title: Meta-analysis of gene-level associations for rare variants based on single-variant statistics.
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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
authors.forename: Yi-Juan
authors.initials: YJ
authors.lastname: Hu
authors.forename: Sonja I
authors.initials: SI
authors.lastname: Berndt
authors.forename: Stefan
authors.initials: S
authors.lastname: Gustafsson
authors.forename: Andrea
authors.initials: A
authors.lastname: Ganna
authors.forename: Joel
authors.initials: J
authors.lastname: Hirschhorn
authors.forename: Kari E
authors.initials: KE
authors.lastname: North
authors.forename: Erik
authors.initials: E
authors.lastname: Ingelsson
authors.forename: Dan-Yu
authors.initials: DY
authors.lastname: Lin
mesh_terms: D003198:Computer Simulation; D005787:Gene Frequency; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011973:Receptors, LDL / Q000235:genetics*; D018035:Receptors, Odorant / Q000235:genetics*; D012984:Software
chemical_list: C573412:LDLR protein, human; D011973:Receptors, LDL; D018035:Receptors, Odorant
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