Abstract
Background
Pleiotropy is a widespread phenomenon in complex human diseases. Jointly analyzing multiple phenotypes can improve power performance of detecting genetic variants and uncover the underlying genetic mechanism.
Objective
This study aims to detect the association between genetic variants in a genomic region and multiple phenotypes.
Methods
We develop the aggregated Cauchy association test to detect the association between rare variants in a genomic region and multiple phenotypes (abbreviated as “Multi-ACAT”). Multi-ACAT first detects the association between each rare variant and multiple phenotypes based on reverse regression and obtains variant-level p-values, then takes linear combination of transformed p-values as the test statistic which approximately follows Cauchy distribution under the null hypothesis.
Results
Extensive simulation studies show that when the proportion of causal variants in a genomic region is extremely small, Multi-ACAT is more powerful than the other several methods and is robust to bi-directional effects of causal variants. Finally, we illustrate our proposed method by analyzing two phenotypes [systolic blood pressure (SBP) and diastolic blood pressure (DBP)] from Genetic Analysis Workshop 19 (GAW19).
Conclusion
The Multi-ACAT computes extremely fast, does not consider complex distributions of multiple correlated phenotypes, and can be applied to the case with noise phenotypes.
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Acknowledgements
This research 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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LC and YZ designed the study. LC and YZ performed statistical simulation analyses. LC performed GAW19 real data analyses. LC drafted the manuscript and YZ contributed to the writing, reviewing, and editing of the manuscript.
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Chen, L., Zhou, Y. A fast and powerful aggregated Cauchy association test for joint analysis of multiple phenotypes. Genes Genom 43, 69–77 (2021). https://doi.org/10.1007/s13258-020-01034-3
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DOI: https://doi.org/10.1007/s13258-020-01034-3