Predicting disease risk using bootstrap ranking and classification algorithms

PLoS Comput Biol. 2013;9(8):e1003200. doi: 10.1371/journal.pcbi.1003200. Epub 2013 Aug 22.

Abstract

Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Area Under Curve
  • Cluster Analysis*
  • Computational Biology / methods*
  • Disease / genetics*
  • Genetic Predisposition to Disease*
  • Genotype
  • Humans
  • Models, Genetic
  • Polymorphism, Single Nucleotide
  • Reproducibility of Results
  • Sequence Analysis, DNA / methods

Grant support

This work was supported by grants from the European Research Council (ERC), the U.S. National Institutes of Health (NIH), and the EU SYSCOL project to ES. ES is the incumbent of the Soretta and Henry Shapiro career development chair. OM is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.