Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants

PLoS One. 2013 May 31;8(5):e62161. doi: 10.1371/journal.pone.0062161. Print 2013.

Abstract

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the "noise" from 6-12 non-causal SNPs will cancel out the "signal" of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Chromosomes, Human, Pair 10*
  • Computer Simulation
  • Gene Frequency
  • Genome-Wide Association Study
  • Haplotypes
  • Humans
  • Linkage Disequilibrium
  • Models, Genetic*
  • Phenotype
  • Polymorphism, Single Nucleotide*
  • Receptors, Complement / genetics*

Substances

  • Receptors, Complement
  • VSIG4 protein, human