Description Usage Arguments Details Value Author(s) References See Also Examples

Performs variable selection for continuous phenotypes in a single iteration, combining the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors, as described in Sanyal et al. (2018) [submitted]

1 2 3 | ```
nlpsLM(y, x, prior, tau, priorDelta = modelbbprior(1,1), k0, rxx,
niter = 2000, verbose = F,
tau.hs.method = "halfCauchy", sigma.hs.method = "Jeffreys" )
``` |

`y` |
The vector of the continuous phenotype values. |

`x` |
The SNP genotype matrix with subjects represented by rows and SNPs represented by columns. The elements of x should be of numeric type. Missing values are currently not accepted. |

`prior` |
"mom" for pMOM prior, "imom" for piMOM prior, "zellner" for Zellner's g-prior, "horseshoe" for horseshoe prior. |

`tau` |
the value of the scale parameter tau of the nonlocal 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 References). |

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

`niter` |
Number of MCMC iterations for nonlocal prior based Bayesian variable selection. Defaults to 2000. |

`verbose` |
If TRUE, prints some details. FALSE by default. |

`tau.hs.method` |
Optional. Necessary only when prior=="horseshoe". |

`sigma.hs.method` |
Optional. Necessary only when prior=="horseshoe". |

The nlpsLM function performs SNP selection in one iteraion for continuous phenotypes. The GWASinlps function repeatedly calls the nlpsLM function. The nlpsLM procedure starts by determining the k0 *leading SNPs* having the highest association with y. The measure of association is absolute value of the Pearson's correlation coefficient. These k0 leading SNPs, in turn, determine the 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. Then non-local prior based Bayesian variable selection is run within each leading set (using package mombf), and the predictors appearing in the HPPM are considered selected. For more details, see the References. For horseshoe prior, package horseshoe is used.

A list with elements

`hppm ` |
The set of predictors appearing in the HPPM of at least one leading set. |

`not.selected ` |
The set of predictors appearing in at least one leading set, but in none of the HPPMs. |

Nilotpal Sanyal <[email protected]>

Sanyal et al. (2018), "GWASinlps: Nonlocal prior based iterative SNP selection tool for genome-wide association studies" [submitted].

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
p = 1000; n = 100
m = 10
# Generate SNP 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 data
causal_snps = sample( 1:p, m )
beta = rep( 0, p )
set.seed(1)
beta[causal_snps] = rnorm(m, mean = 0, sd = 2 )
y = x %*% beta + rnorm(n, 0, 1)
# Fix scale parameter tau
tau = 0.022
# Perform GWASinlps
out = nlpsLM(y, x, prior = "mom", tau = tau, k0 = 10, rxx = .5, niter = 10000, verbose = TRUE)
out
``` |

GWASinlps documentation built on Feb. 3, 2018, 1:04 a.m.

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