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Improving power for rare‐variant tests by integrating external controls
Author(s) -
Lee Seunggeun,
Kim Sehee,
Fuchsberger Christian
Publication year - 2017
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22057
Subject(s) - type i and type ii errors , statistical power , computer science , type 2 diabetes , statistics , computational biology , false discovery rate , odds ratio , word error rate , biology , data mining , genetics , mathematics , gene , artificial intelligence , diabetes mellitus , endocrinology
Due to the drop in sequencing cost, the number of sequenced genomes is increasing rapidly. To improve power of rare‐variant tests, these sequenced samples could be used as external control samples in addition to control samples from the study itself. However, when using external controls, possible batch effects due to the use of different sequencing platforms or genotype calling pipelines can dramatically increase type I error rates. To address this, we propose novel summary statistics based single and gene‐ or region‐based rare‐variant tests that allow the integration of external controls while controlling for type I error. Our approach is based on the insight that batch effects on a given variant can be assessed by comparing odds ratio estimates using internal controls only vs. using combined control samples of internal and external controls. From simulation experiments and the analysis of data from age‐related macular degeneration and type 2 diabetes studies, we demonstrate that our method can substantially improve power while controlling for type I error rate.