Meta-analysis of gene-level associations for rare variants based on single-variant statistics

Am J Hum Genet. 2013 Aug 8;93(2):236-48. doi: 10.1016/j.ajhg.2013.06.011. Epub 2013 Jul 25.

Abstract

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.

Publication types

  • Meta-Analysis
  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural

MeSH terms

  • Computer Simulation
  • Gene Frequency
  • Genetic Variation
  • Genome-Wide Association Study*
  • Genotype
  • Humans
  • Models, Genetic*
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Receptors, LDL / genetics*
  • Receptors, Odorant / genetics*
  • Software*

Substances

  • LDLR protein, human
  • Receptors, LDL
  • Receptors, Odorant

Grant support