SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Sun, Jianle; Lyu, Ruiqi; Deng, Luojia; Li, Qianwen; Zhao, Yang; Zhang, Yue

doi:10.1371/journal.pcbi.1009948

Cited by 3 publications

(2 citation statements)

References 76 publications

(36 reference statements)

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“…Although large-scale biobanks containing genotypes and phenotypes are now available, an increasing number of studies tend to report summary association statistics instead due to concerns on privacy and security. Bayesian meta-analysis is applied to assess pooled genetic relevance (Sun et al, 2022). Recent work has started to focus on learning causal diagrams with summary data (Zhang et al, 2017), while arduous task still remains.…”

Section: Discussionmentioning

confidence: 99%

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference

Sun,

Zhou,

Gong

et al. 2024

Hum. Genet.

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Mendelian randomization is a powerful method for for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian Network-based Mendelian Randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlight the potential to 1 facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

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Section: Discussionmentioning

confidence: 99%

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference

Sun,

Zhou,

Gong

et al. 2024

Hum. Genet.

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“…The combination of two statistical tools from different schools of statistics gives users a comprehensive and complementary perspective. In addition, Bayesian methods have been further extended to more complex analyses, such as GWAS meta-analysis ( Sun et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

STS-BN: An efficient Bayesian network method for detecting causal SNPs

Yuan

et al. 2022

Front. Genet.

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Background: The identification of the causal SNPs of complex diseases in large-scale genome-wide association analysis is beneficial to the studies of pathogenesis, prevention, diagnosis and treatment of these diseases. However, existing applicable methods for large-scale data suffer from low accuracy. Developing powerful and accurate methods for detecting SNPs associated with complex diseases is highly desired.Results: We propose a score-based two-stage Bayesian network method to identify causal SNPs of complex diseases for case-control designs. This method combines the ideas of constraint-based methods and score-and-search methods to learn the structure of the disease-centered local Bayesian network. Simulation experiments are conducted to compare this new algorithm with several common methods that can achieve the same function. The results show that our method improves the accuracy and stability compared to several common methods. Our method based on Bayesian network theory results in lower false-positive rates when all correct loci are detected. Besides, real-world data application suggests that our algorithm has good performance when handling genome-wide association data.Conclusion: The proposed method is designed to identify the SNPs related to complex diseases, and is more accurate than other methods which can also be adapted to large-scale genome-wide analysis studies data.

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Bayesian Network-based Mendelian Randomization for Variant Prioritization and Phenotypic Causal Inference

Sun,

Zhou,

Gong

et al. 2023

Preprint

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Mendelian randomization is a powerful method for for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian Network-based Mendelian Randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, a series of Bayesian network structural learning processes, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlight the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

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SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Cited by 3 publications

References 76 publications

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference

STS-BN: An efficient Bayesian network method for detecting causal SNPs

Bayesian Network-based Mendelian Randomization for Variant Prioritization and Phenotypic Causal Inference

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