Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12864-018-4851-2
Title: LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies
Authors: Yang, Y
Dai, M
Huang, J
Lin, X
Yang, C
Chen, M
Liu, J 
Keywords: Article
case control study
controlled study
Crohn disease
genetic risk
genetic variability
genome-wide association study
human
insulin dependent diabetes mellitus
phenotypic variation
pleiotropy
quantitative trait
rheumatoid arthritis
simulation
single nucleotide polymorphism
algorithm
Bayes theorem
computer interface
genetics
genome-wide association study
procedures
Algorithms
Arthritis, Rheumatoid
Bayes Theorem
Crohn Disease
Diabetes Mellitus, Type 1
Genetic Pleiotropy
Genome-Wide Association Study
Humans
Internet Access
Polymorphism, Single Nucleotide
User-Computer Interface
Issue Date: 2018
Citation: 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. ScholarBank@NUS Repository. https://doi.org/10.1186/s12864-018-4851-2
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.com/Shufeyangyi2015310117/LPG. © 2018 The Author(s).
Source Title: BMC Genomics
URI: https://scholarbank.nus.edu.sg/handle/10635/175377
ISSN: 1471-2164
DOI: 10.1186/s12864-018-4851-2
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