Inference about causation between body mass index and DNA methylation in blood from a twin family study

Li, Shuai; Wong, Ee Ming; Bui, Minh; Nguyen, Tuong L.; Joo, Jihoon E.; Stone, Jennifer; Dite, Gillian S.; Dugué, Pierre-Antoine; Milne, Roger L.; Giles, Graham G.; Saffery, Richard; Southey, Melissa C.; Hopper, John L.

doi:10.1101/223040

Cited by 17 publications

(33 citation statements)

References 45 publications

(22 reference statements)

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“…By analyzing epigenetic signatures in whole blood of 3296 individuals phenotyped for fasting blood lipid levels using a Mendelian randomization framework, Dekkers and colleagues found strong evidence that triglycerides and cholesterol cause alterations in DNA methylation patterns and not vice versa (Dekkers et al, 2016). Similar observations were obtained for associations between DNA methylation patterns and adiposity (Li et al, 2018; Mendelson et al, 2017; Richmond et al, 2016; Wahl et al, 2017) or lung cancer risk (Battram et al, 2018). Thus, while more data about the functional role of epigenetic patterns are warranted, these data incentivize analyses of forward and reverse causality in epidemiological studies of epigenetic phenomena.…”

Section: Pharmacoepigenomicssupporting

confidence: 80%

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Lauschke

Zhou

Ingelman‐Sundberg

2019

Pharmacology & Therapeutics

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Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

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Section: Pharmacoepigenomicssupporting

confidence: 80%

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Lauschke

Zhou

Ingelman‐Sundberg

2019

Pharmacology & Therapeutics

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“…ICE FALCON has been applied to try to understand the causes of several traits including mammographic density, 6 , 7 allergic conditions, 8 psychological behaviours, 9 bone architecture 10 and epigenetic modifications. 11 , 12 In the latter papers we compared our findings with those from MR, and found they agreed. ICE FALCON has similarities and differences to MR; see Discussion.…”

Section: Introductionmentioning

confidence: 66%

“…Our previous applications of ICE FALCON to data on exposures and blood DNA methylation also gave the same conclusion as from MR analyses. 11 , 12 …”

Section: Discussionmentioning

confidence: 99%

Inference about causation from examination of familial confounding (ICE FALCON): a model for assessing causation analogous to Mendelian randomization

Bui

Hopper

2020

International Journal of Epidemiology

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Background We developed a method to make Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using observational data for related individuals and considering changes in a pair of regression coefficients. ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not require genetic data nor make strong assumptions. ICE FALCON can assess tracking of a measure over time, an issue often difficult to assess using MR due to lack of a valid instrumental variable. Methods We describe ICE FALCON and present two empirical applications with simulations. Results We found evidence consistent with body mass index (BMI) having a causal effect on DNA methylation at the ABCG1 locus, the same conclusion as from MR analyses but providing about 2.5 times more information per subject. We found evidence that tracking of BMI is consistent with longitudinal causation, as well as familial confounding. The simulations supported the validity of ICE FALCON. Conclusions There are conceptual similarities between ICE FALCON and MR, but empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable, or when the assumptions underlying MR analysis are suspect. ICE FALCON could provide insights into causality for a wide range of public health questions.

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“…This method was also used to apply the Inference about Causation through Examination of FAmiliaL CONfounding (ICE FAL-CON) models so as to investigate whether there was evidence consistent with an association being due to causal effects or to familial confounding. (32,39,40) In brief, three models were fitted to the data in regression analysis using the generalized estimating equation method to allow for the outcomes being correlated within twin pairs. The first model estimated within individual-association of LM or FM with each bone trait, giving β self .…”

Section: Methodsmentioning

confidence: 99%

“…This method was also used to apply the Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) models so as to investigate whether there was evidence consistent with an association being due to causal effects or to familial confounding. ( 32,39,40 )…”

Section: Methodsmentioning

confidence: 99%

Are the Relationships of Lean Mass and Fat Mass With Bone Microarchitecture Causal or Due to Familial Confounders? A Novel Study of Adult Female Twin Pairs

Bui

Hopper

et al. 2020

JBMR Plus

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It is not known whether the relationships of lean mass (LM) and fat mass (FM) with bone microarchitecture and geometry are causal and/or are because of confounders, including familial confounders arising from genetic and environment effects shared by relatives. We tested the hypotheses that: (i) LM is associated with cortical bone traits, (ii) FM is associated with trabecular bone traits, and (iii) these relationships of LM and FM with bone microarchitecture and geometry have a causal component. Total body composition was quantified for 98 monozygotic (MZ) and 54 dizygotic (DZ) white female twin pairs aged 31 to 77 years. Microarchitecture at the distal tibia and distal radius was quantified using HRpQCT and StrAx software. We applied the Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) method. Within‐individuals, distal tibia total bone area, cortical area, cortical thickness, and trabecular number were positively associated with LM (standardized regression coefficient (β) = 0.13 to 0.43; all p < 0.05); porosity of the inner transitional zone (ITZ) was negatively associated with LM (β = −0.22; p < 0.01). Trabecular number was positively associated with FM (β = 0.40; p < 0.001), and trabecular thickness was negatively associated with FM (β = −0.27; p < 0.001). For porosity of ITZ and trabecular number, the cross‐pair cross‐trait association with LM was significant before and after adjustment for the within‐individual association with LM (all ps < 0.05). For trabecular number, the cross‐pair cross‐trait association with FM was significant before and after adjustment for the within‐individual association with FM (p < 0.01). There were no significant changes in these cross‐pair cross‐trait associations after adjustment for the within‐individual association (p = 0.06 to 0.99). Similar results were found for distal radius measures. We conclude that there was no evidence that the relationships of LM and FM with bone microarchitecture and geometry are causal; they must in part due to by familial confounders affecting both bone architecture and body composition. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC. on behalf of American Society for Bone and Mineral Research.

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Inference about causation between body mass index and DNA methylation in blood from a twin family study

Abstract: Background

Cited by 17 publications

References 45 publications

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Inference about causation from examination of familial confounding (ICE FALCON): a model for assessing causation analogous to Mendelian randomization

Are the Relationships of Lean Mass and Fat Mass With Bone Microarchitecture Causal or Due to Familial Confounders? A Novel Study of Adult Female Twin Pairs

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