Type:	Package
Title:	Non-Local Prior Based Iterative Variable Selection Tool for Genome-Wide Association Studies
Version:	2.4
Date:	2025-10-29
Description:	Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <doi:10.1093/bioinformatics/bty472>).
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Depends:	mombf
Imports:	Rcpp (≥ 1.0.9), RcppArmadillo, fastglm, survival
Suggests:	glmnet
LinkingTo:	Rcpp, RcppArmadillo
URL:	https://nilotpalsanyal.github.io/GWASinlps/
BugReports:	https://github.com/nilotpalsanyal/GWASinlps/issues
Repository:	CRAN
NeedsCompilation:	yes
Packaged:	2025-10-29 07:15:43 UTC; nsanyal
Author:	Nilotpal Sanyal [aut, cre]
Maintainer:	Nilotpal Sanyal <nilotpal.sanyal@gmail.com>
Date/Publication:	2025-10-29 08:40:10 UTC

Non-local prior based iterative variable selection tool for genome-wide association study data, or other high-dimensional data

Description

The GWASinlps package performs variable selection for data from genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework, the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see the References).

Details

The main function:
GWASinlps

The main function calls the following functions:
nlpsLM
nlpsGLM
nlpsAFTM

Author(s)

Nilotpal Sanyal <nilotpal.sanyal@gmail.com>

Maintainer: Nilotpal Sanyal <nilotpal.sanyal@gmail.com>

References

Sanyal et al. (2019), "GWASinlps: Non-local prior based iterative SNP selection tool for genome-wide association studies". Bioinformatics, 35(1), 1-11.

Sanyal, N. (2022). "Iterative variable selection for high-dimensional data with binary outcomes". arXiv preprint arXiv:2211.03190.

Non-local prior based iterative variable selection for GWAS data, or other high-dimensional data

Description

GWASinlps performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework, the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see the References).

Usage

GWASinlps(
  y, 
  event,
  x, 
  family = c("normal","binomial","survival"),
  method = c("rigorous","quick"),
  cor_xy = NULL,
  mmle_xy = NULL,
  mu_xy = NULL,
  prior = c("mom", "imom", "emom", "zellner", "horseshoe"), 
  tau, 
  priorDelta = modelbbprior(1,1), 
  k0, 
  m, 
  rxx, 
  nskip = 3, 
  niter = 2000, 
  verbose = FALSE, 
  seed = NULL, 
  tau.hs.method = "halfCauchy", 
  sigma.hs.method = "Jeffreys"
)

Arguments

Details

The GWASinlps method selects variables (SNPs) iteratively.

For continuous response

For continuous response (phenotype), the procedure starts with an initial set of independent variables (SNPs), a design matrix (SNP genotype matrix) x and a response (phenotype) vector y.

- An iteration proceeds by determining the k0 leading SNPs/variables having the highest association with y. The measure of association is the absolute value of the Pearson's correlation coefficient cor_xy. These k0 leading SNPs/variables, in turn, determine k0 leading sets, where each leading set consists of all SNPs/variables with absolute correlation coefficient more than or equal to rxx with the correspondng leading SNP/variable.

- Within each leading set, non-local prior based Bayesian variable selection for linear models is performed (using package mombf). The variables (SNPs) appearing in the highest posterior probability model (HPPM) are considered selected in the current iteration. Note that a single variable (SNP) can be selected from multiple leading sets. The selected variables (SNPs) are regressed out from y using lm fit. The variables (SNPs) that are included in one or more of the leading sets but do not appear in any HPPM are dropped from further analysis.

- With updated y and variable (SNP) set, next iteration proceeds similarly. And so on like this. The procedure continues until the stopping point, which is determined by the GWASinlps tuning parameters m, rxx, and nskip, is reached. For more details, see References.

For binary response

For binary response (phenotype), the procedure starts with an initial set of variables (SNPs), a design matrix (SNP genotype matrix) x and a binary response (phenotype) vector y. If method="rigorous",

- The first iteration proceeds by determining the k0 leading SNPs/variables having the highest association with y. The measure of association is the absolute value of the maximum marginal likelihood estimate mmle_xy. These k0 leading SNPs/variables, in turn, determine k0 leading sets, where each leading set consists of all SNPs/variables with absolute correlation coefficient more than or equal to rxx with the correspondng leading SNP.

- Within each leading set, non-local prior based Bayesian variable selection for logistic regression model is performed (using package mombf). The variables (SNPs) appearing in the HPPM are considered selected in the first iteration. Note that a single variable (SNP) can be selected from multiple leading sets. The variables (SNPs) which are included in one or more leading sets but do not appear in any HPPM are dropped from further analysis. After this, the selected variables (SNPs) are accounted for by including them in glm fits of y with each of the remaining variables (SNPs). The glm coefficients of the remaining variables, thus obtained, reflect their contribution in presence of the selected variables (SNPs) of the first iteration.

- Considering the absolute values of these glm coefficients as the measure of association, we proceed with the second iteration with updated variable (SNP) set. And so on in this manner. The procedure continues until the stopping point, which is determined by the GWASinlps tuning parameters m, rxx, and nskip, is reached.

If method="quick", the procedure is similar to above except at the following points. In this method, non-local prior based Bayesian variable selection using logistic regression model is performed until one or more variables (SNP) are selected in an iteration. Until a variable is selected, there is no need to account for anything, so the initial maximum marginal likelihood estimates continue to be used. After the first selections (if any) are made, a glm fit of y on the selected variables is performed and the deviance residuals are computed. In the subsequent iterations, considering these (continuous) deviance residuals as response, non-local prior based Bayesian variable selection for linear models is performed till the stopping point is reached.

For survival data

For survival data, the procedure starts with an initial set of variables (SNPs), a design matrix (SNP genotype matrix) x and a binary response (phenotype) vector y.

- The first iteration proceeds by determining the k0 leading SNPs/variables having the highest association with y. The measure of association is the absolute value of the marginal utility mu_xy. These k0 leading SNPs/variables, in turn, determine k0 leading sets, where each leading set consists of all SNPs with absolute correlation coefficient more than or equal to rxx with the correspondng leading SNP.

- Within each leading set, non-local prior based Bayesian variable selection for accelerated failure time model is performed (using package mombf). The variables (SNPs) appearing in the HPPM are considered selected in the first iteration. Note that a single variable (SNP) can be selected from multiple leading sets. The variables (SNPs) which are included in one or more leading sets but do not appear in any HPPM are dropped from further analysis. After this, to account for the selected variables (SNPs), conditional utilities of each of the remaining variables (SNPs) are computed in the presence of the selected variables (SNPs) in the model. These conditional utilities reflect the contribution of the remaining variables (SNPs) in presence of the selected variables (SNPs) of the first iteration.

- Considering the absolute values of these conditional utilities as the measure of association, we proceed with the second iteration with updated variable (SNP) set. And so on in this manner. The procedure continues until the stopping point, which is determined by the GWASinlps tuning parameters m, rxx, and nskip, is reached.

Value

A list containing

Author(s)

Nilotpal Sanyal <nilotpal.sanyal@gmail.com>

References

Sanyal et al. (2019), "GWASinlps: Non-local prior based iterative SNP selection tool for genome-wide association studies". Bioinformatics, 35(1), 1-11.

Sanyal, N. (2022). "Iterative variable selection for high-dimensional data with binary outcomes". arXiv preprint arXiv:2211.03190.

See Also

nlpsLM, nlpsGLM, nlpsAFTM, modelSelection

Examples

n = 200
p = 1000
m = 10

# Generate design matrix (genotype matrix)
set.seed(1) 
f = runif( p, .1, .2 ) # simulate minor allele frequency
x = matrix( nrow = n, ncol = p )
colnames(x) = 1:p
for(j in 1:p)
  x[,j] = rbinom( n, 2, f[j] )

# Generate true effect sizes
causal_snps = sample( 1:p, m )
beta = rep( 0, p )
set.seed(1)
beta[causal_snps] = rnorm(m, mean = 0, sd = 2 )

# Generate continuous (phenotype) data
y = x %*% beta + rnorm(n, 0, 1) 

# Fix scale parameter tau 
tau = 0.2

# GWASinlps analysis
inlps = GWASinlps(y=y, x=x, family="normal", prior="mom", tau=tau, k0=1,  
        m=50, rxx=0.2)
cat( "GWASinlps selected", length(inlps$selected), "SNPs with", 
    length(intersect(inlps$selected, causal_snps)), "true positive(s) and",
    length(setdiff(causal_snps, inlps$selected)), "false negative(s) out  
    of a pool of", p, "SNPs with data from", n, "persons." )

# Compare with LASSO
library(glmnet)
fit.cvlasso = cv.glmnet( x, y, alpha = 1 )
l.min = fit.cvlasso $lambda.min # lambda that gives minimum cvm
l.1se = fit.cvlasso $lambda.1se  # largest lambda such that error is 
                                 # within 1 se of the minimum

lasso_min = which( as.vector( coef( fit.cvlasso, s = l.min ) )[-1] != 0 )  
cat( "LASSO with lambda.min selected", length(lasso_min), "SNPs with", 
     length(intersect(lasso_min, causal_snps)), "true positives and",
    length(setdiff(causal_snps, inlps$selected)), "false negative(s)." )

lasso_1se = which( as.vector( coef( fit.cvlasso, s = l.1se ) )[-1] != 0 )
cat( "LASSO with lambda.1se selected", length(lasso_1se), "SNPs with", 
     length(intersect(lasso_1se, causal_snps)), "true positives and",
    length(setdiff(causal_snps, inlps$selected)), "false negative(s)." )

Non-local prior based single-step variable selection for high-dimensional data

Description

nlpsLM, nlpsGLM, nlpsAFTM perform variable selection in a single iteration respectively for continuous, binary and survival outcomes, combining the computational efficiency of the 'structured screen-and-select' variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see the References).

Usage

nlpsLM(y, x, cor_xy, prior = c("mom", "imom", "emom", "zellner", 
  "horseshoe"), tau, priorDelta = modelbbprior(1,1), 
  k0, rxx, niter = 2000, verbose = F, 
  tau.hs.method = "halfCauchy", sigma.hs.method = "Jeffreys" )

nlpsGLM(y, x, mmle_xy, prior = c("mom", "imom", "zellner"), 
  tau, priorDelta = modelbbprior(1,1), 
  k0, rxx, niter = 2000, verbose = F )

nlpsAFTM(y, event, x, mu_xy, prior = c("mom", "imom", "emom", 
  "zellner"), tau, priorDelta = modelbbprior(1,1), 
  k0, rxx, niter = 2000, verbose = F )

Arguments

Details

The nlpsLM, nlpsGLM and nlpsAFTM functions perform SNP selection in one iteration for continuous data, binary data, and survival data, respectively. The GWASinlps function repeatedly calls these functions. For details of the procedure, see the reference for the GWASinlps function.

Value

A list with elements

Author(s)

Nilotpal Sanyal <nilotpal.sanyal@gmail.com>

References

Sanyal et al. (2019), "GWASinlps: Non-local prior based iterative SNP selection tool for genome-wide association studies". Bioinformatics, 35(1), 1-11.

Sanyal, N. (2022). "Iterative variable selection for high-dimensional data with binary outcomes". arXiv preprint arXiv:2211.03190.

See Also

GWASinlps, modelSelection

Examples

n = 100
p = 1000
m = 10

# Generate design matrix (genotype matrix)
set.seed(1) 
f = runif( p, .1, .2 ) # simulate minor allele frequency
x = matrix( nrow = n, ncol = p )
colnames(x) = 1:p
for(j in 1:p)
  x[,j] = rbinom( n, 2, f[j] )

# Generate true effect sizes
causal_snps = sample( 1:p, m )
beta = rep( 0, p )
set.seed(1)
beta[causal_snps] = rnorm(m, mean = 0, sd = 2 )

# Generate continuous (phenotype) data
y.cont = x %*% beta + rnorm(n, 0, 1) 

# Generate binary (phenotype) data
prob = exp(x %*% beta)/(1 + exp(x %*% beta))
y.bin = sapply(1:n, function(i)rbinom(1,1,prob[i]) )

# Fix scale parameter tau 
tau = 0.022

# GWASinlps analysis
cor_xy = c(cor(x,y.cont))
names(cor_xy) = colnames(x)
nlps_cont = nlpsLM(y.cont, x, cor_xy=cor_xy, prior="mom", 
  tau=tau, k0=2, rxx=0.3, niter=10000, verbose=TRUE) 
nlps_cont

library(fastglm)
mode(x) = "double"  #needed for fastglm() function below
mmle_xy = apply( x, 2, function(z) coef( fastglm(y=y.bin, 
x=cbind(1,matrix(z)), family = binomial(link = "logit")) )[2] )
nlps_bin = nlpsGLM(y.bin, x, mmle_xy=mmle_xy, prior="mom", 
  tau=tau, k0=2, rxx=0.3, niter=10000, verbose=TRUE) 
nlps_bin