Title | Meta-analysis of gene-level associations for rare variants based on single-variant statistics. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Hu, Yi-Juan, Sonja I. Berndt, Stefan Gustafsson, Andrea Ganna, Joel Hirschhorn, Kari E. North, Erik Ingelsson, and Dan-Yu Lin |
Corporate Authors | Genetic Investigation of ANthropometric Traits(GIANT) Consortium |
Journal | Am J Hum Genet |
Volume | 93 |
Issue | 2 |
Pagination | 236-48 |
Date Published | 2013 Aug 08 |
ISSN | 1537-6605 |
Keywords | Computer Simulation, Gene Frequency, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide, Receptors, LDL, Receptors, Odorant, Software |
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. |
DOI | 10.1016/j.ajhg.2013.06.011 |
Alternate Journal | Am J Hum Genet |
Original Publication | Meta-analysis of gene-level associations for rare variants based on single-variant statistics. |
PubMed ID | 23891470 |
PubMed Central ID | PMC3738834 |
Grant List | K05 AA017688 / AA / NIAAA NIH HHS / United States UL1 RR025005 / RR / NCRR NIH HHS / United States R01CA082659 / CA / NCI NIH HHS / United States HHSN268201100011I / HL / NHLBI NIH HHS / United States U01HG004803 / HG / NHGRI NIH HHS / United States U01HG004402 / HG / NHGRI NIH HHS / United States HHSN26820110006C / / PHS HHS / United States U01 HG004803 / HG / NHGRI NIH HHS / United States HHSN26820110005C / / PHS HHS / United States / ImNIH / Intramural NIH HHS / United States 14136 / CRUK_ / Cancer Research UK / United Kingdom P01CA142538 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R01 HL087641 / HL / NHLBI NIH HHS / United States HHSN26800625226C / / PHS HHS / United States R01HL086694 / HL / NHLBI NIH HHS / United States R01 CA082659 / CA / NCI NIH HHS / United States MR/K013351/1 / MRC_ / Medical Research Council / United Kingdom HHSN268201100012C / HL / NHLBI NIH HHS / United States UL1RR025005 / RR / NCRR NIH HHS / United States R01HL59367 / HL / NHLBI NIH HHS / United States HHSN268201100010C / HL / NHLBI NIH HHS / United States HHSN26820110009C / / PHS HHS / United States R01 HL059367 / HL / NHLBI NIH HHS / United States HHSN268201100011C / HL / NHLBI NIH HHS / United States R01 HL086694 / HL / NHLBI NIH HHS / United States R01 DK075787 / DK / NIDDK NIH HHS / United States HHSN268200625226C / / PHS HHS / United States U01 HG004402 / HG / NHGRI NIH HHS / United States 097117 / / Wellcome Trust / United Kingdom HHSN26820110007C / / PHS HHS / United States HHSN26820110008C / / PHS HHS / United States G0401527 / MRC_ / Medical Research Council / United Kingdom R01HL087641 / HL / NHLBI NIH HHS / United States G1000143 / MRC_ / Medical Research Council / United Kingdom 090532 / / Wellcome Trust / United Kingdom MC_PC_U127561128 / MRC_ / Medical Research Council / United Kingdom |
Meta-analysis of gene-level associations for rare variants based on single-variant statistics.
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