|
1
|
GBD 2013 Mortality and Causes of Death
Collaborators: Global, regional, and national age-sex specific
all-cause and cause-specific mortality for 240 causes of death,
1990–2013: A systematic analysis for the Global Burden of Disease
Study 2013. Lancet. 385:117–171. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Benjamin EJ, Blaha MJ, Chiuve SE, Cushman
M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C,
et al: American Heart Association Statistics Committee and Stroke
Statistics Subcommittee: Heart disease and stroke statistics-2017
update: A report from the American Heart Association. Circulation.
135:e146–e603. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
National Center for Health Statistics:
Mortality multiple cause micro-data files, 2014: Public-use data
file and documentation: NHLBI tabulations.
|
|
4
|
Ministry of Health, Labour and Welfare,
Japan: Vital statistics.
|
|
5
|
Schunkert H, König IR, Kathiresan S,
Reilly MP, Assimes TL, Holm H, Preuss M, Stewart AFR, Barbalic M,
Gieger C, et al: Cardiogenics; CARDIoGRAM Consortium: Large-scale
association analysis identifies 13 new susceptibility loci for
coronary artery disease. Nat Genet. 43:333–338. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Golbus JR, Stitziel NO, Zhao W, Xue C,
Farrall M, McPherson R, Erdmann J, Deloukas P, Watkins H, Schunkert
H, et al: CARDIoGRAMplusC4D, Myocardial Infarction Genetics
(MIGen), Exome Sequencing Project and Early-Onset Myocardial
Infarction (ESP EOMI), and the Pakistan Risk of Myocardial
Infarction Study (PROMIS) Consortia*: Common and rare genetic
variation in CCR2, CCR5, or CX3CR1 and risk of atherosclerotic
coronary heart disease and glucometabolic traits. Circ Cardiovasc
Genet. 9:250–258. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Lu X, Wang L, Chen S, He L, Yang X, Shi Y,
Cheng J, Zhang L, Gu CC, Huang J, et al: Coronary ARtery DIsease
Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Consortium:
Genome-wide association study in Han Chinese identifies four new
susceptibility loci for coronary artery disease. Nat Genet.
44:890–894. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Nikpay M, Goel A, Won H-H, Hall LM,
Willenborg C, Kanoni S, Saleheen D, Kyriakou T, Nelson CP, Hopewell
JC, et al: A comprehensive 1,000 Genomes-based genome-wide
association meta-analysis of coronary artery disease. Nat Genet.
47:1121–1130. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Wakil SM, Ram R, Muiya NP, Mehta M, Andres
E, Mazhar N, Baz B, Hagos S, Alshahid M, Meyer BF, et al: A
genome-wide association study reveals susceptibility loci for
myocardial infarction/coronary artery disease in Saudi Arabs.
Atherosclerosis. σ245:62–70. 2016. View Article : Google Scholar
|
|
10
|
Yamada Y, Fuku N, Tanaka M, Aoyagi Y,
Sawabe M, Metoki N, Yoshida H, Satoh K, Kato K, Watanabe S, et al:
Identification of CELSR1 as a susceptibility gene for ischemic
stroke in Japanese individuals by a genome-wide association study.
Atherosclerosis. 207:144–149. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Kubo M, Hata J, Ninomiya T, Matsuda K,
Yonemoto K, Nakano T, Matsushita T, Yamazaki K, Ohnishi Y, Saito S,
et al: A nonsynonymous SNP in PRKCH (protein kinase C η) increases
the risk of cerebral infarction. Nat Genet. 39:212–217. 2007.
View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Gouveia LO, Sobral J, Vicente AM, Ferro JM
and Oliveira SA: Replication of the CELSR1 association with
ischemic stroke in a Portuguese case-control cohort.
Atherosclerosis. 217:260–262. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Akinyemi RO, Ovbiagele B, Akpalu A,
Jenkins C, Sagoe K, Owolabi L, Sarfo F, Obiako R, Gebreziabher M,
Melikam E, et al: SIREN Investigators as Members of the H3Africa
Consortium: Stroke genomics in people of African ancestry: Charting
new paths. Cardiovasc J Afr. 26(Suppl 1): S39–S49. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Meschia JF, Nalls M, Matarin M, Brott TG,
Brown RD Jr, Hardy J, Kissela B, Rich SS, Singleton A, Hernandez D,
et al: Siblings With Ischemic Stroke Study Investigators: Siblings
with ischemic stroke study: Results of a genome-wide scan for
stroke loci. Stroke. 42:2726–2732. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Kilarski LL, Achterberg S, Devan WJ,
Traylor M, Malik R, Lindgren A, Pare G, Sharma P, Slowik A, Thijs
V, et al: GARNET Collaborative Research Group, Wellcome Trust Case
Control Consortium 2, Australian Stroke Genetic Collaborative, the
METASTROKE Consortium, and the International Stroke Genetics
Consortium: Meta-analysis in more than 17,900 cases of ischemic
stroke reveals a novel association at 12q24.12. Neurology.
83:678–685. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Yamada Y, Matsui K, Takeuchi I and
Fujimaki T: Association of genetic variants with dyslipidemia and
chronic kidney disease in a longitudinal population-based genetic
epidemiological study. Int J Mol Med. 35:1290–1300. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Yamada Y, Matsui K, Takeuchi I, Oguri M
and Fujimaki T: Association of genetic variants of the α-kinase 1
gene with type 2 diabetes mellitus in a longitudinal
population-based genetic epidemiological study. Biomed Rep.
3:347–354. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Yamada Y, Matsui K, Takeuchi I and
Fujimaki T: Association of genetic variants with coronary artery
disease and ischemic stroke in a longitudinal population-based
genetic epidemiological study. Biomed Rep. 3:413–419. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Yamada Y, Matsui K, Takeuchi I, Oguri M
and Fujimaki T: Association of genetic variants with hypertension
in a longitudinal population-based genetic epidemiological study.
Int J Mol Med. 35:1189–1198. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Oguri M, Fujimaki T, Horibe H, Kato K,
Matsui K, Takeuchi I and Yamada Y: Obesity-related changes in
clinical parameters and conditions in a longitudinal
population-based epidemiological study. Obes Res Clin Pract.
11:299–314. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Grove ML, Yu B, Cochran BJ, Haritunians T,
Bis JC, Taylor KD, Hansen M, Borecki IB, Cupples LA, Fornage M, et
al: Best practices and joint calling of the HumanExome BeadChip:
The CHARGE Consortium. PLoS One. 8:e680952013. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Price AL, Patterson NJ, Plenge RM,
Weinblatt ME, Shadick NA and Reich D: Principal components analysis
corrects for stratification in genome-wide association studies. Nat
Genet. 38:904–909. 2006. View
Article : Google Scholar : PubMed/NCBI
|
|
23
|
R Development Core Team: R: A language and
environment for statistical computing. Vienna, Austria; 2016
|
|
24
|
RStudio Team: RStudio: Integrated
development environment for R. RStudio, Inc. Boston, MA; 2015
|
|
25
|
Liang KY and Zeger SL: Longitudinal data
analysis using generalized linear models. Biometrika. 73:13–22.
1986. View Article : Google Scholar
|
|
26
|
Hanley JA, Negassa A, Edwardes MD and
Forrester JE: Statistical analysis of correlated data using
generalized estimating equations: An orientation. Am J Epidemiol.
157:364–375. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Halekoh U, Højsgaard S and Yan J: The R
package geepack for generalized estimating equations. J Stat Softw.
15:1–11. 2006. View Article : Google Scholar
|
|
28
|
Williams FMK, Carter AM, Hysi PG,
Surdulescu G, Hodgkiss D, Soranzo N, Traylor M, Bevan S, Dichgans
M, Rothwell PMW, et al: EuroCLOT Investigators; Wellcome Trust Case
Control Consortium 2; MOnica Risk, Genetics, Archiving and
Monograph; MetaStroke; International Stroke Genetics Consortium:
Ischemic stroke is associated with the ABO locus: The EuroCLOT
study. Ann Neurol. 73:16–31. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Holmen OL, Zhang H, Fan Y, Hovelson DH,
Schmidt EM, Zhou W, Guo Y, Zhang J, Langhammer A, Løchen ML, et al:
Systematic evaluation of coding variation identifies a candidate
causal variant in TM6SF2 influencing total cholesterol and
myocardial infarction risk. Nat Genet. 46:345–351. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Benjamini Y and Hochberg Y: Controlling
the false discovery rate: A practical and powerful approach to
multiple testing. J R Stat Soc B. 57:289–300. 1995.
|
|
31
|
Sitlani CM, Rice KM, Lumley T, McKnight B,
Cupples LA, Avery CL, Noordam R, Stricker BHC, Whitsel EA and Psaty
BM: Generalized estimating equations for genome-wide association
studies using longitudinal phenotype data. Stat Med. 34:118–130.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Voorman A, Rice K and Lumley T: Fast
computation for genome-wide association studies using boosted
one-step statistics. Bioinformatics. 28:1818–1822. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
1000 Genomes Project Consortium; Abecasis
GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME
and McVean GA: A map of human genome variation from
population-scale sequencing. Nature. 467:1061–1073. 2010.
View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Machiela MJ and Chanock SJ: LDlink: A
web-based application for exploring population-specific haplotype
structure and linking correlated alleles of possible functional
variants. Bioinformatics. 31:3555–3557. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Leslie R, O'Donnell CJ and Johnson AD:
GRASP: Analysis of genotype-phenotype results from 1390 genome-wide
association studies and corresponding open access database.
Bioinformatics. 30:i185–i194. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Piñero J, Queralt-Rosinach N, Bravo À,
Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F and Furlong LI:
DisGeNET: A discovery platform for the dynamical exploration of
human diseases and their genes. Database (Oxford).
2015:bav0282015.PubMed/NCBI
|
|
37
|
MacArthur J, Bowler E, Cerezo M, Gil L,
Hall P, Hastings E, Junkins H, McMahon A, Milano A, Morales J, et
al: The new NHGRI-EBI Catalog of published genome-wide association
studies (GWAS Catalog). Nucleic Acids Res. 45(D1): D896–D901. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Warde-Farley D, Donaldson SL, Comes O,
Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT,
et al: The GeneMANIA prediction server: Biological network
integration for gene prioritization and predicting gene function.
Nucleic Acids Res. 38:W214–220. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Montojo J, Zuberi K, Rodriguez H, Bader GD
and Morris Q: GeneMANIA: Fast gene network construction and
function prediction for Cytoscape. F1000Res. 3:1532014.PubMed/NCBI
|
|
40
|
Montojo J, Zuberi K, Rodriguez H, Kazi F,
Wright G, Donaldson SL, Morris Q and Bader GD: GeneMANIA Cytoscape
plugin: Fast gene function predictions on the desktop.
Bioinformatics. 26:2927–2928. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Shannon P, Markiel A, Ozier O, Baliga NS,
Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A
software environment for integrated models of biomolecular
interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Sherry ST, Ward MH, Kholodov M, Baker J,
Phan L, Smigielski EM and Sirotkin K: dbSNP: The NCBI database of
genetic variation. Nucleic Acids Res. 29:308–311. 2001. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Warnes G, Gorjanc G, Leisch F and Man M:
Genetics: Population Genetics. 2013
|
|
44
|
Stacey M, Lin HH, Hilyard KL, Gordon S and
McKnight AJ: Human epidermal growth factor (EGF) module-containing
mucin-like hormone receptor 3 is a new member of the EGF-TM7 family
that recognizes a ligand on human macrophages and activated
neutrophils. J Biol Chem. 276:18863–18870. 2001. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Libby P: Inflammation in atherosclerosis.
Nature. 420:868–874. 2002. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Greaves DR and Gordon S: Immunity,
atherosclerosis and cardiovascular disease. Trends Immunol.
22:180–181. 2001. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Libby P: Inflammation in atherosclerosis.
Arterioscler Thromb Vasc Biol. 32:2045–2051. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Uhlén M, Fagerberg L, Hallström BM,
Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C,
Sjöstedt E, Asplund A, et al: Proteomics. Tissue-based map of the
human proteome. Science. 347:12604192015.
|
|
49
|
Thul PJ, Åkesson L, Wiking M, Mahdessian
D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM,
et al: A subcellular map of the human proteome. Science.
356:eaal33212017. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Wang C, Liu X, Liu Y, Zhang Q, Yao Z,
Huang B, Zhang P, Li N and Cao X: Zinc finger protein 64 promotes
Toll-like receptor-triggered proinflammatory and type I interferon
production in macrophages by enhancing p65 subunit activation. J
Biol Chem. 288:24600–24608. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Somia NV, Schmitt MJ, Vetter DE, van
Antwerp D, Heinemann SF and Verma IM: LFG: An anti-apoptotic gene
that provides protection from Fas-mediated cell death. Proc Natl
Acad Sci USA. 96:12667–12672. 1999. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Strasser A, Jost PJ and Nagata S: The many
roles of FAS receptor signaling in the immune system. Immunity.
30:180–192. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Schütz C, Oelke M, Schneck JP, Mackensen A
and Fleck M: Killer artificial antigen-presenting cells: The
synthetic embodiment of a ‘guided missile’. Immunotherapy.
2:539–550. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Lenardo MJ: Fas and the art of lymphocyte
maintenance. J Exp Med. 183:721–724. 1996. View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Hanada R, Leibbrandt A, Hanada T, Kitaoka
S, Furuyashiki T, Fujihara H, Trichereau J, Paolino M, Qadri F,
Plehm R, et al: Central control of fever and female body
temperature by RANKL/RANK. Nature. 462:505–509. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
56
|
Shimamura M, Nakagami H, Osako MK,
Kurinami H, Koriyama H, Zhengda P, Tomioka H, Tenma A, Wakayama K
and Morishita R: OPG/RANKL/RANK axis is a critical inflammatory
signaling system in ischemic brain in mice. Proc Natl Acad Sci USA.
111:8191–8196. 2014. View Article : Google Scholar : PubMed/NCBI
|
