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Title: | LPG : a four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies | Authors: | Yang, Y Dai, MW Huang, J Lin, XY Yang, C Chen, M Liu, J |
Issue Date: | 2018 | Source: | BMC genomics, 28 June 2018, v. 19, 503 | Abstract: | Background: To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding light on the genetic architecture of complex disease. The polygenicity of complex diseases is a widely accepted phenomenon through which a vast number of risk variants, each with a modest individual effect, collectively contribute to the heritability of complex diseases. This imposes a major challenge on fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larger sample size is required to detect individual risk variants with weak/moderate effects. Meanwhile, accumulating evidence suggests that different complex diseases can share genetic risk variants, a phenomenon known as pleiotropy. Results: In this study, we propose a statistical framework for Leveraging Pleiotropic effects in large-scale GWAS data (LPG). LPG utilizes a variational Bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient and scalable for genome-wide-scale analysis. To demonstrate the advantages of LPG over existing methods that do not leverage pleiotropy, we conducted extensive simulation studies and applied LPG to analyze two pairs of disorders (Crohn's disease and Type 1 diabetes, as well as rheumatoid arthritis and Type 1 diabetes). The results indicate that by levelaging pleiotropy, LPG can improve the power of prioritization of risk variants and the accuracy of risk prediction. Conclusions: Our methodology provides a novel and efficient tool to detect pleiotropy among GWAS data for multiple traits/diseases collected from different studies. The software is available at https://github.co/Shufeyangyi2015310117/LPG. |
Keywords: | Pleiotropy Variational Bayesian expectation-maximization Genome-wide association studies |
Publisher: | BioMed Central | Journal: | BMC genomics | EISSN: | 1471-2164 | DOI: | 10.1186/s12864-018-4851-2 | Rights: | © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. The following publication Yang, Y., Dai, M., Huang, J., Lin, X., Yang, C., Chen, M., & Liu, J. (2018). LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies. BMC genomics, 19(1), 503 is available at https://doi.org/10.1186/s12864-018-4851-2 |
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