A 2-phased approach for detecting multiple loci associations with traits

Sunwon Lee, Jaewoo Kang, Junho Oh

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The recent advance in SNP genotyping has made a signifi cant contribution to reduction of the costs for large-scale genotyping. The development also has dramatically increased the size of the SNP genotype data. The increase in the volume of the data, however, has posed a huge obstacle to the conventional analysis techniques that are typically vulnerable to the high-dimensionality problem. To address the issue, we propose a method that exploits two well-tested models: the document-term model and the transaction analysis model. The proposed method consists of two phases. In the fi rst phase, we reduce the dimensions of the SNP genotype data by extracting signifi cant SNPs through transformation of the data in lieu of the document-term model. In the second phase, we discover the association rules that signify the relations between the SNPs and the traits, through the application of transactional analysis in the reduced-dimension genotype data. We validated the discovered rules through literature survey. Experiments were also carried out using the HGDP panel data provided by the Foundation Jean Dausset-CEPH, which prove the validity of our new method for identifying appropriate dimensional reduction and associations of multiple SNPs and traits. This paper is an extended version of our workshop paper presented in the 2010 International Workshop on Data Mining for High Throughput Data from Genome-Wide Association Studies.

Original languageEnglish
Pages (from-to)535-556
Number of pages22
JournalInternational Journal of Data Mining and Bioinformatics
Volume6
Issue number5
DOIs
Publication statusPublished - 2012 Sep 1

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Keywords

  • Apriori algorithm
  • Bioinformatics
  • Class association rule mining
  • Data mining
  • GWAS
  • SNP
  • Term frequency - inverse document frequency
  • TF-IDF

ASJC Scopus subject areas

  • Library and Information Sciences
  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)

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