About Multi-Stage GWASs
In genome-wide association studies (GWASs) of common diseases/traits, commonly we use a multi-stage setting to discover associations and to validate identified findings. Within such settings, we discover associations in primary studies and validate findings in replication studies. Since it is diffcult to access data with detailed individual measurements, summary-statistics-based methods have become popular in GWASs. Here we implement RPower, RRate and RFdr to address three statistical issues in multi-stage GWASs. RPower uses an Empirical Bayes (EB) based method to estimate the power of replication study in GWASs. We can use this method to determine the sample size needed in the replication study to achieve certain statistical power. RRate provides the estimation of replication rate (RR), which is the probability of a primary association being validated in the replication study. We can also use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study. RFdr is a novel method to determine significance levels jointly for two-stage GWASs. It finds the most powerful significance levels when controlling the false discovery rate (Fdr) in the two-stage study at a certain level.

Also, we often analyze multiple GWASs with the same phenotype together to discover associated genetic variants with higher power. Since it is diffcult to access data with detailed individual measurements, summary-statistics-based meta-analysis methods have become popular to jointly analyze data sets from multiple GWASs. Here we implement a novel summary-statistics-based joint analysis method based on controlling the joint local false discovery rate (Jlfdr). This method is the most powerful summary-statistics-based joint analysis method when controlling the false discovery rate (Fdr) at a certain level. Details about the method can be seen in our reference paper below.
Upload Files for Multi-Stage GWASs
Brife User Guide
  1. Upload files should be .txt files and contain only the summary statistics.
  2. The first row should be "column names".
  3. The column names should contain at least one of the two in the following "Reference Type" ("SNPID" or "BP"). The program will first collect the intersection in all files by the reference.
  4. The column names should contain "SE" for the computation of meta-analysis as a comparison.
  5. Please make sure that a single file is less than 30 MB.
  6. Your email will be protected and only be used to send feedback of this application.
  7. We provide an example of input summary statistics and output results HERE.
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  • Determine the sample size needed
    Calculate the replication rates
  • Yes No
Stage 1: Primary Study
Step 1

Upload summary statistics from the primary study
file: none
Stage 2: Replication Study
Step 2

Design the replication study
Step 3

Upload summary statistics from the replication study
file: none
Step 4

Discover all validated associations
Reference
W. Jiang and W. Yu
"Power estimation and sample size determination for replication studies of genome-wide association studies."
BMC Genomics, 17(Suppl 1):3, 2016.

W. Jiang, J-H. Xue and W. Yu
"What is the probability of replicating a statistically significant association in genome-wide association studies?"
Briefings in Bioinformatics, 18(6): 928-939, 2017.

W. Jiang and W. Yu
"Jointly determining significance levels of primary and replication studies by controlling the false discovery rate in two-stage genome-wide association studies."
Statistical Methods in Medical Research, 0962280216687168, 2017.

W. Jiang and W. Yu
"Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies."
Bioinformatics, 33(4): 500-507, 2017.