Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation

Kato, Norihiro; Loh, Marie; Takeuchi, Fumihiko; Verweij, Niek; Wang, Xu; Zhang, Weihua; NKelly, Tanika; Saleheen, Danish; Lehne, Benjamin; Leach, Irene Mateo; Drong, Alexander W.; Abbott, James; Wahl, Simone; Tan, Sian Tsung; Scott, William R.; Campanella, Gianluca; Chadeau‐Hyam, Marc; Afzal, Uzma; Ahluwalia, Tarunveer S.; Bonder, Marc Jan; Chen, Peng; Dehghan, Abbas; Edwards, Todd L.; Esko, Tõnu; Go, Min Jin; Harris, Sarah E.; Hartiala, Jaana; Kasela, Silva; Kasturiratne, Anuradhani; Khor, Chiea‐Chuen; Kleber, Marcus E.; Li, Huaixing; Mok, Zuan Yu; Nakatochi, Masahiro; Sapari, Nur Sabrina; Saxena, Richa; Stewart, Alexandre F.R.; Stolk, Lisette; Tabara, Yasuharu; Teh, Ai Ling; Wu, Ying; Wu, Jer Yuarn; Zhang, Yi; Aits, Imke; Alves, Alexessander Da Silva Couto; Das, Shikta; Dorajoo, Rajkumar; CHopewell, Jemma; Kim, Yun Kyoung; WKoivula, Robert; Luan, Jian’an; Lyytikäinen, Leo-Pekka; NNguyen, Quang; Pereira, Mark A.; Postmus, Iris; TRaitakari, Olli; Bryan, Molly Scannell; Scott, Robert A.; Sorice, Rossella; Tragante, Vinicius; Traglia, Michela; White, Jon; Yamamoto, Ken; Zhang, Yonghong; Adair, Linda S.; Ahmed, Alauddin; Akiyama, Koichi; Rasheed, Awais; Aung, Tin; Barroso, Inês; Bjonnes, Andrew; Braun, Timothy R.; Cai, Hui; Chang, Li; Chen, Chien Hsiun; Cheng, Ching‐Yu; Chong, Yap Seng; Collins, Rory; Courtney, Regina; Davies, Gail; Delgado, Graciela; D., Loi; Doevendans, Pieter A.; Gansevoort, Ron T.; Gao, Yu Tang; Grammer, Tanja B.; Grarup, Niels; Grewal, Jagvir; Gu, Dongfeng; SWander, Gurpreet; Hartikainen, Anna Liisa; Hazen, Stanley L.; He, Jing; Heng, Chew Kiat; Hixso, E. James Ames; Hofman, Albert; Hsu, Chris; Huang, Wei; Husemoen, Lise Lotte Nystrup; Hwang, Joo Yeon; Ichihara, Sahoko; Igase, Michiya; Isono, Motohide; Justesen, Johanne Marie; Katsuya, Tomohiro; GKibriya, Muhammad; Kim, Young Jin; Kishimoto, Mitsumasa; Koh, Woon Puay; Kohara, Katsuhiko; Kumari, Meena; Kwek, Kenneth; Lee, Nanette R.; Lee, Jeannette; Liao, Jiemin; Lieb, Wolfgang; Liewald, David C.; Matsubara, Tatsuaki; Matsushita, Yushu; Meitinger, Thomas; Mihailov, Evelin; Milani, Lili; Mills, Rebecca; Mononen, Nina; Müller‐Nurasyid, Martina; Nabika, Toru; Nakashima, Eitaro; Ng, Hong Kiat; Nikus, Kjell; Nutile, Teresa; Ohkubo, Takayoshi; Ohnaka, Keizo; Parish, Sarah; Paternoster, Lavinia; Peng, Hao; Peters, Annette; TPham, Son; Pinidiyapathirage, Janani; Rahman, Mahfuzar; Rakugi, Hiromi; Rolandsson, Olov; Rozario, Michelle Ann; Ruggiero, Daniela; Sala, Cinzia; Ralhan, Sarju; Shimokawa, Kuniyasu; Snieder, Harold; Sparsø, Thomas; Spiering, Wilko; Starr, John M.; Stott, David J.; OStram, Daniel; Sugiyama, Takao; Szymczak, Silke; Tang, Weihong; Lin, Tong; Trompet, Stella; Turjanmaa, Väinö; Ueshima, Hirotsugu; Uitterlinden, André G.; Umemura, Satoshi; Vääräsmäki, Marja; Dam, Rob M. van; Gilst, Wiek H. van; Veldhuisen, Dirk J. van; Viikari, Jorma; Waldenberger, Mélanie; Wang, Yiqin; Wang, Aili; Wilson, Rory; Wong, Tien Yin; Xiang, Yong Bing; Yamaguchi, Shûhei; Ye, Xingwang; Young, Robin; Young, Terri L.; Yuan, Jian Min; Zhou, Xueya; Asselbergs, Folkert W.; Ciullo, Marina; Clarke, Robert; Deloukas, Panos; Franke, André; Paul, W.; Franks, Paul W.; Friedlander, Yechiel; Gross, Myron D.; Guo, Zhirong; Hansen, Torben; Järvelin, Marjo‐Riitta; Jørgensen, Torben; Jukema, J. Wouter; Kähönen, Mika; Kajio, Hiroshi; Kivimäki, Mika; Lee, Jong‐Young; Lehtimäki, Terho; Linneberg, Allan; Miki, Tetsuro; Pedersen, Oluf; Samani, Nilesh J.; Sørensen, Thorkild I A; Takayanagi, Ryoichi; Toniolo, Daniela; Ahsan, Habibul; Allayee, Hooman; Chen, Yuan Tsong; Danesh, John; Deary, Ian J.; Franco, Oscar H.; Franke, Lude; THeijman, Bastiaan; Holbrook, Joanna D.; Isaacs, Aaron; Kim, Bong-Jo; Lin, Xu; Liu, Jing; März, Winfried; Metspalu, Andres; Mohlke, Karen L.; Sangher, K.; Harambir, Dharambir; Meurs, Joyce B. J. van; Vithana, Eranga N.; Wickremasinghe, Ananda R.; Wijmenga, Cisca; Wolffenbuttel, Bruce H. R.; Yokota, Mitsuhiro; Wang, Zheng; Zhu, Dingliang; Víneis, Paolo; Kyrtopoulos, Soterios A.; Kleinjans, Jos; McCarthy, Mark I.; Soong, Richie; Gieger, Christian; Scott, James A.; Teo, Yik Ying; He, Jiang; Elliott, Paul; Tai, E. Shyong; Harst, Pim van der; Kooner, Jaspal S.; Chambers, John C.

doi:10.1038/ng.3405

Cited by 298 publications

(226 citation statements)

References 51 publications

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“…Importantly, these findings have recently been independently replicated [16]. Other EWAS have also shown that genetic predisposition to adult onset phenotypes may be mediated by DNA methylation [17,18].…”

Section: Dna Methylation Marks Integrate Genetic and Environmental Inflsupporting

confidence: 50%

Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies?

et al. 2017

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Excitement about DNA methylation biomarkers has been tempered by a growing appreciation of the complex causal relations with cell fate. Intersample differences in DNA methylation can be partitioned into those that are independent of cellular heterogeneity and those that are caused by differential mixtures of cell types. Generally, the field has assumed that the former are more likely to be causative of disease. The latter has been considered a likely consequence of disease and a confounder to be removed. We argue that the conceptual separation of these signals is artificial and not necessarily informative about causation. DNA methylation is a very sensitive measure of cell fate mix and therefore reveals much about underlying disease etiology including aspects of causation. DNA methylation marks integrate genetic & environmental influences & hence have potential as prognostic & stratification biomarkers & also as read-outs of intervention efficacyLoci-specific DNA 5-methylcytosine levels at CpG sites are easily measured at scale in biological samples. They vary across individuals and this variation has been robustly associated with a range of disease phenotypes and environmental exposures [1][2][3][4][5][6]. As interindividual DNA methylation is specified by the interaction of both genotypic and environmental influences [7,8], it may be a more powerful prognostic biomarker than either genotypic or lifestyle factors alone [9]. DNA methylation is dynamic throughout the lifecourse and is potentially modifiable by therapeutic interventions. This raises the possibility of sensitive real-time biomarkers of disease status to track intervention efficacy [10].Locus-specific observations were first to demonstrate the role of early life environmental influence on interindividual variation in methylation. For instance, postnatal maternal care in rats or childhood trauma in humans, affects DNA methylation levels at the NR3C1 gene in blood and the hippocampus; methylation levels at this locus then predict adult psychopathology [11][12][13]. Observations such as this have inspired many epigenome wide association studies (EWAS) to determine the epigenetic consequences of early life influences. Although epigenetic programming was originally envisioned to pertain to very early life factors (i.e., fetal programming, see for example [14]), it could be generalized to account for environmental influences throughout the lifecourse. Some of the best evidence for the later life effect of the environment on DNA methylation is from Fasanelli and colleagues, who showed (by EWAS) that smoking reversibly caused hypomethylation of the AHRR and FR2L3 genes in adults and that

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Section: Dna Methylation Marks Integrate Genetic and Environmental Inflsupporting

confidence: 50%

Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies?

et al. 2017

Self Cite

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“…In brief, thoracic aortae were stripped of endothelial and adventitial layers by microdissection, and then SMCs were dispersed by treatment with trypsin and collagenase. Cells were maintained in DMEM supplemented with F12 and 10% FBS and used from passages [5][6][7][8][9][10][11][12][13][14][15]. SMC preparations are routinely tested for smooth muscle differentiation marker gene expression, and only those that are deemed at least 85% pure by these measurements are used for further experimentation.…”

Section: Methodsmentioning

confidence: 99%

Blood pressure–associated polymorphism controls ARHGAP42 expression via serum response factor DNA binding

Xue¹,

Mangum²,

Dee³

et al. 2017

Journal of Clinical Investigation

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“…There have been numerous reports of SNPs affecting the hypermethylation of proximal and trans-gene CpG islands, [2][3][4][5][6] Table 3. Allele frequencies for the non-hg19 SNP alleles that occur most frequently in the indicated data sets and that create or eliminate CpG's.…”

Section: Discussionmentioning

confidence: 99%

Impact of SNPs on CpG Islands in the MYC and HRAS oncogenes and in a wide variety of tumor suppressor genes: A multi-cancer approach

et al. 2016

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Single nucleotide polymorphisms (SNPs) that occur within CpG Islands may lead to increased hypermethylation if a SNP allele has the potential to form a CpG dinucleotide, as well as potentially lead to hypomethylation if a SNP allele eliminates a CpG dinucleotide. We analyzed CpG-related SNP allele frequencies in whole genome sequences (WGS) across 5 TCGA cancer datasets, thereby exploiting a more recent appreciation for signaling pathway degeneracy in cancer. The cancer data sets were analyzed for SNPs in CpG islands associated with the oncogenes, HRAS and MYC, and in the CpG islands associated with the tumor suppressor genes, APC, DCC, and RB1. We determined that one SNP allele (rs3824120) in a CpG island associated with MYC which eliminated a CpG was more common in the cancer datasets than in the 100Genomes databases (p < 0.01). For HRAS, 2 SNP alleles (rs112690925, rs7939028) that created CpG's occurred significantly less frequently in the cancer data sets than in the general SNP databases (e.g., rs7939028, p < 0.0002, in comparison with AllSNPs (142)). Also, one SNP allele (rs4940177) that created a CpG in a CpG island associated with the DCC tumor suppressor gene, was more common in the cancer datasets (p < 0.0007). To understand a broader picture of the potential of SNP alleles to create CpG's in CpG islands of tumor suppressor genes, we developed a scripted algorithm to assess the SNP alleles associated with the CpG islands of 43 tumor suppressor genes. The following tumor suppressor genes have the possibility of significant, percent increases in their CpG counts, depending on which SNP allele(s) is present: VHL, BRCA1, BRCA2, CHEK2, PTEN and RB1.

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Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation

Cited by 298 publications

References 51 publications

Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies?

Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies?

Blood pressure–associated polymorphism controls ARHGAP42 expression via serum response factor DNA binding

Impact of SNPs on CpG Islands in the MYC and HRAS oncogenes and in a wide variety of tumor suppressor genes: A multi-cancer approach

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