Paper Info
Title
FOLD: a method to optimize power in meta-analysis of genetic association studies with overlapping subjects.
Abstract
Motivation: In genetic association studies, meta-analyses are widely used to increase the statistical power by aggregating information from multiple studies. In meta-analyses, participating studies often share the same individuals due to the shared use of publicly available control data or accidental recruiting of the same subjects. As such overlapping can inflate false positive rate, overlapping subjects are traditionally split in the studies prior to meta-analysis, which requires access to genotype data and is not always possible. Fortunately, recently developed meta-analysis methods can systematically account for overlapping subjects at the summary statistics level. Results: We identify and report a phenomenon that these methods for overlapping subjects can yield low power. For instance, in our simulation involving a meta-analysis of five studies that share 20% of individuals, whereas the traditional splitting method achieved 80% power, none of the new methods exceeded 32% power. We found that this low power resulted from the unaccounted differences between shared and unshared individuals in terms of their contributions towards the final statistic. Here, we propose an optimal summary-statistic-based method termed as FOLD that increases the power of meta-analysis involving studies with overlapping subjects.
Year
DOI
Venue
2017
10.1093/bioinformatics/btx463
BIOINFORMATICS
Field
DocType
Volume
Genotype,Computer science,Genome-wide association study,Genetic association,Software,Bioinformatics,Meta-analysis,Meta-Analysis as Topic
Journal
33
Issue
ISSN
Citations 
24
1367-4803
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Emma E Kim100.34
Seunghoon Lee200.34
Cue Hyunkyu Lee300.34
Hyunjung Oh400.34
Kyuyoung Song500.34
Buhm Han6508.89