Learning in glaucoma genetic risk assessment

Zhuo Zhang, Jiang Liu, Chee Keong Kwoh, Xueling Sim, Wan Ting Tay, Yonghua Tan, Fengshou Yin, Tien Yin Wong

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Genome Wide Association (GWA) studies are powerful tools to identify genes involved in common human diseases, and are becoming increasingly important in genetic epidemiology research. However, the statistical approaches behind GWA studies lack capability in taking into account the possible interactions among genetic markers; and true disease variants may be lost in statistical noise due to high threshold. A typical GWA study reports a few highly suspected signals, e.g. Single-nucleotide polymorphisms (SNPs), which usually account for a tiny portion of overall genetic risks for the disease of interest. This study proposes a computational learning approach in addition to parametric statistical methods along with a filtering mechanism, to build glaucoma genetic risk assessment model. Our data set was obtained from Singapore Malay Eye Study (SiMES), genotyped on Illumina 610quad arrays. We constructed case-control data set with 233 glaucoma and 458 healthy samples. A standard case-control association test was conducted on post-QC dataset with more than 500k SNPs. Genetic profile is constructed using genotype information from a list of 412 SNPs filtered by a relaxed pvalue threshold of 1×10-3, and forms the feature space for learning. Among the five learning algorithms we performed, Support Vector Machines with radial kernel (SVM-radial) achieved the best result, with area under curve (ROC) of 99.4% and accuracy of 95.9%. The result illustrates that, learning approach in post GWAS data analysis is able to accurately assess genetic risk for glaucoma. The approach is more robust and comprehensive than individual SNPs matching method. We will further validate our results in several other data sets obtained in consequential population studies conducted in Singapore.

Original languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages6182-6185
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: 31 Aug 20104 Sep 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Conference

Conference2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Country/TerritoryArgentina
CityBuenos Aires
Period31/08/104/09/10

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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