Abstract
Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.
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Acknowledgements
The Genetic Analysis workshops were supported by GAW grant R01 GM031575 from the National Institute of General Medical Sciences. Preparation of the Genetic Analysis Work-shop 17 Simulated Exome Dataset was supported in part by NIH R01 MH059490 and used sequencing data from the 1000 Genomes Project (http://www.1000genomes.org). This work was conducted in the framework of basic research expenditure of universities in Heilongjiang Province, special fund of Heilongjiang University (no. HDJCCX-201631).
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Chen, L., Wang, Y. & Zhou, Y. Association analysis of multiple traits by an approach of combining \(P\) values. J Genet 97, 79–85 (2018). https://doi.org/10.1007/s12041-018-0885-0
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DOI: https://doi.org/10.1007/s12041-018-0885-0