Dataset Information

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.

ABSTRACT: Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

SUBMITTER: Liu C

PROVIDER: S-EPMC5320952 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.

Liu Chunyu C Kraja Aldi T AT Smith Jennifer A JA Brody Jennifer A JA Franceschini Nora N Bis Joshua C JC Rice Kenneth K Morrison Alanna C AC Lu Yingchang Y Weiss Stefan S Guo Xiuqing X Palmas Walter W Martin Lisa W LW Chen Yii-Der Ida YD Surendran Praveen P Drenos Fotios F Cook James P JP Auer Paul L PL Chu Audrey Y AY Giri Ayush A Zhao Wei W Jakobsdottir Johanna J Lin Li-An LA Stafford Jeanette M JM Amin Najaf N Mei Hao H Yao Jie J Voorman Arend A Larson Martin G MG Grove Megan L ML Smith Albert V AV Hwang Shih-Jen SJ Chen Han H Huan Tianxiao T Kosova Gulum G Stitziel Nathan O NO Kathiresan Sekar S Samani Nilesh N Schunkert Heribert H Deloukas Panos P Li Man M Fuchsberger Christian C Pattaro Cristian C Gorski Mathias M Kooperberg Charles C Papanicolaou George J GJ Rossouw Jacques E JE Faul Jessica D JD Kardia Sharon L R SL Bouchard Claude C Raffel Leslie J LJ Uitterlinden André G AG Franco Oscar H OH Vasan Ramachandran S RS O'Donnell Christopher J CJ Taylor Kent D KD Liu Kiang K Bottinger Erwin P EP Gottesman Omri O Daw E Warwick EW Giulianini Franco F Ganesh Santhi S Salfati Elias E Harris Tamara B TB Launer Lenore J LJ Dörr Marcus M Felix Stephan B SB Rettig Rainer R Völzke Henry H Kim Eric E Lee Wen-Jane WJ Lee I-Te IT Sheu Wayne H-H WH Tsosie Krystal S KS Edwards Digna R Velez DR Liu Yongmei Y Correa Adolfo A Weir David R DR Völker Uwe U Ridker Paul M PM Boerwinkle Eric E Gudnason Vilmundur V Reiner Alexander P AP van Duijn Cornelia M CM Borecki Ingrid B IB Edwards Todd L TL Chakravarti Aravinda A Rotter Jerome I JI Psaty Bruce M BM Loos Ruth J F RJ Fornage Myriam M Ehret Georg B GB Newton-Cheh Christopher C Levy Daniel D Chasman Daniel I DI

Nature genetics 20160912 10

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT ...[more]

PMID: 27618448

Similar Datasets

Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the âbiological signatureâ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either a custom long oligonucleotide microarray for the interrogation of 231 genes encompassed by QTL1 and QTL2, or a TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL genes. A âflip-dyeâ or âbalanced blockâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Six âbalanced blockâ normalized files are submitted for the long oligonucleotide array interrogating the hearts from S versus S.LEW(5)x6x9 animals, fourteen âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x9 animals, and twelve âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x11 animals. GPR and MEV files cannot be located.

Project description:Background. Genome-wide association studies have succeeded in detecting novel common variants which associate with complex diseases. As a result of the fast changes in next generation sequencing technology, a large number of sequencing data are generated, which offers great opportunities to identify rare variants that could explain a larger proportion of missing heritability. Many effective and powerful methods are proposed, although they are usually limited to continuous, dichotomous or ordinal traits. Notice that traits having nominal categorical features are commonly observed in complex diseases, especially in mental disorders, which motivates the incorporation of the characteristics of the categorical trait into association studies with rare and common variants. Methods. We construct two simple and intuitive nonparametric tests, MIT and aMIT, based on mutual information for detecting association between genetic variants in a gene or region and a categorical trait. MIT and aMIT can gauge the difference among the distributions of rare and common variants across a region given every categorical trait value. If there is little association between variants and a categorical trait, MIT or aMIT approximately equals zero. The larger the difference in distributions, the greater values MIT and aMIT have. Therefore, MIT and aMIT have the potential for detecting functional variants. Results.We checked the validity of proposed statistics and compared them to the existing ones through extensive simulation studies with varied combinations of the numbers of variants of rare causal, rare non-causal, common causal, and common non-causal, deleterious and protective, various minor allele frequencies and different levels of linkage disequilibrium. The results show our methods have higher statistical power than conventional ones, including the likelihood based score test, in most cases: (1) there are multiple genetic variants in a gene or region; (2) both protective and deleterious variants are present; (3) there exist rare and common variants; and (4) more than half of the variants are neutral. The proposed tests are applied to the data from Collaborative Studies on Genetics of Alcoholism, and a competent performance is exhibited therein. Discussion. As a complementary to the existing methods mainly focusing on quantitative traits, this study provides the nonparametric tests MIT and aMIT for detecting variants associated with categorical trait. Furthermore, we plan to investigate the association between rare variants and multiple categorical traits.

Dataset Information

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.

Publications

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure