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Abstract:
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With the development of sequencing technologies, the number of sequenced genomes is increasing rapidly. If sequenced samples from external studies are used as control samples (external control samples) in addition to control samples from the study itself (internal control samples), power for rare variant tests can be greatly improved without additional sequencing costs. 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 problem, we propose novel single and gene- or region-based rare-variant tests. Our approach is based on the insight that the strength of batch effects can be evaluated by comparing allele frequencies between internal and external control samples. Specifically, to mitigate possible batch effects for the single variant test, we propose a shrinkage estimator of a log odds ratio ; we then extend it to gene- or region-based tests. From simulation experiments and the analysis of real genetic data from AMD and T2D studies, we show that our method can improve power while controlling for type I error rates.
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