IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies

Dai, Mingwei; Ming, Jingsi; Cai, Mingxuan; Liu, Jin; Yang, Can; Wan, Xiang; Xu, Zongben

doi:10.1093/bioinformatics/btx314

Cited by 15 publications

(19 citation statements)

References 36 publications

(38 reference statements)

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“…In our simulation studies, the performance of Lasso was worse than that of its probabilistic counterpart, BVSR. A similar observation was previously reported [ 28 ]. Additional simulation results under different configurations of ρ (strength of the correlation between genetic variants) and h 2 (heritability) (Additional file 1 : Tables S1 and S2) produced similar conclusions (Additional file 1 : Figures S1 - S18).…”

Section: Resultssupporting

confidence: 92%

LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

et al. 2018

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BackgroundTo 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.ResultsIn 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4851-2) contains supplementary material, which is available to authorized users.

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Section: Resultssupporting

confidence: 92%

LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

et al. 2018

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“…The following mathematical setup defines the notations and symbols. Referred to Dai et al [14], a Beta distribution with parameter well cultivate a significant -value. The greater the , the least the -value in GWAS.…”

Section: Variational Inference On the Solution Of "Mutation Data Bridmentioning

confidence: 99%

Bridging Heterogeneous Mutation Data to Enhance Disease-Gene Discovery

Zhou

Wang

Cohen

et al. 2020

Preprint

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Background: Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations which link genotype and phenotype, and it is under-powered for pinpointing causal genes due to high false positive or negative rate. In the meantime, mutation events widely reported in literature unveil typical functional biological process, including mutation types like gain-of-function and loss-of-function. Methods: To bring together the heterogeneous mutation data, we propose a pipeline, “Gene-Disease Association prediction by Mutation Data Bridging (GDAMDB)”, with a statistic generative model. The model learns the distribution parameters of mutation associations and mutation types, and recovers false negative GWAS mutations which fail to pass significant test but represent supportive evidences of functional biological process in literature. Results: Eventually, GDAMDB is applied in Alzheimer’s disease which is a common inheritable neurodegenerative disorder with unknown pathological mechanism, and it predicted 79 AD-associated genes. Besides 12 of them come from the original GWAS study, 57 of them are supported to be AD-related by other GWAS or literature report. Conclusion: Our model is capable of enhancing the GWAS-based gene association discovery by well combining text mining results. The positive result indicates that bridging the heterogeneous mutation data is contributory for the novel disease-related gene discovery.

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“…As discussed in Section 2.3, to overcome the intractability of maximizing the marginal likelihood, we utilize a (PX)-VBEM algorithm where we maximize the ELBO, instead of the marginal likelihood, to obtain parameter estimatesθ 0 andθ. Earlier applications demonstrate that (PX)-VBEM produces practically useful and accurate posterior mean estimates [3,8,43,34] (i.e.θ 0 andθ). While it might seem reasonable to use the estimated posterior distribution from maximizing the ELBO to directly approximate the marginal likelihood in Equation 13, it is well-known that the (PX)-VBEM typically identifies posterior distributions that underestimate the marginal variances [40,36].…”

Section: Evaluate Association Between a A Complex Trait/disease And Amentioning

confidence: 99%

CoMM-S²: a collaborative mixed model using summary statistics in transcriptome-wide association studies

Yang

Shi

Jiao³

et al. 2019

Preprint

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Motivation: Although genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required. Results: In this study, we propose a novel probabilistic model, CoMM-S 2 , to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S 2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S 2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S 2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data.

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IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 15 publications

References 36 publications

LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

Bridging Heterogeneous Mutation Data to Enhance Disease-Gene Discovery

CoMM-S²: a collaborative mixed model using summary statistics in transcriptome-wide association studies

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IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 15 publications

References 36 publications

LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies

Bridging Heterogeneous Mutation Data to Enhance Disease-Gene Discovery

CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

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Product

Resources

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CoMM-S²: a collaborative mixed model using summary statistics in transcriptome-wide association studies