famLBL: Bayesian Lasso for Detecting Rare (or Common) Haplotype...

In mxw010/LBL: Logistic and Quantitative Bayesian Lasso for identifying genetic association between common diseases and rare haplotypes

Description

famLBL is an MCMC algorithm that generates posterior samples for family trio data. This function takes standard pedigree format as input. The input does not allow missing observations and subjects with missing data are removed. The function returns an object containing posterior samples after the burn-in period.

Usage

famLBL(data.fam, baseline = "missing", start.beta = 0.01, lambda = 1,
  D = 0, seed = NULL, a = 15, b = 15, burn.in = 10000,
  num.it = 40000, summary = TRUE, e = 0.1, ci.level = 0.95)

Arguments

data.fam	The input data. It should consist of "3n" rows and 6+2*p columns, where n is the number of of case-parent trios, and p is the number of SNPs. The data should be in standard pedigree format, with the first 6 columns representing the family ID, individual ID, father ID, mother ID, sex, and affection status. The other 2*p columns are genotype data in allelic format, with each allele of a SNP taking up one column. An example can be found in this package under the name "fam". For more information about the format, type "?fam" into R, or see "Linkage Format" section of https://www.broadinstitute.org/haploview/input-file-formats.
baseline	Haplotype to be used for baseline coding; default is the most frequent haplotype according to the initial haplotype frequency estimates. This argument should be a character, starting with an h and followed by the baseline haplotype.
start.beta	Starting value of all regression coefficients, β; default is 0.01.
lambda	Starting value of the λ parameter described above; default is 1.
D	Starting value of the D parameter, which is the within-population inbreeding coefficient; default is 0.
seed	Seed to be used for the MCMC in Bayesian Lasso; default is a random seed. If exact same results need to be reproduced, seed should be fixed to the same number.
a	First hyperparameter of the prior for regression coefficients, β. The prior variance of β is 2/λ^2 and λ has Gamma(a,b) prior. The Gamma prior parameters a and b are formulated such that the mean and variance of the Gamma distribution are a/b and a/b^2. The default value of a is 15.
b	Second hyperparameter of the Gamma(a,b) distribution described above; default is 15.
burn.in	Burn-in period of the MCMC sampling scheme; default is 10000.
num.it	Total number of MCMC iterations including burn-in; default is 40000.
summary	Logical. If TRUE, famLBL will return a summary of the analysis. If FALSE, famLBL will return the posterior samples of MCMC. Default is set to be TRUE.
e	A (small) number ε in the null hypothesis of no association, H_0: |β| ≤ ε. The default is 0.1. Changing e from the default of 0.1 may necessitate choosing a different threshold for Bayes Factor (one of the outputs) to infer association. Only used if summary = TRUE.
ci.level	Credible probability. The probability that the true value of beta will be within the credible interval. Default is 0.95, which corresponds to a 95% posterior credible interval. Only used if summary = TRUE.

Value

If summary = FALSE, return a list with the following components:

haplotypes

The list of haplotypes used in the analysis. The last column is the reference haplotype.

beta

Posterior samples of betas stored in a matrix.

lambda

A vector of (num.it-burn.in) posterior samples of lambda.

freq

Posterior samples of the frequencies of haplotypes stored in a matrix format, in the same order as haplotypes.

init.freq

The haplotype distribution used to initiate the MCMC.

If summary = TRUE, return the result of LBL_summary. For details, see the description of the LBL_summary function.

See Also

LBL, cLBL, LBL_summary, print_LBL_summary, LBL-package.

Examples

 data(fam)
 fam.obj<-famLBL(fam)
 fam.obj
 print_LBL_summary(fam.obj)

mxw010/LBL documentation built on Sept. 26, 2021, 3:44 a.m.