Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics

Yi-Juan Hu, Sonja I Berndt, Stefan Gustafsson, Andrea Ganna, Joel Hirschhorn, Kari E North, Erik Ingelsson, Dan-Yu Lin, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Veronique Vitart

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)236-48
Number of pages13
JournalAmerican Journal of Human Genetics
Volume93
Issue number2
DOIs
Publication statusPublished - 8 Aug 2013

Keywords

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

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