Investigation of Single Nucleotide Polymorphisms Associated to Familial Combined Hyperlipidemia with Random Forests

Antonino Staiano, Maria Donata di Taranto, Elena Bloise, Maria Nicoletta D'Agostino, Antonietta D'Angelo, Gennaro Marotta, Marco Gentile, Fabrizio Jossa, Arcangelo Iannuzzi, Paolo Rubba, Giuliana Fortunato

Research output: Chapter in Book/Report/Conference proceedingChapter


Single nucleotide polymorphisms (SNPs) are the foremost part of many genome association studies. Selecting a subset of SNPs that is sufficiently informative but still small enough to reduce the genotyping overhead is an important step towards disease-gene association. In this work, a Random Forest (RF) approach to informative SNPs selection in Familial Combined Hyperlipidemia (FCH) is proposed. FCH is the most common form of familial hyperlipidemia. Affected patients have elevated levels of plasma triglycerides and/or total cholesterol and show increased risk of premature coronary heart disease. In order to identify susceptibility markers for FCH we perform the analysis of 21 SNPs in ten genes associated with high cardiovascular risk. RF appears to be a useful technique in identifying gene polymorphisms involved in FCH: the identified SNPs confirmed some variants in a couple of genes as genetic markers of FCH as proved in various studies in scientific literature and lead us to report for the first time a further gene association with FCH. This result could be promising and encourages to further investigate on the role of the identified gene in the development of FCH phenotype.

Original languageEnglish
Title of host publicationSmart Innovation, Systems and Technologies
Number of pages10
Publication statusPublished - 2013

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)21903018
ISSN (Electronic)21903026


  • Familial combined hyperlipidemia
  • Feature selection
  • Genome association
  • Machine learning
  • Random forests
  • Single nucleotide polymorphisms

ASJC Scopus subject areas

  • Computer Science(all)
  • Decision Sciences(all)


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