available genotyped data and thus, maximising accounted variance. Methods Measures of self-reported depression, neuroticism and trauma exposure for 148 129 participants with whole genome SNP data are available from the UK Biobank study. Here, we used a mixed-model statistical approach utilising genetic, trauma exposure and genome-by-trauma exposure interaction similarity matrices to explore sources of variation in depression and neuroticism. Findings Our approach estimated the heritability of MDD to be approximately 0.160 [SE 0.016]. Subtypes of self-reported trauma exposure (catastrophic, adult, childhood and full trauma) accounted for a significant proportion of the variance of each trait, ranging from 0.056 [SE 0.013] to 0.176 [SE 0.025]. The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction ranged from 0.074 [SE 0.006] to 0.201 [SE 0.009]. Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in males than in females. Interpretation This is the first study to utilise an approach combining all genome-wide SNP data when exploring genome-by-trauma interaction effects in MDD and present evidence that interaction effects are influential in depression manifestation. This effect accounts for greater trait variance within males which points to potential differences in depression aetiology between the sexes. The methodology utilised in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genome-by-Trauma Exposure Interaction Effects in Depression

Chuong

Adams

Kwong

et al. 2022

Preprint

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“…652 out of these 4445 participants had imputed genotype information with parent-of-origin alleles successfully inferred. Based on a pipeline proposed previously [4], the two datasets were generated, processed and quality controlled in consistent way [22], which was briefly described in text s1.…”

Section: Gs:sfhs Cohort: Dna Methylationmentioning

confidence: 99%

Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome

Zeng¹,

Amador

Gao³

et al. 2021

Preprint

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Background: parent-of-origin effects (POE) play important roles in development and complex disease and thus understanding their regulation and associated molecular and phenotypic variation are warranted. Previous studies have mainly focused on the detection of genomic regions or phenotypes regulated by POE. Understanding whether POE may be modified by environmental or genetic exposures is important for understanding of the source of POE-associated variation, but only a few case studies addressing these modifiable POE exist. Methods: in order to understand this high order of POE regulation, we screened 101 genetic and environmental factors such as "predicted mRNA expression levels" of DNA methylation/imprinting machinery genes and early/late lifestyle/environmental exposures. POE-mQTL-modifier interaction models were proposed to test the potential of these factors to modify POE at DNA methylation using data from Generation Scotland: The Scottish Family Health Study(N=2315). Results: a set of vulnerable/modifiable POE-CpGs were identified (modifiable-POE-regulated CpGs, N=3). Four factors, "lifetime smoking status" and "predicted mRNA expression levels" of TET2, SIRT1 and KDM1A, were found to significantly modify the POE on the three CpGs in both discovery and replication datasets. Importantly, the POE on one of the CpGs were modified by both genetic and environmental factors. We further identified plasma protein and health-related phenotypes associated with the methylation level of one of the identified CpGs. Conclusions: the modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation, motivating future comprehensive evaluation of the role of these modifiers in complex diseases.

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“…DNA methylation in whole blood captures a wide range of putative ALS risk factors at a molecular level, including smoking, alcohol intake, body mass index (BMI), biological age, and various metabolic and inflammatory proteins (12)(13)(14)(15)(16)(17)(18). Leveraging DNA methylation as proxies for these risk factors offers several advantages because it is (i) not prone to recall bias (relevant for smoking and alcohol), (ii) may capture information not (accurately) captured by the self-report (such as passive and past smoking) and provides a quantifiable measure (19), and (iii) is relatively stable in the short term [especially relevant for immunological proteins (18)]. Moreover, many risk factor studies have been conducted in small samples (3,6), whereas our large DNA methylation study can provide a well-powered alternative that jointly considers the molecular correlates of many risk factors.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS

et al. 2022

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Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation–based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.

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Genome-wide methylation data improves dissection of the effect of smoking on body mass index

Cited by 4 publications

References 71 publications

Genome-by-Trauma Exposure Interaction Effects in Depression

Genome-by-Trauma Exposure Interaction Effects in Depression

Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome

Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS

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