ACE2基因多态与冠心病/心梗的关联研究及全基因组关联研究中的通路分析方法
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摘要
第一部分中国汉族人群中ACE2基因多态和冠心病及心肌梗塞的关联研究
越来越多的证据显示ACE2(血管紧张素转换酶2)可能是对心血管具有保护性作用的蛋白;但是相应的人群研究却很少。因此我们进行了一项病例对照研究,考察ACE2基因多态和CHD(冠心病)以及MI(心肌梗塞)的关系。在中国汉族人群中,选取了811名CHD病例(其中508名是MI病例)和905名正常对照,通过PCR-RFLP方法鉴定了三个ACE2基因单核苷酸多态位点(1075A/G、8790A/G和16854G/C)的基因型。这些位点间存在连锁不平衡(r~2,0.854～0.973)。由于ACE2基因处在X染色体上,统计分析根据不同的性别进行。在女性中,校正环境变量后发现1075A/G(P=0.026,OR=1.98)和16854G/C(P=0.028,OR=1.97)两个位点在隐形模型下和MI相关联。在男性中有两种常见的单体型——AAG和GGC。不饮酒的男性个体中,和最常见的单体型AAG的携带者相比,在校正环境因素后携带单体型GGC的个体具有1.76倍的CHD患病风险(95%可信区间是1.15-2.69;P=0.007),以及1.77倍的MI患病风险(95%可信区间是1.12-2.81,P=0.015)。总之,上述结果表明ACE2基因的常见变异可能影响女性MI的发生,它可能与饮酒存在交互作用影响中国男性CHD和MI的患病风险。
第二部分全基因组关联研究中的通路分析方法
全基因组关联(GWA)分析是复杂疾病遗传学研究的革命性进展。目前的研究一般使用单位点分析方法,报告最显著的关联位点和相关基因。由于检验的位点数目众多,遗传背景噪声过多,单位点分析存在一些局限。Wang等人首次将基因表达分析中的通路研究方法GSEA(Gene set enrichment analysis)应用于全基因组关联分析,作为传统单位点分析的补充。但是他们采用一个基因内所有SNP关联统计量的最大值来衡量基因和疾病的关联程度。这种方法受到了基因内部位点数目和局部连锁不平衡的影响,位点数目较多或LD较弱的基因会得到较大的关联统计量,可能在分析中造成偏倚。若在此基础上综合多个基因的统计量来分析基因集合的显著性,检验统计把握度也会受到影响。我们提出VSEA(Variable set enrichment analysis)方法,采用多种方案更加合理地计算基因和疾病关联的统计量,以改进GSEA方法使之更适合全基因组关联数据分析。各种VSEA算法通过模拟数据进行了检验,并在小型全基因组关联数据(LVH前导性研究)中得到了测试。结果显示,采用校正的基因关联统计量后,使用CHI2和类似算法的VSEA方法与Wang的GSEA相比得到了很大的改进;其他一些算法(如基于主成份分析的算法)也显示出一定的潜力。VSEA方法可以作为全基因组关联数据分析中传统统计分析方法的有效补充,为复杂疾病的GWA数据的解释提供更多的帮助。
Association study of ACE2 gene polymorphisms with coronary heart disease and myocardial infarction in Chinese Han population
Results are accumulating that ACE2 (angiotensin I-converting enzyme 2) might act as a protective protein for cardiovascular diseases; however, only a few studies in human populations have been carried out. This prompted us to perform a case-control study to investigate the relationship of ACE2 polymorphisms with CHD (coronary heart disease) and Ml (myocardial infarction). Three single nucleotide polymorphisms in the ACE2 gene (1075A/G, 8790A/G and 16854G/C) were genotyped by PCR-RFLP (restriction-fragment-length polymorphism) in 811 patients with CHD (of which 508 were patients with MI) and 905 normal controls in a Chinese population. The polymorphisms were in linkage disequilibrium (r~2 =0.854-0.973). Analyses were conducted by gender, because the ACE2 gene is on the X chromosome. In females, an association was detected with MI for 1075A/G (P=0.026; odds ratio=1.98) and 16854G/C (P=0.028; odds ratio=1.97) in recessive models after adjusting for covariates. In male subjects, two haplotypes (AAG and GGC) were common in frequency. In male subjects not consuming alcohol, the haplotype GGC was associated with a 1.76-fold risk of CHD [95% CI (confidence interval), 1.15-2.69; P=0.007] and a 1.77-fold risk of MI (95% CI, 1.12-2.81; P=0.015) with environmental factors adjusted, when compared with the most common haplotype AAG. In conclusion, the results of the present study indicate that common genetic variants in the ACE2 gene might impact on Ml in females, and may possibly interact with alcohol consumption to affect the risk of CHD and MI in Chinese males.
Variable Set Enrichment Analaysis for Testing Pathway Significance in Genome-wide Association Studies
The genome-wide association (GWA) approach is revolutionary in the genetic investigation of complex diseases. Currently, single locus analysis is generally assumed in GWAS and reports single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association. This strategy is limited since there are too much variables in the data, and the background noise is high. As a complementary approach, gene set enrichment analysis (GSEA), which evaluates the significance of biologically plausible pathways, was borrowed by Wang et. al. from gene expression studies. They used the maximum SNP association statistics from a gene as the score to measure association between the gene and the disease of interest. Even thought reasonable, such a choice is affected by the gene sizes and local linkage equilibrium within genes. Calculated on the basis of such gene scores, the summary statistics for gene sets tends to be biased. We propose a variable set enrichment analysis (VSEA) method to utilize multiple seemingly more reasonable algorithesms for gene score calculation, endeavoring to make GSEA method more suited in the GWA setting. The VSEA approach is tested by simulation studies and applied to a small scale GWA data set (LVH pilot study). Results show that, with the new adjusted gene scores, VSEA based on CHI2 and related algorithms have substantial improvement in terms of power compared to Wang's GSEA; Some other algorithms (e.g., principal component analysis based methods) are also potentially more effective. VSEA might complement the traditional methods for GWA analysis and provide additional insights into the interpretation of GWA data on complex diseases.
越来越多的证据显示ACE2(血管紧张素转换酶2)可能是对心血管具有保护性作用的蛋白;但是相应的人群研究却很少。因此我们进行了一项病例对照研究,考察ACE2基因多态和CHD(冠心病)以及MI(心肌梗塞)的关系。在中国汉族人群中,选取了811名CHD病例(其中508名是MI病例)和905名正常对照,通过PCR-RFLP方法鉴定了三个ACE2基因单核苷酸多态位点(1075A/G、8790A/G和16854G/C)的基因型。这些位点间存在连锁不平衡(r~2,0.854～0.973)。由于ACE2基因处在X染色体上,统计分析根据不同的性别进行。在女性中,校正环境变量后发现1075A/G(P=0.026,OR=1.98)和16854G/C(P=0.028,OR=1.97)两个位点在隐形模型下和MI相关联。在男性中有两种常见的单体型——AAG和GGC。不饮酒的男性个体中,和最常见的单体型AAG的携带者相比,在校正环境因素后携带单体型GGC的个体具有1.76倍的CHD患病风险(95%可信区间是1.15-2.69;P=0.007),以及1.77倍的MI患病风险(95%可信区间是1.12-2.81,P=0.015)。总之,上述结果表明ACE2基因的常见变异可能影响女性MI的发生,它可能与饮酒存在交互作用影响中国男性CHD和MI的患病风险。
第二部分全基因组关联研究中的通路分析方法
全基因组关联(GWA)分析是复杂疾病遗传学研究的革命性进展。目前的研究一般使用单位点分析方法,报告最显著的关联位点和相关基因。由于检验的位点数目众多,遗传背景噪声过多,单位点分析存在一些局限。Wang等人首次将基因表达分析中的通路研究方法GSEA(Gene set enrichment analysis)应用于全基因组关联分析,作为传统单位点分析的补充。但是他们采用一个基因内所有SNP关联统计量的最大值来衡量基因和疾病的关联程度。这种方法受到了基因内部位点数目和局部连锁不平衡的影响,位点数目较多或LD较弱的基因会得到较大的关联统计量,可能在分析中造成偏倚。若在此基础上综合多个基因的统计量来分析基因集合的显著性,检验统计把握度也会受到影响。我们提出VSEA(Variable set enrichment analysis)方法,采用多种方案更加合理地计算基因和疾病关联的统计量,以改进GSEA方法使之更适合全基因组关联数据分析。各种VSEA算法通过模拟数据进行了检验,并在小型全基因组关联数据(LVH前导性研究)中得到了测试。结果显示,采用校正的基因关联统计量后,使用CHI2和类似算法的VSEA方法与Wang的GSEA相比得到了很大的改进;其他一些算法(如基于主成份分析的算法)也显示出一定的潜力。VSEA方法可以作为全基因组关联数据分析中传统统计分析方法的有效补充,为复杂疾病的GWA数据的解释提供更多的帮助。
Association study of ACE2 gene polymorphisms with coronary heart disease and myocardial infarction in Chinese Han population
Results are accumulating that ACE2 (angiotensin I-converting enzyme 2) might act as a protective protein for cardiovascular diseases; however, only a few studies in human populations have been carried out. This prompted us to perform a case-control study to investigate the relationship of ACE2 polymorphisms with CHD (coronary heart disease) and Ml (myocardial infarction). Three single nucleotide polymorphisms in the ACE2 gene (1075A/G, 8790A/G and 16854G/C) were genotyped by PCR-RFLP (restriction-fragment-length polymorphism) in 811 patients with CHD (of which 508 were patients with MI) and 905 normal controls in a Chinese population. The polymorphisms were in linkage disequilibrium (r~2 =0.854-0.973). Analyses were conducted by gender, because the ACE2 gene is on the X chromosome. In females, an association was detected with MI for 1075A/G (P=0.026; odds ratio=1.98) and 16854G/C (P=0.028; odds ratio=1.97) in recessive models after adjusting for covariates. In male subjects, two haplotypes (AAG and GGC) were common in frequency. In male subjects not consuming alcohol, the haplotype GGC was associated with a 1.76-fold risk of CHD [95% CI (confidence interval), 1.15-2.69; P=0.007] and a 1.77-fold risk of MI (95% CI, 1.12-2.81; P=0.015) with environmental factors adjusted, when compared with the most common haplotype AAG. In conclusion, the results of the present study indicate that common genetic variants in the ACE2 gene might impact on Ml in females, and may possibly interact with alcohol consumption to affect the risk of CHD and MI in Chinese males.
Variable Set Enrichment Analaysis for Testing Pathway Significance in Genome-wide Association Studies
The genome-wide association (GWA) approach is revolutionary in the genetic investigation of complex diseases. Currently, single locus analysis is generally assumed in GWAS and reports single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association. This strategy is limited since there are too much variables in the data, and the background noise is high. As a complementary approach, gene set enrichment analysis (GSEA), which evaluates the significance of biologically plausible pathways, was borrowed by Wang et. al. from gene expression studies. They used the maximum SNP association statistics from a gene as the score to measure association between the gene and the disease of interest. Even thought reasonable, such a choice is affected by the gene sizes and local linkage equilibrium within genes. Calculated on the basis of such gene scores, the summary statistics for gene sets tends to be biased. We propose a variable set enrichment analysis (VSEA) method to utilize multiple seemingly more reasonable algorithesms for gene score calculation, endeavoring to make GSEA method more suited in the GWA setting. The VSEA approach is tested by simulation studies and applied to a small scale GWA data set (LVH pilot study). Results show that, with the new adjusted gene scores, VSEA based on CHI2 and related algorithms have substantial improvement in terms of power compared to Wang's GSEA; Some other algorithms (e.g., principal component analysis based methods) are also potentially more effective. VSEA might complement the traditional methods for GWA analysis and provide additional insights into the interpretation of GWA data on complex diseases.
引文
1. Wang, Q. (2005) Molecular genetics of coronary artery disease. Curr. Opin.Cardiol.20, 182-188
2. Hansson, G. K. (2005) Inflammation, atherosclerosis, and coronary artery disease.N. Engl. J. Med. 352, 1685-1695
3. Unger, T. (2002) The role of the renin-angiotensin system in the development of cardiovascular disease. Am. J. Cardiol. 89, 3A-9A
4. O'Keefe. J. H., Lurk, J. T., Kahatapitiya, R. C. and Haskin, J. A. (2003) The renin-angiotensin-aldosterone system as a target in coronary disease. Curr.Atheroscler. Rep. 5, 124-130
5. Kurdi, M., De Mello, W. C. and Booz, G.W. (2005) Working outside the system:an update on the unconventional behavior of the renin-angiotensin system components. Int. J. Biochem. Cell. Biol. 37, 1357-1367
6. Tipnis, S. R., Hooper, N. M., Hyde, R., Karran, E., Christie, G. and Turner, A. J.(2000) A human homolog of angiotensin-converting enzyme. Cloning and functional expression as a captopril-insensitive carboxypeptidase. J. Biol. Chem. 275,33238-33243
7. Donoghue, M., Hsieh, F., Baronas, E. et al. (2000) Acton S. A novel angiotensin-converting enzyme-related carboxypeptidase (ACE2) converts angiotensin I to angiotensin 1-9. Circ. Res. 87, E1-E9
8. Li, N., Zimpelmann, J., Cheng, K., Wilkins, J. A. and Burns, K. D. (2005) The role of angiotensin converting enzyme 2 in the generation of angiotensin 1-7 by rat proximal tubules. Am. J. Physiol. Renal Physiol. 288, F353-F362
9. Clark, M. A., Diz, D. I. and Tallant, E. A. (2001) Angiotensin-(1-7) downregulates the angiotensin II type 1 receptor in vascular smooth muscle cells.Hypertension 37, 1141-1146
10. Crackower, M. A., Sarao, R., Oudit, G. Y. et al. (2002) Angiotensin-converting enzyme 2 is an essential regulator of heart function. Nature 417, 822-828
11. Oudit, G. Y., Crackower, M. A., Backx, P. H. and Penninger, J. M. (2003) The role of ACE2 in cardiovascular physiology. Trends Cardiovasc. Med. 13, 93-101
12. Itoyama, S., Keicho, N., Hijikata, M. et al. (2005) Identification of an alternative 5_-untranslated exon and new polymorphisms of angiotensin-converting enzyme 2 gene: lack of association with SARS in the Vietnamese population. Am. J. Med.Genet. A 136, 52-57
13. Loot, A. E., Roks, A. J., Henning R.H., A. J. et al. (2002) Angiotensin-(1-7) attenuates the development of heart failure after myocardial infarction in rats.Circulation 105, 1548-1550
14. Burrell, L. M., Risvanis, J., Kubota, E. et al. (2005) Myocardial infarction increases ACE2 expression in rat and humans. Eur. Heart .1. 26, 369-375
15. Benjafield, A. V., Wang, W. Y. and Morris, B. J. (2004) No association of angiotensin-converting enzyme 2 gene (ACE2) polymorphisms with essential hypertension. Am. J. Hypertens. 17, 624-628
16. Chen, A. A., Barnes, G., Foti, A. et al. (2002) Two single nucleotide polymorphisms in the ACE2 locus are associated with cardiovascular disease. Gen.Epidem. 23, 272
17. Lieb, W., Graf, J., Gotz, A. et al. (2006) Association of angiotensin-converting enzyme 2 (ACE2) gene polymorphisms with parameters of left ventricular hypertrophy in men. J. Mol. Med. 84, 88-96
18 Zhou, X., Huang, J., Chen, J. et al. (2004) Thrombospondin-4 A387P polymorphism is not associated with coronary artery disease and myocardial infarction in the Chinese Han population. Clin. Sci. 106,495-500
19. Rose, G. A. and Blackburn, H. (1968) Cardiovascular survey methods. Monogr.Ser. World Health Organ. 56, 1-188
20. Chiu, R. W., Tang, N. L., Hui, D. S. et al. (2004) ACE2 gene polymorphisms do not affect outcome of severe acute respiratory syndrome. Clin. Chem. 50, 1683-1686
21. Abecasis, G. R. and Cookson, W. O. (2000) GOLD: graphical overview of linkage disequilibrium. Bioinformatics 16, 182-183
22. Schaid, D. J., Rowland, C. M., Tines, D. E., Jacobson, R. M. and Poland, G. A.(2002) Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am. J. Hum. Genet. 70, 425-434
23. Lee, W. C. (2003) Searching for disease-susceptibility loci by testing for Hardy-Weinberg disequilibrium in a gene bank of affected individuals. Am. J.Epidemiol. 158, 397-400
24. Hill, J. A. (2005) In vino veritas: alcohol and heart disease. Am. J. Med. Sci.329, 124-135
25. Marcus, F., Andrea, B. and Heribert, S. (2002) Renin angiotensin system and gender differences in the cardiovascular system. Cardiovasc. Res. 53, 672-677
26. Jane, F. R. (2001) Gender differences in the regulation of blood pressure.Hypertension 37, 1199-1208
27. Hunter, D. J. (2005) Gene-environment interactions in human diseases. Nat.Rev. Genet. 6, 287-298
1.Scott,L.J.,et al.,A genome-wide association study of type 2diahetes in Finns detects multiple susceptihility variants.Science,2007.316(5829):p.1341-5.
2.He,J.,et al.,Major causes of death among men and women in China.N Engl J Med,2005.353(11):p.1124-34.
3.WHO,Preventing Chronic Diseases:A Vital Investment:Overview.2005.
4.Stumpf,M.P.and D.B.Goldstein,Demography,recombination hotspot intensity,and the block structure of linkage disequilibrium.Curr Biol,2003.13(1):p.1-8.
5.Couzin,J.,Human genome.HapMap launched with pledges of $100million.Science,2002.298(5595):p.941-2.
6.Frazer,K.A.,et al.,A second generation human haplotype map of over 3.1 million SNPs.Nature,2007.449(7164):p.851-61.
7.Hirschhorn,J.N.,et al.,A comprehensive review of genetic association studies.Genet Med,2002.4(2):p.45-61.
8.Ioannidis,J.P.,Genetic associations:false or true? Trends Mol Med,2003.9(4):p.135-8.
9.Ioannidis,J.P.,et al.,Genetic associations in large versus small studies:an empirical assessment.Lancet,2003.361(9357):p.567-71.
10.Lohmueller,K.E.,et al.,Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.Nat Genet,2003.33(2):p.177-82.
11.Agerholm-Larsen,B.,B.C.Nordestgaard,and A.Tybjaerg-Hansen,ACE gene polymorphism in cardiovascular disease:meta-analyses of small and large studies in whites.Arterioscler Thromb Vasc Biol, 2000.20(2):p.484-92.
12.Fornage,M.,et al.,Variation in the region of the angiotensin-converting enzyme gene influences interindividual differences in blood pressure levels in young white males.Circulation,1998.97(18):p.1773-9.
13.Harrap,S.B.,et al.,The ACE gene I/D polymorphism is not associated with the blood pressure and cardiovascular benefits of ACE inhibition.Hypertension,2003.42(3):p.297-303.
14.Higaki,J.,et al.,Deletion allele of angiotensin-converting enzyme gene increases risk of essential hypertension in Japanese men:the Suita Study.Circulation,2000.101(17):p.2060-5.
15.O'Donnell,C.J.,et al.,Evidence for association and genetic linkage of the angiotensin-converting enzyme locus with hypertension and blood pressure in men but not women in the Framingham Heart Study.Circulation,1998.97(18):p.1766-72.
16.Moore,J.H.and S.M.Williams,New strategies for identifying gene-gene interactions in hypertension.Ann Med,2002.34(2):p.88-95.
17.Williams,S.M.,et al.,Combinations of variations in multiple genes are associated with hypertension.Hypertension,2000.36(1):p.2-6.
18.Moore,J.H.,The ubiquitous nature of epistasis in determining susceptibility to common human diseases.Hum Hered,2003.56(1-3):p.73-82.
19.Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.Nature,2007.447(7145):p.661-78.
20.Zeggini,E.,et al.,Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.Science,2007. 316(5829):p.1336-41.
21.Rao,O.C.,An overview of the genetic dissection of complex traits.Adv Genet,2008.60:p.3-34.
22.Wallace,C.,et al.,Genome-wide association study identifies genes for biomarkers of cardiovascular disease:serum urate and dyslipidemia.Am J Hum Genet,2008,82(1):p.139-49.
23.Wang,K.,M.Li,and M.Bucan,Pathway-Based Approaches for Analysis of Genomewide Association Studies.Am J Hum Genet,2007.81(6).
24.Neale,B.M.and P.C.Sham,The future of association studies:gene-based analysis and replication.Am J Hum Genet,2004.75(3):p.353-62.
25.Roeder,K.,et al.,Using linkage genome scans to improve power of association ingenome scans.Am J Hum Genet,2006.78(2):p.243-52.
26.Subramanian,A.,et al.,Gene set enrichment analysis:a knowledge-based approach for in terprering genome-wide expression profiles.Proc Natl Acad Sci U S A,2005.102(43):p.15545-50.
27.Mootha,V.K.,et al.,PGC-lalpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.Nat Genet,2003.34(3):p.267-73.
28.Patti,M.E.,et al.,Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes:Potential role of PGC1 and NRF1.Proc Natl Acad Sci U S A,2003.100(14):p.8466-71.
29.Petersen,K.F.,et al.,Impaired mitochondrial activity in the insulin-resistant offspring of patients with type 2 diabetes.N Engl J Med,2004.350(7):p.664-71.
30.Purcell,S.,et al.,PLINK:a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet,2007.81(3):p.559-75.
31.Xiong,M.,J.Zhao,and E.Boerwinkle,Generalized T2 test for genome association studies.Am J Hum Genet,2002.70(5):p.1257-68.
32.Balakrishnan,V.and L.D.Sanghvi,Distance between Populations on the Basis of Attribute Data.Biometrics,1968.24(4):p.859-865.
33.Zhou,H.,L.J.Wei,and X.Xu,Combining association tests across multiple genetic markers in case-control studies.Hum Hered,2008.65(3):p.166-74.
34.Meng,Z.,et al.,Selection of genetic markers for association analyses,using linkage disequilibrium and haplotypes.Am J Hum Genet,2003.73(1):p.115-30.
35.Gu,C.C.,et al.,Enhanced Detection of Genetic Association of Hypertensive Heart Disease by Analysis of Latent Phenotypes.Genetic Epidemiology,2008.in press.
36.Fung,H.C.,et al.,Genome-wide genotyping in Parkinson's disease and neurologically normal controis;first stage analysis and public release of data.Lancet Neurol,2006.5(11):p.911-6.
37.Efron,B.and R.Tibshirani,On testing the significance of sets of genes.The Annals of Applied Statistics,2007.1(1):p.107-129.
38.Stranger,B.E.,et al.,Population genomics of human gene expression.Nat Genet,2007.39(10):p.1217-24.
39.Goring,H.H.,et al.,Piscovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes.Nat Genet,2007.39(10):p.1208-16.
40.Dixon,A.L.,et al.,A genome-wide association study of global gene expression.Nat Genet,2007.39(10):p.1202-7.
41.Chasman,D.I.,On the Utility of Gene Set Methods in Genomewide AssociationStudies of Quantitative Traits.Genetic Epidemiology,2008.(Early online publication).
BioConductor:http://www.bioconductor.org/
R软件:http://www.r-project.org/
PLINK软件:http://pngu.mgh.harvard.edu/~purcell/plink/
KEGG:http://www.genome.jp/kegg/
BioCarta:http://www.biocarta.com/
Gene ontology:http://www.geneontology.org/
1. DJ. N. Hirschhorn et al., "A comprehensive review of genetic association studies.," Genet Med 4, no. 2 (2002): 45-61.
2. J. P. A. loannidis et al., "Genetic associations in large versus small studies: an empirical assessment," The Lancet 361, no. 9357 (2003): 567-571.
3. K. E. Lohmueller et al., "Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease," Nature Genetics 33, no. 2 (2003): 177-182.
4. N. J. Risch, "Searching for genetic determinants in the new millennium.," Nature 405, no. 6788 (2000): 847-56.
5. R. Culverhouse et al.. "A Perspective on Epistasis: Limits of Models Displaying No Main Effect,"Am J Hum Genet 70, no. 2 (2002): 461-471.
6. J. N. Hirschhorn and M. J. Daly, "Genome-wide association studies for common diseases and complex traits.," Nat Rev Genet 6, no. 2 (2005): 95-108.
7. B. M. Neale and P. C. Sham, "The Future of Association Studies: Gene-Based Analysis and Replication," Am J Hum Genet 75, no. 3 (2004): 353-362.
8. J. M. Chapman et al., "Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power," Human heredity 56, no. 1-3 (2003): 18-31.
9. Y. Benjamini and Y. Hochberg, "Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing," Journal of the Royal Statistical Society. Series B (Methodological) 57, no. 1 (1995): 289-300.
10. B. Devlin, K. Roeder, and L. Wasserman, "Analysis of multilocus models of association," Genetic Epidemiology 25, no. 1 (2003): 36-47.
11.J. Hoh, A. Wille, and J. Ott, "Trimming, Weighting, and Grouping SNPs in Human Case-Control Association Studies," Genome Research 11, no. 12 (2001):2115-2119.
12. K. Hao et al., "Power estimation of multiple SNP association test of case-control study and application," Genetic Epidemiology 26, no. 1 (2004): 22-30.
13.A. Wille, J. Hoh, and J. Ott, '"Sum statistics for the joint detection of multiple disease loci in case-control association studies with SNP markers." Genetic Epidemiology 25, no. 4 (2003): 350-359.
14. H. Zhou et al., "Combining association tests across multiple genetic markers in case-control studies," Human heredity 65, no. 3 (2008): 166-74.
15. M. Xiong, J. Zhao, and E. Boerwinkle, "Generalized T 2 test for genome association studies," Am J Hum Genet 70, no. 5 (2002): 1257-1268.
16. V. Balakrishnan and L. D. Sanghvi, "Distance between Populations on the Basis of Attribute Data,'" Biometrics 24, no. 4 (1968): 859-865.
17. D. Y. Lin a nd D. Zeng, "Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies," Journal of the American Statistical Association 101,no.473 (2006): 89-104.
18. A. R. Templeton, E. Boerwinkle, and C. F. Sing, "A Cladistic Analysis of Phenotypic Associations With Haplotypes Inferred From Restriction Endonuclease Mapping. I. Basic Theory and an Analysis of Alcohol Dehydrogenase Activity in Drosophila," Genetics 117, no. 2 (1987): 343-351.
19. J. Y. Tzeng, "Evolutionary-based grouping of haplotypes in association analysis,"Genetic Epidemiology 28, no. 3 (2005): 220-231.
20. E. R. B. Waldron, J. C. Whittaker, and D. J. Balding, "Fine mapping of disease genes via haplotype clustering," Genet Epidemiol 30, no. 2 (2006): 170-179.
21.I. Tachmazidou, C. J. Verzilli, and M. De Iorio, "Genetic Association Mapping via Evolution-Based Clustering of Haplotypes," PLoS Genetics 3, no. 7 (2007).
22. S. R. Browning, "Multilocus Association Mapping Using Variable-Length Markov Chains," The American Journal of Human Genetics 78 (2006): 903.
23. D. J. Balding, "A tutorial on statistical methods for population association studies," NATURE REVIEWS| GENETICS 7 (2006): 781.
24. A. Rinaldo et al., "Characterization of multilocus linkage disequilibrium," Genetic Epidemiology 28, no. 3 (2005): 193-206.
25. Kai Wang and Diana Abbott, "A principal components regression approach to multilocus genetic association studies," Genetic epidemiology 32, no. 2 (February 2008): 108-18.
26. Z. Meng et al., "Selection of Genetic Markers for Association Analyses, Using Linkage Disequilibrium and Haplotypes," Am J Hum Genet 73, no. 1 (2003):115-130.
27. J. Li and L. Ji, "Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix," Heredity 95 (2005): 221-227.
28. T. Wang and R. C. Elston, "Improved Power by Use of a Weighted Score Test for Linkage Disequilibrium Mapping," Am J Hum Genet 80, no. 2 (2007): 353-360.
2. Hansson, G. K. (2005) Inflammation, atherosclerosis, and coronary artery disease.N. Engl. J. Med. 352, 1685-1695
3. Unger, T. (2002) The role of the renin-angiotensin system in the development of cardiovascular disease. Am. J. Cardiol. 89, 3A-9A
4. O'Keefe. J. H., Lurk, J. T., Kahatapitiya, R. C. and Haskin, J. A. (2003) The renin-angiotensin-aldosterone system as a target in coronary disease. Curr.Atheroscler. Rep. 5, 124-130
5. Kurdi, M., De Mello, W. C. and Booz, G.W. (2005) Working outside the system:an update on the unconventional behavior of the renin-angiotensin system components. Int. J. Biochem. Cell. Biol. 37, 1357-1367
6. Tipnis, S. R., Hooper, N. M., Hyde, R., Karran, E., Christie, G. and Turner, A. J.(2000) A human homolog of angiotensin-converting enzyme. Cloning and functional expression as a captopril-insensitive carboxypeptidase. J. Biol. Chem. 275,33238-33243
7. Donoghue, M., Hsieh, F., Baronas, E. et al. (2000) Acton S. A novel angiotensin-converting enzyme-related carboxypeptidase (ACE2) converts angiotensin I to angiotensin 1-9. Circ. Res. 87, E1-E9
8. Li, N., Zimpelmann, J., Cheng, K., Wilkins, J. A. and Burns, K. D. (2005) The role of angiotensin converting enzyme 2 in the generation of angiotensin 1-7 by rat proximal tubules. Am. J. Physiol. Renal Physiol. 288, F353-F362
9. Clark, M. A., Diz, D. I. and Tallant, E. A. (2001) Angiotensin-(1-7) downregulates the angiotensin II type 1 receptor in vascular smooth muscle cells.Hypertension 37, 1141-1146
10. Crackower, M. A., Sarao, R., Oudit, G. Y. et al. (2002) Angiotensin-converting enzyme 2 is an essential regulator of heart function. Nature 417, 822-828
11. Oudit, G. Y., Crackower, M. A., Backx, P. H. and Penninger, J. M. (2003) The role of ACE2 in cardiovascular physiology. Trends Cardiovasc. Med. 13, 93-101
12. Itoyama, S., Keicho, N., Hijikata, M. et al. (2005) Identification of an alternative 5_-untranslated exon and new polymorphisms of angiotensin-converting enzyme 2 gene: lack of association with SARS in the Vietnamese population. Am. J. Med.Genet. A 136, 52-57
13. Loot, A. E., Roks, A. J., Henning R.H., A. J. et al. (2002) Angiotensin-(1-7) attenuates the development of heart failure after myocardial infarction in rats.Circulation 105, 1548-1550
14. Burrell, L. M., Risvanis, J., Kubota, E. et al. (2005) Myocardial infarction increases ACE2 expression in rat and humans. Eur. Heart .1. 26, 369-375
15. Benjafield, A. V., Wang, W. Y. and Morris, B. J. (2004) No association of angiotensin-converting enzyme 2 gene (ACE2) polymorphisms with essential hypertension. Am. J. Hypertens. 17, 624-628
16. Chen, A. A., Barnes, G., Foti, A. et al. (2002) Two single nucleotide polymorphisms in the ACE2 locus are associated with cardiovascular disease. Gen.Epidem. 23, 272
17. Lieb, W., Graf, J., Gotz, A. et al. (2006) Association of angiotensin-converting enzyme 2 (ACE2) gene polymorphisms with parameters of left ventricular hypertrophy in men. J. Mol. Med. 84, 88-96
18 Zhou, X., Huang, J., Chen, J. et al. (2004) Thrombospondin-4 A387P polymorphism is not associated with coronary artery disease and myocardial infarction in the Chinese Han population. Clin. Sci. 106,495-500
19. Rose, G. A. and Blackburn, H. (1968) Cardiovascular survey methods. Monogr.Ser. World Health Organ. 56, 1-188
20. Chiu, R. W., Tang, N. L., Hui, D. S. et al. (2004) ACE2 gene polymorphisms do not affect outcome of severe acute respiratory syndrome. Clin. Chem. 50, 1683-1686
21. Abecasis, G. R. and Cookson, W. O. (2000) GOLD: graphical overview of linkage disequilibrium. Bioinformatics 16, 182-183
22. Schaid, D. J., Rowland, C. M., Tines, D. E., Jacobson, R. M. and Poland, G. A.(2002) Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am. J. Hum. Genet. 70, 425-434
23. Lee, W. C. (2003) Searching for disease-susceptibility loci by testing for Hardy-Weinberg disequilibrium in a gene bank of affected individuals. Am. J.Epidemiol. 158, 397-400
24. Hill, J. A. (2005) In vino veritas: alcohol and heart disease. Am. J. Med. Sci.329, 124-135
25. Marcus, F., Andrea, B. and Heribert, S. (2002) Renin angiotensin system and gender differences in the cardiovascular system. Cardiovasc. Res. 53, 672-677
26. Jane, F. R. (2001) Gender differences in the regulation of blood pressure.Hypertension 37, 1199-1208
27. Hunter, D. J. (2005) Gene-environment interactions in human diseases. Nat.Rev. Genet. 6, 287-298
1.Scott,L.J.,et al.,A genome-wide association study of type 2diahetes in Finns detects multiple susceptihility variants.Science,2007.316(5829):p.1341-5.
2.He,J.,et al.,Major causes of death among men and women in China.N Engl J Med,2005.353(11):p.1124-34.
3.WHO,Preventing Chronic Diseases:A Vital Investment:Overview.2005.
4.Stumpf,M.P.and D.B.Goldstein,Demography,recombination hotspot intensity,and the block structure of linkage disequilibrium.Curr Biol,2003.13(1):p.1-8.
5.Couzin,J.,Human genome.HapMap launched with pledges of $100million.Science,2002.298(5595):p.941-2.
6.Frazer,K.A.,et al.,A second generation human haplotype map of over 3.1 million SNPs.Nature,2007.449(7164):p.851-61.
7.Hirschhorn,J.N.,et al.,A comprehensive review of genetic association studies.Genet Med,2002.4(2):p.45-61.
8.Ioannidis,J.P.,Genetic associations:false or true? Trends Mol Med,2003.9(4):p.135-8.
9.Ioannidis,J.P.,et al.,Genetic associations in large versus small studies:an empirical assessment.Lancet,2003.361(9357):p.567-71.
10.Lohmueller,K.E.,et al.,Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.Nat Genet,2003.33(2):p.177-82.
11.Agerholm-Larsen,B.,B.C.Nordestgaard,and A.Tybjaerg-Hansen,ACE gene polymorphism in cardiovascular disease:meta-analyses of small and large studies in whites.Arterioscler Thromb Vasc Biol, 2000.20(2):p.484-92.
12.Fornage,M.,et al.,Variation in the region of the angiotensin-converting enzyme gene influences interindividual differences in blood pressure levels in young white males.Circulation,1998.97(18):p.1773-9.
13.Harrap,S.B.,et al.,The ACE gene I/D polymorphism is not associated with the blood pressure and cardiovascular benefits of ACE inhibition.Hypertension,2003.42(3):p.297-303.
14.Higaki,J.,et al.,Deletion allele of angiotensin-converting enzyme gene increases risk of essential hypertension in Japanese men:the Suita Study.Circulation,2000.101(17):p.2060-5.
15.O'Donnell,C.J.,et al.,Evidence for association and genetic linkage of the angiotensin-converting enzyme locus with hypertension and blood pressure in men but not women in the Framingham Heart Study.Circulation,1998.97(18):p.1766-72.
16.Moore,J.H.and S.M.Williams,New strategies for identifying gene-gene interactions in hypertension.Ann Med,2002.34(2):p.88-95.
17.Williams,S.M.,et al.,Combinations of variations in multiple genes are associated with hypertension.Hypertension,2000.36(1):p.2-6.
18.Moore,J.H.,The ubiquitous nature of epistasis in determining susceptibility to common human diseases.Hum Hered,2003.56(1-3):p.73-82.
19.Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.Nature,2007.447(7145):p.661-78.
20.Zeggini,E.,et al.,Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.Science,2007. 316(5829):p.1336-41.
21.Rao,O.C.,An overview of the genetic dissection of complex traits.Adv Genet,2008.60:p.3-34.
22.Wallace,C.,et al.,Genome-wide association study identifies genes for biomarkers of cardiovascular disease:serum urate and dyslipidemia.Am J Hum Genet,2008,82(1):p.139-49.
23.Wang,K.,M.Li,and M.Bucan,Pathway-Based Approaches for Analysis of Genomewide Association Studies.Am J Hum Genet,2007.81(6).
24.Neale,B.M.and P.C.Sham,The future of association studies:gene-based analysis and replication.Am J Hum Genet,2004.75(3):p.353-62.
25.Roeder,K.,et al.,Using linkage genome scans to improve power of association ingenome scans.Am J Hum Genet,2006.78(2):p.243-52.
26.Subramanian,A.,et al.,Gene set enrichment analysis:a knowledge-based approach for in terprering genome-wide expression profiles.Proc Natl Acad Sci U S A,2005.102(43):p.15545-50.
27.Mootha,V.K.,et al.,PGC-lalpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.Nat Genet,2003.34(3):p.267-73.
28.Patti,M.E.,et al.,Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes:Potential role of PGC1 and NRF1.Proc Natl Acad Sci U S A,2003.100(14):p.8466-71.
29.Petersen,K.F.,et al.,Impaired mitochondrial activity in the insulin-resistant offspring of patients with type 2 diabetes.N Engl J Med,2004.350(7):p.664-71.
30.Purcell,S.,et al.,PLINK:a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet,2007.81(3):p.559-75.
31.Xiong,M.,J.Zhao,and E.Boerwinkle,Generalized T2 test for genome association studies.Am J Hum Genet,2002.70(5):p.1257-68.
32.Balakrishnan,V.and L.D.Sanghvi,Distance between Populations on the Basis of Attribute Data.Biometrics,1968.24(4):p.859-865.
33.Zhou,H.,L.J.Wei,and X.Xu,Combining association tests across multiple genetic markers in case-control studies.Hum Hered,2008.65(3):p.166-74.
34.Meng,Z.,et al.,Selection of genetic markers for association analyses,using linkage disequilibrium and haplotypes.Am J Hum Genet,2003.73(1):p.115-30.
35.Gu,C.C.,et al.,Enhanced Detection of Genetic Association of Hypertensive Heart Disease by Analysis of Latent Phenotypes.Genetic Epidemiology,2008.in press.
36.Fung,H.C.,et al.,Genome-wide genotyping in Parkinson's disease and neurologically normal controis;first stage analysis and public release of data.Lancet Neurol,2006.5(11):p.911-6.
37.Efron,B.and R.Tibshirani,On testing the significance of sets of genes.The Annals of Applied Statistics,2007.1(1):p.107-129.
38.Stranger,B.E.,et al.,Population genomics of human gene expression.Nat Genet,2007.39(10):p.1217-24.
39.Goring,H.H.,et al.,Piscovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes.Nat Genet,2007.39(10):p.1208-16.
40.Dixon,A.L.,et al.,A genome-wide association study of global gene expression.Nat Genet,2007.39(10):p.1202-7.
41.Chasman,D.I.,On the Utility of Gene Set Methods in Genomewide AssociationStudies of Quantitative Traits.Genetic Epidemiology,2008.(Early online publication).
BioConductor:http://www.bioconductor.org/
R软件:http://www.r-project.org/
PLINK软件:http://pngu.mgh.harvard.edu/~purcell/plink/
KEGG:http://www.genome.jp/kegg/
BioCarta:http://www.biocarta.com/
Gene ontology:http://www.geneontology.org/
1. DJ. N. Hirschhorn et al., "A comprehensive review of genetic association studies.," Genet Med 4, no. 2 (2002): 45-61.
2. J. P. A. loannidis et al., "Genetic associations in large versus small studies: an empirical assessment," The Lancet 361, no. 9357 (2003): 567-571.
3. K. E. Lohmueller et al., "Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease," Nature Genetics 33, no. 2 (2003): 177-182.
4. N. J. Risch, "Searching for genetic determinants in the new millennium.," Nature 405, no. 6788 (2000): 847-56.
5. R. Culverhouse et al.. "A Perspective on Epistasis: Limits of Models Displaying No Main Effect,"Am J Hum Genet 70, no. 2 (2002): 461-471.
6. J. N. Hirschhorn and M. J. Daly, "Genome-wide association studies for common diseases and complex traits.," Nat Rev Genet 6, no. 2 (2005): 95-108.
7. B. M. Neale and P. C. Sham, "The Future of Association Studies: Gene-Based Analysis and Replication," Am J Hum Genet 75, no. 3 (2004): 353-362.
8. J. M. Chapman et al., "Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power," Human heredity 56, no. 1-3 (2003): 18-31.
9. Y. Benjamini and Y. Hochberg, "Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing," Journal of the Royal Statistical Society. Series B (Methodological) 57, no. 1 (1995): 289-300.
10. B. Devlin, K. Roeder, and L. Wasserman, "Analysis of multilocus models of association," Genetic Epidemiology 25, no. 1 (2003): 36-47.
11.J. Hoh, A. Wille, and J. Ott, "Trimming, Weighting, and Grouping SNPs in Human Case-Control Association Studies," Genome Research 11, no. 12 (2001):2115-2119.
12. K. Hao et al., "Power estimation of multiple SNP association test of case-control study and application," Genetic Epidemiology 26, no. 1 (2004): 22-30.
13.A. Wille, J. Hoh, and J. Ott, '"Sum statistics for the joint detection of multiple disease loci in case-control association studies with SNP markers." Genetic Epidemiology 25, no. 4 (2003): 350-359.
14. H. Zhou et al., "Combining association tests across multiple genetic markers in case-control studies," Human heredity 65, no. 3 (2008): 166-74.
15. M. Xiong, J. Zhao, and E. Boerwinkle, "Generalized T 2 test for genome association studies," Am J Hum Genet 70, no. 5 (2002): 1257-1268.
16. V. Balakrishnan and L. D. Sanghvi, "Distance between Populations on the Basis of Attribute Data,'" Biometrics 24, no. 4 (1968): 859-865.
17. D. Y. Lin a nd D. Zeng, "Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies," Journal of the American Statistical Association 101,no.473 (2006): 89-104.
18. A. R. Templeton, E. Boerwinkle, and C. F. Sing, "A Cladistic Analysis of Phenotypic Associations With Haplotypes Inferred From Restriction Endonuclease Mapping. I. Basic Theory and an Analysis of Alcohol Dehydrogenase Activity in Drosophila," Genetics 117, no. 2 (1987): 343-351.
19. J. Y. Tzeng, "Evolutionary-based grouping of haplotypes in association analysis,"Genetic Epidemiology 28, no. 3 (2005): 220-231.
20. E. R. B. Waldron, J. C. Whittaker, and D. J. Balding, "Fine mapping of disease genes via haplotype clustering," Genet Epidemiol 30, no. 2 (2006): 170-179.
21.I. Tachmazidou, C. J. Verzilli, and M. De Iorio, "Genetic Association Mapping via Evolution-Based Clustering of Haplotypes," PLoS Genetics 3, no. 7 (2007).
22. S. R. Browning, "Multilocus Association Mapping Using Variable-Length Markov Chains," The American Journal of Human Genetics 78 (2006): 903.
23. D. J. Balding, "A tutorial on statistical methods for population association studies," NATURE REVIEWS| GENETICS 7 (2006): 781.
24. A. Rinaldo et al., "Characterization of multilocus linkage disequilibrium," Genetic Epidemiology 28, no. 3 (2005): 193-206.
25. Kai Wang and Diana Abbott, "A principal components regression approach to multilocus genetic association studies," Genetic epidemiology 32, no. 2 (February 2008): 108-18.
26. Z. Meng et al., "Selection of Genetic Markers for Association Analyses, Using Linkage Disequilibrium and Haplotypes," Am J Hum Genet 73, no. 1 (2003):115-130.
27. J. Li and L. Ji, "Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix," Heredity 95 (2005): 221-227.
28. T. Wang and R. C. Elston, "Improved Power by Use of a Weighted Score Test for Linkage Disequilibrium Mapping," Am J Hum Genet 80, no. 2 (2007): 353-360.
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