Multi-Trait Genetic Analysis Reveals Clinically Interpretable Hypertension Subtypes.
cardiovascular diseases
epidemiology
hypertension
medical genetics
precision medicine
risk factors
risk scores
Journal
Circulation. Genomic and precision medicine
ISSN: 2574-8300
Titre abrégé: Circ Genom Precis Med
Pays: United States
ID NLM: 101714113
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
pubmed:
24
5
2022
medline:
19
8
2022
entrez:
23
5
2022
Statut:
ppublish
Résumé
Hypertension comprises a heterogeneous range of phenotypes. We asked whether underlying genetic structure could explain a part of this heterogeneity. Our study sample comprised N=198 148 FinnGen participants (56% women, mean age 58 years) and N=21 168 well-phenotyped FINRISK participants (53% women, mean age 50 years). First, we identified genetic hypertension components with an unsupervised Bayesian non-negative matrix factorization algorithm using public genome-wide association data for 144 genetic hypertension variants and 16 clinical traits. For these components, we computed their (1) cross-sectional associations with clinical traits in FINRISK using linear regression and (2) longitudinal associations with incident adverse outcomes in FinnGen using Cox regression. We observed 4 genetic hypertension components corresponding to recognizable clinical phenotypes: obesity (high body mass index), dyslipidemia (low high-density lipoprotein cholesterol and high triglycerides), hypolipidemia (low low-density lipoprotein cholesterol and low total cholesterol), and short stature. In FINRISK, all hypertension components had robust associations with their respective clinical characteristics. In FinnGen, the Obesity component was associated with increased diabetes risk (hazard ratio per 1 SD increase 1.08 [Bonferroni corrected CI, 1.05-1.10]) and the Hypolipidemia component with increased autoimmune disease risk (hazard ratio per 1 SD increase 1.05 [Bonferroni corrected CI, 1.03-1.07]). In addition, all hypertension components were related to both hypertension and cardiovascular disease. Our unsupervised analysis demonstrates that the genetic basis of hypertension can be understood as a mixture of 4 broad, clinically interpretable components capturing disease heterogeneity. These components could be used to stratify individuals into specific genetic subtypes and, therefore, to benefit personalized health care and pharmaceutical research.
Sections du résumé
BACKGROUND
Hypertension comprises a heterogeneous range of phenotypes. We asked whether underlying genetic structure could explain a part of this heterogeneity.
METHODS
Our study sample comprised N=198 148 FinnGen participants (56% women, mean age 58 years) and N=21 168 well-phenotyped FINRISK participants (53% women, mean age 50 years). First, we identified genetic hypertension components with an unsupervised Bayesian non-negative matrix factorization algorithm using public genome-wide association data for 144 genetic hypertension variants and 16 clinical traits. For these components, we computed their (1) cross-sectional associations with clinical traits in FINRISK using linear regression and (2) longitudinal associations with incident adverse outcomes in FinnGen using Cox regression.
RESULTS
We observed 4 genetic hypertension components corresponding to recognizable clinical phenotypes: obesity (high body mass index), dyslipidemia (low high-density lipoprotein cholesterol and high triglycerides), hypolipidemia (low low-density lipoprotein cholesterol and low total cholesterol), and short stature. In FINRISK, all hypertension components had robust associations with their respective clinical characteristics. In FinnGen, the Obesity component was associated with increased diabetes risk (hazard ratio per 1 SD increase 1.08 [Bonferroni corrected CI, 1.05-1.10]) and the Hypolipidemia component with increased autoimmune disease risk (hazard ratio per 1 SD increase 1.05 [Bonferroni corrected CI, 1.03-1.07]). In addition, all hypertension components were related to both hypertension and cardiovascular disease.
CONCLUSIONS
Our unsupervised analysis demonstrates that the genetic basis of hypertension can be understood as a mixture of 4 broad, clinically interpretable components capturing disease heterogeneity. These components could be used to stratify individuals into specific genetic subtypes and, therefore, to benefit personalized health care and pharmaceutical research.
Identifiants
pubmed: 35604428
doi: 10.1161/CIRCGEN.121.003583
pmc: PMC9558213
mid: NIHMS1807114
doi:
Substances chimiques
Cholesterol
97C5T2UQ7J
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e003583Subventions
Organisme : NIDDK NIH HHS
ID : K23 DK114551
Pays : United States
Organisme : NIDDK NIH HHS
ID : L30 DK106874
Pays : United States
Organisme : NIDDK NIH HHS
ID : R03 DK131249
Pays : United States
Références
Nat Genet. 2021 Apr;53(4):420-425
pubmed: 33692568
Nat Genet. 2010 Jun;42(6):508-14
pubmed: 20453842
PLoS Genet. 2017 Apr 3;13(4):e1006706
pubmed: 28369058
Genet Epidemiol. 2017 Feb;41(2):145-151
pubmed: 27990689
Nat Genet. 2010 Feb;42(2):105-16
pubmed: 20081858
Ann Epidemiol. 2000 Aug;10(6):389-400
pubmed: 10964005
Sci Rep. 2017 Mar 07;7:43965
pubmed: 28266630
PLoS Med. 2015 Mar 31;12(3):e1001779
pubmed: 25826379
Libyan J Med. 2008 Jun 01;3(2):84-90
pubmed: 21499464
Front Genet. 2020 Feb 28;11:157
pubmed: 32180801
Hypertension. 2021 Apr;77(4):1119-1127
pubmed: 33611940
Trends Genet. 2010 Jun;26(6):266-74
pubmed: 20381893
Lancet. 2010 Jan 16;375(9710):181-3
pubmed: 20109902
Nature. 1999 Oct 21;401(6755):788-91
pubmed: 10548103
Nat Genet. 2018 Apr;50(4):572-580
pubmed: 29632379
Nat Genet. 2015 Sep;47(9):979-986
pubmed: 26192919
Nat Genet. 2013 Jan;45(1):76-82
pubmed: 23202124
Nat Med. 2020 Apr;26(4):549-557
pubmed: 32273609
Nat Genet. 2019 Jun;51(6):957-972
pubmed: 31152163
PLoS Med. 2017 Sep 12;14(9):e1002383
pubmed: 28898252
Nat Genet. 2019 Oct;51(10):1459-1474
pubmed: 31578528
Nat Commun. 2019 Sep 11;10(1):4130
pubmed: 31511532
J Clin Hypertens (Greenwich). 2020 Sep;22(9):1546-1553
pubmed: 33460260
Nat Genet. 2018 Mar;50(3):401-413
pubmed: 29507422
Curr Opin Nephrol Hypertens. 2016 Mar;25(2):87-93
pubmed: 26717315
Hum Mol Genet. 2019 Nov 21;28(R2):R151-R161
pubmed: 31411675
Genes (Basel). 2020 Oct 27;11(11):
pubmed: 33121163
Metab Syndr Relat Disord. 2020 Apr;18(3):121-127
pubmed: 31928498
Genome Med. 2020 May 18;12(1):44
pubmed: 32423490
Diabetes. 2014 Dec;63(12):4369-77
pubmed: 25048195
Bioinformatics. 2019 Apr 15;35(8):1395-1403
pubmed: 30239588
IEEE Trans Pattern Anal Mach Intell. 2013 Jul;35(7):1592-605
pubmed: 23681989
Int J Epidemiol. 2018 Jun 1;47(3):696-696i
pubmed: 29165699
Medicine (Baltimore). 2017 Dec;96(50):e9233
pubmed: 29390353
Metabolism. 2014 May;63(5):633-9
pubmed: 24641884
Nat Genet. 2018 Oct;50(10):1412-1425
pubmed: 30224653
J Immunol Res. 2019 Feb 28;2019:7403796
pubmed: 30944837
J Cardiovasc Transl Res. 2017 Jun;10(3):275-284
pubmed: 28258421
PLoS Med. 2018 Sep 21;15(9):e1002654
pubmed: 30240442
PLoS Genet. 2009 Mar;5(3):e1000409
pubmed: 19266077