Genome- and epigenome-wide association studies identify susceptibility of CpG sites and regions for metabolic syndrome in a Korean population

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Abstract

Background

While multiple studies have investigated the relationship between metabolic syndrome (MetS) and its related traits (fasting glucose, triglyceride, HDL cholesterol, blood pressure, waist circumference) and DNA methylation, our understanding of the epigenetic mechanisms in MetS remains limited. Therefore, we performed an epigenome-wide meta-analysis of blood DNA methylation to identify differentially methylated probes (DMPs) and differentially methylated regions (DMRs) associated with MetS and its components using two independent cohorts comprising a total of 2,334 participants. We also investigated the specific genetic effects on DNA methylation, identified methylation quantitative trait loci (meQTLs) through genome-wide association studies and further utilized Mendelian randomization (MR) to assess how these meQTLs subsequently influence MetS status.

Results

We identified 40 DMPs and 27 DMRs that are significantly associated with MetS. In addition, we identified many novel DMPs and DMRs underlying inflammatory and steroid hormonal processes. The most significant associations were observed in 3 DMPs (cg19693031, cg26974062, cg02988288) and a DMR (chr1:145440444–145441553) at the TXNIP, which are involved in lipid metabolism. These CpG sites were identified as coregulators of DNA methylation in MetS, TG and FAG levels. We identified a total of 144 cis-meQTLs, out of which only 13 were found to be associated with DMPs for MetS. Among these, we confirmed the identified causal mediators of genetic effects at CpG sites cg01881899 at ABCG1 and cg00021659 at the TANK genes for MetS.

Conclusions

This study observed whether specific CpGs and methylated regions act independently or are influenced by genetic effects for MetS and its components in the Korean population. These associations between the identified DNA methylation and MetS, along with its individual components, may serve as promising targets for the development of preventive interventions for MetS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-024-01671-5.

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Most cited references 52

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Second-generation PLINK: rising to the challenge of larger and richer datasets

Christopher Chang, Carson Chow, Laurent Tellier … (2015)

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

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Adjusting batch effects in microarray expression data using empirical Bayes methods.

W Johnson, Cheng Li, Ariel Rabinovic (2007)

Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.

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Integrative analysis of 111 reference human epigenomes

Anshul Kundaje, Wouter Meuleman, Jason Ernst … (2016)

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but a similar reference has lacked for epigenomic studies. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection to-date of human epigenomes for primary cells and tissues. Here, we describe the integrative analysis of 111 reference human epigenomes generated as part of the program, profiled for histone modification patterns, DNA accessibility, DNA methylation, and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically-relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation, and human disease.

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Author and article information

Contributors

Ho-Sun Lee: hs4369@gmail.com

Journal

Journal ID (nlm-ta): Clin Epigenetics

Journal ID (iso-abbrev): Clin Epigenetics

Title: Clinical Epigenetics

Publisher: BioMed Central (London )

ISSN (Print): 1868-7075

ISSN (Electronic): 1868-7083

Publication date (Electronic): 29 April 2024

Publication date PMC-release: 29 April 2024

Publication date Collection: 2024

Volume: 16

Electronic Location Identifier: 60

Affiliations

[1 ]Forensic Toxicology Division, Daegu Institute, National Forensic Service, ( https://ror.org/051269613) Chilgok-gun, 39872 Gyeongsangbuk-do Korea

[2 ]Interdisciplinary Program in Bioinformatics, Seoul National University, ( https://ror.org/04h9pn542) Seoul, 08826 Korea

[3 ]Department of Statistics, Seoul National University, ( https://ror.org/04h9pn542) Seoul, 08826 Korea

Article

Publisher ID: 1671

DOI: 10.1186/s13148-024-01671-5

PMC ID: 11059751

PubMed ID: 38685121

SO-VID: 0b10d97c-0ae5-42ce-9597-a1ccdfc9bfb1

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 14 November 2023

Date accepted : 13 April 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;

Award ID: NO.2022R1F1A1074097

Award Recipient : Ho-Sun Lee

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: metabolic syndrome,epigenome-wide association study,genome-wide association study,txnip

Data availability:

ScienceOpen disciplines: Genetics

Keywords: metabolic syndrome, epigenome-wide association study, genome-wide association study, txnip

Genome- and epigenome-wide association studies identify susceptibility of CpG sites and regions for metabolic syndrome in a Korean population

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Abstract

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Supplementary Information

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Genome Integrity

Most cited references 52

Second-generation PLINK: rising to the challenge of larger and richer datasets

Adjusting batch effects in microarray expression data using empirical Bayes methods.

Integrative analysis of 111 reference human epigenomes

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