Development of risk prediction models for glioma based on genome-wide association study findings and comprehensive evaluation of predictive performances

Yingjie Zhao, Gong Chen, Hongjie Yu, Lingna Hu, Yunmeng Bian, Dapeng Yun, Juxiang Chen, Ying Mao, Hongyan Chen, Daru Lu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Over 14 common single nucleotide polymorphisms (SNP) have been consistently identified from genome-wide association studies (GWAS) as associated with glioma risk in European background. The extent to which and how these genetic variants can improve the prediction of glioma risk has was not been investigated. In this study, we employed three independent case-control datasets in Chinese populations, tested GWAS signals in dataset1, validated association results in dataset2, developed prediction models in dataset2 for the consistently replicated SNPs, refined the consistently replicated SNPs in dataset3 and developed tailored models for Chinese populations. For model construction, we aggregated the contribution of multiple SNPs into genetic risk scores (count GRS and weighed GRS) or predicted risks from logistic regression analyses (PRFLR). In dataset2, the area under receiver operating characteristic curves (AUC) of the 5 consistently replicated SNPs by PRFLR(SNPs) was 0.615, higher than those of all GRSs(ranging from 0.607 to 0.611, all P>0.05). The AUC of genetic profile significantly exceeded that of family history (fmc) alone (AUC=0.535, all P<0.001). The best model in our study comprised "PRURA +fmc" (AUC=0.646) in dataset3. Further model assessment analyses provided additional evidence. This study indicates that genetic markers have potential value for risk prediction of glioma.

Original languageEnglish (US)
Pages (from-to)8311-8325
Number of pages15
JournalOncotarget
Volume9
Issue number9
DOIs
StatePublished - 2018
Externally publishedYes

Keywords

  • Genetic risk score
  • Genome wide association study
  • Glioma
  • Prediction risk from logistic regression analyses
  • Risk prediction

ASJC Scopus subject areas

  • Oncology

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