library(GWAS.BAYES)

1 Introduction

The GWAS.BAYES package provides statistical tools for the analysis of Gaussian GWAS data. GWAS.BAYES contains functions to perform IEB which is a novel iterative two step Bayesian procedure that, when compared to single marker analysis (SMA), increases the recall of true causal SNPs and drastically reduces the rate of false discoveries. Further, when compared to BICOSS (Williams, Ferreira, and Ji 2022) also available in GWAS.BAYES, IEB provides a quicker and more accurate analysis.

This vignette shows an example of how to use the IEB function provided in GWAS.BAYES to analyze GWAS data. Data has been simulated under a linear mixed model from 9,000 SNPs for 328 A. Thaliana ecotypes. The GWAS.BAYES package includes as R objects the 9,000 SNPs, the simulated phenotypes, and the kinship matrix used to simulate the data.

2 Functions

The function implemented in GWAS.BAYES is described below:

IEB Performs IEB, using linear mixed models for a given numeric phenotype vector Y, a SNP matrix encoded numerically SNPs, and a realized relationship matrix or kinship matrix kinship. The IEB function returns the indices of the SNP matrix that were identified in the best model found by the IEB algorithm.

3 Model/Model Assumptions

The model for GWAS analysis used in the GWAS.BAYES package is

\[\begin{equation*} \textbf{Y} = X \boldsymbol{\beta} + Z \textbf{u} + \boldsymbol{\epsilon} \ \text{where} \ \boldsymbol{\epsilon} \sim N(\textbf{0},\sigma^2 I) \ \text{and} \ \textbf{u} \sim N(\textbf{0},\sigma^2 \tau K), \end{equation*}\]

where

\(\textbf{Y}\) is the vector of phenotype responses.
\(X\) is the matrix of SNPs (single nucleotide polymorphisms).
\(\boldsymbol{\beta}\) is the vector of regression coefficients that contains the effects of the SNPs.
\(Z\) is an incidence matrix relating the random effects associated with the kinship structure.
\(\textbf{u}\) is a vector of random effects associated with the kinship structure to the phenotype responses.
\(\boldsymbol{\epsilon}\) is the error vector.
\(\sigma^2\) is the variance of the errors.
\(\tau\) is a parameter related to the variance of the random effects.
\(K\) is the kinship matrix.

Currently, all functions in GWAS.BAYES assume the errors of the fitted model are Gaussian.

4 Example

The IEB function requires a vector of observed phenotypes, a matrix of SNPs, and a kinship matrix. First, the vector of observed phenotypes must be a numeric vector or a numeric \(n \times 1\) matrix. GWAS.BAYES does not allow the analysis of multiple phenotypes at the same time. In this example, the vector of observed phenotypes was simulated from a linear mixed model. Here are the first five elements of the simulated vector of phenotypes:

Y[1:5]
#> [1] 3.330224 2.733632 4.167975 3.705713 4.015575

Second, the SNP matrix has to contain numeric values where each column corresponds to a SNP of interest and the \(i\)th row corresponds to the \(i\)th observed phenotype. In this example, the SNPs are a subset of the TAIR9 genotype dataset and all SNPs have minor allele frequency greater than 0.01. Here are the first five rows and five columns of the SNP matrix:

SNPs[1:5,1:5]
#>      SNP2555 SNP2556 SNP2557 SNP2558 SNP2559
#> [1,]       1       1       1       0       0
#> [2,]       0       1       1       1       1
#> [3,]       0       0       1       1       1
#> [4,]       1       1       0       0       1
#> [5,]       1       1       1       1       1

Third, the kinship matrix is an \(n \times n\) positive semi-definite matrix containing only numeric values. The \(i\)th row or \(i\)th column quantifies how observation \(i\) is related to other observations. Here are the first five rows and five columns of the kinship matrix:

kinship[1:5,1:5]
#>              V1         V2         V3         V4         V5
#> [1,] 0.78515873 0.15800700 0.04264546 0.02057071 0.05643574
#> [2,] 0.15800700 0.78146476 0.05135891 0.01476357 0.05482448
#> [3,] 0.04264546 0.05135891 0.80199976 0.10558970 0.04888596
#> [4,] 0.02057071 0.01476357 0.10558970 0.80030413 0.02935703
#> [5,] 0.05643574 0.05482448 0.04888596 0.02935703 0.78401489

4.1 IEB

The function IEB implements the IEB method for linear mixed models with Gaussian errors. This function takes as inputs the observed phenotypes, the SNPs coded numerically, and the kinship matrix. Further, the other inputs of IEB are the FDR nominal level, the maximum number of iterations of the genetic algorithm in the model selection step, and the number of consecutive iterations of the genetic algorithm with the same best model for convergence. The default values of maximum iterations and the number of iterations are the values used in the IEB manuscript (Williams, Xu, and Ferreira, n.d.), that is, 400 and 40 respectively.

Here we illustrate the use of IEB with a nominal FDR of 0.05.

IEB_Result <- IEB(Y = Y, SNPs = SNPs, 
                     kinship = kinship,FDR_Nominal = 0.05,
                     maxiterations = 400,runs_til_stop = 40)
IEB_Result$best_model
#> [1] 1350 2250 4950 5276 5850 8550 3150

IEB returns a named list where the best model values correspond to the indices of the SNP matrix. Because this is simulated data, we can compute the number of true positives and the number of false positives.

## The true causal SNPs in this example are
True_Causal_SNPs <- c(450,1350,2250,3150,4050,4950,5850,6750,7650,8550)
## Thus, the number of true positives is
sum(IEB_Result$best_model %in% True_Causal_SNPs)
#> [1] 6
## The number of false positives is
sum(!(IEB_Result$best_model %in% True_Causal_SNPs))
#> [1] 1

IEB, when compared to SMA, better controls false discoveries and improves on the number of true positives. When compared to BICOSS, IEB is much quicker the BICOSS while maintaining or even increasing precision and recall of causal SNPs.

sessionInfo()
#> R version 4.4.0 RC (2024-04-16 r86468)
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#> Running under: Ubuntu 22.04.4 LTS
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#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
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#> other attached packages:
#> [1] GWAS.BAYES_1.13.0 BiocStyle_2.31.0 
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#> loaded via a namespace (and not attached):
#>  [1] crayon_1.5.2        cli_3.6.2           knitr_1.46         
#>  [4] rlang_1.1.3         xfun_0.43           jsonlite_1.8.8     
#>  [7] statmod_1.5.0       htmltools_0.5.8.1   sass_0.4.9         
#> [10] rmarkdown_2.26      grid_4.4.0          evaluate_0.23      
#> [13] jquerylib_0.1.4     fastmap_1.1.1       foreach_1.5.2      
#> [16] yaml_2.3.8          lifecycle_1.0.4     memoise_2.0.1      
#> [19] bookdown_0.39       BiocManager_1.30.22 compiler_4.4.0     
#> [22] codetools_0.2-20    Rcpp_1.0.12         limma_3.59.10      
#> [25] lattice_0.22-6      digest_0.6.35       R6_2.5.1           
#> [28] bslib_0.7.0         Matrix_1.7-0        tools_4.4.0        
#> [31] iterators_1.0.14    GA_3.2.4            cachem_1.0.8

References

Williams, Jacob, Marco A. R. Ferreira, and Tieming Ji. 2022. “BICOSS: Bayesian iterative conditional stochastic search for GWAS.” BMC Bioinformatics 23 (475). https://doi.org/10.1186/s12859-022-05030-0.

Williams, Jacob, Shuangshuang Xu, and Marco A. R. Ferreira. n.d. “IEB: Iterative Empirical Bayes for High Recall and Precision in GWAS Analysis.”

IEB in the GWAS.BAYES Package

2024-04-29

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