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Gene association detection via local linear regression method

Journal of Human Genetics volume 65, pages 115–123 (2020)Cite this article

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Abstract

The development of next-generation sequencing technology has provided us with great convenience in genetic association studies and many effective analysis methods were proposed continuously. However, population stratification is still a major issue in current genetic association studies. Many existing methods have been developed to remove the bias due to population stratification for common variant association studies, but such methods may be not effective for rare variant, which will lead to power reduction. Therefore, in this paper, we develop a principal component analysis strategy (called PC-LLR) based on local linear regression method to eliminate population stratification effect in both rare variant and common variant association studies. Simulation results indicate that the new PC-LLR method can eliminate population stratification effect well. It has correct type I error rates in all cases and higher powers in most cases, while most existing methods have inflated type I error rates at least in some cases. We also demonstrate that the PC-LLR is more effective to eliminate population stratification effect through applying the PC-LLR to the whole-exome sequencing data set from genetic analysis workshop 19 (GAW19).

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Acknowledgements

The authors would like to thank the joint editor and reviewers for helpful comments that greatly improved the presentation of the paper. This research was supported by the Natural Science Foundation of Heilongjiang Province of China (LH2019A020). The Genetic Analysis Workshops (GAW) are supported by GAW grant R01 GM031575 from the National Institute of General Medical Sciences. Preparation of the Genetic Analysis Workshop 17 Simulated Exome Data Set was supported in part by NIH R01 MH059490 and used sequencing data from the 1000 Genomes Project (www.1000genomes.org). The GAW19 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW19 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889.

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  1. Department of Statistics, School of Mathematical Sciences, Heilongjiang University and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Harbin, 150080, China

    Jinli He, Weijun Ma & Ying Zhou

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  1. Jinli He
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Correspondence to Ying Zhou.

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He, J., Ma, W. & Zhou, Y. Gene association detection via local linear regression method. J Hum Genet 65, 115–123 (2020). https://doi.org/10.1038/s10038-019-0676-3

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