BICOSS: Bayesian iterative conditional stochastic search for GWAS

Williams, Jacob; Ferreira, Marco A. R.; Ji, Tieming

doi:10.1186/s12859-022-05030-0

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…We identified 65 SNPs highly correlated to changes in RFW Supplementary Material Figure S5 ). In order to narrow the list of candidate SNPs to a more focused set of SNPs, the data were also analyzed using the two-stages procedure implemented in the R package GWAS.BAYES [ 38 , 39 ]. This analysis reduced the number of SNPs highly associated with ∆RFW to 11 ( Table 2 ); however, no SNPs were highly associated with ∆PRL in this analysis.…”

Section: Resultsmentioning

confidence: 99%

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Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T

et al. 2023

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Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant–bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.

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Section: Resultsmentioning

confidence: 99%

“…The genetic algorithm in the model selection step forces the SNPs to compete to appear in the highest ranked models. As shown in [ 39 ], combining a screening step and a model selection step provides a much shorter list of significant SNPs and leads to a much higher true discovery rate.…”

Section: Methodsmentioning

confidence: 99%

Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T

et al. 2023

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“…Specifically, we focus on GLMMs where each random effect has a covariance matrix that is the product of an unknown variance component parameter and a known positive semi‐definite symmetric matrix. The class of GLMMs we consider can be used for the analysis of spatial areal data (Banerjee et al., 2014; Clayton and Kaldor, 1987), genome‐wide association studies (GWAS) (Williams et al., 2022), and longitudinal data (Breslow and Clayton, 1993; Xu et al., 2016). However, inference for GLMMs is difficult because the integrated likelihood function is not available in closed form.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Model Selection for Generalized Linear Mixed Models

Ferreira

Porter

et al. 2023

Biometrics

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We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome‐wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo‐likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half‐Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe–Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.

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“…To resolve the multiple testing issue, many statistical methods have been developed in terms of penalization [4][5][6][7], Bayesian variable selection [8,9], and sure independence screening strategy [10][11][12]. Researchers proposed variable selection methods by incorporating the group information to select genetic variants in both gene and SNP levels simultaneously [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Bayesian bi-level variable selection for genome-wide survival study

2023

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Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer’s disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer’s Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

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BICOSS: Bayesian iterative conditional stochastic search for GWAS

Cited by 5 publications

References 36 publications

Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T

Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T

Bayesian Model Selection for Generalized Linear Mixed Models

Bayesian bi-level variable selection for genome-wide survival study

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