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Association analysis of rare and common variants with multiple traits based on variable reduction method

  • LILI CHEN (a1) (a2), YONG WANG (a1) and YAJING ZHOU (a2)

Summary

Pleiotropy, the effect of one variant on multiple traits, is widespread in complex diseases. Joint analysis of multiple traits can improve statistical power to detect genetic variants and uncover the underlying genetic mechanism. Currently, a large number of existing methods target one common variant or only rare variants. Increasing evidence shows that complex diseases are caused by common and rare variants. Here we propose a region-based method to test both rare and common variant associated multiple traits based on variable reduction method (abbreviated as MULVR). However, in the presence of noise traits, the MULVR method may lose power, so we propose the MULVR-O method, which jointly analyses the optimal number of traits associated with genetic variants by the MULVR method, to guard against the effect of noise traits. Extensive simulation studies show that our proposed method (MULVR-O) is applied to not only multiple quantitative traits but also qualitative traits, and is more powerful than several other comparison methods in most scenarios. An application to the two genes (SHBG and CHRM3) and two phenotypes (systolic blood pressure and diastolic blood pressure) from the GAW19 dataset illustrates that our proposed methods (MULVR and MULVR-O) are feasible and efficient as a region-based method.

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Copyright

Corresponding author

*Corresponding author: Tel: +86 451 86608282. E-mail: chenlili_02_06@163.com

References

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Association analysis of rare and common variants with multiple traits based on variable reduction method

  • LILI CHEN (a1) (a2), YONG WANG (a1) and YAJING ZHOU (a2)

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