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A Powerful Adaptive Cauchy-Variable Combination Method for Rare-Variant Association Analysis

  • MATHEMATICAL MODELS AND METHODS
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Abstract

Set-based association analysis has emerged as a popular tool for testing the association of rare variants within a genomic region with complex diseases. However, when only a small proportion of variants are causal, combining the association signals of multiple markers within a genomic region may cause noise due to the inclusion of non-causal variants, which usually decreases the power of a test. Besides, the existing set-based methods are sensitive to the genetic architecture. Therefore, we extend the aggregated Cauchy association test (ACAT) and propose an adaptive Cauchy-variable combination method (AAC). The AAC method adaptively combines Cauchy-variables transformed from variant-level P-values by using the optimal number of P-values that is determined by the data; the AAC method can remove variants with larger P-values. Extensive simulation studies and Genetic Analysis Workshop 19 real data analysis show that AAC is more powerful than the other comparative methods when only a small proportion of variants are causal. And AAC is robust to the varied genetic architecture. In addition, the AAC method may use summary statistics, without requiring the original genotypic and phenotypic data.

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Funding

This study was supported by the Natural Science Foundation of Heilongjiang Province of China (LH2019A020), and basic research expenditure of universities in Heilongjiang Province, special fund of Heilongjiang University (KJCX201803 and KJCX201804). The Genetic Analysis Workshops are supported by GAW grant R01 GM031575 from the National Institute of General Medical Sciences. Preparation of the Genetic Analysis Workshop 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). The GAW19 unrelated data were provided by Type 2 Diabetes Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Project 1.

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Authors and Affiliations

  1. Department of Statistic, School of Mathematical Sciences, Heilongjiang University, 150080, Harbin, P.R. China

    Y. Tang, Y. Zhou, L. Chen, Y. Bao & R. Zhang

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  1. Y. Tang
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  2. Y. Zhou
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  3. L. Chen
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Correspondence to Y. Zhou.

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The authors declare that they have no conflicts of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Tang, Y., Zhou, Y., Chen, L. et al. A Powerful Adaptive Cauchy-Variable Combination Method for Rare-Variant Association Analysis. Russ J Genet 57, 238–245 (2021). https://doi.org/10.1134/S1022795421020125

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