How to Build CHD@ZJU

CHD related Articles were retrieved from Pubmed, by entering keywords "coronary heart disease" and constrict the publish date from 2000/1/1 to now (2013/1/23). As a result, totally 115898 articles were found and their abstracts were downloaded for text mining. Since some articles didn't contain abstracts, only 88396 abstracts remained.
The text-mining process to get CHD related genes could be divided in to 5 following steps:

  • 1) Extracting all keywords from abstracts and ignoring those keywords start with numbers. 101402 keywords were extracted.

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  • 2) Input these keywords into Gene library in ArrayTrack and find possible related genes. 4674 genes were then found.

  • 3) Put these 4674 genes again into pubmed abstracts to find related aticles. Only genes which offical name or there keyword description (such as prolactin for gene PRL) could be found in the abstract would be remained. As a result, 1247 genes were remained.

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  • 4) Manually examined on the 1247 genes to validate it was acutally related to CHD. Some genes would be filtered if it represents other meanings (such as gene CAD, Entrez ID:790, carbamoyl-phosphate synthetase 2, is mostly meant coronary arterial disease in articles). 681 genes were then validated with at least one reference.

  • 5) All genes was compared with 1078 CHD genes in RGD database, and 370 genes were overlapped. These 370 genes were labels as "RGD_Supported" and the other 293 genes were labels as "REFERED". All 663 genes had supported references in CHD@ZJU which were examined by step 4.

    • How To contact Us

    Collaboration Information: Prof. Xiaohui Fan (fanxh@zju.edu.cn)

    Website using assistance : Leihong Wu (11019004@zju.edu.cn)




    "Candidate gene genotypes, along with conventional risk factor assessment, improve estimation of coronary heart disease risk in healthy UK men."
  • Author:"Humphries, Steve E;Cooper, Jackie A;Talmud, Philippa J;Miller, George J"

  • Published Year:2007

  • Journal:Clinical chemistry

  • Abstract:"BACKGROUND: One of the aims of cardiovascular genetics is to test the efficacy of the use of genetic information to predict cardiovascular risk. We therefore investigated whether inclusion of a set of common variants in candidate genes along with conventional risk factor (CRF) assessment enhanced coronary heart disease (CHD)-risk algorithms. METHODS: We followed middle-aged men in the prospective Northwick Park Heart Study II (NPHSII) for 10.8 years and analyzed complete trait and genotype information available on 2057 men (183 CHD events). RESULTS: Of the 12 genes previously associated with CHD risk, in stepwise multivariate risk analysis, uncoupling protein 2 (UCP2; P = 0.0001), apolipoprotein E (APOE; P = 0.0003), lipoprotein lipase (LPL; P = 0.007), and apolipoprotein AIV (APOA4; P = 0.04) remained in the model. Their combined area under the ROC curve (A(ROC)) was 0.62 (0.58-0.66) [12.6% detection rate for a 5% false positive rate (DR(5))]. The A(ROC) for the CRFs age, triglyceride, cholesterol, systolic blood pressure, and smoking was 0.66 (0.61-0.70) (DR(5) = 14.2%). Combining CRFs and genotypes significantly improved discrimination (P = 0.001). Inclusion of previously demonstrated interactions of smoking with LPL, interleukin-6 (IL6), and platelet/endothelial cell adhesion molecule (PECAM1) genotypes increased the A(ROC) to 0.72 (0.68-0.76) for a DR(5) of 19.1% (P = 0.01 vs CRF combined with genotypes). CONCLUSIONS: For a modest panel of selected genotypes, CHD-risk estimates incorporating CRFs and genotype-risk factor interactions were more effective than risk estimates that used CRFs alone."

  • 10.1373/clinchem.2006.074591

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