Jointly determining significance levels of primary and replication studies by controlling the false discovery rate in two-stage genome-wide association studies. (September 2018)

• Record Type: Journal Article
• Authors: Jiang, Wei; Yu, Weichuan
• Is Part Of: Statistical methods in medical research. Volume 27:Number 9(2018)
• Page Start: 2795
• Page End: 2808
• Publication Date: 2018-09
• Subjects: False discovery rate -- genome-wide association studies -- power -- primary study -- replication study -- significance levels; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216687168
• Languages: English
• ISSNs: 0962-2802

Abstract

In genome-wide association studies, we normally discover associations between genetic variants and diseases/traits in primary studies, and validate the findings in replication studies. We consider the associations identified in both primary and replication studies as true findings. An important question under this two-stage setting is how to determine significance levels in both studies. In traditional methods, significance levels of the primary and replication studies are determined separately. We argue that the separate determination strategy reduces the power in the overall two-stage study. Therefore, we propose a novel method to determine significance levels jointly. Our method is a reanalysis method that needs summary statistics from both studies. We find the most powerful significance levels when controlling the false discovery rate in the two-stage study. To enjoy the power improvement from the joint determination method, we need to select single nucleotide polymorphisms for replication at a less stringent significance level. This is a common practice in studies designed for discovery purpose. We suggest this practice is also suitable in studies with validation purpose in order to identify more true findings. Simulation experiments show that our method can provide more power than traditional methods and that the false discovery rate is well-controlled. Empirical experiments on datasets of five diseases/traits demonstrate that our method can help identify more associations. The R-package is available at:http://bioinformatics.ust.hk/RFdr.html .