DNA methylation and cancer incidence: lymphatic–hematopoietic versus solid cancers in the Strong Heart Study

Domingo‐Relloso, Arce; Huan, Tianxiao; Haack, Karin; Riffo‐Campos, Ángela L.; Levy, Daniel; Fallin, M. Daniele; Terry, Mary Beth; Zhang, Ying; Rhoades, Dorothy A.; Herreros-Martinez, Miguel; García‐Esquinas, Esther; Cole, Shelley A.; Téllez-Plaza, María; Navas‐Acien, Ana

doi:10.1186/s13148-021-01030-8

Cited by 11 publications

(11 citation statements)

References 57 publications

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“…Once these preprocessing steps were completed, four omics blocks of different dimensions were considered for the modeling phase ( Fig. 1 ) (Abayomi et al, 2005 ; Aryee et al, 2014 ; Benton et al, 2017 ; Boks et al, 2009 ; Cazaly et al, 2016 ; Domingo-Relloso et al, 2021 ; Du et al, 2008 ; Friedman et al, 2010 ; Hayati Rezvan et al, 2015 ; Honaker et al, 2011 ; Kaprio et al, 1987 ; Keil et al, 1991 ; Lin et al, 2008 ; Nikpay et al, 2015 ; Price et al, 2006 ; Salvador et al, 2019 ; Triche et al, 2013 ; van Buuren and Groothuis-Oudshoorn, 2011 ; Vilaplana, 2006 ; Waldmann et al, 2013 ; Yengo et al, 2018 ; and Zou and Hastie, 2005 ).…”

Section: Methodsmentioning

confidence: 99%

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Drouard

Ollikainen

Mykkänen

et al. 2022

OMICS: A Journal of Integrative Biology

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Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics—including transcriptomic, methylation, metabolomic—data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

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Section: Methodsmentioning

confidence: 99%

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Drouard

Ollikainen

Mykkänen

et al. 2022

OMICS: A Journal of Integrative Biology

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“…For example, in lung cancer it detects the early stage of disease [ 76 ]. Another study conducted in cancer patients described that DNA methylation data could improve early detection beyond known risk factors [ 77 ]. Identified DNA methylation markers may not only constitute a precision medicine tool, but may also help elucidate novel mechanisms of treatment.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide methylation patterns in Marfan syndrome

et al. 2021

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Background Marfan syndrome (MFS) is a connective tissue disorder caused by mutations in the Fibrillin-1 gene (FBN1). Here, we undertook the first epigenome-wide association study (EWAS) in patients with MFS aiming at identifying DNA methylation loci associated with MFS phenotypes that may shed light on the disease process. Methods The Illumina 450 k DNA-methylation array was used on stored peripheral whole-blood samples of 190 patients with MFS originally included in the COMPARE trial. An unbiased genome-wide approach was used, and methylation of CpG-sites across the entire genome was evaluated. Additionally, we investigated CpG-sites across the FBN1-locus (15q21.1) more closely, since this is the gene defective in MFS. Differentially Methylated Positions (DMPs) and Differentially Methylated Regions (DMRs) were identified through regression analysis. Associations between methylation levels and aortic diameters and presence or absence of 21 clinical features of MFS at baseline were analyzed. Moreover, associations between aortic diameter change, and the occurrence of clinical events (death any cause, type-A or -B dissection/rupture, or aortic surgery) and methylation levels were analyzed. Results We identified 28 DMPs that are significantly associated with aortic diameters in patients with MFS. Seven of these DMPs (25%) could be allocated to a gene that was previously associated with cardiovascular diseases (HDAC4, IGF2BP3, CASZ1, SDK1, PCDHGA1, DIO3, PTPRN2). Moreover, we identified seven DMPs that were significantly associated with aortic diameter change and five DMP’s that associated with clinical events. No significant associations at p < 10–8 or p < 10–6 were found with any of the non-cardiovascular phenotypic MFS features. Investigating DMRs, clusters were seen mostly on X- and Y, and chromosome 18–22. The remaining DMRs indicated involvement of a large family of protocadherins on chromosome 5, which were not reported in MFS before. Conclusion This EWAS in patients with MFS has identified a number of methylation loci significantly associated with aortic diameters, aortic dilatation rate and aortic events. Our findings add to the slowly growing literature on the regulation of gene expression in MFS patients.

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“…In 2016, a Tribal Nation from Arizona declined further participation, leaving 3517 potential participants for this study. Eligibility for blood DNA methylation analysis has been described in a previous publication [ 21 ], leaving 2325 participants for epigenetic research. For this study, 967 participants with prevalent diabetes (assessed by whether participants took diabetes medication, by measurements of fasting glucose and by the HbA1c test) and 46 participants with missing diabetes information at exam 2 (1993–1995) and exam 3 (1998–1999) or missing values in fasting glucose or HOMA-IR at baseline were excluded, leaving a total of 1312 participants (Additional file 1 : Figure S1).…”

Section: Methodsmentioning

confidence: 99%

Epigenetics of type 2 diabetes and diabetes-related outcomes in the Strong Heart Study

et al. 2022

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Background The prevalence of type 2 diabetes has dramatically increased in the past years. Increasing evidence supports that blood DNA methylation, the best studied epigenetic mark, is related to diabetes risk. Few prospective studies, however, are available. We studied the association of blood DNA methylation with diabetes in the Strong Heart Study. We used limma, Iterative Sure Independence Screening and Cox regression to study the association of blood DNA methylation with fasting glucose, HOMA-IR and incident type 2 diabetes among 1312 American Indians from the Strong Heart Study. DNA methylation was measured using Illumina’s MethylationEPIC beadchip. We also assessed the biological relevance of our findings using bioinformatics analyses. Results Among the 358 differentially methylated positions (DMPs) that were cross-sectionally associated either with fasting glucose or HOMA-IR, 49 were prospectively associated with incident type 2 diabetes, although no DMPs remained significant after multiple comparisons correction. Multiple of the top DMPs were annotated to genes with relevant functions for diabetes including SREBF1, associated with obesity, type 2 diabetes and insulin sensitivity; ABCG1, involved in cholesterol and phospholipids transport; and HDAC1, of the HDAC family. (HDAC inhibitors have been proposed as an emerging treatment for diabetes and its complications.) Conclusions Our results suggest that differences in peripheral blood DNA methylation are related to cross-sectional markers of glucose metabolism and insulin activity. While some of these DMPs were modestly associated with prospective incident type 2 diabetes, they did not survive multiple testing. Common DMPs with diabetes epigenome-wide association studies from other populations suggest a partially common epigenomic signature of glucose and insulin activity.

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DNA methylation and cancer incidence: lymphatic–hematopoietic versus solid cancers in the Strong Heart Study

Cited by 11 publications

References 57 publications

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Genome-wide methylation patterns in Marfan syndrome

Epigenetics of type 2 diabetes and diabetes-related outcomes in the Strong Heart Study

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