nlps {GWASinlps}

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

y

The vector of continuous response (phenotype) for linear models (LM), or binary response (phenotype) for generalized linear models (GLM), or survival times for accelerated failure time models (AFTM). Binary response values must be 0 or 1.

event

Only for AFTM. The vector of status indicator for the survival data.

x

The design matrix with subjects in rows and independent variables (SNPs) in columns. Missing values are not accepted currently.

cor_xy

Only for LM. Vector of (Pearson) correlations of y with the columns of x.

mmle_xy

Only for GLM. Vector of maximum marginal likelihood estimates of the regression parameters for the variables (SNPs) in x. These may be obtained from individual GLM fits of y with the columns of x.

mu_xy

Only for AFTM. Vector of marginal utility estimates of the variables (SNPs) in x. These may be obtained by fitting AFT model to y with individual columns of x using the survreg function of the package survival.

prior

"mom" for pMOM prior, "imom" for piMOM prior, "emom" for peMOM prior, "zellner" for Zellner's g-prior, "horseshoe" for horseshoe prior. For GLM, "zellner" considers group Zellner prior and "emom" and "horseshoe" are not available. For AFTM, "horseshoe" is not available.

tau

the value of the scale parameter tau of the non-local prior.

priorDelta

Prior for model space. Defaults to modelbbprior(1,1), which is beta-binomial(1,1) prior.

k0

GWASinlps tuning parameter denoting the number of leading SNPs (see Details).

rxx

GWASinlps tuning parameter denoting the correlation threshold to determine leading sets (see References).

niter

Number of MCMC iterations for non-local prior based Bayesian variable selection. Defaults to 2000.

verbose

If TRUE, prints result from the iterations progressively. FALSE by default.

tau.hs.method

Necessary only when prior="horseshoe". See horseshoe function reference.

sigma.hs.method

Necessary only when prior="horseshoe". See horseshoe function reference.

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

hppm

The names of variables (SNPs) appearing in the highest posterior probability model (HPPM) of at least one leading set.

not.selected

The names of variables (SNPs) appearing in at least one leading set but in none of the HPPMs.

Author(s)

Nilotpal Sanyal <[e-mail]>

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, horseshoe

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